INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97984, 2017, 'bmc', 'Correcting nucleotide-specific biases in high-throughput sequencing data', 'Wang, Jeremy R|Quach, Bryan|Furey, Terrence S', 'Department of Genetics, University of North Carolina at Chapel Hill, CB 7032, 7314 Medical Biomolecular Research Building, 111 Mason Farm Road, Chapel Hill, 27599, NC, USA. jeremy_wang@med.unc.edu.|Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Department of Genetics, University of North Carolina at Chapel Hill, CB 7032, 7314 Medical Biomolecular Research Building, 111 Mason Farm Road, Chapel Hill, 27599, NC, USA.', 'United States|United States|United States', 11, 'Base Sequence|Bias|Binding Sites|Computational Biology|DNA|Deoxyribonucleases|High-Throughput Nucleotide Sequencing|Nucleotides|Principal Component Analysis|Protein Binding|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-017-1766-x', '28764645', 'High-throughput sequence (HTS) data exhibit position-specific nucleotide biases that obscure the intended signal and reduce the effectiveness of these data for downstream analyses. These biases are particularly evident in HTS assays for identifying regulatory regions in DNA (DNase-seq, ChIP-seq, FAIRE-seq, ATAC-seq). Biases may result from many experiment-specific factors, including selectivity of DNA restriction enzymes and fragmentation method, as well as sequencing technology-specific factors, such as choice of adapters/primers and sample amplification methods.'),
(97985, 2017, 'bmc', 'Parallel multiple instance learning for extremely large histopathology image analysis', 'Xu, Yan|Li, Yeshu|Shen, Zhengyang|Wu, Ziwei|Gao, Teng|Fan, Yubo|Lai, Maode|Chang, Eric I-Chao', 'State Key Laboratory of Software Development Environment and Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and Research Institute of Beihang University in Shenzhen, Beijing, China.|School of Computer Science and Engineering, Beihang University, Beijing, China.|State Key Laboratory of Software Development Environment and Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and Research Institute of Beihang University in Shenzhen, Beijing, China.|College of Information and Electrical Engineering, China Agricultural University, Beijing, China.|Microsoft Research Asia, Beijing, China.|State Key Laboratory of Software Development Environment and Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and Research Institute of Beihang University in Shenzhen, Beijing, China.|Department of Pathology, School of Medicine, Zhejiang University, Zhejiang, China.|Microsoft Research Asia, Beijing, China. echang@microsoft.com.', 'China|China|China|China|China|China|China|China', 4, 'Algorithms|Cluster Analysis|Computing Methodologies|Humans|Image Processing, Computer-Assisted|Pathology|ROC Curve|Statistics as Topic', 'Journal Article', '10.1186/s12859-017-1768-8', '28774262', 'Histopathology images are critical for medical diagnosis, e.g., cancer and its treatment. A standard histopathology slice can be easily scanned at a high resolution of, say, 200,000×200,000 pixels. These high resolution images can make most existing imaging processing tools infeasible or less effective when operated on a single machine with limited memory, disk space and computing power.'),
(97986, 2017, 'bmc', 'Evaluation of high-throughput isomiR identification tools: illuminating the early isomiRome of Tribolium castaneum', 'Amsel, Daniel|Vilcinskas, Andreas|Billion, André', 'Fraunhofer Institute for Molecular Biology and Applied Ecology, Department of Bioresources, Winchester Str. 2, 35394, Giessen, Germany. Daniel.Amsel@ime.fraunhofer.de.|Fraunhofer Institute for Molecular Biology and Applied Ecology, Department of Bioresources, Winchester Str. 2, 35394, Giessen, Germany.|Fraunhofer Institute for Molecular Biology and Applied Ecology, Department of Bioresources, Winchester Str. 2, 35394, Giessen, Germany.', 'Germany|Germany|Germany', 4, 'Algorithms|Animals|Embryo, Nonmammalian|Gene Expression Regulation|High-Throughput Nucleotide Sequencing|MicroRNAs|Polymorphism, Single Nucleotide|Protein Isoforms|RNA, Messenger|Sequence Deletion|Software|Templates, Genetic|Tribolium', 'Journal Article', '10.1186/s12859-017-1772-z', '28774263', 'MicroRNAs carry out post-transcriptional gene regulation in animals by binding to the 3 untranslated regions of mRNAs, causing their degradation or translational repression. MicroRNAs influence many biological functions, and dysregulation can therefore disrupt development or even cause death. High-throughput sequencing and the mining of animal small RNA data has shown that microRNA genes can yield differentially expressed isoforms, known as isomiRs. Such isoforms are particularly relevant during early development, and the extension or truncation of the 5 end can change the profile of mRNA targets compared to the original mature sequence. We used the publicly available small RNA dataset of the model beetle Tribolium castaneum to create the first comparative isomiRome of early developmental stages in this species. Standard microRNA analysis software does not specifically account for isomiRs. We therefore carried out the first comparative evaluation of the specialized tools isomiRID, isomiR-SEA and miraligner, which can be downloaded for local use and can handle next generation sequencing data.'),
(97987, 2017, 'bmc', 'Repliscan: a tool for classifying replication timing regions', 'Zynda, Gregory J|Song, Jawon|Concia, Lorenzo|Wear, Emily E|Hanley-Bowdoin, Linda|Thompson, William F|Vaughn, Matthew W', 'Texas Advanced Computing Center, University of Texas at Austin, 10100 Burnet Road, Austin, 78758-4497, TX, USA.|Texas Advanced Computing Center, University of Texas at Austin, 10100 Burnet Road, Austin, 78758-4497, TX, USA.|Department of Plant and Microbial Biology, North Carolina State University, Raleigh, 27695-7612, NC, USA.|Department of Plant and Microbial Biology, North Carolina State University, Raleigh, 27695-7612, NC, USA.|Department of Plant and Microbial Biology, North Carolina State University, Raleigh, 27695-7612, NC, USA.|Department of Plant and Microbial Biology, North Carolina State University, Raleigh, 27695-7612, NC, USA.|Texas Advanced Computing Center, University of Texas at Austin, 10100 Burnet Road, Austin, 78758-4497, TX, USA. vaughn@tacc.utexas.edu.', 'United States|United States|United States|United States|United States|United States|United States', 3, 'DNA Replication Timing|Genome|Genome Size|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1774-x', '28784090', 'Replication timing experiments that use label incorporation and high throughput sequencing produce peaked data similar to ChIP-Seq experiments. However, the differences in experimental design, coverage density, and possible results make traditional ChIP-Seq analysis methods inappropriate for use with replication timing.'),
(97988, 2017, 'bmc', 'A nonparametric Bayesian method of translating machine learning scores to probabilities in clinical decision support', 'Connolly, Brian|Cohen, K Bretonnel|Santel, Daniel|Bayram, Ulya|Pestian, John', 'Department of Biomedical Informatics, Cincinnati Childrens Hospital Medical Center, 3333 Burnet Ave., MLC 7024, Cincinnati, OH, 45229-3039, USA.|Computational Bioscience Program, University of Colorado School of Medicine, Denver, CO, USA.|Department of Biomedical Informatics, Cincinnati Childrens Hospital Medical Center, 3333 Burnet Ave., MLC 7024, Cincinnati, OH, 45229-3039, USA.|Department of Biomedical Informatics, Cincinnati Childrens Hospital Medical Center, 3333 Burnet Ave., MLC 7024, Cincinnati, OH, 45229-3039, USA.|Department of Biomedical Informatics, Cincinnati Childrens Hospital Medical Center, 3333 Burnet Ave., MLC 7024, Cincinnati, OH, 45229-3039, USA. john.pestian@cchmc.org.', 'United States|United States|United States|United States|United States', 3, 'Adolescent|Bayes Theorem|Calibration|Decision Support Systems, Clinical|Humans|Machine Learning|Statistics, Nonparametric|Suicide|Support Vector Machine', 'Journal Article', '10.1186/s12859-017-1736-3', '28784111', 'Probabilistic assessments of clinical care are essential for quality care. Yet, machine learning, which supports this care process has been limited to categorical results. To maximize its usefulness, it is important to find novel approaches that calibrate the ML output with a likelihood scale. Current state-of-the-art calibration methods are generally accurate and applicable to many ML models, but improved granularity and accuracy of such methods would increase the information available for clinical decision making. This novel non-parametric Bayesian approach is demonstrated on a variety of data sets, including simulated classifier outputs, biomedical data sets from the University of California, Irvine (UCI) Machine Learning Repository, and a clinical data set built to determine suicide risk from the language of emergency department patients.'),
(97989, 2017, 'bmc', 'CIPHER: a flexible and extensive workflow platform for integrative next-generation sequencing data analysis and genomic regulatory element prediction', 'Guzman, Carlos|DOrso, Iván', 'Department of Microbiology, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA. cag104@ucsd.edu.|Department of Microbiology, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA. Ivan.Dorso@utsouthwestern.edu.', 'United States|United States', 0, 'Chromatin Immunoprecipitation|Chromosome Mapping|Databases, Genetic|Enhancer Elements, Genetic|Gene Expression Regulation|High-Throughput Nucleotide Sequencing|Regulatory Sequences, Nucleic Acid|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-017-1770-1', '28789639', 'Next-generation sequencing (NGS) approaches are commonly used to identify key regulatory networks that drive transcriptional programs. Although these technologies are frequently used in biological studies, NGS data analysis remains a challenging, time-consuming, and often irreproducible process. Therefore, there is a need for a comprehensive and flexible workflow platform that can accelerate data processing and analysis so more time can be spent on functional studies.'),
(97990, 2017, 'bmc', 'Local sequence and sequencing depth dependent accuracy of RNA-seq reads', 'Cai, Guoshuai|Liang, Shoudan|Zheng, Xiaofeng|Xiao, Feifei', 'Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA. Guoshuai.Cai@dartmouth.edu.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA. xiaof@mailbox.sc.edu.', 'United States|United States|United States|United States', 1, 'Cell Line|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Models, Theoretical|RNA|Real-Time Polymerase Chain Reaction|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-017-1780-z', '28793860', 'Many biases and spurious effects are inherent in RNA-seq technology, resulting in a non-uniform distribution of sequencing read counts for each base position in a gene. Therefore, a base-level strategy is required to model the non-uniformity. Also, the properties of sequencing read counts can be leveraged to achieve a more precise estimation of the mean and variance of measurement.'),
(97991, 2017, 'bmc', 'Network design and analysis for multi-enzyme biocatalysis', 'Blaß, Lisa Katharina|Weyler, Christian|Heinzle, Elmar', 'Biochemical Engineering Institute, Saarland University, Campus A1.5, Saarbrücken, 66123, Germany.|Biochemical Engineering Institute, Saarland University, Campus A1.5, Saarbrücken, 66123, Germany.|Biochemical Engineering Institute, Saarland University, Campus A1.5, Saarbrücken, 66123, Germany. e.heinzle@mx.uni-saarland.de.', 'Germany|Germany|Germany', 2, 'Algorithms|Biocatalysis|Biosynthetic Pathways|Computer Simulation|Enzymes|Software|Thermodynamics', 'Journal Article', '10.1186/s12859-017-1773-y', '28797226', 'As more and more biological reaction data become available, the full exploration of the enzymatic potential for the synthesis of valuable products opens up exciting new opportunities but is becoming increasingly complex. The manual design of multi-step biosynthesis routes involving enzymes from different organisms is very challenging. To harness the full enzymatic potential, we developed a computational tool for the directed design of biosynthetic production pathways for multi-step catalysis with in vitro enzyme cascades, cell hydrolysates and permeabilized cells.'),
(97992, 2017, 'bmc', 'ODG: Omics database generator - a tool for generating, querying, and analyzing multi-omics comparative databases to facilitate biological understanding', 'Guhlin, Joseph|Silverstein, Kevin A T|Zhou, Peng|Tiffin, Peter|Young, Nevin D', 'Department of Plant and Microbial Biology, 140 Gortner Laboratory, 1479 Gortner Avenue, University of Minnesota, St. Paul, MN, 55108, USA. guhli007@umn.edu.|Minnesota Supercomputing Institute, 599 Walter Library, 117 Pleasant St. SE, Minneapolis, MN, 55455, USA.|Department of Plant Pathology, 495 Borlaug Hall, 1991 Upper Buford Circle, St. Paul, MN, 55108, USA.|Department of Plant and Microbial Biology, 140 Gortner Laboratory, 1479 Gortner Avenue, University of Minnesota, St. Paul, MN, 55108, USA.|Department of Plant Pathology, 495 Borlaug Hall, 1991 Upper Buford Circle, St. Paul, MN, 55108, USA.', 'United States|United States|United States|United States|United States', 4, 'Databases, Nucleic Acid|Genomics|Molecular Sequence Annotation|Software', 'Journal Article', '10.1186/s12859-017-1777-7', '28797229', 'Rapid generation of omics data in recent years have resulted in vast amounts of disconnected datasets without systemic integration and knowledge building, while individual groups have made customized, annotated datasets available on the web with few ways to link them to in-lab datasets. With so many research groups generating their own data, the ability to relate it to the larger genomic and comparative genomic context is becoming increasingly crucial to make full use of the data.'),
(97993, 2017, 'bmc', 'Full L1-regularized Traction Force Microscopy over whole cells', 'Suñé-Auñón, Alejandro|Jorge-Peñas, Alvaro|Aguilar-Cuenca, Rocío|Vicente-Manzanares, Miguel|Van Oosterwyck, Hans|Muñoz-Barrutia, Arrate', 'Bioengineering and Aerospace Engineering Department, Universidad Carlos III de Madrid, Leganés, Spain.|Biomechanics Section, Department of Mechanical Engineering, KU Leuven, 3001, Leuven, Belgium.|Instituto de Investigación Sanitaria-Hospital Universitario de la Princesa, Universidad Autónoma de Madrid, School of Medicine, 28006, Madrid, Spain.|Instituto de Investigación Sanitaria-Hospital Universitario de la Princesa, Universidad Autónoma de Madrid, School of Medicine, 28006, Madrid, Spain.|Biomechanics Section, Department of Mechanical Engineering, KU Leuven, 3001, Leuven, Belgium.|Bioengineering and Aerospace Engineering Department, Universidad Carlos III de Madrid, Leganés, Spain. mamunozb@ing.uc3m.es.', 'Spain|Belgium|Spain|Spain|Belgium|Spain', 0, 'Algorithms|Animals|Biomechanical Phenomena|CHO Cells|Cricetinae|Cricetulus|Hydrogels|Microscopy, Fluorescence', 'Journal Article', '10.1186/s12859-017-1771-0', '28797233', 'Background Traction Force Microscopy (TFM) is a widespread technique to estimate the tractions that cells exert on the surrounding substrate. To recover the tractions, it is necessary to solve an inverse problem, which is ill-posed and needs regularization to make the solution stable. The typical regularization scheme is given by the minimization of a cost functional, which is divided in two terms: the error present in the data or data fidelity term; and the regularization or penalty term. The classical approach is to use zero-order Tikhonov or L2-regularization, which uses the L2-norm for both terms in the cost function. Recently, some studies have demonstrated an improved performance using L1-regularization (L1-norm in the penalty term) related to an increase in the spatial resolution and sensitivity of the recovered traction field. In this manuscript, we present a comparison between the previous two regularization schemes (relying in the L2-norm for the data fidelity term) and the full L1-regularization (using the L1-norm for both terms in the cost function) for synthetic and real data.  Results Our results reveal that L1-regularizations give an improved spatial resolution (more important for full L1-regularization) and a reduction in the background noise with respect to the classical zero-order Tikhonov regularization. In addition, we present an approximation, which makes feasible the recovery of cellular tractions over whole cells on typical full-size microscope images when working in the spatial domain.  Conclusions The proposed full L1-regularization improves the sensitivity to recover small stress footprints. Moreover, the proposed method has been validated to work on full-field microscopy images of real cells, what certainly demonstrates it is a promising tool for biological applications.'),
(97994, 2017, 'bmc', 'Improving fold resistance prediction of HIV-1 against protease and reverse transcriptase inhibitors using artificial neural networks', 'Sheik Amamuddy, Olivier|Bishop, Nigel T|Tastan Bishop, Özlem', 'Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, 6140, South Africa.|Department of Mathematics (Pure and Applied), Rhodes University, Grahamstown, 6140, South Africa.|Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, 6140, South Africa. O.TastanBishop@ru.ac.za.', 'South Africa|South Africa|South Africa', 10, 'Databases, Factual|Drug Resistance, Viral|HIV Infections|HIV Protease|HIV Protease Inhibitors|HIV Reverse Transcriptase|HIV-1|Humans|Neural Networks (Computer)|Reverse Transcriptase Inhibitors', 'Journal Article', '10.1186/s12859-017-1782-x', '28810826', 'Drug resistance in HIV treatment is still a worldwide problem. Predicting resistance to antiretrovirals (ARVs) before starting any treatment is important. Prediction accuracy is essential, as low-accuracy predictions increase the risk of prescribing sub-optimal drug regimens leading to patients developing resistance sooner. Artificial Neural Networks (ANNs) are a powerful tool that would be able to assist in drug resistance prediction. In this study, we constrained the dataset to subtype B, sacrificing generalizability for a higher predictive performance, and demonstrated that the predictive quality of the ANN regression models have definite improvement for most ARVs.'),
(97995, 2017, 'bmc', 'A neural network multi-task learning approach to biomedical named entity recognition', 'Crichton, Gamal|Pyysalo, Sampo|Chiu, Billy|Korhonen, Anna', 'Language Technology Laboratory, DTAL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK. gkoc2@cam.ac.uk.|Language Technology Laboratory, DTAL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK.|Language Technology Laboratory, DTAL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK.|Language Technology Laboratory, DTAL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 36, 'Data Mining|Databases, Factual|Machine Learning|Models, Theoretical|Neural Networks (Computer)', 'Journal Article', '10.1186/s12859-017-1776-8', '28810903', 'Named Entity Recognition (NER) is a key task in biomedical text mining. Accurate NER systems require task-specific, manually-annotated datasets, which are expensive to develop and thus limited in size. Since such datasets contain related but different information, an interesting question is whether it might be possible to use them together to improve NER performance. To investigate this, we develop supervised, multi-task, convolutional neural network models and apply them to a large number of varied existing biomedical named entity datasets. Additionally, we investigated the effect of dataset size on performance in both single- and multi-task settings.'),
(97996, 2017, 'bmc', 'Cancerouspdomains: comprehensive analysis of cancer type-specific recurrent somatic mutations in proteins and domains', 'Hashemi, Seirana|Nowzari Dalini, Abbas|Jalali, Adrin|Banaei-Moghaddam, Ali Mohammad|Razaghi-Moghadam, Zahra', 'College of Science, University of Tehran, Tehran, Iran.|College of Science, University of Tehran, Tehran, Iran.|Max Planck Institute for Informatics, Saarland Informatics, Campus, 66123, Saarbrücken, Germany.|Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.|Faculty of New Sciences and Technologies, University of Tehran, North Kargar St, Tehran, Tehran, 1439957131, Iran. razzaghi@ut.ac.ir.', '-|-|Germany|-|-', 1, 'DNA Repair|Databases, Genetic|Humans|Internet|Mitochondria|Mutation|Neoplasms|Neoplastic Stem Cells|Protein Interaction Maps|Proteins|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1779-5', '28814324', 'Discriminating driver mutations from the ones that play no role in cancer is a severe bottleneck in elucidating molecular mechanisms underlying cancer development. Since protein domains are representatives of functional regions within proteins, mutations on them may disturb the protein functionality. Therefore, studying mutations at domain level may point researchers to more accurate assessment of the functional impact of the mutations.'),
(97997, 2017, 'bmc', 'eccCL: parallelized GPU implementation of Ensemble Classifier Chains', 'Riemenschneider, Mona|Herbst, Alexander|Rasch, Ari|Gorlatch, Sergei|Heider, Dominik', 'Department of Bioinformatics, Straubing Center of Science, Petersgasse 18, Straubing, 94315, Germany.|Institute of Computer Science, University of Münster, Einsteinstr. 62, Münster, 48149, Germany.|Institute of Computer Science, University of Münster, Einsteinstr. 62, Münster, 48149, Germany.|Institute of Computer Science, University of Münster, Einsteinstr. 62, Münster, 48149, Germany.|Department of Bioinformatics, Straubing Center of Science, Petersgasse 18, Straubing, 94315, Germany. d.heider@wz-straubing.de.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Computer Graphics|High-Throughput Nucleotide Sequencing|Software', 'Journal Article', '10.1186/s12859-017-1783-9', '28818036', 'Multi-label classification has recently gained great attention in diverse fields of research, e.g., in biomedical application such as protein function prediction or drug resistance testing in HIV. In this context, the concept of Classifier Chains has been shown to improve prediction accuracy, especially when applied as Ensemble Classifier Chains. However, these techniques lack computational efficiency when applied on large amounts of data, e.g., derived from next-generation sequencing experiments. By adapting algorithms for the use of graphics processing units, computational efficiency can be greatly improved due to parallelization of computations.'),
(97998, 2017, 'bmc', 'Coreference annotation and resolution in the Colorado Richly Annotated Full Text (CRAFT) corpus of biomedical journal articles', 'Cohen, K Bretonnel|Lanfranchi, Arrick|Choi, Miji Joo-Young|Bada, Michael|Baumgartner, William A|Panteleyeva, Natalya|Verspoor, Karin|Palmer, Martha|Hunter, Lawrence E', 'Computational Bioscience Program, University of Colorado School of Medicine, Denver, CO, USA. kevin.cohen@gmail.com.|Department of Linguistics, University of Colorado at Boulder, Boulder, Colorado, USA.|School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia.|Computational Bioscience Program, University of Colorado School of Medicine, Denver, CO, USA.|Computational Bioscience Program, University of Colorado School of Medicine, Denver, CO, USA.|Computational Bioscience Program, University of Colorado School of Medicine, Denver, CO, USA.|School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia.|Computational Bioscience Program, University of Colorado School of Medicine, Denver, CO, USA.|Computational Bioscience Program, University of Colorado School of Medicine, Denver, CO, USA.', 'United States|United States|Australia|United States|United States|United States|Australia|United States|United States', 0, 'Data Mining|Periodicals as Topic|Semantics', 'Journal Article', '10.1186/s12859-017-1775-9', '28818042', 'Coreference resolution is the task of finding strings in text that have the same referent as other strings. Failures of coreference resolution are a common cause of false negatives in information extraction from the scientific literature. In order to better understand the nature of the phenomenon of coreference in biomedical publications and to increase performance on the task, we annotated the Colorado Richly Annotated Full Text (CRAFT) corpus with coreference relations.'),
(97999, 2017, 'bmc', 'SG-ADVISER mtDNA: a web server for mitochondrial DNA annotation with data from 200 samples of a healthy aging cohort', 'Rueda, Manuel|Torkamani, Ali', 'The Scripps Translational Science Institute, Scripps Health, and The Scripps Research Institute, La Jolla, CA, 92037, USA. mrueda@scripps.edu.|The Scripps Translational Science Institute, Scripps Health, and The Scripps Research Institute, La Jolla, CA, 92037, USA. atorkama@scripps.edu.', 'United States|United States', 0, 'Body Mass Index|Cohort Studies|DNA, Mitochondrial|Female|Genetic Variation|Genome, Human|Healthy Aging|High-Throughput Nucleotide Sequencing|Humans|Male|Software', 'Journal Article', '10.1186/s12859-017-1778-6', '28821228', 'Whole genome and exome sequencing usually include reads containing mitochondrial DNA (mtDNA). Yet, state-of-the-art pipelines and services for human nuclear genome variant calling and annotation do not handle mitochondrial genome data appropriately. As a consequence, any researcher desiring to add mtDNA variant analysis to their investigations is forced to explore the literature for mtDNA pipelines, evaluate them, and implement their own instance of the desired tool. This task is far from trivial, and can be prohibitive for non-bioinformaticians.'),
(98000, 2017, 'bmc', 'Evaluation of the impact of Illumina error correction tools on de novo genome assembly', 'Heydari, Mahdi|Miclotte, Giles|Demeester, Piet|Van de Peer, Yves|Fostier, Jan', 'Department of Information Technology, Ghent University-imec, IDLab, Ghent, B-9052, Belgium.|Department of Information Technology, Ghent University-imec, IDLab, Ghent, B-9052, Belgium.|Department of Information Technology, Ghent University-imec, IDLab, Ghent, B-9052, Belgium.|Center for Plant Systems Biology, VIB, Ghent, B-9052, Belgium.|Department of Information Technology, Ghent University-imec, IDLab, Ghent, B-9052, Belgium. jan.fostier@ugent.be.', 'Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Animals|Bacteria|Caenorhabditis elegans|DNA|Drosophila|Genome|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-017-1784-8', '28821237', 'Recently, many standalone applications have been proposed to correct sequencing errors in Illumina data. The key idea is that downstream analysis tools such as de novo genome assemblers benefit from a reduced error rate in the input data. Surprisingly, a systematic validation of this assumption using state-of-the-art assembly methods is lacking, even for recently published methods.'),
(98001, 2017, 'bmc', 'A regulation probability model-based meta-analysis of multiple transcriptomics data sets for cancer biomarker identification', 'Xie, Xin-Ping|Xie, Yu-Feng|Wang, Hong-Qiang', 'School of Mathematics and Physics, Anhui Jianzhu University, Hefei, Anhui, 230022, China.|School of Mathematics and Physics, Anhui Jianzhu University, Hefei, Anhui, 230022, China.|Cancer Hospital, CAS, Hefei, Anhui, 230031, China. hqwang126@126.com.', 'China|China|China', 0, 'Biomarkers, Tumor|Databases, Genetic|Gene Expression Profiling|Humans|Models, Statistical|Neoplasms|Oligonucleotide Array Sequence Analysis|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-017-1794-6', '28830341', 'Large-scale accumulation of omics data poses a pressing challenge of integrative analysis of multiple data sets in bioinformatics. An open question of such integrative analysis is how to pinpoint consistent but subtle gene activity patterns across studies. Study heterogeneity needs to be addressed carefully for this goal.'),
(98002, 2017, 'bmc', 'Identifying pleiotropic genes in genome-wide association studies from related subjects using the linear mixed model and Fisher combination function', 'Yang, James J|Williams, L Keoki|Buu, Anne', 'School of Nursing, University of Michigan, Ann Arbor, 48104, Michigan, USA. jjyang@umich.edu.|Department of Internal Medicine, Henry Ford Health System, Detroit, 48202, Michigan, USA.|Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, 48104, Michigan, USA.', 'United States|United States|United States', 0, 'Alcoholism|Genetic Pleiotropy|Genome-Wide Association Study|Genotype|Humans|Models, Genetic|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-017-1791-9', '28836938', 'A multivariate genome-wide association test is proposed for analyzing data on multivariate quantitative phenotypes collected from related subjects. The proposed method is a two-step approach. The first step models the association between the genotype and marginal phenotype using a linear mixed model. The second step uses the correlation between residuals of the linear mixed model to estimate the null distribution of the Fisher combination test statistic.'),
(98003, 2017, 'bmc', 'A weighted string kernel for protein fold recognition', 'Nojoomi, Saghi|Koehl, Patrice', 'Biotechnology program, University of California, Davis, 1, Shields Avenue, Davis, 95616, CA, USA.|Department of Computer Science and Genome Center, 1, Shields Avenue, Davis, 95616, CA, USA. pakoehl@ucdavis.edu.', 'United States|United States', 0, 'Algorithms|Amino Acids|Area Under Curve|Principal Component Analysis|Protein Folding|Proteins|ROC Curve', 'Journal Article', '10.1186/s12859-017-1795-5', '28841820', 'Alignment-free methods for comparing protein sequences have proved to be viable alternatives to approaches that first rely on an alignment of the sequences to be compared. Much work however need to be done before those methods provide reliable fold recognition for proteins whose sequences share little similarity. We have recently proposed an alignment-free method based on the concept of string kernels, SeqKernel (Nojoomi and Koehl, BMC Bioinformatics, 2017, 18:137). In this previous study, we have shown that while Seqkernel performs better than standard alignment-based methods, its applications are potentially limited, because of biases due mostly to sequence length effects.'),
(98004, 2017, 'bmc', 'Improved protein structure reconstruction using secondary structures, contacts at higher distance thresholds, and non-contacts', 'Adhikari, Badri|Cheng, Jianlin', 'Department of Mathematics and Computer Science, University of Missouri-St.Louis, St. Louis, MO, 63121, USA.|Department of Electrical Engineering & Computer Science, Informatics Institute, University of Missouri, Columbia, MO, 65211, USA. chengji@missouri.edu.', 'United States|United States', 0, 'Caspases|Models, Molecular|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1186/s12859-017-1807-5', '28851269', 'Residue-residue contacts are key features for accurate de novo protein structure prediction. For the optimal utilization of these predicted contacts in folding proteins accurately, it is important to study the challenges of reconstructing protein structures using true contacts. Because contact-guided protein modeling approach is valuable for predicting the folds of proteins that do not have structural templates, it is necessary for reconstruction studies to focus on hard-to-predict protein structures.'),
(98005, 2017, 'bmc', 'EL_PSSM-RT: DNA-binding residue prediction by integrating ensemble learning with PSSM Relation Transformation', 'Zhou, Jiyun|Lu, Qin|Xu, Ruifeng|He, Yulan|Wang, Hongpeng', 'School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, Guangdong, 518055, China.|Department of Computing, the Hong Kong Polytechnic University, Kowloon, Hong Kong.|School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, Guangdong, 518055, China. xuruifeng@hit.edu.cn.|School of Engineering and Applied Science, Aston University, Birmingham, UK.|School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, Guangdong, 518055, China.', 'China|Hong Kong|China|United Kingdom|China', 0, 'Area Under Curve|DNA|Internet|Position-Specific Scoring Matrices|ROC Curve|Support Vector Machine|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1792-8', '28851273', 'Prediction of DNA-binding residue is important for understanding the protein-DNA recognition mechanism. Many computational methods have been proposed for the prediction, but most of them do not consider the relationships of evolutionary information between residues.'),
(98006, 2017, 'bmc', 'QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model', 'Liu, Lian|Zhang, Shao-Wu|Huang, Yufei|Meng, Jia', 'Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xian, 710072, China.|Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xian, 710072, China. zhangsw@nwpu.edu.cn.|Department of Electrical and Computation Engineering, University of Texas at San Antonio, San Antonio, TX, 78230, USA.|Department of Biological Sciences, HRINU, SUERI, Xian Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China. jia.meng@xjtlu.edu.cn.', 'China|China|United States|China', 13, 'Algorithms|Animals|Binomial Distribution|Cell Line, Tumor|High-Throughput Nucleotide Sequencing|Humans|Mesencephalon|Methylation|Mice|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-017-1808-4', '28859631', 'As a newly emerged research area, RNA epigenetics has drawn increasing attention recently for the participation of RNA methylation and other modifications in a number of crucial biological processes. Thanks to high throughput sequencing techniques, such as, MeRIP-Seq, transcriptome-wide RNA methylation profile is now available in the form of count-based data, with which it is often of interests to study the dynamics at epitranscriptomic layer. However, the sample size of RNA methylation experiment is usually very small due to its costs, and additionally, there usually exist a large number of genes whose methylation level cannot be accurately estimated due to their low expression level, making differential RNA methylation analysis a difficult task.'),
(98007, 2017, 'bmc', 'Robust gene selection methods using weighting schemes for microarray data analysis', 'Kang, Suyeon|Song, Jongwoo', 'Department of Statistics, Ewha Womans University, Seoul, South Korea.|Department of Statistics, Ewha Womans University, Seoul, South Korea. josong@ewha.ac.kr.', '-|-', 0, 'Algorithms|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Sample Size|Transcriptome', 'Journal Article', '10.1186/s12859-017-1810-x', '28865426', 'A common task in microarray data analysis is to identify informative genes that are differentially expressed between two different states. Owing to the high-dimensional nature of microarray data, identification of significant genes has been essential in analyzing the data. However, the performances of many gene selection techniques are highly dependent on the experimental conditions, such as the presence of measurement error or a limited number of sample replicates.'),
(98008, 2017, 'bmc', 'A new and updated resource for codon usage tables', 'Athey, John|Alexaki, Aikaterini|Osipova, Ekaterina|Rostovtsev, Alexandre|Santana-Quintero, Luis V|Katneni, Upendra|Simonyan, Vahan|Kimchi-Sarfaty, Chava', 'Division of Plasma Protein Therapeutics, Office of Tissue and Advanced Therapies, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, USA.|Division of Plasma Protein Therapeutics, Office of Tissue and Advanced Therapies, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, USA.|High Performance Integrated Environment, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, USA.|High Performance Integrated Environment, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, USA.|High Performance Integrated Environment, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, USA.|Division of Plasma Protein Therapeutics, Office of Tissue and Advanced Therapies, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, USA.|High Performance Integrated Environment, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, USA.|Division of Plasma Protein Therapeutics, Office of Tissue and Advanced Therapies, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, USA. Chava.Kimchi-Sarfaty@fda.hhs.gov.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Animals|Codon|Databases, Nucleic Acid|Humans', 'Journal Article', '10.1186/s12859-017-1793-7', '28865429', 'Due to the degeneracy of the genetic code, most amino acids can be encoded by multiple synonymous codons. Synonymous codons naturally occur with different frequencies in different organisms. The choice of codons may affect protein expression, structure, and function. Recombinant gene technologies commonly take advantage of the former effect by implementing a technique termed codon optimization, in which codons are replaced with synonymous ones in order to increase protein expression. This technique relies on the accurate knowledge of codon usage frequencies. Accurately quantifying codon usage bias for different organisms is useful not only for codon optimization, but also for evolutionary and translation studies: phylogenetic relations of organisms, and host-pathogen co-evolution relationships, may be explored through their codon usage similarities. Furthermore, codon usage has been shown to affect protein structure and function through interfering with translation kinetics, and cotranslational protein folding.'),
(98009, 2017, 'bmc', 'RRCRank: a fusion method using rank strategy for residue-residue contact prediction', 'Jing, Xiaoyang|Dong, Qiwen|Lu, Ruqian', 'School of Computer Science, Fudan University, Shanghai, 200433, Peoples Republic of China.|School of Data Science and Engineering, East China Normal University, Shanghai, 200062, Peoples Republic of China. qwdong@dase.ecnu.edu.cn.|School of Computer Science, Fudan University, Shanghai, 200433, Peoples Republic of China.', 'China|China|China', 0, 'Algorithms|Machine Learning|Proteins', 'Evaluation Studies', '10.1186/s12859-017-1811-9', '28865433', 'In structural biology area, protein residue-residue contacts play a crucial role in protein structure prediction. Some researchers have found that the predicted residue-residue contacts could effectively constrain the conformational search space, which is significant for de novo protein structure prediction. In the last few decades, related researchers have developed various methods to predict residue-residue contacts, especially, significant performance has been achieved by using fusion methods in recent years. In this work, a novel fusion method based on rank strategy has been proposed to predict contacts. Unlike the traditional regression or classification strategies, the contact prediction task is regarded as a ranking task. First, two kinds of features are extracted from correlated mutations methods and ensemble machine-learning classifiers, and then the proposed method uses the learning-to-rank algorithm to predict contact probability of each residue pair.'),
(98010, 2017, 'bmc', 'Investigating MicroRNA and transcription factor co-regulatory networks in colorectal cancer', 'Wang, Hao|Luo, Jiamao|Liu, Chun|Niu, Huilin|Wang, Jing|Liu, Qi|Zhao, Zhongming|Xu, Hua|Ding, Yanqing|Sun, Jingchun|Zhang, Qingling', 'Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.|Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.|Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.|Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.|Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA.|Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA.|School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.|School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.|Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.|School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. jingchun.sun@uth.tmc.edu.|Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China. zqllc8@fimmu.com.', 'China|China|China|China|United States|United States|United States|United States|China|United States|China', 0, 'Colorectal Neoplasms|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|MicroRNAs|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1186/s12859-017-1796-4', '28865443', 'Colorectal cancer (CRC) is one of the most common malignancies worldwide with poor prognosis. Studies have showed that abnormal microRNA (miRNA) expression can affect CRC pathogenesis and development through targeting critical genes in cellular system. However, it is unclear about which miRNAs play central roles in CRCs pathogenesis and how they interact with transcription factors (TFs) to regulate the cancer-related genes.'),
(98011, 2017, 'bmc', 'Phylo_dCor: distance correlation as a novel metric for phylogenetic profiling', 'Sferra, Gabriella|Fratini, Federica|Ponzi, Marta|Pizzi, Elisabetta', 'Dipartimento di Malattie Infettive, Parassitarie e Immunomediate, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161, Rome, Italy.|Dipartimento di Malattie Infettive, Parassitarie e Immunomediate, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161, Rome, Italy.|Dipartimento di Malattie Infettive, Parassitarie e Immunomediate, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161, Rome, Italy.|Dipartimento di Malattie Infettive, Parassitarie e Immunomediate, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161, Rome, Italy. elisabetta.pizzi@iss.it.', 'Italy|Italy|Italy|Italy', 0, 'Algorithms|Area Under Curve|Databases, Genetic|Escherichia coli|Escherichia coli Proteins|Protein Interaction Maps|ROC Curve|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/s12859-017-1815-5', '28870256', 'Elaboration of powerful methods to predict functional and/or physical protein-protein interactions from genome sequence is one of the main tasks in the post-genomic era. Phylogenetic profiling allows the prediction of protein-protein interactions at a whole genome level in both Prokaryotes and Eukaryotes. For this reason it is considered one of the most promising methods.'),
(98012, 2017, 'bmc', 'BUFET: boosting the unbiased miRNA functional enrichment analysis using bitsets', 'Zagganas, Konstantinos|Vergoulis, Thanasis|Paraskevopoulou, Maria D|Vlachos, Ioannis S|Skiadopoulos, Spiros|Dalamagas, Theodore', 'University of Peloponnese, Department of Informatics and Telecommunications, Tripoli, 22100, Greece. kzagganas@uop.gr.|Athena Research and Innovation Center, Athens, 15125, Greece.|DIANA-Lab, Department of Electrical & Computer Engineering, University of Thessaly, Volos, 38221, Greece.|DIANA-Lab, Department of Electrical & Computer Engineering, University of Thessaly, Volos, 38221, Greece.|University of Peloponnese, Department of Informatics and Telecommunications, Tripoli, 22100, Greece.|Athena Research and Innovation Center, Athens, 15125, Greece.', 'Greece|Greece|Greece|Greece|Greece|Greece', 4, 'Computational Biology|MicroRNAs|Software', 'Journal Article', '10.1186/s12859-017-1812-8', '28874117', 'A group of miRNAs can regulate a biological process by targeting genes involved in the process. The unbiased miRNA functional enrichment analysis is the most precise in silico approach to predict the biological processes that may be regulated by a given miRNA group. However, it is computationally intensive and significantly more expensive than its alternatives.'),
(98013, 2017, 'bmc', 'High-throughput PCR assay design for targeted resequencing using primerXL', 'Lefever, Steve|Pattyn, Filip|De Wilde, Bram|Coppieters, Frauke|De Keulenaer, Sarah|Hellemans, Jan|Vandesompele, Jo', 'Center for Medical Genetics, Ghent University, De Pintelaan 185, 9000, Ghent, Belgium. Steve.Lefever@UGent.be.|Center for Medical Genetics, Ghent University, De Pintelaan 185, 9000, Ghent, Belgium.|Center for Medical Genetics, Ghent University, De Pintelaan 185, 9000, Ghent, Belgium.|Center for Medical Genetics, Ghent University, De Pintelaan 185, 9000, Ghent, Belgium.|Center for Medical Genetics, Ghent University, De Pintelaan 185, 9000, Ghent, Belgium.|Center for Medical Genetics, Ghent University, De Pintelaan 185, 9000, Ghent, Belgium.|Center for Medical Genetics, Ghent University, De Pintelaan 185, 9000, Ghent, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Animals|DNA Primers|High-Throughput Nucleotide Sequencing|Humans|Plants|Polymerase Chain Reaction|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1809-3', '28877663', 'Although the sequencing landscape is rapidly evolving and sequencing costs are continuously decreasing, whole genome sequencing is still too expensive for use on a routine basis. Targeted resequencing of only the regions of interest decreases both costs and the complexity of the downstream data-analysis. Various target enrichment strategies are available, but none of them obtain the degree of coverage uniformity, flexibility and specificity of PCR-based enrichment. On the other hand, the biggest limitation of target enrichment by PCR is the need to design large numbers of partially overlapping assays to cover the target.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98014, 2017, 'bmc', 'Statistical classifiers for diagnosing disease from immune repertoires: a case study using multiple sclerosis', 'Ostmeyer, Jared|Christley, Scott|Rounds, William H|Toby, Inimary|Greenberg, Benjamin M|Monson, Nancy L|Cowell, Lindsay G', 'Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA.|Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA.|Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA.|Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA.|Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9036, USA.|Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9036, USA.|Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA. lindsay.cowell@utsouthwestern.edu.', 'United States|United States|United States|United States|United States|United States|United States', 17, 'Amino Acid Sequence|Area Under Curve|B-Lymphocytes|Complementarity Determining Regions|High-Throughput Nucleotide Sequencing|Humans|Models, Statistical|Multiple Sclerosis, Relapsing-Remitting|Nervous System Diseases|ROC Curve', 'Journal Article', '10.1186/s12859-017-1814-6', '28882107', 'Deep sequencing of lymphocyte receptor repertoires has made it possible to comprehensively profile the clonal composition of lymphocyte populations. This opens the door for novel approaches to diagnose and prognosticate diseases with a driving immune component by identifying repertoire sequence patterns associated with clinical phenotypes. Indeed, recent studies support the feasibility of this, demonstrating an association between repertoire-level summary statistics (e.g., diversity) and patient outcomes for several diseases. In our own prior work, we have shown that six codons in VH4-containing genes in B cells from the cerebrospinal fluid of patients with relapsing remitting multiple sclerosis (RRMS) have higher replacement mutation frequencies than observed in healthy controls or patients with other neurological diseases. However, prior methods to date have been limited to focusing on repertoire-level summary statistics, ignoring the vast amounts of information in the millions of individual immune receptors comprising a repertoire. We have developed a novel method that addresses this limitation by using innovative approaches for accommodating the extraordinary sequence diversity of immune receptors and widely used machine learning approaches. We applied our method to RRMS, an autoimmune disease that is notoriously difficult to diagnose.'),
(98015, 2017, 'bmc', 'Interactive visual exploration and refinement of cluster assignments', 'Kern, Michael|Lex, Alexander|Gehlenborg, Nils|Johnson, Chris R', 'Scientific Computing and Imaging Institute, University of Utah, 72 Sout Central Campus Drive, Salt Lake City, 84112, USA.|Scientific Computing and Imaging Institute, University of Utah, 72 Sout Central Campus Drive, Salt Lake City, 84112, USA. alex@sci.utah.edu.|Department of Biomedical Informatics, Harvard Medical School, Boston, 02115, USA.|Scientific Computing and Imaging Institute, University of Utah, 72 Sout Central Campus Drive, Salt Lake City, 84112, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Genotype|Humans|Internet|Neoplasms|Phenotype|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1813-7', '28899361', 'With ever-increasing amounts of data produced in biology research, scientists are in need of efficient data analysis methods. Cluster analysis, combined with visualization of the results, is one such method that can be used to make sense of large data volumes. At the same time, cluster analysis is known to be imperfect and depends on the choice of algorithms, parameters, and distance measures. Most clustering algorithms dont properly account for ambiguity in the source data, as records are often assigned to discrete clusters, even if an assignment is unclear. While there are metrics and visualization techniques that allow analysts to compare clusterings or to judge cluster quality, there is no comprehensive method that allows analysts to evaluate, compare, and refine cluster assignments based on the source data, derived scores, and contextual data.'),
(98016, 2017, 'bmc', 'mmquant: how to count multi-mapping reads?', 'Zytnicki, Matthias', 'MIAT, Toulouse INRA, BP 52627, Castanet-Tolosan cedex, 31326, France. matthias.zytnicki@inra.fr.', 'France', 8, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-017-1816-4', '28915787', 'RNA-Seq is currently used routinely, and it provides accurate information on gene transcription. However, the method cannot accurately estimate duplicated genes expression. Several strategies have been previously used (drop duplicated genes, distribute uniformly the reads, or estimate expression), but all of them provide biased results.'),
(98017, 2017, 'bmc', 'Segmentation and classification of two-channel C elegans nucleus-labeled fluorescence images', 'Zhao, Mengdi|An, Jie|Li, Haiwen|Zhang, Jiazhi|Li, Shang-Tong|Li, Xue-Mei|Dong, Meng-Qiu|Mao, Heng|Tao, Louis', 'Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Yiheyuan Road, Beijing, 100871, China.|LMAM, School of Mathematical Sciences, Peking University, Yiheyuan Road, Beijing, 100871, China.|LMAM, School of Mathematical Sciences, Peking University, Yiheyuan Road, Beijing, 100871, China.|Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, School of Life Sciences, Peking University, Yiheyuan Road, Beijing, 100871, China.|National Institute of Biological Sciences, Beijing, Kexueyuan Road, Beijing, 102206, China.|National Institute of Biological Sciences, Beijing, Kexueyuan Road, Beijing, 102206, China.|National Institute of Biological Sciences, Beijing, Kexueyuan Road, Beijing, 102206, China.|LMAM, School of Mathematical Sciences, Peking University, Yiheyuan Road, Beijing, 100871, China. heng.mao@pku.edu.cn.|Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Yiheyuan Road, Beijing, 100871, China. taolt@mail.cbi.pku.edu.cn.', 'China|China|China|China|China|China|China|China|China', 0, 'Aging|Algorithms|Animals|Caenorhabditis elegans|Cell Nucleus|Fluorescence|Image Processing, Computer-Assisted|Staining and Labeling', 'Journal Article', '10.1186/s12859-017-1817-3', '28915791', 'Aging is characterized by a gradual breakdown of cellular structures. Nuclear abnormality is a hallmark of progeria in human. Analysis of age-dependent nuclear morphological changes in Caenorhabditis elegans is of great value to aging research, and this calls for an automatic image processing method that is suitable for both normal and abnormal structures.'),
(98018, 2017, 'bmc', 'Deep learning methods for protein torsion angle prediction', 'Li, Haiou|Hou, Jie|Adhikari, Badri|Lyu, Qiang|Cheng, Jianlin', 'Department of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, 215006, China.|Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.|Department of Mathematics and Computer Science, University of Missouri-St. Louis, 1 University Blvd. 311 Express Scripts Hall, St. Louis, MO, 63121, USA.|Department of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, 215006, China.|Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA. chengji@missouri.edu.', 'China|United States|United States|China|United States', 0, 'Machine Learning|Molecular Structure|Neural Networks (Computer)|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1186/s12859-017-1834-2', '28923002', 'Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins.'),
(98019, 2017, 'bmc', 'Cleaning by clustering: methodology for addressing data quality issues in biomedical metadata', 'Hu, Wei|Zaveri, Amrapali|Qiu, Honglei|Dumontier, Michel', 'State Key Laboratory for Novel Software Technology, Nanjing University, 163 Xianlin Avenue, Nanjing, 210023, Jiangsu, China.|Institute of Data Science, Maastricht University, Maastricht, 6200, MD, The Netherlands.|State Key Laboratory for Novel Software Technology, Nanjing University, 163 Xianlin Avenue, Nanjing, 210023, Jiangsu, China.|Institute of Data Science, Maastricht University, Maastricht, 6200, MD, The Netherlands. michel.dumontier@maastrichtuniversity.nl.', 'China|Netherlands|China|Netherlands', 3, 'Algorithms|Cluster Analysis|Data Accuracy|Metadata', 'Evaluation Studies', '10.1186/s12859-017-1832-4', '28923003', 'The ability to efficiently search and filter datasets depends on access to high quality metadata. While most biomedical repositories require data submitters to provide a minimal set of metadata, some such as the Gene Expression Omnibus (GEO) allows users to specify additional metadata in the form of textual key-value pairs (e.g. sex: female). However, since there is no structured vocabulary to guide the submitter regarding the metadata terms to use, consequently, the 44,000,000+ key-value pairs in GEO suffer from numerous quality issues including redundancy, heterogeneity, inconsistency, and incompleteness. Such issues hinder the ability of scientists to hone in on datasets that meet their requirements and point to a need for accurate, structured and complete description of the data.'),
(98020, 2017, 'bmc', 'Methods for discovering genomic loci exhibiting complex patterns of differential methylation', 'Hardcastle, Thomas J', 'Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA, UK. tjh48@cam.ac.uk.', 'United Kingdom', 0, 'Arabidopsis|Bayes Theorem|Cytosine|DNA Methylation|Genetic Loci|Genome|Genomics|High-Throughput Nucleotide Sequencing|Models, Statistical|Ribonuclease III|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1186/s12859-017-1836-0', '28923005', 'Cytosine methylation is widespread in most eukaryotic genomes and is known to play a substantial role in various regulatory pathways. Unmethylated cytosines may be converted to uracil through the addition of sodium bisulphite, allowing genome-wide quantification of cytosine methylation via high-throughput sequencing. The data thus acquired allows the discovery of methylation loci, contiguous regions of methylation consistently methylated across biological replicates. The mapping of these loci allows for associations with other genomic factors to be identified, and for analyses of differential methylation to take place.'),
(98021, 2017, 'bmc', 'C-State: an interactive web app for simultaneous multi-gene visualization and comparative epigenetic pattern search', 'Sowpati, Divya Tej|Srivastava, Surabhi|Dhawan, Jyotsna|Mishra, Rakesh K', 'CSIR- Centre for Cellular and Molecular Biology, Hyderabad, India.|CSIR- Centre for Cellular and Molecular Biology, Hyderabad, India. ssurabhi@ccmb.res.in.|CSIR- Centre for Cellular and Molecular Biology, Hyderabad, India.|CSIR- Centre for Cellular and Molecular Biology, Hyderabad, India. mishra@ccmb.res.in.', 'India|India|India|India', 1, 'Algorithms|Embryonic Stem Cells|Epigenomics|Genes|Genomics|HeLa Cells|Humans|Internet|K562 Cells|Promoter Regions, Genetic|Software|Transcription, Genetic|Web Browser', 'Journal Article', '10.1186/s12859-017-1786-6', '28929968', 'Comparative epigenomic analysis across multiple genes presents a bottleneck for bench biologists working with NGS data. Despite the development of standardized peak analysis algorithms, the identification of novel epigenetic patterns and their visualization across gene subsets remains a challenge.'),
(98022, 2017, 'bmc', 'CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components', 'Heberle, Henry|Carazzolle, Marcelo Falsarella|Telles, Guilherme P|Meirelles, Gabriela Vaz|Minghim, Rosane', 'University of São Paulo, Instituto de Ciências Matemáticas e de Computação, Av. Trabalhador São-carlense, 400, São Carlos-SP, Brazil.|University of Campinas, Institute of Biology, Av. Albert Einstein, 1251, Campinas-SP, Brazil.|University of Campinas, Institute of Computing, Av. Albert Einstein, 1251, Campinas-SP, Brazil.|Biosciences National Laboratory, Caixa Postal 6192, Campinas-SP, Brazil.|University of São Paulo, Instituto de Ciências Matemáticas e de Computação, Av. Trabalhador São-carlense, 400, São Carlos-SP, Brazil. rminghim@icmc.usp.br.', 'Brazil|Brazil|Brazil|Brazil|Brazil', 0, 'Algorithms|Cells|Databases, Factual|Gene Ontology|Humans|Internet|MAP Kinase Signaling System|Metabolic Networks and Pathways|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1787-5', '28929969', 'The advent of omics science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability.'),
(98023, 2017, 'bmc', 'Bayesian Unidimensional Scaling for visualizing uncertainty in high dimensional datasets with latent ordering of observations', 'Nguyen, Lan Huong|Holmes, Susan', 'Institute for Computational and Mathematical Engineering, Stanford University, Stanford, 94305, USA. lanhuong@stanford.edu.|Department of Statistics, Stanford University, Stanford, 94305, USA.', 'United States|United States', 0, 'Animals|Anura|Bayes Theorem|Cluster Analysis|Databases as Topic|Gastrointestinal Microbiome|Gene Expression Regulation, Developmental|Humans|Infant|Models, Statistical|Oceans and Seas|Uncertainty|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1790-x', '28929970', 'Detecting patterns in high-dimensional multivariate datasets is non-trivial. Clustering and dimensionality reduction techniques often help in discerning inherent structures. In biological datasets such as microbial community composition or gene expression data, observations can be generated from a continuous process, often unknown. Estimating data points natural ordering and their corresponding uncertainties can help researchers draw insights about the mechanisms involved.'),
(98024, 2017, 'bmc', 'MediSyn: uncertainty-aware visualization of multiple biomedical datasets to support drug treatment selection', 'He, Chen|Micallef, Luana|Tanoli, Zia-Ur-Rehman|Kaski, Samuel|Aittokallio, Tero|Jacucci, Giulio', 'Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Gustaf Hällströmin katu 2b, Helsinki, 00560, Finland. chen.he@helsinki.fi.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Konemiehentie 2, Espoo, 02150, Finland.|Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, 00014, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Konemiehentie 2, Espoo, 02150, Finland.|Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, 00014, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Gustaf Hällströmin katu 2b, Helsinki, 00560, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland', 2, 'Adult|Databases, Factual|Drug Therapy|Female|Humans|Software|Surveys and Questionnaires|Uncertainty', 'Journal Article', '10.1186/s12859-017-1785-7', '28929971', 'Dispersed biomedical databases limit user exploration to generate structured knowledge. Linked Data unifies data structures and makes the dispersed data easy to search across resources, but it lacks supporting human cognition to achieve insights. In addition, potential errors in the data are difficult to detect in their free formats. Devising a visualization that synthesizes multiple sources in such a way that links between data sources are transparent, and uncertainties, such as data conflicts, are salient is challenging.'),
(98025, 2017, 'bmc', 'Vermont: a multi-perspective visual interactive platform for mutational analysis', 'Fassio, Alexandre V|Martins, Pedro M|Guimarães, Samuel da S|Junior, Sócrates S A|Ribeiro, Vagner S|de Melo-Minardi, Raquel C|Silveira, Sabrina de A', 'Department of Computer Science, Universidade Federal de Minas Gerais, 6627, Antônio Carlos avenue, Pampulha, Belo Horizonte, 31270-901, Brazil. alexandrefassio@dcc.ufmg.br.|Department of Computer Science, Universidade Federal de Minas Gerais, 6627, Antônio Carlos avenue, Pampulha, Belo Horizonte, 31270-901, Brazil.|Department of Computer Science, Universidade Federal de Viçosa, Peter Henry Rolfs avenue, Campus Universitário, Viçosa, 36570-900, Brazil.|Department of Computer Science, Universidade Federal de Viçosa, Peter Henry Rolfs avenue, Campus Universitário, Viçosa, 36570-900, Brazil.|Department of Computer Science, Universidade Federal de Viçosa, Peter Henry Rolfs avenue, Campus Universitário, Viçosa, 36570-900, Brazil.|Department of Computer Science, Universidade Federal de Minas Gerais, 6627, Antônio Carlos avenue, Pampulha, Belo Horizonte, 31270-901, Brazil.|Department of Computer Science, Universidade Federal de Viçosa, Peter Henry Rolfs avenue, Campus Universitário, Viçosa, 36570-900, Brazil.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 5, 'Amino Acid Sequence|Conserved Sequence|DNA Mutational Analysis|Humans|Hydrophobic and Hydrophilic Interactions|Mutation|Polymorphism, Single Nucleotide|Sequence Alignment|Software|Tumor Suppressor Protein p53', 'Journal Article', '10.1186/s12859-017-1789-3', '28929973', 'A huge amount of data about genomes and sequence variation is available and continues to grow on a large scale, which makes experimentally characterizing these mutations infeasible regarding disease association and effects on protein structure and function. Therefore, reliable computational approaches are needed to support the understanding of mutations and their impacts. Here, we present VERMONT 2.0, a visual interactive platform that combines sequence and structural parameters with interactive visualizations to make the impact of protein point mutations more understandable.'),
(98026, 2017, 'bmc', 'Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies', 'Abdellah, Marwan|Hernando, Juan|Antille, Nicolas|Eilemann, Stefan|Markram, Henry|Schxc3xbcrmann, Felix', 'Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Biotech Campus, Chemin des Mines 9, Geneva, 1202, Switzerland.|Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Biotech Campus, Chemin des Mines 9, Geneva, 1202, Switzerland.|Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Biotech Campus, Chemin des Mines 9, Geneva, 1202, Switzerland.|Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Biotech Campus, Chemin des Mines 9, Geneva, 1202, Switzerland.|Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Biotech Campus, Chemin des Mines 9, Geneva, 1202, Switzerland.|Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Biotech Campus, Chemin des Mines 9, Geneva, 1202, Switzerland. felix.schuermann@epfl.ch.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 6, 'Animals|Computer Simulation|Image Processing, Computer-Assisted|Microscopy|Models, Neurological|Neocortex|Nerve Net|Neurons|Optical Phenomena|Rats', 'Journal Article', '10.1186/s12859-017-1788-4', '28929974', 'We present a software workflow capable of building large scale, highly detailed and realistic volumetric models of neocortical circuits from the morphological skeletons of their digitally reconstructed neurons. The limitations of the existing approaches for creating those models are explained, and then, a multi-stage pipeline is discussed to overcome those limitations. Starting from the neuronal morphologies, we create smooth piecewise watertight polygonal models that can be efficiently utilized to synthesize continuous and plausible volumetric models of the neurons with solid voxelization. The somata of the neurons are reconstructed on a physically-plausible basis relying on the physics engine in Blender.'),
(98027, 2017, 'bmc', 'VISMapper: ultra-fast exhaustive cartography of viral insertion sites for gene therapy', 'Juanes, José M|Gallego, Asunción|Tárraga, Joaquín|Chaves, Felipe J|Marín-Garcia, Pablo|Medina, Ignacio|Arnau, Vicente|Dopazo, Joaquín', 'Departamento de Informática, Escuela Técnica Superior de Ingeniería (ETSE), Universidad de Valencia, 46100, Valencia, Burjassot, Spain.|Clinical Bioinformatics Research Area, Fundación Progreso y Salud, Hospital Virgen del Rocío, 41013, Sevilla, Spain.|Computational Genomics Department, Prince Felipe Research Center, 46012, Valencia, Spain.|Genotyping and Genetic Diagnosis Unit, Health Research Institute, INCLIVA, Valencia, Spain.|Genotyping and Genetic Diagnosis Unit, Health Research Institute, INCLIVA, Valencia, Spain.|HPC Service, University Information Services, University of Cambridge, Cambridge, UK.|Departamento de Informática, Escuela Técnica Superior de Ingeniería (ETSE), Universidad de Valencia, 46100, Valencia, Burjassot, Spain.|Clinical Bioinformatics Research Area, Fundación Progreso y Salud, Hospital Virgen del Rocío, 41013, Sevilla, Spain. joaquin.dopazo@juntadeandalucia.es.', 'Spain|Spain|Spain|Spain|Spain|United Kingdom|Spain|Spain', 0, 'Base Sequence|Genetic Therapy|Genetic Vectors|High-Throughput Nucleotide Sequencing|Humans|Internet|User-Computer Interface|Virus Integration', 'Journal Article', '10.1186/s12859-017-1837-z', '28931371', 'The possibility of integrating viral vectors to become a persistent part of the host genome makes them a crucial element of clinical gene therapy. However, viral integration has associated risks, such as the unintentional activation of oncogenes that can result in cancer. Therefore, the analysis of integration sites of retroviral vectors is a crucial step in developing safer vectors for therapeutic use.'),
(98028, 2017, 'bmc', 'Improving contig binning of metagenomic data using [Formula: see text] oligonucleotide frequency dissimilarity', 'Wang, Ying|Wang, Kun|Lu, Yang Young|Sun, Fengzhu', 'Department of Automation, Xiamen University, Xiamen, Fujian, 361005, China. wangying@xmu.edu.cn.|Department of Automation, Xiamen University, Xiamen, Fujian, 361005, China.|Molecular and Computational Biology Program, University of Southern California, Los Angeles, California, CA, 90089, USA.|Molecular and Computational Biology Program, University of Southern California, Los Angeles, California, CA, 90089, USA. fsun@usc.edu.', 'China|China|United States|United States', 6, 'Algorithms|Gene Frequency|Genome|High-Throughput Nucleotide Sequencing|Internet|Markov Chains|Metagenomics|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1835-1', '28931373', 'Metagenomics sequencing provides deep insights into microbial communities. To investigate their taxonomic structure, binning assembled contigs into discrete clusters is critical. Many binning algorithms have been developed, but their performance is not always satisfactory, especially for complex microbial communities, calling for further development.'),
(98029, 2017, 'bmc', 'VCFtoTree: a user-friendly tool to construct locus-specific alignments and phylogenies from thousands of anthropologically relevant genome sequences', 'Xu, Duo|Jaber, Yousef|Pavlidis, Pavlos|Gokcumen, Omer', 'Department of Biological Sciences, State University of New York at Buffalo, New York, 14260, USA.|Department of Biological Sciences, State University of New York at Buffalo, New York, 14260, USA.|Institute of Molecular Biology and biotechnology (IMBB), Foundation of Research and Technology--Hellas, Heraklion, Crete, Greece.|Department of Biological Sciences, State University of New York at Buffalo, New York, 14260, USA. omergokc@buffalo.edu.', 'United States|United States|Greece|United States', 5, 'Algorithms|Animals|Base Sequence|Genetic Loci|Genome, Human|Humans|INDEL Mutation|Phylogeny|Primates|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1844-0', '28950836', 'Constructing alignments and phylogenies for a given locus from large genome sequencing studies with relevant outgroups allow novel evolutionary and anthropological insights. However, no user-friendly tool has been developed to integrate thousands of recently available and anthropologically relevant genome sequences to construct complete sequence alignments and phylogenies.'),
(98030, 2017, 'bmc', 'pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms', 'Molnos, Sophie|Baumbach, Clemens|Wahl, Simone|Müller-Nurasyid, Martina|Strauch, Konstantin|Wang-Sattler, Rui|Waldenberger, Melanie|Meitinger, Thomas|Adamski, Jerzy|Kastenmüller, Gabi|Suhre, Karsten|Peters, Annette|Grallert, Harald|Theis, Fabian J|Gieger, Christian', 'Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany. Sophie.molnos@helmholtz-muenchen.de.|Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany.|Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany.|Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universität, Munich, Germany.|Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany.|Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany.|Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany.|German Center for Diabetes Research (DZD), Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany.|Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germ', 0, 'Algorithms|Computer Simulation|CpG Islands|Humans|Linear Models|Polymorphism, Single Nucleotide|Software|Time Factors', 'Journal Article', '10.1186/s12859-017-1838-y', '28962546', 'Genome-wide association studies allow us to understand the genetics of complex diseases. Human metabolism provides information about the disease-causing mechanisms, so it is usual to investigate the associations between genetic variants and metabolite levels. However, only considering genetic variants and their effects on one trait ignores the possible interplay between different omics layers. Existing tools only consider single-nucleotide polymorphism (SNP)-SNP interactions, and no practical tool is available for large-scale investigations of the interactions between pairs of arbitrary quantitative variables.'),
(98031, 2017, 'bmc', 'A machine learning approach for predicting methionine oxidation sites', 'Aledo, Juan C|Cantón, Francisco R|Veredas, Francisco J', 'Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, Bulevar de Louis Pasteur s/n, Málaga, 29071, Spain. caledo@uma.es.|Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, Bulevar de Louis Pasteur s/n, Málaga, 29071, Spain.|Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Bulevar de Louis Pasteur s/n, Málaga, 29071, Spain.', 'Spain|Spain|Spain', 12, 'Algorithms|Likelihood Functions|Machine Learning|Methionine|Models, Theoretical|Oxidation-Reduction|ROC Curve', 'Journal Article', '10.1186/s12859-017-1848-9', '28962549', 'The oxidation of protein-bound methionine to form methionine sulfoxide, has traditionally been regarded as an oxidative damage. However, recent evidences support the view of this reversible reaction as a regulatory post-translational modification. The perception that methionine sulfoxidation may provide a mechanism to the redox regulation of a wide range of cellular processes, has stimulated some proteomic studies. However, these experimental approaches are expensive and time-consuming. Therefore, computational methods designed to predict methionine oxidation sites are an attractive alternative. As a first approach to this matter, we have developed models based on random forests, support vector machines and neural networks, aimed at accurate prediction of sites of methionine oxidation.'),
(98032, 2017, 'bmc', 'RiceMetaSys for salt and drought stress responsive genes in rice: a web interface for crop improvement', 'Sandhu, Maninder|Sureshkumar, V|Prakash, Chandra|Dixit, Rekha|Solanke, Amolkumar U|Sharma, Tilak Raj|Mohapatra, Trilochan|S V, Amitha Mithra', 'ICAR-National Research Centre on Plant Biotechnology, LBS Building, Pusa Campus, New Delhi, 110012, India.|ICAR-National Research Centre on Plant Biotechnology, LBS Building, Pusa Campus, New Delhi, 110012, India.|ICAR-National Research Centre on Plant Biotechnology, LBS Building, Pusa Campus, New Delhi, 110012, India.|Shobhit University, Modipuram, Meerut, 250110, Uttar Pradesh, India.|ICAR-National Research Centre on Plant Biotechnology, LBS Building, Pusa Campus, New Delhi, 110012, India.|ICAR-National Research Centre on Plant Biotechnology, LBS Building, Pusa Campus, New Delhi, 110012, India.|Indian Council of Agricultural Research, Krishi Bhawan, New Delhi, 110001, India.|ICAR-National Research Centre on Plant Biotechnology, LBS Building, Pusa Campus, New Delhi, 110012, India. amithamithra.nrcpb@gmail.com.', 'India|India|India|India|India|India|India|India', 3, 'Databases, Genetic|Droughts|Genetic Markers|Internet|Microsatellite Repeats|Oryza|Sodium Chloride|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1846-y', '28964253', 'Genome-wide microarray has enabled development of robust databases for functional genomics studies in rice. However, such databases do not directly cater to the needs of breeders. Here, we have attempted to develop a web interface which combines the information from functional genomic studies across different genetic backgrounds with DNA markers so that they can be readily deployed in crop improvement. In the current version of the database, we have included drought and salinity stress studies since these two are the major abiotic stresses in rice.'),
(98033, 2017, 'bmc', 'Fangorn Forest (F2): a machine learning approach to classify genes and genera in the family Geminiviridae', 'Silva, José Cleydson F|Carvalho, Thales F M|Fontes, Elizabeth P B|Cerqueira, Fabio R', 'Department of Informatics, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-900, Brazil.|Department of Informatics, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-900, Brazil.|National Institute of Science and Technology in Plant-Pest Interactions/BIOAGRO, Campus Universitário, Viçosa, Minas Gerais, 36570-900, Brazil. bbfontes@ufv.br.|Department of Informatics, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-900, Brazil. frcerqueira@id.uff.br.', 'Brazil|Brazil|Brazil|Brazil', 3, 'Area Under Curve|DNA, Satellite|Geminiviridae|Internet|Machine Learning|Open Reading Frames|Plants|ROC Curve|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1839-x', '28964254', 'Geminiviruses infect a broad range of cultivated and non-cultivated plants, causing significant economic losses worldwide. The studies of the diversity of species, taxonomy, mechanisms of evolution, geographic distribution, and mechanisms of interaction of these pathogens with the host have greatly increased in recent years. Furthermore, the use of rolling circle amplification (RCA) and advanced metagenomics approaches have enabled the elucidation of viromes and the identification of many viral agents in a large number of plant species. As a result, determining the nomenclature and taxonomically classifying geminiviruses turned into complex tasks. In addition, the gene responsible for viral replication (particularly, the viruses belonging to the genus Mastrevirus) may be spliced due to the use of the transcriptional/splicing machinery in the host cells. However, the current tools have limitations concerning the identification of introns.'),
(98034, 2017, 'bmc', 'SILVA tree viewer: interactive web browsing of the SILVA phylogenetic guide trees', 'Beccati, Alan|Gerken, Jan|Quast, Christian|Yilmaz, Pelin|Glöckner, Frank Oliver', 'Department of Life Sciences & Chemistry, Jacobs University gGmbH, Bremen, Germany.|Department of Life Sciences & Chemistry, Jacobs University gGmbH, Bremen, Germany.|Microbial Genomics and Bioinformatics Research Group, Max Planck Institute for Marine Microbiology, Bremen, Germany.|Microbial Genomics and Bioinformatics Research Group, Max Planck Institute for Marine Microbiology, Bremen, Germany.|Department of Life Sciences & Chemistry, Jacobs University gGmbH, Bremen, Germany. fog@mpi-bremen.de.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Databases, Genetic|Internet|Phylogeny|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1841-3', '28964270', 'Phylogenetic trees are an important tool to study the evolutionary relationships among organisms. The huge amount of available taxa poses difficulties in their interactive visualization. This hampers the interaction with the users to provide feedback for the further improvement of the taxonomic framework.'),
(98035, 2017, 'bmc', 'SimBA: A methodology and tools for evaluating the performance of RNA-Seq bioinformatic pipelines', 'Audoux, Jérôme|Salson, Mikaël|Grosset, Christophe F|Beaumeunier, Sacha|Holder, Jean-Marc|Commes, Thérèse|Philippe, Nicolas', 'SeqOne, IRMB, CHRU de Montpellier -Hopital St Eloi, 80 avenue Augustin Fliche, Montpellier, 34295, France.|University Lille, CNRS, Centrale Lille, Inria, UMR 9189 - CRIStAL - Centre de Recherche en Informatique Signal et Automatique de Lille, Lille, F-59000, France.|University Bordeaux, Inserm, BMGIC, U1035, Bordeaux, 33076, France.|SeqOne, IRMB, CHRU de Montpellier -Hopital St Eloi, 80 avenue Augustin Fliche, Montpellier, 34295, France.|SeqOne, IRMB, CHRU de Montpellier -Hopital St Eloi, 80 avenue Augustin Fliche, Montpellier, 34295, France.|SeqOne, IRMB, CHRU de Montpellier -Hopital St Eloi, 80 avenue Augustin Fliche, Montpellier, 34295, France.|SeqOne, IRMB, CHRU de Montpellier -Hopital St Eloi, 80 avenue Augustin Fliche, Montpellier, 34295, France. nphilippe.research@gmail.com.', 'France|France|France|France|France|France|France', 0, 'Computational Biology|Computer Simulation|Gene Fusion|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Polymorphism, Single Nucleotide|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-017-1831-5', '28969586', 'The evolution of next-generation sequencing (NGS) technologies has led to increased focus on RNA-Seq. Many bioinformatic tools have been developed for RNA-Seq analysis, each with unique performance characteristics and configuration parameters. Users face an increasingly complex task in understanding which bioinformatic tools are best for their specific needs and how they should be configured. In order to provide some answers to these questions, we investigate the performance of leading bioinformatic tools designed for RNA-Seq analysis and propose a methodology for systematic evaluation and comparison of performance to help users make well informed choices.'),
(98036, 2017, 'bmc', 'BioCarian: search engine for exploratory searches in heterogeneous biological databases', 'Zaki, Nazar|Tennakoon, Chandana', 'Department of Comp. Science and Software Engineering, College of Info. Technology, United Arab Emirates University (UAEU), Al Ain, PO Box 15551, United Arab Emirates. nzaki@uaeu.ac.ae.|Department of Comp. Science and Software Engineering, College of Info. Technology, United Arab Emirates University (UAEU), Al Ain, PO Box 15551, United Arab Emirates.', 'United Arab Emirates|United Arab Emirates', 3, 'Databases, Factual|Internet|Search Engine|Software', 'Journal Article', '10.1186/s12859-017-1840-4', '28969593', 'There are a large number of biological databases publicly available for scientists in the web. Also, there are many private databases generated in the course of research projects. These databases are in a wide variety of formats. Web standards have evolved in the recent times and semantic web technologies are now available to interconnect diverse and heterogeneous sources of data. Therefore, integration and querying of biological databases can be facilitated by techniques used in semantic web. Heterogeneous databases can be converted into Resource Description Format (RDF) and queried using SPARQL language. Searching for exact queries in these databases is trivial. However, exploratory searches need customized solutions, especially when multiple databases are involved. This process is cumbersome and time consuming for those without a sufficient background in computer science. In this context, a search engine facilitating exploratory searches of databases would be of great help to the scientific community.'),
(98037, 2017, 'bmc', 'MetaComp: comprehensive analysis software for comparative meta-omics including comparative metagenomics', 'Zhai, Peng|Yang, Longshu|Guo, Xiao|Wang, Zhe|Guo, Jiangtao|Wang, Xiaoqi|Zhu, Huaiqiu', 'State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.|State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.|Center for Quantitative Biology, Peking University, Beijing, 100871, China.|State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.|State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.|State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.|State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China. hqzhu@pku.edu.cn.', 'China|China|China|China|China|China|China', 6, 'Data Interpretation, Statistical|Gene Expression Profiling|Genes, Microbial|Humans|Metabolomics|Metagenomics|Microbiota|Proteomics|Software', 'Journal Article', '10.1186/s12859-017-1849-8', '28969605', 'During the past decade, the development of high throughput nucleic sequencing and mass spectrometry analysis techniques have enabled the characterization of microbial communities through metagenomics, metatranscriptomics, metaproteomics and metabolomics data. To reveal the diversity of microbial communities and interactions between living conditions and microbes, it is necessary to introduce comparative analysis based upon integration of all four types of data mentioned above. Comparative meta-omics, especially comparative metageomics, has been established as a routine process to highlight the significant differences in taxon composition and functional gene abundance among microbiota samples. Meanwhile, biologists are increasingly concerning about the correlations between meta-omics features and environmental factors, which may further decipher the adaptation strategy of a microbial community.'),
(98038, 2017, 'bmc', 'Integrated pipeline for inferring the evolutionary history of a gene family embedded in the species tree: a case study on the STIMATE gene family', 'Song, Jia|Zheng, Sisi|Nguyen, Nhung|Wang, Youjun|Zhou, Yubin|Lin, Kui', 'MOE Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, 100875, China.|Beijing Key Laboratory of Gene Resources and Molecular Development College of Life Sciences, Beijing Normal University, Beijing, 100875, China.|Center for Translational Cancer Research, Institute of Biosciences and Technology, Department of Medical Physiology, College of Medicine, Texas A&M University, Houston, TX, 77030, USA.|Beijing Key Laboratory of Gene Resources and Molecular Development College of Life Sciences, Beijing Normal University, Beijing, 100875, China.|Center for Translational Cancer Research, Institute of Biosciences and Technology, Department of Medical Physiology, College of Medicine, Texas A&M University, Houston, TX, 77030, USA.|MOE Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, 100875, China. linkui@bnu.edu.cn.', 'China|China|United States|China|United States|China', 1, 'Algorithms|Animals|Evolution, Molecular|Gene Duplication|Humans|Likelihood Functions|Membrane Proteins|Models, Genetic|Multigene Family|Phylogeny', 'Journal Article', '10.1186/s12859-017-1850-2', '28974198', 'Because phylogenetic inference is an important basis for answering many evolutionary problems, a large number of algorithms have been developed. Some of these algorithms have been improved by integrating gene evolution models with the expectation of accommodating the hierarchy of evolutionary processes. To the best of our knowledge, however, there still is no single unifying model or algorithm that can take all evolutionary processes into account through a stepwise or simultaneous method.'),
(98039, 2017, 'bmc', 'Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data', 'Paulson, Joseph N|Chen, Cho-Yi|Lopes-Ramos, Camila M|Kuijjer, Marieke L|Platig, John|Sonawane, Abhijeet R|Fagny, Maud|Glass, Kimberly|Quackenbush, John', 'Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.|Channing Division of Network Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA. johnq@jimmy.harvard.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 12, 'Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Humans|Molecular Sequence Annotation|Organ Specificity|Principal Component Analysis|Quality Control|Reference Standards|Sample Size|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-017-1847-x', '28974199', 'Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data - critical first steps for any subsequent analysis.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98040, 2017, 'bmc', 'Anaconda: AN automated pipeline for somatic COpy Number variation Detection and Annotation from tumor exome sequencing data', 'Gao, Jianing|Wan, Changlin|Zhang, Huan|Li, Ao|Zang, Qiguang|Ban, Rongjun|Ali, Asim|Yu, Zhenghua|Shi, Qinghua|Jiang, Xiaohua|Zhang, Yuanwei', 'Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Hefei, Anhui, 230027, China.|Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Hefei, Anhui, 230027, China.|Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Hefei, Anhui, 230027, China.|School of Information Science and Technology, University of Science and Technology of China, Hefei, 230027, China.|School of Information Science and Technology, University of Science and Technology of China, Hefei, 230027, China.|Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Hefei, Anhui, 230027, China.|Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Hefei, Anhui, 230027, China.|School of Information Science and Technology, University of Science and Technology of China, Hefei, 230027, China.|Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Hefei, Anhui, 230027, China.|Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Hefei, Anhui, 230027, China. biojxh@ustc.edu.cn.|Molecular and Cell Genetics Laboratory, The CAS Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, CAS Center for Excellence in Molecular Cell Science, University of Science and Technology of China, Hefei, Anhui, 230027, China. zyuanwei@ustc.edu.cn.', 'China|China|China|China|China|China|China|China|China|China|China', 0, 'Algorithms|Automation|DNA Copy Number Variations|Databases, Genetic|Exome|Exons|Humans|Molecular Sequence Annotation|Neoplasms|Reproducibility of Results|Whole Exome Sequencing', 'Journal Article', '10.1186/s12859-017-1833-3', '28974218', 'Copy number variations (CNVs) are the main genetic structural variations in cancer genome. Detecting CNVs in genetic exome region is efficient and cost-effective in identifying cancer associated genes. Many tools had been developed accordingly and yet these tools lack of reliability because of high false negative rate, which is intrinsically caused by genome exonic bias.'),
(98041, 2017, 'bmc', 'Block-based characterization of protease specificity from substrate sequence profile', 'Qi, Enfeng|Wang, Dongyu|Gao, Bo|Li, Yang|Li, Guojun', 'School of Mathematics, Shandong University, Jinan, 250100, China.|The State Key Laboratory of Microbial Technology, Shandong University, Jinan, 250100, China.|School of Mathematics, Shandong University, Jinan, 250100, China.|School of Mathematics, Shandong University, Jinan, 250100, China.|School of Mathematics, Shandong University, Jinan, 250100, China. guojunsdu@gmail.com.', 'China|China|China|China|China', 3, 'Amino Acid Sequence|Amino Acids|Databases, Protein|Entropy|Peptide Hydrolases|Principal Component Analysis|Substrate Specificity', 'Journal Article', '10.1186/s12859-017-1851-1', '28974219', 'The mechanism of action of proteases has been widely studied based on substrate specificity. Prior research has been focused on the amino acids at a single amino acid site, but rarely on combinations of amino acids around the cleavage bond.'),
(98042, 2017, 'bmc', 'VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization', 'Bolgár, Bence|Antal, Péter', 'Department of Measurement and Information Systems, Budapest University of Technology and Economics, Magyar tudósok krt. 2., Budapest, 1117, Hungary. bolgar@mit.bme.hu.|Department of Measurement and Information Systems, Budapest University of Technology and Economics, Magyar tudósok krt. 2., Budapest, 1117, Hungary.', 'Hungary|Hungary', 8, 'Algorithms|Area Under Curve|Bayes Theorem|Drug Interactions|Humans', 'Journal Article', '10.1186/s12859-017-1845-z', '28978313', 'Computational fusion approaches to drug-target interaction (DTI) prediction, capable of utilizing multiple sources of background knowledge, were reported to achieve superior predictive performance in multiple studies. Other studies showed that specificities of the DTI task, such as weighting the observations and focusing the side information are also vital for reaching top performance.'),
(98043, 2017, 'bmc', 'Interpretation of microbiota-based diagnostics by explaining individual classifier decisions', 'Eck, A|Zintgraf, L M|de Groot, E F J|de Meij, T G J|Cohen, T S|Savelkoul, P H M|Welling, M|Budding, A E', 'Department of Medical Microbiology and Infection Control, VU University medical center, Amsterdam, The Netherlands. a.eckhauer@vumc.nl.|Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands.|Department of Gastroenterology and Hepatology, VU University medical center, Amsterdam, The Netherlands.|Department of Pediatric Gastroenterology, VU University medical center, Amsterdam, The Netherlands.|Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands.|Department of Medical Microbiology and Infection Control, VU University medical center, Amsterdam, The Netherlands.|Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands.|Department of Medical Microbiology and Infection Control, VU University medical center, Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 3, 'Algorithms|Bacteria|Enteral Nutrition|Gastrointestinal Microbiome|Humans|Inflammatory Bowel Diseases|Meta-Analysis as Topic|Reproducibility of Results|Skin|Software|Species Specificity', 'Journal Article', '10.1186/s12859-017-1843-1', '28978318', 'The human microbiota is associated with various disease states and holds a great promise for non-invasive diagnostics. However, microbiota data is challenging for traditional diagnostic approaches: It is high-dimensional, sparse and comprises of high inter-personal variation. State of the art machine learning tools are therefore needed to achieve this goal. While these tools have the ability to learn from complex data and interpret patterns therein that cannot be identified by humans, they often operate as black boxes, offering no insight into their decision-making process. In most cases, it is difficult to represent the learning of a classifier in a comprehensible way, which makes them prone to be mistrusted, or even misused, in a clinical environment. In this study, we aim to elucidate microbiota-based classifier decisions in a biologically meaningful context to allow their interpretation.'),
(98044, 2017, 'bmc', 'CNN-based ranking for biomedical entity normalization', 'Li, Haodi|Chen, Qingcai|Tang, Buzhou|Wang, Xiaolong|Xu, Hua|Wang, Baohua|Huang, Dong', 'Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology, Shenzhen, GuangDong, China.|Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology, Shenzhen, GuangDong, China. qingcai.chen@gmail.com.|Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology, Shenzhen, GuangDong, China. tangbuzhou@gmail.com.|Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology, Shenzhen, GuangDong, China.|School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA.|College of Mathematics and statistics, Shenzhen University, Shenzhen, GuangDong, China.|Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology, Shenzhen, GuangDong, China.', 'China|China|China|China|United States|China|China', 16, 'Algorithms|Biomedical Research|Databases as Topic|Humans|Neural Networks (Computer)|Reference Standards|Semantics', 'Journal Article', '10.1186/s12859-017-1805-7', '28984180', 'Most state-of-the-art biomedical entity normalization systems, such as rule-based systems, merely rely on morphological information of entity mentions, but rarely consider their semantic information. In this paper, we introduce a novel convolutional neural network (CNN) architecture that regards biomedical entity normalization as a ranking problem and benefits from semantic information of biomedical entities.'),
(98045, 2017, 'bmc', 'Identification of genome-wide non-canonical spliced regions and analysis of biological functions for spliced sequences using Read-Split-Fly', 'Bai, Yongsheng|Kinne, Jeff|Ding, Lizhong|Rath, Ethan C|Cox, Aaron|Naidu, Siva Dharman', 'Department of Biology, Indiana State University, 600 Chestnut Street, Terre Haute, IN, 47809, USA. Yongsheng.Bai@indstate.edu.|Department of Mathematics and Computer Science, Indiana State University, 200 North Seventh Street, Terre Haute, IN, 47809, USA.|Department of Biology, Indiana State University, 600 Chestnut Street, Terre Haute, IN, 47809, USA.|Department of Biology, Indiana State University, 600 Chestnut Street, Terre Haute, IN, 47809, USA.|Department of Mathematics and Computer Science, Indiana State University, 200 North Seventh Street, Terre Haute, IN, 47809, USA.|Department of Mathematics and Computer Science, Indiana State University, 200 North Seventh Street, Terre Haute, IN, 47809, USA.', 'India|India|India|India|India|India', 2, 'Algorithms|Alternative Splicing|Base Sequence|Databases, Nucleic Acid|Genome|Humans|Introns|MicroRNAs|RNA Splice Sites', 'Journal Article', '10.1186/s12859-017-1801-y', '28984182', 'It is generally thought that most canonical or non-canonical splicing events involving U2- and U12 spliceosomes occur within nuclear pre-mRNAs. However, the question of whether at least some U12-type splicing occurs in the cytoplasm is still unclear. In recent years next-generation sequencing technologies have revolutionized the field. The Read-Split-Walk (RSW) and Read-Split-Run (RSR) methods were developed to identify genome-wide non-canonical spliced regions including special events occurring in cytoplasm. As the significant amount of genome/transcriptome data such as, Encyclopedia of DNA Elements (ENCODE) project, have been generated, we have advanced a newer more memory-efficient version of the algorithm, Read-Split-Fly (RSF), which can detect non-canonical spliced regions with higher sensitivity and improved speed. The RSF algorithm also outputs the spliced sequences for further downstream biological function analysis.'),
(98046, 2017, 'bmc', 'XBSeq2: a fast and accurate quantification of differential expression and differential polyadenylation', 'Liu, Yuanhang|Wu, Ping|Zhou, Jingqi|Johnson-Pais, Teresa L|Lai, Zhao|Chowdhury, Wasim H|Rodriguez, Ronald|Chen, Yidong', 'Greehey Childrens Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.|Department of Urology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.|Greehey Childrens Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.|Department of Urology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.|Greehey Childrens Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.|Department of Urology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.|Department of Urology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.|Greehey Childrens Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA. Cheny8@uthscsa.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States', 8, 'Algorithms|Carcinoma, Renal Cell|Databases, Nucleic Acid|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|High-Throughput Nucleotide Sequencing|Humans|Kidney Neoplasms|Polyadenylation|ROC Curve|Sequence Analysis, RNA|Software|Statistics as Topic', 'Journal Article', '10.1186/s12859-017-1803-9', '28984183', 'RNA sequencing (RNA-seq) is a high throughput technology that profiles gene expression in a genome-wide manner. RNA-seq has been mainly used for testing differential expression (DE) of transcripts between two conditions and has recently been used for testing differential alternative polyadenylation (APA). In the past, many algorithms have been developed for detecting differentially expressed genes (DEGs) from RNA-seq experiments, including the one we developed, XBSeq, which paid special attention to the context-specific background noise that is ignored in conventional gene expression quantification and DE analysis of RNA-seq data.'),
(98047, 2017, 'bmc', 'Using machine learning algorithms to identify genes essential for cell survival', 'Philips, Santosh|Wu, Heng-Yi|Li, Lang', 'Center for Computational Biology and Bioinformatics, Indiana University, 410 West 10th Street, HITS 5003 lab, Indianapolis, IN, 46202, USA.|Center for Computational Biology and Bioinformatics, Indiana University, 410 West 10th Street, HITS 5003 lab, Indianapolis, IN, 46202, USA.|Center for Computational Biology and Bioinformatics, Indiana University, 410 West 10th Street, HITS 5003 lab, Indianapolis, IN, 46202, USA. lali@iu.edu.', 'India|India|India', 3, 'Algorithms|Animals|Cell Line|Cell Survival|Genes, Essential|Humans|Machine Learning|Neoplasms|PubMed|RNA Interference|RNA, Small Interfering|Reproducibility of Results', 'Journal Article', '10.1186/s12859-017-1799-1', '28984184', 'With the explosion of data comes a proportional opportunity to identify novel knowledge with the potential for application in targeted therapies. In spite of this huge amounts of data, the solutions to treating complex disease is elusive. One reason being that these diseases are driven by a network of genes that need to be targeted in order to understand and treat them effectively. Part of the solution lies in mining and integrating information from various disciplines. Here we propose a machine learning method to mining through publicly available literature on RNA interference with the goal of identifying genes essential for cell survival.'),
(98048, 2017, 'bmc', 'STRScan: targeted profiling of short tandem repeats in whole-genome sequencing data', 'Tang, Haixu|Nzabarushimana, Etienne', 'School of Informatics and Computing, Indiana University, 150 S. Woodlawn Avenue, Bloomington, 47405, IN, USA. hatang@indiana.edu.|School of Informatics and Computing, Indiana University, 150 S. Woodlawn Avenue, Bloomington, 47405, IN, USA.', 'India|India', 8, 'Algorithms|Databases, Nucleic Acid|High-Throughput Nucleotide Sequencing|Humans|Microsatellite Repeats|Software|Whole Genome Sequencing', 'Journal Article', '10.1186/s12859-017-1800-z', '28984185', 'Short tandem repeats (STRs) are found in many prokaryotic and eukaryotic genomes, and are commonly used as genetic markers, in particular for identity and parental testing in DNA forensics. The unstable expansion of some STRs was associated with various genetic disorders (e.g., the Huntington disease), and thus was used in genetic testing for screening individuals at high risk. Traditional STR analyses were based on the PCR amplification of STR loci followed by gel electrophoresis. With the availability of massive whole genome sequencing data, it becomes practical to mine STR profiles in silico from genome sequences. Software tools such as lobSTR and STR-FM have been developed to address these demands, which are, however, built upon whole genome reads mapping tools, and thus may not be sensitive enough.'),
(98049, 2017, 'bmc', 'PennCNV in whole-genome sequencing data', 'de Araújo Lima, Leandro|Wang, Kai', 'Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, 90089, CA, USA.|Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, 90089, CA, USA. kw2701@cumc.columbia.edu.', 'United States|United States', 5, 'Algorithms|DNA Copy Number Variations|Databases, Nucleic Acid|Gene Deletion|Gene Duplication|Gene Frequency|Genome, Human|Genome-Wide Association Study|Humans|Loss of Heterozygosity|Markov Chains|Polymorphism, Single Nucleotide|Reproducibility of Results|Whole Genome Sequencing', 'Journal Article', '10.1186/s12859-017-1802-x', '28984186', 'The use of high-throughput sequencing data has improved the results of genomic analysis due to the resolution of mapping algorithms. Although several tools for copy-number variation calling in whole genome sequencing have been published, the noisy nature of sequencing data is still a limitation for accuracy and concordance among such tools. To assess the performance of PennCNV original algorithm for array data in whole genome sequencing data, we processed mapping (BAM) files to extract coverage, representing log R ratio (LRR) of signal intensity, and B allele frequency (BAF).'),
(98050, 2017, 'bmc', 'New statistical methods for estimation of recombination fractions in F2 population', 'Tan, Yuan-De|Zhang, Xiang H F|Mo, Qianxing', 'Dan L. Ducan Cancer Center, Baylor College of Medicine, Houston, TX, USA.|Dan L. Ducan Cancer Center, Baylor College of Medicine, Houston, TX, USA. xiangz@bcm.edu.|Dan L. Ducan Cancer Center, Baylor College of Medicine, Houston, TX, USA. qmo@bcm.edu.', 'United States|United States|United States', 1, 'Algorithms|Animals|Computer Simulation|Crosses, Genetic|Female|Genes, Dominant|Genetic Linkage|Genetic Loci|Genetic Markers|Least-Squares Analysis|Male|Mice|Models, Genetic|Recombination, Genetic|Statistics as Topic', 'Journal Article', '10.1186/s12859-017-1804-8', '28984187', 'Background Dominant markers in an F2 population or a hybrid population have much less linkage information in repulsion phase than in coupling phase. Linkage analysis produces two separate complementary marker linkage maps that have little use in disease association analysis and breeding. There is a need to develop efficient statistical methods and computational algorithms to construct or merge a complete linkage dominant marker maps. The key for doing so is to efficiently estimate recombination fractions between dominant markers in repulsion phases.  Result We proposed an expectation least square (ELS) algorithm and binomial analysis of three-point gametes (BAT) for estimating gamete frequencies from F2 dominant and codominant marker data, respectively. The results obtained from simulated and real genotype datasets showed that the ELS algorithm was able to accurately estimate frequencies of gametes and outperformed the EM algorithm in estimating recombination fractions between dominant loci and recovering true linkage maps of 6 dominant loci in coupling and unknown linkage phases. Our BAT method also had smaller variances in estimation of two-point recombination fractions than the EM algorithm.  Conclusion ELS is a powerful method for accurate estimation of gamete frequencies in dominant three-locus system in an F2 population and BAT is a computationally efficient and fast method for estimating frequencies of three-point codominant gametes.'),
(98051, 2017, 'bmc', 'ScaPD: a database for human scaffold proteins', 'Han, Xiaomei|Wang, Jenny|Wang, Jie|Liu, Sheng|Hu, Jianfei|Zhu, Heng|Qian, Jiang', 'Department of Ophthalmology, Johns Hopkins School of Medicine, Baltimore, MD, USA.|The Horace Mann School in Bronx, Bronx, NY, USA.|Department of Gastroenterology, Zhejiang Provincial Peoples Hospital, Hangzhou, Zhejiang, China.|Department of Gastroenterology, Zhejiang Provincial Peoples Hospital, Hangzhou, Zhejiang, China.|Department of Ophthalmology, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Department of Ophthalmology, Johns Hopkins School of Medicine, Baltimore, MD, USA. jiang.qian@jhmi.edu.', 'United States|United States|China|China|United States|United States|United States', 3, 'Amino Acid Sequence|Databases, Protein|Gene Ontology|Humans|Protein Domains|Proteins|Signal Transduction|Statistics as Topic', 'Journal Article', '10.1186/s12859-017-1806-6', '28984188', 'Scaffold proteins play a critical role in an increasing number of biological signaling processes, including simple tethering mechanism, regulating selectivity in pathways, shaping cellular behaviors. While many databases document the signaling pathways, few databases are devoted to the scaffold proteins that medicate signal transduction.'),
(98052, 2017, 'bmc', 'The International Conference on Intelligent Biology and Medicine (ICIBM) 2016: from big data to big analytical tools', 'Liu, Zhandong|Zheng, W Jim|Allen, Genevera I|Liu, Yin|Ruan, Jianhua|Zhao, Zhongming', 'Jan and Dan Duncan Neurological Research Institute, Texas Childrens Hospital, Houston, TX, 77030, USA. zhandonl@bcm.edu.|School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, 77030, USA.|Jan and Dan Duncan Neurological Research Institute, Texas Childrens Hospital, Houston, TX, 77030, USA.|Department of Neurobiology and Anatomy, The University of Texas Medical School at Houston, Houston, TX, 77030, USA.|Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX, 78249, USA.|Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. zhongming.zhao@uth.tmc.edu.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Biology|Congresses as Topic|DNA Copy Number Variations|Humans|Internationality|Machine Learning|Medicine|Neural Networks (Computer)|RNA Splicing|Sequence Analysis, RNA|Statistics as Topic', 'Editorial', '10.1186/s12859-017-1797-3', '28984189', 'The 2016 International Conference on Intelligent Biology and Medicine (ICIBM 2016) was held on December 8-10, 2016 in Houston, Texas, USA. ICIBM included eight scientific sessions, four tutorials, one poster session, four highlighted talks and four keynotes that covered topics on 3D genomics structural analysis, next generation sequencing (NGS) analysis, computational drug discovery, medical informatics, cancer genomics, and systems biology. Here, we present a summary of the nine research articles selected from ICIBM 2016 program for publishing in BMC Bioinformatics.'),
(98053, 2017, 'bmc', 'Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma', 'Young, Jonathan D|Cai, Chunhui|Lu, Xinghua', 'Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA, 15206, USA. jdy10@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA, 15206, USA.|Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA, 15206, USA.', 'United States|United States|United States', 17, 'Algorithms|Brain Neoplasms|Cluster Analysis|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Glioblastoma|Humans|Machine Learning|Prognosis', 'Journal Article', '10.1186/s12859-017-1798-2', '28984190', 'One approach to improving the personalized treatment of cancer is to understand the cellular signaling transduction pathways that cause cancer at the level of the individual patient. In this study, we used unsupervised deep learning to learn the hierarchical structure within cancer gene expression data. Deep learning is a group of machine learning algorithms that use multiple layers of hidden units to capture hierarchically related, alternative representations of the input data. We hypothesize that this hierarchical structure learned by deep learning will be related to the cellular signaling system.'),
(98054, 2017, 'bmc', 'An integrative approach to predicting the functional effects of small indels in non-coding regions of the human genome', 'Ferlaino, Michael|Rogers, Mark F|Shihab, Hashem A|Mort, Matthew|Cooper, David N|Gaunt, Tom R|Campbell, Colin', 'Big Data Institute, University of Oxford, Oxford, OX3 7LF, UK. michael.ferlaino@bdi.ox.ac.uk.|Intelligent Systems Laboratory, University of Bristol, Bristol, BS8 1UB, UK.|MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK.|Institute of Medical Genetics, Cardiff University, Cardiff, CF14 4XN, UK.|Institute of Medical Genetics, Cardiff University, Cardiff, CF14 4XN, UK.|MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK.|Intelligent Systems Laboratory, University of Bristol, Bristol, BS8 1UB, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|DNA, Intergenic|Genetics, Population|Genome, Human|Humans|INDEL Mutation|Phenotype|ROC Curve|Reproducibility of Results|Software', 'Journal Article', '10.1186/s12859-017-1862-y', '28985712', 'Small insertions and deletions (indels) have a significant influence in human disease and, in terms of frequency, they are second only to single nucleotide variants as pathogenic mutations. As the majority of mutations associated with complex traits are located outside the exome, it is crucial to investigate the potential pathogenic impact of indels in non-coding regions of the human genome.'),
(98055, 2017, 'bmc', 'A new synonym-substitution method to enrich the human phenotype ontology', 'Taboada, Maria|Rodriguez, Hadriana|Gudivada, Ranga C|Martinez, Diego', 'Department of Electronics & Computer Science, University of Santiago de Compostela, Campus Vida, Santiago de Compostela, 15705, Spain. maria.taboada@usc.es.|Department of Electronics & Computer Science, University of Santiago de Compostela, Campus Vida, Santiago de Compostela, 15705, Spain.|CareCentrix, Hartford, 06103, Conneticut, USA.|Department of Applied Physics, University of Santiago de Compostela, 15705, Santiago de Compostela, Campus Vida, Spain.', 'Spain|Spain|United States|Spain', 0, 'Biological Ontologies|Data Mining|Humans|Phenotype|Semantics', 'Journal Article', '10.1186/s12859-017-1858-7', '29017443', 'Named entity recognition is critical for biomedical text mining, where it is not unusual to find entities labeled by a wide range of different terms. Nowadays, ontologies are one of the crucial enabling technologies in bioinformatics, providing resources for improved natural language processing tasks. However, biomedical ontology-based named entity recognition continues to be a major research problem.'),
(98056, 2017, 'bmc', 'Protein remote homology detection based on bidirectional long short-term memory', 'Li, Shumin|Chen, Junjie|Liu, Bin', 'School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, 518055, China.|School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, 518055, China.|School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, 518055, China. bliu@hit.edu.cn.', 'China|China|China', 20, 'Algorithms|Databases, Protein|Proteins|ROC Curve|Structural Homology, Protein', 'Journal Article', '10.1186/s12859-017-1842-2', '29017445', 'Protein remote homology detection plays a vital role in studies of protein structures and functions. Almost all of the traditional machine leaning methods require fixed length features to represent the protein sequences. However, it is never an easy task to extract the discriminative features with limited knowledge of proteins. On the other hand, deep learning technique has demonstrated its advantage in automatically learning representations. It is worthwhile to explore the applications of deep learning techniques to the protein remote homology detection.'),
(98057, 2017, 'bmc', 'LSTrAP: efficiently combining RNA sequencing data into co-expression networks', 'Proost, Sebastian|Krawczyk, Agnieszka|Mutwil, Marek', 'Max-Planck Institute for Molecular Plant Physiology, Am Muehlenberg 1, 14476, Potsdam, Germany.|Max-Planck Institute for Molecular Plant Physiology, Am Muehlenberg 1, 14476, Potsdam, Germany.|Max-Planck Institute for Molecular Plant Physiology, Am Muehlenberg 1, 14476, Potsdam, Germany. mutwil@mpimp-golm.mpg.de.', 'Germany|Germany|Germany', 0, 'Arabidopsis|Gene Expression Profiling|Gene Expression Regulation, Plant|Gene Regulatory Networks|Principal Component Analysis|Quality Control|Sequence Analysis, RNA|Software|Sorghum|Statistics as Topic', 'Journal Article', '10.1186/s12859-017-1861-z', '29017446', 'Since experimental elucidation of gene function is often laborious, various in silico methods have been developed to predict gene function of uncharacterized genes. Since functionally related genes are often expressed in the same tissues, conditions and developmental stages (co-expressed), functional annotation of characterized genes can be transferred to co-expressed genes lacking annotation. With genome-wide expression data available, the construction of co-expression networks, where genes are nodes and edges connect significantly co-expressed genes, provides unprecedented opportunities to predict gene function. However, the construction of such networks requires large volumes of high-quality data, multiple processing steps and a considerable amount of computation power. While efficient tools exist to process RNA-Seq data, pipelines which combine them to construct co-expression networks efficiently are currently lacking.'),
(98058, 2017, 'bmc', 'An attention-based effective neural model for drug-drug interactions extraction', 'Zheng, Wei|Lin, Hongfei|Luo, Ling|Zhao, Zhehuan|Li, Zhengguang|Zhang, Yijia|Yang, Zhihao|Wang, Jian', 'College of Computer Science and Technology, Dalian University of Technology, Dalian, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, China. hflin@dlut.edu.cn.|College of Computer Science and Technology, Dalian University of Technology, Dalian, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, China.', 'China|China|China|China|China|China|China|China', 27, 'Algorithms|Databases as Topic|Drug Interactions|Humans|Models, Theoretical|Neural Networks (Computer)|Publications|Support Vector Machine', 'Journal Article', '10.1186/s12859-017-1855-x', '29017459', 'Drug-drug interactions (DDIs) often bring unexpected side effects. The clinical recognition of DDIs is a crucial issue for both patient safety and healthcare cost control. However, although text-mining-based systems explore various methods to classify DDIs, the classification performance with regard to DDIs in long and complex sentences is still unsatisfactory.'),
(98059, 2017, 'bmc', 'Identify Huntingtons disease associated genes based on restricted Boltzmann machine with RNA-seq data', 'Jiang, Xue|Zhang, Han|Duan, Feng|Quan, Xiongwen', 'College of Computer and Control Engineering, Nankai University, Tongyan Road, Tianjin, 300350, China.|College of Computer and Control Engineering, Nankai University, Tongyan Road, Tianjin, 300350, China.|College of Computer and Control Engineering, Nankai University, Tongyan Road, Tianjin, 300350, China.|College of Computer and Control Engineering, Nankai University, Tongyan Road, Tianjin, 300350, China. quanxw@nankai.edu.cn.', 'China|China|China|China', 0, 'Algorithms|Cluster Analysis|Computer Simulation|Disease Progression|Gene Expression Regulation|Genetic Association Studies|Genetic Predisposition to Disease|Humans|Huntington Disease|Molecular Sequence Annotation|Neurons|ROC Curve|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-017-1859-6', '29020921', 'Predicting disease-associated genes is helpful for understanding the molecular mechanisms during the disease progression. Since the pathological mechanisms of neurodegenerative diseases are very complex, traditional statistic-based methods are not suitable for identifying key genes related to the disease development. Recent studies have shown that the computational models with deep structure can learn automatically the features of biological data, which is useful for exploring the characteristics of gene expression during the disease progression.'),
(98060, 2017, 'bmc', 'VDJPipe: a pipelined tool for pre-processing immune repertoire sequencing data', 'Christley, Scott|Levin, Mikhail K|Toby, Inimary T|Fonner, John M|Monson, Nancy L|Rounds, William H|Rubelt, Florian|Scarborough, Walter|Scheuermann, Richard H|Cowell, Lindsay G', 'Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, 75390, USA.|Bank of America Corporate Center, Charlotte, NC, 28202, USA.|Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, 75390, USA.|Texas Advanced Computing Center, Austin, TX, 78758-4497, USA.|Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Dallas, TX, 75390, USA.|Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, 75390, USA.|Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, 94305, USA.|Texas Advanced Computing Center, Austin, TX, 78758-4497, USA.|J. Craig Venter Institute, La Jolla, CA, 92037, USA.|Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, 75390, USA. lindsay.cowell@utsouthwestern.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 6, 'Animals|B-Lymphocytes|DNA Primers|High-Throughput Nucleotide Sequencing|Humans|Immunoglobulin G|Mice|Software|Time Factors', 'Journal Article', '10.1186/s12859-017-1853-z', '29020925', 'Pre-processing of high-throughput sequencing data for immune repertoire profiling is essential to insure high quality input for downstream analysis. VDJPipe is a flexible, high-performance tool that can perform multiple pre-processing tasks with just a single pass over the data files.'),
(98061, 2017, 'bmc', 'Partitioning of functional gene expression data using principal points', 'Kim, Jaehee|Kim, Haseong', 'Department of Statistics, Duksung Womens University, Seoul, South Korea. jaehee@duksung.ac.kr.|Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, South Korea.', '-|-', 0, 'Algorithms|Cluster Analysis|Escherichia coli|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Gene Expression Regulation, Fungal|Gene Ontology|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Stochastic Processes', 'Journal Article', '10.1186/s12859-017-1860-0', '29025390', 'DNA microarrays offer motivation and hope for the simultaneous study of variations in multiple genes. Gene expression is a temporal process that allows variations in expression levels with a characterized gene function over a period of time. Temporal gene expression curves can be treated as functional data since they are considered as independent realizations of a stochastic process. This process requires appropriate models to identify patterns of gene functions. The partitioning of the functional data can find homogeneous subgroups of entities for the massive genes within the inherent biological networks. Therefor it can be a useful technique for the analysis of time-course gene expression data. We propose a new self-consistent partitioning method of functional coefficients for individual expression profiles based on the orthonormal basis system.'),
(98062, 2017, 'bmc', 'Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods', 'Notaro, Marco|Schubach, Max|Robinson, Peter N|Valentini, Giorgio', 'Anacleto Lab - Dipartimento di Informatica, Universitá degli Studi di Milano, Via Comelico 39, Milan, 20135, Italy.|Institute for Medical and Human Genetics, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, Berlin, 13353, Germany.|Institute for Medical and Human Genetics, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, Berlin, 13353, Germany.|Anacleto Lab - Dipartimento di Informatica, Universitá degli Studi di Milano, Via Comelico 39, Milan, 20135, Italy. valentini@di.unimi.it.', 'Italy|Germany|Germany|Italy', 5, 'Algorithms|Area Under Curve|Biological Ontologies|Genetic Association Studies|Humans|Molecular Sequence Annotation|Phenotype|ROC Curve', 'Journal Article', '10.1186/s12859-017-1854-y', '29025394', 'The prediction of human gene-abnormal phenotype associations is a fundamental step toward the discovery of novel genes associated with human disorders, especially when no genes are known to be associated with a specific disease. In this context the Human Phenotype Ontology (HPO) provides a standard categorization of the abnormalities associated with human diseases. While the problem of the prediction of gene-disease associations has been widely investigated, the related problem of gene-phenotypic feature (i.e., HPO term) associations has been largely overlooked, even if for most human genes no HPO term associations are known and despite the increasing application of the HPO to relevant medical problems. Moreover most of the methods proposed in literature are not able to capture the hierarchical relationships between HPO terms, thus resulting in inconsistent and relatively inaccurate predictions.'),
(98063, 2017, 'bmc', 'A method for named entity normalization in biomedical articles: application to diseases and plants', 'Cho, Hyejin|Choi, Wonjun|Lee, Hyunju', 'School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123 Chemdangwagi-ro, Buk-gu, Gwangju, Republic of Korea.|School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123 Chemdangwagi-ro, Buk-gu, Gwangju, Republic of Korea.|School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123 Chemdangwagi-ro, Buk-gu, Gwangju, Republic of Korea. hyunjulee@gist.ac.kr.', '-|-|-', 0, 'Algorithms|Biomedical Research|Data Mining|Disease|Female|Humans|Male|Plants|PubMed|Reference Standards|Semantics', 'Journal Article', '10.1186/s12859-017-1857-8', '29029598', 'In biomedical articles, a named entity recognition (NER) technique that identifies entity names from texts is an important element for extracting biological knowledge from articles. After NER is applied to articles, the next step is to normalize the identified names into standard concepts (i.e., disease names are mapped to the National Library of Medicines Medical Subject Headings disease terms). In biomedical articles, many entity normalization methods rely on domain-specific dictionaries for resolving synonyms and abbreviations. However, the dictionaries are not comprehensive except for some entities such as genes. In recent years, biomedical articles have accumulated rapidly, and neural network-based algorithms that incorporate a large amount of unlabeled data have shown considerable success in several natural language processing problems.'),
(98064, 2017, 'bmc', 'LiverWiki: a wiki-based database for human liver', 'Chen, Tao|Li, Mansheng|He, Qiang|Zou, Lei|Li, Youhuan|Chang, Cheng|Zhao, Dongyan|Zhu, Yunping', 'Beijing Institute of Life Omics, State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, 33 Life Science Park Rd, Changping District, Beijing, 102206, China.|Beijing Institute of Life Omics, State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, 33 Life Science Park Rd, Changping District, Beijing, 102206, China.|School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Victoria, 3122, Australia.|Institute of Computer Science and Technology, Peking University, No.5 Yiheyuan Road Haidian District, Beijing, 100871, China.|Institute of Computer Science and Technology, Peking University, No.5 Yiheyuan Road Haidian District, Beijing, 100871, China.|Beijing Institute of Life Omics, State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, 33 Life Science Park Rd, Changping District, Beijing, 102206, China.|Institute of Computer Science and Technology, Peking University, No.5 Yiheyuan Road Haidian District, Beijing, 100871, China.|Beijing Institute of Life Omics, State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, 33 Life Science Park Rd, Changping District, Beijing, 102206, China. zhuyunping@gmail.com.', 'China|China|Australia|China|China|China|China|China', 0, 'Databases, Factual|Humans|Information Storage and Retrieval|Internet|Liver|Liver Diseases|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1852-0', '29029599', 'Recent advances in omics technology have produced a large amount of liver-related data. A comprehensive and up-to-date source of liver-related data is needed to allow biologists to access the latest data. However, current liver-related data sources each cover only a specific part of the liver. It is difficult for them to keep pace with the rapid increase of liver-related data available at those data resources. Integrating diverse liver-related data is a critical yet formidable challenge, as it requires sustained human effort.'),
(98065, 2017, 'bmc', 'A strategy for evaluating pathway analysis methods', 'Yu, Chenggang|Woo, Hyung Jun|Yu, Xueping|Oyama, Tatsuya|Wallqvist, Anders|Reifman, Jaques', 'Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Fort Detrick, MD, 21702, USA.|Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Fort Detrick, MD, 21702, USA.|Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Fort Detrick, MD, 21702, USA.|Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Fort Detrick, MD, 21702, USA.|Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Fort Detrick, MD, 21702, USA.|Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Fort Detrick, MD, 21702, USA. jaques.reifman.civ@mail.mil.', 'United States|United States|United States|United States|United States|United States', 10, 'Databases, Genetic|Disease|Gene Expression|Humans|Reproducibility of Results|Signal Transduction', 'Journal Article', '10.1186/s12859-017-1866-7', '29029625', 'Researchers have previously developed a multitude of methods designed to identify biological pathways associated with specific clinical or experimental conditions of interest, with the aim of facilitating biological interpretation of high-throughput data. Before practically applying such pathway analysis (PA) methods, we must first evaluate their performance and reliability, using datasets where the pathways perturbed by the conditions of interest have been well characterized in advance. However, such ground truths (or gold standards) are often unavailable. Furthermore, previous evaluation strategies that have focused on defining true answers are unable to systematically and objectively assess PA methods under a wide range of conditions.'),
(98066, 2017, 'bmc', 'chngpt: threshold regression model estimation and inference', 'Fong, Youyi|Huang, Ying|Gilbert, Peter B|Permar, Sallie R', 'Department of Biostatistics, Bioinformatics and Epidemiology, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, USA, 1100 Fairview Ave N., Seattle, USA. yfong@fhcrc.org.|Department of Biostatistics, Bioinformatics and Epidemiology, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, USA, 1100 Fairview Ave N., Seattle, USA.|Department of Biostatistics, Bioinformatics and Epidemiology, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, USA, 1100 Fairview Ave N., Seattle, USA.|Human Vaccine Institute, Duke University Medical Center, 2 Genome Court, Durham, USA.', 'United States|United States|United States|United States', 0, 'HIV Infections|HIV-1|Humans|Models, Statistical|Monte Carlo Method|Regression Analysis|Software|Time Factors', 'Journal Article', '10.1186/s12859-017-1863-x', '29037149', 'Threshold regression models are a diverse set of non-regular regression models that all depend on change points or thresholds. They provide a simple but elegant and interpretable way to model certain kinds of nonlinear relationships between the outcome and a predictor.'),
(98067, 2017, 'bmc', 'Mapping eQTL by leveraging multiple tissues and DNA methylation', 'Acharya, Chaitanya R|Owzar, Kouros|Allen, Andrew S', 'Program in Computational Biology and Bioinformatics, Duke University, 2424 Erwin Road, Suite 1104, Durham, 27710, NC, USA.|Department of Biostatistics and Bioinformatics, Duke University, 2424 Erwin Road, Suite 1104, Durham, 27710, NC, USA.|Program in Computational Biology and Bioinformatics, Duke University, 2424 Erwin Road, Suite 1104, Durham, 27710, NC, USA. asallen@duke.edu.', 'United States|United States|United States', 2, 'Adult|Chromosome Mapping|Computer Simulation|DNA Methylation|Gene Expression Regulation|Humans|Monte Carlo Method|Organ Specificity|Polymorphism, Single Nucleotide|Quantitative Trait Loci', 'Journal Article', '10.1186/s12859-017-1856-9', '29047346', 'DNA methylation is an important tissue-specific epigenetic event that influences transcriptional regulation of gene expression. Differentially methylated CpG sites may act as mediators between genetic variation and gene expression, and this relationship can be exploited while mapping multi-tissue expression quantitative trait loci (eQTL). Current multi-tissue eQTL mapping techniques are limited to only exploiting gene expression patterns across multiple tissues either in a joint tissue or tissue-by-tissue frameworks. We present a new statistical approach that enables us to model the effect of germ-line variation on tissue-specific gene expression in the presence of effects due to DNA methylation.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98068, 2017, 'bmc', 'LCS-TA to identify similar fragments in RNA 3D structures', 'Wiedemann, Jakub|Zok, Tomasz|Milostan, Maciej|Szachniuk, Marta', 'Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland.|Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland.|Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland.|Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. marta.szachniuk@cs.put.poznan.pl.', 'Poland|Poland|Poland|Poland', 0, 'Algorithms|Base Sequence|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-017-1867-6', '29058576', 'In modern structural bioinformatics, comparison of molecular structures aimed to identify and assess similarities and differences between them is one of the most commonly performed procedures. It gives the basis for evaluation of in silico predicted models. It constitutes the preliminary step in searching for structural motifs. In particular, it supports tracing the molecular evolution. Faced with an ever-increasing amount of available structural data, researchers need a range of methods enabling comparative analysis of the structures from either global or local perspective.'),
(98069, 2017, 'bmc', 'A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE)', 'Stacey, R Greg|Skinnider, Michael A|Scott, Nichollas E|Foster, Leonard J', 'Michael Smith Laboratories, University of British Columbia, Vancouver, V6T 1Z4, Canada. richard.greg.stacey@ubc.msl.ca.|Michael Smith Laboratories, University of British Columbia, Vancouver, V6T 1Z4, Canada.|Michael Smith Laboratories, University of British Columbia, Vancouver, V6T 1Z4, Canada.|Michael Smith Laboratories, University of British Columbia, Vancouver, V6T 1Z4, Canada. foster@msl.ubc.ca.', 'Canada|Canada|Canada|Canada', 12, 'Algorithms|Databases, Protein|Humans|Molecular Sequence Annotation|Protein Interaction Mapping|Proteomics|Reproducibility of Results|Saccharomyces cerevisiae', 'Journal Article', '10.1186/s12859-017-1865-8', '29061110', 'An organisms protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes. We recently developed a complementary technique based on the use of protein correlation profiling (PCP) and stable isotope labeling in amino acids in cell culture (SILAC) to assess chromatographic co-elution as evidence of interacting proteins. Importantly, PCP-SILAC is also capable of measuring protein interactions simultaneously under multiple biological conditions, allowing the detection of treatment-specific changes to an interactome. Given the uniqueness and high dimensionality of co-elution data, new tools are needed to compare protein elution profiles, control false discovery rates, and construct an accurate interactome.'),
(98070, 2017, 'bmc', 'An optimized approach for annotation of large eukaryotic genomic sequences using genetic algorithm', 'Chowdhury, Biswanath|Garai, Arnav|Garai, Gautam', 'Department of Biophysics, Molecular Biology and Bioinformatics, University of Calcutta, Kolkata, 700009, WB, India. bchowdhury2410@gmail.com.|Unit of Energy, Utilities, Communications and Services, Infosys Technologies Ltd., Bhubaneswar, 751024, Odisha, India.|Computational Sciences Division, Saha Institute of Nuclear Physics, Kolkata, 700064, WB, India.', 'India|India|India', 1, 'Algorithms|Animals|Chromosomes, Human, Pair 21|Exons|Humans|Mice|Molecular Sequence Annotation|Open Reading Frames', 'Journal Article', '10.1186/s12859-017-1874-7', '29065853', 'Detection of important functional and/or structural elements and identification of their positions in a large eukaryotic genomic sequence are an active research area. Gene is an important functional and structural unit of DNA. The computation of gene prediction is, therefore, very essential for detailed genome annotation.'),
(98071, 2017, 'bmc', 'A heuristic model for computational prediction of human branch point sequence', 'Wen, Jia|Wang, Jue|Zhang, Qing|Guo, Dianjing', 'School of Life Science, State Key Laboratory of Agrobiotechnology and ShenZhen Research Institute, The Chinese University of Hong Kong, Hong Kong, China.|School of Life Science, State Key Laboratory of Agrobiotechnology and ShenZhen Research Institute, The Chinese University of Hong Kong, Hong Kong, China.|School of Life Science, State Key Laboratory of Agrobiotechnology and ShenZhen Research Institute, The Chinese University of Hong Kong, Hong Kong, China.|School of Life Science, State Key Laboratory of Agrobiotechnology and ShenZhen Research Institute, The Chinese University of Hong Kong, Hong Kong, China. djguo@cuhk.edu.hk.', 'China|China|China|China', 4, 'Exons|Humans|Introns|Models, Molecular|Protein Binding|RNA Precursors|RNA Splicing|RNA, Messenger|Ribonucleoprotein, U2 Small Nuclear|Thermodynamics', 'Journal Article', '10.1186/s12859-017-1864-9', '29065858', 'Pre-mRNA splicing is the removal of introns from precursor mRNAs (pre-mRNAs) and the concurrent ligation of the flanking exons to generate mature mRNA. This process is catalyzed by the spliceosome, where the splicing factor 1 (SF1) specifically recognizes the seven-nucleotide branch point sequence (BPS) and the U2 snRNP later displaces the SF1 and binds to the BPS. In mammals, the degeneracy of BPS motifs together with the lack of a large set of experimentally verified BPSs complicates the task of BPS prediction in silico.'),
(98072, 2017, 'bmc', 'QuaDMutEx: quadratic driver mutation explorer', 'Bokhari, Yahya|Arodz, Tomasz', 'Department of Computer Science, School of Engineering, Virginia Commonwealth University, 401 W. Main St., Richmond, 23284, VA, USA.|Department of Computer Science, School of Engineering, Virginia Commonwealth University, 401 W. Main St., Richmond, 23284, VA, USA. tarodz@vcu.edu.', 'United States|United States', 1, 'Algorithms|Databases, Factual|Humans|Internet|Monte Carlo Method|Mutation|Neoplasms|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1869-4', '29065872', 'Somatic mutations accumulate in human cells throughout life. Some may have no adverse consequences, but some of them may lead to cancer. A cancer genome is typically unstable, and thus more mutations can accumulate in the DNA of cancer cells. An ongoing problem is to figure out which mutations are drivers - play a role in oncogenesis, and which are passengers - do not play a role. One way of addressing this question is through inspection of somatic mutations in DNA of cancer samples from a cohort of patients and detection of patterns that differentiate driver from passenger mutations.'),
(98073, 2017, 'bmc', 'riboviz: analysis and visualization of ribosome profiling datasets', 'Carja, Oana|Xing, Tongji|Wallace, Edward W J|Plotkin, Joshua B|Shah, Premal', 'Department of Biology, University of Pennsylvania, 204K Lynch Labs, 433 S University Ave, Philadelphia, 19104, PA, USA. ocarja@sas.upenn.edu.|Department of Genetics, Rutgers University, Piscataway, NJ, USA.|School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK.|Department of Biology, University of Pennsylvania, 204K Lynch Labs, 433 S University Ave, Philadelphia, 19104, PA, USA.|Department of Genetics, Rutgers University, Piscataway, NJ, USA. premal.shah@rutgers.edu.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 14, 'Databases, Genetic|High-Throughput Nucleotide Sequencing|Internet|Ribosomes|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1873-8', '29070028', 'Using high-throughput sequencing to monitor translation in vivo, ribosome profiling can provide critical insights into the dynamics and regulation of protein synthesis in a cell. Since its introduction in 2009, this technique has played a key role in driving biological discovery, and yet it requires a rigorous computational toolkit for widespread adoption.'),
(98074, 2017, 'bmc', 'Detection and quantification of mitochondrial DNA deletions from next-generation sequence data', 'Bosworth, Colleen M|Grandhi, Sneha|Gould, Meetha P|LaFramboise, Thomas', 'Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.|Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.|Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.|Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA. thomas.laframboise@case.edu.', 'United States|United States|United States|United States', 14, 'Adult|Aged|Brain|Computer Simulation|DNA, Mitochondrial|Genetic Variation|Genome, Mitochondrial|High-Throughput Nucleotide Sequencing|Humans|Mitochondria|Sequence Deletion|Time Factors', 'Journal Article', '10.1186/s12859-017-1821-7', '29072135', 'Chromosomal deletions represent an important class of human genetic variation. Various methods have been developed to mine next-generation sequencing (NGS) data to detect deletions and quantify their clonal abundances. These methods have focused almost exclusively on the nuclear genome, ignoring the mitochondrial chromosome (mtDNA). Detecting mtDNA deletions requires special care. First, the chromosomes relatively small size (16,569 bp) necessitates the ability to detect extremely focal events. Second, the chromosome can be present at thousands of copies in a single cell (in contrast to two copies of nuclear chromosomes), and mtDNA deletions may be present on only a very small percentage of chromosomes. Here we present a method, termed MitoDel, to detect mtDNA deletions from NGS data.'),
(98075, 2017, 'bmc', 'An effective approach to detecting both small and large complexes from protein-protein interaction networks', 'Xu, Bin|Wang, Yang|Wang, Zewei|Zhou, Jiaogen|Zhou, Shuigeng|Guan, Jihong', 'Department of Computer Science and Technology, Tongji University, 4800 Caoan Road, Shanghai, 201804, China.|School of Software, Jiangxi Normal University, 99 Ziyang Avenue, Nanchang, 330022, China.|Shanghai Southwest Model Middle School, 67 Huicheng Vallige-1, Baise Road, Shanghai, 200237, China.|The institute of subtropical Agriculture, China Academy of Sciences, 444 Yuandaer Road, Mapoling, Changsha, 410125, China.|Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, 220 Handan Road, Shanghai, 200433, China.|Department of Computer Science and Technology, Tongji University, 4800 Caoan Road, Shanghai, 201804, China. jhguan@tongji.edu.cn.', 'China|China|China|China|China|China', 10, 'Algorithms|Cluster Analysis|Databases, Protein|Multiprotein Complexes|Protein Interaction Mapping|Protein Interaction Maps|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/s12859-017-1820-8', '29072136', 'Predicting protein complexes from protein-protein interaction (PPI) networks has been studied for decade. Various methods have been proposed to address some challenging issues of this problem, including overlapping clusters, high false positive/negative rates of PPI data and diverse complex structures. It is well known that most current methods can detect effectively only complexes of size xe2x89xa53, which account for only about half of the total existing complexes. Recently, a method was proposed specifically for finding small complexes (size = 2 and 3) from PPI networks. However, up to now there is no effective approach that can predict both small (size xe2x89xa4 3) and large (size >3) complexes from PPI networks.'),
(98076, 2017, 'bmc', 'Predicting combinative drug pairs towards realistic screening via integrating heterogeneous features', 'Shi, Jian-Yu|Li, Jia-Xin|Gao, Ke|Lei, Peng|Yiu, Siu-Ming', 'School of Life Sciences, Northwestern Polytechnical University, Xian, 710072, China. jianyushi@nwpu.edu.cn.|School of Life Sciences, Northwestern Polytechnical University, Xian, 710072, China.|School of Computer Science, Northwestern Polytechnical University, Xian, China.|Department of Chinese Medicine, Shaanxi Provincial Peoples Hospital, Xian, China.|Department of Computer Science, the University of Hong Kong, Hong Kong, China. smyiu@cs.hku.hk.', 'China|China|China|China|China', 8, 'Computational Biology|Databases as Topic|Drug Combinations|Drug Evaluation, Preclinical|Humans', 'Journal Article', '10.1186/s12859-017-1818-2', '29072137', 'Drug Combination is one of the effective approaches for treating complex diseases. However, determining combinative drug pairs in clinical trials is still costly. Thus, computational approaches are used to identify potential drug pairs in advance. Existing computational approaches have the following shortcomings: (i) the lack of an effective integration of heterogeneous features leads to a time-consuming training and even results in an over-fitted classifier, and (ii) the narrow consideration of predicting potential drug combinations only among known drugs having known combinations cannot meet the demand of realistic screenings, which pay more attention to potential combinative pairs among newly-coming drugs that have no approved combination with other drugs at all.'),
(98077, 2017, 'bmc', 'Fusing multiple protein-protein similarity networks to effectively predict lncRNA-protein interactions', 'Zheng, Xiaoxiong|Wang, Yang|Tian, Kai|Zhou, Jiaogen|Guan, Jihong|Luo, Libo|Zhou, Shuigeng', 'Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, 220 Handan Road, Shanghai, 200433, China.|School of Software, Jiangxi Normal University, 99 Ziyang Avenue, Nanchang, 330022, China.|Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, 220 Handan Road, Shanghai, 200433, China.|The institute of subtropical Agriculture, China Academy of Sciences, 444 Yuandaer Road, Mapoling, Changsha, 410125, China.|Department of Computer Science and Technology, Tongji University, 4800 Caoan Road, Shanghai, 201804, China.|The Bioinformatics Lab at Changzhou NO. 7 Peoples Hospital, Changzhou, Jiangsu, 213011, China.|Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, 220 Handan Road, Shanghai, 200433, China. sgzhou@fudan.edu.cn.', 'China|China|China|China|China|China|China', 21, 'Area Under Curve|Humans|Proteins|RNA, Long Noncoding|ROC Curve|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/s12859-017-1819-1', '29072138', 'Long non-coding RNA (lncRNA) plays important roles in many biological and pathological processes, including transcriptional regulation and gene regulation. As lncRNA interacts with multiple proteins, predicting lncRNA-protein interactions (lncRPIs) is an important way to study the functions of lncRNA. Up to now, there have been a few works that exploit protein-protein interactions (PPIs) to help the prediction of new lncRPIs.'),
(98078, 2017, 'bmc', 'NemoProfile as an efficient approach to network motif analysis with instance collection', 'Kim, Wooyoung|Haukap, Lynnette', 'Division of Computing and Software Systems, School of Science, Technology, Engineering, and Mathematics (STEM), University of Washington Bothell, 18115 Campus Way NE, Bothell, 98011-8246, WA, USA. kimw6@uw.edu.|Division of Computing and Software Systems, School of Science, Technology, Engineering, and Mathematics (STEM), University of Washington Bothell, 18115 Campus Way NE, Bothell, 98011-8246, WA, USA.', 'United States|United States', 1, 'Algorithms|Computational Biology|Gene Regulatory Networks|Protein Interaction Maps|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/s12859-017-1822-6', '29072139', 'A network motif is defined as a statistically significant and recurring subgraph pattern within a network. Most existing instance collection methods are not feasible due to high memory usage issues and provision of limited network motif information. They require a two-step process that requires network motif identification prior to instance collection. Due to the impracticality in obtaining motif instances, the significance of their contribution to problem solving is debated within the field of biology.'),
(98079, 2017, 'bmc', 'A sensitive short read homology search tool for paired-end read sequencing data', 'Techa-Angkoon, Prapaporn|Sun, Yanni|Lei, Jikai', 'Department of Computer Science and Engineering, Michigan State University, East Lansing, 48824, MI, USA.|Department of Computer Science and Engineering, Michigan State University, East Lansing, 48824, MI, USA. yannisun@msu.edu.|Department of Computer Science and Engineering, Michigan State University, East Lansing, 48824, MI, USA.', 'United States|United States|United States', 0, 'Amino Acid Sequence|Arabidopsis|Bayes Theorem|High-Throughput Nucleotide Sequencing|Metagenomics|ROC Curve|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software|Time Factors', 'Journal Article', '10.1186/s12859-017-1826-2', '29072140', 'Homology search is still a significant step in functional analysis for genomic data. Profile Hidden Markov Model-based homology search has been widely used in protein domain analysis in many different species. In particular, with the fast accumulation of transcriptomic data of non-model species and metagenomic data, profile homology search is widely adopted in integrated pipelines for functional analysis. While the state-of-the-art tool HMMER has achieved high sensitivity and accuracy in domain annotation, the sensitivity of HMMER on short reads declines rapidly. The low sensitivity on short read homology search can lead to inaccurate domain composition and abundance computation. Our experimental results showed that half of the reads were missed by HMMER for a RNA-Seq dataset. Thus, there is a need for better methods to improve the homology search performance for short reads.'),
(98080, 2017, 'bmc', 'Inferring transposons activity chronology by TRANScendence - TEs database and de-novo mining tool', 'Startek, Michaxc5x82 Piotr|Nogxc5x82y, Jakub|Gromadka, Agnieszka|Grzebelus, Dariusz|Gambin, Anna', 'Institute of Informatics, University of Warsaw, Banacha 2, Warsaw, 02097, Poland.|Institute of Informatics, University of Warsaw, Banacha 2, Warsaw, 02097, Poland.|Institute of Informatics, University of Warsaw, Banacha 2, Warsaw, 02097, Poland.|Institute of Plant Biology and Biotechnology, University of Agriculture in Krakxc3xb3w, 29 Listopada 54, Krakxc3xb3w, 31425, Poland.|Institute of Informatics, University of Warsaw, Banacha 2, Warsaw, 02097, Poland. aniag@mimuw.edu.pl.', 'Poland|Poland|Poland|Poland|Poland', 1, 'Algorithms|Animals|DNA Transposable Elements|Data Mining|Databases, Genetic|Drosophila melanogaster|Genome, Human|Humans|Models, Theoretical|Reproducibility of Results|Software', 'Journal Article', '10.1186/s12859-017-1824-4', '29072141', 'The constant progress in sequencing technology leads to ever increasing amounts of genomic data. In the light of current evidence transposable elements (TEs for short) are becoming useful tools for learning about the evolution of host genome. Therefore the software for genome-wide detection and analysis of TEs is of great interest.'),
(98081, 2017, 'bmc', 'MegaGTA: a sensitive and accurate metagenomic gene-targeted assembler using iterative de Bruijn graphs', 'Li, Dinghua|Huang, Yukun|Leung, Chi-Ming|Luo, Ruibang|Ting, Hing-Fung|Lam, Tak-Wah', 'Department of Computer Science, University of Hong Kong, Pokfulam, Hong Kong.|Department of Computer Science, University of Hong Kong, Pokfulam, Hong Kong.|Department of Computer Science, University of Hong Kong, Pokfulam, Hong Kong.|Department of Computer Science, University of Hong Kong, Pokfulam, Hong Kong.|Department of Computer Science, University of Hong Kong, Pokfulam, Hong Kong.|Department of Computer Science, University of Hong Kong, Pokfulam, Hong Kong. twlam@cs.hku.hk.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong', 4, 'Algorithms|Databases, Genetic|Genes|Humans|Metagenomics|Pilot Projects|Reference Standards|Rhizosphere|Software|Soil|Statistics as Topic', 'Journal Article', '10.1186/s12859-017-1825-3', '29072142', 'The recent release of the gene-targeted metagenomics assembler Xander has demonstrated that using the trained Hidden Markov Model (HMM) to guide the traversal of de Bruijn graph gives obvious advantage over other assembly methods. Xander, as a pilot study, indeed has a lot of room for improvement. Apart from its slow speed, Xander uses only 1 k-mer size for graph construction and whatever choice of k will compromise either sensitivity or accuracy. Xander uses a Bloom-filter representation of de Bruijn graph to achieve a lower memory footprint. Bloom filters bring in false positives, and it is not clear how this would impact the quality of assembly. Xander does not keep track of the multiplicity of k-mers, which would have been an effective way to differentiate between erroneous k-mers and correct k-mers.'),
(98082, 2017, 'bmc', 'Detecting large deletions at base pair level by combining split read and paired read data', 'Hayes, Matthew|Pearson, Jeremy S', 'Xavier University of Louisiana, 1 Drexel Dr, New Orleans, 70125, LA, USA. mhayes5@xula.edu.|Department of Computer Science, Tennessee State University, 3500 John A. Merritt Blvd., Nashville, 37221, Tennessee, USA.', 'United States|United States', 0, 'Algorithms|Base Pairing|Computer Simulation|Databases, Genetic|High-Throughput Nucleotide Sequencing|Humans|Male|Prostatic Neoplasms|Sequence Deletion', 'Journal Article', '10.1186/s12859-017-1829-z', '29072143', 'Genomic structural variants (SV) play a significant role in the onset and progression of cancer. Genomic deletions can create oncogenic fusion genes or cause the loss of tumor suppressing gene function which can lead to tumorigenesis by downregulating these genes. Detecting these variants has clinical importance in the treatment of diseases. Furthermore, it is also clinically important to detect their breakpoint boundaries at high resolution. We have generalized the framework of a previously-published algorithm that located translocations, and we have applied that framework to develop a method to locate deletions at base pair level using next-generation sequencing data. Our method uses abnormally mapped read pairs, and then subsequently maps split reads to identify precise breakpoints.'),
(98083, 2017, 'bmc', 'A new method for enhancer prediction based on deep belief network', 'Bu, Hongda|Gan, Yanglan|Wang, Yang|Zhou, Shuigeng|Guan, Jihong', 'Department of Computer Science and Technology, Tongji University, 4800 Caoan Road, Shanghai, 201804, China.|School of Computer, Donghua University, 2999 Renming North Road, Shanghai, 201620, China.|School of Software, Jiangxi Normal University, 99 Ziyang Avenue, Jiangxi, 330022, China.|Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, 220 Handan Road, Shanghai, 200433, China.|Department of Computer Science and Technology, Tongji University, 4800 Caoan Road, Shanghai, 201804, China. jhguan@tongji.edu.cn.', 'China|China|China|China|China', 17, 'Algorithms|Computational Biology|Databases, Genetic|Enhancer Elements, Genetic|Humans|ROC Curve', 'Journal Article', '10.1186/s12859-017-1828-0', '29072144', 'Studies have shown that enhancers are significant regulatory elements to play crucial roles in gene expression regulation. Since enhancers are unrelated to the orientation and distance to their target genes, it is a challenging mission for scholars and researchers to accurately predicting distal enhancers. In the past years, with the high-throughout ChiP-seq technologies development, several computational techniques emerge to predict enhancers using epigenetic or genomic features. Nevertheless, the inconsistency of computational models across different cell-lines and the unsatisfactory prediction performance call for further research in this area.'),
(98084, 2017, 'bmc', 'DCJ-RNA - double cut and join for RNA secondary structures', 'Badr, Ghada H|Al-Aqel, Haifa A', 'IRI- The City of Scientific Research and Technological Applications, University and Research District, P. O. 21934, New Borg Alarab, Alexandria, Egypt. badrghada@hotmail.com.|Imam Mohammad ibn Saud Islamic University, College of Computer and Information Sciences, Riyadh, Saudi Arabia. haagel@imamu.edu.sa.', 'Egypt|Saudi Arabia', 0, 'Algorithms|Base Sequence|Chromosome Inversion|Databases, Nucleic Acid|Models, Genetic|Nucleic Acid Conformation|RNA|Time Factors', 'Journal Article', '10.1186/s12859-017-1830-6', '29072145', 'Genome rearrangements are essential processes for evolution and are responsible for existing varieties of genome architectures. Many studies have been conducted to obtain an algorithm that identifies the minimum number of inversions that are necessary to transform one genome into another, this allows for genome sequence representation in polynomial time. Studies have not been conducted on the topic of rearranging a genome when it is represented as a secondary structure. Unlike sequences, the secondary structure preserves the functionality of the genome. Sequences can be different, but they all share the same structure and, therefore, the same functionality.'),
(98085, 2017, 'bmc', 'VAliBS: a visual aligner for bisulfite sequences', 'Li, Min|Huang, Ping|Yan, Xiaodong|Wang, Jianxin|Pan, Yi|Wu, Fang-Xiang', 'School of Information Science and Engineering, Central South University, Changsha, 410083, China.|School of Information Science and Engineering, Central South University, Changsha, 410083, China.|School of Information Science and Engineering, Central South University, Changsha, 410083, China.|School of Information Science and Engineering, Central South University, Changsha, 410083, China. jxwang@mail.csu.edu.cn.|School of Information Science and Engineering, Central South University, Changsha, 410083, China. yipan@gsu.edu.|School of Information Science and Engineering, Central South University, Changsha, 410083, China.', 'China|China|China|China|China|China', 1, 'Animals|Base Sequence|Computer Simulation|DNA Methylation|Humans|Mice|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1186/s12859-017-1827-1', '29072146', 'Methylation is a common modification of DNA. It has been a very important and hot topic to study the correlation between methylation and diseases in medical science. Because of the special process with bisulfite treatment, traditional mapping tools do not work well with such methylation experimental reads. Traditional aligners are not designed for mapping bisulfite-treated reads, where the un-methylated Cs are converted to Ts.'),
(98086, 2017, 'bmc', 'Tractable RNA-ligand interaction kinetics', 'Kühnl, Felix|Stadler, Peter F|Will, Sebastian', 'Department of Computer Science and Interdisciplinary Center for Bioinformatics, University Leipzig, Härtelstr. 16-18, Leipzig, D-04107, Germany.|Department of Computer Science and Interdisciplinary Center for Bioinformatics, University Leipzig, Härtelstr. 16-18, Leipzig, D-04107, Germany.|Department of Computer Science and Interdisciplinary Center for Bioinformatics, University Leipzig, Härtelstr. 16-18, Leipzig, D-04107, Germany. will@bioinf.uni-leipzig.de.', 'Germany|Germany|Germany', 8, 'Dimerization|Kinetics|Ligands|Models, Theoretical|Nucleic Acid Conformation|Probability|RNA|Thermodynamics', 'Journal Article', '10.1186/s12859-017-1823-5', '29072147', 'The binding of small ligands to RNA elements can cause substantial changes in the RNA structure. This constitutes an important, fast-acting mechanism of ligand-controlled transcriptional and translational gene regulation implemented by a wide variety of riboswitches. The associated refolding processes often cannot be explained by thermodynamic effects alone. Instead, they are governed by the kinetics of RNA folding. While the computational analysis of RNA folding can make use of well-established models of the thermodynamics of RNA structures formation, RNA-RNA interaction, and RNA-ligand interaction, kinetic effects pose fundamentally more challenging problems due to the enormous size of the conformation space. The analysis of the combined process of ligand binding and structure formation even for small RNAs is plagued by intractably large state spaces. Moreover, the interaction is concentration-dependent and thus is intrinsically non-linear. This precludes the direct transfer of the strategies previously used for the analysis of RNA folding kinetics.'),
(98087, 2017, 'bmc', 'Long short-term memory RNN for biomedical named entity recognition', 'Lyu, Chen|Chen, Bo|Ren, Yafeng|Ji, Donghong', 'School of Computer Science, Wuhan University, Wuhan, 430072, Hubei, China.|Department of Chinese Language & Literature, Hubei University of Art & Science, Xiangyang, 24105, Hubei, China.|Guangdong Collaborative Innovation Center for Language Research & Services, Guangdong University of Foreign Studies, Guangzhou, 510420, Guangdong, China.|School of Computer Science, Wuhan University, Wuhan, 430072, Hubei, China. dhji@whu.edu.cn.', 'China|China|China|China', 38, 'Algorithms|Biomedical Research|Information Storage and Retrieval|Machine Learning|Neural Networks (Computer)', 'Journal Article', '10.1186/s12859-017-1868-5', '29084508', 'Biomedical named entity recognition(BNER) is a crucial initial step of information extraction in biomedical domain. The task is typically modeled as a sequence labeling problem. Various machine learning algorithms, such as Conditional Random Fields (CRFs), have been successfully used for this task. However, these state-of-the-art BNER systems largely depend on hand-crafted features.'),
(98088, 2017, 'bmc', 'Identification of gene pairs through penalized regression subject to constraints', 'Shen, Rex|Luo, Lan|Jiang, Hui', 'The Blake School, Minneapolis, 55403, MN, USA.|Department of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA.|Department of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA. jianghui@umich.edu.', 'United States|United States|United States', 0, 'Algorithms|Biomarkers, Tumor|Humans|Keratin-15|Linear Models|Male|Prostate-Specific Antigen|Prostatic Neoplasms|RNA, Neoplasm|Real-Time Polymerase Chain Reaction', 'Journal Article', '10.1186/s12859-017-1872-9', '29100492', 'This article concerns the identification of gene pairs or combinations of gene pairs associated with biological phenotype or clinical outcome, allowing for building predictive models that are not only robust to normalization but also easily validated and measured by qPCR techniques. However, given a small number of biological samples yet a large number of genes, this problem suffers from the difficulty of high computational complexity and imposes challenges to the accuracy of identification statistically.'),
(98089, 2017, 'bmc', 'K-mer clustering algorithm using a MapReduce framework: application to the parallelization of the Inchworm module of Trinity', 'Kim, Chang Sik|Winn, Martyn D|Sachdeva, Vipin|Jordan, Kirk E', 'The Hartree Centre and Scientific Computing Department, STFC Daresbury Laboratory, Warrington, WA4 4AD, UK.|The Hartree Centre and Scientific Computing Department, STFC Daresbury Laboratory, Warrington, WA4 4AD, UK. martyn.winn@stfc.ac.uk.|Computational Science Center, IBM T.J. Watson Research, Cambridge, MA, USA.|Computational Science Center, IBM T.J. Watson Research, Cambridge, MA, USA.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Cluster Analysis|High-Throughput Nucleotide Sequencing|RNA|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1186/s12859-017-1881-8', '29100493', 'De novo transcriptome assembly is an important technique for understanding gene expression in non-model organisms. Many de novo assemblers using the de Bruijn graph of a set of the RNA sequences rely on in-memory representation of this graph. However, current methods analyse the complete set of read-derived k-mer sequence at once, resulting in the need for computer hardware with large shared memory.'),
(98090, 2017, 'bmc', 'RNA inverse folding using Monte Carlo tree search', 'Yang, Xiufeng|Yoshizoe, Kazuki|Taneda, Akito|Tsuda, Koji', 'Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, 277-8561, Japan.|RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihombashi Chuo-ku, Tokyo, 103-0027, Japan.|Graduate School of Science and Technology, Hirosaki University, 3 Bunkyo-cho, Hirosaki, 036-8561, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, 277-8561, Japan. tsuda@k.u-tokyo.ac.jp.', 'Japan|Japan|Japan|Japan', 0, 'Algorithms|Internet|Monte Carlo Method|Nucleic Acid Conformation|RNA|RNA Folding|Sequence Analysis, RNA|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1882-7', '29110632', 'Artificially synthesized RNA molecules provide important ways for creating a variety of novel functional molecules. State-of-the-art RNA inverse folding algorithms can design simple and short RNA sequences of specific GC content, that fold into the target RNA structure. However, their performance is not satisfactory in complicated cases.'),
(98091, 2017, 'bmc', 'JS-MS: a cross-platform, modular javascript viewer for mass spectrometry signals', 'Rosen, Jebediah|Handy, Kyle|Gillan, André|Smith, Rob', 'Department of Computer Science, University of Montana, 32 Campus Drive, Missoula, 59812, MT, USA.|Department of Computer Science, University of Montana, 32 Campus Drive, Missoula, 59812, MT, USA.|Department of Computer Science, University of Montana, 32 Campus Drive, Missoula, 59812, MT, USA.|Department of Computer Science, University of Montana, 32 Campus Drive, Missoula, 59812, MT, USA. robert.smith@mso.umt.edu.', 'United States|United States|United States|United States', 2, 'Algorithms|Electronic Data Processing|Internet|Mass Spectrometry|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1883-6', '29110634', 'Despite the ubiquity of mass spectrometry (MS), data processing tools can be surprisingly limited. To date, there is no stand-alone, cross-platform 3-D visualizer for MS data. Available visualization toolkits require large libraries with multiple dependencies and are not well suited for custom MS data processing modules, such as MS storage systems or data processing algorithms.'),
(98092, 2017, 'bmc', 'Sequence-based information-theoretic features for gene essentiality prediction', 'Nigatu, Dawit|Sobetzko, Patrick|Yousef, Malik|Henkel, Werner', 'Transmission Systems Group, Jacobs University Bremen, Campus Ring 1, Bremen, D-28759, Germany. d.nigatu@jacobs-university.de.|Philipps-Universität Marburg, LOEWE-Zentrum für Synthetische Mikrobiologie, Hans-Meerwein-Straße, Mehrzweckgebäude, Marburg, 35043, Germany.|Community Information Systems, Zefat Academic College, Zefat, 13206, Israel.|Transmission Systems Group, Jacobs University Bremen, Campus Ring 1, Bremen, D-28759, Germany.', 'Germany|Germany|Israel|Germany', 14, 'Area Under Curve|Bacteria|Base Sequence|Genes, Essential|Machine Learning|Markov Chains|Models, Theoretical|ROC Curve', 'Journal Article', '10.1186/s12859-017-1884-5', '29121868', 'Identification of essential genes is not only useful for our understanding of the minimal gene set required for cellular life but also aids the identification of novel drug targets in pathogens. In this work, we present a simple and effective gene essentiality prediction method using information-theoretic features that are derived exclusively from the gene sequences.'),
(98093, 2017, 'bmc', 'Reference-guided de novo assembly approach improves genome reconstruction for related species', 'Lischer, Heidi E L|Shimizu, Kentaro K', 'Department of Evolutionary Biology and Environmental Studies (IEU), University of Zurich, Zurich, Switzerland. heidi.lischer@ieu.uzh.ch.|Department of Evolutionary Biology and Environmental Studies (IEU), University of Zurich, Zurich, Switzerland.', 'Switzerland|Switzerland', 0, 'Arabidopsis|Genome, Plant|Genomics|High-Throughput Nucleotide Sequencing|Reference Standards|Reproducibility of Results|Species Specificity', 'Journal Article', '10.1186/s12859-017-1911-6', '29126390', 'The development of next-generation sequencing has made it possible to sequence whole genomes at a relatively low cost. However, de novo genome assemblies remain challenging due to short read length, missing data, repetitive regions, polymorphisms and sequencing errors. As more and more genomes are sequenced, reference-guided assembly approaches can be used to assist the assembly process. However, previous methods mostly focused on the assembly of other genotypes within the same species. We adapted and extended a reference-guided de novo assembly approach, which enables the usage of a related reference sequence to guide the genome assembly. In order to compare and evaluate de novo and our reference-guided de novo assembly approaches, we used a simulated data set of a repetitive and heterozygotic plant genome.'),
(98094, 2017, 'bmc', 'fLPS: Fast discovery of compositional biases for the protein universe', 'Harrison, Paul M', 'Department of Biology, McGill University, Montreal, QC, Canada. paul.harrison@mcgill.ca.', 'Canada', 0, 'Algorithms|Bias|Proteome|Saccharomyces cerevisiae|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/s12859-017-1906-3', '29132292', 'Proteins often contain regions that are compositionally biased (CB), i.e., they are made from a small subset of amino-acid residue types. These CB regions can be functionally important, e.g., the prion-forming and prion-like regions that are rich in asparagine and glutamine residues.'),
(98095, 2017, 'bmc', 'A fragment based method for modeling of protein segments into cryo-EM density maps', 'Ismer, Jochen|Rose, Alexander S|Tiemann, Johanna K S|Hildebrand, Peter W', 'Institute of Medical Physics and Biophysics, University Medicine Berlin, Charitéplatz 1, 10117, Berlin, Germany.|Institute of Medical Physics and Biophysics, University Medicine Berlin, Charitéplatz 1, 10117, Berlin, Germany.|Institute of Medical Physics and Biophysics, University Medicine Berlin, Charitéplatz 1, 10117, Berlin, Germany.|Institute of Medical Physics and Biophysics, University Medicine Berlin, Charitéplatz 1, 10117, Berlin, Germany. peter.hildebrand@charite.de.', 'Germany|Germany|Germany|Germany', 0, 'Cryoelectron Microscopy|Databases, Protein|Models, Molecular|Protein Structure, Secondary|Proteins|Software', 'Journal Article', '10.1186/s12859-017-1904-5', '29132296', 'Single-particle analysis of electron cryo-microscopy (cryo-EM) is a key technology for elucidation of macromolecular structures. Recent technical advances in hardware and software developments significantly enhanced the resolution of cryo-EM density maps and broadened the applicability and the circle of users. To facilitate modeling of macromolecules into cryo-EM density maps, fast and easy to use methods for modeling are now demanded.'),
(98096, 2017, 'bmc', 'BugMat and FindNeighbour: command line and server applications for investigating bacterial relatedness', 'Mazariegos-Canellas, Oriol|Do, Trien|Peto, Tim|Eyre, David W|Underwood, Anthony|Crook, Derrick|Wyllie, David H', 'Nuffield Department of Medicine, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.|Nuffield Department of Medicine, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.|Nuffield Department of Medicine, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.|Nuffield Department of Medicine, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.|Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK.|Nuffield Department of Medicine, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.|Nuffield Department of Medicine, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK. David.wyllie@ndm.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 7, 'Bacteria|Genome, Bacterial|Genomics|Likelihood Functions|Mycobacterium tuberculosis|Phylogeny|Software', 'Journal Article', '10.1186/s12859-017-1907-2', '29132318', 'Large scale bacterial sequencing has made the determination of genetic relationships within large sequence collections of bacterial genomes derived from the same microbial species an increasingly common task. Solutions to the problem have application to public health (for example, in the detection of possible disease transmission), and as part of divide-and-conquer strategies selecting groups of similar isolates for computationally intensive methods of phylogenetic inference using (for example) maximal likelihood methods. However, the generation and maintenance of distance matrices is computationally intensive, and rapid methods of doing so are needed to allow translation of microbial genomics into public health actions.'),
(98097, 2017, 'bmc', 'Analysis of breast cancer subtypes by AP-ISA biclustering', 'Yang, Liying|Shen, Yunyan|Yuan, Xiguo|Zhang, Junying|Wei, Jianhua', 'School of Computer Science and Technology, Xidian University, Xian, Shaanxi, 710071, China. yangliying1208@163.com.|School of Computer Science and Technology, Xidian University, Xian, Shaanxi, 710071, China.|School of Computer Science and Technology, Xidian University, Xian, Shaanxi, 710071, China.|School of Computer Science and Technology, Xidian University, Xian, Shaanxi, 710071, China.|State Key Laboratory of Military Stomatology & National Clinical Research Center for Oral Diseases & Shaanxi Clinical Research Center for Oral Diseases, Department of Maxillofacial Surgery, School of Stomatology, The Fourth Military Medical University, Xian, Shaanxi, 710032, China. weiyoyo@fmmu.edu.cn.', 'China|China|China|China|China', 2, 'Algorithms|Breast Neoplasms|Cluster Analysis|DNA Methylation|Female|Gene Expression Profiling|Humans', 'Journal Article', '10.1186/s12859-017-1926-z', '29137596', 'Gene expression profiling has led to the definition of breast cancer molecular subtypes: Basal-like, HER2-enriched, LuminalA, LuminalB and Normal-like. Different subtypes exhibit diverse responses to treatment. In the past years, several traditional clustering algorithms have been applied to analyze gene expression profiling. However, accurate identification of breast cancer subtypes, especially within highly variable LuminalA subtype, remains a challenge. Furthermore, the relationship between DNA methylation and expression level in different breast cancer subtypes is not clear.'),
(98098, 2017, 'bmc', 'GFF3sort: a novel tool to sort GFF3 files for tabix indexing', 'Zhu, Tao|Liang, Chengzhen|Meng, Zhigang|Guo, Sandui|Zhang, Rui', 'Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.|Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.|Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.|Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China. guosandui@caas.cn.|Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China. zhangrui@caas.cn.', 'China|China|China|China|China', 0, 'Animals|Genomics|Humans|Molecular Sequence Annotation|Software', 'Journal Article', '10.1186/s12859-017-1930-3', '29137598', 'The traditional method of visualizing gene annotation data in JBrowse is converting GFF3 files to JSON format, which is time-consuming. The latest version of JBrowse supports rendering sorted GFF3 files indexed by tabix, a novel strategy that is more convenient than the original conversion process. However, current tools available for GFF3 file sorting have some limitations and their sorting results would lead to erroneous rendering in JBrowse.'),
(98099, 2017, 'bmc', 'Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs', 'Le, Duc-Hau|Verbeke, Lieven|Son, Le Hoang|Chu, Dinh-Toi|Pham, Van-Huy', 'Vinmec Research Institute of Stem Cell and Gene Technology, 458 Minh Khai, Hai Ba Trung, Hanoi, Vietnam.|Department of Information Technology, Ghent University - imec, Ghent, Belgium.|VNU University of Science, Vietnam National University, Hanoi, Vietnam.|Faculty of Biology, Hanoi National University of Education, Hanoi, Vietnam.|Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam. phamvanhuy@tdt.edu.vn.', '-|Belgium|-|-|-', 8, 'Algorithms|Disease|Gene Regulatory Networks|Humans|MicroRNAs', 'Journal Article', '10.1186/s12859-017-1924-1', '29137601', 'MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model.'),
(98100, 2017, 'bmc', 'ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data', 'Oluwadare, Oluwatosin|Cheng, Jianlin', 'Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, 65211, USA.|Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, 65211, USA. chengji@missouri.edu.', 'United States|United States', 8, 'Animals|Binding Sites|CCCTC-Binding Factor|Chromatin Immunoprecipitation|Chromosomes|Cluster Analysis|Enhancer Elements, Genetic|Genomics|Histone Code|Mice|Promoter Regions, Genetic|Sequence Analysis, DNA|Unsupervised Machine Learning', 'Journal Article', '10.1186/s12859-017-1931-2', '29137603', 'With the development of chromosomal conformation capturing techniques, particularly, the Hi-C technique, the study of the spatial conformation of a genome is becoming an important topic in bioinformatics and computational biology. The Hi-C technique can generate genome-wide chromosomal interaction (contact) data, which can be used to investigate the higher-level organization of chromosomes, such as Topologically Associated Domains (TAD), i.e., locally packed chromosome regions bounded together by intra chromosomal contacts. The identification of the TADs for a genome is useful for studying gene regulation, genomic interaction, and genome function.'),
(98101, 2017, 'bmc', 'Usability of human Infinium MethylationEPIC BeadChip for mouse DNA methylation studies', 'Needhamsen, Maria|Ewing, Ewoud|Lund, Harald|Gomez-Cabrero, David|Harris, Robert Adam|Kular, Lara|Jagodic, Maja', 'Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.|Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.|Applied Immunology and Immunotherapy, Department of Clinical Neuroscience, Karolinska Institutet Center for Molecular Medicine, Karolinska Hospital at Solna, Stockholm, Sweden.|Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.|Applied Immunology and Immunotherapy, Department of Clinical Neuroscience, Karolinska Institutet Center for Molecular Medicine, Karolinska Hospital at Solna, Stockholm, Sweden.|Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.|Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden. maja.jagodic@ki.se.', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 7, 'Animals|CpG Islands|DNA Methylation|Genomics|Humans|Mice|Oligonucleotide Array Sequence Analysis|Reproducibility of Results', 'Evaluation Studies', '10.1186/s12859-017-1870-y', '29141580', 'The advent of array-based genome-wide DNA methylation methods has enabled quantitative measurement of single CpG methylation status at relatively low cost and sample input. Whereas the use of Infinium Human Methylation BeadChips has shown great utility in clinical studies, no equivalent tool is available for rodent animal samples. We examined the feasibility of using the new Infinium MethylationEPIC BeadChip for studying DNA methylation in mouse.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98102, 2017, 'bmc', 'SPRINT: ultrafast protein-protein interaction prediction of the entire human interactome', 'Li, Yiwei|Ilie, Lucian', 'Department of Computer Science, The University of Western Ontario, London, N6A 5B7, Ontario, Canada.|Department of Computer Science, The University of Western Ontario, London, N6A 5B7, Ontario, Canada. ilie@uwo.ca.', 'Canada|Canada', 10, 'Algorithms|Humans|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|Software', 'Evaluation Studies', '10.1186/s12859-017-1871-x', '29141584', 'Proteins perform their functions usually by interacting with other proteins. Predicting which proteins interact is a fundamental problem. Experimental methods are slow, expensive, and have a high rate of error. Many computational methods have been proposed among which sequence-based ones are very promising. However, so far no such method is able to predict effectively the entire human interactome: they require too much time or memory.'),
(98103, 2017, 'bmc', 'Reconstructing cancer karyotypes from short read data: the half empty and half full glass', 'Eitan, Rami|Shamir, Ron', 'Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv-Yafo, Israel.|Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv-Yafo, Israel. rami.eitan@gmail.com.', 'Israel|Israel', 0, 'Algorithms|Chromosome Aberrations|Chromosome Breakpoints|DNA Copy Number Variations|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Karyotype|Karyotyping|Neoplasms|Sequence Analysis, DNA', 'Evaluation Studies', '10.1186/s12859-017-1929-9', '29141589', 'During cancer progression genomes undergo point mutations as well as larger segmental changes. The latter include, among others, segmental deletions duplications, translocations and inversions.The result is a highly complex, patient-specific cancer karyotype. Using high-throughput technologies of deep sequencing and microarrays it is possible to interrogate a cancer genome and produce chromosomal copy number profiles and a list of breakpoints (jumps) relative to the normal genome. This information is very detailed but local, and does not give the overall picture of the cancer genome. One of the basic challenges in cancer genome research is to use such information to infer the cancer karyotype. We present here an algorithmic approach, based on graph theory and integer linear programming, that receives segmental copy number and breakpoint data as input and produces a cancer karyotype that is most concordant with them. We used simulations to evaluate the utility of our approach, and applied it to real data.'),
(98104, 2017, 'bmc', 'TRAVeLer: a tool for template-based RNA secondary structure visualization', 'Elias, Richard|Hoksza, David', 'Faculty of Mathematics and Physics, Charles University, Prague, 11800, Czech Republic.|Faculty of Mathematics and Physics, Charles University, Prague, 11800, Czech Republic. david.hoksza@mff.cuni.cz.', '-|-', 7, 'Algorithms|Nucleic Acid Conformation|RNA|Software', 'Journal Article', '10.1186/s12859-017-1885-4', '29141608', 'Visualization of RNA secondary structures is a complex task, and, especially in the case of large RNA structures where the expected layout is largely habitual, the existing visualization tools often fail to produce suitable visualizations. This led us to the idea to use existing layouts as templates for the visualization of new RNAs similarly to how templates are used in homology-based structure prediction.'),
(98105, 2017, 'bmc', 'ANAT 20: reconstructing functional protein subnetworks', 'Almozlino, Yomtov|Atias, Nir|Silverbush, Dana|Sharan, Roded', 'School of Computer Science, Tel Aviv University, 69978, Tel Aviv, Israel.|School of Computer Science, Tel Aviv University, 69978, Tel Aviv, Israel.|School of Computer Science, Tel Aviv University, 69978, Tel Aviv, Israel.|School of Computer Science, Tel Aviv University, 69978, Tel Aviv, Israel. roded@post.tau.ac.il.', 'Israel|Israel|Israel|Israel', 6, 'Algorithms|Humans|Proteins|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1186/s12859-017-1932-1', '29145805', 'ANAT is a graphical, Cytoscape-based tool for the inference of protein networks that underlie a process of interest. The ANAT tool allows the user to perform network reconstruction under several scenarios in a number of organisms including yeast and human.'),
(98106, 2017, 'bmc', 'dbMDEGA: a database for meta-analysis of differentially expressed genes in autism spectrum disorder', 'Zhang, Shuyun|Deng, Libin|Jia, Qiyue|Huang, Shaoting|Gu, Junwang|Zhou, Fankun|Gao, Meng|Sun, Xinyi|Feng, Chang|Fan, Guangqin', 'Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, BaYi Road 461, Nanchang, 330006, China.|Institute for Translational Medicine, Nanchang University, Nanchang, 330000, China.|Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, BaYi Road 461, Nanchang, 330006, China.|Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, BaYi Road 461, Nanchang, 330006, China.|Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, BaYi Road 461, Nanchang, 330006, China.|Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, BaYi Road 461, Nanchang, 330006, China.|Institute for Translational Medicine, Nanchang University, Nanchang, 330000, China.|Institute for Translational Medicine, Nanchang University, Nanchang, 330000, China.|Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, BaYi Road 461, Nanchang, 330006, China.|Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, BaYi Road 461, Nanchang, 330006, China. fanguangqin@ncu.edu.cn.', 'China|China|China|China|China|China|China|China|China|China', 4, 'Animals|Autism Spectrum Disorder|Cerebellum|Cerebral Cortex|Databases, Genetic|Female|Humans|Male|Mice|Transcriptome', 'Journal Article', '10.1186/s12859-017-1915-2', '29145823', 'Autism spectrum disorders (ASD) are hereditary, heterogeneous and biologically complex neurodevelopmental disorders. Individual studies on gene expression in ASD cannot provide clear consensus conclusions. Therefore, a systematic review to synthesize the current findings from brain tissues and a search tool to share the meta-analysis results are urgently needed.'),
(98107, 2017, 'bmc', 'GPU-based detection of protein cavities using Gaussian surfaces', 'Dias, Sérgio E D|Martins, Ana Mafalda|Nguyen, Quoc T|Gomes, Abel J P', 'Universidade da Beira Interior, Av. Marques DÁvila e Bolama, Covilhã, 6200-001, Portugal.|Universidade da Beira Interior, Av. Marques DÁvila e Bolama, Covilhã, 6200-001, Portugal.|Universidade da Beira Interior, Av. Marques DÁvila e Bolama, Covilhã, 6200-001, Portugal.|Universidade da Beira Interior, Av. Marques DÁvila e Bolama, Covilhã, 6200-001, Portugal. agomes@di.ubi.pt.', 'Portugal|Portugal|Portugal|Portugal', 7, 'Algorithms|Ligands|Normal Distribution|Proteins|Surface Properties', 'Journal Article', '10.1186/s12859-017-1913-4', '29145826', 'Protein cavities play a key role in biomolecular recognition and function, particularly in protein-ligand interactions, as usual in drug discovery and design. Grid-based cavity detection methods aim at finding cavities as aggregates of grid nodes outside the molecule, under the condition that such cavities are bracketed by nodes on the molecule surface along a set of directions (not necessarily aligned with coordinate axes). Therefore, these methods are sensitive to scanning directions, a problem that we call cavity ground-and-walls ambiguity, i.e., they depend on the position and orientation of the protein in the discretized domain. Also, it is hard to distinguish grid nodes belonging to protein cavities amongst all those outside the protein, a problem that we call cavity ceiling ambiguity.'),
(98108, 2017, 'bmc', 'bcROCsurface: an R package for correcting verification bias in estimation of the ROC surface and its volume for continuous diagnostic tests', 'To Duc, Khanh', 'Department of Statistical Sciences, University of Padova, via C. Battisti, 241, Padova, 35121, Italy. toduc@stat.unipd.it.', 'Italy', 4, 'Adult|Aged|Bias|Diagnostic Tests, Routine|Female|Humans|Middle Aged|Ovarian Neoplasms|ROC Curve|Software', 'Journal Article', '10.1186/s12859-017-1914-3', '29151019', 'Receiver operating characteristic (ROC) surface analysis is usually employed to assess the accuracy of a medical diagnostic test when there are three ordered disease status (e.g. non-diseased, intermediate, diseased). In practice, verification bias can occur due to missingness of the true disease status and can lead to a distorted conclusion on diagnostic accuracy. In such situations, bias-corrected inference tools are required.'),
(98109, 2017, 'bmc', 'Swellix: a computational tool to explore RNA conformational space', 'Sloat, Nathan|Liu, Jui-Wen|Schroeder, Susan J', ', 101 Stephenson Parkway, Norman, OK, 73019, USA.|, 101 Stephenson Parkway, Norman, OK, 73019, USA.|, 101 Stephenson Parkway, Norman, OK, 73019, USA. susan.schroeder@ou.edu.', 'United States|United States|United States', 2, 'Algorithms|Base Pairing|Base Sequence|Computational Biology|Endogenous Retroviruses|Humans|Nucleic Acid Conformation|Nucleotides|Pharmaceutical Preparations|RNA|RNA Folding|RNA, Transfer|RNA, Viral|RNA-Binding Proteins|Thermodynamics', 'Journal Article', '10.1186/s12859-017-1910-7', '29157200', 'The sequence of nucleotides in an RNA determines the possible base pairs for an RNA fold and thus also determines the overall shape and function of an RNA. The Swellix program presented here combines a helix abstraction with a combinatorial approach to the RNA folding problem in order to compute all possible non-pseudoknotted RNA structures for RNA sequences. The Swellix program builds on the Crumple program and can include experimental constraints on global RNA structures such as the minimum number and lengths of helices from crystallography, cryoelectron microscopy, or in vivo crosslinking and chemical probing methods.'),
(98110, 2017, 'bmc', 'Integration of RNA-Seq data with heterogeneous microarray data for breast cancer profiling', 'Castillo, Daniel|Gálvez, Juan Manuel|Herrera, Luis Javier|Román, Belén San|Rojas, Fernando|Rojas, Ignacio', 'Department of Computer Architecture and Technology, University of Granada, Periodista Rafael Gómez Montero, 2, Granada, 18014, Spain. cased@ugr.es.|Department of Computer Architecture and Technology, University of Granada, Periodista Rafael Gómez Montero, 2, Granada, 18014, Spain.|Department of Computer Architecture and Technology, University of Granada, Periodista Rafael Gómez Montero, 2, Granada, 18014, Spain.|Department of Computer Architecture and Technology, University of Granada, Periodista Rafael Gómez Montero, 2, Granada, 18014, Spain.|Department of Computer Architecture and Technology, University of Granada, Periodista Rafael Gómez Montero, 2, Granada, 18014, Spain.|Department of Computer Architecture and Technology, University of Granada, Periodista Rafael Gómez Montero, 2, Granada, 18014, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Algorithms|Breast Neoplasms|Cluster Analysis|Databases, Genetic|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-017-1925-0', '29157215', 'Nowadays, many public repositories containing large microarray gene expression datasets are available. However, the problem lies in the fact that microarray technology are less powerful and accurate than more recent Next Generation Sequencing technologies, such as RNA-Seq. In any case, information from microarrays is truthful and robust, thus it can be exploited through the integration of microarray data with RNA-Seq data. Additionally, information extraction and acquisition of large number of samples in RNA-Seq still entails very high costs in terms of time and computational resources.This paper proposes a new model to find the gene signature of breast cancer cell lines through the integration of heterogeneous data from different breast cancer datasets, obtained from microarray and RNA-Seq technologies. Consequently, data integration is expected to provide a more robust statistical significance to the results obtained. Finally, a classification method is proposed in order to test the robustness of the Differentially Expressed Genes when unseen data is presented for diagnosis.'),
(98111, 2017, 'bmc', 'Brain medical image diagnosis based on corners with importance-values', 'Gao, Linlin|Pan, Haiwei|Li, Qing|Xie, Xiaoqin|Zhang, Zhiqiang|Han, Jinming|Zhai, Xiao|Alzheimerxe2x80x99s Disease Neuroimaging Initiative', 'Research center for intelligent information processing, College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China.|Research center for intelligent information processing, College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China. panhaiwei@hrbeu.edu.cn.|Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.|Research center for intelligent information processing, College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China.|Research center for intelligent information processing, College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China.|Department of Neurology and Neuroscience Center, the First Hospital of Jilin University, Changchun, 130021, China.|Research center for intelligent information processing, College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China.|Research center for intelligent information processing, College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China.', 'China|China|Hong Kong|China|China|China|China|China', 5, 'Algorithms|Brain|Humans|Imaging, Three-Dimensional|Magnetic Resonance Imaging|Reproducibility of Results|Tomography, X-Ray Computed|Uncertainty', 'Journal Article', '10.1186/s12859-017-1903-6', '29157218', 'Brain disorders are one of the top causes of human death. Generally, neurologists analyze brain medical images for diagnosis. In the image analysis field, corners are one of the most important features, which makes corner detection and matching studies essential. However, existing corner detection studies do not consider the domain information of brain. This leads to many useless corners and the loss of significant information. Regarding corner matching, the uncertainty and structure of brain are not employed in existing methods. Moreover, most corner matching studies are used for 3D image registration. They are inapplicable for 2D brain image diagnosis because of the different mechanisms. To address these problems, we propose a novel corner-based brain medical image classification method. Specifically, we automatically extract multilayer texture images (MTIs) which embody diagnostic information from neurologists. Moreover, we present a corner matching method utilizing the uncertainty and structure of brain medical images and a bipartite graph model. Finally, we propose a similarity calculation method for diagnosis.'),
(98112, 2017, 'bmc', 'Fast batch searching for protein homology based on compression and clustering', 'Ge, Hongwei|Sun, Liang|Yu, Jinghong', 'College of Computer Science and Technology, Dalian University of Technology, No.2, Linggong Road, Dalian, China.|College of Computer Science and Technology, Dalian University of Technology, No.2, Linggong Road, Dalian, China. liangsun@dlut.edu.cn.|College of Computer Science and Technology, Dalian University of Technology, No.2, Linggong Road, Dalian, China.', 'China|China|China', 3, 'Algorithms|Amino Acid Sequence|Cluster Analysis|Data Compression|Databases, Protein|Proteins|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/s12859-017-1938-8', '29162030', 'In bioinformatics community, many tasks associate with matching a set of protein query sequences in large sequence datasets. To conduct multiple queries in the database, a common used method is to run BLAST on each original querey or on the concatenated queries. It is inefficient since it doesnt exploit the common subsequences shared by queries.'),
(98113, 2017, 'bmc', 'Norgal: extraction and de novo assembly of mitochondrial DNA from whole-genome sequencing data', 'Al-Nakeeb, Kosai|Petersen, Thomas Nordahl|Sicheritz-Pontén, Thomas', 'Department of Bio and Health Informatics, Technical University of Denmark, Kemitorvet, Building 208, Kgs Lyngby, 2800, Denmark. kosai@bioinformatics.dtu.dk.|Department of Bio and Health Informatics, Technical University of Denmark, Kemitorvet, Building 208, Kgs Lyngby, 2800, Denmark.|Department of Bio and Health Informatics, Technical University of Denmark, Kemitorvet, Building 208, Kgs Lyngby, 2800, Denmark.', 'Denmark|Denmark|Denmark', 15, 'Animals|DNA, Chloroplast|DNA, Mitochondrial|Genome, Chloroplast|Genome, Mitochondrial|Software|Whole Genome Sequencing', 'Journal Article', '10.1186/s12859-017-1927-y', '29162031', 'Whole-genome sequencing (WGS) projects provide short read nucleotide sequences from nuclear and possibly organelle DNA depending on the source of origin. Mitochondrial DNA is present in animals and fungi, while plants contain DNA from both mitochondria and chloroplasts. Current techniques for separating organelle reads from nuclear reads in WGS data require full reference or partial seed sequences for assembling.'),
(98114, 2017, 'bmc', 'Synima: a Synteny imaging tool for annotated genome assemblies', 'Farrer, Rhys A', 'Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK. rfarrer@broadinstitute.org.', 'United Kingdom', 7, 'Algorithms|Cryptococcus|Genome, Fungal|Molecular Sequence Annotation|Phylogeny|Software|Synteny', 'Journal Article', '10.1186/s12859-017-1939-7', '29162056', 'Ortholog prediction and synteny visualization across whole genomes are valuable methods for detecting and representing a range of evolutionary processes such as genome expansion, chromosomal rearrangement, and chromosomal translocation. Few standalone methods are currently available to visualize synteny across any number of annotated genomes.'),
(98115, 2017, 'bmc', 'Unsupervised gene selection using biological knowledge : application in sample clustering', 'Acharya, Sudipta|Saha, Sriparna|Nikhil, N', 'IIT Patna, Department of Computer Science and engineering, Patna, India. sudiptaacharya.2012@gmail.com.|IIT Patna, Department of Computer Science and engineering, Patna, India.|IIT Ropar, Department of Computer Science and engineering, Punjab, India.', 'India|India|India', 9, 'Algorithms|Cluster Analysis|Computational Biology|Gene Expression Profiling|Gene Ontology|Genes|Humans|Molecular Sequence Annotation|Organ Specificity|Saccharomyces cerevisiae', 'Journal Article', '10.1186/s12859-017-1933-0', '29166852', 'Classification of biological samples of gene expression data is a basic building block in solving several problems in the field of bioinformatics like cancer and other disease diagnosis and making a proper treatment plan. One big challenge in sample classification is handling large dimensional and redundant gene expression data. To reduce the complexity of handling this high dimensional data, gene/feature selection plays a major role.'),
(98116, 2017, 'bmc', 'ADAGE signature analysis: differential expression analysis with data-defined gene sets', 'Tan, Jie|Huyck, Matthew|Hu, Dongbo|Zelaya, Renxc3xa9 A|Hogan, Deborah A|Greene, Casey S', 'Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA.|Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, 19104, USA.|Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, 19104, USA.|Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, 19104, USA.|Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA.|Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, 19104, USA. greenescientist@gmail.com.', 'United States|United States|United States|United States|United States|United States', 6, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Internet|Pseudomonas aeruginosa|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1905-4', '29166858', 'Gene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data.'),
(98117, 2017, 'bmc', 'HashClone: a new tool to quantify the minimal residual disease in B-cell lymphoma from deep sequencing data', 'Beccuti, Marco|Genuardi, Elisa|Romano, Greta|Monitillo, Luigia|Barbero, Daniela|Boccadoro, Mario|Ladetto, Marco|Calogero, Raffaele|Ferrero, Simone|Cordero, Francesca', 'Department of Computer Science, University of Torino, Via Pesinetto 12, Turin, 10149, Italy.|Division of Hematology, Department of Molecular Biotechnologies and Health Sciences, University of Torino, Via Genova 3, Turin, 10126, Italy.|Department of Computer Science, University of Torino, Via Pesinetto 12, Turin, 10149, Italy.|Division of Hematology, Department of Molecular Biotechnologies and Health Sciences, University of Torino, Via Genova 3, Turin, 10126, Italy.|Division of Hematology, Department of Molecular Biotechnologies and Health Sciences, University of Torino, Via Genova 3, Turin, 10126, Italy.|Division of Hematology, Department of Molecular Biotechnologies and Health Sciences, University of Torino, Via Genova 3, Turin, 10126, Italy.|Division of Hematology, Az. Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Via Venezia 16, Alessandria, 15121, Italy.|Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, Turin, 10125, Italy.|Division of Hematology, Department of Molecular Biotechnologies and Health Sciences, University of Torino, Via Genova 3, Turin, 10126, Italy.|Department of Computer Science, University of Torino, Via Pesinetto 12, Turin, 10149, Italy. fcordero@di.unito.it.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 5, 'Algorithms|Alleles|B-Lymphocytes|Clone Cells|Humans|Lymphoma, B-Cell|Neoplasm, Residual|Reproducibility of Results', 'Journal Article', '10.1186/s12859-017-1923-2', '29169317', 'Mantle Cell Lymphoma (MCL) is a B cell aggressive neoplasia accounting for about the 6% of all lymphomas. The most common molecular marker of clonality in MCL, as in other B lymphoproliferative disorders, is the ImmunoGlobulin Heavy chain (IGH) rearrangement, occurring in B-lymphocytes. The patient-specific IGH rearrangement is extensively used to monitor the Minimal Residual Disease (MRD) after treatment through the standardized Allele-Specific Oligonucleotides Quantitative Polymerase Chain Reaction based technique. Recently, several studies have suggested that the IGH monitoring through deep sequencing techniques can produce not only comparable results to Polymerase Chain Reaction-based methods, but also might overcome the classical technique in terms of feasibility and sensitivity. However, no standard bioinformatics tool is available at the moment for data analysis in this context.'),
(98118, 2017, 'bmc', 'MVQTLCIM: composite interval mapping of multivariate traits in a hybrid F1 population of outbred species', 'Liu, Fenxiang|Tong, Chunfa|Tao, Shentong|Wu, Jiyan|Chen, Yuhua|Yao, Dan|Li, Huogen|Shi, Jisen', 'The Southern Modern Forestry Collaborative Innovation Center, College of Forestry, Nanjing Forestry University, Nanjing, 210037, China.|The Southern Modern Forestry Collaborative Innovation Center, College of Forestry, Nanjing Forestry University, Nanjing, 210037, China. tongchf@njfu.edu.cn.|The Southern Modern Forestry Collaborative Innovation Center, College of Forestry, Nanjing Forestry University, Nanjing, 210037, China.|The Southern Modern Forestry Collaborative Innovation Center, College of Forestry, Nanjing Forestry University, Nanjing, 210037, China.|The Southern Modern Forestry Collaborative Innovation Center, College of Forestry, Nanjing Forestry University, Nanjing, 210037, China.|The Southern Modern Forestry Collaborative Innovation Center, College of Forestry, Nanjing Forestry University, Nanjing, 210037, China.|The Southern Modern Forestry Collaborative Innovation Center, College of Forestry, Nanjing Forestry University, Nanjing, 210037, China.|The Southern Modern Forestry Collaborative Innovation Center, College of Forestry, Nanjing Forestry University, Nanjing, 210037, China.', 'China|China|China|China|China|China|China|China', 2, 'Chromosome Mapping|Chromosome Segregation|Computer Simulation|Crosses, Genetic|Genetic Association Studies|Genetic Linkage|Genetic Markers|Genetics, Population|Genome, Plant|Hybridization, Genetic|Likelihood Functions|Monte Carlo Method|Phenotype|Populus|Quantitative Trait Loci|Quantitative Trait, Heritable|Species Specificity', 'Journal Article', '10.1186/s12859-017-1908-1', '29169342', 'Background With the plummeting cost of the next-generation sequencing technologies, high-density genetic linkage maps could be constructed in a forest hybrid F1 population. However, based on such genetic maps, quantitative trait loci (QTL) mapping cannot be directly conducted with traditional statistical methods or tools because the linkage phase and segregation pattern of molecular markers are not always fixed as in inbred lines.  Results We implemented the traditional composite interval mapping (CIM) method to multivariate trait data in forest trees and developed the corresponding software, mvqtlcim. Our method not only incorporated the various segregations and linkage phases of molecular markers, but also applied Takeuchi’s information criterion (TIC) to discriminate the QTL segregation type among several possible alternatives. QTL mapping was performed in a hybrid F1 population of Populus deltoides and P. simonii, and 12 QTLs were detected for tree height over 6 time points. The software package allowed many options for parameters as well as parallel computing for permutation tests. The features of the software were demonstrated with the real data analysis and a large number of Monte Carlo simulations.  Conclusions We provided a powerful tool for QTL mapping of multiple or longitudinal traits in an outbred F1 population, in which the traditional software for QTL mapping cannot be used. This tool will facilitate studying of QTL mapping and thus will accelerate molecular breeding programs especially in forest trees. The tool package is freely available from https://github.com/tongchf /mvqtlcim.'),
(98119, 2017, 'bmc', 'A modified GC-specific MAKER gene annotation method reveals improved and novel gene predictions of high and low GC content in Oryza sativa', 'Bowman, Megan J|Pulman, Jane A|Liu, Tiffany L|Childs, Kevin L', 'Department of Plant Biology, Michigan State University, 612 Wilson Rd, Room 166, East Lansing, MI, 48824, USA.|Department of Plant Biology, Michigan State University, 612 Wilson Rd, Room 166, East Lansing, MI, 48824, USA.|Department of Plant Biology, Michigan State University, 612 Wilson Rd, Room 166, East Lansing, MI, 48824, USA.|Department of Plant Biology, Michigan State University, 612 Wilson Rd, Room 166, East Lansing, MI, 48824, USA. kchilds@msu.edu.', 'United States|United States|United States|United States', 3, 'Base Composition|Genome, Plant|Markov Chains|Molecular Sequence Annotation|Oryza|RNA, Plant|Ribosomes|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-017-1942-z', '29178822', 'Accurate structural annotation depends on well-trained gene prediction programs. Training data for gene prediction programs are often chosen randomly from a subset of high-quality genes that ideally represent the variation found within a genome. One aspect of gene variation is GC content, which differs across species and is bimodal in grass genomes. When gene prediction programs are trained on a subset of grass genes with random GC content, they are effectively being trained on two classes of genes at once, and this can be expected to result in poor results when genes are predicted in new genome sequences.'),
(98120, 2017, 'bmc', 'Software for rapid time dependent ChIP-sequencing analysis (TDCA)', 'Myschyshyn, Mike|Farren-Dai, Marco|Chuang, Tien-Jui|Vocadlo, David', 'Department of Molecular Biology and Biochemistry, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada. mmyschyshyn@gmail.com.|Chemistry, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada.|Department of Molecular Biology and Biochemistry, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada.|Department of Molecular Biology and Biochemistry, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada. dvocadlo@sfu.ca.', 'Canada|Canada|Canada|Canada', 1, 'Algorithms|Animals|Cell Line|Chromatin Immunoprecipitation|Chromosomes|DNA|DNA-Binding Proteins|High-Throughput Nucleotide Sequencing|Histones|Humans|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1186/s12859-017-1936-x', '29178831', 'Chromatin immunoprecipitation followed by DNA sequencing (ChIP-seq) and associated methods are widely used to define the genome wide distribution of chromatin associated proteins, post-translational epigenetic marks, and modifications found on DNA bases. An area of emerging interest is to study time dependent changes in the distribution of such proteins and marks by using serial ChIP-seq experiments performed in a time resolved manner. Despite such time resolved studies becoming increasingly common, software to facilitate analysis of such data in a robust automated manner is limited.'),
(98121, 2017, 'bmc', 'VISPA2: a scalable pipeline for high-throughput identification and annotation of vector integration sites', 'Spinozzi, Giulio|Calabria, Andrea|Brasca, Stefano|Beretta, Stefano|Merelli, Ivan|Milanesi, Luciano|Montini, Eugenio', 'San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS, San Raffaele Scientific Institute, Via Olgettina, 58, 20132, Milan, Italy.|San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS, San Raffaele Scientific Institute, Via Olgettina, 58, 20132, Milan, Italy.|San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS, San Raffaele Scientific Institute, Via Olgettina, 58, 20132, Milan, Italy.|Department of Computer Science, University of Milano Bicocca, Viale Sarca, 336, 20126, Milan, Italy.|National Research Council, Institute for Biomedical Technologies, Via Fratelli Cervi, 93, 20090, Segrate, Italy.|National Research Council, Institute for Biomedical Technologies, Via Fratelli Cervi, 93, 20090, Segrate, Italy.|San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS, San Raffaele Scientific Institute, Via Olgettina, 58, 20132, Milan, Italy. montini.eugenio@hsr.it.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 6, 'Algorithms|Genetic Therapy|Genetic Vectors|Hematopoietic Stem Cell Transplantation|Hematopoietic Stem Cells|High-Throughput Nucleotide Sequencing|Humans|Lentivirus|Sequence Alignment|User-Computer Interface|Virus Internalization', 'Journal Article', '10.1186/s12859-017-1937-9', '29178837', 'Bioinformatics tools designed to identify lentiviral or retroviral vector insertion sites in the genome of host cells are used to address the safety and long-term efficacy of hematopoietic stem cell gene therapy applications and to study the clonal dynamics of hematopoietic reconstitution. The increasing number of gene therapy clinical trials combined with the increasing amount of Next Generation Sequencing data, aimed at identifying integration sites, require both highly accurate and efficient computational software able to correctly process big data in a reasonable computational time.'),
(98122, 2017, 'bmc', 'BoolFilter: an R package for estimation and identification of partially-observed Boolean dynamical systems', 'Mcclenny, Levi D|Imani, Mahdi|Braga-Neto, Ulisses M', 'Electrical and Computer Engineering Department, College Station, Texas, USA. levimcclenny@tamu.edu.|Electrical and Computer Engineering Department, College Station, Texas, USA.|Electrical and Computer Engineering Department, College Station, Texas, USA.', 'United States|United States|United States', 15, 'Algorithms|Gene Expression Profiling|Gene Regulatory Networks|Models, Biological|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1886-3', '29178844', 'Gene regulatory networks govern the function of key cellular processes, such as control of the cell cycle, response to stress, DNA repair mechanisms, and more. Boolean networks have been used successfully in modeling gene regulatory networks. In the Boolean network model, the transcriptional state of each gene is represented by 0 (inactive) or 1 (active), and the relationship among genes is represented by logical gates updated at discrete time points. However, the Boolean gene states are never observed directly, but only indirectly and incompletely through noisy measurements based on expression technologies such as cDNA microarrays, RNA-Seq, and cell imaging-based assays. The Partially-Observed Boolean Dynamical System (POBDS) signal model is distinct from other deterministic and stochastic Boolean network models in removing the requirement of a directly observable Boolean state vector and allowing uncertainty in the measurement process, addressing the scenario encountered in practice in transcriptomic analysis.'),
(98123, 2017, 'bmc', 'Detecting transitions in protein dynamics using a recurrence quantification analysis based bootstrap method', 'Karain, Wael I', 'Department of Physics, Birzeit University, P.O.Box 14, Birzeit, Palestine. wqaran@birzeit.edu.', '-', 1, 'Algorithms|Cluster Analysis|Computational Biology|Molecular Dynamics Simulation|Principal Component Analysis|Protein Structure, Secondary|Proteins|Time|Time Factors', 'Journal Article', '10.1186/s12859-017-1943-y', '29179670', 'Proteins undergo conformational transitions over different time scales. These transitions are closely intertwined with the proteins function. Numerous standard techniques such as principal component analysis are used to detect these transitions in molecular dynamics simulations. In this work, we add a new method that has the ability to detect transitions in dynamics based on the recurrences in the dynamical system. It combines bootstrapping and recurrence quantification analysis. We start from the assumption that a protein has a baseline recurrence structure over a given period of time. Any statistically significant deviation from this recurrence structure, as inferred from complexity measures provided by recurrence quantification analysis, is considered a transition in the dynamics of the protein.'),
(98124, 2017, 'bmc', 'An efficient error correction algorithm using FM-index', 'Huang, Yao-Ting|Huang, Yu-Wen', 'Department of Computer Science and Information Engineering, National Chuang Cheng University, Chiayi, Taiwan. ythuang@cs.ccu.edu.tw.|Department of Computer Science and Information Engineering, National Chuang Cheng University, Chiayi, Taiwan.', '-|-', 1, 'Algorithms|Animals|Bacteria|Base Sequence|Caenorhabditis elegans|Genome|High-Throughput Nucleotide Sequencing|Sequence Alignment|Time Factors', 'Journal Article', '10.1186/s12859-017-1940-1', '29179672', 'High-throughput sequencing offers higher throughput and lower cost for sequencing a genome. However, sequencing errors, including mismatches and indels, may be produced during sequencing. Because, errors may reduce the accuracy of subsequent de novo assembly, error correction is necessary prior to assembly. However, existing correction methods still face trade-offs among correction power, accuracy, and speed.'),
(98125, 2017, 'bmc', 'Modeling, validation and verification of three-dimensional cell-scaffold contacts from terabyte-sized images', 'Bajcsy, Peter|Yoon, Soweon|Florczyk, Stephen J|Hotaling, Nathan A|Simon, Mylene|Szczypinski, Piotr M|Schaub, Nicholas J|Simon, Carl G|Brady, Mary|Sriram, Ram D', 'Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA. peter.bajcsy@nist.gov.|Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.|Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.|National Eye Institute, National Institute of Health, Bethesda, MD, USA. nathan.hotaling@nih.gov.|Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.|Lodz University of Technology, xc5x81xc3xb3dxc5xba, Poland.|Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.|Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.|Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.|Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.', 'United States|United States|United States|United States|United States|Poland|United States|United States|United States|United States', 2, 'Algorithms|Biocompatible Materials|Bone Marrow Cells|Humans|Imaging, Three-Dimensional|Internet|Male|Mesenchymal Stem Cells|Microscopy, Atomic Force|Microscopy, Confocal|Microscopy, Electron, Scanning|Models, Biological|Tissue Scaffolds|User-Computer Interface|X-Ray Microtomography|Young Adult', 'Journal Article', '10.1186/s12859-017-1928-x', '29183290', 'Cell-scaffold contact measurements are derived from pairs of co-registered volumetric fluorescent confocal laser scanning microscopy (CLSM) images (z-stacks) of stained cells and three types of scaffolds (i.e., spun coat, large microfiber, and medium microfiber). Our analysis of the acquired terabyte-sized collection is motivated by the need to understand the nature of the shape dimensionality (1D vs 2D vs 3D) of cell-scaffold interactions relevant to tissue engineers that grow cells on biomaterial scaffolds.'),
(98126, 2017, 'bmc', 'PON-SC - program for identifying steric clashes caused by amino acid substitutions', 'xc4x8calyxc5xa1eva, Jelena|Vihinen, Mauno', 'Protein Structure and Bioinformatics, Department of Experimental Medical Science, Lund University, BMC B13, SE-22 184, Lund, Sweden.|Protein Structure and Bioinformatics, Department of Experimental Medical Science, Lund University, BMC B13, SE-22 184, Lund, Sweden. mauno.vihinen@med.lu.se.', 'Sweden|Sweden', 0, 'Algorithms|Amino Acid Substitution|Amino Acids|Databases, Protein|Protein Conformation|Protein Engineering|Proteins|Software', 'Journal Article', '10.1186/s12859-017-1947-7', '29187139', 'Amino acid substitutions due to DNA nucleotide replacements are frequently disease-causing because of affecting functionally important sites. If the substituting amino acid does not fit into the protein, it causes structural alterations that are often harmful. Clashes of amino acids cause local or global structural changes. Testing structural compatibility of variations has been difficult due to the lack of a dedicated method that could handle vast amounts of variation data produced by next generation sequencing technologies.'),
(98127, 2017, 'bmc', 'DMRfinder: efficiently identifying differentially methylated regions from MethylC-seq data', 'Gaspar, John M|Hart, Ronald P', 'Department of Pharmaceutics, Rutgers University, Piscataway, NJ, USA. jsh58@wildcats.unh.edu.|Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ, USA.', 'United States|United States', 13, 'Cell Line|Cluster Analysis|CpG Islands|DNA Methylation|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1909-0', '29187143', 'DNA methylation is an epigenetic modification that is studied at a single-base resolution with bisulfite treatment followed by high-throughput sequencing. After alignment of the sequence reads to a reference genome, methylation counts are analyzed to determine genomic regions that are differentially methylated between two or more biological conditions. Even though a variety of software packages is available for different aspects of the bioinformatics analysis, they often produce results that are biased or require excessive computational requirements.'),
(98128, 2017, 'bmc', 'DBNorm: normalizing high-density oligonucleotide microarray data based on distributions', 'Meng, Qinxue|Catchpoole, Daniel|Skillicorn, David|Kennedy, Paul J', 'School of Software, Faculty of Engineering and Information Technology and the Centre for Artificial Intelligence, University of Technology Sydney (UTS), PO Box 123, 15 Broadway, Ultimo, NSW, 2007, Australia. Qinxue.Meng@uts.edu.au.|Childrens Cancer Research Unit, The Childrens Hospital at Westmead, 180 Hawkesbury Rd, Westmead, NSW, 2145, Australia.|School of Computing, Queens University at Kingston, 99 University Ave, ON, K7L3N6, Kingston, Canada.|School of Software, Faculty of Engineering and Information Technology and the Centre for Artificial Intelligence, University of Technology Sydney (UTS), PO Box 123, 15 Broadway, Ultimo, NSW, 2007, Australia.', 'Australia|Australia|Canada|Australia', 1, 'Area Under Curve|Gene Expression Regulation, Neoplastic|Humans|Normal Distribution|Oligonucleotide Array Sequence Analysis|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Principal Component Analysis|ROC Curve|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1912-5', '29187149', 'Data from patients with rare diseases is often produced using different platforms and probe sets because patients are widely distributed in space and time. Aggregating such data requires a method of normalization that makes patient records comparable.'),
(98129, 2017, 'bmc', 'A proximity-based graph clustering method for the identification and application of transcription factor clusters', 'Spadafore, Maxwell|Najarian, Kayvan|Boyle, Alan P', 'University of Michigan Medical School, 1301 Catherine, Ann Arbor, 48109-5624, USA. maxspad@umich.edu.|University of Michigan Department of Computational Medicine and Bioinformatics, 100 Washtenaw Avenue, Ann Arbor, 48109, USA.|University of Michigan Department of Computational Medicine and Bioinformatics, 100 Washtenaw Avenue, Ann Arbor, 48109, USA.', 'United States|United States|United States', 0, 'Algorithms|Chromatin Immunoprecipitation|Cluster Analysis|DNA|Gene Regulatory Networks|Humans|K562 Cells|Markov Chains|Protein Interaction Maps|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/s12859-017-1935-y', '29187152', 'Transcription factors (TFs) form a complex regulatory network within the cell that is crucial to cell functioning and human health. While methods to establish where a TF binds to DNA are well established, these methods provide no information describing how TFs interact with one another when they do bind. TFs tend to bind the genome in clusters, and current methods to identify these clusters are either limited in scope, unable to detect relationships beyond motif similarity, or not applied to TF-TF interactions.'),
(98130, 2017, 'bmc', 'ImageJ2: ImageJ for the next generation of scientific image data', 'Rueden, Curtis T|Schindelin, Johannes|Hiner, Mark C|DeZonia, Barry E|Walter, Alison E|Arena, Ellen T|Eliceiri, Kevin W', 'Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA.|Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA.|Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA.|Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA.|Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA.|Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA.|Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA. eliceiri@wisc.edu.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Humans|Image Processing, Computer-Assisted|Reproducibility of Results|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1934-z', '29187165', 'ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the softwares ability to handle the requirements of modern science.'),
(98131, 2017, 'bmc', 'Using variant databases for variant prioritization and to detect erroneous genotype-phenotype associations', 'Broeckx, Bart J G|Peelman, Luc|Saunders, Jimmy H|Deforce, Dieter|Clement, Lieven', 'Laboratory of Animal Genetics, Faculty of Veterinary Medicine, Ghent University, Heidestraat 19, B-9820, Merelbeke, Belgium. bart.broeckx@ugent.be.|Laboratory of Animal Genetics, Faculty of Veterinary Medicine, Ghent University, Heidestraat 19, B-9820, Merelbeke, Belgium.|Department of Medical Imaging and Orthopedics, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium.|Laboratory of Pharmaceutical Biotechnology, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium.|Department of Applied Mathematics, Computer Science and Statistics, Faculty of Sciences, Ghent University, Ghent, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium', 3, 'Alleles|Congenital Abnormalities|Databases, Genetic|Gene Frequency|Genetic Association Studies|Genotype|Humans|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-017-1951-y', '29191167', 'In the search for novel causal mutations, public and/or private variant databases are nearly always used to facilitate the search as they result in a massive reduction of putative variants in one step. Practically, variant filtering is often done by either using all variants from the variant database (called the absence-approach, i.e. it is assumed that disease-causing variants do not reside in variant databases) or by using the subset of variants with an allelic frequencyxe2x80x89>xe2x80x891% (called the 1%-approach). We investigate the validity of these two approaches in terms of false negatives (the true disease-causing variant does not pass all filters) and false positives (a harmless mutation passes all filters and is erroneously retained in the list of putative disease-causing variants) and compare it with an novel approach which we named the quantile-based approach. This approach applies variable instead of static frequency thresholds and the calculation of these thresholds is based on prior knowledge of disease prevalence, inheritance models, database size and database characteristics.'),
(98132, 2017, 'bmc', 'A common base method for analysis of qPCR data and the application of simple blocking in qPCR experiments', 'Ganger, Michael T|Dietz, Geoffrey D|Ewing, Sarah J', 'Department of Biology, Gannon University, 109 University Square, Erie, PA, 16541, USA. ganger001@gannon.edu.|Department of Mathematics, Gannon University, 109 University Square, Erie, PA, 16541, USA.|Department of Biology, Gannon University, 109 University Square, Erie, PA, 16541, USA.', 'United States|United States|United States', 14, 'Analysis of Variance|Gene Expression|Models, Theoretical|Real-Time Polymerase Chain Reaction', 'Journal Article', '10.1186/s12859-017-1949-5', '29191175', 'qPCR has established itself as the technique of choice for the quantification of gene expression. Procedures for conducting qPCR have received significant attention, however, more rigorous approaches to the statistical analysis of qPCR data are needed.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98133, 2017, 'bmc', 'Decontaminating eukaryotic genome assemblies with machine learning', 'Fierst, Janna L|Murdock, Duncan A', 'Department of Biological Sciences, University of Alabama, Tuscaloosa, 35487, AL, USA. jlfierst@ua.edu.|Department of Biological Sciences, University of Alabama, Tuscaloosa, 35487, AL, USA.', 'United States|United States', 0, 'Animals|Base Composition|Caenorhabditis|DNA, Helminth|Databases, Genetic|Genome|High-Throughput Nucleotide Sequencing|Machine Learning|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-017-1941-0', '29191179', 'High-throughput sequencing has made it theoretically possible to obtain high-quality de novo assembled genome sequences but in practice DNA extracts are often contaminated with sequences from other organisms. Currently, there are few existing methods for rigorously decontaminating eukaryotic assemblies. Those that do exist filter sequences based on nucleotide similarity to contaminants and risk eliminating sequences from the target organism.'),
(98134, 2017, 'bmc', 'Explorative visual analytics on interval-based genomic data and their metadata', 'Jalili, Vahid|Matteucci, Matteo|Masseroli, Marco|Ceri, Stefano', 'Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, 20133, Italy. vahid.jalili@polimi.it.|Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, 20133, Italy.|Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, 20133, Italy.|Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, 20133, Italy.', 'Italy|Italy|Italy|Italy', 4, 'A549 Cells|Databases, Genetic|Dexamethasone|Ethanol|Genomics|Humans|Metadata|Models, Theoretical|Pattern Recognition, Automated|Protein Interaction Mapping|Software', 'Journal Article', '10.1186/s12859-017-1945-9', '29202689', 'With the wide-spreading of public repositories of NGS processed data, the availability of user-friendly and effective tools for data exploration, analysis and visualization is becoming very relevant. These tools enable interactive analytics, an exploratory approach for the seamless sense-making of data through on-the-fly integration of analysis and visualization phases, suggested not only for evaluating processing results, but also for designing and adapting NGS data analysis pipelines.'),
(98135, 2017, 'bmc', 'CONSTAX: a tool for improved taxonomic resolution of environmental fungal ITS sequences', 'Gdanetz, Kristi|Benucci, Gian Maria Niccolò|Vande Pol, Natalie|Bonito, Gregory', 'Department of Plant Biology, Michigan State University, East Lansing, Michigan, 48824, USA.|Department of Plant, Soil, & Microbial Sciences, Michigan State University, East Lansing, Michigan, 48824, USA. benucci@msu.edu.|Department of Microbiology & Molecular Genetics, Michigan State University, East Lansing, Michigan, 48824, USA.|Department of Plant, Soil, & Microbial Sciences, Michigan State University, East Lansing, Michigan, 48824, USA.', 'United States|United States|United States|United States', 16, 'DNA, Fungal|DNA, Intergenic|Environmental Microbiology|Fungi|Genome, Fungal|Genomics|High-Throughput Nucleotide Sequencing|Software', 'Journal Article', '10.1186/s12859-017-1952-x', '29212440', 'One of the most crucial steps in high-throughput sequence-based microbiome studies is the taxonomic assignment of sequences belonging to operational taxonomic units (OTUs). Without taxonomic classification, functional and biological information of microbial communities cannot be inferred or interpreted. The internal transcribed spacer (ITS) region of the ribosomal DNA is the conventional marker region for fungal community studies. While bioinformatics pipelines that cluster reads into OTUs have received much attention in the literature, less attention has been given to the taxonomic classification of these sequences, upon which biological inference is dependent.'),
(98136, 2017, 'bmc', 'Chromosome structures: reduction of certain problems with unequal gene content and gene paralogs to integer linear programming', 'Lyubetsky, Vassily|Gershgorin, Roman|Gorbunov, Konstantin', 'Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), Bolshoy Karetny per. 19, build.1, Moscow, 127051, Russia.|Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), Bolshoy Karetny per. 19, build.1, Moscow, 127051, Russia.|Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), Bolshoy Karetny per. 19, build.1, Moscow, 127051, Russia. gorbunov@iitp.ru.', '-|-|-', 5, 'Biological Evolution|Chromosome Structures|Computational Biology|Genome|Models, Genetic|Phylogeny|Programming, Linear', 'Journal Article', '10.1186/s12859-017-1944-x', '29212445', 'Chromosome structure is a very limited model of the genome including the information about its chromosomes such as their linear or circular organization, the order of genes on them, and the DNA strand encoding a gene. Gene lengths, nucleotide composition, and intergenic regions are ignored. Although highly incomplete, such structure can be used in many cases, e.g., to reconstruct phylogeny and evolutionary events, to identify gene synteny, regulatory elements and promoters (considering highly conserved elements), etc. Three problems are considered, all assume unequal gene content and the presence of gene paralogs. The distance problem is to determine the minimum number of operations required to transform one chromosome structure into another and the corresponding transformation itself including the identification of paralogs in two structures. We use the DCJ model which is one of the most studied combinatorial rearrangement models. Double-, sesqui-, and single-operations as well as deletion and insertion of a chromosome region are considered in the model, the single ones comprise cut and join. In the reconstruction problem, a phylogenetic tree with chromosome structures in the leaves is given. It is necessary to assign the structures to inner nodes of the tree to minimize the sum of distances between terminal structures of each edge and to identify the mutual paralogs in a fairly large set of structures. A linear algorithm is known for the distance problem without paralogs, while the presence of paralogs makes it NP-hard. If paralogs are allowed but the insertion and deletion operations are missing (and special constraints are imposed), the reduction of the distance problem to integer linear programming is known. Apparently, the reconstruction problem is NP-hard even in the absence of paralogs. The problem of contigs is to find the optimal arrangements for each given set of contigs, which also includes the mutual identification of paralogs.'),
(98137, 2017, 'bmc', 'A comparison of graph- and kernel-based -omics data integration algorithms for classifying complex traits', 'Yan, Kang K|Zhao, Hongyu|Pang, Herbert', 'School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.|Department of Biostatistics, Yale University, New Haven, CT, USA.|School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China. herbpang@hku.hk.', 'China|United States|China', 0, 'Algorithms|Bayes Theorem|Computational Biology|Humans|Support Vector Machine', 'Journal Article', '10.1186/s12859-017-1982-4', '29212468', 'High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking.'),
(98138, 2017, 'bmc', 'Bayesian estimation of scaled mutation rate under the coalescent: a sequential Monte Carlo approach', 'Ogundijo, Oyetunji E|Wang, Xiaodong', 'Department of Electrical Engineering, Columbia University, New York, 10027, USA.|Department of Electrical Engineering, Columbia University, New York, 10027, USA. wangx@ee.columbia.edu.', 'United States|United States', 0, 'Algorithms|Bayes Theorem|Computational Biology|Genetics, Population|Humans|Monte Carlo Method|Mutation Rate|Random Allocation', 'Journal Article', '10.1186/s12859-017-1948-6', '29216822', '-'),
(98139, 2017, 'bmc', 'A multi-network clustering method for detecting protein complexes from multiple heterogeneous networks', 'Ou-Yang, Le|Yan, Hong|Zhang, Xiao-Fei', 'College of Information Engineering & Shenzhen Key Laboratory of Media Security, Shenzhen University, Nanhai Ave 3688, Shenzhen, 518060, China.|College of Information Engineering & Shenzhen Key Laboratory of Media Security, Shenzhen University, Nanhai Ave 3688, Shenzhen, 518060, China.|School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, 430079, China. zhangxf@mail.ccnu.edu.cn.', 'China|China|China', 0, 'Algorithms|Cluster Analysis|Computational Biology|Protein Interaction Mapping|Protein Interaction Maps|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/s12859-017-1877-4', '29219066', 'The accurate identification of protein complexes is important for the understanding of cellular organization. Up to now, computational methods for protein complex detection are mostly focus on mining clusters from protein-protein interaction (PPI) networks. However, PPI data collected by high-throughput experimental techniques are known to be quite noisy. It is hard to achieve reliable prediction results by simply applying computational methods on PPI data. Behind protein interactions, there are protein domains that interact with each other. Therefore, based on domain-protein associations, the joint analysis of PPIs and domain-domain interactions (DDI) has the potential to obtain better performance in protein complex detection. As traditional computational methods are designed to detect protein complexes from a single PPI network, it is necessary to design a new algorithm that could effectively utilize the information inherent in multiple heterogeneous networks.'),
(98140, 2017, 'bmc', 'Prediction of essential proteins based on subcellular localization and gene expression correlation', 'Fan, Yetian|Tang, Xiwei|Hu, Xiaohua|Wu, Wei|Ping, Qing', 'School of Mathematics, Liaoning University, Shenyang, 110036, China.|Department of Information Science and Engineering, Hunan First Normal University, Changsha, 410205, China. tangxiwei2010@gmail.com.|College of Computing and Informatics, Drexel University, Philadelphia, 19104, USA.|School of Mathematical Sciences, Dalian University of Technology, Dalian, 116023, China.|College of Computing and Informatics, Drexel University, Philadelphia, 19104, USA.', 'China|China|United States|China|United States', 7, 'Algorithms|Computational Biology|Gene Expression Regulation, Fungal|Genes, Essential|Protein Interaction Maps|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Subcellular Fractions', 'Journal Article', '10.1186/s12859-017-1876-5', '29219067', 'Essential proteins are indispensable to the survival and development process of living organisms. To understand the functional mechanisms of essential proteins, which can be applied to the analysis of disease and design of drugs, it is important to identify essential proteins from a set of proteins first. As traditional experimental methods designed to test out essential proteins are usually expensive and laborious, computational methods, which utilize biological and topological features of proteins, have attracted more attention in recent years. Protein-protein interaction networks, together with other biological data, have been explored to improve the performance of essential protein prediction.'),
(98141, 2017, 'bmc', 'Predicting enhancers with deep convolutional neural networks', 'Min, Xu|Zeng, Wanwen|Chen, Shengquan|Chen, Ning|Chen, Ting|Jiang, Rui', 'MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Beijing, 100084, China.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Beijing, 100084, China.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Beijing, 100084, China.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Beijing, 100084, China.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Beijing, 100084, China.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Beijing, 100084, China. ruijiang@tsinghua.edu.cn.', 'China|China|China|China|China|China', 16, 'Algorithms|Computational Biology|DNA|Databases, Factual|Enhancer Elements, Genetic|Genome, Human|Genomics|Humans|Machine Learning|Models, Genetic|Neural Networks (Computer)', 'Journal Article', '10.1186/s12859-017-1878-3', '29219068', 'With the rapid development of deep sequencing techniques in the recent years, enhancers have been systematically identified in such projects as FANTOM and ENCODE, forming genome-wide landscapes in a series of human cell lines. Nevertheless, experimental approaches are still costly and time consuming for large scale identification of enhancers across a variety of tissues under different disease status, making computational identification of enhancers indispensable.'),
(98142, 2017, 'bmc', 'A boosting approach for prediction of protein-RNA binding residues', 'Tang, Yongjun|Liu, Diwei|Wang, Zixiang|Wen, Ting|Deng, Lei', 'Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, China.|School of Software, Central South University, No.22 Shaoshan South Road, Changsha, 410075, China.|School of Software, Central South University, No.22 Shaoshan South Road, Changsha, 410075, China.|School of Software, Central South University, No.22 Shaoshan South Road, Changsha, 410075, China.|School of Software, Central South University, No.22 Shaoshan South Road, Changsha, 410075, China. leideng@csu.edu.cn.', 'China|China|China|China|China', 0, 'Algorithms|Amino Acids|Binding Sites|Computational Biology|Humans|Machine Learning|Position-Specific Scoring Matrices|Protein Binding|Protein Conformation|RNA|RNA-Binding Proteins|Support Vector Machine', 'Journal Article', '10.1186/s12859-017-1879-2', '29219069', 'RNA binding proteins play important roles in post-transcriptional RNA processing and transcriptional regulation. Distinguishing the RNA-binding residues in proteins is crucial for understanding how protein and RNA recognize each other and function together as a complex.'),
(98143, 2017, 'bmc', 'Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods', 'Zhang, Wen|Zhu, Xiaopeng|Fu, Yu|Tsuji, Junko|Weng, Zhiping', 'School of Computer, Wuhan University, Wuhan, 430072, China. zhangwen@whu.edu.cn.|School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA.|Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA, 01605, USA.|Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA, 01605, USA.|Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA, 01605, USA.', 'China|United States|United States|United States|United States', 20, 'Base Sequence|Computational Biology|Exons|Genome, Human|Humans|Introns|Machine Learning|Nucleotide Motifs|Position-Specific Scoring Matrices|RNA Splicing', 'Journal Article', '10.1186/s12859-017-1875-6', '29219070', 'Alternative splicing is the critical process in a single gene coding, which removes introns and joins exons, and splicing branchpoints are indicators for the alternative splicing. Wet experiments have identified a great number of human splicing branchpoints, but many branchpoints are still unknown. In order to guide wet experiments, we develop computational methods to predict human splicing branchpoints.'),
(98144, 2017, 'bmc', 'Diverse effects of distance cutoff and residue interval on the performance of distance-dependent atom-pair potential in protein structure prediction', 'Yao, Yuangen|Gui, Rong|Liu, Quan|Yi, Ming|Deng, Haiyou', 'Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China.|Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China.|Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China.|Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China.|Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China. hydeng@mail.hzau.edu.cn.', 'China|China|China|China|China', 0, 'Computational Biology|Knowledge Bases|Protein Conformation|Proteins', 'Journal Article', '10.1186/s12859-017-1983-3', '29221443', 'As one of the most successful knowledge-based energy functions, the distance-dependent atom-pair potential is widely used in all aspects of protein structure prediction, including conformational search, model refinement, and model assessment. During the last two decades, great efforts have been made to improve the reference state of the potential, while other factors that also strongly affect the performance of the potential have been relatively less investigated.'),
(98145, 2017, 'bmc', 'pyAmpli: an amplicon-based variant filter pipeline for targeted resequencing data', 'Beyens, Matthias|Boeckx, Nele|Van Camp, Guy|Op de Beeck, Ken|Vandeweyer, Geert', 'Center of Medical Genetics, University of Antwerp, Prins Boudewijnlaan 43, 2650, Antwerp, Belgium. matthias.beyens@uantwerpen.be.|Center of Medical Genetics, University of Antwerp, Prins Boudewijnlaan 43, 2650, Antwerp, Belgium.|Center of Medical Genetics, University of Antwerp, Prins Boudewijnlaan 43, 2650, Antwerp, Belgium.|Center of Medical Genetics, University of Antwerp, Prins Boudewijnlaan 43, 2650, Antwerp, Belgium.|Center of Medical Genetics, University of Antwerp, Prins Boudewijnlaan 43, 2650, Antwerp, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'DNA Mutational Analysis|Databases, Genetic|Genetic Variation|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1985-1', '29237398', 'Haloplex targeted resequencing is a popular method to analyze both germline and somatic variants in gene panels. However, involved wet-lab procedures may introduce false positives that need to be considered in subsequent data-analysis. No variant filtering rationale addressing amplicon enrichment related systematic errors, in the form of an all-in-one package, exists to our knowledge.'),
(98146, 2017, 'bmc', 'Redundancy analysis allows improved detection of methylation changes in large genomic regions', 'Ruiz-Arenas, Carlos|González, Juan R', 'ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.|ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain. juanr.gonzalez@isglobal.org.', 'Spain|Spain', 0, 'Breast Neoplasms|DNA Methylation|Databases, Genetic|Epigenesis, Genetic|Female|Genome|Genomics|Humans', 'Journal Article', '10.1186/s12859-017-1986-0', '29237399', 'DNA methylation is an epigenetic process that regulates gene expression. Methylation can be modified by environmental exposures and changes in the methylation patterns have been associated with diseases. Methylation microarrays measure methylation levels at more than 450,000 CpGs in a single experiment, and the most common analysis strategy is to perform a single probe analysis to find methylation probes associated with the outcome of interest. However, methylation changes usually occur at the regional level: for example, genomic structural variants can affect methylation patterns in regions up to several megabases in length. Existing DMR methods provide lists of Differentially Methylated Regions (DMRs) of up to only few kilobases in length, and cannot check if a target region is differentially methylated. Therefore, these methods are not suitable to evaluate methylation changes in large regions. To address these limitations, we developed a new DMR approach based on redundancy analysis (RDA) that assesses whether a target region is differentially methylated.'),
(98147, 2017, 'bmc', 'Detecting intermediate protein conformations using algebraic topology', 'Haspel, Nurit|Luo, Dong|González, Eduardo', 'Department of Computer Science, University of Massachusetts Boston, 100 Morrissey Blvd., Boston, 02125, MA, USA. nurit.haspel@umb.edu.|Department of Computer Science, University of Massachusetts Boston, 100 Morrissey Blvd., Boston, 02125, MA, USA.|Department of Mathematics, University of Massachusetts Boston, 100 Morrissey Blvd., Boston, 02125, MA, USA.', 'United States|United States|United States', 3, 'Cluster Analysis|Computational Biology|Principal Component Analysis|Protein Conformation|Proteins', 'Journal Article', '10.1186/s12859-017-1918-z', '29244007', 'Understanding protein structure and dynamics is essential for understanding their function. This is a challenging task due to the high complexity of the conformational landscapes of proteins and their rugged energy levels. In particular, it is important to detect highly populated regions which could correspond to intermediate structures or local minima.'),
(98148, 2017, 'bmc', 'RocSampler: regularizing overlapping protein complexes in protein-protein interaction networks', 'Maruyama, Osamu|Kuwahara, Yuki', 'Institute of Mathematics for Industry, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan. om@imi.kyushu-u.ac.jp.|Graduate School of Mathematics, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan.', 'Japan|Japan', 0, 'Computational Biology|Databases, Protein|Protein Interaction Mapping|Protein Interaction Maps|Software', 'Journal Article', '10.1186/s12859-017-1920-5', '29244010', 'In recent years, protein-protein interaction (PPI) networks have been well recognized as important resources to elucidate various biological processes and cellular mechanisms. In this paper, we address the problem of predicting protein complexes from a PPI network. This problem has two difficulties. One is related to small complexes, which contains two or three components. It is relatively difficult to identify them due to their simpler internal structure, but unfortunately complexes of such sizes are dominant in major protein complex databases, such as CYC2008. Another difficulty is how to model overlaps between predicted complexes, that is, how to evaluate different predicted complexes sharing common proteins because CYC2008 and other databases include such protein complexes. Thus, it is critical how to model overlaps between predicted complexes to identify them simultaneously.'),
(98149, 2017, 'bmc', 'A deep learning method for lincRNA detection using auto-encoder algorithm', 'Yu, Ning|Yu, Zeng|Pan, Yi', 'Department of Computing Sciences, The College at Brockport, State University of New York, 350 New Campus Drive, Brockport, 14420, NY, USA. nyu@brockport.edu.|School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 610031, Sihuan, China.|Department of Computer Science, Georgia State University, 25 Park Place, Atlanta, 30303, GA, USA.', 'United States|China|Georgia', 0, 'Algorithms|Computational Biology|Humans|Machine Learning|RNA, Long Noncoding|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-017-1922-3', '29244011', 'RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods. By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression of lincRNAs appears to be regulated, that is, the relevance exists along the DNA sequences, (2) lincRNAs contain some conversed patterns/motifs tethered together by non-conserved regions. The two evidences give the reasoning for adopting knowledge-based deep learning methods in lincRNA detection. Similar to coding region transcription, non-coding regions are split at transcriptional sites. However, regulatory RNAs rather than message RNAs are generated. That is, the transcribed RNAs participate the biological process as regulatory units instead of generating proteins. Identifying these transcriptional regions from non-coding regions is the first step towards lincRNA recognition.'),
(98150, 2017, 'bmc', 'Utilizing knowledge base of amino acids structural neighborhoods to predict protein-protein interaction sites', 'Jelínek, Jan|Škoda, Petr|Hoksza, David', 'Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Ke Karlovu 3, Prague 2, Czech Republic. jelinek@ksi.mff.cuni.cz.|Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Ke Karlovu 3, Prague 2, Czech Republic.|Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Ke Karlovu 3, Prague 2, Czech Republic.', '-|-|-', 3, 'Amino Acids|Computational Biology|Databases, Protein|Knowledge Bases|Models, Statistical|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1186/s12859-017-1921-4', '29244012', 'Protein-protein interactions (PPI) play a key role in an investigation of various biochemical processes, and their identification is thus of great importance. Although computational prediction of which amino acids take part in a PPI has been an active field of research for some time, the quality of in-silico methods is still far from perfect.'),
(98151, 2017, 'bmc', 'Cache and energy efficient algorithms for Nussinovs RNA Folding', 'Zhao, Chunchun|Sahni, Sartaj', 'Department of Computer and Information Science and Engineering, University of Florida, Gainesville, 32611, FL, USA. czhao@cise.ufl.edu.|Department of Computer and Information Science and Engineering, University of Florida, Gainesville, 32611, FL, USA.', 'United States|United States', 0, 'Algorithms|Computational Biology|Nucleic Acid Conformation|RNA|RNA Folding', 'Journal Article', '10.1186/s12859-017-1917-0', '29244013', 'An RNA folding/RNA secondary structure prediction algorithm determines the non-nested/pseudoknot-free structure by maximizing the number of complementary base pairs and minimizing the energy. Several implementations of Nussinovs classical RNA folding algorithm have been proposed. Our focus is to obtain run time and energy efficiency by reducing the number of cache misses.'),
(98152, 2017, 'bmc', 'CAMSA: a tool for comparative analysis and merging of scaffold assemblies', 'Aganezov, Sergey S|Alekseyev, Max A', 'Princeton University, 35 Olden St., Princeton, 08450, NJ, USA. aganezov@cs.princeton.edu.|The George Washington University, 45085 University Dr., Suite 305, Ashburn, 20147, VA, USA.', 'United States|United States', 5, 'Algorithms|Chromosome Mapping|Genome|Genomics|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1919-y', '29244014', 'Despite the recent progress in genome sequencing and assembly, many of the currently available assembled genomes come in a draft form. Such draft genomes consist of a large number of genomic fragments (scaffolds), whose positions and orientations along the genome are unknown. While there exists a number of methods for reconstruction of the genome from its scaffolds, utilizing various computational and wet-lab techniques, they often can produce only partial error-prone scaffold assemblies. It therefore becomes important to compare and merge scaffold assemblies produced by different methods, thus combining their advantages and highlighting present conflicts for further investigation. These tasks may be labor intensive if performed manually.'),
(98153, 2017, 'bmc', 'Fast algorithms for computing phylogenetic divergence time', 'Crosby, Ralph W|Williams, Tiffani L', 'Department of Computer Science, College of Charleston, Charleston, SC, USA. crosbyrw@cofc.edu.|Department of Computer Science, Northeastern University, Charlotte, NC, USA.', 'United States|United States', 0, 'Algorithms|Animals|Computational Biology|Phylogeny|Primates|Sequence Analysis, DNA|Software|Time Factors', 'Journal Article', '10.1186/s12859-017-1916-1', '29244015', 'The inference of species divergence time is a key step in most phylogenetic studies. Methods have been available for the last ten years to perform the inference, but the performance of the methods does not yet scale well to studies with hundreds of taxa and thousands of DNA base pairs. For example a study of 349 primate taxa was estimated to require over 9 months of processing time. In this work, we present a new algorithm, AncestralAge, that significantly improves the performance of the divergence time process.'),
(98154, 2017, 'bmc', 'An Eigenvalue test for spatial principal component analysis', 'Montano, V|Jombart, T', 'School of Biology, University of St Andrews, Bute Building, St Andrews, KY16 9TS, UK. mirainoshojo@gmail.com.|Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analysis and Modelling, Imperial College, St Marys Campus, Norfolk Place, London, W2 1PG, UK.', 'United Kingdom|United Kingdom', 0, 'Algorithms|Computational Biology|DNA, Mitochondrial|Genetic Variation|Genetics, Population|Humans|Population Groups|Principal Component Analysis', 'Journal Article', '10.1186/s12859-017-1988-y', '29246102', 'The spatial Principal Component Analysis (sPCA, Jombart (Heredity 101:92-103, 2008) is designed to investigate non-random spatial distributions of genetic variation. Unfortunately, the associated tests used for assessing the existence of spatial patterns (global and local test, (Heredity 101:92-103, 2008) lack statistical power and may fail to reveal existing spatial patterns. Here, we present a non-parametric test for the significance of specific patterns recovered by sPCA.'),
(98155, 2017, 'bmc', 'Canary: an atomic pipeline for clinical amplicon assays', 'Doig, Kenneth D|Ellul, Jason|Fellowes, Andrew|Thompson, Ella R|Ryland, Georgina|Blombery, Piers|Papenfuss, Anthony T|Fox, Stephen B', 'Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia. Ken.Doig@petermac.org.|Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.|Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.|Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.|Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.|Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.|Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.|Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Computational Biology|Databases, Genetic|Genetic Variation|High-Throughput Nucleotide Sequencing|Humans|Software', 'Journal Article', '10.1186/s12859-017-1950-z', '29246107', 'High throughput sequencing requires bioinformatics pipelines to process large volumes of data into meaningful variants that can be translated into a clinical report. These pipelines often suffer from a number of shortcomings: they lack robustness and have many components written in multiple languages, each with a variety of resource requirements. Pipeline components must be linked together with a workflow system to achieve the processing of FASTQ files through to a VCF file of variants. Crafting these pipelines requires considerable bioinformatics and IT skills beyond the reach of many clinical laboratories.'),
(98156, 2017, 'bmc', 'MetaGenyo: a web tool for meta-analysis of genetic association studies', 'Martorell-Marugan, Jordi|Toro-Dominguez, Daniel|Alarcon-Riquelme, Marta E|Carmona-Saez, Pedro', 'Bioinformatics Unit, Centre for Genomics and Oncological Research (GENYO), Granada, Spain.|Bioinformatics Unit, Centre for Genomics and Oncological Research (GENYO), Granada, Spain.|Medical Genomics, Centre for Genomics and Oncological Research (GENYO), Granada, Spain.|Bioinformatics Unit, Centre for Genomics and Oncological Research (GENYO), Granada, Spain. pedro.carmona@genyo.es.', 'Spain|Spain|Spain|Spain', 0, 'Genetic Association Studies|Humans|Internet|Metagenomics|Software', 'Journal Article', '10.1186/s12859-017-1990-4', '29246109', 'Genetic association studies (GAS) aims to evaluate the association between genetic variants and phenotypes. In the last few years, the number of this type of study has increased exponentially, but the results are not always reproducible due to experimental designs, low sample sizes and other methodological errors. In this field, meta-analysis techniques are becoming very popular tools to combine results across studies to increase statistical power and to resolve discrepancies in genetic association studies. A meta-analysis summarizes research findings, increases statistical power and enables the identification of genuine associations between genotypes and phenotypes. Meta-analysis techniques are increasingly used in GAS, but it is also increasing the amount of published meta-analysis containing different errors. Although there are several software packages that implement meta-analysis, none of them are specifically designed for genetic association studies and in most cases their use requires advanced programming or scripting expertise.'),
(98157, 2017, 'bmc', 'HISEA: HIerarchical SEed Aligner for PacBio data', 'Khiste, Nilesh|Ilie, Lucian', 'Department of Computer Science, University of Western Ontario, LondonOntario, N6A 5B7, Canada.|Department of Computer Science, University of Western Ontario, LondonOntario, N6A 5B7, Canada. ilie@uwo.ca.', 'Canada|Canada', 5, 'Algorithms|Databases, Genetic|High-Throughput Nucleotide Sequencing|Sequence Alignment|Sequence Analysis, DNA|Time Factors', 'Journal Article', '10.1186/s12859-017-1953-9', '29258419', 'The next generation sequencing (NGS) techniques have been around for over a decade. Many of their fundamental applications rely on the ability to compute good genome assemblies. As the technology evolves, the assembly algorithms and tools have to continuously adjust and improve. The currently dominant technology of Illumina produces reads that are too short to bridge many repeats, setting limits on what can be successfully assembled. The emerging SMRT (Single Molecule, Real-Time) sequencing technique from Pacific Biosciences produces uniform coverage and long reads of length up to sixty thousand base pairs, enabling significantly better genome assemblies. However, SMRT reads are much more expensive and have a much higher error rate than Illuminas - around 10-15% - mostly due to indels. New algorithms are very much needed to take advantage of the long reads while mitigating the effect of high error rate and lowering the required coverage.'),
(98158, 2017, 'bmc', 'A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data', 'Kang, Tianyu|Ding, Wei|Zhang, Luoyan|Ziemek, Daniel|Zarringhalam, Kourosh', 'Department of Computer Science, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, 02125, MA, USA.|Department of Computer Science, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, 02125, MA, USA.|Department of Computer Science, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, 02125, MA, USA.|Inflammation and Immunology, Pfizer Worldwide Research & Development, Berlin, Germany.|Department of Mathematics, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, 0212, MA, USA. kourosh.zarringhalam@umb.edu.', 'United States|United States|United States|Germany|United States', 8, 'Gene Expression Regulation|Gene Regulatory Networks|Humans|Models, Theoretical|Neural Networks (Computer)|Phenotype|Reproducibility of Results|Support Vector Machine', 'Journal Article', '10.1186/s12859-017-1984-2', '29258445', 'Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model.'),
(98159, 2017, 'bmc', 'A comprehensive analysis on preservation patterns of gene co-expression networks during Alzheimers disease progression', 'Ray, Sumanta|Hossain, Sk Md Mosaddek|Khatun, Lutfunnesa|Mukhopadhyay, Anirban', 'Department of Computer Science and Engineering, Aliah University, Kolkata, 700156, West Bengal, India.|Department of Computer Science and Engineering, Aliah University, Kolkata, 700156, West Bengal, India. mosaddek.hossain@gmail.com.|Department of Computer Science and Engineering, Aliah University, Kolkata, 700156, West Bengal, India.|Department of Computer Science and Engineering, University of Kalyani, Kalyani, 741235, West Bengal, India.', 'India|India|India|India', 0, 'Alzheimer Disease|Brain|Computational Biology|Disease Progression|Gene Expression Profiling|Gene Regulatory Networks|Humans|Transcriptome', 'Journal Article', '10.1186/s12859-017-1946-8', '29262769', 'Alzheimers disease (AD) is a chronic neuro-degenerative disruption of the brain which involves in large scale transcriptomic variation. The disease does not impact every regions of the brain at the same time, instead it progresses slowly involving somewhat sequential interaction with different regions. Analysis of the expression patterns of the genes in different regions of the brain influenced in AD surely contribute for a enhanced comprehension of AD pathogenesis and shed light on the early characterization of the disease.'),
(98160, 2017, 'bmc', 'An integrated meta-analysis approach to identifying medications with potential to alter breast cancer risk through connectivity mapping', 'Thillaiyampalam, Gayathri|Liberante, Fabio|Murray, Liam|Cardwell, Chris|Mills, Ken|Zhang, Shu-Dong', 'Centre for Cancer Research and Cell Biology (CCRCB), Queens University Belfast, Belfast, UK.|Centre for Cancer Research and Cell Biology (CCRCB), Queens University Belfast, Belfast, UK.|Centre for Public Health, Queens University Belfast, Belfast, UK.|Centre for Public Health, Queens University Belfast, Belfast, UK.|Centre for Cancer Research and Cell Biology (CCRCB), Queens University Belfast, Belfast, UK. k.mills@qub.ac.uk.|Centre for Cancer Research and Cell Biology (CCRCB), Queens University Belfast, Belfast, UK. k.mills@qub.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, 'Antineoplastic Agents|Breast Neoplasms|Chromosome Mapping|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Principal Component Analysis|Risk Factors', 'Journal Article', '10.1186/s12859-017-1989-x', '29268695', 'Gene expression connectivity mapping has gained much popularity in recent years with a number of successful applications in biomedical research testifying its utility and promise. A major application of connectivity mapping is the identification of small molecule compounds capable of inhibiting a disease state. In this study, we are additionally interested in small molecule compounds that may enhance a disease state or increase the risk of developing that disease. Using breast cancer as a case study, we aim to develop and test a methodology for identifying commonly prescribed drugs that may have a suppressing or inducing effect on the target disease (breast cancer).'),
(98161, 2017, 'bmc', 'Super-delta: a new differential gene expression analysis procedure with robust data normalization', 'Liu, Yuhang|Zhang, Jinfeng|Qiu, Xing', 'Department of Statistics, Florida State University, Tallahassee, 32306, FL, USA.|Department of Statistics, Florida State University, Tallahassee, 32306, FL, USA. jinfeng@stat.fsu.edu.|Department of Biostatistics and Computational Biology, University of Rochester, Rochester, 14624, NY, USA. Xing_Qiu@URMC.Rochester.edu.', 'United States|United States|United States', 1, 'Algorithms|Breast Neoplasms|Computer Simulation|Databases, Genetic|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genes, Neoplasm|Humans|Molecular Sequence Annotation|Reference Standards', 'Journal Article', '10.1186/s12859-017-1992-2', '29268715', 'Normalization is an important data preparation step in gene expression analyses, designed to remove various systematic noise. Sample variance is greatly reduced after normalization, hence the power of subsequent statistical analyses is likely to increase. On the other hand, variance reduction is made possible by borrowing information across all genes, including differentially expressed genes (DEGs) and outliers, which will inevitably introduce some bias. This bias typically inflates type I error, and can reduce statistical power in certain situations. In this study we propose a new differential expression analysis pipeline, dubbed as super-delta, that consists of a multivariate extension of the global normalization and a modified t-test. A robust procedure is designed to minimize the bias introduced by DEGs in the normalization step. The modified t-test is derived based on asymptotic theory for hypothesis testing that suitably pairs with the proposed robust normalization.'),
(98162, 2017, 'bmc', 'CSmetaPred: a consensus method for prediction of catalytic residues', 'Choudhary, Preeti|Kumar, Shailesh|Bachhawat, Anand Kumar|Pandit, Shashi Bhushan', 'Department of Biological Sciences, Indian Institute of Science Education and Research, Mohali, Knowledge City, Sector 81, SAS Nagar, Manuali PO 140306, India.|Department of Biological Sciences, Indian Institute of Science Education and Research, Mohali, Knowledge City, Sector 81, SAS Nagar, Manuali PO 140306, India.|Department of Biological Sciences, Indian Institute of Science Education and Research, Mohali, Knowledge City, Sector 81, SAS Nagar, Manuali PO 140306, India.|Department of Biological Sciences, Indian Institute of Science Education and Research, Mohali, Knowledge City, Sector 81, SAS Nagar, Manuali PO 140306, India. shashibp@iisermohali.ac.in.', 'India|India|India|India', 0, 'Algorithms|Amino Acids|Catalysis|Catalytic Domain|Computational Biology|Consensus Sequence|Databases, Protein|Models, Molecular|ROC Curve', 'Journal Article', '10.1186/s12859-017-1987-z', '29273005', 'Knowledge of catalytic residues can play an essential role in elucidating mechanistic details of an enzyme. However, experimental identification of catalytic residues is a tedious and time-consuming task, which can be expedited by computational predictions. Despite significant development in active-site prediction methods, one of the remaining issues is ranked positions of putative catalytic residues among all ranked residues. In order to improve ranking of catalytic residues and their prediction accuracy, we have developed a meta-approach based method CSmetaPred. In this approach, residues are ranked based on the mean of normalized residue scores derived from four well-known catalytic residue predictors. The mean residue score of CSmetaPred is combined with predicted pocket information to improve prediction performance in meta-predictor, CSmetaPred_poc.'),
(98163, 2017, 'bmc', 'A comparison of genotyping-by-sequencing analysis methods on low-coverage crop datasets shows advantages of a new workflow, GB-eaSy', 'Wickland, Daniel P|Battu, Gopal|Hudson, Karen A|Diers, Brian W|Hudson, Matthew E', 'Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.|Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.|USDA-ARS Crop Production and Pest Control Research Unit, 915 West State Street, West Lafayette, IN, 47907, USA.|Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.|Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. mhudson@illinois.edu.', 'United States|United States|United States|United States|United States', 10, 'Crops, Agricultural|Genome, Plant|Genotype|Genotyping Techniques|High-Throughput Nucleotide Sequencing|Polymorphism, Single Nucleotide|Polyploidy|Software|Soybeans|Whole Genome Sequencing|Workflow', 'Comparative Study', '10.1186/s12859-017-2000-6', '29281959', 'Genotyping-by-sequencing (GBS), a method to identify genetic variants and quickly genotype samples, reduces genome complexity by using restriction enzymes to divide the genome into fragments whose ends are sequenced on short-read sequencing platforms. While cost-effective, this method produces extensive missing data and requires complex bioinformatics analysis. GBS is most commonly used on crop plant genomes, and because crop plants have highly variable ploidy and repeat content, the performance of GBS analysis software can vary by target organism. Here we focus our analysis on soybean, a polyploid crop with a highly duplicated genome, relatively little public GBS data and few dedicated tools.'),
(98164, 2017, 'bmc', 'Improved high-dimensional prediction with Random Forests by the use of co-data', 'Te Beest, Dennis E|Mes, Steven W|Wilting, Saskia M|Brakenhoff, Ruud H|van de Wiel, Mark A', 'Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, 1007 MB, The Netherlands.|Department of Otolaryngology-Head and Neck Surgery, VU University Medical Center, Amsterdam, 1007 MB, The Netherlands.|Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, 3015 CE, The Netherlands.|Department of Otolaryngology-Head and Neck Surgery, VU University Medical Center, Amsterdam, 1007 MB, The Netherlands.|Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, 1007 MB, The Netherlands. mark.vdwiel@vumc.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Algorithms|Bayes Theorem|Databases as Topic|Humans|Neoplasms|ROC Curve|Time Factors', 'Journal Article', '10.1186/s12859-017-1993-1', '29281963', 'Prediction in high dimensional settings is difficult due to the large number of variables relative to the sample size. We demonstrate how auxiliary co-data can be used to improve the performance of a Random Forest in such a setting.'),
(98165, 2017, 'bmc', 'Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction', 'Han, Youngmahn|Kim, Dongsup', 'Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea. kds@kaist.ac.kr.', '-|-', 0, 'Alleles|Amino Acid Sequence|Animals|Deep Learning|Histocompatibility Antigens Class I|Humans|Internet|Machine Learning|Peptides|Protein Binding|Reproducibility of Results', 'Journal Article', '10.1186/s12859-017-1997-x', '29281985', 'Computational scanning of peptide candidates that bind to a specific major histocompatibility complex (MHC) can speed up the peptide-based vaccine development process and therefore various methods are being actively developed. Recently, machine-learning-based methods have generated successful results by training large amounts of experimental data. However, many machine learning-based methods are generally less sensitive in recognizing locally-clustered interactions, which can synergistically stabilize peptide binding. Deep convolutional neural network (DCNN) is a deep learning method inspired by visual recognition process of animal brain and it is known to be able to capture meaningful local patterns from 2D images. Once the peptide-MHC interactions can be encoded into image-like array(ILA) data, DCNN can be employed to build a predictive model for peptide-MHC binding prediction. In this study, we demonstrated that DCNN is able to not only reliably predict peptide-MHC binding, but also sensitively detect locally-clustered interactions.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98166, 2018, 'bmc', 'Simulating autosomal genotypes with realistic linkage disequilibrium and a spiked-in genetic effect', 'Shi, M|Umbach, D M|Wise, A S|Weinberg, C R', 'Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, NC, USA. shi2@niehs.nih.gov.|Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, NC, USA.|Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, NC, USA.|Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, NC, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Alleles|Case-Control Studies|Computer Simulation|Gene Frequency|Genetic Association Studies|Genome-Wide Association Study|Genotype|Humans|Linkage Disequilibrium|Models, Genetic|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-017-2004-2', '29291710', 'To evaluate statistical methods for genome-wide genetic analyses, one needs to be able to simulate realistic genotypes. We here describe a method, applicable to a broad range of association study designs, that can simulate autosome-wide single-nucleotide polymorphism data with realistic linkage disequilibrium and with spiked in, user-specified, single or multi-SNP causal effects.'),
(98167, 2018, 'bmc', 'Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data', 'Marco-Ramell, Anna|Palau-Rodriguez, Magali|Alay, Ania|Tulipani, Sara|Urpi-Sarda, Mireia|Sanchez-Pla, Alex|Andres-Lacueva, Cristina', 'Biomarkers & Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, Food Technology Reference Net (XaRTA), Nutrition and Food Safety Research Institute (INSA-UB), Faculty of Pharmacy and Food Sciences, Pharmacy and Food Science Faculty, University of Barcelona, Barcelona, Spain.|Biomarkers & Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, Food Technology Reference Net (XaRTA), Nutrition and Food Safety Research Institute (INSA-UB), Faculty of Pharmacy and Food Sciences, Pharmacy and Food Science Faculty, University of Barcelona, Barcelona, Spain.|Genetics, Microbiology and Statistics Department, Biology Faculty, University of Barcelona, Barcelona, Spain.|Biomarkers & Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, Food Technology Reference Net (XaRTA), Nutrition and Food Safety Research Institute (INSA-UB), Faculty of Pharmacy and Food Sciences, Pharmacy and Food Science Faculty, University of Barcelona, Barcelona, Spain.|Biomarkers & Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, Food Technology Reference Net (XaRTA), Nutrition and Food Safety Research Institute (INSA-UB), Faculty of Pharmacy and Food Sciences, Pharmacy and Food Science Faculty, University of Barcelona, Barcelona, Spain.|Genetics, Microbiology and Statistics Department, Biology Faculty, University of Barcelona, Barcelona, Spain.|Biomarkers & Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, Food Technology Reference Net (XaRTA), Nutrition and Food Safety Research Institute (INSA-UB), Faculty of Pharmacy and Food Sciences, Pharmacy and Food Science Faculty, University of Barcelona, Barcelona, Spain. candres@ub.edu.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Computational Biology|Databases, Factual|Humans|Metabolome|Metabolomics', 'Comparative Study', '10.1186/s12859-017-2006-0', '29291722', 'Bioinformatic tools for the enrichment of omics datasets facilitate interpretation and understanding of data. To date few are suitable for metabolomics datasets. The main objective of this work is to give a critical overview, for the first time, of the performance of these tools. To that aim, datasets from metabolomic repositories were selected and enriched data were created. Both types of data were analysed with these tools and outputs were thoroughly examined.'),
(98168, 2018, 'bmc', 'fDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization', 'Meng, Fanchi|Wang, Chen|Kurgan, Lukasz', 'Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada.|Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA.|Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA. lkurgan@vcu.edu.', 'Canada|United States|United States', 9, 'Amino Acid Sequence|Crystallization|Crystallography, X-Ray|Databases, Protein|Humans|Internet|Proteins|ROC Curve|Time Factors', 'Journal Article', '10.1186/s12859-017-1995-z', '29295714', 'Development of predictors of propensity of protein sequences for successful crystallization has been actively pursued for over a decade. A few novel methods that expanded the scope of these predictions to address additional steps of protein production and structure determination pipelines were released in recent years. The predictive performance of the current methods is modest. This is because the only input that they use is the protein sequence and since the experimental annotations of these data might be inconsistent given that they were collected across many laboratories and centers. However, even these modest levels of predictive quality are still practical compared to the reported low success rates of crystallization, which are below 10%. We focus on another important aspect related to a high computational cost of running the predictors that offer the expanded scope.'),
(98169, 2017, 'bmc', 'Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks', 'Sharifi, Mohsen|Buzatu, Dan|Harris, Stephen|Wilkes, Jon', 'Division of Systems Biology, FDAs National Center for Toxicological Research, Jefferson, AR, 72079, USA.|Division of Systems Biology, FDAs National Center for Toxicological Research, Jefferson, AR, 72079, USA. Dan.Buzatu@fda.hhs.gov.|Division of Systems Biology, FDAs National Center for Toxicological Research, Jefferson, AR, 72079, USA.|Division of Systems Biology, FDAs National Center for Toxicological Research, Jefferson, AR, 72079, USA.', 'United States|United States|United States|United States', 0, 'Action Potentials|Arrhythmias, Cardiac|Electrocardiography|Ether-A-Go-Go Potassium Channels|Heart Ventricles|Humans|Long QT Syndrome|Male|Middle Aged|Models, Cardiovascular|Myocytes, Cardiac|Neural Networks (Computer)|ROC Curve|Torsades de Pointes', 'Journal Article', '10.1186/s12859-017-1895-2', '29297274', '-'),
(98170, 2017, 'bmc', 'Identification and characterization of conserved lncRNAs in human and rat brain', 'Li, Dan|Yang, Mary Qu', 'MidSouth Bioinformatics Center and Joint Bioinformatics Ph.D. Program, University of Arkansas at Little Rock and University of Arkansas for Medical Sciences, 2801 S. University Avenue, Little Rock, AR, 72204, USA.|MidSouth Bioinformatics Center and Joint Bioinformatics Ph.D. Program, University of Arkansas at Little Rock and University of Arkansas for Medical Sciences, 2801 S. University Avenue, Little Rock, AR, 72204, USA. mqyang@ualr.edu.', 'United States|United States', 0, 'Animals|Brain|Conserved Sequence|Gene Expression Profiling|Gene Ontology|Gene Regulatory Networks|Humans|Molecular Sequence Annotation|Open Reading Frames|Protein Isoforms|RNA, Long Noncoding|Rats|Time Factors', 'Journal Article', '10.1186/s12859-017-1890-7', '29297275', 'Long noncoding RNAs (lncRNAs) are involved in diverse biological processes and play an essential role in various human diseases. The number of lncRNAs identified has increased rapidly in recent years owing to RNA sequencing (RNA-Seq) technology. However, presently, most lncRNAs are not well characterized, and their regulatory mechanisms remain elusive. Many lncRNAs show poor evolutionary conservation. Thus, the lncRNAs that are conserved across species can provide insight into their critical functional roles.'),
(98171, 2017, 'bmc', 'ALE: automated label extraction from GEO metadata', 'Giles, Cory B|Brown, Chase A|Ripperger, Michael|Dennis, Zane|Roopnarinesingh, Xiavan|Porter, Hunter|Perz, Aleksandra|Wren, Jonathan D', 'Arthritis & Clinical Immunology Program, Oklahoma Medical Research Foundation, 825 N.E. 13th Street, Oklahoma City, OK, 73104, USA.|Arthritis & Clinical Immunology Program, Oklahoma Medical Research Foundation, 825 N.E. 13th Street, Oklahoma City, OK, 73104, USA.|Vanderbilt University, 2201 West End Ave, Nashville, TN, USA.|Department of Computer Science, Baylor University, Hankamer Academic Building, 105 Baylor Ave, Waco, TX, 76706, USA.|Arthritis & Clinical Immunology Program, Oklahoma Medical Research Foundation, 825 N.E. 13th Street, Oklahoma City, OK, 73104, USA.|Arthritis & Clinical Immunology Program, Oklahoma Medical Research Foundation, 825 N.E. 13th Street, Oklahoma City, OK, 73104, USA.|Arthritis & Clinical Immunology Program, Oklahoma Medical Research Foundation, 825 N.E. 13th Street, Oklahoma City, OK, 73104, USA.|Arthritis & Clinical Immunology Program, Oklahoma Medical Research Foundation, 825 N.E. 13th Street, Oklahoma City, OK, 73104, USA. jonathan-wren@omrf.org.', 'United States|United States|United States|United States|United States|United States|United States|United States', 4, 'Age Factors|Algorithms|Animals|Automation|Databases, Genetic|Female|Gene Expression|Gene Ontology|Humans|Machine Learning|Male|Metadata|Middle Aged|Molecular Sequence Annotation|Rats|Reference Standards', 'Journal Article', '10.1186/s12859-017-1888-1', '29297276', 'NCBIs Gene Expression Omnibus (GEO) is a rich community resource containing millions of gene expression experiments from human, mouse, rat, and other model organisms. However, information about each experiment (metadata) is in the format of an open-ended, non-standardized textual description provided by the depositor. Thus, classification of experiments for meta-analysis by factors such as gender, age of the sample donor, and tissue of origin is not feasible without assigning labels to the experiments. Automated approaches are preferable for this, primarily because of the size and volume of the data to be processed, but also because it ensures standardization and consistency. While some of these labels can be extracted directly from the textual metadata, many of the data available do not contain explicit text informing the researcher about the age and gender of the subjects with the study. To bridge this gap, machine-learning methods can be trained to use the gene expression patterns associated with the text-derived labels to refine label-prediction confidence.'),
(98172, 2017, 'bmc', 'Proceedings of the 2017 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference', 'Wren, Jonathan D|Dozmorov, Mikhail G|Toby, Inimary|Nanduri, Bindu|Homayouni, Ramin|Manda, Prashanti|Thakkar, Shraddha', 'Arthritis and Clinical Immunology Research Program, Division of Genomics and Data Sciences, Oklahoma Medical Research Foundation, 825 N.E. 13th Street, Oklahoma City, OK, 73104-5005, USA. jonathan-wren@omrf.org.|Department of Biostatistics, Virginia Commonwealth University, 830 East Main Street, Richmond, Virginia, 23298, USA.|Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA.|Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS, 39762, USA.|Department of Biological Sciences, Bioinformatics Program, University of Memphis, Memphis, TN, 38152, USA.|Department of Computer Science, University of North Carolina at Greensboro, Greensboro, NC, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Introductory Journal Article', '10.1186/s12859-017-1887-2', '29297277', '-'),
(98173, 2017, 'bmc', 'Incorporating biological prior knowledge for Bayesian learning via maximal knowledge-driven information priors', 'Boluki, Shahin|Esfahani, Mohammad Shahrokh|Qian, Xiaoning|Dougherty, Edward R', 'Department of Electrical and Computer Engineering, Texas A&M University, MS3128 TAMU, College Station, 77843, TX, USA. s.boluki@tamu.edu.|Division of Oncology and Center for Cancer Systems Biology, Stanford School of Medicine, 291 Campus Drive, Stanford, 94305, CA, USA.|Department of Electrical and Computer Engineering, Texas A&M University, MS3128 TAMU, College Station, 77843, TX, USA.|Department of Electrical and Computer Engineering, Texas A&M University, MS3128 TAMU, College Station, 77843, TX, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Animals|Bayes Theorem|Carcinoma, Non-Small-Cell Lung|Cell Cycle|Entropy|Humans|Information Theory|Lung Neoplasms|Mammals|Probability|Signal Transduction|Tumor Suppressor Protein p53', 'Journal Article', '10.1186/s12859-017-1893-4', '29297278', 'Phenotypic classification is problematic because small samples are ubiquitous, and, for these, use of prior knowledge is critical. If knowledge concerning the feature-label distribution - for instance, genetic pathways - is available, then it can be used in learning. Optimal Bayesian classification provides optimal classification under model uncertainty. It differs from classical Bayesian methods in which a classification model is assumed and prior distributions are placed on model parameters. With optimal Bayesian classification, uncertainty is treated directly on the feature-label distribution, which assures full utilization of prior knowledge and is guaranteed to outperform classical methods.'),
(98174, 2017, 'bmc', 'CUFID-query: accurate network querying through random walk based network flow estimation', 'Jeong, Hyundoo|Qian, Xiaoning|Yoon, Byung-Jun', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77843, TX, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77843, TX, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77843, TX, USA. bjyoon@ece.tamu.edu.', 'United States|United States|United States', 0, 'Algorithms|Animals|Drosophila melanogaster|Humans|Protein Interaction Mapping|Saccharomyces cerevisiae|Search Engine|Time Factors', 'Journal Article', '10.1186/s12859-017-1899-y', '29297279', 'Functional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks.'),
(98175, 2017, 'bmc', 'Mutation status coupled with RNA-sequencing data can efficiently identify important non-significantly mutated genes serving as diagnostic biomarkers of endometrial cancer', 'Liu, Keqin|He, Li|Liu, Zhichao|Xu, Junmei|Liu, Yuan|Kuang, Qifan|Wen, Zhining|Li, Menglong', 'College of Chemistry, Sichuan University, Chengdu, Sichuan, China.|Biogas Appliance Quality Supervision and Inspection Center, Biogas Institute of Ministry of Agriculture, Chengdu, Sichuan, China.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), 3900 NCTR Road, Jefferson, AR, 72079, USA.|College of Chemistry, Sichuan University, Chengdu, Sichuan, China.|College of Chemistry, Sichuan University, Chengdu, Sichuan, China.|College of Chemistry, Sichuan University, Chengdu, Sichuan, China.|College of Chemistry, Sichuan University, Chengdu, Sichuan, China. w_zhining@163.com.|College of Chemistry, Sichuan University, Chengdu, Sichuan, China. liml@scu.edu.cn.', 'China|China|United States|China|China|China|China|China', 0, 'Biomarkers, Tumor|Endometrial Neoplasms|Female|Genes, Neoplasm|Humans|Middle Aged|Models, Genetic|Mutation|Neoplasm Staging|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-017-1891-6', '29297280', 'Endometrial cancers (ECs) are one of the most common types of malignant tumor in females. Substantial efforts had been made to identify significantly mutated genes (SMGs) in ECs and use them as biomarkers for the classification of histological subtypes and the prediction of clinical outcomes. However, the impact of non-significantly mutated genes (non-SMGs), which may also play important roles in the prognosis of EC patients, has not been extensively studied. Therefore, it is essential for the discovery of biomarkers in ECs to further investigate the non-SMGs that were highly associated with clinical outcomes.'),
(98176, 2017, 'bmc', 'Suggested mechanisms for Zika virus causing microcephaly: what do the genomes tell us?', 'Jun, Se-Ran|Wassenaar, Trudy M|Wanchai, Visanu|Patumcharoenpol, Preecha|Nookaew, Intawat|Ussery, David W', 'Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA. sjun@uams.edu.|Molecular Microbiology and Genomics Consultants, Zotzenheim, Germany.|Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.|Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.|Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.|Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA. dwussery@uams.edu.', 'United States|Germany|United States|United States|United States|United States', 0, 'Adaptation, Physiological|Africa|Asia|Base Sequence|Brazil|Cell Line|Codon|Female|Genetic Variation|Genome, Viral|Host-Pathogen Interactions|Humans|Microcephaly|Phosphorylation|Phylogeny|Pregnancy|RNA Stability|Recombination, Genetic|Selection, Genetic|Virulence|Zika Virus|Zika Virus Infection', 'Journal Article', '10.1186/s12859-017-1894-3', '29297281', 'Zika virus (ZIKV) is an emerging human pathogen. Since its arrival in the Western hemisphere, from Africa via Asia, it has become a serious threat to pregnant women, causing microcephaly and other neuropathies in developing fetuses. The mechanisms behind these teratogenic effects are unknown, although epidemiological evidence suggests that microcephaly is not associated with the original, African lineage of ZIKV. The sequences of 196 published ZIKV genomes were used to assess whether recently proposed mechanistic explanations for microcephaly are supported by molecular level changes that may have increased its virulence since the virus left Africa. For this we performed phylogenetic, recombination, adaptive evolution and tetramer frequency analyses, and compared protein sequences for the presence of protease cleavage sites, Pfam domains, glycosylation sites, signal peptides, trans-membrane protein domains, and phosphorylation sites.'),
(98177, 2017, 'bmc', 'Using 16S rRNA gene as marker to detect unknown bacteria in microbial communities', 'Tran, Quang|Pham, Diem-Trang|Phan, Vinhthuy', 'Department of Computer Science, University of Memphis, Memphis, 38152, TN, USA.|Department of Computer Science, University of Memphis, Memphis, 38152, TN, USA.|Department of Computer Science, University of Memphis, Memphis, 38152, TN, USA. vphan@memphis.edu.', 'United States|United States|United States', 0, 'Algorithms|Bacteria|Genetic Markers|Genome, Bacterial|High-Throughput Nucleotide Sequencing|Humans|Metagenome|Microbiota|RNA, Ribosomal, 16S|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-017-1901-8', '29297282', 'Quantification and identification of microbial genomes based on next-generation sequencing data is a challenging problem in metagenomics. Although current methods have mostly focused on analyzing bacteria whose genomes have been sequenced, such analyses are, however, complicated by the presence of unknown bacteria or bacteria whose genomes have not been sequence.'),
(98178, 2017, 'bmc', 'R3D-BLAST2: an improved search tool for similar RNA 3D substructures', 'Yen, Ching-Yu|Lin, Jian-Cheng|Chen, Kun-Tze|Lu, Chin Lung', 'Department of Computer Science, National Tsing Hua University, Hsinchu, 30013, Taiwan.|Department of Computer Science, National Tsing Hua University, Hsinchu, 30013, Taiwan.|Department of Computer Science, National Tsing Hua University, Hsinchu, 30013, Taiwan.|Department of Computer Science, National Tsing Hua University, Hsinchu, 30013, Taiwan. cllu@cs.nthu.edu.tw.', '-|-|-|-', 2, 'Algorithms|Amino Acid Sequence|Databases, Nucleic Acid|Nucleic Acid Conformation|Nucleotides|RNA|Search Engine|Software|Time Factors|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1956-6', '29297283', 'RNA molecules have been known to play a variety of significant roles in cells. In principle, the functions of RNAs are largely determined by their three-dimensional (3D) structures. As more and more RNA 3D structures are available in the Protein Data Bank (PDB), a bioinformatics tool, which is able to rapidly and accurately search the PDB database for similar RNA 3D structures or substructures, is helpful to understand the structural and functional relationships of RNAs.'),
(98179, 2017, 'bmc', 'Automated classification and characterization of the mitotic spindle following knockdown of a mitosis-related protein', 'Khushi, Matloob|Dean, Imraan M|Teber, Erdahl T|Chircop, Megan|Arthur, Jonathan W|Flores-Rodriguez, Neftali', 'Childrens Medical Research Institute, The University of Sydney, Westmead, NSW, Australia. mkhushi@uni.sydney.edu.au.|Childrens Medical Research Institute, The University of Sydney, Westmead, NSW, Australia.|Childrens Medical Research Institute, The University of Sydney, Westmead, NSW, Australia.|Childrens Medical Research Institute, The University of Sydney, Westmead, NSW, Australia.|Childrens Medical Research Institute, The University of Sydney, Westmead, NSW, Australia.|Childrens Medical Research Institute, The University of Sydney, Westmead, NSW, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Humans|Mitosis|Spindle Apparatus', 'Journal Article', '10.1186/s12859-017-1966-4', '29297284', 'Cell division (mitosis) results in the equal segregation of chromosomes between two daughter cells. The mitotic spindle plays a pivotal role in chromosome alignment and segregation during metaphase and anaphase. Structural or functional errors of this spindle can cause aneuploidy, a hallmark of many cancers. To investigate if a given protein associates with the mitotic spindle and regulates its assembly, stability, or function, fluorescence microscopy can be performed to determine if disruption of that protein induces phenotypes indicative of spindle dysfunction. Importantly, functional disruption of proteins with specific roles during mitosis can lead to cancer cell death by inducing mitotic insult. However, there is a lack of automated computational tools to detect and quantify the effects of such disruption on spindle integrity.'),
(98180, 2017, 'bmc', 'Effective computational detection of piRNAs using n-gram models and support vector machine', 'Chen, Chun-Chi|Qian, Xiaoning|Yoon, Byung-Jun', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA. bjyoon@ece.tamu.edu.', 'United States|United States|United States', 0, 'Animals|Area Under Curve|Computational Biology|Databases, Genetic|Humans|RNA, Small Interfering|ROC Curve|Support Vector Machine', 'Journal Article', '10.1186/s12859-017-1896-1', '29297285', 'Piwi-interacting RNAs (piRNAs) are a new class of small non-coding RNAs that are known to be associated with RNA silencing. The piRNAs play an important role in protecting the genome from invasive transposons in the germline. Recent studies have shown that piRNAs are linked to the genome stability and a variety of human cancers. Due to their clinical importance, there is a pressing need for effective computational methods that can be used for computational identification of piRNAs. However, piRNAs lack conserved structural motifs and show relatively low sequence similarity across different species, which makes accurate computational prediction of piRNAs challenging.'),
(98181, 2017, 'bmc', 'Exploration of charge states of balanol analogues acting as ATP-competitive inhibitors in kinases', 'Hardianto, Ari|Yusuf, Muhammad|Liu, Fei|Ranganathan, Shoba', 'Department of Molecular Sciences, Macquarie University, Sydney, NSW, 2109, Australia.|Department of Chemistry, Universitas Padjadjaran, Jatinangor, West Java, 45363, Indonesia.|Department of Molecular Sciences, Macquarie University, Sydney, NSW, 2109, Australia.|Department of Molecular Sciences, Macquarie University, Sydney, NSW, 2109, Australia. shoba.ranganathan@mq.edu.au.', 'Australia|Indonesia|Australia|Australia', 6, 'Acids|Adenosine Triphosphate|Azepines|Cyclic AMP-Dependent Protein Kinases|Fluorine|Humans|Hydroxybenzoates|Isoenzymes|Kinetics|Ligands|Molecular Dynamics Simulation|Protein Kinase C|Protein Kinase Inhibitors|Static Electricity|Structural Homology, Protein', 'Journal Article', '10.1186/s12859-017-1955-7', '29297286', '(-)-Balanol is an ATP mimic that inhibits protein kinase C (PKC) isozymes and cAMP-dependent protein kinase (PKA) withxc2xa0limited selectivity. While PKA is a tumour promoter, PKC isozymes act as tumour promoters or suppressors, depending on the cancer type. In particular, PKCxcexb5 is frequently implicated in cancer promotion, making it a potential target for anticancer drugs. To improve isozyme selectivity of balanol, exhaustive structural and activity relationship (SAR) studies have been performed in the last two decades, but with limited success. More recently, fluorination on balanol has shown improved selectivity for PKCxcexb5, although the fluorine effect is not yet clearly understood. Understanding the origin to this fluorine-based selectivity will be valuable for designing better balanol-based ATP mimicking inhibitors. Computational approaches such as molecular dynamics (MD) simulations can decipher the fluorine effect, provided thatxc2xa0correct charges have been assigned to a ligand. Balanol analogues have multiple ionisable functional groups and the effect of fluorine substitutions on the exact charge state of each analogue bound to PKA and to PKCxcexb5 needs to be thoroughly investigated in orderxc2xa0toxc2xa0design highly selective inhibitors for therapeutic applications.'),
(98182, 2017, 'bmc', 'Scaling bioinformatics applications on HPC', 'Mikailov, Mike|Luo, Fu-Jyh|Barkley, Stuart|Valleru, Lohit|Whitney, Stephen|Liu, Zhichao|Thakkar, Shraddha|Tong, Weida|Petrick, Nicholas', 'Office of Science and Engineering Labs, Center for Devices and Radiological Health, US Food and Drug Administration, 10903 New Hampshire Ave., Silver Spring, MD, 20993, USA.|Office of Science and Engineering Labs, Center for Devices and Radiological Health, US Food and Drug Administration, 10903 New Hampshire Ave., Silver Spring, MD, 20993, USA.|Office of Science and Engineering Labs, Center for Devices and Radiological Health, US Food and Drug Administration, 10903 New Hampshire Ave., Silver Spring, MD, 20993, USA.|Office of Science and Engineering Labs, Center for Devices and Radiological Health, US Food and Drug Administration, 10903 New Hampshire Ave., Silver Spring, MD, 20993, USA.|Office of Science and Engineering Labs, Center for Devices and Radiological Health, US Food and Drug Administration, 10903 New Hampshire Ave., Silver Spring, MD, 20993, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA.|Office of Science and Engineering Labs, Center for Devices and Radiological Health, US Food and Drug Administration, 10903 New Hampshire Ave., Silver Spring, MD, 20993, USA. Nicholas.Petrick@fda.hhs.gov.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Databases, Nucleic Acid|Humans|Search Engine|Software', 'Journal Article', '10.1186/s12859-017-1902-7', '29297287', 'Recent breakthroughs in molecular biology and next generation sequencing technologies have led to the expenential growh of the sequence databases. Researchrs use BLAST for processing these sequences. However traditional software parallelization techniques (threads, message passing interface) applied in newer versios of BLAST are not adequate for processing these sequences in timely manner.'),
(98183, 2017, 'bmc', 'Deep learning architectures for multi-label classification of intelligent health risk prediction', 'Maxwell, Andrew|Li, Runzhi|Yang, Bei|Weng, Heng|Ou, Aihua|Hong, Huixiao|Zhou, Zhaoxian|Gong, Ping|Zhang, Chaoyang', 'School of Computing, University of Southern Mississippi, Hattiesburg, MS, 39406, USA.|Cooperative Innovation Center of Internet Healthcare, School of Information & Engineering, Zhengzhou University, Zhengzhou, 450000, China.|Cooperative Innovation Center of Internet Healthcare, School of Information & Engineering, Zhengzhou University, Zhengzhou, 450000, China.|Department of Big Medical Data, Health Construction Administration Center, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. ww128@qq.com.|Department of Big Medical Data, Health Construction Administration Center, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration (FDA), Jefferson, AR, 72079, USA.|School of Computing, University of Southern Mississippi, Hattiesburg, MS, 39406, USA.|Environmental Lab, US Army Engineer Research and Development Center, Vicksburg, MS, 39180, USA.|School of Computing, University of Southern Mississippi, Hattiesburg, MS, 39406, USA. Chaoyang.Zhang@usm.edu.', 'United States|China|China|China|China|United States|United States|United States|United States', 17, 'Algorithms|Chronic Disease|Deep Learning|Health|Humans|Neural Networks (Computer)|ROC Curve|Risk Assessment|Support Vector Machine', 'Journal Article', '10.1186/s12859-017-1898-z', '29297288', 'Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases.'),
(98184, 2017, 'bmc', 'dBBQs: dataBase of Bacterial Quality scores', 'Wanchai, Visanu|Patumcharoenpol, Preecha|Nookaew, Intawat|Ussery, David', 'Arkansas Center for Genomic Epidemiology & Medicine and The Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.|Arkansas Center for Genomic Epidemiology & Medicine and The Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.|Arkansas Center for Genomic Epidemiology & Medicine and The Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.|Arkansas Center for Genomic Epidemiology & Medicine and The Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA. dwussery@uams.edu.', 'United States|United States|United States|United States', 0, 'Bacteria|Base Sequence|Chromosome Mapping|Databases, Genetic|Genome, Bacterial|Genomics|Internet|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1900-9', '29297289', 'It is well-known that genome sequencing technologies are becoming significantly cheaper and faster. As a result of this, the exponential growth in sequencing data in public databases allows us to explore ever growing large collections of genome sequences. However, it is less known that the majority of available sequenced genome sequences in public databases are not complete, drafts of varying qualities. We have calculated quality scores for around 100,000 bacterial genomes from all major genome repositories and put them in a fast and easy-to-use database.'),
(98185, 2017, 'bmc', 'Texture based skin lesion abruptness quantification to detect malignancy', 'Erol, Recep|Bayraktar, Mustafa|Kockara, Sinan|Kaya, Sertan|Halic, Tansel', 'Department of Computer Science, UCA, Conway, AR, 72034, USA.|Bioinformatics, UA Little Rock, Little Rock, AR, 72204, USA.|Department of Computer Science, UCA, Conway, AR, 72034, USA. skockara@uca.edu.|HP, San Diego, CA, 92127, USA.|Department of Computer Science, UCA, Conway, AR, 72034, USA.', 'United States|United States|United States|United States|United States', 3, 'Algorithms|Data Analysis|Dermoscopy|Entropy|Humans|Image Interpretation, Computer-Assisted|Melanoma|Neural Networks (Computer)|Pattern Recognition, Automated|Skin|Skin Neoplasms', 'Journal Article', '10.1186/s12859-017-1892-5', '29297290', 'Abruptness of pigment patterns at the periphery of a skin lesion is one of the most important dermoscopic features for detection of malignancy. In current clinical setting, abrupt cutoff of a skin lesion determined by an examination of a dermatologist. This process is subjective, nonquantitative, and error-prone. We present an improved computational model to quantitatively measure abruptness of a skin lesion over our previous method. To achieve this, we quantitatively analyze the texture features of a region within the lesion boundary. This region is bounded by an interior border line of the lesion boundary which is determined using level set propagation (LSP) method. This method provides a fast border contraction without a need for extensive boolean operations. Then, we build feature vectors of homogeneity, standard deviation of pixel values, and mean of the pixel values of the region between the contracted border and the original border. These vectors are then classified using neural networks (NN) and SVM classifiers.'),
(98186, 2017, 'bmc', 'The node-weighted Steiner tree approach to identify elements of cancer-related signaling pathways', 'Sun, Yahui|Ma, Chenkai|Halgamuge, Saman', 'Department of Mechanical Engineering, The University of Melbourne, Melbourne, 3010, Australia. yahuis@student.unimelb.edu.au.|Department of Surgery, The University of Melbourne, Melbourne, 3010, Australia.|Research School of Engineering, College of Engineering & Computer Science, The Australian National University, Canberra, 2601, ACT, Australia.', 'Australia|Australia|Australia', 0, 'Algorithms|Computational Biology|Humans|Neoplasms|Protein Interaction Maps|Signal Transduction', 'Journal Article', '10.1186/s12859-017-1958-4', '29297291', 'Cancer constitutes a momentous health burden in our society. Critical information on cancer may be hidden in its signaling pathways. However, even though a large amount of money has been spent on cancer research, some critical information on cancer-related signaling pathways still remains elusive. Hence, new works towards a complete understanding of cancer-related signaling pathways will greatly benefit the prevention, diagnosis, and treatment of cancer.'),
(98187, 2017, 'bmc', 'Repositioning drugs by targeting network modules: a Parkinsons disease case study', 'Yue, Zongliang|Arora, Itika|Zhang, Eric Y|Laufer, Vincent|Bridges, S Louis|Chen, Jake Y', 'Center for Biomedical Big Data, Wenzhou Medical University First Affiliated Hospital, Wenzhou, Zhejiang Province, China.|Informatics Institute in School of Medicine, University of Alabama at Birmingham, Birmingham, 35233, AL, USA.|Informatics Institute in School of Medicine, University of Alabama at Birmingham, Birmingham, 35233, AL, USA.|Division of Clinical Immunology and Rheumatology in School of Medicine, University of Alabama at Birmingham, Birmingham, 35233, AL, USA.|Division of Clinical Immunology and Rheumatology in School of Medicine, University of Alabama at Birmingham, Birmingham, 35233, AL, USA.|Center for Biomedical Big Data, Wenzhou Medical University First Affiliated Hospital, Wenzhou, Zhejiang Province, China. jakechen@uab.edu.', 'China|United States|United States|United States|United States|China', 9, 'Drug Repositioning|Gene Expression Regulation|Gene Ontology|Gene Regulatory Networks|Genome-Wide Association Study|Humans|Parkinson Disease|Phenotype|Protein Interaction Maps', 'Journal Article', '10.1186/s12859-017-1889-0', '29297292', 'Much effort has been devoted to the discovery of specific mechanisms between drugs and single targets to date. However, as biological systems maintain homeostasis at the level of functional networks robustly controlling the internal environment, such networks commonly contain multiple redundant mechanisms designed to counteract loss or perturbation of a single member of the network. As such, investigation of therapeutics that target dysregulated pathways or processes, rather than single targets, may identify agents that function at a level of the biological organization more relevant to the pathology of complex diseases such as Parkinsons Disease (PD). Genome-wide association studies (GWAS) in PD have identified common variants underlying disease susceptibility, while gene expression microarray data provide genome-wide transcriptional profiles. These genomic studies can illustrate upstream perturbations causing the dysfunction in signaling pathways and downstream biochemical mechanisms leading to the PD phenotype. We hypothesize that drugs acting at the level of a gene expression module specific to PD can overcome the lack of efficacy associated with targeting a single gene in polygenic diseases. Thus, this approach represents a promising new direction for module-based drug discovery in human diseases such as PD.'),
(98188, 2017, 'bmc', 'Combining agent based-models and virtual screening techniques to predict the best citrus-derived vaccine adjuvants against human papilloma virus', 'Pennisi, Marzio|Russo, Giulia|Ravalli, Silvia|Pappalardo, Francesco', 'Department of Mathematics and Computer Science, University of Catania, 95125, Catania, Italy.|Department of Biomedical and Biotechnological Sciences, University of Catania, 95123, Catania, Italy.|Department of Drug Sciences, University of Catania, 95125, Catania, Italy.|Department of Drug Sciences, University of Catania, 95125, Catania, Italy. francesco.pappalardo@unict.it.', 'Italy|Italy|Italy|Italy', 0, 'Adjuvants, Pharmaceutic|Animals|Citrus|Female|Humans|Mass Screening|Papillomaviridae|Papillomavirus Infections|Papillomavirus Vaccines|Vaccination', 'Journal Article', '10.1186/s12859-017-1961-9', '29297294', 'Human papillomavirus infection is a global social burden that, every year, leads to thousands new diagnosis of cancer. The introduction of a protocol of immunization, with Gardasil and Cervarix vaccines, has radically changed the way this infection easily spreads among people. Even though vaccination is only preventive and not therapeutic, it is a strong tool capable to avoid the consequences that this pathogen could cause. Gardasil vaccine is not free from side effects and the duration of immunity is not always well determined. This work aim to enhance the effects of the vaccination by using a new class of adjuvants and a different administration protocol. Due to their minimum side effects, their easy extraction, their low production costs and their proven immune stimulating activity, citrus-derived molecules are valid candidates to be administered as adjuvants in a vaccine formulation against Hpv.'),
(98189, 2017, 'bmc', 'CoMet: a workflow using contig coverage and composition for binning a metagenomic sample with high precision', 'Herath, Damayanthi|Tang, Sen-Lin|Tandon, Kshitij|Ackland, David|Halgamuge, Saman Kumara', 'Department of Mechanical Engineering, The University of Melbourne, Parkville, Melbourne, 3010, Australia. damayanthi@ce.pdn.ac.lk.|Biodiversity Research Center, Academia Sinica, Nan-Kang, Taipei, 11529, Taiwan.|Biodiversity Research Center, Academia Sinica, Nan-Kang, Taipei, 11529, Taiwan.|Department of Biomedical Engineering, The University of Melbourne, Victoria, 3010, Australia.|Research School of Engineering, College of Engineering and Computer Science, The Australian National University, Canberra ACT, 2601, Australia.', 'Australia|-|-|Australia|Australia', 4, 'Algorithms|Base Sequence|Cluster Analysis|Contig Mapping|Databases, Genetic|Genome|Humans|Metagenome|Metagenomics|Workflow', 'Journal Article', '10.1186/s12859-017-1967-3', '29297295', 'In metagenomics, the separation of nucleotide sequences belonging to an individual or closely matched populations is termed binning. Binning helps the evaluation of underlying microbial population structure as well as the recovery of individual genomes from a sample of uncultivable microbial organisms. Both supervised and unsupervised learning methods have been employed in binning, however, characterizing a metagenomic sample containing multiple strains remains a significant challenge. In this study, we designed and implemented a new workflow, Coverage and composition based binning of Metagenomes (CoMet), for binning contigs in a single metagenomic sample. CoMet utilizes coverage values and the compositional features of metagenomic contigs. The binning strategy in CoMet includes the initial grouping of contigs in guanine-cytosine (GC) content-coverage space and refinement of bins in tetranucleotide frequencies space in a purely unsupervised manner. With CoMet, the clustering algorithm DBSCAN is employed for binning contigs. The performances of CoMet were compared against four existing approaches for binning a single metagenomic sample, including MaxBin, Metawatt, MyCC (default) and MyCC (coverage) using multiple datasets including a sample comprised of multiple strains.'),
(98190, 2017, 'bmc', 'GTZ: a fast compression and cloud transmission tool optimized for FASTQ files', 'Xing, Yuting|Li, Gen|Wang, Zhenguo|Feng, Bolun|Song, Zhuo|Wu, Chengkun', 'School of Computer Science, National University of Defense Technology, Changsha, 410000, China.|Genetalks Biotech. Co.,Ltd., Beijing, 100000, China.|Genetalks Biotech. Co.,Ltd., Beijing, 100000, China.|Genetalks Biotech. Co.,Ltd., Beijing, 100000, China.|Genetalks Biotech. Co.,Ltd., Beijing, 100000, China. zhuosong@gmail.com.|School of Computer Science, National University of Defense Technology, Changsha, 410000, China. Chengkun_wu@nudt.edu.cn.', 'China|China|China|China|China|China', 0, 'Cloud Computing|Data Compression|High-Throughput Nucleotide Sequencing|Humans', 'Journal Article', '10.1186/s12859-017-1973-5', '29297296', 'The dramatic development of DNA sequencing technology is generating real big data, craving for more storage and bandwidth. To speed up data sharing and bring data to computing resource faster and cheaper, it is necessary to develop a compression tool than can support efficient compression and transmission of sequencing data onto the cloud storage.'),
(98191, 2017, 'bmc', 'Divisive hierarchical maximum likelihood clustering', 'Sharma, Alok|López, Yosvany|Tsunoda, Tatsuhiko', 'Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan.|Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan.|Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan. tsunoda.mesm@mri.tmd.ac.jp.', 'Japan|Japan|Japan', 11, 'Algorithms|Cluster Analysis|Humans|Probability', 'Journal Article', '10.1186/s12859-017-1965-5', '29297297', 'Biological data comprises various topologies or a mixture of forms, which makes its analysis extremely complicated. With this data increasing in a daily basis, the design and development of efficient and accurate statistical methods has become absolutely necessary. Specific analyses, such as those related to genome-wide association studies and multi-omics information, are often aimed at clustering sub-conditions of cancers and other diseases. Hierarchical clustering methods, which can be categorized into agglomerative and divisive, have been widely used in such situations. However, unlike agglomerative methods divisive clustering approaches have consistently proved to be computationally expensive.'),
(98192, 2017, 'bmc', '2D-EM clustering approach for high-dimensional data through folding feature vectors', 'Sharma, Alok|Kamola, Piotr J|Tsunoda, Tatsuhiko', 'Center for Integrative Medical Sciences, RIKEN Yokohama, Yokohama, 230-0045, Japan.|Center for Integrative Medical Sciences, RIKEN Yokohama, Yokohama, 230-0045, Japan.|Center for Integrative Medical Sciences, RIKEN Yokohama, Yokohama, 230-0045, Japan. tatsuhiko.tsunoda@riken.jp.', 'Japan|Japan|Japan', 4, 'Algorithms|Cluster Analysis|Humans|Transcriptome', 'Journal Article', '10.1186/s12859-017-1970-8', '29297298', 'Clustering methods are becoming widely utilized in biomedical research where the volume and complexity of data is rapidly increasing. Unsupervised clustering of patient information can reveal distinct phenotype groups with different underlying mechanism, risk prognosis and treatment response. However, biological datasets are usually characterized by a combination of low sample number and very high dimensionality, something that is not adequately addressed by current algorithms. While the performance of the methods is satisfactory for low dimensional data, increasing number of features results in either deterioration of accuracy or inability to cluster. To tackle these challenges, new methodologies designed specifically for such data are needed.'),
(98193, 2017, 'bmc', 'A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction', 'Deng, Lei|Fan, Chao|Zeng, Zhiwen', 'School of Software, Central South University, No.22 Shaoshan South Road, Changsha, 410075, China.|School of Software, Central South University, No.22 Shaoshan South Road, Changsha, 410075, China.|School of Information Science and Engineering, Central South University, No.932 South Lushan Road, Changsha, 410083, China. zengzhiwen@csu.edu.cn.', 'China|China|China', 0, 'Algorithms|Machine Learning|Models, Molecular|Neural Networks (Computer)|Proteins|Solvents', 'Journal Article', '10.1186/s12859-017-1971-7', '29297299', 'Direct prediction of the three-dimensional (3D) structures of proteins from one-dimensional (1D) sequences is a challenging problem. Significant structural characteristics such as solvent accessibility and contact number are essential for deriving restrains in modeling protein folding and protein 3D structure. Thus, accurately predicting these features is a critical step for 3D protein structure building.'),
(98194, 2017, 'bmc', 'Dependency-based long short term memory network for drug-drug interaction extraction', 'Wang, Wei|Yang, Xi|Yang, Canqun|Guo, Xiaowei|Zhang, Xiang|Wu, Chengkun', 'School of Computer Science, National University of Defense Technology, Changsha, 410073, China.|School of Computer Science, National University of Defense Technology, Changsha, 410073, China.|School of Computer Science, National University of Defense Technology, Changsha, 410073, China.|School of Computer Science, National University of Defense Technology, Changsha, 410073, China.|School of Computer Science, National University of Defense Technology, Changsha, 410073, China.|School of Computer Science, National University of Defense Technology, Changsha, 410073, China. Chenkun_wu@nudt.edu.cn.', 'China|China|China|China|China|China', 0, 'Algorithms|Databases as Topic|Drug Interactions|Neural Networks (Computer)|Statistics as Topic', 'Journal Article', '10.1186/s12859-017-1962-8', '29297301', 'Drug-drug interaction extraction (DDI) needs assistance from automated methods to address the explosively increasing biomedical texts. In recent years, deep neural network based models have been developed to address such needs and they have made significant progress in relation identification.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98195, 2017, 'bmc', 'An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information', 'Kumar, Shiu|Sharma, Alok|Tsunoda, Tatsuhiko', 'Department of Electronics, Instrumentation and Control Engineering, School of Electrical & Electronics Engineering, Fiji National University, Suva, Fiji. shiu.kumar@fnu.ac.fj.|School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Suva, Fiji.|RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.', 'Fiji|Fiji|Japan', 42, 'Electroencephalography|Humans|Signal Processing, Computer-Assisted', 'Journal Article', '10.1186/s12859-017-1964-6', '29297303', 'Common spatial pattern (CSP) has been an effective technique for feature extraction in electroencephalography (EEG) based brain computer interfaces (BCIs). However, motor imagery EEG signal feature extraction using CSP generally depends on the selection of the frequency bands to a great extent.'),
(98196, 2017, 'bmc', 'Novel human microbe-disease association prediction using network consistency projection', 'Bao, Wenzheng|Jiang, Zhichao|Huang, De-Shuang', 'Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Caoan Road 4800, Shanghai, 201804, China.|Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Caoan Road 4800, Shanghai, 201804, China.|Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Caoan Road 4800, Shanghai, 201804, China. dshuang@tongji.edu.cn.', 'China|China|China', 0, 'Algorithms|Computer Simulation|Host Microbial Interactions|Humans|Prognosis', 'Journal Article', '10.1186/s12859-017-1968-2', '29297304', 'Accumulating biological and clinical reports have indicated that imbalance of microbial community is closely associated with occurrence and development of various complex human diseases. Identifying potential microbe-disease associations, which could provide better understanding of disease pathology and further boost disease diagnostic and prognostic, has attracted more and more attention. However, hardly any computational models have been developed for large scale microbe-disease association prediction.'),
(98197, 2017, 'bmc', 'Pharmacophore anchor models of flaviviral NS3 proteases lead to drug repurposing for DENV infection', 'Pathak, Nikhil|Lai, Mei-Ling|Chen, Wen-Yu|Hsieh, Betty-Wu|Yu, Guann-Yi|Yang, Jinn-Moon', 'TIGP-Bioinformatics, Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan.|National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Zhunan, 35053, Taiwan.|Graduate Institute of Immunology, National Taiwan University- College of Medicine, Taipei, 10617, Taiwan.|Graduate Institute of Immunology, National Taiwan University- College of Medicine, Taipei, 10617, Taiwan.|National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Zhunan, 35053, Taiwan.|TIGP-Bioinformatics, Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan. moon@faculty.nctu.edu.tw.', '-|-|-|-|-|-', 2, 'Dengue Virus|Drug Repositioning|Flavivirus|Humans|Peptide Hydrolases', 'Journal Article', '10.1186/s12859-017-1957-5', '29297305', 'Viruses of the flaviviridae family are responsible for some of the major infectious viral diseases around the world and there is an urgent need for drug development for these diseases. Most of the virtual screening methods in flaviviral drug discovery suffer from a low hit rate, strain-specific efficacy differences, and susceptibility to resistance. It is because they often fail to capture the key pharmacological features of the target active site critical for protein function inhibition. So in our current work,xc2xa0for the flaviviral NS3 protease, we summarized the pharmacophore features at the protease active site as anchors (subsite-moiety interactions).'),
(98198, 2017, 'bmc', 'CORNAS: coverage-dependent RNA-Seq analysis of gene expression data without biological replicates', 'Low, Joel Z B|Khang, Tsung Fei|Tammi, Martti T', 'Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, 50603, Malaysia.|Institute of Mathematical Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, 50603, Malaysia. tfkhang@um.edu.my.|Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, 50603, Malaysia.', 'Malaysia|Malaysia|Malaysia', 5, 'Area Under Curve|Bayes Theorem|Computer Simulation|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Humans|Organ Specificity|Polymerase Chain Reaction|Predictive Value of Tests|RNA|ROC Curve|Reproducibility of Results|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-017-1974-4', '29297307', 'In current statistical methods for calling differentially expressed genes in RNA-Seq experiments, the assumption is that an adjusted observed gene count represents an unknown true gene count. This adjustment usually consists of a normalization step to account for heterogeneous sample library sizes, and then the resulting normalized gene counts are used as input for parametric or non-parametric differential gene expression tests. A distribution of true gene counts, each with a different probability, can result in the same observed gene count. Importantly, sequencing coverage information is currently not explicitly incorporated into any of the statistical models used for RNA-Seq analysis.'),
(98199, 2017, 'bmc', 'Identifying term relations cross different gene ontology categories', 'Peng, Jiajie|Wang, Honggang|Lu, Junya|Hui, Weiwei|Wang, Yadong|Shang, Xuequn', 'School of Computer Science, Northwestern Polytechnical University, Xian, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.|School of Computer Science, Northwestern Polytechnical University, Xian, China.|School of Computer Science, Northwestern Polytechnical University, Xian, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. ydwang@hit.edu.cn.|School of Computer Science, Northwestern Polytechnical University, Xian, China. shang@nwpu.edu.cn.', 'China|China|China|China|China|China', 0, 'Algorithms|Computational Biology|Gene Ontology|Gene Regulatory Networks|ROC Curve|Reference Standards|Saccharomyces cerevisiae', 'Journal Article', '10.1186/s12859-017-1959-3', '29297309', 'The Gene Ontology (GO) is a community-based bioinformatics resource that employs ontologies to represent biological knowledge and describes information about gene and gene product function. GO includes three independent categories: molecular function, biological process and cellular component. For better biological reasoning, identifying the biological relationships between terms in different categories are important. However, the existing measurements to calculate similarity between terms in different categories are either developed by using the GO data only or only take part of combined gene co-function network information.'),
(98200, 2017, 'bmc', 'MultiDCoX: Multi-factor analysis of differential co-expression', 'Liany, Herty|Rajapakse, Jagath C|Karuturi, R Krishna Murthy', 'School of Computing, National University of Singapore, 21 Lower Kent Ridge Rd, Singapore, 119077, Singapore.|School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore, 639798, Singapore.|Computational and System Biology, Genome Institute of Singapore, A-STAR, 60 Biopolis Street, Singapore, 138672, Singapore. krish.karuturi@jax.org.', 'Singapore|Singapore|Singapore', 3, 'Algorithms|Breast Neoplasms|Chemokine CXCL13|Computer Simulation|Factor Analysis, Statistical|Female|Gene Expression Profiling|Gene Expression Regulation|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Matrix Metalloproteinase 1|Mutation|Receptors, Estrogen|Survival Analysis|Tumor Suppressor Protein p53', 'Journal Article', '10.1186/s12859-017-1963-7', '29297310', 'Differential co-expression (DCX) signifies change in degree of co-expression of a set of genes among different biological conditions. It has been used to identify differential co-expression networks or interactomes. Many algorithms have been developed for single-factor differential co-expression analysis and applied in a variety of studies. However, in many studies, the samples are characterized by multiple factors such as genetic markers, clinical variables and treatments. No algorithm or methodology is available for multi-factor analysis of differential co-expression.'),
(98201, 2017, 'bmc', 'Utilizing random Forest QSAR models with optimized parameters for target identification and its application to target-fishing server', 'Lee, Kyoungyeul|Lee, Minho|Kim, Dongsup', 'Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 291, Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.|Catholic Precision Medicine Research Center, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea. MinhoLee@catholic.ac.kr.|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 291, Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea. kds@kaist.ac.kr.', '-|-|-', 0, 'Algorithms|Computer Simulation|Drug Delivery Systems|Ligands|Models, Theoretical|Probability|Quantitative Structure-Activity Relationship|ROC Curve|Reproducibility of Results', 'Journal Article', '10.1186/s12859-017-1960-x', '29297315', 'The identification of target molecules is important for understanding the mechanism of target deconvolution in phenotypic screening and polypharmacology of drugs. Because conventional methods of identifying targets require time and cost, in-silico target identification has been considered an alternative solution. One of the well-known in-silico methods of identifying targets involves structure activity relationships (SARs). SARs have advantages such as low computational cost and high feasibility, however, the data dependency in the SAR approach causes imbalance of active data and ambiguity of inactive data throughout targets.'),
(98202, 2017, 'bmc', '16SPIP: a comprehensive analysis pipeline for rapid pathogen detection in clinical samples based on 16S metagenomic sequencing', 'Miao, Jiaojiao|Han, Na|Qiang, Yujun|Zhang, Tingting|Li, Xiuwen|Zhang, Wen', 'State Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.|State Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.|State Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.|State Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.|State Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.|State Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China. zhangwen@icdc.cn.', 'China|China|China|China|China|China', 8, 'Bacteria|Computational Biology|Databases, Genetic|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|RNA, Ribosomal, 16S|Software', 'Journal Article', '10.1186/s12859-017-1975-3', '29297318', 'Pathogen detection in clinical samples based on 16S metagenomic sequencing technology in microbiology laboratories is an important strategy for clinical diagnosis, public health surveillance, and investigations of outbreaks. However, the implementation of the technology is limited by its accuracy and the time required for bioinformatics analysis. Therefore, a simple, standardized, and rapid analysis pipeline from the receipt of clinical samples to the generation of a test report is needed to increase the use of metagenomic analyses in clinical settings.'),
(98203, 2017, 'bmc', 'CNN-BLPred: a Convolutional neural network based predictor for xcexb2-Lactamases (BL) and their classes', 'White, Clarence|Ismail, Hamid D|Saigo, Hiroto|Kc, Dukka B', 'Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, 27411, USA.|Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, 27411, USA.|Faculty of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan.|Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, 27411, USA. dbkc@ncat.edu.', 'United States|United States|Japan|United States', 8, 'Algorithms|Amino Acid Sequence|Databases, Protein|Models, Molecular|Neural Networks (Computer)|ROC Curve|Reproducibility of Results|beta-Lactamases', 'Journal Article', '10.1186/s12859-017-1972-6', '29297322', 'The xcexb2-Lactamase (BL) enzyme family is an important class of enzymes that plays a key role in bacterial resistance to antibiotics. As the newly identified number of BL enzymes is increasing daily, it is imperative to develop a computational tool to classify the newly identified BL enzymes into one of its classes. There are two types of classification of BL enzymes: Molecular Classification and Functional Classification. Existing computational methods only address Molecular Classification and the performance of these existing methods is unsatisfactory.'),
(98204, 2017, 'bmc', 'Automatic plankton image classification combining multiple view features via multiple kernel learning', 'Zheng, Haiyong|Wang, Ruchen|Yu, Zhibin|Wang, Nan|Gu, Zhaorui|Zheng, Bing', 'Department of Electronic Engineering, Ocean University of China, No. 238 Songling Road, Qingdao, 266100, China.|Department of Electronic Engineering, Ocean University of China, No. 238 Songling Road, Qingdao, 266100, China.|Department of Electronic Engineering, Ocean University of China, No. 238 Songling Road, Qingdao, 266100, China.|Department of Electronic Engineering, Ocean University of China, No. 238 Songling Road, Qingdao, 266100, China.|Department of Electronic Engineering, Ocean University of China, No. 238 Songling Road, Qingdao, 266100, China.|College of Information Science and Engineering, Ocean University of China, No. 238 Songling Road, Qingdao, 266100, China. bingzh@ouc.edu.cn.', 'China|China|China|China|China|China', 0, 'Algorithms|Automation|Databases as Topic|Deep Learning|Image Processing, Computer-Assisted|Plankton|Support Vector Machine', 'Journal Article', '10.1186/s12859-017-1954-8', '29297354', 'Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap.'),
(98205, 2017, 'bmc', 'Identification of streptococcal small RNAs that are putative targets of RNase III through bioinformatics analysis of RNA sequencing data', 'Rath, Ethan C|Pitman, Stephanie|Cho, Kyu Hong|Bai, Yongsheng', 'Department of Biology, Indiana State University, Terre Haute, IN, 47809, USA.|Department of Biology, Indiana State University, Terre Haute, IN, 47809, USA.|Department of Biology, Indiana State University, Terre Haute, IN, 47809, USA. KyuHong.Cho@indstate.edu.|Department of Biology, Indiana State University, Terre Haute, IN, 47809, USA. Yongsheng.Bai@indstate.edu.', 'India|India|India|India', 1, 'Base Sequence|Computational Biology|DNA, Intergenic|Gene Deletion|Genome, Bacterial|RNA, Bacterial|RNA, Messenger|RNA, Small Untranslated|Reproducibility of Results|Ribonuclease III|Sequence Analysis, RNA|Streptococcus pyogenes', 'Journal Article', '10.1186/s12859-017-1897-0', '29297355', 'Small noncoding regulatory RNAs (sRNAs) are post-transcriptional regulators, regulating mRNAs, proteins, and DNA in bacteria. One class of sRNAs, trans-acting sRNAs, are the most abundant sRNAs transcribed from the intergenic regions (IGRs) of the bacterial genome. In Streptococcus pyogenes, a common and potentially deadly pathogen, many sRNAs have been identified, but only a few have been studied. The goal of this study is to identify trans-acting sRNAs that can be substrates of RNase III. The endoribonuclease RNase III cleaves double stranded RNAs, which can be formed during the interaction between an sRNA and target mRNAs.'),
(98206, 2017, 'bmc', 'Refine gene functional similarity network based on interaction networks', 'Tian, Zhen|Guo, Maozu|Wang, Chunyu|Liu, Xiaoyan|Wang, Shiming', 'Department of computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Peoples Republic of China.|Department of computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Peoples Republic of China. guomaozu@bucea.edu.cn.|Department of computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Peoples Republic of China.|Department of computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Peoples Republic of China.|Department of computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Peoples Republic of China.', 'China|China|China|China|China', 4, 'Algorithms|Gene Regulatory Networks|Humans|Protein Interaction Mapping|Protein Interaction Maps', 'Journal Article', '10.1186/s12859-017-1969-1', '29297381', 'In recent years, biological interaction networks have become the basis of some essential study and achieved success in many applications. Some typical networks such as protein-protein interaction networks have already been investigated systematically. However, little work has been available for the construction of gene functional similarity networks so far. In this research, we will try to build a high reliable gene functional similarity network to promote its further application.'),
(98207, 2018, 'bmc', 'Fast genomic prediction of breeding values using parallel Markov chain Monte Carlo with convergence diagnosis', 'Guo, Peng|Zhu, Bo|Niu, Hong|Wang, Zezhao|Liang, Yonghu|Chen, Yan|Zhang, Lupei|Ni, Hemin|Guo, Yong|Hay, El Hamidi A|Gao, Xue|Gao, Huijiang|Wu, Xiaolin|Xu, Lingyang|Li, Junya', 'Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Yuanmingyuan West Road 2#, Haidian District, Beijing, 100193, China.|Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Yuanmingyuan West Road 2#, Haidian District, Beijing, 100193, China.|Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Yuanmingyuan West Road 2#, Haidian District, Beijing, 100193, China.|Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Yuanmingyuan West Road 2#, Haidian District, Beijing, 100193, China.|Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Yuanmingyuan West Road 2#, Haidian District, Beijing, 100193, China.|Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Yuanmingyuan West Road 2#, Haidian District, Beijing, 100193, China.|Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Yuanmingyuan West Road 2#, Haidian District, Beijing, 100193, China.|Animal Science and Technology College, Beijing University of Agriculture, Beijing, China.|Animal Science and Technology College, Beijing University of Agriculture, Beijing, China.|Livestock and Range Research Laboratory, ARS, USDA, Miles City, MT, USA.|Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Yuanmingyuan West Road 2#, Haidian District, Beijing, 100193, China.|Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Yuanmingyuan West Road 2#, Haidian District, Beijing, 100193, China.|Biostatistics and Bioinformatics, GeneSeek (A Neogen company), Lincoln, NE, 68504, USA.|Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Yuanmingyuan West Road 2#, Haidian District, Beijing, 100193, China. xulingyang@caas.cn.|Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Yuanmingyuan West Road 2#, Haidian District, Beijing, 100193, China. jl1@iascaas.net.cn.', 'China|China|China|China|China|China|China|China|China|United States|China|China|United States|China|China', 0, 'Animals|Bayes Theorem|Cattle|China|Genome|Markov Chains|Models, Genetic|Monte Carlo Method|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-017-2003-3', '29298666', 'Running multiple-chain Markov Chain Monte Carlo (MCMC) provides an efficient parallel computing method for complex Bayesian models, although the efficiency of the approach critically depends on the length of the non-parallelizable burn-in period, for which all simulated data are discarded. In practice, this burn-in period is set arbitrarily and often leads to the performance of far more iterations than required. In addition, the accuracy of genomic predictions does not improve after the MCMC reaches equilibrium.'),
(98208, 2018, 'bmc', 'Sequence motif finder using memetic algorithm', 'Caldonazzo Garbelini, Jader M|Kashiwabara, André Y|Sanches, Danilo S', 'Department of Computer Science, Bioinformatics Graduate Program, Federal University of Technology - Paraná, Cornélio Procópio, PR, Brazil. jadermcg@hotmail.com.|Department of Computer Science, Bioinformatics Graduate Program, Federal University of Technology - Paraná, Cornélio Procópio, PR, Brazil.|Department of Computer Science, Bioinformatics Graduate Program, Federal University of Technology - Paraná, Cornélio Procópio, PR, Brazil.', 'Brazil|Brazil|Brazil', 3, 'Algorithms|Base Sequence|Binding Sites|Internet|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-2005-1', '29298679', 'De novo prediction of Transcription Factor Binding Sites (TFBS) using computational methods is a difficult task and it is an important problem in Bioinformatics. The correct recognition of TFBS plays an important role in understanding the mechanisms of gene regulation and helps to develop new drugs.'),
(98209, 2018, 'bmc', 'Strategies for identification of somatic variants using the Ion Torrent deep targeted sequencing platform', 'Deshpande, Aditya|Lang, Wenhua|McDowell, Tina|Sivakumar, Smruthy|Zhang, Jiexin|Wang, Jing|San Lucas, F Anthony|Fowler, Jerry|Kadara, Humam|Scheet, Paul', 'Departments of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Departments of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Departments of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Departments of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon. hk94@aub.edu.lb.|Departments of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA. pscheet@alum.wustl.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|Lebanon|United States', 7, 'Carcinoma, Non-Small-Cell Lung|High-Throughput Nucleotide Sequencing|Humans|Lung Neoplasms|Mutation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1991-3', '29301485', 'Next-generation (NGS) sequencing has wide application in medical genetics, including the detection of somatic variation in cancer. The Ion Torrent-based (IONT) platform is among NGS technologies employed in clinical, research and diagnostic settings. However, identifying mutations from IONT deep sequencing with high confidence has remained a challenge. We compared various computational variant-calling methods to derive a variant identification pipeline that may improve the molecular diagnostic and research utility of IONT.'),
(98210, 2018, 'bmc', 'PIVOT: platform for interactive analysis and visualization of transcriptomics data', 'Zhu, Qin|Fisher, Stephen A|Dueck, Hannah|Middleton, Sarah|Khaladkar, Mugdha|Kim, Junhyong', 'Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.|Department of Biology, University of Pennsylvania, Philadelphia, PA, USA.|Department of Biology, University of Pennsylvania, Philadelphia, PA, USA.|Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.|Department of Biology, University of Pennsylvania, Philadelphia, PA, USA.|Department of Biology, University of Pennsylvania, Philadelphia, PA, USA. junhyong@sas.upenn.edu.', 'United States|United States|United States|United States|United States|United States', 0, 'Animals|Cell Transdifferentiation|Databases, Factual|Embryonic Stem Cells|Fibroblasts|Gene Expression Profiling|Internet|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1994-0', '29304726', 'Many R packages have been developed for transcriptome analysis but their use often requires familiarity with R and integrating results of different packages requires scripts to wrangle the datatypes. Furthermore, exploratory data analyses often generate multiple derived datasets such as data subsets or data transformations, which can be difficult to track.'),
(98211, 2018, 'bmc', 'Inferring ontology graph structures using OWL reasoning', 'Rodríguez-García, Miguel Ángel|Hoehndorf, Robert', 'Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Kingdom of Saudi Arabia.|Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Kingdom of Saudi Arabia. robert.hoehndorf@kaust.edu.sa.', 'Saudi Arabia|Saudi Arabia', 9, 'Algorithms|Animals|Area Under Curve|Caenorhabditis elegans|Drosophila|Gene Ontology|Mice|Protein Interaction Maps|ROC Curve|Saccharomyces cerevisiae|Zebrafish', 'Journal Article', '10.1186/s12859-017-1999-8', '29304741', 'Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies semantic content remains a challenge.'),
(98212, 2018, 'bmc', 'A powerful parent-of-origin effects test for qualitative traits on X chromosome in general pedigrees', 'Zou, Qi-Lei|You, Xiao-Ping|Li, Jian-Long|Fung, Wing Kam|Zhou, Ji-Yuan', 'State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, No. 1023, South Shatai Road, Baiyun District, Guangzhou, 510515, China.|State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, No. 1023, South Shatai Road, Baiyun District, Guangzhou, 510515, China.|State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, No. 1023, South Shatai Road, Baiyun District, Guangzhou, 510515, China.|Department of Statistics and Actuarial Science, University of Hong Kong, Pokfulam Road, Hong Kong, China. wingfung@hku.hk.|State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, No. 1023, South Shatai Road, Baiyun District, Guangzhou, 510515, China. zhoujiyuan5460@hotmail.com.', 'China|China|China|China|China', 3, 'Arthritis, Rheumatoid|Chromosomes, Human, X|Genomic Imprinting|Genotype|Humans|Monte Carlo Method|Pedigree|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-017-2001-5', '29304743', 'Genomic imprinting is one of the well-known epigenetic factors causing the association between traits and genes, and has generally been examined by detecting parent-of-origin effects of alleles. A lot of methods have been proposed to test for parent-of-origin effects on autosomes based on nuclear families and general pedigrees. Although these parent-of-origin effects tests on autosomes have been available for more than 15 years, there has been no statistical test developed to test for parent-of-origin effects on X chromosome, until the parental-asymmetry test on X chromosome (XPAT) and its extensions were recently proposed. However, these methods on X chromosome are only applicable to nuclear families and thus are not suitable for general pedigrees.'),
(98213, 2018, 'bmc', 'Gene flow analysis method, the D-statistic, is robust in a wide parameter space', 'Zheng, Yichen|Janke, Axel', 'Biodiversität und Klima Forschungszentrum, Senckenberg Gesellschaft für Naturforschung, 60325, Frankfurt, Germany. yzheng2@uni-koeln.de.|Biodiversität und Klima Forschungszentrum, Senckenberg Gesellschaft für Naturforschung, 60325, Frankfurt, Germany.', 'Germany|Germany', 0, 'Animals|Gene Flow|Genomics|Models, Genetic|Population Density|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-017-2002-4', '29310567', 'We evaluated the sensitivity of the D-statistic, a parsimony-like method widely used to detect gene flow between closely related species. This method has been applied to a variety of taxa with a wide range of divergence times. However, its parameter space and thus its applicability to a wide taxonomic range has not been systematically studied. Divergence time, population size, time of gene flow, distance of outgroup and number of loci were examined in a sensitivity analysis.'),
(98214, 2018, 'bmc', 'Thresher: determining the number of clusters while removing outliers', 'Wang, Min|Abrams, Zachary B|Kornblau, Steven M|Coombes, Kevin R', 'Department of Biomedical Informatics, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, 43210, OH, USA.|Department of Biomedical Informatics, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, 43210, OH, USA.|Department of Leukemia, The University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Blvd., Box 448, Houston, 77030, TX, USA.|Department of Biomedical Informatics, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, 43210, OH, USA. coombes.3@osu.edu.', 'United States|United States|United States|United States', 7, 'Algorithms|Breast Neoplasms|Cluster Analysis|Female|Humans|Monte Carlo Method|Principal Component Analysis', 'Journal Article', '10.1186/s12859-017-1998-9', '29310570', 'Cluster analysis is the most common unsupervised method for finding hidden groups in data. Clustering presents two main challenges: (1) finding the optimal number of clusters, and (2) removing outliers among the objects being clustered. Few clustering algorithms currently deal directly with the outlier problem. Furthermore, existing methods for identifying the number of clusters still have some drawbacks. Thus, there is a need for a better algorithm to tackle both challenges.'),
(98215, 2017, 'bmc', 'Usage of cell nomenclature in biomedical literature', 'Kafkas, xc5x9eenay|Sarntivijai, Sirarat|Hoehndorf, Robert', 'Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Saudi Arabia. senay.kafkas@kaust.edu.sa.|The European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, SD, CB10 1, UK.|Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Saudi Arabia.', 'Saudi Arabia|United Kingdom|Saudi Arabia', 1, 'Cell Line|Cells|Data Mining|Databases, Factual|Humans|Medical Subject Headings|Publications|Terminology as Topic', 'Journal Article', '10.1186/s12859-017-1978-0', '29322912', 'Cell lines and cell types are extensively studied in biomedical research yielding to a significant amount of publications each year. Identifying cell lines and cell types precisely in publications is crucial for science reproducibility and knowledge integration. There are efforts for standardisation of the cell nomenclature based on ontology development to support FAIR principles of the cell knowledge. However, it is important to analyse the usage of cell nomenclature in publications at a large scale for understanding the level of uptake of cell nomenclature in literature by scientists. In this study, we analyse the usage of cell nomenclature, both in Vivo, and in Vitro in biomedical literature by using text mining methods and present our results.'),
(98216, 2017, 'bmc', 'Cell type discovery and representation in the era of high-content single cell phenotyping', 'Bakken, Trygve|Cowell, Lindsay|Aevermann, Brian D|Novotny, Mark|Hodge, Rebecca|Miller, Jeremy A|Lee, Alexandra|Chang, Ivan|McCorrison, Jamison|Pulendran, Bali|Qian, Yu|Schork, Nicholas J|Lasken, Roger S|Lein, Ed S|Scheuermann, Richard H', 'Allen Institute for Brain Science, Seattle, Washington, 98103, USA.|Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA.|J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA.|J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA.|Allen Institute for Brain Science, Seattle, Washington, 98103, USA.|Allen Institute for Brain Science, Seattle, Washington, 98103, USA.|J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA.|J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA.|J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA.|Department of Pathology and Laboratory Medicine, Emory University, 201 Dowman Dr, Atlanta, GA, 30322, USA.|J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA.|J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA.|J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA.|Allen Institute for Brain Science, Seattle, Washington, 98103, USA.|J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA. RScheuermann@jcvi.org.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Biological Ontologies|Biomarkers|Cells|Computational Biology|Humans|Sequence Analysis, RNA|Single-Cell Analysis', 'Journal Article', '10.1186/s12859-017-1977-1', '29322913', 'A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs and tissues, they can also be used as cellular biomarkers of a variety of different disease states and therapeutic/vaccine responses. In order to serve as a reference for cell type representation, the Cell Ontology has been developed to provide a standard nomenclature of defined cell types for comparative analysis and biomarker discovery. Historically, these cell types have been defined based on unique cellular shapes and structures, anatomic locations, and marker protein expression. However, we are now experiencing a revolution in cellular characterization resulting from the application of new high-throughput, high-content cytometry and sequencing technologies. The resulting explosion in the number of distinct cell types being identified is challenging the current paradigm for cell type definition in the Cell Ontology.'),
(98217, 2017, 'bmc', 'Cell ontology in an age of data-driven cell classification', 'Osumi-Sutherland, David', 'European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK. davidos@ebi.ac.uk.', 'United Kingdom', 4, 'Animals|Biological Ontologies|Cells|Computational Biology|Databases, Factual|Humans|Mice|Models, Statistical', 'Journal Article', '10.1186/s12859-017-1980-6', '29322914', 'Data-driven cell classification is becoming common and is now being implemented on a massive scale by projects such as the Human Cell Atlas. The scale of these efforts poses a challenge. How can the results be made searchable and accessible to biologists in general? How can they be related back to the rich classical knowledge of cell-types, anatomy and development? How will data from the various types of single cell analysis be made cross-searchable? Structured annotation with ontology terms provides a potential solution to these problems. In turn, there is great potential for using the outputs of data-driven cell classification to structure ontologies and integrate them with data-driven cell query systems.'),
(98218, 2017, 'bmc', 'Comparison, alignment, and synchronization of cell line information between CLO and EFO', 'Ong, Edison|Sarntivijai, Sirarat|Jupp, Simon|Parkinson, Helen|He, Yongqun', 'Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.|Samples, Phenotypes, and Ontologies Team, European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridge, UK.|Samples, Phenotypes, and Ontologies Team, European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridge, UK.|Samples, Phenotypes, and Ontologies Team, European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridge, UK.|Center of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. yongqunh@med.umich.edu.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 3, 'Algorithms|Biological Ontologies|Cell Line|Cell Physiological Phenomena|Computational Biology|Data Mining|Databases, Factual|Gene Expression Profiling|Humans|Semantics', 'Journal Article', '10.1186/s12859-017-1979-z', '29322915', 'The Experimental Factor Ontology (EFO) is an application ontology driven by experimental variables including cell lines to organize and describe the diverse experimental variables and data resided in the EMBL-EBI resources. The Cell Line Ontology (CLO) is an OBO community-based ontology that contains information of immortalized cell lines and relevant experimental components. EFO integrates and extends ontologies from the bio-ontology community to drive a number of practical applications. It is desirable that the community shares design patterns and therefore that EFO reuses the cell line representation from the Cell Line Ontology (CLO). There are, however, challenges to be addressed when developing a common ontology design pattern for representing cell lines in both EFO and CLO.'),
(98219, 2017, 'bmc', 'Cells in experimental life sciences - challenges and solution to the rapid evolution of knowledge', 'Sarntivijai, Sirarat|Diehl, Alexander D|He, Yongqun', 'European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. siiraa@ebi.ac.uk.|Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, 14203, USA.|Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, and Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.', 'United Kingdom|United Kingdom|United Kingdom', 1, 'Biological Science Disciplines|Computational Biology|Data Mining|Humans|Research Design', 'Editorial', '10.1186/s12859-017-1976-2', '29322916', 'Cell cultures used in biomedical experiments come in the form of both sample biopsy primary cells, and maintainable immortalised cell lineages. The rise of bioinformatics and high-throughput technologies has led us to the requirement of ontology representation of cell types and cell lines. The Cell Ontology (CL) and Cell Line Ontology (CLO) have long been established as reference ontologies in the OBO framework. We have compiled a series of the challenges and the proposals of solutions in this CELLS (Cells in ExperimentaL Life Sciences) thematic series that cover the grounds of standing issues and the directions, which were discussed in the First International Workshop on CELLS at the the International Conference on Biomedical Ontology (ICBO). This workshop focused on the extension of the current CL and CLO to cover a wider set of biological questions and challenges needing semantic infrastructure for information modeling. We discussed data-driven use cases that leverage linkage of CL, CLO and other bio-ontologies. This is an established approach in data-driven ontologies such as the Experimental Factor Ontology (EFO), and the Ontology for Biomedical Investigation (OBI). The First International Workshop on CELLS at the International Conference on Biomedical Ontology has brought together experimental biologists and biomedical ontologists to discuss solutions to organizing and representing the rapidly evolving knowledge gained from experimental cells. The workshop has successfully identified the areas of challenge, and the gap in connecting the two domains of knowledge. The outcome of this workshop yielded practical implementation plans to filled in this gap.This CELLS workshop also provided a venue for panel discussions of innovative solutions as well as challenges in the development and applications of biomedical ontologies to represent and analyze experimental cell data.'),
(98220, 2017, 'bmc', 'Ontological representation, integration, and analysis of LINCS cell line cells and their cellular responses', 'Ong, Edison|Xie, Jiangan|Ni, Zhaohui|Liu, Qingping|Sarntivijai, Sirarat|Lin, Yu|Cooper, Daniel|Terryn, Raymond|Stathias, Vasileios|Chung, Caty|Schürer, Stephan|He, Yongqun', 'Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.|Unit of Laboratory Animal Medicine and Department of Micro biology and Immunology, University of Michigan, Ann Arbor, MI, USA.|Unit of Laboratory Animal Medicine and Department of Micro biology and Immunology, University of Michigan, Ann Arbor, MI, USA.|Unit of Laboratory Animal Medicine and Department of Micro biology and Immunology, University of Michigan, Ann Arbor, MI, USA.|Samples, Phenotypes and Ontologies Team, European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridge, UK.|Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL, USA.|Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL, USA.|Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL, USA.|Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL, USA.|BD2K LINCS Data Coordination and Integration Center, University of Miami, Miami, FL, USA.|Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL, USA. sschurer@med.miami.edu.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. yongqunh@med.umich.edu.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 7, 'Apoptosis|Breast|Cell Line|Cells, Cultured|Computational Biology|Female|Gene Expression Profiling|Gene Expression Regulation|High-Throughput Screening Assays|Humans|Macrolides|Neoplasms|Thiazolidines', 'Journal Article', '10.1186/s12859-017-1981-5', '29322930', 'Aiming to understand cellular responses to different perturbations, the NIH Common Fund Library of Integrated Network-based Cellular Signatures (LINCS) program involves many institutes and laboratories working on over a thousand cell lines. The community-based Cell Line Ontology (CLO) is selected as the default ontology for LINCS cell line representation and integration.'),
(98221, 2018, 'bmc', 'Bi-objective integer programming for RNA secondary structure prediction with pseudoknots', 'Legendre, Audrey|Angel, Eric|Tahi, Fariza', 'IBISC, Univ Evry, Université Paris-Saclay, Evry, 91025, France.|IBISC, Univ Evry, Université Paris-Saclay, Evry, 91025, France.|IBISC, Univ Evry, Université Paris-Saclay, Evry, 91025, France. fariza.tahi@univ-evry.fr.', 'France|France|France', 4, 'Algorithms|Computational Biology|Databases, Genetic|Models, Molecular|Nucleic Acid Conformation|RNA|Time Factors', 'Journal Article', '10.1186/s12859-018-2007-7', '29334887', 'RNA structure prediction is an important field in bioinformatics, and numerous methods and tools have been proposed. Pseudoknots are specific motifs of RNA secondary structures that are difficult to predict. Almost all existing methods are based on a single model and return one solution, often missing the real structure. An alternative approach would be to combine different models and return a (small) set of solutions, maximizing its quality and diversity in order to increase the probability that it contains the real structure.'),
(98222, 2018, 'bmc', 'diceR: an R package for class discovery using an ensemble driven approach', 'Chiu, Derek S|Talhouk, Aline', 'Department of Molecular Oncology, BC Cancer Agency, Vancouver, BC, Canada.|Department of Molecular Oncology, BC Cancer Agency, Vancouver, BC, Canada. atalhouk@bccrc.ca.', 'Canada|Canada', 6, 'Algorithms|Cluster Analysis|Databases as Topic|Humans|Software', 'Journal Article', '10.1186/s12859-017-1996-y', '29334888', 'Given a set of features, researchers are often interested in partitioning objects into homogeneous clusters. In health research, cancer research in particular, high-throughput data is collected with the aim of segmenting patients into sub-populations to aid in disease diagnosis, prognosis or response to therapy. Cluster analysis, a class of unsupervised learning techniques, is often used for class discovery. Cluster analysis suffers from some limitations, including the need to select up-front the algorithm to be used as well as the number of clusters to generate, in addition, there may exist several groupings consistent with the data, making it very difficult to validate a final solution. Ensemble clustering is a technique used to mitigate these limitations and facilitate the generalization and reproducibility of findings in new cohorts of patients.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98223, 2018, 'bmc', 'Protein-protein interface hot spots prediction based on a hybrid feature selection strategy', 'Qiao, Yanhua|Xiong, Yi|Gao, Hongyun|Zhu, Xiaolei|Chen, Peng', 'School of Life Sciences, Anhui University, Hefei, Anhui, 230601, China.|State Key Laboratory of Microbial Metabolism, Shanghai JiaoTong University, Shanghai, 200240, China.|Information and Engineering College, Dalian University, Dalian, Liaoning, 116622, China.|School of Life Sciences, Anhui University, Hefei, Anhui, 230601, China. xlzhu_mdl@hotmail.com.|Institute of Health Sciences, Anhui University, Hefei, Anhui, 230601, China. pchen@ahu.edu.cn.', 'China|China|China|China|China', 37, 'Algorithms|Computational Biology|Databases, Protein|Humans|Proteins|Support Vector Machine', 'Journal Article', '10.1186/s12859-018-2009-5', '29334889', 'Hot spots are interface residues that contribute most binding affinity to protein-protein interaction. A compact and relevant feature subset is important for building machine learning methods to predict hot spots on protein-protein interfaces. Although different methods have been used to detect the relevant feature subset from a variety of features related to interface residues, it is still a challenge to detect the optimal feature subset for building the final model.'),
(98224, 2018, 'bmc', 'Tuning iteration space slicing based tiled multi-core code implementing Nussinovs RNA folding', 'Palkowski, Marek|Bielecki, Wlodzimierz', 'West Pomeranian University of Technology, Faculty of Computer Science, Zolnierska 49, Szczecin, 71-210, Poland. mpalkowski@wi.zut.edu.pl.|West Pomeranian University of Technology, Faculty of Computer Science, Zolnierska 49, Szczecin, 71-210, Poland.', 'Poland|Poland', 4, 'Algorithms|Computational Biology|Nucleic Acid Conformation|RNA|RNA Folding|Time Factors', 'Journal Article', '10.1186/s12859-018-2008-6', '29334891', 'RNA folding is an ongoing compute-intensive task of bioinformatics. Parallelization and improving code locality for this kind of algorithms is one of the most relevant areas in computational biology. Fortunately, RNA secondary structure approaches, such as Nussinovs recurrence, involve mathematical operations over affine control loops whose iteration space can be represented by the polyhedral model. This allows us to apply powerful polyhedral compilation techniques based on the transitive closure of dependence graphs to generate parallel tiled code implementing Nussinovs RNA folding. Such techniques are within the iteration space slicing framework - the transitive dependences are applied to the statement instances of interest to produce valid tiles. The main problem at generating parallel tiled code is defining a proper tile size and tile dimension which impact parallelism degree and code locality.'),
(98225, 2018, 'bmc', 'LocText: relation extraction of protein localizations to assist database curation', 'Cejuela, Juan Miguel|Vinchurkar, Shrikant|Goldberg, Tatyana|Prabhu Shankar, Madhukar Sollepura|Baghudana, Ashish|Bojchevski, Aleksandar|Uhlig, Carsten|Ofner, André|Raharja-Liu, Pandu|Jensen, Lars Juhl|Rost, Burkhard', 'Bioinformatics & Computational Biology, Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, Garching, 85748, Germany. loctext@rostlab.org.|Microsoft, Microsoft Development Center Copenhagen, Kanalvej 7, Kongens Lyngby, 2800, Denmark.|Bioinformatics & Computational Biology, Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, Garching, 85748, Germany.|Bioinformatics & Computational Biology, Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, Garching, 85748, Germany.|Department of Computer Science and Information Systems, Birla Institute of Technology and Science K. K. Birla Goa Campus, Zuarinagar, 403726, Goa, India.|Bioinformatics & Computational Biology, Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, Garching, 85748, Germany.|Bioinformatics & Computational Biology, Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, Garching, 85748, Germany.|Bioinformatics & Computational Biology, Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, Garching, 85748, Germany.|Bioinformatics & Computational Biology, Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, Garching, 85748, Germany.|Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, 2200, Denmark. lars.juhl.jensen@cpr.ku.dk.|Bioinformatics & Computational Biology, Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, Garching, 85748, Germany. rost@rostlab.org.', 'Germany|Denmark|Germany|Germany|India|Germany|Germany|Germany|Germany|Denmark|Germany', 0, 'Data Mining|Databases, Protein|Gene Ontology|Humans|Molecular Sequence Annotation|Proteins|Software', 'Journal Article', '10.1186/s12859-018-2021-9', '29343218', 'The subcellular localization of a protein is an important aspect of its function. However, the experimental annotation of locations is not even complete for well-studied model organisms. Text mining might aid database curators to add experimental annotations from the scientific literature. Existing extraction methods have difficulties to distinguish relationships between proteins and cellular locations co-mentioned in the same sentence.'),
(98226, 2018, 'bmc', 'Feature selection for high-dimensional temporal data', 'Tsagris, Michail|Lagani, Vincenzo|Tsamardinos, Ioannis', 'Department of Computer Science, University of Crete, Voutes Campus, Heraklion, 70013, Greece. mtsagris@csd.uoc.gr.|Department of Computer Science, University of Crete, Voutes Campus, Heraklion, 70013, Greece.|Department of Computer Science, University of Crete, Voutes Campus, Heraklion, 70013, Greece.', 'Greece|Greece|Greece', 12, 'Algorithms|Genomics|Linear Models', 'Journal Article', '10.1186/s12859-018-2023-7', '29357817', 'Feature selection is commonly employed for identifying collectively-predictive biomarkers and biosignatures, it facilitates the construction of small statistical models that are easier to verify, visualize, and comprehend while providing insight to the human expert. In this work we extend established constrained-based, feature-selection methods to high-dimensional omics temporal data, where the number of measurements is orders of magnitude larger than the sample size. The extension required the development of conditional independence tests for temporal and/or static variables conditioned on a set of temporal variables.'),
(98227, 2018, 'bmc', 'MutScan: fast detection and visualization of target mutations by scanning FASTQ data', 'Chen, Shifu|Huang, Tanxiao|Wen, Tiexiang|Li, Hong|Xu, Mingyan|Gu, Jia', 'Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. sf.chen@siat.ac.cn.|HaploX Biotechnology, Shenzhen, China.|Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.|HaploX Biotechnology, Shenzhen, China.|HaploX Biotechnology, Shenzhen, China.|Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. jia.gu@siat.ac.cn.', 'China|China|China|China|China|China', 1, 'Algorithms|Circulating Tumor DNA|High-Throughput Nucleotide Sequencing|Humans|Mutation|Neoplasms|Search Engine|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2024-6', '29357822', 'Some types of clinical genetic tests, such as cancer testing using circulating tumor DNA (ctDNA), require sensitive detection of known target mutations. However, conventional next-generation sequencing (NGS) data analysis pipelines typically involve different steps of filtering, which may cause miss-detection of key mutations with low frequencies. Variant validation is also indicated for key mutations detected by bioinformatics pipelines. Typically, this process can be executed using alignment visualization tools such as IGV or GenomeBrowse. However, these tools are too heavy and therefore unsuitable for validating mutations in ultra-deep sequencing data.'),
(98228, 2018, 'bmc', 'Spherical: an iterative workflow for assembling metagenomic datasets', 'Hitch, Thomas C A|Creevey, Christopher J', 'Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University, Aberystwyth, Wales, SY23 3FG, UK. thitch@ukaachen.de.|Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University, Aberystwyth, Wales, SY23 3FG, UK.', 'United Kingdom|United Kingdom', 4, 'Animals|Cecum|Chickens|Groundwater|Humans|Internet|Metagenome|Mouth|User-Computer Interface', 'Journal Article', '10.1186/s12859-018-2028-2', '29361904', 'The consensus emerging from the study of microbiomes is that they are far more complex than previously thought, requiring better assemblies and increasingly deeper sequencing. However, current metagenomic assembly techniques regularly fail to incorporate all, or even the majority in some cases, of the sequence information generated for many microbiomes, negating this effort. This can especially bias the information gathered and the perceived importance of the minor taxa in a microbiome.'),
(98229, 2018, 'bmc', 'Three-dimensional spatial analysis of missense variants in RTEL1 identifies pathogenic variants in patients with Familial Interstitial Pneumonia', 'Sivley, R Michael|Sheehan, Jonathan H|Kropski, Jonathan A|Cogan, Joy|Blackwell, Timothy S|Phillips, John A|Bush, William S|Meiler, Jens|Capra, John A', 'Department of Biomedical Informatics, Vanderbilt University, Nashville, USA.|Department of Biochemistry and Center for Structural Biology, Vanderbilt University, Nashville, USA.|Department of Medicine, Vanderbilt University, Nashville, USA.|Department of Pediatrics, Vanderbilt University, Nashville, USA.|Department of Medicine, Vanderbilt University, Nashville, USA.|Department of Pediatrics, Vanderbilt University, Nashville, USA.|Department of Quantitative and Population Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA.|Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, USA.|Department of Biological Sciences, Vanderbilt Genetics Institute, and Center for Structural Biology, Vanderbilt University, Nashville, USA. tony.capra@vanderbilt.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Area Under Curve|DNA Helicases|Humans|Lung Diseases, Interstitial|Mutation, Missense|Protein Structure, Tertiary|ROC Curve|Spatial Analysis', 'Journal Article', '10.1186/s12859-018-2010-z', '29361909', 'Next-generation sequencing of individuals with genetic diseases often detects candidate rare variants in numerous genes, but determining which are causal remains challenging. We hypothesized that the spatial distribution of missense variants in protein structures contains information about function and pathogenicity that can help prioritize variants of unknown significance (VUS) and elucidate the structural mechanisms leading to disease.'),
(98230, 2018, 'bmc', 'Shrinkage Clustering: a fast and size-constrained clustering algorithm for biomedical applications', 'Hu, Chenyue W|Li, Hanyang|Qutub, Amina A', 'Department of Bioengineering, Rice University, Main Street, Houston, 77030, USA.|Department of Bioengineering, Rice University, Main Street, Houston, 77030, USA.|Department of Bioengineering, Rice University, Main Street, Houston, 77030, USA. aminaq@rice.edu.', 'United States|United States|United States', 1, 'Algorithms|Brain|Breast Neoplasms|Cluster Analysis|Databases, Factual|Female|Gene Expression Regulation|Humans|Neoplasms', 'Journal Article', '10.1186/s12859-018-2022-8', '29361928', 'Many common clustering algorithms require a two-step process that limits their efficiency. The algorithms need to be performed repetitively and need to be implemented together with a model selection criterion. These two steps are needed in order to determine both the number of clusters present in the data and the corresponding cluster memberships. As biomedical datasets increase in size and prevalence, there is a growing need for new methods that are more convenient to implement and are more computationally efficient. In addition, it is often essential to obtain clusters of sufficient sample size to make the clustering result meaningful and interpretable for subsequent analysis.'),
(98231, 2018, 'bmc', 'Deep learning of mutation-gene-drug relations from the literature', 'Lee, Kyubum|Kim, Byounggun|Choi, Yonghwa|Kim, Sunkyu|Shin, Wonho|Lee, Sunwon|Park, Sungjoon|Kim, Seongsoon|Tan, Aik Choon|Kang, Jaewoo', 'Department of Computer Science and Engineering, Korea University, Seoul, South Korea.|Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, South Korea.|Department of Computer Science and Engineering, Korea University, Seoul, South Korea.|Department of Computer Science and Engineering, Korea University, Seoul, South Korea.|Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, South Korea.|Department of Computer Science and Engineering, Korea University, Seoul, South Korea.|Department of Computer Science and Engineering, Korea University, Seoul, South Korea.|Department of Computer Science and Engineering, Korea University, Seoul, South Korea.|Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA. aikchoon.tan@ucdenver.edu.|Department of Computer Science and Engineering, Korea University, Seoul, South Korea. kangj@korea.ac.kr.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 14, 'Antineoplastic Agents|Databases, Factual|Drug Resistance, Neoplasm|Humans|Mutation|Neoplasms|Neural Networks (Computer)|Precision Medicine|Search Engine', 'Journal Article', '10.1186/s12859-018-2029-1', '29368597', 'Molecular biomarkers that can predict drug efficacy in cancer patients are crucial components for the advancement of precision medicine. However, identifying these molecular biomarkers remains a laborious and challenging task. Next-generation sequencing of patients and preclinical models have increasingly led to the identification of novel gene-mutation-drug relations, and these results have been reported and published in the scientific literature.'),
(98232, 2018, 'bmc', 'CONFOLD2: improved contact-driven ab initio protein structure modeling', 'Adhikari, Badri|Cheng, Jianlin', 'Department of Mathematics and Computer Science, University of Missouri-St. Louis, St. Louis, 63121, MO, USA.|Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, MO, USA. chengji@missouri.edu.', 'United States|United States', 0, 'Algorithms|Databases, Protein|Internet|Protein Conformation|Protein Folding|Proteins|User-Computer Interface', 'Journal Article', '10.1186/s12859-018-2032-6', '29370750', 'Contact-guided protein structure prediction methods are becoming more and more successful because of the latest advances in residue-residue contact prediction. To support contact-driven structure prediction, effective tools that can quickly build tertiary structural models of good quality from predicted contacts need to be developed.'),
(98233, 2018, 'bmc', 'Scuba: scalable kernel-based gene prioritization', 'Zampieri, Guido|Tran, Dinh Van|Donini, Michele|Navarin, Nicolò|Aiolli, Fabio|Sperduti, Alessandro|Valle, Giorgio', 'CRIBI Biotechnology Center, University of Padova, viale G. Colombo, 3, Padova, Italy.|Department of Mathematics, University of Padova, via Trieste, 63, Padova, Italy.|Istituto Italiano di Tecnologia, Via Morego, 30, Genoa, Italy.|Department of Mathematics, University of Padova, via Trieste, 63, Padova, Italy.|Department of Mathematics, University of Padova, via Trieste, 63, Padova, Italy.|Department of Mathematics, University of Padova, via Trieste, 63, Padova, Italy.|CRIBI Biotechnology Center, University of Padova, viale G. Colombo, 3, Padova, Italy. giorgio.valle@unipd.it.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 5, 'Algorithms|Databases, Factual|Genome-Wide Association Study|Humans|Internet|User-Computer Interface', 'Journal Article', '10.1186/s12859-018-2025-5', '29370760', 'The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability.'),
(98234, 2018, 'bmc', 'SLALOM, a flexible method for the identification and statistical analysis of overlapping continuous sequence elements in sequence- and time-series data', 'Prytuliak, Roman|Pfeiffer, Friedhelm|Habermann, Bianca Hermine', 'Computational Biology Group, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152, Martinsried, Germany.|Computational Biology Group, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152, Martinsried, Germany.|Computational Biology Group, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152, Martinsried, Germany. bianca.habermann@univ-amu.fr.', 'Germany|Germany|Germany', 0, 'Archaea|Computational Biology|Molecular Sequence Annotation|Open Reading Frames|Proteins', 'Journal Article', '10.1186/s12859-018-2020-x', '29373955', 'Protein or nucleic acid sequences contain a multitude of associated annotations representing continuous sequence elements (CSEs). Comparing these CSEs is needed, whenever we want to match identical annotations or integrate distinctive ones. Currently, there is no ready-to-use software available that provides comprehensive statistical readout for comparing two annotations of the same type with each other, which can be adapted to the application logic of the scientific question.'),
(98235, 2018, 'bmc', 'GenIO: a phenotype-genotype analysis web server for clinical genomics of rare diseases', 'Koile, Daniel|Cordoba, Marta|de Sousa Serro, Maximiliano|Kauffman, Marcelo Andres|Yankilevich, Patricio', 'Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA), CONICET - Partner Institute of the Max Planck Society, Buenos Aires, Argentina.|Consultorio de Neurogenética, Centro Universitario de Neurología y División Neurología, Hospital J.M. Ramos Mejia, Facultad de Medicina, UBA, Buenos Aires, Argentina.|Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA), CONICET - Partner Institute of the Max Planck Society, Buenos Aires, Argentina.|Consultorio de Neurogenética, Centro Universitario de Neurología y División Neurología, Hospital J.M. Ramos Mejia, Facultad de Medicina, UBA, Buenos Aires, Argentina. marcelokauffman@gmail.com.|Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA), CONICET - Partner Institute of the Max Planck Society, Buenos Aires, Argentina. pyankilevich@ibioba-mpsp-conicet.gov.ar.', 'Argentina|Argentina|Argentina|Argentina|Argentina', 0, 'Genetic Association Studies|Genotype|Humans|Internet|Phenotype|Rare Diseases|User-Computer Interface', 'Journal Article', '10.1186/s12859-018-2027-3', '29374474', 'GenIO is a novel web-server, designed to assist clinical genomics researchers and medical doctors in the diagnostic process of rare genetic diseases. The tool identifies the most probable variants causing a rare disease, using the genomic and clinical information provided by a medical practitioner. Variants identified in a whole-genome, whole-exome or target sequencing studies are annotated, classified and filtered by clinical significance. Candidate genes associated with the patients symptoms, suspected disease and complementary findings are identified to obtain a small manageable number of the most probable recessive and dominant candidate gene variants associated with the rare disease case. Additionally, following the American College of Medical Genetics and Genomics and the Association of Molecular Pathology (ACMG-AMP) guidelines and recommendations, all potentially pathogenic variants that might be contributing to disease and secondary findings are identified.'),
(98236, 2018, 'bmc', 'Large scale analysis of protein conformational transitions from aqueous to non-aqueous media', 'Rueda, Ana Julia Velez|Monzon, Alexander Miguel|Ardanaz, Sebastián M|Iglesias, Luis E|Parisi, Gustavo', 'Departamento de Ciencia y Tecnología, CONICET, Universidad Nacional de Quilmes, Roque Sáenz Peña 352, B1876BXD, Bernal, Provincia de Buenos Aires, Argentina.|Departamento de Ciencia y Tecnología, CONICET, Universidad Nacional de Quilmes, Roque Sáenz Peña 352, B1876BXD, Bernal, Provincia de Buenos Aires, Argentina.|Laboratorio de Biocatálisis y Biotransformaciones, Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Roque Sáenz Peña 352, B1876BXD, Bernal, Provincia de Buenos Aires, Argentina.|Laboratorio de Biocatálisis y Biotransformaciones, Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Roque Sáenz Peña 352, B1876BXD, Bernal, Provincia de Buenos Aires, Argentina.|Departamento de Ciencia y Tecnología, CONICET, Universidad Nacional de Quilmes, Roque Sáenz Peña 352, B1876BXD, Bernal, Provincia de Buenos Aires, Argentina. gusparisi@gmail.com.', 'Argentina|Argentina|Argentina|Argentina|Argentina', 3, 'Biocatalysis|Computational Biology|Databases, Protein|Humans|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins|Solvents|Water|ras Proteins', 'Journal Article', '10.1186/s12859-018-2044-2', '29382320', 'Biocatalysis in organic solvents is nowadays a common practice with a large potential in Biotechnology. Several studies report that proteins which are co-crystallized or soaked in organic solvents preserve their fold integrity showing almost identical arrangements when compared to their aqueous forms. However, it is well established that the catalytic activity of proteins in organic solvents is much lower than in water. In order to explain this diminished activity and to further characterize the behaviour of proteins in non-aqueous environments, we performed a large-scale analysis (1737 proteins) of the conformational diversity of proteins crystallized in aqueous and co-crystallized or soaked in non-aqueous media.'),
(98237, 2018, 'bmc', 'Inferring synteny between genome assemblies: a systematic evaluation', 'Liu, Dang|Hunt, Martin|Tsai, Isheng J', 'Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei, Taiwan.|Nuffield Department of Clinical Medicine, Experimental Medicine Division, John Radcliffe Hospital, University of Oxford, Oxford, OX1 1NF, UK.|Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei, Taiwan. ijtsai@gate.sinica.edu.tw.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Animals|Caenorhabditis elegans|Genome|Genomics|Nematoda', 'Journal Article', '10.1186/s12859-018-2026-4', '29382321', 'Genome assemblies across all domains of life are being produced routinely. Initial analysis of a new genome usually includes annotation and comparative genomics. Synteny provides a framework in which conservation of homologous genes and gene order is identified between genomes of different species. The availability of human and mouse genomes paved the way for algorithm development in large-scale synteny mapping, which eventually became an integral part of comparative genomics. Synteny analysis is regularly performed on assembled sequences that are fragmented, neglecting the fact that most methods were developed using complete genomes. It is unknown to what extent draft assemblies lead to errors in such analysis.'),
(98238, 2018, 'bmc', 'Germline contamination and leakage in whole genome somatic single nucleotide variant detection', 'Sendorek, Dorota H|Caloian, Cristian|Ellrott, Kyle|Bare, J Christopher|Yamaguchi, Takafumi N|Ewing, Adam D|Houlahan, Kathleen E|Norman, Thea C|Margolin, Adam A|Stuart, Joshua M|Boutros, Paul C', 'Informatics & Biocomputing Program, Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, Ontario, M5G 0A3, Canada.|Informatics & Biocomputing Program, Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, Ontario, M5G 0A3, Canada.|Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.|Sage Bionetworks, Seattle, WA, USA.|Informatics & Biocomputing Program, Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, Ontario, M5G 0A3, Canada.|Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.|Informatics & Biocomputing Program, Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, Ontario, M5G 0A3, Canada.|Sage Bionetworks, Seattle, WA, USA.|Sage Bionetworks, Seattle, WA, USA.|Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.|Informatics & Biocomputing Program, Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, Ontario, M5G 0A3, Canada. paul.boutros@oicr.on.ca.', 'Canada|Canada|United States|United States|Canada|United States|Canada|United States|United States|United States|Canada', 2, 'Algorithms|Genome, Human|Germ Cells|Humans|Internet|Neoplasms|Polymorphism, Single Nucleotide|User-Computer Interface|Whole Genome Sequencing', 'Journal Article', '10.1186/s12859-018-2046-0', '29385983', 'The clinical sequencing of cancer genomes to personalize therapy is becoming routine across the world. However, concerns over patient re-identification from these data lead to questions about how tightly access should be controlled. It is not thought to be possible to re-identify patients from somatic variant data. However, somatic variant detection pipelines can mistakenly identify germline variants as somatic ones, a process called germline leakage. The rate of germline leakage across different somatic variant detection pipelines is not well-understood, and it is uncertain whether or not somatic variant calls should be considered re-identifiable. To fill this gap, we quantified germline leakage across 259 sets of whole-genome somatic single nucleotide variant (SNVs) predictions made by 21 teams as part of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge.'),
(98239, 2018, 'bmc', 'Grid-based prediction of torsion angle probabilities of protein backbone and its application to discrimination of protein intrinsic disorder regions and selection of model structures', 'Gao, Jianzhao|Yang, Yuedong|Zhou, Yaoqi', 'School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, Peoples Republic of China.|School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510000, Peoples Republic of China. yangyd25@mail.sysu.edu.cn.|Institute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Dr, Southport, QLD, 4222, Australia. yaoqi.zhou@griffith.edu.au.', 'China|China|Australia', 0, 'Area Under Curve|Neural Networks (Computer)|Probability|Protein Domains|Protein Structure, Tertiary|Proteins|ROC Curve|User-Computer Interface', 'Journal Article', '10.1186/s12859-018-2031-7', '29390958', '-'),
(98240, 2018, 'bmc', 'Cross-linking BioThings APIs through JSON-LD to facilitate knowledge exploration', 'Xin, Jiwen|Afrasiabi, Cyrus|Lelong, Sebastien|Adesara, Julee|Tsueng, Ginger|Su, Andrew I|Wu, Chunlei', 'Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA. cwu@scripps.edu.', 'United States|United States|United States|United States|United States|United States|United States', 6, 'Biological Science Disciplines|Databases, Factual|Humans|Information Storage and Retrieval|Internet|Software', 'Journal Article', '10.1186/s12859-018-2041-5', '29390967', 'Application Programming Interfaces (APIs) are now widely used to distribute biological data. And many popular biological APIs developed by many different research teams have adopted Javascript Object Notation (JSON) as their primary data format. While usage of a common data format offers significant advantages, that alone is not sufficient for rich integrative queries across APIs.'),
(98241, 2018, 'bmc', 'Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus', 'Condon, David E|Tran, Phu V|Lien, Yu-Chin|Schug, Jonathan|Georgieff, Michael K|Simmons, Rebecca A|Won, Kyoung-Jae', 'Department of Genetics, The Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.|Department of Pediatrics, University of Minnesota, 2450 Riverside Avenue, Minneapolis, MN, 55454, USA.|Center for Research on Reproduction and Womens Health, University of Pennsylvania, 421 Curie Blvd, Philadelphia, PA, 19104, USA.|Department of Genetics, The Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.|Department of Pediatrics, University of Minnesota, 2450 Riverside Avenue, Minneapolis, MN, 55454, USA.|Center for Research on Reproduction and Womens Health, University of Pennsylvania, 421 Curie Blvd, Philadelphia, PA, 19104, USA.|Department of Genetics, The Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. kyoung.won@bric.ku.dk.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Animals, Newborn|CpG Islands|DNA Methylation|Databases, Genetic|Female|Fetus|Hippocampus|Iron|Molecular Sequence Annotation|Rats, Sprague-Dawley|Software|Time Factors', 'Journal Article', '10.1186/s12859-018-2037-1', '29402210', 'Identification of differentially methylated regions (DMRs) is the initial step towards the study of DNA methylation-mediated gene regulation. Previous approaches to call DMRs suffer from false prediction, use extreme resources, and/or require library installation and input conversion.'),
(98242, 2018, 'bmc', 'Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine', 'Chiu, Billy|Pyysalo, Sampo|Vulixc4x87, Ivan|Korhonen, Anna', 'Language Technology Laboratory, DTAL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK. hwc25@cam.ac.uk.|Language Technology Laboratory, DTAL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK.|Language Technology Laboratory, DTAL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK.|Language Technology Laboratory, DTAL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, 'Biomedical Technology|Databases as Topic|Humans|Language|Natural Language Processing|Semantics|Software', 'Journal Article', '10.1186/s12859-018-2039-z', '29402212', 'Word representations support a variety of Natural Language Processing (NLP) tasks. The quality of these representations is typically assessed by comparing the distances in the induced vector spaces against human similarity judgements. Whereas comprehensive evaluation resources have recently been developed for the general domain, similar resources for biomedicine currently suffer from the lack of coverage, both in terms of word types included and with respect to the semantic distinctions. Notably, verbs have been excluded, although they are essential for the interpretation of biomedical language. Further, current resources do not discern between semantic similarity and semantic relatedness, although this has been proven as an important predictor of the usefulness of word representations and their performance in downstream applications.'),
(98243, 2018, 'bmc', 'xenoGI: reconstructing the history of genomic island insertions in clades of closely related bacteria', 'Bush, Eliot C|Clark, Anne E|DeRanek, Carissa A|Eng, Alexander|Forman, Juliet|Heath, Kevin|Lee, Alexander B|Stoebel, Daniel M|Wang, Zunyan|Wilber, Matthew|Wu, Helen', 'Department of Biology, Harvey Mudd College, 301 Platt Blvd., Claremont, 91711, CA, USA. bush@hmc.edu.|Department of Biology, Harvey Mudd College, 301 Platt Blvd., Claremont, 91711, CA, USA.|Department of Biology, Harvey Mudd College, 301 Platt Blvd., Claremont, 91711, CA, USA.|Department of Biology, Harvey Mudd College, 301 Platt Blvd., Claremont, 91711, CA, USA.|Department of Biology, Harvey Mudd College, 301 Platt Blvd., Claremont, 91711, CA, USA.|Department of Biology, Harvey Mudd College, 301 Platt Blvd., Claremont, 91711, CA, USA.|Department of Biology, Harvey Mudd College, 301 Platt Blvd., Claremont, 91711, CA, USA.|Department of Biology, Harvey Mudd College, 301 Platt Blvd., Claremont, 91711, CA, USA.|Department of Biology, Harvey Mudd College, 301 Platt Blvd., Claremont, 91711, CA, USA.|Department of Biology, Harvey Mudd College, 301 Platt Blvd., Claremont, 91711, CA, USA.|Department of Biology, Harvey Mudd College, 301 Platt Blvd., Claremont, 91711, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Bacteria|Computer Simulation|Evolution, Molecular|Genome, Bacterial|Genomic Islands|Phylogeny|Reproducibility of Results|Software|Time Factors', 'Journal Article', '10.1186/s12859-018-2038-0', '29402213', 'Genomic islands play an important role in microbial genome evolution, providing a mechanism for strains to adapt to new ecological conditions. A variety of computational methods, both genome-composition based and comparative, have been developed to identify them. Some of these methods are explicitly designed to work in single strains, while others make use of multiple strains. In general, existing methods do not identify islands in the context of the phylogeny in which they evolved. Even multiple strain approaches are best suited to identifying genomic islands that are present in one strain but absent in others. They do not automatically recognize islands which are shared between some strains in the clade or determine the branch on which these islands inserted within the phylogenetic tree.'),
(98244, 2018, 'bmc', 'G4PromFinder: an algorithm for predicting transcription promoters in GC-rich bacterial genomes based on AT-rich elements and G-quadruplex motifs', 'Di Salvo, Marco|Pinatel, Eva|Talxc3xa0, Adelfia|Fondi, Marco|Peano, Clelia|Alifano, Pietro', 'Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy.|Institute of Biomedical Technologies National Research Council, Milan, Segrate, Italy.|Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy.|Department of Biology, University of Florence, Florence, Italy.|Institute of Genetic and Biomedical Research (IRGB), UOS of Milan, National Research Council, Milan, Italy.|Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy. pietro.alifano@unisalento.it.', 'Italy|Italy|Italy|Italy|Italy|Italy', 8, 'Algorithms|Bacteria|G-Quadruplexes|Genome, Bacterial|Nucleotide Motifs|Promoter Regions, Genetic|Pseudomonas aeruginosa|Streptomyces coelicolor', 'Journal Article', '10.1186/s12859-018-2049-x', '29409441', '-'),
(98245, 2018, 'bmc', 'Semantic annotation of consumer health questions', 'Kilicoglu, Halil|Ben Abacha, Asma|Mrabet, Yassine|Shooshan, Sonya E|Rodriguez, Laritza|Masterton, Kate|Demner-Fushman, Dina', 'Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, 8600 Rockville Pike, Bethesda, MD, USA. kilicogluh@mail.nih.gov.|Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, 8600 Rockville Pike, Bethesda, MD, USA.|Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, 8600 Rockville Pike, Bethesda, MD, USA.|Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, 8600 Rockville Pike, Bethesda, MD, USA.|Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, 8600 Rockville Pike, Bethesda, MD, USA.|Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, 8600 Rockville Pike, Bethesda, MD, USA.|Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, 8600 Rockville Pike, Bethesda, MD, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Electronic Mail|Health Status|Humans|Semantics|Surveys and Questionnaires|Web Browser', 'Journal Article', '10.1186/s12859-018-2045-1', '29409442', 'Consumers increasingly use online resources for their health information needs. While current search engines can address these needs to some extent, they generally do not take into account that most health information needs are complex and can only fully be expressed in natural language. Consumer health question answering (QA) systems aim to fill this gap. A major challenge in developing consumer health QA systems is extracting relevant semantic content from the natural language questions (question understanding). To develop effective question understanding tools, question corpora semantically annotated for relevant question elements are needed. In this paper, we present a two-part consumer health question corpus annotated with several semantic categories: named entities, question triggers/types, question frames, and question topic. The first part (CHQA-email) consists of relatively long email requests received by the U.S. National Library of Medicine (NLM) customer service, while the second part (CHQA-web) consists of shorter questions posed to MedlinePlus search engine as queries. Each question has been annotated by two annotators. The annotation methodology is largely the same between the two parts of the corpus, however, we also explain and justify the differences between them. Additionally, we provide information about corpus characteristics, inter-annotator agreement, and our attempts to measure annotation confidence in the absence of adjudication of annotations.'),
(98246, 2018, 'bmc', 'Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction', 'Daberdaku, Sebastian|Ferrari, Carlo', 'Department of Information Engineering, University of Padova, via Gradenigo 6/A, Padova, 35131, Italy. sebastian.daberdaku@dei.unipd.it.|Department of Information Engineering, University of Padova, via Gradenigo 6/A, Padova, 35131, Italy.', 'Italy|Italy', 14, 'Amino Acids|Area Under Curve|Binding Sites|Protein Interaction Domains and Motifs|Proteins|ROC Curve|Support Vector Machine', 'Journal Article', '10.1186/s12859-018-2043-3', '29409446', 'The correct determination of protein-protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task.'),
(98247, 2018, 'bmc', 'The research on gene-disease association based on text-mining of PubMed', 'Zhou, Jie|Fu, Bo-Quan', 'Guangdong Key Laboratory of Computer Network, School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China. jiezhou@scut.edu.cn.|Guangdong Key Laboratory of Computer Network, School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China.', 'China|China', 0, 'Algorithms|Data Mining|Databases, Factual|Genetic Association Studies|Genetic Predisposition to Disease|Humans|Medical Subject Headings|PubMed|Research', 'Journal Article', '10.1186/s12859-018-2048-y', '29415654', 'The associations between genes and diseases are of critical significance in aspects of prevention, diagnosis and treatment. Although gene-disease relationships have been investigated extensively, much of the underpinnings of these associations are yet to be elucidated.'),
(98248, 2018, 'bmc', 'OVAS: an open-source variant analysis suite with inheritance modelling', 'Mozere, Monika|Tekman, Mehmet|Kari, Jameela|Bockenhauer, Detlef|Kleta, Robert|Stanescu, Horia', 'Division of Medicine, University College London, London, NW3 2PF, UK.|Division of Medicine, University College London, London, NW3 2PF, UK.|Pediatric Nephrology Center of Excellence and Pediatric Department, Faculty of Medicine, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia.|Division of Medicine, University College London, London, NW3 2PF, UK.|Division of Medicine, University College London, London, NW3 2PF, UK. r.kleta@ucl.ac.uk.|Division of Medicine, University College London, London, NW3 2PF, UK.', 'United Kingdom|United Kingdom|Saudi Arabia|United Kingdom|United Kingdom|United Kingdom', 0, 'Databases, Genetic|Genetic Variation|Humans|Inheritance Patterns|Internet|Models, Genetic|Molecular Sequence Annotation|Mutation|Software|User-Computer Interface|Web Browser', 'Journal Article', '10.1186/s12859-018-2030-8', '29422027', 'The advent of modern high-throughput genetics continually broadens the gap between the rising volume of sequencing data, and the tools required to process them. The need to pinpoint a small subset of functionally important variants has now shifted towards identifying the critical differences between normal variants and disease-causing ones. The ever-increasing reliance on cloud-based services for sequence analysis and the non-transparent methods they utilize has prompted the need for more in-situ services that can provide a safer and more accessible environment to process patient data, especially in circumstances where continuous internet usage is limited.'),
(98249, 2018, 'bmc', 'Prioritizing disease genes with an improved dual label propagation framework', 'Zhang, Yaogong|Liu, Jiahui|Liu, Xiaohu|Fan, Xin|Hong, Yuxiang|Wang, Yuan|Huang, YaLou|Xie, MaoQiang', 'College of Software, Nankai University, TianJin, 300350, China.|College of Software, Nankai University, TianJin, 300350, China.|College of Software, Nankai University, TianJin, 300350, China.|College of Software, Nankai University, TianJin, 300350, China.|College of Software, Nankai University, TianJin, 300350, China.|School of Computer Science and Information Engineering, Tianjin University of Science and Technology, TianJin, 300222, China.|College of Software, Nankai University, TianJin, 300350, China.|College of Software, Nankai University, TianJin, 300350, China. xiemq@nankai.edu.cn.', 'China|China|China|China|China|China|China|China', 4, 'Algorithms|Area Under Curve|Computational Biology|Disease|Humans|Parkinson Disease|Phenotype|Protein Interaction Maps|Statistics as Topic', 'Journal Article', '10.1186/s12859-018-2040-6', '29422030', 'Prioritizing disease genes is trying to identify potential disease causing genes for a given phenotype, which can be applied to reveal the inherited basis of human diseases and facilitate drug development. Our motivation is inspired by label propagation algorithm and the false positive protein-protein interactions that exist in the dataset. To the best of our knowledge, the false positive protein-protein interactions have not been considered before in disease gene prioritization. Label propagation has been successfully applied to prioritize disease causing genes in previous network-based methods. These network-based methods use basic label propagation, i.e. random walk, on networks to prioritize disease genes in different ways. However, all these methods can not deal with the situation in which plenty false positive protein-protein interactions exist in the dataset, because the PPI network is used as a fixed input in previous methods. This important characteristic of data source may cause a large deviation in results.'),
(98250, 2018, 'bmc', 'FMLRC: Hybrid long read error correction using an FM-index', 'Wang, Jeremy R|Holt, James|McMillan, Leonard|Jones, Corbin D', 'Department of Genetics, University of North Carolina at Chapel Hill, CB 3280, 3144 Genome Sciences Building, 250 Bell Tower Dr, Chapel Hill, 27599, NC, USA. jeremy_wang@med.unc.edu.|Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Department of Biology and Integrative Program for Biological and Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Databases, Genetic|Genome, Fungal|Saccharomyces cerevisiae|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2051-3', '29426289', 'Long read sequencing is changing the landscape of genomic research, especially de novo assembly. Despite the high error rate inherent to long read technologies, increased read lengths dramatically improve the continuity and accuracy of genome assemblies. However, the cost and throughput of these technologies limits their application to complex genomes. One solution is to decrease the cost and time to assemble novel genomes by leveraging hybrid assemblies that use long reads for scaffolding and short reads for accuracy.'),
(98251, 2018, 'bmc', 'Antigenic cartography of H1N1 influenza viruses using sequence-based antigenic distance calculation', 'Anderson, Christopher S|McCall, Patrick R|Stern, Harry A|Yang, Hongmei|Topham, David J', 'New York Influenza Center of Excellence at David Smith Center for Immunology and Vaccine Biology, Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA.|New York Influenza Center of Excellence at David Smith Center for Immunology and Vaccine Biology, Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA.|Center for Integrated Research Computing, University of Rochester, Rochester, NY, USA.|Department of Biostatistics and Computational Biology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA.|New York Influenza Center of Excellence at David Smith Center for Immunology and Vaccine Biology, Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA. David_Topham@URMC.rochester.edu.', 'United States|United States|United States|United States|United States', 11, 'Algorithms|Amino Acid Sequence|Antigens, Viral|Epitope Mapping|Epitopes|Hemagglutinin Glycoproteins, Influenza Virus|Humans|Influenza A Virus, H1N1 Subtype|Reproducibility of Results', 'Journal Article', '10.1186/s12859-018-2042-4', '29433425', 'The ease at which influenza virus sequence data can be used to estimate antigenic relationships between strains and the existence of databases containing sequence data for hundreds of thousands influenza strains make sequence-based antigenic distance estimates an attractive approach to researchers. Antigenic mismatch between circulating strains and vaccine strains results in significantly decreased vaccine effectiveness. Furthermore, antigenic relatedness between the vaccine strain and the strains an individual was originally primed with can affect the cross-reactivity of the antibody response. Thus, understanding the antigenic relationships between influenza viruses that have circulated is important to both vaccinologists and immunologists.');
