INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97434, 2016, 'bmc', 'A corpus for plant-chemical relationships in the biomedical domain', 'Choi, Wonjun|Kim, Baeksoo|Cho, Hyejin|Lee, Doheon|Lee, Hyunju', 'School of Information and Communications, Gwangju Institute of Science and Technology, Chemdangwagi-ro, Gwangju, Republic of Korea.|School of Information and Communications, Gwangju Institute of Science and Technology, Chemdangwagi-ro, Gwangju, Republic of Korea.|School of Information and Communications, Gwangju Institute of Science and Technology, Chemdangwagi-ro, Gwangju, Republic of Korea.|Department of Bio and Brain Engineering, KAIST, Yuseong-gu, Daejeon, Republic of Korea.|School of Information and Communications, Gwangju Institute of Science and Technology, Chemdangwagi-ro, Gwangju, Republic of Korea. hyunjulee@gist.ac.kr.', '-|-|-|-|-', 2, 'Data Mining|Natural Language Processing|Phytochemicals|Plants|PubMed', 'Journal Article', '10.1186/s12859-016-1249-5', '27650402', 'Plants are natural products that humans consume in various ways including food and medicine. They have a long empirical history of treating diseases with relatively few side effects. Based on these strengths, many studies have been performed to verify the effectiveness of plants in treating diseases. It is crucial to understand the chemicals contained in plants because these chemicals can regulate activities of proteins that are key factors in causing diseases. With the accumulation of a large volume of biomedical literature in various databases such as PubMed, it is possible to automatically extract relationships between plants and chemicals in a large-scale way if we apply a text mining approach. A cornerstone of achieving this task is a corpus of relationships between plants and chemicals.'),
(97435, 2016, 'bmc', 'A covert authentication and security solution for GMOs', 'Mueller, Siguna|Jafari, Farhad|Roth, Don', 'Department of Molecular Biology, University of Wyoming, 1000 E. University Ave, Laramie, WY, 82071, USA.|Department of Mathematics, University of Wyoming, 1000 E. University Ave, Laramie, WY, 82071, USA.|Department of Molecular Biology, University of Wyoming, 1000 E. University Ave, Laramie, WY, 82071, USA. RothDon@uwyo.edu.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1186/s12859-016-1256-6', '27653779', 'Proliferation and expansion of security risks necessitates new measures to ensure authenticity and validation of GMOs. Watermarking and other cryptographic methods are available which conceal and recover the original signature, but in the process reveal the authentication information. In many scenarios watermarking and standard cryptographic methods are necessary but not sufficient and new, more advanced, cryptographic protocols are necessary.'),
(97436, 2016, 'bmc', 'solveME: fast and reliable solution of nonlinear ME models', 'Yang, Laurence|Ma, Ding|Ebrahim, Ali|Lloyd, Colton J|Saunders, Michael A|Palsson, Bernhard O', 'Department of Bioengineering, University of California at San Diego, La Jolla, 92093, CA, USA.|Department of Management Science and Engineering, Stanford University, Stanford, 94305, CA, USA.|Department of Bioengineering, University of California at San Diego, La Jolla, 92093, CA, USA.|Department of Bioengineering, University of California at San Diego, La Jolla, 92093, CA, USA.|Department of Management Science and Engineering, Stanford University, Stanford, 94305, CA, USA.|Department of Bioengineering, University of California at San Diego, La Jolla, 92093, CA, USA. palsson@ucsd.edu.', 'United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1186/s12859-016-1240-1', '27659412', 'Genome-scale models of metabolism and macromolecular expression (ME) significantly expand the scope and predictive capabilities of constraint-based modeling. ME models present considerable computational challenges: they are much (>30 times) larger than corresponding metabolic reconstructions (M models), are multiscale, and growth maximization is a nonlinear programming (NLP) problem, mainly due to macromolecule dilution constraints.'),
(97437, 2016, 'bmc', 'Automatic classification of registered clinical trials towards the Global Burden of Diseases taxonomy of diseases and injuries', 'Atal, Ignacio|Zeitoun, Jean-David|Névéol, Aurélie|Ravaud, Philippe|Porcher, Raphaël|Trinquart, Ludovic', 'Centre dÉpidémiologie Clinique, Hôpital Hôtel-Dieu, Paris, France. ignacio.atal-ext@aphp.fr.|Centre dÉpidémiologie Clinique, Hôpital Hôtel-Dieu, Paris, France.|LIMSI, CNRS UPR 3251, Université Paris-Saclay, Orsay, France.|Centre dÉpidémiologie Clinique, Hôpital Hôtel-Dieu, Paris, France.|Centre dÉpidémiologie Clinique, Hôpital Hôtel-Dieu, Paris, France.|Centre dÉpidémiologie Clinique, Hôpital Hôtel-Dieu, Paris, France.', 'France|France|France|France|France|France', 3, '-', 'Journal Article', '10.1186/s12859-016-1247-7', '27659604', 'Clinical trial registries may allow for producing a global mapping of health research. However, health conditions are not described with standardized taxonomies in registries. Previous work analyzed clinical trial registries to improve the retrieval of relevant clinical trials for patients. However, no previous work has classified clinical trials across diseases using a standardized taxonomy allowing a comparison between global health research and global burden across diseases. We developed a knowledge-based classifier of health conditions studied in registered clinical trials towards categories of diseases and injuries from the Global Burden of Diseases (GBD) 2010 study. The classifier relies on the UMLS® knowledge source (Unified Medical Language System®) and on heuristic algorithms for parsing data. It maps trial records to a 28-class grouping of the GBD categories by automatically extracting UMLS concepts from text fields and by projecting concepts between medical terminologies. The classifier allows deriving pathways between the clinical trial record and candidate GBD categories using natural language processing and links between knowledge sources, and selects the relevant GBD classification based on rules of prioritization across the pathways found. We compared automatic and manual classifications for an external test set of 2,763 trials. We automatically classified 109,603 interventional trials registered before February 2014 at WHO ICTRP.'),
(97438, 2016, 'bmc', 'MicroScope: ChIP-seq and RNA-seq software analysis suite for gene expression heatmaps', 'Khomtchouk, Bohdan B|Hennessy, James R|Wahlestedt, Claes', 'Center for Therapeutic Innovation and Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, 1120 NW 14th ST, Miami, 33136, FL, USA. b.khomtchouk@med.miami.edu.|Department of Mathematics, University of Miami, 1365 Memorial Drive, Coral Gables, 33146, FL, USA.|Center for Therapeutic Innovation and Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, 1120 NW 14th ST, Miami, 33136, FL, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1186/s12859-016-1260-x', '27659774', 'Heatmaps are an indispensible visualization tool for examining large-scale snapshots of genomic activity across various types of next-generation sequencing datasets. However, traditional heatmap software do not typically offer multi-scale insight across multiple layers of genomic analysis (e.g., differential expression analysis, principal component analysis, gene ontology analysis, and network analysis) or multiple types of next-generation sequencing datasets (e.g., ChIP-seq and RNA-seq). As such, it is natural to want to interact with a heatmaps contents using an extensive set of integrated analysis tools applicable to a broad array of genomic data types.'),
(97439, 2016, 'bmc', 'rapidGSEA: Speeding up gene set enrichment analysis on multi-core CPUs and CUDA-enabled GPUs', 'Hundt, Christian|Hildebrandt, Andreas|Schmidt, Bertil', 'Department of Computer Science, Johannes Gutenberg University, Staudingerweg 9, Mainz, 55128, Germany. hundt@uni-mainz.de.|Department of Computer Science, Johannes Gutenberg University, Staudingerweg 9, Mainz, 55128, Germany.|Department of Computer Science, Johannes Gutenberg University, Staudingerweg 9, Mainz, 55128, Germany.', 'Germany|Germany|Germany', 1, '-', 'Journal Article', '10.1186/s12859-016-1244-x', '27663265', 'Gene Set Enrichment Analysis (GSEA) is a popular method to reveal significant dependencies between predefined sets of gene symbols and observed phenotypes by evaluating the deviation of gene expression values between cases and controls. An established measure of inter-class deviation, the enrichment score, is usually computed using a weighted running sum statistic over the whole set of gene symbols. Due to the lack of analytic expressions the significance of enrichment scores is determined using a non-parametric estimation of their null distribution by permuting the phenotype labels of the probed patients. Accordingly, GSEA is a time-consuming task due to the large number of required permutations to accurately estimate the nominal p-value - a circumstance that is even more pronounced during multiple hypothesis testing since its estimate is lower-bounded by the inverse number of samples in permutation space.'),
(97440, 2016, 'bmc', 'Combinatorial identification of DNA methylation patterns over age in the human brain', 'Torabi Moghadam, Behrooz|Dabrowski, Michal|Kaminska, Bozena|Grabherr, Manfred G|Komorowski, Jan', 'Department of Cell and Molecular Biology, Computational and Systems Biology, Uppsala University, Uppsala, Sweden.|Laboratory of Bioinformatics, Neurobiology Center, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland.|Laboratory of Molecular Neurobiology, Neurobiology Center, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland.|Department of Medical Biochemistry and Microbiology/BILS, Genomics, Uppsala University, Uppsala, Sweden.|Department of Cell and Molecular Biology, Computational and Systems Biology, Uppsala University, Uppsala, Sweden. jan.komorowski@icm.uu.se.', 'Sweden|Poland|Poland|Sweden|Sweden', 0, '-', 'Journal Article', '10.1186/s12859-016-1259-3', '27663458', 'DNA methylation plays a key role in developmental processes, which is reflected in changing methylation patterns at specific CpG sites over the lifetime of an individual. The underlying mechanisms are complex and possibly affect multiple genes or entire pathways.'),
(97441, 2016, 'bmc', 'General continuous-time Markov model of sequence evolution via insertions/deletions: local alignment probability computation', 'Ezawa, Kiyoshi', 'Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, 820-8502, Japan. kezawa.ezawa3@gmail.com.', 'Japan', 3, '-', 'Journal Article', '10.1186/s12859-016-1167-6', '27677569', 'Insertions and deletions (indels) account for more nucleotide differences between two related DNA sequences than substitutions do, and thus it is imperative to develop a method to reliably calculate the occurrence probabilities of sequence alignments via evolutionary processes on an entire sequence. Previously, we presented a perturbative formulation that facilitates the ab initio calculation of alignment probabilities under a continuous-time Markov model, which describes the stochastic evolution of an entire sequence via indels with quite general rate parameters. And we demonstrated that, under some conditions, the ab initio probability of an alignment can be factorized into the product of an overall factor and contributions from regions (or local alignments) delimited by gapless columns.'),
(97442, 2016, 'bmc', 'Predicting protein-protein interactions via multivariate mutual information of protein sequences', 'Ding, Yijie|Tang, Jijun|Guo, Fei', 'School of Computer Science and Technology, Tianjin University, No.135, Yaguan Road, Tianjin Haihe Education Park, Tianjin, Peoples Republic of China.|School of Computer Science and Technology, Tianjin University, No.135, Yaguan Road, Tianjin Haihe Education Park, Tianjin, Peoples Republic of China.|School of Computer Science and Technology, Tianjin University, No.135, Yaguan Road, Tianjin Haihe Education Park, Tianjin, Peoples Republic of China. fguo@tju.edu.cn.', 'China|China|China', 65, '-', 'Journal Article', '10.1186/s12859-016-1253-9', '27677692', 'Protein-protein interactions (PPIs) are central to a lot of biological processes. Many algorithms and methods have been developed to predict PPIs and protein interaction networks. However, the application of most existing methods is limited since they are difficult to compute and rely on a large number of homologous proteins and interaction marks of protein partners. In this paper, we propose a novel sequence-based approach with multivariate mutual information (MMI) of protein feature representation, for predicting PPIs via Random Forest (RF).'),
(97443, 2016, 'bmc', 'Representing and decomposing genomic structural variants as balanced integer flows on sequence graphs', 'Zerbino, Daniel R|Ballinger, Tracy|Paten, Benedict|Hickey, Glenn|Haussler, David', 'European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, UK. zerbino@ebi.ac.uk.|European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, UK.|Center for Biomolecular Sciences and Engineering, CBSE/ITI, UC Santa Cruz, 1156 High St, Santa Cruz, 95064, CA, USA.|Center for Biomolecular Sciences and Engineering, CBSE/ITI, UC Santa Cruz, 1156 High St, Santa Cruz, 95064, CA, USA.|Center for Biomolecular Sciences and Engineering, CBSE/ITI, UC Santa Cruz, 1156 High St, Santa Cruz, 95064, CA, USA.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1186/s12859-016-1258-4', '27687569', 'The study of genomic variation has provided key insights into the functional role of mutations. Predominantly, studies have focused on single nucleotide variants (SNV), which are relatively easy to detect and can be described with rich mathematical models. However, it has been observed that genomes are highly plastic, and that whole regions can be moved, removed or duplicated in bulk. These structural variants (SV) have been shown to have significant impact on phenotype, but their study has been held back by the combinatorial complexity of the underlying models.'),
(97444, 2016, 'bmc', 'SAMSA: a comprehensive metatranscriptome analysis pipeline', 'Westreich, Samuel T|Korf, Ian|Mills, David A|Lemay, Danielle G', 'Department of Molecular and Cellular Biology, University of California - Davis, Davis, CA, USA.|Department of Molecular and Cellular Biology, University of California - Davis, Davis, CA, USA.|Department of Food Science and Technology, University of California - Davis, Davis, CA, USA.|Genome Center, University of California - Davis, Davis, CA, USA. dglemay@ucdavis.edu.', 'United States|United States|United States|United States', 24, '-', 'Journal Article', '10.1186/s12859-016-1270-8', '27687690', 'Although metatranscriptomics-the study of diverse microbial population activity based on RNA-seq data-is rapidly growing in popularity, there are limited options for biologists to analyze this type of data. Current approaches for processing metatranscriptomes rely on restricted databases and a dedicated computing cluster, or metagenome-based approaches that have not been fully evaluated for processing metatranscriptomic datasets. We created a new bioinformatics pipeline, designed specifically for metatranscriptome dataset analysis, which runs in conjunction with Metagenome-RAST (MG-RAST) servers. Designed for use by researchers with relatively little bioinformatics experience, SAMSA offers a breakdown of metatranscriptome transcription activity levels by organism or transcript function, and is fully open source. We used this new tool to evaluate best practices for sequencing stool metatranscriptomes.'),
(97445, 2016, 'bmc', 'Identification of gene-drug interactions that impact patient survival in TCGA', 'Spainhour, John Christian Givhan|Qiu, Peng', 'Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Dr. NW, Atlanta, GA, 30332, USA. John.spainhour@bme.gatech.edu.|Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Dr. NW, Atlanta, GA, 30332, USA.', 'Georgia|Georgia', 0, 'Antineoplastic Agents|DNA Copy Number Variations|Drug Interactions|Humans|Neoplasms|Software|Survival Rate', 'Journal Article', '10.1186/s12859-016-1255-7', '27716027', 'With the advent of large scale biological data collection for various diseases, data analysis pipelines and workflows need to be established to build frameworks for integrative analysis. Here the authors present a pipeline for identifying disease specific gene-drug interactions using CNV (Copy Number Variation) and clinical data from the TCGA (The Cancer Genome Atlas) project. Two cancer types were selected for analysis, LGG (Brain lower grade glioma) and GBM (Glioblastoma multiforme), due to the possible progression from LGG to GBM in some cases. The copy number and clinical data were then used to preform survival analysis on a gene by gene basis on sub-populations of patients exposed to a given drug.'),
(97446, 2016, 'bmc', 'Simulating cryo electron tomograms of crowded cell cytoplasm for assessment of automated particle picking', 'Pei, Long|Xu, Min|Frazier, Zachary|Alber, Frank', 'Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, CA, 90089, USA.|Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, CA, 90089, USA.|Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, CA, 90089, USA.|Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, CA, 90089, USA. alber@usc.edu.', 'United States|United States|United States|United States', 13, 'Cells|Cryoelectron Microscopy|Cytoplasm|Electron Microscope Tomography|Humans|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Macromolecular Substances', 'Journal Article', '10.1186/s12859-016-1283-3', '27716029', 'Cryo-electron tomography is an important tool to study structures of macromolecular complexes in close to native states. A whole cell cryo electron tomogram contains structural information of all its macromolecular complexes. However, extracting this information remains challenging, and relies on sophisticated image processing, in particular for template-free particle extraction, classification and averaging. To develop these methods it is crucial to realistically simulate tomograms of crowded cellular environments, which can then serve as ground truth models for assessing and optimizing methods for detection of complexes in cell tomograms.'),
(97447, 2016, 'bmc', 'Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework', 'Voillet, Valentin|Besse, Philippe|Liaubet, Laurence|San Cristobal, Magali|González, Ignacio', 'Université de Toulouse, INRA, INPT, INP-ENVT, UMR1388, GenPhySE, Castanet-Tolosan, F-31326, France.|Université de Toulouse INSA, UMR5219 Institut de Mathématiques, Toulouse, F-31077, France.|Université de Toulouse, INRA, INPT, INP-ENVT, UMR1388, GenPhySE, Castanet-Tolosan, F-31326, France.|Université de Toulouse, INRA, INPT, INP-ENVT, UMR1388, GenPhySE, Castanet-Tolosan, F-31326, France.|INRAUR875 Mathématiques et Informatiques Appliquées, F-31326, Castanet-Tolosan, France. Ignacio.Gonzalez@toulouse.inra.fr.', 'France|France|France|France|France', 13, 'Acetaminophen|Analgesics, Non-Narcotic|Animals|Chemical and Drug Induced Liver Injury|Data Interpretation, Statistical|Factor Analysis, Statistical|Gene Expression Regulation|Genomics|Humans|Male|Multivariate Analysis|Neoplasms|Proteomics|Rats|Rats, Wistar|Reproducibility of Results|Tumor Cells, Cultured', 'Journal Article', '10.1186/s12859-016-1273-5', '27716030', 'In omics data integration studies, it is common, for a variety of reasons, for some individuals to not be present in all data tables. Missing row values are challenging to deal with because most statistical methods cannot be directly applied to incomplete datasets. To overcome this issue, we propose a multiple imputation (MI) approach in a multivariate framework. In this study, we focus on multiple factor analysis (MFA) as a tool to compare and integrate multiple layers of information. MI involves filling the missing rows with plausible values, resulting in M completed datasets. MFA is then applied to each completed dataset to produce M different configurations (the matrices of coordinates of individuals). Finally, the M configurations are combined to yield a single consensus solution.'),
(97448, 2016, 'bmc', 'Boolean regulatory network reconstruction using literature based knowledge with a genetic algorithm optimization method', 'Dorier, Julien|Crespo, Isaac|Niknejad, Anne|Liechti, Robin|Ebeling, Martin|Xenarios, Ioannis', 'Vital-IT, Systems biology and medicine department, SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland. julien.dorier@sib.swiss.|Vital-IT, Systems biology and medicine department, SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.|Vital-IT, Systems biology and medicine department, SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.|Vital-IT, Systems biology and medicine department, SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.|Pharmaceutical Sciences/Translational Technologies and Bioinformatics, Roche Innovation Center Basel, 124 Grenzacherstrasse, 4070, Basel, Switzerland.|Vital-IT, Systems biology and medicine department, SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland. ioannis.xenarios@sib.swiss.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Algorithms|Computer Simulation|Gene Regulatory Networks|Humans|Knowledge Bases|Models, Biological|Models, Genetic|Publications|Software', 'Journal Article', '10.1186/s12859-016-1287-z', '27716031', 'Prior knowledge networks (PKNs) provide a framework for the development of computational biological models, including Boolean models of regulatory networks which are the focus of this work. PKNs are created by a painstaking process of literature curation, and generally describe all relevant regulatory interactions identified using a variety of experimental conditions and systems, such as specific cell types or tissues. Certain of these regulatory interactions may not occur in all biological contexts of interest, and their presence may dramatically change the dynamical behaviour of the resulting computational model, hindering the elucidation of the underlying mechanisms and reducing the usefulness of model predictions. Methods are therefore required to generate optimized contextual network models from generic PKNs.'),
(97449, 2016, 'bmc', 'VennDiagramWeb: a web application for the generation of highly customizable Venn and Euler diagrams', 'Lam, Felix|Lalansingh, Christopher M|Babaran, Holly E|Wang, Zhiyuan|Prokopec, Stephenie D|Fox, Natalie S|Boutros, Paul C', 'Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Canada.|Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Canada.|Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Canada.|Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Canada.|Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Canada.|Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Canada.|Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Canada. Paul.Boutros@oicr.on.ca.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 7, 'Computational Biology|Computer Graphics|Data Interpretation, Statistical|Documentation|Humans|Internet|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1281-5', '27716034', 'Visualization of data generated by high-throughput, high-dimensionality experiments is rapidly becoming a rate-limiting step in computational biology. There is an ongoing need to quickly develop high-quality visualizations that can be easily customized or incorporated into automated pipelines. This often requires an interface for manual plot modification, rapid cycles of tweaking visualization parameters, and the generation of graphics code. To facilitate this process for the generation of highly-customizable, high-resolution Venn and Euler diagrams, we introduce VennDiagramWeb: a web application for the widely used VennDiagram R package. VennDiagramWeb is hosted at http://venndiagram.res.oicr.on.ca/ .'),
(97450, 2016, 'bmc', 'Identification of large disjoint motifs in biological networks', 'Elhesha, Rasha|Kahveci, Tamer', 'CISE Department, University of Florida, 432 Newell Dr, Gainesville, Florida, 32611, USA. relhesha@cise.ufl.edu.|CISE Department, University of Florida, 432 Newell Dr, Gainesville, Florida, 32611, USA.', 'United States|United States', 9, 'Algorithms|Amino Acid Motifs|Computational Biology|Gene Regulatory Networks|Herpesvirus 8, Human|Humans|Pattern Recognition, Automated|Protein Interaction Mapping|Viral Proteins', 'Journal Article', '10.1186/s12859-016-1271-7', '27716036', 'Biological networks provide great potential to understand how cells function. Network motifs, frequent topological patterns, are key structures through which biological networks operate. Finding motifs in biological networks remains to be computationally challenging task as the size of the motif and the underlying network grow. Often, different copies of a given motif topology in a network share nodes or edges. Counting such overlapping copies introduces significant problems in motif identification.'),
(97451, 2016, 'bmc', 'Impact of post-alignment processing in variant discovery from whole exome data', 'Tian, Shulan|Yan, Huihuang|Kalmbach, Michael|Slager, Susan L', 'Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.|Division of Research and Education Support Systems, Department of Information Technology Mayo Clinic, Rochester, MN, 55905, USA.|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA. Slager.Susan@mayo.edu.', 'United States|United States|United States|United States', 8, 'Computational Biology|Exome|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Mutation|Polymorphism, Single Nucleotide|Sequence Alignment|Software|Workflow', 'Journal Article', '10.1186/s12859-016-1279-z', '27716037', 'GATK Best Practices workflows are widely used in large-scale sequencing projects and recommend post-alignment processing before variant calling. Two key post-processing steps include the computationally intensive local realignment around known INDELs and base quality score recalibration (BQSR). Both have been shown to reduce erroneous calls, however, the findings are mainly supported by the analytical pipeline that incorporates BWA and GATK UnifiedGenotyper. It is not known whether there is any benefit of post-processing and to what extent the benefit might be for pipelines implementing other methods, especially given that both mappers and callers are typically updated. Moreover, because sequencing platforms are upgraded regularly and the new platforms provide better estimations of read quality scores, the need for post-processing is also unknown. Finally, some regions in the human genome show high sequence divergence from the reference genome, it is unclear whether there is benefit from post-processing in these regions.'),
(97452, 2016, 'bmc', 'ChiLin: a comprehensive ChIP-seq and DNase-seq quality control and analysis pipeline', 'Qin, Qian|Mei, Shenglin|Wu, Qiu|Sun, Hanfei|Li, Lewyn|Taing, Len|Chen, Sujun|Li, Fugen|Liu, Tao|Zang, Chongzhi|Xu, Han|Chen, Yiwen|Meyer, Clifford A|Zhang, Yong|Brown, Myles|Long, Henry W|Liu, X Shirley', 'Shanghai Key laboratory of tuberculosis, Shanghai Pulmonary Hospital, Shanghai, China.|Shanghai Key laboratory of tuberculosis, Shanghai Pulmonary Hospital, Shanghai, China.|Shanghai Key laboratory of tuberculosis, Shanghai Pulmonary Hospital, Shanghai, China.|Shanghai Key laboratory of tuberculosis, Shanghai Pulmonary Hospital, Shanghai, China.|Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA, USA.|Shanghai Key laboratory of tuberculosis, Shanghai Pulmonary Hospital, Shanghai, China.|Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.|Department of Biochemistry, University at Buffalo, Buffalo, NY, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA, USA.|Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, China.|Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.|Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA. henry_long@dfci.harvard.edu.|Shanghai Key laboratory of tuberculosis, Shanghai Pulmonary Hospital, Shanghai, China. xsliu@jimmy.harvard.edu.', 'China|China|China|China|United States|United States|China|United States|United States|United States|United States|United States|United States|China|United States|United States|China', 40, 'Chromatin Immunoprecipitation|Chromosome Mapping|Data Interpretation, Statistical|Databases, Genetic|Deoxyribonucleases|Gene Expression Regulation|High-Throughput Nucleotide Sequencing|Humans|Quality Control|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-016-1274-4', '27716038', 'Transcription factor binding, histone modification, and chromatin accessibility studies are important approaches to understanding the biology of gene regulation. ChIP-seq and DNase-seq have become the standard techniques for studying protein-DNA interactions and chromatin accessibility respectively, and comprehensive quality control (QC) and analysis tools are critical to extracting the most value from these assay types. Although many analysis and QC tools have been reported, few combine ChIP-seq and DNase-seq data analysis and quality control in a unified framework with a comprehensive and unbiased reference of data quality metrics.'),
(97453, 2016, 'bmc', 'Parametric bootstrapping for biological sequence motifs', 'ONeill, Patrick K|Erill, Ivan', 'Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, 21250, US.|Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, 21250, US. erill@umbc.edu.', '-|-', 3, 'Algorithms|Binding Sites|Computational Biology|Databases, Genetic|Eukaryotic Cells|Humans|Nucleotide Motifs|Prokaryotic Cells|Proteins|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/s12859-016-1246-8', '27716039', 'Biological sequence motifs drive the specific interactions of proteins and nucleic acids. Accordingly, the effective computational discovery and analysis of such motifs is a central theme in bioinformatics. Many practical questions about the properties of motifs can be recast as random sampling problems. In this light, the task is to determine for a given motif whether a certain feature of interest is statistically unusual among relevantly similar alternatives. Despite the generality of this framework, its use has been frustrated by the difficulties of defining an appropriate reference class of motifs for comparison and of sampling from it effectively.'),
(97454, 2016, 'bmc', 'A decision analysis model for KEGG pathway analysis', 'Du, Junli|Li, Manlin|Yuan, Zhifa|Guo, Mancai|Song, Jiuzhou|Xie, Xiaozhen|Chen, Yulin', 'College of sciences, Northwest A&F University, Yangling, 712100, Peoples Republic of China.|College of sciences, Northwest A&F University, Yangling, 712100, Peoples Republic of China.|College of sciences, Northwest A&F University, Yangling, 712100, Peoples Republic of China.|College of sciences, Northwest A&F University, Yangling, 712100, Peoples Republic of China.|Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.|College of sciences, Northwest A&F University, Yangling, 712100, Peoples Republic of China.|College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, Peoples Republic of China. liml75@126.com.', 'China|China|China|China|United States|China|China', 14, 'Animals|Cattle|Computational Biology|Databases, Factual|Decision Support Techniques|Female|Gene Expression Profiling|Genome|Lactation|Mammary Glands, Animal|Signal Transduction|Transcriptome', 'Journal Article', '10.1186/s12859-016-1285-1', '27716040', 'The knowledge base-driven pathway analysis is becoming the first choice for many investigators, in that it not only can reduce the complexity of functional analysis by grouping thousands of genes into just several hundred pathways, but also can increase the explanatory power for the experiment by identifying active pathways in different conditions. However, current approaches are designed to analyze a biological system assuming that each pathway is independent of the other pathways.'),
(97455, 2016, 'bmc', 'Je, a versatile suite to handle multiplexed NGS libraries with unique molecular identifiers', 'Girardot, Charles|Scholtalbers, Jelle|Sauer, Sajoscha|Su, Shu-Yi|Furlong, Eileen E M', 'European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, D-69117, Germany. charles.girardot@embl.de.|European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, D-69117, Germany.|European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, D-69117, Germany.|European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, D-69117, Germany.|European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, D-69117, Germany.', 'Germany|Germany|Germany|Germany|Germany', 30, 'Electronic Data Processing|Gene Library|Genomics|High-Throughput Nucleotide Sequencing|Humans|Polymerase Chain Reaction|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-016-1284-2', '27717304', 'The yield obtained from next generation sequencers has increased almost exponentially in recent years, making sample multiplexing common practice. While barcodes (known sequences of fixed length) primarily encode the sample identity of sequenced DNA fragments, barcodes made of random sequences (Unique Molecular Identifier or UMIs) are often used to distinguish between PCR duplicates and transcript abundance in, for example, single-cell RNA sequencing (scRNA-seq). In paired-end sequencing, different barcodes can be inserted at each fragment end to either increase the number of multiplexed samples in the library or to use one of the barcodes as UMI. Alternatively, UMIs can be combined with the sample barcodes into composite barcodes, or with standard Illumina® indexing. Subsequent analysis must take read duplicates and sample identity into account, by identifying UMIs.'),
(97456, 2016, 'bmc', 'RStrucFam: a web server to associate structure and cognate RNA for RNA-binding proteins from sequence information', 'Ghosh, Pritha|Mathew, Oommen K|Sowdhamini, Ramanathan', 'National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bellary Road, Bangalore, Karnataka, 560 065, India.|National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bellary Road, Bangalore, Karnataka, 560 065, India.|National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bellary Road, Bangalore, Karnataka, 560 065, India. mini@ncbs.res.in.', 'India|India|India', 3, 'Amino Acid Sequence|Humans|Protein Structure, Tertiary|RNA|RNA-Binding Proteins', 'Journal Article', '10.1186/s12859-016-1289-x', '27717309', 'RNA-binding proteins (RBPs) interact with their cognate RNA(s) to form large biomolecular assemblies. They are versatile in their functionality and are involved in a myriad of processes inside the cell. RBPs with similar structural features and common biological functions are grouped together into families and superfamilies. It will be useful to obtain an early understanding and association of RNA-binding property of sequences of gene products. Here, we report a web server, RStrucFam, to predict the structure, type of cognate RNA(s) and function(s) of proteins, where possible, from mere sequence information.'),
(97457, 2016, 'bmc', 'FMAP: Functional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies', 'Kim, Jiwoong|Kim, Min Soo|Koh, Andrew Y|Xie, Yang|Zhan, Xiaowei', 'Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390, USA.|Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390, USA.|Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390, USA.|Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390, USA. Yang.Xie@utsouthwestern.edu.|Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390, USA. Xiaowei.Zhan@utsouthwestern.edu.', 'United States|United States|United States|United States|United States', 27, 'Bacteria|Computational Biology|Crohn Disease|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Metabolic Networks and Pathways|Metagenome|Metagenomics|Molecular Sequence Annotation|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1186/s12859-016-1278-0', '27724866', 'Given the lack of a complete and comprehensive library of microbial reference genomes, determining the functional profile of diverse microbial communities is challenging. The available functional analysis pipelines lack several key features: (i) an integrated alignment tool, (ii) operon-level analysis, and (iii) the ability to process large datasets.'),
(97458, 2016, 'bmc', 'Extending digital PCR analysis by modelling quantification cycle data', 'Wilson, Philip J|Ellison, Stephen L R', 'LGC, Queens Road, Teddington, Middlesex, TW11 0LY, UK. philip.wilson@lgcgroup.com.|LGC, Queens Road, Teddington, Middlesex, TW11 0LY, UK.', 'United Kingdom|United Kingdom', 0, 'DNA|Humans|Plasmids|Poisson Distribution|Polymerase Chain Reaction', 'Journal Article', '10.1186/s12859-016-1275-3', '27733121', '-'),
(97459, 2016, 'bmc', 'A scalability study of phylogenetic network inference methods using empirical datasets and simulations involving a single reticulation', 'Hejase, Hussein A|Liu, Kevin J', 'Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, MI, USA.|Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, MI, USA. kjl@msu.edu.', 'United States|United States', 10, 'Animals|Biological Evolution|Computational Biology|Genetic Speciation|High-Throughput Nucleotide Sequencing|Humans|Models, Genetic|Phylogeny|Probability', 'Journal Article', '10.1186/s12859-016-1277-1', '27737628', 'Branching events in phylogenetic trees reflect bifurcating and/or multifurcating speciation and splitting events. In the presence of gene flow, a phylogeny cannot be described by a tree but is instead a directed acyclic graph known as a phylogenetic network. Both phylogenetic trees and networks are typically reconstructed using computational analysis of multi-locus sequence data. The advent of high-throughput sequencing technologies has brought about two main scalability challenges: (1) dataset size in terms of the number of taxa and (2) the evolutionary divergence of the taxa in a study. The impact of both dimensions of scale on phylogenetic tree inference has been well characterized by recent studies, in contrast, the scalability limits of phylogenetic network inference methods are largely unknown.'),
(97460, 2016, 'bmc', 'Roar: detecting alternative polyadenylation with standard mRNA sequencing libraries', 'Grassi, Elena|Mariella, Elisa|Lembo, Antonio|Molineris, Ivan|Provero, Paolo', 'Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, Via Nizza 52, Torino, 10126, Italy. elena.grassi@unito.t.|Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, Via Nizza 52, Torino, 10126, Italy.|Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, Via Nizza 52, Torino, 10126, Italy.|Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, Via Nizza 52, Torino, 10126, Italy.|Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, Via Nizza 52, Torino, 10126, Italy.', 'Italy|Italy|Italy|Italy|Italy', 0, '3 Untranslated Regions|Algorithms|Brain|Breast Neoplasms|Female|Gene Expression Profiling|Gene Expression Regulation|High-Throughput Nucleotide Sequencing|Humans|Polyadenylation|RNA, Messenger|Reproducibility of Results', 'Journal Article', '10.1186/s12859-016-1254-8', '27756200', 'Post-transcriptional regulation is a complex mechanism that plays a central role in defining multiple cellular identities starting from a common genome. Modifications in the length of 3UTRs have been found to play an important role in this context, since alternative 3 UTRs could lead to differences for example in regulation by microRNAs and cellular localization of the transcripts thus altering their fate.'),
(97461, 2016, 'bmc', 'RNAdualPF: software to compute the dual partition function with sample applications in molecular evolution theory', 'Garcia-Martin, Juan Antonio|Bayegan, Amir H|Dotu, Ivan|Clote, Peter', 'Biology Department, Boston College, 140 Commonwealth Avenue, Chestnut Hill, 02467, MA, USA.|Biology Department, Boston College, 140 Commonwealth Avenue, Chestnut Hill, 02467, MA, USA.|Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, IMIM (Hospital del Mar Medical Research Institute), Dr. Aiguader, 88, Barcelona, Spain.|Biology Department, Boston College, 140 Commonwealth Avenue, Chestnut Hill, 02467, MA, USA. clote@bc.edu.', 'United States|United States|Spain|United States', 7, 'Algorithms|Animals|Caenorhabditis elegans|Computational Biology|Databases, Factual|Escherichia coli|Evolution, Molecular|MicroRNAs|RNA|RNA Folding|RNA, Small Nuclear|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-016-1280-6', '27756204', '-'),
(97462, 2016, 'bmc', 'Convert your favorite protein modeling program into a mutation predictor: MODICT', 'Tanyalcin, Ibrahim|Stouffs, Katrien|Daneels, Dorien|Al Assaf, Carla|Lissens, Willy|Jansen, Anna|Gheldof, Alexander', 'Center for Medical Genetics, UZ Brussel, Laarbeeklaan 101, Brussel, 1090, Belgium. itanyalc@vub.ac.be.|Center for Medical Genetics, Reproduction and Genetics, Reproduction Genetics and Regenerative Medicine, Vrije Universiteit Brussel (VUB), UZ Brussel, Laarbeeklaan 101, Brussel, 1090, Belgium.|Center for Medical Genetics, Reproduction and Genetics, Reproduction Genetics and Regenerative Medicine, Vrije Universiteit Brussel (VUB), UZ Brussel, Laarbeeklaan 101, Brussel, 1090, Belgium.|Center for Human Genetics, KU Leuven and University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium.|Center for Medical Genetics, Reproduction and Genetics, Reproduction Genetics and Regenerative Medicine, Vrije Universiteit Brussel (VUB), UZ Brussel, Laarbeeklaan 101, Brussel, 1090, Belgium.|Center for Medical Genetics, UZ Brussel, Laarbeeklaan 101, Brussel, 1090, Belgium.|Center for Medical Genetics, Reproduction and Genetics, Reproduction Genetics and Regenerative Medicine, Vrije Universiteit Brussel (VUB), UZ Brussel, Laarbeeklaan 101, Brussel, 1090, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Acyl-CoA Dehydrogenase|Computational Biology|Humans|Models, Molecular|Mutation|Protein Conformation|Proteins|Renin|Software|Tubulin', 'Journal Article', '10.1186/s12859-016-1286-0', '27760515', 'Predict whether a mutation is deleterious based on the custom 3D model of a protein.'),
(97463, 2016, 'bmc', 'Proceedings of the 2016 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference', 'Wren, Jonathan D|Toby, Inimary|Hong, Huxiao|Nanduri, Bindu|Kaundal, Rakesh|Dozmorov, Mikhail G|Thakkar, Shraddha', 'Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, 825 N.E. 13th Street, Oklahoma City, OK, 73104-5005, USA. jonathan-wren@omrf.org.|Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA.|Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi, MS, USA.|Bioinformatics Facility, Institute for Integrative Genome Biology, University of California, Riverside, California, USA.|Department of Biostatistics, Richmond Academy of Medicine, Virginia Commonwealth University, Virginia, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Gene Regulatory Networks|Genomics|Imaging, Three-Dimensional|Systems Biology', 'Congress', '10.1186/s12859-016-1213-4', '27766933', '-'),
(97464, 2016, 'bmc', 'CC-PROMISE effectively integrates two forms of molecular data with multiple biologically related endpoints', 'Cao, Xueyuan|Crews, Kristine R|Downing, James|Lamba, Jatinder|Pounds, Stanley B', 'Department of Biostatistics, St. Jude Childrens Research Hospital, 262 Danny Thomas Place, Memphis, 38105, USA.|Department of Pharmaceutical Sciences, St. Jude Childrens Research Hospital, 262 Danny Thomas Place, Memphis, 38105, USA.|Department of Pathology, St. Jude Childrens Research Hospital, 262 Danny Thomas Place, Memphis, 38105, USA.|Department of Pharmacotherapy and Translational Research, University of Florida, 1333 Center Drive, Gainesville, 32610, USA.|Department of Biostatistics, St. Jude Childrens Research Hospital, 262 Danny Thomas Place, Memphis, 38105, USA. stanley.pounds@stjude.org.', 'United States|United States|United States|United States|United States', 0, 'DNA Methylation|Genomics|Humans|Leukemia|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software|Transcriptome', 'Journal Article', '10.1186/s12859-016-1217-0', '27766934', 'As new technologies allow investigators to collect multiple forms of molecular data (genomic, epigenomic, transcriptomic, etc) and multiple endpoints on a clinical trial cohort, it will become necessary to effectively integrate all these data in a way that reliably identifies biologically important genes.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97465, 2016, 'bmc', 'Analysis of optimal alignments unfolds aligners bias in existing variant profiles', 'Tran, Quang|Gao, Shanshan|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', 4, 'Alleles|Data Accuracy|Genome, Human|Genomics|Humans|INDEL Mutation|Polymorphism, Genetic|Sequence Alignment|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1186/s12859-016-1216-1', '27766935', 'Efforts such as International HapMap Project and 1000 Genomes Project resulted in a catalog of millions of single nucleotides and insertion/deletion (INDEL) variants of the human population. Viewed as a reference of existing variants, this resource commonly serves as a gold standard for studying and developing methods to detect genetic variants. Our analysis revealed that this reference contained thousands of INDELs that were constructed in a biased manner. This bias occurred at the level of aligning short reads to reference genomes to detect variants. The bias is caused by the existence of many theoretically optimal alignments between the reference genome and reads containing alternative alleles at those INDEL locations. We examined several popular aligners and showed that these aligners could be divided into groups whose alignments yielded INDELs that agreed strongly or disagreed strongly with reported INDELs. This finding suggests that the agreement or disagreement between the aligners called INDEL and the reported INDEL is merely a result of the arbitrary selection of one of the optimal alignments. The existence of bias in INDEL calling might have a serious influence in downstream analyses. As such, our finding suggests that this phenomenon should be further addressed.'),
(97466, 2016, 'bmc', 'Dissecting the biological relationship between TCGA miRNA and mRNA sequencing data using MMiRNA-Viewer', 'Bai, Yongsheng|Ding, Lizhong|Baker, Steve|Bai, Jenny M|Rath, Ethan|Jiang, Feng|Wu, Jianghong|Jiang, Hui|Stuart, Gary', 'Department of Biology, Indiana State University, Terre Haute, IN, 47809, USA. Yongsheng.Bai@indstate.edu.|Department of Biology, Indiana State University, Terre Haute, IN, 47809, USA.|Department of Mathematics and Computer Science, Indiana State University, Terre Haute, IN, 47809, USA.|Terre Haute South Vigo High School, Terre Haute, IN, 47809, USA.|Department of Biology, Indiana State University, Terre Haute, IN, 47809, USA.|Department of Mathematics and Computer Science, Indiana State University, Terre Haute, IN, 47809, USA.|Animal Husbandry Institute, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot, 010031, Peoples Republic of China.|Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.|Department of Biology, Indiana State University, Terre Haute, IN, 47809, USA.', 'India|India|India|United States|India|India|China|United States|India', 6, '3 Untranslated Regions|Computational Biology|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|MicroRNAs|Neoplasms|RNA, Messenger|Regulatory Sequences, Nucleic Acid|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-016-1219-y', '27766936', 'MicroRNAs (miRNA) are short nucleotides that interact with their target genes through 3 untranslated regions (UTRs). The Cancer Genome Atlas (TCGA) harbors an increasing amount of cancer genome data for both tumor and normal samples. However, there are few visualization tools focusing on concurrently displaying important relationships and attributes between miRNAs and mRNAs of both cancer tumor and normal samples. Moreover, a deep investigation of miRNA-mRNA target and biological relationships across multiple cancer types by integrating web-based analysis has not been thoroughly conducted.'),
(97467, 2016, 'bmc', 'Leveraging graph topology and semantic context for pharmacovigilance through twitter-streams', 'Eshleman, Ryan|Singh, Rahul', 'Department of Computer Science, San Francisco State University, San Francisco, CA, 94132, USA.|Department of Computer Science, San Francisco State University, San Francisco, CA, 94132, USA. rahul@sfsu.edu.', 'United States|United States', 13, 'Data Accuracy|Data Mining|Databases, Factual|Drug-Related Side Effects and Adverse Reactions|Humans|Models, Theoretical|Pharmacovigilance|Semantics|Social Media', 'Journal Article', '10.1186/s12859-016-1220-5', '27766937', 'Adverse drug events (ADEs) constitute one of the leading causes of post-therapeutic death and their identification constitutes an important challenge of modern precision medicine. Unfortunately, the onset and effects of ADEs are often underreported complicating timely intervention. At over 500 million posts per day, Twitter is a commonly used social media platform. The ubiquity of day-to-day personal information exchange on Twitter makes it a promising target for data mining for ADE identification and intervention. Three technical challenges are central to this problem: (1) identification of salient medical keywords in (noisy) tweets, (2) mapping drug-effect relationships, and (3) classification of such relationships as adverse or non-adverse.'),
(97468, 2016, 'bmc', 'Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model', 'Jeong, Hyundoo|Qian, Xiaoning|Yoon, Byung-Jun', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, USA. bjyoon@ece.tamu.edu.', 'United States|United States|United States', 9, 'Algorithms|Animals|Computational Biology|Humans|Models, Statistical|Protein Interaction Maps|Saccharomyces cerevisiae', 'Journal Article', '10.1186/s12859-016-1215-2', '27766938', 'Comparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison.'),
(97469, 2016, 'bmc', 'Phylogenetic tree construction using trinucleotide usage profile (TUP)', 'Chen, Si|Deng, Lih-Yuan|Bowman, Dale|Shiau, Jyh-Jen Horng|Wong, Tit-Yee|Madahian, Behrouz|Lu, Henry Horng-Shing', 'Key Laboratory of Combinatorial Biosynthesis and Drug Discovery Ministry of Education and School of Pharmaceutical Sciences Wuhan University, Wuhan, China.|Department of Mathematical Sciences, University of Memphis, Memphis, TN, USA.|Department of Mathematical Sciences, University of Memphis, Memphis, TN, USA.|Institute of Statistics, National Chiao Tung University, Hsinchu, Taiwan.|Department of Biological Sciences, University of Memphis, Memphis, TN, USA.|Department of Mathematical Sciences, University of Memphis, Memphis, TN, USA.|Institute of Statistics, National Chiao Tung University, Hsinchu, Taiwan. hslu@stat.nctu.edu.tw.', 'China|United States|United States|United States|United States|United States|United States', 4, 'Algorithms|Bacteria|Codon|Computational Biology|Phylogeny|Reading Frames', 'Journal Article', '10.1186/s12859-016-1222-3', '27766939', '-'),
(97470, 2016, 'bmc', 'Prioritization, clustering and functional annotation of MicroRNAs using latent semantic indexing of MEDLINE abstracts', 'Roy, Sujoy|Curry, Brandon C|Madahian, Behrouz|Homayouni, Ramin', 'Bioinformatics Program, University of Memphis, Memphis, 38152, USA.|Bioinformatics Program, University of Memphis, Memphis, 38152, USA.|Department of Mathematical Sciences, University of Memphis, Memphis, 38152, USA.|Bioinformatics Program, University of Memphis, Memphis, 38152, USA. rhomayon@memphis.edu.', 'United States|United States|United States|United States', 4, 'Cluster Analysis|Computational Biology|Data Mining|Humans|MEDLINE|MicroRNAs|Molecular Sequence Annotation|Semantics|Software', 'Journal Article', '10.1186/s12859-016-1223-2', '27766940', 'The amount of scientific information about MicroRNAs (miRNAs) is growing exponentially, making it difficult for researchers to interpret experimental results. In this study, we present an automated text mining approach using Latent Semantic Indexing (LSI) for prioritization, clustering and functional annotation of miRNAs.'),
(97471, 2016, 'bmc', 'Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images', 'Kaya, Sertan|Bayraktar, Mustafa|Kockara, Sinan|Mete, Mutlu|Halic, Tansel|Field, Halle E|Wong, Henry K', 'Department of Computer Science, University of Central Arkansas, Conway, AR, USA.|Department of Bioinformatics, University of Arkansas-Little Rock, Little Rock, AR, 72204, USA.|Department of Computer Science, University of Central Arkansas, Conway, AR, USA. skockara@uca.edu.|Department of Computer Science and Information Systems, Texas A&M-Commerce, Commerce, TX, USA.|Department of Computer Science, University of Central Arkansas, Conway, AR, USA.|Department of Dermatology, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.|Department of Dermatology, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.', 'United States|United States|United States|United States|United States|United States|United States', 7, 'Algorithms|Color|Data Accuracy|Dermoscopy|Humans|Image Interpretation, Computer-Assisted|Melanoma|Pattern Recognition, Automated|Sensitivity and Specificity|Skin Neoplasms', 'Journal Article', '10.1186/s12859-016-1221-4', '27766942', 'Automated skin lesion border examination and analysis techniques have become an important field of research for distinguishing malignant pigmented lesions from benign lesions. An abrupt pigment pattern cutoff at the periphery of a skin lesion is one of the most important dermoscopic features for detection of neoplastic behavior. In current clinical setting, the lesion is divided into a virtual pie with eight sections. Each section is examined by a dermatologist for abrupt cutoff and scored accordingly, which can be tedious and subjective.'),
(97472, 2016, 'bmc', 'Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach', 'Mete, Mutlu|Sakoglu, Unal|Spence, Jeffrey S|Devous, Michael D|Harris, Thomas S|Adinoff, Bryon', 'Department of Computer Science and Information Systems, Texas A&M University-Commerce, Commerce, TX, USA. Mutlu.Mete@tamuc.edu.|Computer Engineering, University of Houston - Clear Lake, Houston, TX, USA.|Center for Brain Health, University of Texas at Dallas, Richardson, TX, USA.|Department of Neurology, UT Southwestern Medical Center, Dallas, TX, USA.|Avid Radiopharmaceuticals, Philadelphia, PA, USA.|Veterans Affairs North Texas Health Care System, Dallas, TX, USA.', 'United States|United States|United States|United States|United States|United States', 9, 'Adult|Algorithms|Brain|Cluster Analysis|Cocaine-Related Disorders|Female|Humans|Male|Middle Aged|Neuroimaging|Sensitivity and Specificity|Support Vector Machine|Young Adult', 'Journal Article', '10.1186/s12859-016-1218-z', '27766943', 'Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented. Single Photon Emission Computerized Tomography (SPECT) images obtained during rest or a saline infusion in three cohorts of 2-4 week abstinent cocaine-dependent participants (nxe2x80x89=xe2x80x8993) and healthy controls (nxe2x80x89=xe2x80x8969) were used to develop a classification model. An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels. A density-based clustering algorithm was then used to form spatially connected voxel clouds in three-dimensional space. A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification. Statistically insignificant voxels of spatially connected brain regions were removed iteratively and classification accuracy was reported through the iterations.'),
(97473, 2016, 'bmc', 'Incorporating topological information for predicting robust cancer subnetwork markers in human protein-protein interaction network', 'Khunlertgit, Navadon|Yoon, Byung-Jun', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77843-3128, TX, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77843-3128, TX, USA. bjyoon@ece.tamu.edu.', 'United States|United States', 5, 'Algorithms|Breast Neoplasms|Cluster Analysis|Computational Biology|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Prognosis|Protein Interaction Mapping|Protein Interaction Maps', 'Journal Article', '10.1186/s12859-016-1224-1', '27766944', 'Discovering robust markers for cancer prognosis based on gene expression data is an important yet challenging problem in translational bioinformatics. By integrating additional information in biological pathways or a protein-protein interaction (PPI) network, we can find better biomarkers that lead to more accurate and reproducible prognostic predictions. In fact, recent studies have shown that, modular markers, that integrate multiple genes with potential interactions can improve disease classification and also provide better understanding of the disease mechanisms.'),
(97474, 2016, 'bmc', 'Improving sensitivity of linear regression-based cell type-specific differential expression deconvolution with per-gene vs global significance threshold', 'Glass, Edmund R|Dozmorov, Mikhail G', 'Department of Biostatistics, Virginia Commonwealth University, School of Medicine, PO Box 980032, Richmond, VA, 23298, USA.|Department of Biostatistics, Virginia Commonwealth University, School of Medicine, PO Box 980032, Richmond, VA, 23298, USA. mikhail.dozmorov@vcuhealth.org.', 'United States|United States', 4, 'Algorithms|Gene Expression Profiling|Humans|Linear Models|Models, Genetic|Sample Size|Sensitivity and Specificity', 'Journal Article', '10.1186/s12859-016-1226-z', '27766949', 'The goal of many human disease-oriented studies is to detect molecular mechanisms different between healthy controls and patients. Yet, commonly used gene expression measurements from blood samples suffer from variability of cell composition. This variability hinders the detection of differentially expressed genes and is often ignored. Combined with cell counts, heterogeneous gene expression may provide deeper insights into the gene expression differences on the cell type-specific level. Published computational methods use linear regression to estimate cell type-specific differential expression, and a global cutoff to judge significance, such as False Discovery Rate (FDR). Yet, they do not consider many artifacts hidden in high-dimensional gene expression data that may negatively affect linear regression. In this paper we quantify the parameter space affecting the performance of linear regression (sensitivity of cell type-specific differential expression detection) on a per-gene basis.'),
(97475, 2016, 'bmc', 'PGen: large-scale genomic variations analysis workflow and browser in SoyKB', 'Liu, Yang|Khan, Saad M|Wang, Juexin|Rynge, Mats|Zhang, Yuanxun|Zeng, Shuai|Chen, Shiyuan|Maldonado Dos Santos, Joao V|Valliyodan, Babu|Calyam, Prasad P|Merchant, Nirav|Nguyen, Henry T|Xu, Dong|Joshi, Trupti', 'Informatics Institute, University of Missouri, Columbia, MO, USA.|Informatics Institute, University of Missouri, Columbia, MO, USA.|Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.|Information Sciences Institute, University of Southern California, Los Angeles, CA, USA.|Department of Computer Science, University of Missouri, Columbia, MO, USA.|Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.|Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.|Division of Plant Sciences, University of Missouri, Columbia, MO, USA.|Division of Plant Sciences, University of Missouri, Columbia, MO, USA.|Department of Computer Science, University of Missouri, Columbia, MO, USA.|iPlant Collaborative, University of Arizona, Tucson, AZ, USA.|Division of Plant Sciences, University of Missouri, Columbia, MO, USA.|Informatics Institute, University of Missouri, Columbia, MO, USA.|Informatics Institute, University of Missouri, Columbia, MO, USA. joshitr@missouri.edu.', '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', 11, 'Genome, Plant|Genomics|High-Throughput Nucleotide Sequencing|Polymorphism, Genetic|Sequence Analysis, DNA|Software|Soybeans|Workflow', 'Journal Article', '10.1186/s12859-016-1227-y', '27766951', 'With the advances in next-generation sequencing (NGS) technology and significant reductions in sequencing costs, it is now possible to sequence large collections of germplasm in crops for detecting genome-scale genetic variations and to apply the knowledge towards improvements in traits. To efficiently facilitate large-scale NGS resequencing data analysis of genomic variations, we have developed PGen, an integrated and optimized workflow using the Extreme Science and Engineering Discovery Environment (XSEDE) high-performance computing (HPC) virtual system, iPlant cloud data storage resources and Pegasus workflow management system (Pegasus-WMS). The workflow allows users to identify single nucleotide polymorphisms (SNPs) and insertion-deletions (indels), perform SNP annotations and conduct copy number variation analyses on multiple resequencing datasets in a user-friendly and seamless way.'),
(97476, 2016, 'bmc', 'Application of dynamic topic models to toxicogenomics data', 'Lee, Mikyung|Liu, Zhichao|Huang, Ruili|Tong, Weida', 'NIH/National Center for Advancing Translational Sciences, Rockville, MD, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Jefferson, AR, USA.|NIH/National Center for Advancing Translational Sciences, Rockville, MD, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Jefferson, AR, USA. weida.tong@fda.hhs.gov.', 'United States|United States|United States|United States', 8, 'Algorithms|Animals|Cluster Analysis|Computational Biology|Data Mining|Hepatocytes|Humans|Metabolic Networks and Pathways|Models, Biological|Oligonucleotide Array Sequence Analysis|Rats|Toxicogenetics|Toxins, Biological|Transcriptome', 'Journal Article', '10.1186/s12859-016-1225-0', '27766956', 'All biological processes are inherently dynamic. Biological systems evolve transiently or sustainably according to sequential time points after perturbation by environment insults, drugs and chemicals. Investigating the temporal behavior of molecular events has been an important subject to understand the underlying mechanisms governing the biological system in response to, such as, drug treatment. The intrinsic complexity of time series data requires appropriate computational algorithms for data interpretation. In this study, we propose, for the first time, the application of dynamic topic models (DTM) for analyzing time-series gene expression data.'),
(97477, 2016, 'bmc', 'VDJML: a file format with tools for capturing the results of inferring immune receptor rearrangements', 'Toby, Inimary T|Levin, Mikhail K|Salinas, Edward A|Christley, Scott|Bhattacharya, Sanchita|Breden, Felix|Buntzman, Adam|Corrie, Brian|Fonner, John|Gupta, Namita T|Hershberg, Uri|Marthandan, Nishanth|Rosenfeld, Aaron|Rounds, William|Rubelt, Florian|Scarborough, Walter|Scott, Jamie K|Uduman, Mohamed|Vander Heiden, Jason A|Scheuermann, Richard H|Monson, Nancy|Kleinstein, Steven H|Cowell, Lindsay G', 'Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA.|Bank of America Corporate Center, 100 North Tryon Street, Charlotte, NC, 28202, USA.|Broad Institute, 75 Ames Street, Cambridge, MA, 02142, USA.|Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA.|Institute for Computational Health Sciences, University of California San Francisco, Mission Hall, 550 16th Street, 4th Floor, Box 0110, San Francisco, CA, 94158, USA.|Department of Biological Sciences and The IRMACS Centre, Simon Fraser University, 8888 University Drive, Burnaby, V5A 1S6, British Columbia, Canada.|Department of Immunobiology, University of Arizona School of Medicine, 1656 E. Mabel Street, P.O. Box 245221, Tucson, AZ, 85724-5221, USA.|New Zealand eScience Infrastructure, University of Auckland, Level 10, 49 Symonds Street, Auckland, New Zealand.|Texas Advanced Computing Center, Research Office Complex 1.101, J.J. Pickle Research Campus, Building 196, 10100 Burnet Road (R8700), Austin, TX, 78758-4497, USA.|Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, 300 George Street, Suite 505, New Haven, CT, 06511, USA.|School of Biomedical Engineering, Science and Health Systems and Department of Microbiology and Immunology, College of Medicine, Drexel University, 3141 Chestnut Street, Philadelphia, PA, 19104, USA.|The IRMACS Centre (ASB 10905), Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada.|School of Biomedical Engineering, Science and Health Systems, Drexel University, 3141 Chestnut Street, Philadelphia, PA, 19104, USA.|Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9036, USA.|Stanford University School of Medicine, 279 Campus Drive, Stanford, CA, 94305-5101, USA.|Texas Advanced Computing Center, Research Office Complex 1.101, J.J. Pickle Research Campus, Building 196, 10100 Burnet Road (R8700), Austin, TX, 78758-4497, USA.|Department of Molecular Biology and Biochemistry and Faculty of Health Sciences, Simon Fraser University, Blusson Hall, Room 11300, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada.|Department of Pathology, Yale School of Medicine, 300 George Street, Suite 505, New Haven, CT, 06511, USA.|Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, 300 George Street, Suite 505, New Haven, CT, 06511, USA.|J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA.|Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9036, USA.|Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, 300 George Street, Suite 505, New Haven, CT, 06511, 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|Canada|United States|New Zealand|United States|United States|United States|Canada|United States|United States|United States|United States|Canada|United States|United States|United State', 6, 'Genomics|Humans|Information Dissemination|Receptors, Immunologic|Software|V(D)J Recombination', 'Journal Article', '10.1186/s12859-016-1214-3', '27766961', 'The genes that produce antibodies and the immune receptors expressed on lymphocytes are not germline encoded, rather, they are somatically generated in each developing lymphocyte by a process called V(D)J recombination, which assembles specific, independent gene segments into mature composite genes. The full set of composite genes in an individual at a single point in time is referred to as the immune repertoire. V(D)J recombination is the distinguishing feature of adaptive immunity and enables effective immune responses against an essentially infinite array of antigens. Characterization of immune repertoires is critical in both basic research and clinical contexts. Recent technological advances in repertoire profiling via high-throughput sequencing have resulted in an explosion of research activity in the field. This has been accompanied by a proliferation of software tools for analysis of repertoire sequencing data. Despite the widespread use of immune repertoire profiling and analysis software, there is currently no standardized format for output files from V(D)J analysis. Researchers utilize software such as IgBLAST and IMGT/High V-QUEST to perform V(D)J analysis and infer the structure of germline rearrangements. However, each of these software tools produces results in a different file format, and can annotate the same result using different labels. These differences make it challenging for users to perform additional downstream analyses.'),
(97478, 2016, 'bmc', 'dupRadar: a Bioconductor package for the assessment of PCR artifacts in RNA-Seq data', 'Sayols, Sergi|Scherzinger, Denise|Klein, Holger', 'Bioinformatics Core Facility, Institute of Molecular Biology, Ackermannweg 4, 55128, Mainz, Germany. sergisayolspuig@imb-mainz.de.|Bioinformatics Core Facility, Institute of Molecular Biology, Ackermannweg 4, 55128, Mainz, Germany.|Bioinformatics Core Facility, Institute of Molecular Biology, Ackermannweg 4, 55128, Mainz, Germany. holger.klein@boehringer-ingelheim.com.', 'Germany|Germany|Germany', 22, 'Artifacts|Gene Expression Profiling|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Polymerase Chain Reaction|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-016-1276-2', '27769170', 'PCR clonal artefacts originating from NGS library preparation can affect both genomic as well as RNA-Seq applications when protocols are pushed to their limits. In RNA-Seq however the artifactual reads are not easy to tell apart from normal read duplication due to natural over-sequencing of highly expressed genes. Especially when working with little input material or single cells assessing the fraction of duplicate reads is an important quality control step for NGS data sets. Up to now there are only tools to calculate the global duplication rates that do not take into account the effect of gene expression levels which leaves them of limited use for RNA-Seq data.'),
(97479, 2016, 'bmc', 'A statistical approach to detection of copy number variations in PCR-enriched targeted sequencing data', 'Demidov, German|Simakova, Tamara|Vnuchkova, Julia|Bragin, Anton', 'Parseq Lab, Birzhevaya, 16, Saint-Petersburg, 199053, Russia.|Parseq Lab, Birzhevaya, 16, Saint-Petersburg, 199053, Russia.|Parseq Lab, Birzhevaya, 16, Saint-Petersburg, 199053, Russia.|Parseq Lab, Birzhevaya, 16, Saint-Petersburg, 199053, Russia. abragin@parseq.pro.', '-|-|-|-', 3, 'Algorithms|DNA Copy Number Variations|Data Interpretation, Statistical|Genetic Variation|High-Throughput Nucleotide Sequencing|Humans|Polymerase Chain Reaction|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-016-1272-6', '27770783', 'Multiplex polymerase chain reaction (PCR) is a common enrichment technique for targeted massive parallel sequencing (MPS) protocols. MPS is widely used in biomedical research and clinical diagnostics as the fast and accurate tool for the detection of short genetic variations. However, identification of larger variations such as structure variants and copy number variations (CNV) is still being a challenge for targeted MPS. Some approaches and tools for structural variants detection were proposed, but they have limitations and often require datasets of certain type, size and expected number of amplicons affected by CNVs. In the paper, we describe novel algorithm for high-resolution germinal CNV detection in the PCR-enriched targeted sequencing data and present accompanying tool.'),
(97480, 2016, 'bmc', 'Subcellular protein expression models for microsatellite instability in colorectal adenocarcinoma tissue images', 'Kovacheva, Violeta N|Rajpoot, Nasir M', 'Department of Systems Biology, University of Warwick, Coventry, CV4 7AL, UK. violet.kovacheva@icr.ac.uk.|Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK.', 'United Kingdom|United Kingdom', 0, 'Adenocarcinoma|Biomarkers, Tumor|Colorectal Neoplasms|DNA-Binding Proteins|Humans|Immunohistochemistry|Microsatellite Instability|Microsatellite Repeats|Protein Processing, Post-Translational|Subcellular Fractions', 'Journal Article', '10.1186/s12859-016-1243-y', '27770786', 'New bioimaging techniques capable of visualising the co-location of numerous proteins within individual cells have been proposed to study tumour heterogeneity of neighbouring cells within the same tissue specimen. These techniques have highlighted the need to better understand the interplay between proteins in terms of their colocalisation.'),
(97481, 2016, 'bmc', 'TRIg: a robust alignment pipeline for non-regular T-cell receptor and immunoglobulin sequences', 'Hung, Sheng-Jou|Chen, Yi-Lin|Chu, Chia-Hung|Lee, Chuan-Chun|Chen, Wan-Li|Lin, Ya-Lan|Lin, Ming-Ching|Ho, Chung-Liang|Liu, Tsunglin', 'Department of Biotechnology and Bioindustry Sciences, National Cheng Kung University, Tainan City, Taiwan.|Molecular Diagnostic Laboratory, Department of Pathology, National Cheng Kung University Hospital, Tainan City, Taiwan.|Department of Biotechnology and Bioindustry Sciences, National Cheng Kung University, Tainan City, Taiwan.|Molecular Diagnostic Laboratory, Department of Pathology, National Cheng Kung University Hospital, Tainan City, Taiwan.|Molecular Diagnostic Laboratory, Department of Pathology, National Cheng Kung University Hospital, Tainan City, Taiwan.|Molecular Diagnostic Laboratory, Department of Pathology, National Cheng Kung University Hospital, Tainan City, Taiwan.|Molecular Diagnostic Laboratory, Department of Pathology, National Cheng Kung University Hospital, Tainan City, Taiwan.|Molecular Diagnostic Laboratory, Department of Pathology, National Cheng Kung University Hospital, Tainan City, Taiwan.|Department of Biotechnology and Bioindustry Sciences, National Cheng Kung University, Tainan City, Taiwan. tsunglin@mail.ncku.edu.tw.', '-|-|-|-|-|-|-|-|-', 0, 'Algorithms|Animals|Computational Biology|DNA Primers|Humans|Immunoglobulins|Mice|Molecular Sequence Annotation|Receptors, Antigen, T-Cell|Recombination, Genetic|Sequence Alignment|Software|T-Lymphocytes', 'Journal Article', '10.1186/s12859-016-1304-2', '27782801', 'T cells and B cells are essential in the adaptive immunity via expressing T cell receptors and immunoglogulins respectively for recognizing antigens. To recognize a wide variety of antigens, a highly diverse repertoire of receptors is generated via complex recombination of the receptor genes. Reasonably, frequencies of the recombination events have been shown to predict immune diseases and provide insights into the development of immunity. The field is further boosted by high-throughput sequencing and several computational tools have been released to analyze the recombined sequences. However, all current tools assume regular recombination of the receptor genes, which is not always valid in data prepared using a RACE approach. Compared to the traditional multiplex PCR approach, RACE is free of primer bias, therefore can provide accurate estimation of recombination frequencies. To handle the non-regular recombination events, a new computational program is needed.'),
(97482, 2016, 'bmc', 'AlignStat: a web-tool and R package for statistical comparison of alternative multiple sequence alignments', 'Shafee, Thomas|Cooke, Ira', 'Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, 3086, Australia. T.Shafee@LaTrobe.edu.au.|Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, 3086, Australia.', 'Australia|Australia', 0, 'Algorithms|Amino Acid Sequence|Computational Biology|Data Interpretation, Statistical|Humans|Sequence Alignment|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/s12859-016-1300-6', '27784265', 'Alternative sequence alignment algorithms yield different results. It is therefore useful to quantify the similarities and differences between alternative alignments of the same sequences. These measurements can identify regions of consensus that are likely to be most informative in downstream analysis. They can also highlight systematic differences between alignments that relate to differences in the alignment algorithms themselves.'),
(97483, 2016, 'bmc', 'An analysis of simple computational strategies to facilitate the design of functional molecular information processors', 'Lee, Yiling|Roslan, Rozieffa|Azizan, Shariza|Firdaus-Raih, Mohd|Ramlan, Effirul I', 'Natural Computing Laboratory, Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia.|School of Biosciences & Biotechnology, Faculty of Science & Technology, and Institute of Systems Biology, Universiti Kebangsaan Malaysia, 43600, Bangi, Malaysia.|Bioscience Institute, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia.|School of Biosciences & Biotechnology, Faculty of Science & Technology, and Institute of Systems Biology, Universiti Kebangsaan Malaysia, 43600, Bangi, Malaysia. firdaus@mfrlab.org.|Natural Computing Laboratory, Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia. effirul@um.edu.my.', 'Malaysia|Malaysia|Malaysia|Malaysia|Malaysia', 0, 'Computational Biology|Computer Simulation|Electrophoresis, Polyacrylamide Gel|RNA|RNA, Catalytic|Reproducibility of Results', 'Journal Article', '10.1186/s12859-016-1297-x', '27793081', 'Biological macromolecules (DNA, RNA and proteins) are capable of processing physical or chemical inputs to generate outputs that parallel conventional Boolean logical operators. However, the design of functional modules that will enable these macromolecules to operate as synthetic molecular computing devices is challenging.'),
(97484, 2016, 'bmc', 'MEvoLib v10: the first molecular evolution library for Python', 'Álvarez-Jarreta, Jorge|Ruiz-Pesini, Eduardo', 'Depto. de Informática e Ingeniería de Sistemas (DIIS), Universidad de Zaragoza, María de Luna 1, Zaragoza, 50018, Spain. jorgeal@unizar.es.|Depto. de Bioquímica, Biología Molecular y Celular, Universidad de Zaragoza, Miguel Server 177, Zaragoza, 50013, Spain.', 'Spain|Spain', 1, 'Algorithms|Base Sequence|Computational Biology|DNA, Mitochondrial|Evolution, Molecular|Gene Library|Genes|Humans|Software', 'Journal Article', '10.1186/s12859-016-1303-3', '27793083', 'Molecular evolution studies involve many different hard computational problems solved, in most cases, with heuristic algorithms that provide a nearly optimal solution. Hence, diverse software tools exist for the different stages involved in a molecular evolution workflow.'),
(97485, 2016, 'bmc', 'BIGMAC : breaking inaccurate genomes and merging assembled contigs for long read metagenomic assembly', 'Lam, Ka-Kit|Hall, Richard|Clum, Alicia|Rao, Satish', 'Department of Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley, USA.|Pacific Biosciences, Menlo Park, USA.|Joint Genome Institute, Walnut Creek, USA.|Department of Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley, USA. satishr@cs.berkeley.edu.', 'United States|United States|United States|United States', 0, 'Algorithms|Computer Simulation|Metagenome|Metagenomics|Software|Statistics as Topic', 'Journal Article', '10.1186/s12859-016-1288-y', '27793084', 'The problem of de-novo assembly for metagenomes using only long reads is gaining attention. We study whether post-processing metagenomic assemblies with the original input long reads can result in quality improvement. Previous approaches have focused on pre-processing reads and optimizing assemblers. BIGMAC takes an alternative perspective to focus on the post-processing step.'),
(97486, 2016, 'bmc', 'Erratum to: Detection and sequence/structure mapping of biophysical constraints to protein variation in saturated mutational libraries and protein sequence alignments with a dedicated server', 'Abriata, Luciano A|Bovigny, Christophe|Peraro, Matteo Dal', 'Laboratory for Biomolecular Modeling, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, and Swiss Institute of Bioinformatics, AAB014 Station 19, Lausanne, 1015, Switzerland. luciano.abriata@epfl.ch.|Laboratory for Biomolecular Modeling, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, and Swiss Institute of Bioinformatics, AAB014 Station 19, Lausanne, 1015, Switzerland.|Laboratory for Biomolecular Modeling, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, and Swiss Institute of Bioinformatics, AAB014 Station 19, Lausanne, 1015, Switzerland.', 'Switzerland|Switzerland|Switzerland', 0, '-', 'Journal Article', '10.1186/s12859-016-1315-z', '27799049', '-'),
(97487, 2016, 'bmc', 'InDel marker detection by integration of multiple softwares using machine learning techniques', 'Yang, Jianqiu|Shi, Xinyi|Hu, Lun|Luo, Daipeng|Peng, Jing|Xiong, Shengwu|Kong, Fanjing|Liu, Baohui|Yuan, Xiaohui', 'School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.|School of Computer Science and Technology, Heilongjiang University, Harbin, China.|School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.|School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.|School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.|School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.|The Key Lab of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China.|The Key Lab of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China.|The Key Lab of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China. yuanxh@iga.ac.cn.', 'China|China|China|China|China|China|China|China|China', 1, '-', 'Journal Article', '10.1186/s12859-016-1312-2', '27806691', 'In the biological experiments of soybean species, molecular markers are widely used to verify the soybean genome or construct its genetic map. Among a variety of molecular markers, insertions and deletions (InDels) are preferred with the advantages of wide distribution and high density at the whole-genome level. Hence, the problem of detecting InDels based on next-generation sequencing data is of great importance for the design of InDel markers. To tackle it, this paper integrated machine learning techniques with existing software and developed two algorithms for InDel detection, one is the best F-score method (BF-M) and the other is the Support Vector Machine (SVM) method (SVM-M), which is based on the classical SVM model.'),
(97488, 2016, 'bmc', 'Evaluating tools for transcription factor binding site prediction', 'Jayaram, Narayan|Usvyat, Daniel|R Martin, Andrew C', 'Institute of Structural and Molecular Biology, Division of Biosciences, University College London, Darwin Building, Gower Street, London, WC1E 6BT, UK.|Institute of Structural and Molecular Biology, Division of Biosciences, University College London, Darwin Building, Gower Street, London, WC1E 6BT, UK.|Institute of Structural and Molecular Biology, Division of Biosciences, University College London, Darwin Building, Gower Street, London, WC1E 6BT, UK. andrew@bioinf.org.uk.', 'United Kingdom|United Kingdom|United Kingdom', 25, '-', 'Journal Article', '10.1186/s12859-016-1298-9', '27806697', 'Binding of transcription factors to transcription factor binding sites (TFBSs) is key to the mediation of transcriptional regulation. Information on experimentally validated functional TFBSs is limited and consequently there is a need for accurate prediction of TFBSs for gene annotation and in applications such as evaluating the effects of single nucleotide variations in causing disease. TFBSs are generally recognized by scanning a position weight matrix (PWM) against DNA using one of a number of available computer programs. Thus we set out to evaluate the best tools that can be used locally (and are therefore suitable for large-scale analyses) for creating PWMs from high-throughput ChIP-Seq data and for scanning them against DNA.'),
(97489, 2016, 'bmc', 'muBLASTP: database-indexed protein sequence search on multicore CPUs', 'Zhang, Jing|Misra, Sanchit|Wang, Hao|Feng, Wu-Chun', 'Department of Computer Science, Virginia Tech, 225 Stanger Street, Blacksburg, 24060, VA, USA. zjing14@vt.edu.|Parallel Computing Lab, Intel Corporation, Bengaluru, Karnataka, 560102, India.|Department of Computer Science, Virginia Tech, 225 Stanger Street, Blacksburg, 24060, VA, USA.|Department of Computer Science, Virginia Tech, 225 Stanger Street, Blacksburg, 24060, VA, USA.', 'United States|India|United States|United States', 0, 'Algorithms|Databases, Protein|Humans|Information Storage and Retrieval|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1302-4', '27809763', 'The Basic Local Alignment Search Tool (BLAST) is a fundamental program in the life sciences that searches databases for sequences that are most similar to a query sequence. Currently, the BLAST algorithm utilizes a query-indexed approach. Although many approaches suggest that sequence search with a database index can achieve much higher throughput (e.g., BLAT, SSAHA, and CAFE), they cannot deliver the same level of sensitivity as the query-indexed BLAST, i.e., NCBI BLAST, or they can only support nucleotide sequence search, e.g., MegaBLAST. Due to different challenges and characteristics between query indexing and database indexing, the existing techniques for query-indexed search cannot be used into database indexed search.'),
(97490, 2016, 'bmc', 'Unsupervised gene set testing based on random matrix theory', 'Frost, H Robert|Amos, Christopher I', 'Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, 03755, NH, USA. rob.frost@dartmouth.edu.|Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, 03755, NH, USA.', 'United States|United States', 0, 'Algorithms|Biomarkers, Tumor|Computer Simulation|Gene Expression Profiling|Gene Regulatory Networks|Genomics|Humans|Leukemia|Models, Theoretical', 'Journal Article', '10.1186/s12859-016-1299-8', '27809777', 'Gene set testing, or pathway analysis, is a bioinformatics technique that performs statistical testing on biologically meaningful sets of genomic variables. Although originally developed for supervised analyses, i.e., to test the association between gene sets and an outcome variable, gene set testing also has important unsupervised applications, e.g., p-value weighting. For unsupervised testing, however, few effective gene set testing methods are available with support especially poor for several biologically relevant use cases.'),
(97491, 2016, 'bmc', 'PSE-HMM: genome-wide CNV detection from NGS data using an HMM with Position-Specific Emission probabilities', 'Malekpour, Seyed Amir|Pezeshk, Hamid|Sadeghi, Mehdi', 'School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, 14155-6455, Iran.|School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, 14155-6455, Iran. pezeshk@khayam.ut.ac.ir.|National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.', '-|-|-', 0, 'Algorithms|DNA Copy Number Variations|Data Accuracy|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Probability|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-016-1296-y', '27809781', 'Copy Number Variation (CNV) is envisaged to be a major source of large structural variations in the human genome. In recent years, many studies apply Next Generation Sequencing (NGS) data for the CNV detection. However, still there is a necessity to invent more accurate computational tools.'),
(97492, 2016, 'bmc', 'SGFSC: speeding the gene functional similarity calculation based on hash tables', 'Tian, Zhen|Wang, Chunyu|Guo, Maozu|Liu, Xiaoyan|Teng, Zhixia', 'School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Peoples Republic of China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Peoples Republic of China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Peoples Republic of China. maozuguo@hit.edu.cn.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Peoples Republic of China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Peoples Republic of China.', 'China|China|China|China|China', 3, 'Algorithms|Biomarkers|Computational Biology|Computer Simulation|Gene Ontology|Genomics|Humans|Molecular Sequence Annotation|Programming Languages|Semantics', 'Journal Article', '10.1186/s12859-016-1294-0', '27814675', 'In recent years, many measures of gene functional similarity have been proposed and widely used in all kinds of essential research. These methods are mainly divided into two categories: pairwise approaches and group-wise approaches. However, a common problem with these methods is their time consumption, especially when measuring the gene functional similarities of a large number of gene pairs. The problem of computational efficiency for pairwise approaches is even more prominent because they are dependent on the combination of semantic similarity. Therefore, the efficient measurement of gene functional similarity remains a challenging problem.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97493, 2016, 'bmc', 'WUFlux: an open-source platform for 13C metabolic flux analysis of bacterial metabolism', 'He, Lian|Wu, Stephen G|Zhang, Muhan|Chen, Yixin|Tang, Yinjie J', 'Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, 63130, USA. lianheenvi@gmail.com.|Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, 63130, USA.|Department of Computer Science and Engineering, Washington University, St. Louis, MO, 63130, USA.|Department of Computer Science and Engineering, Washington University, St. Louis, MO, 63130, USA.|Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, 63130, USA. yinjie.tang@wustl.edu.', 'United States|United States|United States|United States|United States', 14, 'Carbon Isotopes|Cyanobacteria|Humans|Metabolic Flux Analysis|Metabolic Networks and Pathways|Models, Biological|Models, Theoretical|Software', 'Journal Article', '10.1186/s12859-016-1314-0', '27814681', 'Background: Flux analyses, including flux balance analysis (FBA) and 13C-metabolic flux analysis (13C-MFA), offer direct insights into cell metabolism, and have been widely used to characterize model and non-model microbial species. Nonetheless, constructing the 13C-MFA model and performing flux calculation are demanding for new learners, because they require knowledge of metabolic networks, carbon transitions, and computer programming. To facilitate and standardize the 13C-MFA modeling work, we set out to publish a user-friendly and programming-free platform (WUFlux) for flux calculations in MATLAB®. Results: We constructed an open-source platform for steady-state 13C-MFA. Using GUIDE (graphical user interface design environment) in MATLAB, we built a user interface that allows users to modify models based on their own experimental conditions. WUFlux is capable of directly correcting mass spectrum data of TBDMS (N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide)-derivatized proteinogenic amino acids by removing background noise. To simplify 13C-MFA of different prokaryotic species, the software provides several metabolic network templates, including those for chemoheterotrophic bacteria and mixotrophic cyanobacteria. Users can modify the network and constraints, and then analyze the microbial carbon and energy metabolisms of various carbon substrates (e.g., glucose, pyruvate/lactate, acetate, xylose, and glycerol). WUFlux also offers several ways of visualizing the flux results with respect to the constructed network. To validate our model’s applicability, we have compared and discussed the flux results obtained from WUFlux and other MFA software. We have also illustrated how model constraints of cofactor and ATP balances influence fluxome results. Conclusion: Open-source software for 13C-MFA, WUFlux, with a user-friendly interface and easy-to-modify templates, is now available at http://www.13cmfa.org/or (http://tang.eece.wustl.edu/ToolDevelopment.htm). We will continue documenting curated models of non-model microbial species and improving WUFlux performance.'),
(97494, 2016, 'bmc', 'Distributed gene expression modelling for exploring variability in epigenetic function', 'Budden, David M|Crampin, Edmund J', 'Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, Cambridge, 02139, USA. budden@csail.mit.edu.|Systems Biology Laboratory, Melbourne School of Engineering, the University of Melbourne, Parkville, 3010, Australia.', 'United States|Australia', 0, 'Chromatin Immunoprecipitation|Computational Biology|Epigenomics|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genome, Human|High-Throughput Nucleotide Sequencing|Histones|Humans|Neoplasms|Sequence Analysis, DNA|Transcription, Genetic', 'Journal Article', '10.1186/s12859-016-1313-1', '27816056', 'Predictive gene expression modelling is an important tool in computational biology due to the volume of high-throughput sequencing data generated by recent consortia. However, the scope of previous studies has been restricted to a small set of cell-lines or experimental conditions due an inability to leverage distributed processing architectures for large, sharded data-sets.'),
(97495, 2016, 'bmc', 'Large scale analysis of amino acid substitutions in bacterial proteomics', 'Ischenko, Dmitry|Alexeev, Dmitry|Shitikov, Egor|Kanygina, Alexandra|Malakhova, Maja|Kostryukova, Elena|Larin, Andrey|Kovalchuk, Sergey|Pobeguts, Olga|Butenko, Ivan|Anikanov, Nikolay|Altukhov, Ilya|Ilina, Elena|Govorun, Vadim', 'Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russian Federation. dmitry.ischenko@phystech.edu.|Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russian Federation.|Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russian Federation.|Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russian Federation.|Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russian Federation.|Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russian Federation.|Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russian Federation.|Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russian Federation.|Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russian Federation.|Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russian Federation.|Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russian Federation.|Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russian Federation.|Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russian Federation.|Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russian Federation.', 'Russian Federation|Russian Federation|Russian Federation|Russian Federation|Russian Federation|Russi', 0, 'Algorithms|Amino Acid Substitution|Amino Acids|Bacteria|Bacterial Proteins|Databases, Protein|Genome, Bacterial|Genomics|Mutation|Peptide Fragments|Proteome|Proteomics|Tandem Mass Spectrometry', 'Journal Article', '10.1186/s12859-016-1301-5', '27821049', 'Proteomics of bacterial pathogens is a developing field exploring microbial physiology, gene expression and the complex interactions between bacteria and their hosts. One of the complications in proteomic approach is micro- and macro-heterogeneity of bacterial species, which makes it impossible to build a comprehensive database of bacterial genomes for identification, while most of the existing algorithms rely largely on genomic data.'),
(97496, 2016, 'bmc', 'Disease gene prioritization by integrating tissue-specific molecular networks using a robust multi-network model', 'Ni, Jingchao|Koyuturk, Mehmet|Tong, Hanghang|Haines, Jonathan|Xu, Rong|Zhang, Xiang', 'Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, 44106, OH, USA.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, 44106, OH, USA.|School of Computing, Informatics, Decision Systems Engineering, Arizona State University, 699 S. Mill Ave., Tempe, 85281, AZ, USA.|Department of Epidemiology and Biostatistics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, 44106, OH, USA.|Department of Epidemiology and Biostatistics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, 44106, OH, USA.|College of Information Sciences and Technology, Pennsylvania State University, 332 Information Sciences and Technology Building, University Park, 16802, PA, USA. xzhang@ist.psu.edu.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Area Under Curve|Computational Biology|Databases, Genetic|Disease|Gene Regulatory Networks|Humans|Models, Genetic|Organ Specificity|ROC Curve', 'Journal Article', '10.1186/s12859-016-1317-x', '27829360', 'Accurately prioritizing candidate disease genes is an important and challenging problem. Various network-based methods have been developed to predict potential disease genes by utilizing the disease similarity network and molecular networks such as protein interaction or gene co-expression networks. Although successful, a common limitation of the existing methods is that they assume all diseases share the same molecular network and a single generic molecular network is used to predict candidate genes for all diseases. However, different diseases tend to manifest in different tissues, and the molecular networks in different tissues are usually different. An ideal method should be able to incorporate tissue-specific molecular networks for different diseases.'),
(97497, 2016, 'bmc', 'LSCplus: a fast solution for improving long read accuracy by short read alignment', 'Hu, Ruifeng|Sun, Guibo|Sun, Xiaobo', 'Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China.|Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China.|Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China. xbsun@implad.ac.cn.', 'China|China|China', 6, 'Computational Biology|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Reproducibility of Results|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-016-1316-y', '27829364', 'The single molecule, real time (SMRT) sequencing technology of Pacific Biosciences enables the acquisition of transcripts from end to end due to its ability to produce extraordinarily long reads (>10 kb). This new method of transcriptome sequencing has been applied to several projects on humans and model organisms. However, the raw data from SMRT sequencing are of relatively low quality, with a random error rate of approximately 15 %, for which error correction using next-generation sequencing (NGS) short reads is typically necessary. Few tools have been designed that apply a hybrid sequencing approach that combines NGS and SMRT data, and the most popular existing tool for error correction, LSC, has computing resource requirements that are too intensive for most laboratory and research groups. These shortcomings severely limit the application of SMRT long reads for transcriptome analysis.'),
(97498, 2016, 'bmc', 'libFLASM: a software library for fixed-length approximate string matching', 'Ayad, Lorraine A K|Pissis, Solon P P|Retha, Ahmad', 'Department of Informatics, Kings College London, The Strand, London, WC2R 2LS, UK.|Department of Informatics, Kings College London, The Strand, London, WC2R 2LS, UK. solon.pissis@kcl.ac.uk.|Department of Informatics, Kings College London, The Strand, London, WC2R 2LS, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Computational Biology|Databases as Topic|Gene Library|Likelihood Functions|Nucleotide Motifs|Sequence Alignment|Software|Time Factors', 'Journal Article', '10.1186/s12859-016-1320-2', '27832739', 'Approximate string matching is the problem of finding all factors of a given text that are at a distance at most k from a given pattern. Fixed-length approximate string matching is the problem of finding all factors of a text of length n that are at a distance at most k from any factor of length xe2x84x93 of a pattern of length m. There exist bit-vector techniques to solve the fixed-length approximate string matching problem in time [Formula: see text] and space [Formula: see text] under the edit and Hamming distance models, where w is the size of the computer word, as such these techniques are independent of the distance threshold k or the alphabet size. Fixed-length approximate string matching is a generalisation of approximate string matching and, hence, has numerous direct applications in computational molecular biology and elsewhere.'),
(97499, 2016, 'bmc', 'Erratum to: General continuous-time Markov model of sequence evolution via insertions/deletions: are alignment probabilities factorable?', 'Ezawa, Kiyoshi', 'Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, 820-8502, Japan. kezawa.ezawa3@gmail.com.', 'Japan', 1, '-', 'Journal Article', '10.1186/s12859-016-1282-4', '27832741', '-'),
(97500, 2016, 'bmc', 'DASP3: identification of protein sequences belonging to functionally relevant groups', 'Leuthaeuser, Janelle B|Morris, John H|Harper, Angela F|Ferrin, Thomas E|Babbitt, Patricia C|Fetrow, Jacquelyn S', 'Molecular Genetics and Genomics Program, Wake Forest University, Winston-Salem, NC, 27106, USA. jleuthae@gmail.edu.|Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA.|Department of Physics, Wake Forest University, Winston-Salem, NC, 27106, USA.|Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA.|Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA.|Department of Chemistry, University of Richmond, Richmond, VA, 23173, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Catalytic Domain|Cluster Analysis|Databases, Protein|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/s12859-016-1295-z', '27835946', 'Development of automatable processes for clustering proteins into functionally relevant groups is a critical hurdle as an increasing number of sequences are deposited into databases. Experimental function determination is exceptionally time-consuming and cant keep pace with the identification of protein sequences. A tool, DASP (Deacon Active Site Profiler), was previously developed to identify protein sequences with active site similarity to a query set. Development of two iterative, automatable methods for clustering proteins into functionally relevant groups exposed algorithmic limitations to DASP.'),
(97501, 2016, 'bmc', 'Feature-based classification of human transcription factors into hypothetical sub-classes related to regulatory function', 'Ehsani, Rezvan|Bahrami, Shahram|Drabløs, Finn', 'Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, PO Box 8905, NO-7491, Trondheim, Norway.|Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, PO Box 8905, NO-7491, Trondheim, Norway.|Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, PO Box 8905, NO-7491, Trondheim, Norway. finn.drablos@ntnu.no.', 'Norway|Norway|Norway', 7, 'Chromatin|DNA|Gene Expression Regulation|Genomics|Humans|Transcription Factors', 'Journal Article', '10.1186/s12859-016-1349-2', '27842491', 'Transcription factors are key proteins in the regulation of gene transcription. An important step in this process is the opening of chromatin in order to make genomic regions available for transcription. Data on DNase I hypersensitivity has previously been used to label a subset of transcription factors as Pioneers, Settlers and Migrants to describe their potential role in this process. These labels represent an interesting hypothesis on gene regulation and possibly a useful approach for data analysis, and therefore we wanted to expand the set of labeled transcription factors to include as many known factors as possible. We have used a well-annotated dataset of 1175 transcription factors as input to supervised machine learning methods, using the subset with previously assigned labels as training set. We then used the final classifier to label the additional transcription factors according to their potential role as Pioneers, Settlers and Migrants. The full set of labeled transcription factors was used to investigate associated properties and functions of each class, including an analysis of interaction data for transcription factors based on DNA co-binding and protein-protein interactions. We also used the assigned labels to analyze a previously published set of gene lists associated with a time course experiment on cell differentiation.'),
(97502, 2016, 'bmc', 'SABRE: a method for assessing the stability of gene modules in complex tissues and subject populations', 'Shannon, Casey P|Chen, Virginia|Takhar, Mandeep|Hollander, Zsuzsanna|Balshaw, Robert|McManus, Bruce M|Tebbutt, Scott J|Sin, Don D|Ng, Raymond T', 'PROOF Centre of Excellence, Vancouver, BC, Canada. casey.shannon@hli.ubc.ca.|PROOF Centre of Excellence, Vancouver, BC, Canada.|Department of Computer Science, UBC, Vancouver, BC, Canada.|PROOF Centre of Excellence, Vancouver, BC, Canada.|PROOF Centre of Excellence, Vancouver, BC, Canada.|PROOF Centre of Excellence, Vancouver, BC, Canada.|PROOF Centre of Excellence, Vancouver, BC, Canada.|Department of Medicine, Division of Respiratory Medicine, UBC, Vancouver, BC, Canada.|PROOF Centre of Excellence, Vancouver, BC, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Algorithms|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Humans|Software|Systems Biology|Transcriptome', 'Evaluation Studies', '10.1186/s12859-016-1319-8', '27842512', 'Gene network inference (GNI) algorithms can be used to identify sets of coordinately expressed genes, termed network modules from whole transcriptome gene expression data. The identification of such modules has become a popular approach to systems biology, with important applications in translational research. Although diverse computational and statistical approaches have been devised to identify such modules, their performance behavior is still not fully understood, particularly in complex human tissues. Given human heterogeneity, one important question is how the outputs of these computational methods are sensitive to the input sample set, or stability. A related question is how this sensitivity depends on the size of the sample set. We describe here the SABRE (Similarity Across Bootstrap RE-sampling) procedure for assessing the stability of gene network modules using a re-sampling strategy, introduce a novel criterion for identifying stable modules, and demonstrate the utility of this approach in a clinically-relevant cohort, using two different gene network module discovery algorithms.'),
(97503, 2016, 'bmc', 'GEN3VA: aggregation and analysis of gene expression signatures from related studies', 'Gundersen, Gregory W|Jagodnik, Kathleen M|Woodland, Holly|Fernandez, Nicholas F|Sani, Kevin|Dohlman, Anders B|Ung, Peter Man-Un|Monteiro, Caroline D|Schlessinger, Avner|Maayan, Avi', 'Department of Pharmacological Sciences, One Gustave L. Levy Place, Box 1603, New York, NY, 10029, USA.|Fluid Physics and Transport Processes Branch, NASA Glenn Research Center, 21000 Brookpark Rd, Cleveland, OH, 44135, USA.|, Daylesford, The Fairway, Weybridge, Surrey, KT13 0RZ, UK.|Department of Pharmacological Sciences, One Gustave L. Levy Place, Box 1603, New York, NY, 10029, USA.|Department of Pharmacological Sciences, One Gustave L. Levy Place, Box 1603, New York, NY, 10029, USA.|Department of Pharmacological Sciences, One Gustave L. Levy Place, Box 1603, New York, NY, 10029, USA.|Department of Pharmacological Sciences, One Gustave L. Levy Place, Box 1603, New York, NY, 10029, USA.|Department of Pharmacological Sciences, One Gustave L. Levy Place, Box 1603, New York, NY, 10029, USA.|Department of Pharmacological Sciences, One Gustave L. Levy Place, Box 1603, New York, NY, 10029, USA.|Department of Pharmacological Sciences, One Gustave L. Levy Place, Box 1603, New York, NY, 10029, USA. avi.maayan@mssm.edu.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Aging|Animals|Dexamethasone|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Reproducibility of Results|Software|Transcriptome', 'Journal Article', '10.1186/s12859-016-1321-1', '27846806', 'Genome-wide gene expression profiling of mammalian cells is becoming a staple of many published biomedical and biological research studies. Such data is deposited into data repositories such as the Gene Expression Omnibus (GEO) for potential reuse. However, these repositories currently do not provide simple interfaces to systematically analyze collections of related studies.'),
(97504, 2016, 'bmc', 'Comments on: fold change rank ordering statistics: a new method for detecting differentially expressed genes', 'Dembélé, Doulaye|Kastner, Philippe', 'Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), CNRS UMR 7104, INSERM U964, Université de Strasbourg, Illkirch, 67404, France. doulaye@igbmc.fr.|Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), CNRS UMR 7104, INSERM U964, Université de Strasbourg, Illkirch, 67404, France.', 'France|France', 1, 'Algorithms|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis', 'Letter', '10.1186/s12859-016-1322-0', '27846811', 'We published a new method (BMC Bioinformatics 2014, 15:14) for searching for differentially expressed genes from two biological conditions datasets. The presentation of theorem 1 in this paper was incomplete. We received an anonymous comment about our publication that motivates the present work. Here, we present a complementary result which is necessary from the theoretical point of view to demonstrate our theorem. We also show that this result has no negative impact on our conclusions obtained with synthetic and experimental microarrays datasets.'),
(97505, 2016, 'bmc', 'Bioimage classification with subcategory discriminant transform of high dimensional visual descriptors', 'Song, Yang|Cai, Weidong|Huang, Heng|Feng, Dagan|Wang, Yue|Chen, Mei', 'School of Information Technologies, The University of Sydney, Sydney, Australia. yang.song@sydney.edu.au.|School of Information Technologies, The University of Sydney, Sydney, Australia.|Department of Computer Science and Engineering, University of Texas, Arlington, USA.|School of Information Technologies, The University of Sydney, Sydney, Australia.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, USA.|Computer Engineering Department, University of Albany State University of New York, Albany, USA.', 'Australia|Australia|United States|Australia|United States|United States', 0, 'Algorithms|Animals|Databases, Factual|Discriminant Analysis|Humans|Image Processing, Computer-Assisted', 'Evaluation Studies', '10.1186/s12859-016-1318-9', '27852213', 'Bioimage classification is a fundamental problem for many important biological studies that require accurate cell phenotype recognition, subcellular localization, and histopathological classification. In this paper, we present a new bioimage classification method that can be generally applicable to a wide variety of classification problems. We propose to use a high-dimensional multi-modal descriptor that combines multiple texture features. We also design a novel subcategory discriminant transform (SDT) algorithm to further enhance the discriminative power of descriptors by learning convolution kernels to reduce the within-class variation and increase the between-class difference.'),
(97506, 2016, 'bmc', 'Protein ligand-specific binding residue predictions by an ensemble classifier', 'Hu, Xiuzhen|Wang, Kai|Dong, Qiwen', 'College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, Peoples Republic of China.|College of Animal Science and Technology, Jilin Agricultural University, Changchun, 130118, Peoples Republic of China.|Institute for Data Science and Engineering, East China Normal University, Shanghai, 200062, Peoples Republic of China. qwdong@sei.ecnu.edu.cn.', 'China|China|China', 10, 'Algorithms|Amino Acid Sequence|Binding Sites|Databases, Protein|Ligands|Protein Binding|Proteins|Reproducibility of Results', 'Journal Article', '10.1186/s12859-016-1348-3', '27855637', 'Prediction of ligand binding sites is important to elucidate protein functions and is helpful for drug design. Although much progress has been made, many challenges still need to be addressed. Prediction methods need to be carefully developed to account for chemical and structural differences between ligands.'),
(97507, 2016, 'bmc', 'Introducing Explorer of Taxon Concepts with a case study on spider measurement matrix building', 'Cui, Hong|Xu, Dongfang|Chong, Steven S|Ramirez, Martin|Rodenhausen, Thomas|Macklin, James A|Ludäscher, Bertram|Morris, Robert A|Soto, Eduardo M|Koch, Nicolás Mongiardino', 'University of Arizona, Tucson, AZ, USA. hongcui@email.arizona.edu.|University of Arizona, Tucson, AZ, USA.|University of Arizona, Tucson, AZ, USA.|Museo Argentino de Ciencias, Naturales, CONICET, Buenos Aires, Argentina.|University of Arizona, Tucson, AZ, USA.|Agriculture and Agri-Food Canada, Ottawa, Canada.|University of Illinois at Urbana-Champaign, Champaign, USA.|University of Massachusetts at Boston and Harvard University Herbaria, Massachusetts, USA.|Department of Geology & Geophysics, Yale University, New Haven, Connecticut, USA.|Department of Geology & Geophysics, Yale University, New Haven, Connecticut, USA.', 'United States|United States|United States|Argentina|United States|Canada|United States|United States|United States|United States', 0, 'Animals|Biological Evolution|Humans|Software|Spiders', 'Journal Article', '10.1186/s12859-016-1352-7', '27855645', 'Taxonomic descriptions are traditionally composed in natural language and published in a format that cannot be directly used by computers. The Exploring Taxon Concepts (ETC) project has been developing a set of web-based software tools that convert morphological descriptions published in telegraphic style to character data that can be reused and repurposed. This paper introduces the first semi-automated pipeline, to our knowledge, that converts morphological descriptions into taxon-character matrices to support systematics and evolutionary biology research. We then demonstrate and evaluate the use of the ETC Input Creation - Text Capture - Matrix Generation pipeline to generate body part measurement matrices from a set of 188 spider morphological descriptions and report the findings.'),
(97508, 2016, 'bmc', 'Most of the tight positional conservation of transcription factor binding sites near the transcription start site reflects their co-localization within regulatory modules', 'Acevedo-Luna, Natalia|Marixc3xb1o-Ramxc3xadrez, Leonardo|Halbert, Armand|Hansen, Ulla|Landsman, David|Spouge, John L', 'Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, 50011, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.|Department of Biology, Boston University, 5 Cummington Mall, Boston, MA, 02215, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA. spouge@nih.gov.', 'United States|United States|United States|United States|United States|United States', 0, 'Binding Sites|DNA|Gene Expression Regulation|Gene Ontology|Humans|Protein Binding|Regulatory Elements, Transcriptional|Sequence Analysis, DNA|Transcription Factors|Transcription Initiation Site', 'Journal Article', '10.1186/s12859-016-1354-5', '27871221', 'Transcription factors (TFs) form complexes that bind regulatory modules (RMs) within DNA, to control specific sets of genes. Some transcription factor binding sites (TFBSs) near the transcription start site (TSS) display tight positional preferences relative to the TSS. Furthermore, near the TSS, RMs can co-localize TFBSs with each other and the TSS. The proportion of TFBS positional preferences due to TFBS co-localization within RMs is unknown, however. ChIP experiments confirm co-localization of some TFBSs genome-wide, including near the TSS, but they typically examine only a few TFs at a time, using non-physiological conditions that can vary from lab to lab. In contrast, sequence analysis can examine many TFs uniformly and methodically, broadly surveying the co-localization of TFBSs with tight positional preferences relative to the TSS.'),
(97509, 2016, 'bmc', 'A statistical model for the analysis of beta values in DNA methylation studies', 'Weinhold, Leonie|Wahl, Simone|Pechlivanis, Sonali|Hoffmann, Per|Schmid, Matthias', 'Department of Medical Biometry, Informatics and Epidemiology, University of Bonn, Sigmund-Freud-Str. 25, Bonn, D-53127, Germany. weinhold@imbie.uni-bonn.de.|Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Ingolstädter Landstr. 1, Neuherber, D-85764, Germany.|Department of Medical Informatics, Biometry and Epidemiology, University Hospital Essen, Hufelandstr. 55, Essen, D-45122, Germany.|Human Genomics Research Group, Department of Biomedicine, University Hospital Basel, Hebelstr. 20, Basel, CH-4031, Switzerland.|Department of Medical Biometry, Informatics and Epidemiology, University of Bonn, Sigmund-Freud-Str. 25, Bonn, D-53127, Germany.', 'Germany|Germany|Germany|Switzerland|Germany', 0, 'Aged|Aging|Behavior|Cohort Studies|Computer Simulation|CpG Islands|DNA Methylation|Humans|Middle Aged|Models, Statistical|Smoking', 'Journal Article', '10.1186/s12859-016-1347-4', '27875981', 'The analysis of DNA methylation is a key component in the development of personalized treatment approaches. A common way to measure DNA methylation is the calculation of beta values, which are bounded variables of the form M/(M+U) that are generated by Illuminas 450k BeadChip array. The statistical analysis of beta values is considered to be challenging, as traditional methods for the analysis of bounded variables, such as M-value regression and beta regression, are based on regularity assumptions that are often too strong to adequately describe the distribution of beta values.'),
(97510, 2016, 'bmc', 'Clustering of disulfide-rich peptides provides scaffolds for hit discovery by phage display: application to interleukin-23', 'Barkan, David T|Cheng, Xiao-Li|Celino, Herodion|Tran, Tran T|Bhandari, Ashok|Craik, Charles S|Sali, Andrej|Smythe, Mark L', 'Protagonist Therapeutics, Inc., 521 Cottonwood Drive, Suite 100, Milpitas, CA, 95035-74521, USA.|Protagonist Therapeutics, Inc., 521 Cottonwood Drive, Suite 100, Milpitas, CA, 95035-74521, USA.|Protagonist Therapeutics, Inc., 521 Cottonwood Drive, Suite 100, Milpitas, CA, 95035-74521, USA.|Protagonist Therapeutics, Inc., 521 Cottonwood Drive, Suite 100, Milpitas, CA, 95035-74521, USA.|Protagonist Therapeutics, Inc., 521 Cottonwood Drive, Suite 100, Milpitas, CA, 95035-74521, USA.|Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, 94158, USA.|Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, 94158, USA.|Protagonist Therapeutics, Inc., 521 Cottonwood Drive, Suite 100, Milpitas, CA, 95035-74521, USA. m.smythe@protagonist-inc.com.', 'United States|United States|United States|United States|United States|United States|United States|United States', 6, 'Amino Acid Sequence|Bacteriophages|Cluster Analysis|Disulfides|Drug Discovery|Humans|Interleukin-23|Peptide Library|Peptides|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/s12859-016-1350-9', '27881076', 'Disulfide-rich peptides (DRPs) are found throughout nature. They are suitable scaffolds for drug development due to their small cores, whose disulfide bonds impart extraordinary chemical and biological stability. A challenge in developing a DRP therapeutic is to engineer binding to a specific target. This challenge can be overcome by (i) sampling the large sequence space of a given scaffold through a phage display library and by (ii) panning multiple libraries encoding structurally distinct scaffolds. Here, we implement a protocol for defining these diverse scaffolds, based on clustering structurally defined DRPs according to their conformational similarity.'),
(97511, 2016, 'bmc', 'variancePartition: interpreting drivers of variation in complex gene expression studies', 'Hoffman, Gabriel E|Schadt, Eric E', 'Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA. gabriel.hoffman@mssm.edu.|Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA.', 'United States|United States', 0, 'Algorithms|Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Genetic Variation|Genomics|High-Throughput Nucleotide Sequencing|Humans|Linear Models|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-016-1323-z', '27884101', 'As large-scale studies of gene expression with multiple sources of biological and technical variation become widely adopted, characterizing these drivers of variation becomes essential to understanding disease biology and regulatory genetics.'),
(97512, 2016, 'bmc', 'ampliMethProfiler: a pipeline for the analysis of CpG methylation profiles of targeted deep bisulfite sequenced amplicons', 'Scala, Giovanni|Affinito, Ornella|Palumbo, Domenico|Florio, Ermanno|Monticelli, Antonella|Miele, Gennaro|Chiariotti, Lorenzo|Cocozza, Sergio', 'Istituto Nazionale di Fisica Nucleare, Sezione di Napoli, Naples, Italy. gscala@na.infn.it.|Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università degli Studi di Napoli Federico II, Naples, Italy.|Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università degli Studi di Napoli Federico II, Naples, Italy.|Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università degli Studi di Napoli Federico II, Naples, Italy.|Istituto di Endocrinologia ed Oncologia Sperimentale (IEOS) Gaetano Salvatore, Consiglio Nazionale delle Ricerche CNR, Naples, Italy.|Istituto Nazionale di Fisica Nucleare, Sezione di Napoli, Naples, Italy.|Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università degli Studi di Napoli Federico II, Naples, Italy.|Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università degli Studi di Napoli Federico II, Naples, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Animals|CpG Islands|D-Aspartate Oxidase|DNA|DNA Methylation|Gastrointestinal Tract|High-Throughput Nucleotide Sequencing|Humans|Mice|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1186/s12859-016-1380-3', '27884103', 'CpG sites in an individual molecule may exist in a binary state (methylated or unmethylated) and each individual DNA molecule, containing a certain number of CpGs, is a combination of these states defining an epihaplotype. Classic quantification based approaches to study DNA methylation are intrinsically unable to fully represent the complexity of the underlying methylation substrate. Epihaplotype based approaches, on the other hand, allow methylation profiles of cell populations to be studied at the single molecule level. For such investigations, next-generation sequencing techniques can be used, both for quantitative and for epihaplotype analysis. Currently available tools for methylation analysis lack output formats that explicitly report CpG methylation profiles at the single molecule level and that have suited statistical tools for their interpretation.'),
(97513, 2016, 'bmc', 'SPECtre: a spectral coherence--based classifier of actively translated transcripts from ribosome profiling sequence data', 'Chun, Sang Y|Rodriguez, Caitlin M|Todd, Peter K|Mills, Ryan E', 'Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.|Department of Neurology, University of Michigan, Ann Arbor, MI, 48109, USA.|Department of Neurology, University of Michigan, Ann Arbor, MI, 48109, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA. remills@umich.edu.', 'United States|United States|United States|United States', 19, 'Algorithms|Gene Expression Profiling|HEK293 Cells|Humans|Open Reading Frames|Protein Biosynthesis|RNA, Messenger|Ribosomes|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1186/s12859-016-1355-4', '27884106', 'Active protein translation can be assessed and measured using ribosome profiling sequencing strategies. Prevailing analytical approaches applied to this technology make use of sequence fragment length profiling or reading frame occupancy enrichment to differentiate between active translation and background noise, however they do not consider additional characteristics inherent to the technology which limits their overall accuracy.'),
(97514, 2016, 'bmc', 'A new method of finding groups of coexpressed genes and conditions of coexpression', 'Anand, Rajat|Ravichandran, Srikanth|Chatterjee, Samrat', 'Drug Discovery Research Centre, Translational Health Science and Technology Institute, NCR Biotech science cluster, 3rd milestone, Faridabad-Gurgaon expressway, Faridabad, 121001, India.|Immunology group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi, 110067, India.|Drug Discovery Research Centre, Translational Health Science and Technology Institute, NCR Biotech science cluster, 3rd milestone, Faridabad-Gurgaon expressway, Faridabad, 121001, India. samrat.chatterjee@thsti.res.in.', 'India|India|India', 2, 'Algorithms|Animals|Biomarkers|Breast Neoplasms|Cluster Analysis|Diet|Female|Gene Expression Profiling|Humans|Liver|Mice|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/s12859-016-1356-3', '27887568', 'To study a biological phenomenon such as finding mechanism of disease, common methodology is to generate the microarray data in different relevant conditions and find groups of genes co-expressed across conditions from such data. These groups might enable us to find biological processes involved in a disease condition. However, more detailed understanding can be made when information of a biological process associated with a particular condition is obtained from the data. Many algorithms are available which finds groups of co-expressed genes and associated conditions of co-expression that can help finding processes associated with particular condition. However, these algorithms depend on different input parameters for generating groups. For real datasets, it is difficult to use these algorithms due to unknown values of these parameters.'),
(97515, 2016, 'bmc', 'MetaMIS: a metagenomic microbial interaction simulator based on microbial community profiles', 'Shaw, Grace Tzun-Wen|Pao, Yueh-Yang|Wang, Daryi', 'Biodiversity Research Center, Academia Sinica, Taipei, 115, Taiwan.|Biodiversity Research Center, Academia Sinica, Taipei, 115, Taiwan.|Biodiversity Research Center, Academia Sinica, Taipei, 115, Taiwan. dywang@gate.sinica.edu.tw.', '-|-|-', 27, 'Algorithms|Bacteria|Computational Biology|Ecology|Feces|Female|Humans|Male|Metagenome|Microbial Interactions|Microbiota|Software', 'Journal Article', '10.1186/s12859-016-1359-0', '27887570', 'The complexity and dynamics of microbial communities are major factors in the ecology of a system. With the NGS technique, metagenomics data provides a new way to explore microbial interactions. Lotka-Volterra models, which have been widely used to infer animal interactions in dynamic systems, have recently been applied to the analysis of metagenomic data.'),
(97516, 2016, 'bmc', 'LBSizeCleav: improved support vector machine (SVM)-based prediction of Dicer cleavage sites using loop/bulge length', 'Bao, Yu|Hayashida, Morihiro|Akutsu, Tatsuya', 'Laboratory of Mathematical Bioinformatics, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan. houu@kuicr.kyoto-u.ac.jp.|Laboratory of Mathematical Bioinformatics, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan.|Laboratory of Mathematical Bioinformatics, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan.', 'Japan|Japan|Japan', 0, 'Algorithms|Base Sequence|Bayes Theorem|DEAD-box RNA Helicases|Humans|MicroRNAs|Nucleic Acid Conformation|RNA Precursors|Ribonuclease III|Software|Support Vector Machine', 'Journal Article', '10.1186/s12859-016-1353-6', '27887571', 'Dicer is necessary for the process of mature microRNA (miRNA) formation because the Dicer enzyme cleaves pre-miRNA correctly to generate miRNA with correct seed regions. Nonetheless, the mechanism underlying the selection of a Dicer cleavage site is still not fully understood. To date, several studies have been conducted to solve this problem, for example, a recent discovery indicates that the loop/bulge structure plays a central role in the selection of Dicer cleavage sites. In accordance with this breakthrough, a support vector machine (SVM)-based method called PHDCleav was developed to predict Dicer cleavage sites which outperforms other methods based on random forest and naive Bayes. PHDCleav, however, tests only whether a position in the shift window belongs to a loop/bulge structure.'),
(97517, 2016, 'bmc', 'FluxFix: automatic isotopologue normalization for metabolic tracer analysis', 'Trefely, Sophie|Ashwell, Peter|Snyder, Nathaniel W', 'AJ Drexel Autism Institute, Drexel University, Philadelphia, PA, 19104, USA. sjt65@drexel.edu.|AJ Drexel Autism Institute, Drexel University, Philadelphia, PA, 19104, USA.|AJ Drexel Autism Institute, Drexel University, Philadelphia, PA, 19104, USA.', 'United States|United States|United States', 23, 'Algorithms|Biomarkers, Tumor|Carbon Isotopes|HeLa Cells|Hep G2 Cells|Humans|Isotope Labeling|Mass Spectrometry|Metabolomics|Software', 'Journal Article', '10.1186/s12859-016-1360-7', '27887574', '-'),
(97518, 2016, 'bmc', 'The Lair: a resource for exploratory analysis of published RNA-Seq data', 'Pimentel, Harold|Sturmfels, Pascal|Bray, Nicolas|Melsted, Páll|Pachter, Lior', 'Department of Computer Science, University of California, Berkeley, 387 Soda Hall, Berkeley, 94720, USA.|Department of Computer Science, University of Michigan, 486 Vassar Avenue, Berkeley, CA, 94708, USA.|Innovative Genomics Initiative, University of California, Berkeley, 188 Li Ka Shing Center, Berkeley, CA, 94720, USA.|Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Dunhagi 5, 107, Reykjavík, Iceland.|Department of Computer Science, University of California, Berkeley, 387 Soda Hall, Berkeley, 94720, USA. lpachter@math.berkeley.edu.', 'United States|United States|United States|Iceland|United States', 9, 'Databases, Nucleic Acid|High-Throughput Nucleotide Sequencing|Humans|Reproducibility of Results|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-016-1357-2', '27905880', 'Increased emphasis on reproducibility of published research in the last few years has led to the large-scale archiving of sequencing data. While this data can, in theory, be used to reproduce results in papers, it is difficult to use in practice. We introduce a series of tools for processing and analyzing RNA-Seq data in the Sequence Read Archive, that together have allowed us to build an easily extendable resource for analysis of data underlying published papers. Our system makes the exploration of data easily accessible and usable without technical expertise. Our database and associated tools can be accessed at The Lair: http://pachterlab.github.io/lair .'),
(97519, 2016, 'bmc', 'MeFiT: merging and filtering tool for illumina paired-end reads for 16S rRNA amplicon sequencing', 'Parikh, Hardik I|Koparde, Vishal N|Bradley, Steven P|Buck, Gregory A|Sheth, Nihar U', 'Department of Microbiology and Immunology, Virginia Commonwealth University, Richmond, Virginia, USA.|Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, USA.|Department of Microbiology and Immunology, Virginia Commonwealth University, Richmond, Virginia, USA.|Department of Microbiology and Immunology, Virginia Commonwealth University, Richmond, Virginia, USA.|Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, USA. nsheth@vcu.edu.', 'United States|United States|United States|United States|United States', 0, 'Bacteria|Genes, Bacterial|Genes, rRNA|Genomics|High-Throughput Nucleotide Sequencing|RNA, Ribosomal, 16S|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-016-1358-1', '27905885', 'Recent advances in next-generation sequencing have revolutionized genomic research. 16S rRNA amplicon sequencing using paired-end sequencing on the MiSeq platform from Illumina, Inc., is being used to characterize the composition and dynamics of extremely complex/diverse microbial communities. For this analysis on the Illumina platform, merging and quality filtering of paired-end reads are essential first steps in data analysis to ensure the accuracy and reliability of downstream analysis.'),
(97520, 2016, 'bmc', 'Predicting the recurrence of noncoding regulatory mutations in cancer', 'Yang, Woojin|Bang, Hyoeun|Jang, Kiwon|Sung, Min Kyung|Choi, Jung Kyoon', 'Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea. jungkyoon@kaist.ac.kr.', '-|-|-|-|-', 5, 'Breast Neoplasms|Chromatin|Female|Genome|Genomics|Humans|Models, Statistical|Mutation|Transcription Factors', 'Evaluation Studies', '10.1186/s12859-016-1385-y', '27912731', 'One of the greatest challenges in cancer genomics is to distinguish driver mutations from passenger mutations. Whereas recurrence is a hallmark of driver mutations, it is difficult to observe recurring noncoding mutations owing to a limited amount of whole-genome sequenced samples. Hence, it is required to develop a method to predict potentially recurrent mutations.'),
(97521, 2016, 'bmc', 'STON: exploring biological pathways using the SBGN standard and graph databases', 'Touré, Vasundra|Mazein, Alexander|Waltemath, Dagmar|Balaur, Irina|Saqi, Mansoor|Henkel, Ron|Pellet, Johann|Auffray, Charles', 'Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18051, Germany. vasundra.toure@uni-rostock.de.|European Institute for Systems Biology and Medicine (EISBM), CIRI UMR 5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France.|Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18051, Germany.|European Institute for Systems Biology and Medicine (EISBM), CIRI UMR 5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France.|European Institute for Systems Biology and Medicine (EISBM), CIRI UMR 5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France.|Scientific Databases and Visualization, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.|European Institute for Systems Biology and Medicine (EISBM), CIRI UMR 5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France.|European Institute for Systems Biology and Medicine (EISBM), CIRI UMR 5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France.', 'Germany|France|Germany|France|France|Germany|France|France', 8, 'Databases, Factual|Gene Regulatory Networks|Humans|Metabolic Networks and Pathways|Signal Transduction|Software|Systems Biology', 'Journal Article', '10.1186/s12859-016-1394-x', '27919219', 'When modeling in Systems Biology and Systems Medicine, the data is often extensive, complex and heterogeneous. Graphs are a natural way of representing biological networks. Graph databases enable efficient storage and processing of the encoded biological relationships. They furthermore support queries on the structure of biological networks.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97522, 2016, 'bmc', 'DeepQA: improving the estimation of single protein model quality with deep belief networks', 'Cao, Renzhi|Bhattacharya, Debswapna|Hou, Jie|Cheng, Jianlin', 'Department of Computer Science, Pacific Lutheran University, Tacoma, WA, 98447, USA.|Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS, 67260, USA.|Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA.|Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA. chengji@missouri.edu.', 'United States|United States|United States|United States', 87, 'Algorithms|Data Accuracy|Machine Learning|Models, Molecular|Neural Networks (Computer)|Protein Structure, Tertiary|Proteins|Support Vector Machine', 'Journal Article', '10.1186/s12859-016-1405-y', '27919220', 'Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem.'),
(97523, 2016, 'bmc', 'Comparison of different statistical approaches for urinary peptide biomarker detection in the context of coronary artery disease', 'Stanley, Eleanor|Delatola, Eleni Ioanna|Nkuipou-Kenfack, Esther|Spooner, William|Kolch, Walter|Schanstra, Joost P|Mischak, Harald|Koeck, Thomas', 'Eagle Genomics Ltd, The Biodata Innovation Centre, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1DR, UK.|Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland.|Mosaiques Diagnostics GmbH, Hanover, Germany.|Eagle Genomics Ltd, The Biodata Innovation Centre, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1DR, UK.|Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland.|Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institute of Cardiovascular and Metabolic Disease, Toulouse, France.|Mosaiques Diagnostics GmbH, Hanover, Germany. harald.mischak@glasgow.ac.uk.|Mosaiques Diagnostics GmbH, Hanover, Germany.', 'United Kingdom|Ireland|Germany|United Kingdom|Ireland|France|Germany|Germany', 0, 'Adult|Biomarkers|Coronary Artery Disease|Discriminant Analysis|Female|Humans|Male|Middle Aged|Peptides|Prognosis|Proteomics', 'Journal Article', '10.1186/s12859-016-1390-1', '27923348', 'When combined with a clinical outcome variable, the size, complexity and nature of mass-spectrometry proteomics data impose great statistical challenges in the discovery of potential disease-associated biomarkers. The purpose of this study was thus to evaluate the effectiveness of different statistical methods applied for urinary proteomic biomarker discovery and different methods of classifier modelling in respect of the diagnosis of coronary artery disease in 197 study subjects and the prognostication of acute coronary syndromes in 368 study subjects.'),
(97524, 2016, 'bmc', 'ConEVA: a toolbox for comprehensive assessment of protein contacts', 'Adhikari, Badri|Nowotny, Jackson|Bhattacharya, Debswapna|Hou, Jie|Cheng, Jianlin', 'Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA.|Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA.|Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA.|Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA.|Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA. chengji@missouri.edu.', 'United States|United States|United States|United States|United States', 13, 'Models, Molecular|Protein Conformation|Protein Folding|Software', 'Journal Article', '10.1186/s12859-016-1404-z', '27923350', '-'),
(97525, 2016, 'bmc', 'PCAN: phenotype consensus analysis to support disease-gene association', 'Godard, Patrice|Page, Matthew', 'Clarivate Analytics (formerly the IP & Science business of Thomson Reuters), 5901 Priestly Dr., #200, Carlsbad, CA, 92008, USA.|Translational Bioinformatics, UCB Pharma, 208 Bath Road, Slough, SL1 3WE, UK. matthew.page@ucb.com.', 'United Kingdom|United Kingdom', 8, 'Abnormalities, Multiple|Cerebellum|Disease|Eye Abnormalities|Genes|Genotype|Humans|Kidney Diseases, Cystic|Phenotype|Retina|Signal Transduction|Software', 'Journal Article', '10.1186/s12859-016-1401-2', '27923364', 'Bridging genotype and phenotype is a fundamental biomedical challenge that underlies more effective target discovery and patient-tailored therapy. Approaches that can flexibly and intuitively, integrate known gene-phenotype associations in the context of molecular signaling networks are vital to effectively prioritize and biologically interpret genes underlying disease traits of interest.'),
(97526, 2016, 'bmc', 'Integrating mean and variance heterogeneities to identify differentially expressed genes', 'Ouyang, Weiwei|An, Qiang|Zhao, Jinying|Qin, Huaizhen', 'Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, Suite 2001, New Orleans, LA, 70112, USA.|Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, Suite 2001, New Orleans, LA, 70112, USA.|Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, 2004 Mowry Rd, Gainesville, FL, 32610, USA.|Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, Suite 2001, New Orleans, LA, 70112, USA. hqin2@tulane.edu.', 'United States|United States|United States|United States', 0, 'Analysis of Variance|Gene Expression|Gene Expression Profiling|Genetic Heterogeneity|Genomics|Models, Genetic', 'Journal Article', '10.1186/s12859-016-1393-y', '27923367', 'In functional genomics studies, tests on mean heterogeneity have been widely employed to identify differentially expressed genes with distinct mean expression levels under different experimental conditions. Variance heterogeneity (aka, the difference between condition-specific variances) of gene expression levels is simply neglected or calibrated for as an impediment. The mean heterogeneity in the expression level of a gene reflects one aspect of its distribution alteration, and variance heterogeneity induced by condition change may reflect another aspect. Change in condition may alter both mean and some higher-order characteristics of the distributions of expression levels of susceptible genes.'),
(97527, 2016, 'bmc', 'SCIFIO: an extensible framework to support scientific image formats', 'Hiner, Mark C|Rueden, Curtis T|Eliceiri, Kevin W', 'Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, 271 Animal Science Building, 1675 Observatory Dr., Madison, 53706, WI, USA.|Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, 271 Animal Science Building, 1675 Observatory Dr., Madison, 53706, WI, USA.|Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, 271 Animal Science Building, 1675 Observatory Dr., Madison, 53706, WI, USA. eliceiri@wisc.edu.', 'United States|United States|United States', 0, 'Image Processing, Computer-Assisted|Microscopy|Software', 'Journal Article', '10.1186/s12859-016-1383-0', '27927161', 'No gold standard exists in the world of scientific image acquisition, a proliferation of instruments each with its own proprietary data format has made out-of-the-box sharing of that data nearly impossible. In the field of light microscopy, the Bio-Formats library was designed to translate such proprietary data formats to a common, open-source schema, enabling sharing and reproduction of scientific results. While Bio-Formats has proved successful for microscopy images, the greater scientific community was lacking a domain-independent framework for format translation.'),
(97528, 2016, 'bmc', 'The role of nucleotide composition in premature termination codon recognition', 'Zahdeh, Fouad|Carmel, Liran', 'Department of Genetics, The Alexander Silberman Institute of Life Sciences, Faculty of Science, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem, 91904, Israel.|Department of Genetics, The Alexander Silberman Institute of Life Sciences, Faculty of Science, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem, 91904, Israel. liran.carmel@huji.ac.il.', 'Israel|Israel', 2, '3 Untranslated Regions|Base Composition|Codon, Nonsense|Codon, Terminator|Exons|Humans|Nonsense Mediated mRNA Decay|Nucleotides|Open Reading Frames|Peptide Chain Termination, Translational|RNA Stability|RNA, Messenger', 'Journal Article', '10.1186/s12859-016-1384-z', '27927164', 'It is not fully understood how a termination codon is recognized as premature (PTC) by the nonsense-mediated decay (NMD) machinery. This is particularly true for transcripts lacking an exon junction complex (EJC) along their 3 untranslated region (3UTR), and thus degrade through the EJC-independent NMD pathway.'),
(97529, 2016, 'bmc', 'Development of a group contribution method for estimating free energy of peptides in a dodecane-water system via molecular dynamic simulations', 'Mora Osorio, Camilo Andrés|González Barrios, Andrés Fernando', 'Grupo de Diseño de Productos y Procesos (GDPP), Departamento de Ingeniería Química, Universidad de los Andes, Carrera 1E No. 19A-40, Edificio Mario Laserna, Bogotá, DC, Colombia.|Grupo de Diseño de Productos y Procesos (GDPP), Departamento de Ingeniería Química, Universidad de los Andes, Carrera 1E No. 19A-40, Edificio Mario Laserna, Bogotá, DC, Colombia. andgonza@uniandes.edu.co.', 'Colombia|Colombia', 0, 'Alkanes|Amino Acids|Hydrophobic and Hydrophilic Interactions|Molecular Dynamics Simulation|Peptides|Thermodynamics|Water', 'Journal Article', '10.1186/s12859-016-1399-5', '27927167', 'Calculation of the Gibbs free energy changes of biological molecules at the oil-water interface is commonly performed with Molecular Dynamics simulations (MD). It is a process that could be performed repeatedly in order to find some molecules of high stability in this medium. Here, an alternative method of calculation has been proposed: a group contribution method (GCM) for peptides based on MD of the twenty classic amino acids to obtain free energy change during the insertion of any peptide chain in water-dodecane interfaces. Multiple MD of the twenty classic amino acids located at the interface of rectangular simulation boxes with a dodecane-water medium were performed.'),
(97530, 2016, 'bmc', 'Finding an appropriate equation to measure similarity between binary vectors: case studies on Indonesian and Japanese herbal medicines', 'Wijaya, Sony Hartono|Afendi, Farit Mochamad|Batubara, Irmanida|Darusman, Latifah K|Altaf-Ul-Amin, Md|Kanaya, Shigehiko', 'Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan.|Department of Statistics, Bogor Agricultural University, Jl. Meranti Wing 22 Level 4 Kampus IPB Dramaga, Bogor, 16680, Indonesia.|Tropical Biopharmaca Research Center, Bogor Agricultural University, Kampus IPB Taman Kencana, Jl. Taman Kencana No. 3, Bogor, 16128, Indonesia.|Tropical Biopharmaca Research Center, Bogor Agricultural University, Kampus IPB Taman Kencana, Jl. Taman Kencana No. 3, Bogor, 16128, Indonesia.|Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan.|Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan. skanaya@gtc.naist.jp.', 'Japan|Indonesia|Indonesia|Indonesia|Japan|Japan', 12, 'Cluster Analysis|Herbal Medicine|Indonesia|Japan|Plants, Medicinal|ROC Curve', 'Journal Article', '10.1186/s12859-016-1392-z', '27927171', 'The binary similarity and dissimilarity measures have critical roles in the processing of data consisting of binary vectors in various fields including bioinformatics and chemometrics. These metrics express the similarity and dissimilarity values between two binary vectors in terms of the positive matches, absence mismatches or negative matches. To our knowledge, there is no published work presenting a systematic way of finding an appropriate equation to measure binary similarity that performs well for certain data type or application. A proper method to select a suitable binary similarity or dissimilarity measure is needed to obtain better classification results.'),
(97531, 2016, 'bmc', 'SurvivalGWAS_Power: a user friendly tool for power calculations in pharmacogenetic studies with time to event outcomes', 'Syed, Hamzah|Jorgensen, Andrea L|Morris, Andrew P', 'Department of Biostatistics, University of Liverpool, Liverpool, UK. Hamzah.Syed@liverpool.ac.uk.|Department of Biostatistics, University of Liverpool, Liverpool, UK.|Department of Biostatistics, University of Liverpool, Liverpool, UK.', 'United Kingdom|United Kingdom|United Kingdom', 2, 'Computational Biology|Genome-Wide Association Study|Humans|Pharmacogenetics|Pharmacogenomic Testing|Phenotype|Polymorphism, Single Nucleotide|Proportional Hazards Models|Research Design|Sample Size|Software|Survival Rate', 'Journal Article', '10.1186/s12859-016-1407-9', '27931206', 'Power calculators are currently available for the design of genetic association studies of binary phenotypes and quantitative traits, but not for time to event outcomes, which are of particular relevance in pharmacogenetics. With the rapid emergence of pharmacogenetic association studies of single nucleotide polymorphisms (SNPs), and the complexity of clinical outcomes they consider, there is a need for software to perform power calculations of time to event data over a range of design scenarios and analytical methodologies.'),
(97532, 2016, 'bmc', 'Combining independent de novo assemblies optimizes the coding transcriptome for nonconventional model eukaryotic organisms', 'Cerveau, Nicolas|Jackson, Daniel J', 'Department of Geobiology, Goldschmidtstr.3, Georg-August University of Göttingen, 37077, Göttingen, Germany.|Department of Geobiology, Goldschmidtstr.3, Georg-August University of Göttingen, 37077, Göttingen, Germany. djackso@gwdg.de.', 'Germany|Germany', 0, 'Animals|Cluster Analysis|Computational Biology|Eukaryota|Genome|High-Throughput Nucleotide Sequencing|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1186/s12859-016-1406-x', '27938328', 'Next-generation sequencing (NGS) technologies are arguably the most revolutionary technical development to join the list of tools available to molecular biologists since PCR. For researchers working with nonconventional model organisms one major problem with the currently dominant NGS platform (Illumina) stems from the obligatory fragmentation of nucleic acid material that occurs prior to sequencing during library preparation. This step creates a significant bioinformatic challenge for accurate de novo assembly of novel transcriptome data. This challenge becomes apparent when a variety of modern assembly tools (of which there is no shortage) are applied to the same raw NGS dataset. With the same assembly parameters these tools can generate markedly different assembly outputs.'),
(97533, 2016, 'bmc', 'Robust multi-group gene set analysis with few replicates', 'Mishra, Pashupati P|Medlar, Alan|Holm, Liisa|Törönen, Petri', 'Institute of Biotechnology, University of Helsinki, P.O. Box 56, Viikinkaari 5, Helsinki, 00014, Finland. pashupati.mishra@helsinki.fi.|Institute of Biotechnology, University of Helsinki, P.O. Box 56, Viikinkaari 5, Helsinki, 00014, Finland.|Institute of Biotechnology, University of Helsinki, P.O. Box 56, Viikinkaari 5, Helsinki, 00014, Finland.|Institute of Biotechnology, University of Helsinki, P.O. Box 56, Viikinkaari 5, Helsinki, 00014, Finland.', 'Finland|Finland|Finland|Finland', 0, 'Animals|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Mice|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/s12859-016-1403-0', '27938331', 'Competitive gene set analysis is a standard exploratory tool for gene expression data. Permutation-based competitive gene set analysis methods are preferable to parametric ones because the latter make strong statistical assumptions which are not always met. For permutation-based methods, we permute samples, as opposed to genes, as doing so preserves the inter-gene correlation structure. Unfortunately, up until now, sample permutation-based methods have required a minimum of six replicates per sample group.'),
(97534, 2016, 'bmc', 'PROPER: global protein interaction network alignment through percolation matching', 'Kazemi, Ehsan|Hassani, Hamed|Grossglauser, Matthias|Pezeshgi Modarres, Hassan', 'School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland. ehsan.kazemi@epfl.ch.|Department of Computer Science, ETHZ, Zurich, Switzerland.|School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland.|School of Life Sciences, EPFL, Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 6, 'Algorithms|Animals|Humans|Mice|Protein Interaction Mapping|Protein Interaction Maps', 'Journal Article', '10.1186/s12859-016-1395-9', '27955623', 'The alignment of protein-protein interaction (PPI) networks enables us to uncover the relationships between different species, which leads to a deeper understanding of biological systems. Network alignment can be used to transfer biological knowledge between species. Although different PPI-network alignment algorithms were introduced during the last decade, developing an accurate and scalable algorithm that can find alignments with high biological and structural similarities among PPI networks is still challenging.'),
(97535, 2016, 'bmc', 'Microbial phenomics information extractor (MicroPIE): a natural language processing tool for the automated acquisition of prokaryotic phenotypic characters from text sources', 'Mao, Jin|Moore, Lisa R|Blank, Carrine E|Wu, Elvis Hsin-Hui|Ackerman, Marcia|Ranade, Sonali|Cui, Hong', 'School of Information, University of Arizona, Tucson, 85721, AZ, USA.|Department of Biological Sciences, University of Southern Maine, Portland, 04103, ME, USA.|Department of Geosciences, University of Montana, Missoula, 59812, MT, USA.|School of Information, University of Arizona, Tucson, 85721, AZ, USA.|Department of Biological Sciences, University of Southern Maine, Portland, 04103, ME, USA.|School of Information, University of Arizona, Tucson, 85721, AZ, USA.|School of Information, University of Arizona, Tucson, 85721, AZ, USA. hongcui@email.arizona.edu.', 'United States|United States|United States|United States|United States|United States|United States', 7, 'Algorithms|Automation|Bacteria|Biological Evolution|Computational Biology|Data Mining|Information Storage and Retrieval|Phenotype|Prokaryotic Cells', 'Evaluation Studies', '10.1186/s12859-016-1396-8', '27955641', 'The large-scale analysis of phenomic data (i.e., full phenotypic traits of an organism, such as shape, metabolic substrates, and growth conditions) in microbial bioinformatics has been hampered by the lack of tools to rapidly and accurately extract phenotypic data from existing legacy text in the field of microbiology. To quickly obtain knowledge on the distribution and evolution of microbial traits, an information extraction system needed to be developed to extract phenotypic characters from large numbers of taxonomic descriptions so they can be used as input to existing phylogenetic analysis software packages.'),
(97536, 2016, 'bmc', 'Pathway collages: personalized multi-pathway diagrams', 'Paley, Suzanne|OMaille, Paul E|Weaver, Daniel|Karp, Peter D', 'Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, Menlo Park, 94025, USA.|John Innes Centre, Department of Metabolic Biology, and the Institute of Food Research, Food & Health Programme, Norwich Research Park, Norfolk, NR4 7UH, UK.|Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, Menlo Park, 94025, USA.|Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, Menlo Park, 94025, USA. pkarp@ai.sri.com.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Anti-Bacterial Agents|Escherichia coli|Metabolic Networks and Pathways|Metabolomics|Models, Biological|Software', 'Evaluation Studies', '10.1186/s12859-016-1382-1', '27964719', 'Metabolic pathway diagrams are a classical way of visualizing a linked cascade of biochemical reactions. However, to understand some biochemical situations, viewing a single pathway is insufficient, whereas viewing the entire metabolic network results in information overload. How do we enable scientists to rapidly construct personalized multi-pathway diagrams that depict a desired collection of interacting pathways that emphasize particular pathway interactions?'),
(97537, 2016, 'bmc', 'Prior knowledge guided eQTL mapping for identifying candidate genes', 'Wang, Yunli|Richard, Rene|Pan, Youlian', 'National Research Council Canada, 1200 Montreal Rd., Ottawa, K1A 0R6, Canada. Yunli.Wang@nrc-cnrc.gc.ca.|National Research Council Canada, 46 Dineen Dr., Fredericton, E3B 9W4, Canada.|National Research Council Canada, 1200 Montreal Rd., Ottawa, K1A 0R6, Canada.', 'Canada|Canada|Canada', 0, 'Animals|Chromosome Mapping|Gene Regulatory Networks|Hordeum|Mice|Phenotype|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Saccharomyces cerevisiae', 'Evaluation Studies', '10.1186/s12859-016-1387-9', '27964730', 'Expression quantitative trait loci (eQTL) mapping is often used to identify genetic loci and candidate genes correlated with traits. Although usually a group of genes affect complex traits, genes in most eQTL mapping methods are considered as independent. Recently, some eQTL mapping methods have accounted for correlated genes, used biological prior knowledge and applied these in model species such as yeast or mouse. However, biological prior knowledge might be very limited for most species.'),
(97538, 2016, 'bmc', 'New tools to analyze overlapping coding regions', 'Bayegan, Amir H|Garcia-Martin, Juan Antonio|Clote, Peter', 'Biology Department, Boston College, 140 Commonwealth Avenue, Chestnut Hill MA, 02467, USA.|Biology Department, Boston College, 140 Commonwealth Avenue, Chestnut Hill MA, 02467, USA.|Biology Department, Boston College, 140 Commonwealth Avenue, Chestnut Hill MA, 02467, USA. clote@bc.edu.', 'United States|United States|United States', 1, 'Base Sequence|Codon|Computational Biology|HIV Infections|HIV-1|Humans|Molecular Sequence Data|Nucleic Acid Conformation|Open Reading Frames|Position-Specific Scoring Matrices|RNA, Viral|Reading Frames|Software', 'Evaluation Studies', '10.1186/s12859-016-1389-7', '27964762', 'Retroviruses transcribe messenger RNA for the overlapping Gag and Gag-Pol polyproteins, by using a programmed -1 ribosomal frameshift which requires a slippery sequence and an immediate downstream stem-loop secondary structure, together called frameshift stimulating signal (FSS). It follows that the molecular evolution of this genomic region of HIV-1 is highly constrained, since the retroviral genome must contain a slippery sequence (sequence constraint), code appropriate peptides in reading frames 0 and 1 (coding requirements), and form a thermodynamically stable stem-loop secondary structure (structure requirement).'),
(97539, 2016, 'bmc', 'COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator', 'Rawi, Reda|Mall, Raghvendra|Kunji, Khalid|El Anbari, Mohammed|Aupetit, Michael|Ullah, Ehsan|Bensmail, Halima', 'Computational Science and Engineering, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar. redarawi411@gmail.com.|Computational Science and Engineering, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|Computational Science and Engineering, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|Division of Biomedical Informatics, Sidra Medical and Research Center, Doha, Qatar.|Computational Science and Engineering, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|Computational Science and Engineering, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|Computational Science and Engineering, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.', 'Qatar|Qatar|Qatar|Qatar|Qatar|Qatar|Qatar', 0, 'Algorithms|Bayes Theorem|Computational Biology|Models, Molecular|Proteins|Sequence Analysis, Protein|Software', 'Evaluation Studies', '10.1186/s12859-016-1400-3', '27978812', 'The post-genomic era with its wealth of sequences gave rise to a broad range of protein residue-residue contact detecting methods. Although various coevolution methods such as PSICOV, DCA and plmDCA provide correct contact predictions, they do not completely overlap. Hence, new approaches and improvements of existing methods are needed to motivate further development and progress in the field. We present a new contact detecting method, COUSCOus, by combining the best shrinkage approach, the empirical Bayes covariance estimator and GLasso.'),
(97540, 2016, 'bmc', 'SwitchFinder - a novel method and query facility for discovering dynamic gene expression patterns', 'Bulashevska, Svetlana|Priest, Colin|Speicher, Daniel|Zimmermann, Jörg|Westermann, Frank|Cremers, Armin B', 'B-IT Bonn-Aachen International Center for Information Technology, University of Bonn, Dahlmannstr. 2, Bonn, 53113, Germany. sbulashevska@gmail.com.|Sigma Plus Consulting Pty Ltd, Crows Nest, 2065, NSW, Australia.|B-IT Bonn-Aachen International Center for Information Technology, University of Bonn, Dahlmannstr. 2, Bonn, 53113, Germany.|B-IT Bonn-Aachen International Center for Information Technology, University of Bonn, Dahlmannstr. 2, Bonn, 53113, Germany.|Neuroblastoma Genomics Group, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.|B-IT Bonn-Aachen International Center for Information Technology, University of Bonn, Dahlmannstr. 2, Bonn, 53113, Germany.', 'Germany|Australia|Germany|Germany|Germany|Germany', 1, 'Animals|Cell Cycle|Gene Expression Profiling|Humans|Internet|Models, Theoretical|Monte Carlo Method|Proteins|Software', 'Journal Article', '10.1186/s12859-016-1391-0', '27978814', 'Biological systems and processes are highly dynamic. To gain insights into their functioning time-resolved measurements are necessary. Time-resolved gene expression data captures temporal behaviour of the genes genome-wide under various biological conditions: in response to stimuli, during cell cycle, differentiation or developmental programs. Dissecting dynamic gene expression patterns from this data may shed light on the functioning of the gene regulatory system. The present approach facilitates this discovery. The fundamental idea behind it is the following: there are change-points (switches) in the gene behaviour separating intervals of increasing and decreasing activity, whereas the intervals may have different durations. Elucidating the switch-points is important for the identification of biologically meanigfull features and patterns of the gene dynamics.'),
(97541, 2016, 'bmc', 'Robust differential expression analysis by learning discriminant boundary in multi-dimensional space of statistical attributes', 'Bei, Yuanzhe|Hong, Pengyu', 'Computer Science Department, Brandeis University, Waltham, MA, 02453, USA.|Computer Science Department, Brandeis University, Waltham, MA, 02453, USA. hongpeng@brandeis.edu.', 'United States|United States', 0, 'Algorithms|Discriminant Analysis|Gene Expression Profiling|Genome-Wide Association Study|Humans|Machine Learning|Models, Statistical|Normal Distribution', 'Journal Article', '10.1186/s12859-016-1386-x', '27993137', 'Performing statistical tests is an important step in analyzing genome-wide datasets for detecting genomic features differentially expressed between conditions. Each type of statistical test has its own advantages in characterizing certain aspects of differences between population means and often assumes a relatively simple data distribution (e.g., Gaussian, Poisson, negative binomial, etc.), which may not be well met by the datasets of interest. Making insufficient distributional assumptions can lead to inferior results when dealing with complex differential expression patterns.'),
(97542, 2016, 'bmc', 'acdc - Automated Contamination Detection and Confidence estimation for single-cell genome data', 'Lux, Markus|Krüger, Jan|Rinke, Christian|Maus, Irena|Schlüter, Andreas|Woyke, Tanja|Sczyrba, Alexander|Hammer, Barbara', 'Computational Methods for the Analysis of the Diversity and Dynamics of Genomes, Bielefeld University, Universitätsstr. 25, Bielefeld, 33615, Germany. mlux@techfak.uni-bielefeld.de.|Center for Biotechnology - CeBiTec, Bielefeld University, Universitätsstr. 27, Bielefeld, 33615, Germany.|Australian Centre for Ecogenomics, University of Queensland, ST LUCIA, Brisbane, QLD 4072, Australia.|Center for Biotechnology - CeBiTec, Bielefeld University, Universitätsstr. 27, Bielefeld, 33615, Germany.|Center for Biotechnology - CeBiTec, Bielefeld University, Universitätsstr. 27, Bielefeld, 33615, Germany.|, 2800 Mitchell Drive, Walnut Creek, 94598, CA, USA.|Center for Biotechnology - CeBiTec, Bielefeld University, Universitätsstr. 27, Bielefeld, 33615, Germany.|CITEC centre of excellence, Bielefeld University, Inspiration 1, Bielefeld, 33619, Germany.', 'Germany|Germany|Australia|Germany|Germany|United States|Germany|Germany', 13, 'Cluster Analysis|DNA|DNA Contamination|Genome|Machine Learning|Quality Control|Sequence Analysis, DNA|Single-Cell Analysis', 'Journal Article', '10.1186/s12859-016-1397-7', '27998267', 'A major obstacle in single-cell sequencing is sample contamination with foreign DNA. To guarantee clean genome assemblies and to prevent the introduction of contamination into public databases, considerable quality control efforts are put into post-sequencing analysis. Contamination screening generally relies on reference-based methods such as database alignment or marker gene search, which limits the set of detectable contaminants to organisms with closely related reference species. As genomic coverage in the tree of life is highly fragmented, there is an urgent need for a reference-free methodology for contaminant identification in sequence data.'),
(97543, 2016, 'bmc', 'Efficient randomization of biological networks while preserving functional characterization of individual nodes', 'Iorio, Francesco|Bernardo-Faura, Marti|Gobbi, Andrea|Cokelaer, Thomas|Jurman, Giuseppe|Saez-Rodriguez, Julio', 'European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK. iorio@ebi.ac.uk.|European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK.|Fondazione Bruno Kessler, Povo, Trento, I-38122, Italy.|European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK.|Fondazione Bruno Kessler, Povo, Trento, I-38122, Italy.|European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK. saezrodriguez@gmail.com.', 'United Kingdom|United Kingdom|Italy|United Kingdom|Italy|United Kingdom', 10, 'Algorithms|Computational Biology|Data Interpretation, Statistical|Gene Regulatory Networks|Humans|Models, Biological|Protein Interaction Maps|Random Allocation|Software', 'Journal Article', '10.1186/s12859-016-1402-1', '27998275', 'Networks are popular and powerful tools to describe and model biological processes. Many computational methods have been developed to infer biological networks from literature, high-throughput experiments, and combinations of both. Additionally, a wide range of tools has been developed to map experimental data onto reference biological networks, in order to extract meaningful modules. Many of these methods assess results significance against null distributions of randomized networks. However, these standard unconstrained randomizations do not preserve the functional characterization of the nodes in the reference networks (i.e. their degrees and connection signs), hence including potential biases in the assessment.'),
(97544, 2016, 'bmc', 'Block network mapping approach to quantitative trait locus analysis', 'Shreif, Zeina Z|Gatti, Daniel M|Periwal, Vipul', 'Laboratory of Biological Modeling, NIDDK, National Institutes of Health, Bethesda, MD 20892, USA.|The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA.|Laboratory of Biological Modeling, NIDDK, National Institutes of Health, Bethesda, MD 20892, USA. vipulp@mail.nih.gov.', 'United States|United States|United States', 0, 'Algorithms|Animals|Bayes Theorem|Chromosome Mapping|Genomics|Genotype|Mice|Models, Genetic|Polymorphism, Single Nucleotide|Quantitative Trait Loci', 'Journal Article', '10.1186/s12859-016-1351-8', '28007037', 'Advances in experimental biology have enabled the collection of enormous troves of data on genomic variation in living organisms. The interpretation of this data to extract actionable information is one of the keys to developing novel therapeutic strategies to treat complex diseases. Network organization of biological data overcomes measurement noise in several biological contexts. Does a network approach, combining information about the linear organization of genomic markers with correlative information on these markers in a Bayesian formulation, lead to an analytic method with higher power for detecting quantitative trait loci?'),
(97545, 2016, 'bmc', 'Gene regulatory network inference using PLS-based methods', 'Guo, Shun|Jiang, Qingshan|Chen, Lifei|Guo, Donghui', 'Department of Electronic Engineering, Xiamen University, Fujian, 361005, China.|Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518000, China.|School of Mathematics and Computer Science, Fujian Normal University, Fujian, 350117, China.|Department of Electronic Engineering, Xiamen University, Fujian, 361005, China. dhguo@xmu.edu.cn.', 'China|China|China|China', 0, 'Gene Expression|Gene Regulatory Networks|Least-Squares Analysis|Models, Genetic|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/s12859-016-1398-6', '28031031', 'Inferring the topology of gene regulatory networks (GRNs) from microarray gene expression data has many potential applications, such as identifying candidate drug targets and providing valuable insights into the biological processes. It remains a challenge due to the fact that the data is noisy and high dimensional, and there exists a large number of potential interactions.'),
(97546, 2016, 'bmc', 'UQlust: combining profile hashing with linear-time ranking for efficient clustering and analysis of big macromolecular data', 'Adamczak, Rafal|Meller, Jarek', 'Department of Informatics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Grudziadzka 5, 87-100, Torun, Poland. raad@is.umk.pl.|Department of Informatics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Grudziadzka 5, 87-100, Torun, Poland. jmeller@cchmc.org.', 'Poland|Poland', 0, 'Algorithms|Cluster Analysis|Data Interpretation, Statistical|Databases, Protein|Protein Structure, Secondary|Proteins|RNA|Software', 'Journal Article', '10.1186/s12859-016-1381-2', '28031034', 'Advances in computing have enabled current protein and RNA structure prediction and molecular simulation methods to dramatically increase their sampling of conformational spaces. The quickly growing number of experimentally resolved structures, and databases such as the Protein Data Bank, also implies large scale structural similarity analyses to retrieve and classify macromolecular data. Consequently, the computational cost of structure comparison and clustering for large sets of macromolecular structures has become a bottleneck that necessitates further algorithmic improvements and development of efficient software solutions.'),
(97547, 2017, 'bmc', 'Detailed simulation of cancer exome sequencing data reveals differences and common limitations of variant callers', 'Hofmann, Ariane L|Behr, Jonas|Singer, Jochen|Kuipers, Jack|Beisel, Christian|Schraml, Peter|Moch, Holger|Beerenwinkel, Niko', 'Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr, Basel, 26, 4058, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr, Basel, 26, 4058, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr, Basel, 26, 4058, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr, Basel, 26, 4058, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr, Basel, 26, 4058, Switzerland.|Institute for Surgical Pathology, University Hospital Zurich, Schmelzbergstrasse 12, Zurich, 8091, Switzerland.|Institute for Surgical Pathology, University Hospital Zurich, Schmelzbergstrasse 12, Zurich, 8091, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Mattenstr, Basel, 26, 4058, Switzerland. niko.beerenwinkel@bsse.ethz.ch.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 17, 'Algorithms|DNA, Neoplasm|Exome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Kidney Neoplasms|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-016-1417-7', '28049408', 'Next-generation sequencing of matched tumor and normal biopsy pairs has become a technology of paramount importance for precision cancer treatment. Sequencing costs have dropped tremendously, allowing the sequencing of the whole exome of tumors for just a fraction of the total treatment costs. However, clinicians and scientists cannot take full advantage of the generated data because the accuracy of analysis pipelines is limited. This particularly concerns the reliable identification of subclonal mutations in a cancer tissue sample with very low frequencies, which may be clinically relevant.'),
(97548, 2017, 'bmc', 'Negative binomial mixed models for analyzing microbiome count data', 'Zhang, Xinyan|Mallick, Himel|Tang, Zaixiang|Zhang, Lei|Cui, Xiangqin|Benson, Andrew K|Yi, Nengjun', 'Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, 35294-0022, USA.|Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.|Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, China.|Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, China.|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, 35294-0022, USA.|Department of Food Science and Technology and Core for Applied Genomics and Ecology, University of Nebraska, Lincoln, NE, 68583, USA.|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, 35294-0022, USA. nyi@uab.edu.', 'United States|United States|China|China|United States|United States|United States', 0, 'Algorithms|Animals|Bacteria|High-Throughput Nucleotide Sequencing|Humans|Internet|Intestines|Male|Mice|Mice, Inbred C57BL|Microbiota|Models, Statistical|RNA, Ribosomal, 16S|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1441-7', '28049409', 'Recent advances in next-generation sequencing (NGS) technology enable researchers to collect a large volume of metagenomic sequencing data. These data provide valuable resources for investigating interactions between the microbiome and host environmental/clinical factors. In addition to the well-known properties of microbiome count measurements, for example, varied total sequence reads across samples, over-dispersion and zero-inflation, microbiome studies usually collect samples with hierarchical structures, which introduce correlation among the samples and thus further complicate the analysis and interpretation of microbiome count data.'),
(97549, 2017, 'bmc', 'TCGA2BED: extracting, extending, integrating, and querying The Cancer Genome Atlas', 'Cumbo, Fabio|Fiscon, Giulia|Ceri, Stefano|Masseroli, Marco|Weitschek, Emanuel', 'Institute of Systems Analysis and Computer Science A. Ruberti, National Research Council, Via dei Taurini 19, Rome, 00185, Italy.|Institute of Systems Analysis and Computer Science A. Ruberti, National Research Council, Via dei Taurini 19, Rome, 00185, Italy.|Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza L. da Vinci 32, Milan, 20133, Italy.|Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza L. da Vinci 32, Milan, 20133, Italy.|Institute of Systems Analysis and Computer Science A. Ruberti, National Research Council, Via dei Taurini 19, Rome, 00185, Italy. emanuel@iasi.cnr.it.', 'Italy|Italy|Italy|Italy|Italy', 19, 'DNA Copy Number Variations|DNA Methylation|Databases, Genetic|High-Throughput Nucleotide Sequencing|Humans|Internet|MicroRNAs|Neoplasms|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1419-5', '28049410', 'Data extraction and integration methods are becoming essential to effectively access and take advantage of the huge amounts of heterogeneous genomics and clinical data increasingly available. In this work, we focus on The Cancer Genome Atlas, a comprehensive archive of tumoral data containing the results of high-throughout experiments, mainly Next Generation Sequencing, for more than 30 cancer types.'),
(97550, 2017, 'bmc', 'Accelerating metagenomic read classification on CUDA-enabled GPUs', 'Kobus, Robin|Hundt, Christian|Müller, André|Schmidt, Bertil', 'Institute of Computer Science, Johannes Gutenberg University Mainz, Staudingerweg 9, Mainz, 55435, Germany. rkobus@students.uni-mainz.de.|Institute of Computer Science, Johannes Gutenberg University Mainz, Staudingerweg 9, Mainz, 55435, Germany.|Institute of Computer Science, Johannes Gutenberg University Mainz, Staudingerweg 9, Mainz, 55435, Germany.|Institute of Computer Science, Johannes Gutenberg University Mainz, Staudingerweg 9, Mainz, 55435, Germany.', 'Germany|Germany|Germany|Germany', 0, 'High-Throughput Nucleotide Sequencing|Humans|Internet|Metagenomics|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1434-6', '28049411', 'Metagenomic sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification, i.e. the assignment of each read to a taxonomic label. Due to the large number of reads produced by modern high-throughput sequencing technologies and the rapidly increasing number of available reference genomes software tools for fast and accurate metagenomic read classification are urgently needed.'),
(97551, 2017, 'bmc', 'Genomic prediction with epistasis models: on the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE)', 'Martini, Johannes W R|Gao, Ning|Cardoso, Diercles F|Wimmer, Valentin|Erbe, Malena|Cantet, Rodolfo J C|Simianer, Henner', 'Department of Animal Sciences, Georg-August University, Albrecht Thaer-Weg 3, Göttingen, Germany. jmartin2@gwdg.de.|Department of Animal Sciences, Georg-August University, Albrecht Thaer-Weg 3, Göttingen, Germany.|Department of Animal Sciences, Georg-August University, Albrecht Thaer-Weg 3, Göttingen, Germany.|KWS SAAT SE, Einbeck, Germany.|Department of Animal Sciences, Georg-August University, Albrecht Thaer-Weg 3, Göttingen, Germany.|Department of Animal Production, University of Buenos Aires, INPA-CONICET, Buenos Aires, Argentina.|Department of Animal Sciences, Georg-August University, Albrecht Thaer-Weg 3, Göttingen, Germany.', 'Germany|Germany|Germany|Germany|Germany|Argentina|Germany', 0, 'Alleles|Animals|Epistasis, Genetic|Mice|Models, Genetic|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Triticum', 'Journal Article', '10.1186/s12859-016-1439-1', '28049412', 'Epistasis marker effect models incorporating products of marker values as predictor variables in a linear regression approach (extended GBLUP, EGBLUP) have been assessed as potentially beneficial for genomic prediction, but their performance depends on marker coding. Although this fact has been recognized in literature, the nature of the problem has not been thoroughly investigated so far.'),
(97552, 2017, 'bmc', 'Minimum redundancy maximum relevance feature selection approach for temporal gene expression data', 'Radovic, Milos|Ghalwash, Mohamed|Filipovic, Nenad|Obradovic, Zoran', 'Center for Data Analytics and Biomedical Informatics, College of Science and Technology, Temple University, North 12th Street, Philadelphia, 19122, PA, USA. milos.radovic@temple.edu.|Center for Data Analytics and Biomedical Informatics, College of Science and Technology, Temple University, North 12th Street, Philadelphia, 19122, PA, USA.|Bioengineering Research and Development Center - BioIRC, Prvoslava Stojanovica 6, Kragujevac, 34000, Serbia.|Center for Data Analytics and Biomedical Informatics, College of Science and Technology, Temple University, North 12th Street, Philadelphia, 19122, PA, USA.', 'United States|United States|Serbia|United States', 68, 'Algorithms|Analysis of Variance|Bayes Theorem|Gene Expression|Humans|Influenza A Virus, H3N2 Subtype|Respiratory Syncytial Viruses|Rhinovirus|Support Vector Machine', 'Journal Article', '10.1186/s12859-016-1423-9', '28049413', 'Feature selection, aiming to identify a subset of features among a possibly large set of features that are relevant for predicting a response, is an important preprocessing step in machine learning. In gene expression studies this is not a trivial task for several reasons, including potential temporal character of data. However, most feature selection approaches developed for microarray data cannot handle multivariate temporal data without previous data flattening, which results in loss of temporal information. We propose a temporal minimum redundancy - maximum relevance (TMRMR) feature selection approach, which is able to handle multivariate temporal data without previous data flattening. In the proposed approach we compute relevance of a gene by averaging F-statistic values calculated across individual time steps, and we compute redundancy between genes by using a dynamical time warping approach.'),
(97553, 2017, 'bmc', 'Bon-EV: an improved multiple testing procedure for controlling false discovery rates', 'Li, Dongmei|Xie, Zidian|Zand, Martin|Fogg, Thomas|Dye, Timothy', 'Clinical and Translational Science Institute, School of Medicine and Dentistry, University of Rochester, 265 Crittenden Boulevard CU 420708, Rochester, 14642, NY, USA. dongmei_li@urmc.rochester.edu.|Goergen Institute for Data Science, University of Rochester, Computer Studies Building, Rochester, 14642, NY, USA.|Clinical and Translational Science Institute, School of Medicine and Dentistry, University of Rochester, 265 Crittenden Boulevard CU 420708, Rochester, 14642, NY, USA.|Clinical and Translational Science Institute, School of Medicine and Dentistry, University of Rochester, 265 Crittenden Boulevard CU 420708, Rochester, 14642, NY, USA.|Clinical and Translational Science Institute, School of Medicine and Dentistry, University of Rochester, 265 Crittenden Boulevard CU 420708, Rochester, 14642, NY, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|DNA Methylation|Humans|Internet|Polymorphism, Single Nucleotide|RNA|Sequence Analysis, RNA|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1414-x', '28049414', 'Stability of multiple testing procedures, defined as the standard deviation of total number of discoveries, can be used as an indicator of variability of multiple testing procedures. Improving stability of multiple testing procedures can help to increase the consistency of findings from replicated experiments. Benjamini-Hochbergs and Storeys q-value procedures are two commonly used multiple testing procedures for controlling false discoveries in genomic studies. Storeys q-value procedure has higher power and lower stability than Benjamini-Hochbergs procedure. To improve upon the stability of Storeys q-value procedure and maintain its high power in genomic data analysis, we propose a new multiple testing procedure, named Bon-EV, to control false discovery rate (FDR) based on Bonferronis approach.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97554, 2017, 'bmc', 'Improving protein complex prediction by reconstructing a high-confidence protein-protein interaction network of Escherichia coli from different physical interaction data sources', 'Taghipour, Shirin|Zarrineh, Peyman|Ganjtabesh, Mohammad|Nowzari-Dalini, Abbas', 'Department of Computer Science, School of Mathematics, Statistics, and Computer Science, University of Tehran, P.O.Box: 14155-6455, Tehran, Iran.|Department of Computer Science, School of Mathematics, Statistics, and Computer Science, University of Tehran, P.O.Box: 14155-6455, Tehran, Iran.|Department of Computer Science, School of Mathematics, Statistics, and Computer Science, University of Tehran, P.O.Box: 14155-6455, Tehran, Iran. mgtabesh@ut.ac.ir.|Department of Computer Science, School of Mathematics, Statistics, and Computer Science, University of Tehran, P.O.Box: 14155-6455, Tehran, Iran.', '-|-|-|-', 9, 'Algorithms|Bacterial Proteins|Bayes Theorem|Chromatography, Affinity|Escherichia coli|Mass Spectrometry|Protein Interaction Mapping|Protein Interaction Maps', 'Journal Article', '10.1186/s12859-016-1422-x', '28049415', 'Although different protein-protein physical interaction (PPI) datasets exist for Escherichia coli, no common methodology exists to integrate these datasets and extract reliable modules reflecting the existing biological process and protein complexes. Naïve Bayesian formula is the highly accepted method to integrate different PPI datasets into a single weighted PPI network, but detecting proper weights in such network is still a major problem.'),
(97555, 2017, 'bmc', 'Fast-GBS: a new pipeline for the efficient and highly accurate calling of SNPs from genotyping-by-sequencing data', 'Torkamaneh, Davoud|Laroche, Jérôme|Bastien, Maxime|Abed, Amina|Belzile, François', 'Département de Phytologie, Université Laval, Quebec City, QC, Canada.|Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Quebec City, QC, Canada.|Département de Phytologie, Université Laval, Quebec City, QC, Canada.|Département de Phytologie, Université Laval, Quebec City, QC, Canada.|Département de Phytologie, Université Laval, Quebec City, QC, Canada. francois.belzile@fsaa.ulaval.ca.', 'Canada|Canada|Canada|Canada|Canada', 0, 'Alleles|DNA|Genome, Plant|Genotype|Genotyping Techniques|Haplotypes|High-Throughput Nucleotide Sequencing|Hordeum|Internet|Polymorphism, Single Nucleotide|Soybeans|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1431-9', '28049422', 'Next-generation sequencing (NGS) technologies have accelerated considerably the investigation into the composition of genomes and their functions. Genotyping-by-sequencing (GBS) is a genotyping approach that makes use of NGS to rapidly and economically scan a genome. It has been shown to allow the simultaneous discovery and genotyping of thousands to millions of SNPs across a wide range of species. For most users, the main challenge in GBS is the bioinformatics analysis of the large amount of sequence information derived from sequencing GBS libraries in view of calling alleles at SNP loci. Herein we describe a new GBS bioinformatics pipeline, Fast-GBS, designed to provide highly accurate genotyping, to require modest computing resources and to offer ease of use.'),
(97556, 2017, 'bmc', 'A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks', 'Röhl, Annika|Bockmayr, Alexander', 'Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, Berlin, Germany. annika.roehl@fu-berlin.de.|Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, Berlin, Germany.', 'Germany|Germany', 0, 'Algorithms|Animals|Computational Biology|Genome|Genome, Bacterial|Helicobacter pylori|Metabolic Networks and Pathways|Models, Biological', 'Journal Article', '10.1186/s12859-016-1412-z', '28049424', 'Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement.'),
(97557, 2017, 'bmc', 'PERGOLA: fast and deterministic linkage mapping of polyploids', 'Grandke, Fabian|Ranganathan, Soumya|van Bers, Nikkie|de Haan, Jorn R|Metzler, Dirk', 'Genetwister Technologies B.V., Wageningen, The Netherlands. grandke@biologie.uni-muenchen.de.|Fakultät für Biologie, University of Munich (LMU), Munich, Germany.|Genetwister Technologies B.V., Wageningen, The Netherlands.|Genetwister Technologies B.V., Wageningen, The Netherlands.|Fakultät für Biologie, University of Munich (LMU), Munich, Germany.', 'Netherlands|Germany|Netherlands|Netherlands|Germany', 5, 'Algorithms|Chromosome Mapping|Genetic Linkage|Internet|Polyploidy|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1416-8', '28049428', 'A large share of agriculturally and horticulturally important plant species are polyploid. Linkage maps are used to locate associations between genes and traits by breeders and geneticists. Linkage map creation for polyploid species is not supported by standard tools. We want to overcome this limitation and validate our results with simulation studies.'),
(97558, 2017, 'bmc', 'JACUSA: site-specific identification of RNA editing events from replicate sequencing data', 'Piechotta, Michael|Wyler, Emanuel|Ohler, Uwe|Landthaler, Markus|Dieterich, Christoph', 'Max Planck Institute for Biology of Ageing, Joseph-Stelzmann Str. 9b, Cologne, 50931, Germany.|Berlin Institute for Medical Systems Biology, Max-Delbrxc3xbcck-Center for Molecular Medicine, Robert-Rxc3xb6ssle-Strasse 10, Berlin, 13125, Germany.|Berlin Institute for Medical Systems Biology, Max-Delbrxc3xbcck-Center for Molecular Medicine, Robert-Rxc3xb6ssle-Strasse 10, Berlin, 13125, Germany.|Berlin Institute for Medical Systems Biology, Max-Delbrxc3xbcck-Center for Molecular Medicine, Robert-Rxc3xb6ssle-Strasse 10, Berlin, 13125, Germany.|Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology at the Department of Internal Medicine III, University Hospital Heidelberg, Im Neuenheimer Feld 669, Heidelberg, 69120, Germany. christoph.dieterich@uni-heidelberg.de.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Animals|Drosophila melanogaster|HEK293 Cells|High-Throughput Nucleotide Sequencing|Humans|Polymorphism, Single Nucleotide|RNA|RNA Editing|RNA Interference|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1186/s12859-016-1432-8', '28049429', 'RNA editing is a co-transcriptional modification that increases the molecular diversity, alters secondary structure and protein coding sequences by changing the sequence of transcripts. The most common RNA editing modification is the single base substitution (Axe2x86x92I) that is catalyzed by the members of the Adenosine deaminases that act on RNA (ADAR) family. Typically, editing sites are identified as RNA-DNA-differences (RDDs) in a comparison of genome and transcriptome data from next-generation sequencing experiments. However, a method for robust detection of site-specific editing events from replicate RNA-seq data has not been published so far. Even more surprising, condition-specific editing events, which would show up as differences in RNA-RNA comparisons (RRDs) and depend on particular cellular states, are rarely discussed in the literature.'),
(97559, 2017, 'bmc', 'MPD: multiplex primer design for next-generation targeted sequencing', 'Wingo, Thomas S|Kotlar, Alex|Cutler, David J', 'Division of Neurology, Atlanta VA Medical Center, Decatur, 30033, GA, USA. thomas.wingo@emory.edu.|Department of Human Genetics, Emory University School of Medicine, 615 Michael Street NE, Atlanta, GA, 30322, USA.|Department of Human Genetics, Emory University School of Medicine, 615 Michael Street NE, Atlanta, GA, 30322, USA.', 'United States|United States|United States', 6, 'DNA Primers|Exons|Genome, Human|Genome-Wide Association Study|Genomics|High-Throughput Nucleotide Sequencing|Humans|Internet|Polymerase Chain Reaction|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-016-1453-3', '28056760', 'Targeted resequencing offers a cost-effective alternative to whole-genome and whole-exome sequencing when investigating regions known to be associated with a trait or disease. There are a number of approaches to targeted resequencing, including microfluidic PCR amplification, which may be enhanced by multiplex PCR. Currently, there is no open-source software that can design next-generation multiplex PCR experiments that ensures primers are unique at a genome-level and efficiently pools compatible primers.'),
(97560, 2017, 'bmc', 'The use of novel selectivity metrics in kinase research', 'Bosc, Nicolas|Meyer, Christophe|Bonnet, Pascal', 'Institut de Chimie Organique et Analytique (ICOA), UMR CNRS-Université dOrléans 7311, Université dOrléans BP 6759, Orléans Cedex 2, 45067, France.|Janssen-Cilag, Centre de Recherche Pharma Janssen-Cilag, Campus de Maigremont-CS 10615, Chaussée du Vexin, Val de Reuil, 27106, France.|Institut de Chimie Organique et Analytique (ICOA), UMR CNRS-Université dOrléans 7311, Université dOrléans BP 6759, Orléans Cedex 2, 45067, France. pascal.bonnet@univ-orleans.fr.', 'France|France|France', 0, 'Cell Line, Tumor|Computational Biology|Databases, Factual|Drug Discovery|Humans|Models, Theoretical|Protein Kinase Inhibitors|Protein Kinases', 'Journal Article', '10.1186/s12859-016-1413-y', '28056771', 'Compound selectivity is an important issue when developing a new drug. In many instances, a lack of selectivity can translate to increased toxicity. Protein kinases are particularly concerned with this issue because they share high sequence and structural similarity. However, selectivity may be assessed early on using data generated from protein kinase profiling panels.'),
(97561, 2017, 'bmc', 'Structural prediction of the interaction of the tumor suppressor p27KIP1 with cyclin A/CDK2 identifies a novel catalytically relevant determinant', 'Li, Jinyu|Vervoorts, Jxc3xb6rg|Carloni, Paolo|Rossetti, Giulia|Lxc3xbcscher, Bernhard', 'College of Chemistry, Fuzhou University, Fuzhou, 350002, China.|Institute of Biochemistry and Molecular Biology, Medical School, RWTH Aachen University, 52057, Aachen, Germany.|Computational Biomedicine, Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jxc3xbclich, 52425, Jxc3xbclich, Germany.|Computational Biomedicine, Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jxc3xbclich, 52425, Jxc3xbclich, Germany. g.rossetti@fz-juelich.de.|Institute of Biochemistry and Molecular Biology, Medical School, RWTH Aachen University, 52057, Aachen, Germany. luescher@rwth-aachen.de.', 'China|Germany|Germany|Germany|Germany', 2, 'Amino Acid Sequence|Animals|Computational Biology|Cyclin A|Cyclin E|Cyclin-Dependent Kinase 2|Cyclin-Dependent Kinase Inhibitor p27|Humans|Phosphorylation|Protein Conformation|Spodoptera', 'Journal Article', '10.1186/s12859-016-1411-0', '28056778', 'Background The cyclin-dependent kinase 2 (CDK2) together with its cyclin E and A partners is a central regulator of cell growth and division. Deregulation of CDK2 activity is associated with diseases such as cancer. The analysis of substrates identified S/T-P-X-R/K/H as the CDK2 consensus sequence. The crystal structure of cyclin A/CDK2 with a short model peptide supports this sequence and identifies key interactions. However, CDKs use additional determinants to recognize substrates, including the RXL motif that is read by the cyclin subunits. We were interested to determine whether additional amino acids beyond the minimal consensus sequence of the well-studied substrate and tumor suppressor p27KIP1 were relevant for catalysis.  Results To address whether additional amino acids, close to the minimal consensus sequence, play a role in binding, we investigate the interaction of cyclin A/CDK2 with an in vivo cellular partner and CDK inhibitor p27KIP1. This protein is an intrinsically unfolded protein and, in particular, the C-terminal half of the protein has not been accessible to structural analysis. This part harbors the CDK2 phosphorylation site. We used bioinformatics tools, including MODELLER, iTASSER and HADDOCK, along with partial structural information to build a model of the C-terminal region of p27KIP1 with cyclin A/CDK2. This revealed novel interactions beyond the consensus sequence with a proline and a basic amino acid at the P?+?1 and the P?+?3 sites, respectively. We suggest that the lysine at P?+?2 might regulate the reversible association of the second counter ion in the active site of CDK2. The arginine at P?+?7 interacts with both cyclin A and CDK2 and is important for the catalytic turnover rate.  Conclusion Our modeling identifies additional amino acids in p27KIP1 beyond the consensus sequence that contribute to the efficiency of substrate phosphorylation.'),
(97562, 2017, 'bmc', 'KinMap: a web-based tool for interactive navigation through human kinome data', 'Eid, Sameh|Turk, Samo|Volkamer, Andrea|Rippmann, Friedrich|Fulle, Simone', 'BioMed X Innovation Center, Im Neuenheimer Feld 515, 69120, Heidelberg, Germany.|BioMed X Innovation Center, Im Neuenheimer Feld 515, 69120, Heidelberg, Germany.|BioMed X Innovation Center, Im Neuenheimer Feld 515, 69120, Heidelberg, Germany.|Computational Chemistry and Biology, Merck KGaA, Frankfurter Str. 250, 64293, Darmstadt, Germany.|BioMed X Innovation Center, Im Neuenheimer Feld 515, 69120, Heidelberg, Germany. fulle@bio.mx.', 'Germany|Germany|Germany|Germany|Germany', 63, 'Carcinoma, Non-Small-Cell Lung|Databases, Protein|Drug Design|Humans|Internet|Models, Molecular|Molecular Biology|Molecular Sequence Annotation|Phylogeny|Protein Kinase Inhibitors|Protein Kinases|Software', 'Journal Article', '10.1186/s12859-016-1433-7', '28056780', 'Annotations of the phylogenetic tree of the human kinome is an intuitive way to visualize compound profiling data, structural features of kinases or functional relationships within this important class of proteins. The increasing volume and complexity of kinase-related data underlines the need for a tool that enables complex queries pertaining to kinase disease involvement and potential therapeutic uses of kinase inhibitors.'),
(97563, 2017, 'bmc', 'Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data', 'Zhang, Wen|Chen, Yanlin|Liu, Feng|Luo, Fei|Tian, Gang|Li, Xiaohong', 'State Key Lab of Software Engineering, Wuhan University, Wuhan, 430072, China. zhangwen@whu.edu.cn.|School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China.|International School of software, Wuhan University, Wuhan, 430072, China.|State Key Lab of Software Engineering, Wuhan University, Wuhan, 430072, China.|State Key Lab of Software Engineering, Wuhan University, Wuhan, 430072, China.|State Key Lab of Software Engineering, Wuhan University, Wuhan, 430072, China.', 'China|China|China|China|China|China', 0, 'Computational Biology|Databases, Factual|Drug Interactions|Drug-Related Side Effects and Adverse Reactions|Humans|Logistic Models|Models, Theoretical', 'Journal Article', '10.1186/s12859-016-1415-9', '28056782', 'Drug-drug interactions (DDIs) are one of the major concerns in drug discovery. Accurate prediction of potential DDIs can help to reduce unexpected interactions in the entire lifecycle of drugs, and are important for the drug safety surveillance.'),
(97564, 2017, 'bmc', 'T-RECs: rapid and large-scale detection of recombination events among different evolutionary lineages of viral genomes', 'Tsimpidis, Michail|Bachoumis, Georgios|Mimouli, Kalliopi|Kyriakopoulou, Zaharoula|Robertson, David L|Markoulatos, Panayotis|Amoutzias, Grigoris D', 'Department of Biochemistry and Biotechnology, Bioinformatics Laboratory, University of Thessaly, Larisa, Greece.|Department of Biochemistry and Biotechnology, Bioinformatics Laboratory, University of Thessaly, Larisa, Greece.|Department of Biochemistry and Biotechnology, Bioinformatics Laboratory, University of Thessaly, Larisa, Greece.|Department of Biochemistry and Biotechnology, Molecular Virology Laboratory, University of Thessaly, Larisa, Greece.|Faculty of Life Sciences, University of Manchester, Manchester, UK.|Department of Biochemistry and Biotechnology, Molecular Virology Laboratory, University of Thessaly, Larisa, Greece.|Department of Biochemistry and Biotechnology, Bioinformatics Laboratory, University of Thessaly, Larisa, Greece. amoutzias@bio.uth.gr.', 'Greece|Greece|Greece|Greece|United Kingdom|Greece|Greece', 4, 'Cluster Analysis|DNA Fragmentation|DNA, Viral|Databases, Genetic|Evolution, Molecular|Genome, Viral|Genotyping Techniques|Norovirus|Recombination, Genetic|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-016-1420-z', '28056784', 'Many computational tools that detect recombination in viruses are not adapted for the ongoing genomic revolution. A computational tool is needed, that will rapidly scan hundreds/thousands of genomes or sequence fragments and detect candidate recombination events that may later be further analyzed with more sensitive and specialized methods.'),
(97565, 2017, 'bmc', 'GTB - an online genome tolerance browser', 'Shihab, Hashem A|Rogers, Mark F|Ferlaino, Michael|Campbell, Colin|Gaunt, Tom R', 'MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, BS8 2BN, UK.|Intelligent Systems Laboratory, University of Bristol, Bristol, BS8 1UB, UK.|Intelligent Systems Laboratory, University of Bristol, Bristol, BS8 1UB, UK.|Intelligent Systems Laboratory, University of Bristol, Bristol, BS8 1UB, UK.|MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, BS8 2BN, UK. tom.gaunt@bristol.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 3, 'Algorithms|Databases, Genetic|Genome, Human|Homeodomain Proteins|Humans|Internet|Models, Theoretical|Neoplasms|Receptors, LDL|Web Browser', 'Journal Article', '10.1186/s12859-016-1436-4', '28061747', 'Accurate methods capable of predicting the impact of single nucleotide variants (SNVs) are assuming ever increasing importance. There exists a plethora of in silico algorithms designed to help identify and prioritize SNVs across the human genome for further investigation. However, no tool exists to visualize the predicted tolerance of the genome to mutation, or the similarities between these methods.'),
(97566, 2017, 'bmc', 'GenomeCAT: a versatile tool for the analysis and integrative visualization of DNA copy number variants', 'Tebel, Katrin|Boldt, Vivien|Steininger, Anne|Port, Matthias|Ebert, Grit|Ullmann, Reinhard', 'Max Planck Institute for Molecular Genetics, 14195, Berlin, Germany.|Max Planck Institute for Molecular Genetics, 14195, Berlin, Germany.|Max Planck Institute for Molecular Genetics, 14195, Berlin, Germany.|Institut für Radiobiologie der Bundeswehr in Verb. mit der Universität Ulm, 80937, Munich, Germany.|Max Planck Institute for Molecular Genetics, 14195, Berlin, Germany.|Max Planck Institute for Molecular Genetics, 14195, Berlin, Germany. reinhard1ullmann@bundeswehr.org.', 'Germany|Germany|Germany|Germany|Germany|Germany', 3, 'Computer Simulation|DNA Copy Number Variations|Genome, Human|Genomics|Humans|Models, Theoretical|Reproducibility of Results|Software', 'Journal Article', '10.1186/s12859-016-1430-x', '28061750', 'The analysis of DNA copy number variants (CNV) has increasing impact in the field of genetic diagnostics and research. However, the interpretation of CNV data derived from high resolution array CGH or NGS platforms is complicated by the considerable variability of the human genome. Therefore, tools for multidimensional data analysis and comparison of patient cohorts are needed to assist in the discrimination of clinically relevant CNVs from others.'),
(97567, 2017, 'bmc', 'An internet-based bioinformatics toolkit for plant biosecurity diagnosis and surveillance of viruses and viroids', 'Barrero, Roberto A|Napier, Kathryn R|Cunnington, James|Liefting, Lia|Keenan, Sandi|Frampton, Rebekah A|Szabo, Tamas|Bulman, Simon|Hunter, Adam|Ward, Lisa|Whattam, Mark|Bellgard, Matthew I', 'Centre for Comparative Genomics, Murdoch University, Murdoch, WA, 6150, Australia. rbarrero@ccg.murdoch.edu.au.|Centre for Comparative Genomics, Murdoch University, Murdoch, WA, 6150, Australia.|Department of Agriculture and Water Resources, Mickleham, VIC, 3064, Australia.|Ministry for Primary Industries, Wellington, New Zealand.|The New Zealand Institute for Plant Food and Research Limited, Better Border Biosecurity, Lincoln, 7608, New Zealand.|The New Zealand Institute for Plant Food and Research Limited, Better Border Biosecurity, Lincoln, 7608, New Zealand.|Centre for Comparative Genomics, Murdoch University, Murdoch, WA, 6150, Australia.|The New Zealand Institute for Plant Food and Research Limited, Better Border Biosecurity, Lincoln, 7608, New Zealand.|Centre for Comparative Genomics, Murdoch University, Murdoch, WA, 6150, Australia.|Ministry for Primary Industries, Wellington, New Zealand.|Department of Agriculture and Water Resources, Mickleham, VIC, 3064, Australia.|Centre for Comparative Genomics, Murdoch University, Murdoch, WA, 6150, Australia. mbellgard@ccg.murdoch.edu.au.', 'Australia|Australia|Australia|New Zealand|New Zealand|New Zealand|Australia|New Zealand|Australia|Ne', 20, 'Australia|Computational Biology|Internet|New Zealand|Plant Diseases|Plant Viruses|RNA, Plant|RNA, Small Interfering|RNA, Viral|Viroids', 'Journal Article', '10.1186/s12859-016-1428-4', '28077064', 'Detection and preventing entry of exotic viruses and viroids at the border is critical for protecting plant industries trade worldwide. Existing post entry quarantine screening protocols rely on time-consuming biological indicators and/or molecular assays that require knowledge of infecting viral pathogens. Plants have developed the ability to recognise and respond to viral infections through Dicer-like enzymes that cleave viral sequences into specific small RNA products. Many studies reported the use of a broad range of small RNAs encompassing the product sizes of several Dicer enzymes involved in distinct biological pathways. Here we optimise the assembly of viral sequences by using specific small RNA subsets.'),
(97568, 2017, 'bmc', 'Structure alignment-based classification of RNA-binding pockets reveals regional RNA recognition motifs on protein surfaces', 'Liu, Zhi-Ping|Liu, Shutang|Chen, Ruitang|Huang, Xiaopeng|Wu, Ling-Yun', 'Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China.|Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China.|Department of Computer Science, Stanford University, Stanford, CA, 94305, USA.|Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.|Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China. lywu@amss.ac.cn.', 'China|China|United States|China|China', 5, 'Computational Biology|Models, Molecular|Molecular Conformation|RNA Recognition Motif|RNA-Binding Proteins', 'Journal Article', '10.1186/s12859-016-1410-1', '28077065', 'Many critical biological processes are strongly related to protein-RNA interactions. Revealing the protein structure motifs for RNA-binding will provide valuable information for deciphering protein-RNA recognition mechanisms and benefit complementary structural design in bioengineering. RNA-binding events often take place at pockets on protein surfaces. The structural classification of local binding pockets determines the major patterns of RNA recognition.'),
(97569, 2017, 'bmc', 'SCOPA and META-SCOPA: software for the analysis and aggregation of genome-wide association studies of multiple correlated phenotypes', 'Mxc3xa4gi, Reedik|Suleimanov, Yury V|Clarke, Geraldine M|Kaakinen, Marika|Fischer, Krista|Prokopenko, Inga|Morris, Andrew P', 'Estonian Genome Center, University of Tartu, Tartu, Estonia.|Computation-based Science and Technology Research Center, Cyprus Institute, Nicosia, Cyprus.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.|Genomics of Common Disease, Imperial College, London, UK.|Estonian Genome Center, University of Tartu, Tartu, Estonia.|Genomics of Common Disease, Imperial College, London, UK.|Estonian Genome Center, University of Tartu, Tartu, Estonia. apmorris@liverpool.ac.uk.', 'Estonia|Cyprus|United Kingdom|United Kingdom|Estonia|United Kingdom|Estonia', 8, 'Body Mass Index|Cholesterol, HDL|Cholesterol, LDL|Genome-Wide Association Study|Humans|Models, Theoretical|Obesity|Phenotype|Software|Triglycerides', 'Journal Article', '10.1186/s12859-016-1437-3', '28077070', 'Genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have been successful in identifying loci contributing genetic effects to a wide range of complex human diseases and quantitative traits. The traditional approach to GWAS analysis is to consider each phenotype separately, despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. Multivariate analyses of correlated phenotypes have been demonstrated, by simulation, to increase power to detect association with SNPs, and thus may enable improved detection of novel loci contributing to diseases and quantitative traits.'),
(97570, 2017, 'bmc', 'MVIAeval: a web tool for comprehensively evaluating the performance of a new missing value imputation algorithm', 'Wu, Wei-Sheng|Jhou, Meng-Jhun', 'Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan. wessonwu@mail.ncku.edu.tw.|Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan.', '-|-', 3, 'Algorithms|Biostatistics|Computational Biology|Humans|Internet|Models, Statistical|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/s12859-016-1429-3', '28086746', 'Missing value imputation is important for microarray data analyses because microarray data with missing values would significantly degrade the performance of the downstream analyses. Although many microarray missing value imputation algorithms have been developed, an objective and comprehensive performance comparison framework is still lacking. To solve this problem, we previously proposed a framework which can perform a comprehensive performance comparison of different existing algorithms. Also the performance of a new algorithm can be evaluated by our performance comparison framework. However, constructing our framework is not an easy task for the interested researchers. To save researchers time and efforts, here we present an easy-to-use web tool named MVIAeval (Missing Value Imputation Algorithm evaluator) which implements our performance comparison framework.'),
(97571, 2017, 'bmc', 'MicroRNA based Pan-Cancer Diagnosis and Treatment Recommendation', 'Cheerla, Nikhil|Gevaert, Olivier', 'Monta Vista High School, Cupertino, CA, USA. nikcheerla@gmail.com.|Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, and Department of Biomedical Data Science, Stanford University, 1265 Welch Rd, Stanford, CA, USA.', 'United States|United States', 18, 'Algorithms|Biomarkers, Tumor|Humans|MicroRNAs|Neoplasms|Prognosis|Support Vector Machine|Treatment Outcome', 'Journal Article', '10.1186/s12859-016-1421-y', '28086747', 'The current state-of-the-art in cancer diagnosis and treatment is not ideal, diagnostic tests are accurate but invasive, and treatments are one-size fits-all instead of being personalized. Recently, miRNAs have garnered significant attention as cancer biomarkers, owing to their ease of access (circulating miRNA in the blood) and stability. There have been many studies showing the effectiveness of miRNA data in diagnosing specific cancer types, but few studies explore the role of miRNA in predicting treatment outcome.'),
(97572, 2017, 'bmc', 'TTCA: an R package for the identification of differentially expressed genes in time course microarray data', 'Albrecht, Marco|Stichel, Damian|Müller, Benedikt|Merkle, Ruth|Sticht, Carsten|Gretz, Norbert|Klingmüller, Ursula|Breuhahn, Kai|Matthäus, Franziska', 'Complex Biological Systems Group (BIOMS/IWR), Heidelberg, Im Neuenheimer Feld 294, Heidelberg, 69120, Germany. marco.albrecht@posteo.de.|Complex Biological Systems Group (BIOMS/IWR), Heidelberg, Im Neuenheimer Feld 294, Heidelberg, 69120, Germany.|Institute of Pathology, Heidelberg University Hospital, Im Neuenheimer Feld 672, Heidelberg, 69120, Germany.|German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.|Medical Research Center, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, Mannheim, 68167, Germany.|Medical Research Center, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, Mannheim, 68167, Germany.|German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.|Institute of Pathology, Heidelberg University Hospital, Im Neuenheimer Feld 672, Heidelberg, 69120, Germany.|Complex Biological Systems Group (BIOMS/IWR), Heidelberg, Im Neuenheimer Feld 294, Heidelberg, 69120, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 7, 'Computational Biology|Gene Expression|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/s12859-016-1440-8', '28088176', 'The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements.'),
(97573, 2017, 'bmc', 'iGC-an integrated analysis package of gene expression and copy number alteration', 'Lai, Yi-Pin|Wang, Liang-Bo|Wang, Wei-An|Lai, Liang-Chuan|Tsai, Mong-Hsun|Lu, Tzu-Pin|Chuang, Eric Y', 'Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan.|Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan.|Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan.|Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan.|Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan.|Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan. tplu@ntu.edu.tw.|Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan. chuangey@ntu.edu.tw.', '-|-|-|-|-|-|-', 4, 'DNA Copy Number Variations|Gene Expression|Gene Expression Profiling|Genome|Humans|Software|Transcriptome', 'Journal Article', '10.1186/s12859-016-1438-2', '28088185', 'With the advancement in high-throughput technologies, researchers can simultaneously investigate gene expression and copy number alteration (CNA) data from individual patients at a lower cost. Traditional analysis methods analyze each type of data individually and integrate their results using Venn diagrams. Challenges arise, however, when the results are irreproducible and inconsistent across multiple platforms. To address these issues, one possible approach is to concurrently analyze both gene expression profiling and CNAs in the same individual.'),
(97574, 2017, 'bmc', 'Predicting probable Alzheimers disease using linguistic deficits and biomarkers', 'Orimaye, Sylvester O|Wong, Jojo S-M|Golden, Karen J|Wong, Chee P|Soyiri, Ireneous N', 'Intelligent Health Research Group, School of Information Technology, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Malaysia. sylvester.orimaye@monash.edu.|Intelligent Health Research Group, School of Information Technology, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Malaysia.|Jeffrey Cheah School of Medicine and Health Sciences, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Malaysia.|Jeffrey Cheah School of Medicine and Health Sciences, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Malaysia.|Centre for Medical Informatics, Usher Institute for Population Health Sciences & Informatics, The University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, UK.', 'Malaysia|Malaysia|Malaysia|Malaysia|United Kingdom', 25, 'Aged|Aged, 80 and over|Algorithms|Alzheimer Disease|Biomarkers|Humans|Linguistics|Machine Learning|Middle Aged|Speech', 'Journal Article', '10.1186/s12859-016-1456-0', '28088191', 'The manual diagnosis of neurodegenerative disorders such as Alzheimers disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.'),
(97575, 2017, 'bmc', 'PCM-SABRE: a platform for benchmarking and comparing outcome prediction methods in precision cancer medicine', 'Eyal-Altman, Noah|Last, Mark|Rubin, Eitan', 'Shraga Segal Department of Microbiology and Immunology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel. eyalnoa@post.bgu.ac.il.|Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel.|Shraga Segal Department of Microbiology and Immunology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel.', 'Israel|Israel|Israel', 5, 'Algorithms|Benchmarking|Breast Neoplasms|Female|Humans|Logistic Models|Machine Learning|Neoplasm Recurrence, Local|Neural Networks (Computer)|Precision Medicine|Software', 'Journal Article', '10.1186/s12859-016-1435-5', '28095769', 'Numerous publications attempt to predict cancer survival outcome from gene expression data using machine-learning methods. A direct comparison of these works is challenging for the following reasons: (1) inconsistent measures used to evaluate the performance of different models, and (2) incomplete specification of critical stages in the process of knowledge discovery. There is a need for a platform that would allow researchers to replicate previous works and to test the impact of changes in the knowledge discovery process on the accuracy of the induced models.'),
(97576, 2017, 'bmc', 'Empirical assessment of analysis workflows for differential expression analysis of human samples using RNA-Seq', 'Williams, Claire R|Baccarella, Alyssa|Parrish, Jay Z|Kim, Charles C', 'Department of Biology, University of Washington, Seattle, WA, 98195, USA.|Division of Experimental Medicine, Department of Medicine, University of California, San Francisco, CA, 94143, USA.|Department of Biology, University of Washington, Seattle, WA, 98195, USA.|Division of Experimental Medicine, Department of Medicine, University of California, San Francisco, CA, 94143, USA. cckim47@gmail.com.', 'United States|United States|United States|United States', 22, 'Gene Expression Profiling|Humans|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1186/s12859-016-1457-z', '28095772', 'RNA-Seq has supplanted microarrays as the preferred method of transcriptome-wide identification of differentially expressed genes. However, RNA-Seq analysis is still rapidly evolving, with a large number of tools available for each of the three major processing steps: read alignment, expression modeling, and identification of differentially expressed genes. Although some studies have benchmarked these tools against gold standard gene expression sets, few have evaluated their performance in concert with one another. Additionally, there is a general lack of testing of such tools on real-world, physiologically relevant datasets, which often possess qualities not reflected in tightly controlled reference RNA samples or synthetic datasets.'),
(97577, 2017, 'bmc', 'ARA-PEPs: a repository of putative sORF-encoded peptides in Arabidopsis thaliana', 'Hazarika, Rashmi R|De Coninck, Barbara|Yamamoto, Lidia R|Martin, Laura R|Cammue, Bruno P A|van Noort, Vera', 'KU Leuven, Centre of Microbial and Plant Genetics, Kasteelpark Arenberg 20, Leuven, B-3001, Belgium.|KU Leuven, Centre of Microbial and Plant Genetics, Kasteelpark Arenberg 20, Leuven, B-3001, Belgium.|KU Leuven, Centre of Microbial and Plant Genetics, Kasteelpark Arenberg 20, Leuven, B-3001, Belgium.|KU Leuven, Centre of Microbial and Plant Genetics, Kasteelpark Arenberg 20, Leuven, B-3001, Belgium.|KU Leuven, Centre of Microbial and Plant Genetics, Kasteelpark Arenberg 20, Leuven, B-3001, Belgium.|KU Leuven, Centre of Microbial and Plant Genetics, Kasteelpark Arenberg 20, Leuven, B-3001, Belgium. vera.vannoort@kuleuven.be.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 9, 'Arabidopsis|Arabidopsis Proteins|Databases, Factual|Molecular Sequence Annotation|Open Reading Frames|Peptides', 'Journal Article', '10.1186/s12859-016-1458-y', '28095775', 'Many eukaryotic RNAs have been considered non-coding as they only contain short open reading frames (sORFs). However, there is increasing evidence for the translation of these sORFs into bioactive peptides with potent signaling, antimicrobial, developmental, antioxidant roles etc. Yet only a few peptides encoded by sORFs are annotated in the model organism Arabidopsis thaliana.'),
(97578, 2017, 'bmc', 'Link prediction in drug-target interactions network using similarity indices', 'Lu, Yiding|Guo, Yufan|Korhonen, Anna', 'Computer Laboratory, University of Cambridge, JJ Thompson Avenue, Cambridge, UK.|Computer Laboratory, University of Cambridge, JJ Thompson Avenue, Cambridge, UK.|Computer Laboratory, University of Cambridge, JJ Thompson Avenue, Cambridge, UK. alk23@cam.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 39, 'Computer Simulation|Databases, Factual|Drug Repositioning|Support Vector Machine', 'Journal Article', '10.1186/s12859-017-1460-z', '28095781', 'In silico drug-target interaction (DTI) prediction plays an integral role in drug repositioning: the discovery of new uses for existing drugs. One popular method of drug repositioning is network-based DTI prediction, which uses complex network theory to predict DTIs from a drug-target network. Currently, most network-based DTI prediction is based on machine learning - methods such as Restricted Boltzmann Machines (RBM) or Support Vector Machines (SVM). These methods require additional information about the characteristics of drugs, targets and DTIs, such as chemical structure, genome sequence, binding types, causes of interactions, etc., and do not perform satisfactorily when such information is unavailable. We propose a new, alternative method for DTI prediction that makes use of only network topology information attempting to solve this problem.'),
(97579, 2017, 'bmc', 'MultiDataSet: an R package for encapsulating multiple data sets with application to omic data integration', 'Hernandez-Ferrer, Carles|Ruiz-Arenas, Carlos|Beltran-Gomila, Alba|González, Juan R', 'Institut de Salut Global de Barcelona (ISGlobal) - Campus Mar, Barcelona Biulding: Biomedical Research Park, c/Dr. Aiguader, 88, 08003, Barcelona, Spain.|Institut de Salut Global de Barcelona (ISGlobal) - Campus Mar, Barcelona Biulding: Biomedical Research Park, c/Dr. Aiguader, 88, 08003, Barcelona, Spain.|Institut de Salut Global de Barcelona (ISGlobal) - Campus Mar, Barcelona Biulding: Biomedical Research Park, c/Dr. Aiguader, 88, 08003, Barcelona, Spain.|Institut de Salut Global de Barcelona (ISGlobal) - Campus Mar, Barcelona Biulding: Biomedical Research Park, c/Dr. Aiguader, 88, 08003, Barcelona, Spain. juanr.gonzalez@isglobal.org.', 'Spain|Spain|Spain|Spain', 12, 'DNA Methylation|Gene Expression|Genomics|Humans|Multivariate Analysis|Software', 'Journal Article', '10.1186/s12859-016-1455-1', '28095799', 'Reduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple experiments in the same subjects. While Bioconductors methods and classes implemented in different packages manage individual experiments, there is not a standard class to properly manage different omic datasets from the same subjects. In addition, most R/Bioconductor packages that have been designed to integrate and visualize biological data often use basic data structures with no clear general methods, such as subsetting or selecting samples.'),
(97580, 2017, 'bmc', 'SMITE: an R/Bioconductor package that identifies network modules by integrating genomic and epigenomic information', 'Wijetunga, N Ari|Johnston, Andrew D|Maekawa, Ryo|Delahaye, Fabien|Ulahannan, Netha|Kim, Kami|Greally, John M', 'Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY, 10461, USA.|Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY, 10461, USA.|Division of Obstetrics and Gynecology, Yamaguchi University, 677-1 Yoshida, Yamaguchi Prefecture, 753-8511, Japan.|Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY, 10461, USA.|Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY, 10461, USA.|Department of Microbiology and Immunology, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY, 10461, USA.|Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY, 10461, USA. john.greally@einstein.yu.edu.', 'United States|United States|Japan|United States|United States|United States|United States', 10, 'Algorithms|Databases, Genetic|Epigenesis, Genetic|Epigenomics|Fibroblasts|Foreskin|Gene Regulatory Networks|Humans|Male|Models, Theoretical|Software|Toxoplasma|Transcriptome', 'Journal Article', '10.1186/s12859-017-1477-3', '28100166', 'The molecular assays that test gene expression, transcriptional, and epigenetic regulation are increasingly diverse and numerous. The information generated by each type of assay individually gives an insight into the state of the cells tested. What should be possible is to add the information derived from separate, complementary assays to gain higher-confidence insights into cellular states. At present, the analysis of multi-dimensional, massive genome-wide data requires an initial pruning step to create manageable subsets of observations that are then used for integration, which decreases the sizes of the intersecting data sets and the potential for biological insights. Our Significance-based Modules Integrating the Transcriptome and Epigenome (SMITE) approach was developed to integrate transcriptional and epigenetic regulatory data without a loss of resolution.'),
(97581, 2015, 'bmc', 'GIW and InCoB are advancing bioinformatics in the Asia-Pacific', 'Schönbach, Christian|Horton, Paul|Yiu, Siu-Ming|Tan, Tin Wee|Ranganathan, Shoba', 'Department of Biology, School of Science and Technology, Nazarbayev University, Astana, 010000, Republic of Kazakhstan. christian.schoenbach@nu.edu.kz.|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, 135-0064, Japan.|Department of Computer Science, Faculty of Engineering, The University of Hong Kong, Hong Kong, HKSAR.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599.', 'Kazakhstan|Japan|Hong Kong|Singapore|Singapore', 0, 'Asia|Biomarkers|Biomedical Research|Computational Biology|Congresses as Topic|Genomics|Protein Conformation|Protein Interaction Mapping|Protein Processing, Post-Translational|Systems Biology', 'Journal Article', '10.1186/1471-2105-16-S18-I1', '28102114', 'GIW/InCoB2015 the joint 26th International Conference on Genome Informatics (GIW) and 14th International Conference on Bioinformatics (InCoB) held in Tokyo, September 9-11, 2015 was attended by over 200 delegates. Fifty-one out of 89 oral presentations were based on research articles accepted for publication in four BMC journal supplements and three other journals. Sixteen articles in this supplement and six articles in the BMC Systems Biology GIW/InCoB2015 Supplement are covered by this introduction. The topics range from genome informatics, protein structure informatics, image analysis to biological networks and biomarker discovery.'),
(97582, 2017, 'bmc', 'Nonlinear dimensionality reduction methods for synthetic biology biobricks visualization', 'Yang, Jiaoyun|Wang, Haipeng|Ding, Huitong|An, Ning|Alterovitz, Gil', 'School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei, 230009, China.|School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei, 230009, China.|School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei, 230009, China.|School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei, 230009, China. ning.g.an@acm.org.|Harvard Medical School, Boston Childrens Hospital, Boston, 02115, MA, USA.', 'China|China|China|China|United States', 1, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Factual|Models, Theoretical|Nonlinear Dynamics|Reproducibility of Results|Sequence Analysis, DNA|Synthetic Biology', 'Journal Article', '10.1186/s12859-017-1484-4', '28103789', 'Visualizing data by dimensionality reduction is an important strategy in Bioinformatics, which could help to discover hidden data properties and detect data quality issues, e.g. data noise, inappropriately labeled data, etc. As crowdsourcing-based synthetic biology databases face similar data quality issues, we propose to visualize biobricks to tackle them. However, existing dimensionality reduction methods could not be directly applied on biobricks datasets. Hereby, we use normalized edit distance to enhance dimensionality reduction methods, including Isomap and Laplacian Eigenmaps.'),
(97583, 2017, 'bmc', 'Variational inference for rare variant detection in deep, heterogeneous next-generation sequencing data', 'Zhang, Fan|Flaherty, Patrick', 'Department of Biomedical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, 01609, USA.|Department of Biomedical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, 01609, USA. flaherty@math.umass.edu.', 'United States|United States', 1, 'Algorithms|Bayes Theorem|Gene Frequency|High-Throughput Nucleotide Sequencing|Markov Chains|Models, Theoretical|Monte Carlo Method|Sequence Analysis, DNA', 'Comparative Study', '10.1186/s12859-016-1451-5', '28103803', 'The detection of rare single nucleotide variants (SNVs) is important for understanding genetic heterogeneity using next-generation sequencing (NGS) data. Various computational algorithms have been proposed to detect variants at the single nucleotide level in mixed samples. Yet, the noise inherent in the biological processes involved in NGS technology necessitates the development of statistically accurate methods to identify true rare variants.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97584, 2017, 'bmc', 'Inactivity periods and postural change speed can explain atypical postural change patterns of Caenorhabditis elegans mutants', 'Fukunaga, Tsukasa|Iwasaki, Wataru', 'Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, 277-8568, Japan. t.fukunaga@kurenai.waseda.jp.|Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, 277-8568, Japan. iwasaki@bs.s.u-tokyo.ac.jp.', 'Japan|Japan', 0, 'Animals|Behavior, Animal|Caenorhabditis elegans|Caenorhabditis elegans Proteins|Models, Theoretical|Mutation|Receptors, Neuropeptide Y', 'Journal Article', '10.1186/s12859-016-1408-8', '28103804', 'With rapid advances in genome sequencing and editing technologies, systematic and quantitative analysis of animal behavior is expected to be another key to facilitating data-driven behavioral genetics. The nematode Caenorhabditis elegans is a model organism in this field. Several video-tracking systems are available for automatically recording behavioral data for the nematode, but computational methods for analyzing these data are still under development.'),
(97585, 2016, 'bmc', 'DegreeCox - a network-based regularization method for survival analysis', 'Veríssimo, André|Oliveira, Arlindo Limede|Sagot, Marie-France|Vinga, Susana', 'IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, 1049-001, Portugal.|Instituto Superior Técnico, Universidade de Lisboa, Lisbon, 1049-001, Portugal.|ERABLE, Inria, Villeurbanne, France.|IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, 1049-001, Portugal. susanavinga@tecnico.ulisboa.pt.', 'Portugal|Portugal|France|Portugal', 0, 'Algorithms|Female|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Models, Genetic|Ovarian Neoplasms|Proportional Hazards Models', 'Journal Article', '10.1186/s12859-016-1310-4', '28105908', 'Modeling survival oncological data has become a major challenge as the increase in the amount of molecular information nowadays available means that the number of features greatly exceeds the number of observations. One possible solution to cope with this dimensionality problem is the use of additional constraints in the cost function optimization. LASSO and other sparsity methods have thus already been successfully applied with such idea. Although this leads to more interpretable models, these methods still do not fully profit from the relations between the features, specially when these can be represented through graphs. We propose DEGREECOX, a method that applies network-based regularizers to infer Cox proportional hazard models, when the features are genes and the outcome is patient survival. In particular, we propose to use network centrality measures to constrain the model in terms of significant genes.'),
(97586, 2016, 'bmc', 'On the inconsistency of ? 1-penalised sparse precision matrix estimation', 'Heinxc3xa4vaara, Otte|Leppxc3xa4-Aho, Janne|Corander, Jukka|Honkela, Antti', 'Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.|Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland. antti.honkela@helsinki.fi.', 'Finland|Finland|Finland|Finland', 6, 'Animals|Computational Biology|Gene Regulatory Networks|Humans|Machine Learning|Models, Statistical|Transcriptome', 'Journal Article', '10.1186/s12859-016-1309-x', '28105909', 'Background Various ? 1-penalised estimation methods such as graphical lasso and CLIME are widely used for sparse precision matrix estimation and learning of undirected network structure from data. Many of these methods have been shown to be consistent under various quantitative assumptions about the underlying true covariance matrix. Intuitively, these conditions are related to situations where the penalty term will dominate the optimisation.  Results We explore the consistency of ? 1-based methods for a class of bipartite graphs motivated by the structure of models commonly used for gene regulatory networks. We show that all ? 1-based methods fail dramatically for models with nearly linear dependencies between the variables. We also study the consistency on models derived from real gene expression data and note that the assumptions needed for consistency never hold even for modest sized gene networks and ? 1-based methods also become unreliable in practice for larger networks.  Conclusions Our results demonstrate that ? 1-penalised undirected network structure learning methods are unable to reliably learn many sparse bipartite graph structures, which arise often in gene expression data. Users of such methods should be aware of the consistency criteria of the methods and check if they are likely to be met in their application of interest.'),
(97587, 2016, 'bmc', 'Selected proceedings of Machine Learning in Systems Biology: MLSB 2016', 'van Dijk, Aalt D J|Lähdesmäki, Harri|de Ridder, Dick|Rousu, Juho', 'Biometris, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands.|Department of Computer Science, Aalto University, 00076, Aalto, Finland.|Bioinformatics Group, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands.|Department of Computer Science, Aalto University, 00076, Aalto, Finland. juho.rousu@aalto.fi.', 'Netherlands|Finland|Netherlands|Finland', 3, '-', 'Journal Article', '10.1186/s12859-016-1305-1', '28105910', '-'),
(97588, 2016, 'bmc', 'Integrating gene set analysis and nonlinear predictive modeling of disease phenotypes using a Bayesian multitask formulation', 'Gxc3xb6nen, Mehmet', 'Department of Industrial Engineering, Koxc3xa7 University, xc4xb0stanbul, 34450, Turkey. mehmetgonen@ku.edu.tr.', 'Turkey', 2, 'Adult|Algorithms|Bayes Theorem|Child|Computational Biology|Gene Expression Profiling|Humans|Neoplasms|Phenotype|Transcriptome|Tuberculosis', 'Journal Article', '10.1186/s12859-016-1311-3', '28105911', 'Identifying molecular signatures of disease phenotypes is studied using two mainstream approaches: (i) Predictive modeling methods such as linear classification and regression algorithms are used to find signatures predictive of phenotypes from genomic data, which may not be robust due to limited sample size or highly correlated nature of genomic data. (ii) Gene set analysis methods are used to find gene sets on which phenotypes are linearly dependent by bringing prior biological knowledge into the analysis, which may not capture more complex nonlinear dependencies. Thus, formulating an integrated model of gene set analysis and nonlinear predictive modeling is of great practical importance.'),
(97589, 2016, 'bmc', 'DGW: an exploratory data analysis tool for clustering and visualisation of epigenomic marks', 'Lukauskas, Saulius|Visintainer, Roberto|Sanguinetti, Guido|Schweikert, Gabriele B', 'Department of Chemical Engineering, Imperial College London, London, SW7 2AZ, UK. saulius.lukauskas13@imperial.ac.uk.|Fondazione Bruno Kessler, Via Sommarive 18, Povo, I-38123, TN, Italy.|School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh, EH8 9AB, Scotland.|School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh, EH8 9AB, Scotland.', 'United Kingdom|Italy|United Kingdom|United Kingdom', 5, 'Chromatin Immunoprecipitation|Cluster Analysis|Computer Simulation|DNA|DNA-Binding Proteins|Epigenesis, Genetic|Epigenomics|Genome, Human|Histone Code|Humans|Leukemia|Software', 'Journal Article', '10.1186/s12859-016-1306-0', '28105912', 'Functional genomic and epigenomic research relies fundamentally on sequencing based methods like ChIP-seq for the detection of DNA-protein interactions. These techniques return large, high dimensional data sets with visually complex structures, such as multi-modal peaks extended over large genomic regions. Current tools for visualisation and data exploration represent and leverage these complex features only to a limited extent.'),
(97590, 2016, 'bmc', 'MUMAL2: Improving sensitivity in shotgun proteomics using cost sensitive artificial neural networks and a threshold selector algorithm', 'Cerqueira, Fabio Ribeiro|Ricardo, Adilson Mendes|de Paiva Oliveira, Alcione|Graber, Armin|Baumgartner, Christian', 'Department of Informatics, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil. fabio.cerqueira@ufv.br.|Department of Informatics, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil.|Department of Informatics, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil.|Research and Product Development of Genoptix, a Novartis company, 2110 Rutherford Rd, Carlsbad, 92008, USA.|Institute of Health Care Engineering with European Notified Body of Medical Devices, Graz University of Technology, Stremayrgasse 16/II, Graz, A-8010, Austria.', 'Brazil|Brazil|Brazil|United States|Austria', 2, 'Algorithms|Databases, Protein|Neural Networks (Computer)|Peptides|Probability|Proteome|Proteomics|Software|Tandem Mass Spectrometry', 'Evaluation Studies', '10.1186/s12859-016-1341-x', '28105913', 'This work presents a machine learning strategy to increase sensitivity in tandem mass spectrometry (MS/MS) data analysis for peptide/protein identification. MS/MS yields thousands of spectra in a single run which are then interpreted by software. Most of these computer programs use a protein database to match peptide sequences to the observed spectra. The peptide-spectrum matches (PSMs) must also be assessed by computational tools since manual evaluation is not practicable. The target-decoy database strategy is largely used for error estimation in PSM assessment. However, in general, that strategy does not account for sensitivity.'),
(97591, 2016, 'bmc', 'Homology modeling and molecular dynamics provide structural insights into tospovirus nucleoprotein', 'Lima, Rayane Nunes|Faheem, Muhammad|Barbosa, João Alexandre Ribeiro Gonçalves|Polêto, Marcelo Depólo|Verli, Hugo|Melo, Fernando Lucas|Resende, Renato Oliveira', 'Laboratório de Virologia Vegetal, Departamento de Biologia Celular, Instituto de Ciências Biológicas, Universidade de Brasília, Brasília, DF, Brazil.|Laboratório de Biofísica, Departamento de Biologia Celular, Instituto de Ciências Biológicas, Universidade de Brasília, Brasília, DF, Brazil.|Laboratório de Biofísica, Departamento de Biologia Celular, Instituto de Ciências Biológicas, Universidade de Brasília, Brasília, DF, Brazil.|Centro de Biotecnologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.|Centro de Biotecnologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.|Laboratório de Virologia Vegetal, Departamento de Biologia Celular, Instituto de Ciências Biológicas, Universidade de Brasília, Brasília, DF, Brazil.|Laboratório de Virologia Vegetal, Departamento de Biologia Celular, Instituto de Ciências Biológicas, Universidade de Brasília, Brasília, DF, Brazil. rresende@unb.br.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 4, 'Amino Acid Sequence|Base Sequence|Molecular Dynamics Simulation|Molecular Sequence Data|Nucleoproteins|Sequence Alignment|Tospovirus', 'Journal Article', '10.1186/s12859-016-1339-4', '28105914', 'Tospovirus is a plant-infecting genus within the family Bunyaviridae, which also includes four animal-infecting genera: Hantavirus, Nairovirus, Phlebovirus and Orthobunyavirus. Compared to these members, the structures of Tospovirus proteins still are poorly understood. Despite multiple studies have attempted to identify candidate N protein regions involved in RNA binding and protein multimerization for tospovirus using yeast two-hybrid systems (Y2HS) and site-directed mutagenesis, the tospovirus ribonucleocapsids (RNPs) remains largely uncharacterized at the molecular level and the lack of structural information prevents detailed insight into these interactions.'),
(97592, 2016, 'bmc', 'Spectral consensus strategy for accurate reconstruction of large biological networks', 'Affeldt, Séverine|Sokolovska, Nataliya|Prifti, Edi|Zucker, Jean-Daniel', 'Integromics, Institute of Cardiometabolism and Nutrition, ICAN, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, 75013, France.|Integromics, Institute of Cardiometabolism and Nutrition, ICAN, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, 75013, France.|Integromics, Institute of Cardiometabolism and Nutrition, ICAN, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, 75013, France.|Integromics, Institute of Cardiometabolism and Nutrition, ICAN, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, 75013, France. jean-daniel.zucker@ird.fr.', 'France|France|France|France', 4, 'Algorithms|Bacteria|Computational Biology|Gastrointestinal Microbiome|Humans|Unsupervised Machine Learning', 'Journal Article', '10.1186/s12859-016-1308-y', '28105915', 'The last decades witnessed an explosion of large-scale biological datasets whose analyses require the continuous development of innovative algorithms. Many of these high-dimensional datasets are related to large biological networks with few or no experimentally proven interactions. A striking example lies in the recent gut bacterial studies that provided researchers with a plethora of information sources. Despite a deeper knowledge of microbiome composition, inferring bacterial interactions remains a critical step that encounters significant issues, due in particular to high-dimensional settings, unknown gut bacterial taxa and unavoidable noise in sparse datasets. Such data type make any a priori choice of a learning method particularly difficult and urge the need for the development of new scalable approaches.'),
(97593, 2016, 'bmc', 'GPCRs from fusarium graminearum detection, modeling and virtual screening - the search for new routes to control head blight disease', 'Bresso, Emmanuel|Togawa, Roberto|Hammond-Kosack, Kim|Urban, Martin|Maigret, Bernard|Martins, Natalia Florencio', 'EMBRAPA Genetic Resources and Biotechnology, Brasília, DF, 70770-917, Brazil.|EMBRAPA Genetic Resources and Biotechnology, Brasília, DF, 70770-917, Brazil.|Department of Plant Biology and Crop Science, Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, UK.|Department of Plant Biology and Crop Science, Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, UK.|EMBRAPA Genetic Resources and Biotechnology, Brasília, DF, 70770-917, Brazil.|EMBRAPA Genetic Resources and Biotechnology, Brasília, DF, 70770-917, Brazil. natalia.martins@embrapa.br.', 'Brazil|Brazil|United Kingdom|United Kingdom|Brazil|Brazil', 6, 'Fungal Proteins|Fusarium|Molecular Dynamics Simulation|Plant Diseases|Receptors, G-Protein-Coupled|Signal Transduction', 'Journal Article', '10.1186/s12859-016-1342-9', '28105916', 'Fusarium graminearum (FG) is one of the major cereal infecting pathogens causing high economic losses worldwide and resulting in adverse effects on human and animal health. Therefore, the development of new fungicides against FG is an important issue to reduce cereal infection and economic impact. In the strategy for developing new fungicides, a critical step is the identification of new targets against which innovative chemicals weapons can be designed. As several G-protein coupled receptors (GPCRs) are implicated in signaling pathways critical for the fungi development and survival, such proteins could be valuable efficient targets to reduce Fusarium growth and therefore to prevent food contamination.'),
(97594, 2016, 'bmc', 'ProClaT, a new bioinformatics tool for in silico protein reclassification: case study of DraB, a protein coded from the draTGB operon in Azospirillum brasilense', 'Rubel, Elisa Terumi|Raittz, Roberto Tadeu|Coimbra, Nilson Antonio da Rocha|Gehlen, Michelly Alves Coutinho|Pedrosa, Fábio de Oliveira', 'Laboratory of Bioinformatics, Professional and Technological Education Sector, Federal University of Paraná, Curitiba, PR, Brazil.|Laboratory of Bioinformatics, Professional and Technological Education Sector, Federal University of Paraná, Curitiba, PR, Brazil.|Laboratory of Bioinformatics, Professional and Technological Education Sector, Federal University of Paraná, Curitiba, PR, Brazil.|Laboratory of Bioinformatics, Professional and Technological Education Sector, Federal University of Paraná, Curitiba, PR, Brazil.|Department of Biochemistry and Molecular Biology, Federal University of Paraná, Curitiba, PR, Brazil. fpedrosa@ufpr.br.', 'Brazil|Brazil|Brazil|Brazil|Brazil', 1, 'Azospirillum brasilense|Bacterial Proteins|Computational Biology|Genes, Bacterial|Nitrogen Fixation|Nitrogenase|Operon', 'Evaluation Studies', '10.1186/s12859-016-1338-5', '28105917', '-'),
(97595, 2016, 'bmc', 'Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction', 'Marques, Yuri Bento|de Paiva Oliveira, Alcione|Ribeiro Vasconcelos, Ana Tereza|Cerqueira, Fabio Ribeiro', 'Department of Informatics, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil.|Department of Informatics, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil.|Laboratório Nacional de Computação Científica, Rua Getúlio Vargas 333, Petropólis, 25651-071, Brazil.|Department of Informatics, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil. fabio.cerqueira@ufv.br.', 'Brazil|Brazil|Brazil|Brazil', 7, 'Animals|Computational Biology|Computer Simulation|Eukaryota|Genome|Genomics|Humans|Machine Learning|MicroRNAs|Nucleic Acid Conformation|Plants', 'Evaluation Studies', '10.1186/s12859-016-1343-8', '28105918', 'MicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets.'),
(97596, 2016, 'bmc', 'SnoReport 20: new features and a refined Support Vector Machine to improve snoRNA identification', 'de Araujo Oliveira, Joxc3xa3o Victor|Costa, Fabrizio|Backofen, Rolf|Stadler, Peter Florian|Machado Telles Walter, Maria Emxc3xadlia|Hertel, Jana', 'Department of Computer Science, University of Brasilia, Brasxc3xadlia, BR-70910-900, Brazil. joaovicers@gmail.com.|Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Kxc3xb6hler-Allee 106, Freiburg, 79110, Germany.|Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Kxc3xb6hler-Allee 106, Freiburg, 79110, Germany.|Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Haertelstraxc3x9fe 16-18, Leipzig, D-04107, Germany.|Department of Computer Science, University of Brasilia, Brasxc3xadlia, BR-70910-900, Brazil.|Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Haertelstraxc3x9fe 16-18, Leipzig, D-04107, Germany.', 'Brazil|Germany|Germany|Germany|Brazil|Germany', 6, 'Animals|Base Sequence|Computational Biology|Eukaryota|Humans|Molecular Sequence Data|RNA, Small Nucleolar|Support Vector Machine|Vertebrates', 'Evaluation Studies', '10.1186/s12859-016-1345-6', '28105919', 'snoReport uses RNA secondary structure prediction combined with machine learning as the basis to identify the two main classes of small nucleolar RNAs, the box H/ACA snoRNAs and the box C/D snoRNAs. Here, we present snoReport 2.0, which substantially improves and extends in the original method by: extracting new features for both box C/D and H/ACA box snoRNAs, developing a more sophisticated technique in the SVM training phase with recent data from vertebrate organisms and a careful choice of the SVM parameters C and xcexb3, and using updated versions of tools and databases used for the construction of the original version of snoReport. To validate the new version and to demonstrate its improved performance, we tested snoReport 2.0 in different organisms.'),
(97597, 2016, 'bmc', 'Compromise or optimize? The breakpoint anti-median', 'Larlee, Caroline Anne|Brandts, Alex|Sankoff, David', 'Department of Mathematics and Statistics, University of Ottawa, 585 King Edward Avenue, Ottawa, K1N 6N5, Canada.|Department of Mathematics and Statistics, University of Ottawa, 585 King Edward Avenue, Ottawa, K1N 6N5, Canada.|Department of Mathematics and Statistics, University of Ottawa, 585 King Edward Avenue, Ottawa, K1N 6N5, Canada. sankoff@uottawa.ca.', 'Canada|Canada|Canada', 0, 'Algorithms|Evolution, Molecular|Gene Order|Genome|Genomics|Models, Genetic', 'Journal Article', '10.1186/s12859-016-1340-y', '28105920', 'The median of kxe2x89xa53 genomes was originally defined to find a compromise genome indicative of a common ancestor. However, in gene order comparisons, the usual definitions based on minimizing the sum of distances to the input genomes lead to degenerate medians reflecting only one of the input genomes. Near-medians, consisting of equal samples of gene adjacencies from all the input genomes, were designed to restore the idea of compromise to the median problem.'),
(97598, 2016, 'bmc', 'SIMBA: a web tool for managing bacterial genome assembly generated by Ion PGM sequencing technology', 'Mariano, Diego C B|Pereira, Felipe L|Aguiar, Edgar L|Oliveira, Letícia C|Benevides, Leandro|Guimarães, Luís C|Folador, Edson L|Sousa, Thiago J|Ghosh, Preetam|Barh, Debmalya|Figueiredo, Henrique C P|Silva, Artur|Ramos, Rommel T J|Azevedo, Vasco A C', 'Laboratory of Cellular and Molecular Genetics, Department of General Biology, Institute of Biological Sciences, Federal University of Minas Gerais, CEP 31270-901, Belo Horizonte, Minas Gerais, Brazil.|National Reference Laboratory for Aquatic Animal Diseases of Ministry of Agriculture, Livestock and Food Supply, Federal University of Minas Gerais, CEP 31270-901, Belo Horizonte, Minas Gerais, Brazil.|Laboratory of Cellular and Molecular Genetics, Department of General Biology, Institute of Biological Sciences, Federal University of Minas Gerais, CEP 31270-901, Belo Horizonte, Minas Gerais, Brazil.|Laboratory of Cellular and Molecular Genetics, Department of General Biology, Institute of Biological Sciences, Federal University of Minas Gerais, CEP 31270-901, Belo Horizonte, Minas Gerais, Brazil.|Laboratory of Cellular and Molecular Genetics, Department of General Biology, Institute of Biological Sciences, Federal University of Minas Gerais, CEP 31270-901, Belo Horizonte, Minas Gerais, Brazil.|Laboratory of Cellular and Molecular Genetics, Department of General Biology, Institute of Biological Sciences, Federal University of Minas Gerais, CEP 31270-901, Belo Horizonte, Minas Gerais, Brazil.|Laboratory of Cellular and Molecular Genetics, Department of General Biology, Institute of Biological Sciences, Federal University of Minas Gerais, CEP 31270-901, Belo Horizonte, Minas Gerais, Brazil.|Laboratory of Cellular and Molecular Genetics, Department of General Biology, Institute of Biological Sciences, Federal University of Minas Gerais, CEP 31270-901, Belo Horizonte, Minas Gerais, Brazil.|Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, PurbaMedinipur, WB-721172, India.|Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, PurbaMedinipur, WB-721172, India.|National Reference Laboratory for Aquatic Animal Diseases of Ministry of Agriculture, Livestock and Food Supply, Federal University of Minas Gerais, CEP 31270-901, Belo Horizonte, Minas Gerais, Brazil.|Institute of Biological Sciences, Federal University of Pará, Belém, Pará, Brazil.|Institute of Biological Sciences, Federal University of Pará, Belém, Pará, Brazil.|Laboratory of Cellular and Molecular Genetics, Department of General Biology, Institute of Biological Sciences, Federal University of Minas Gerais, CEP 31270-901, Belo Horizonte, Minas Gerais, Brazil. vasco@icb.ufmg.br.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|India|India|Brazil|Brazil|Brazil|Brazil', 5, 'Bacteria|Base Sequence|Chromosome Mapping|Computational Biology|Data Mining|Genome, Bacterial|High-Throughput Nucleotide Sequencing|Internet|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1186/s12859-016-1344-7', '28105921', 'The evolution of Next-Generation Sequencing (NGS) has considerably reduced the cost per sequenced-base, allowing a significant rise of sequencing projects, mainly in prokaryotes. However, the range of available NGS platforms requires different strategies and software to correctly assemble genomes. Different strategies are necessary to properly complete an assembly project, in addition to the installation or modification of various software. This requires users to have significant expertise in these software and command line scripting experience on Unix platforms, besides possessing the basic expertise on methodologies and techniques for genome assembly. These difficulties often delay the complete genome assembly projects.'),
(97599, 2016, 'bmc', 'ChARM: Discovery of combinatorial chromatin modification patterns in hepatitis B virus X-transformed mouse liver cancer using association rule mining', 'Park, Sung Hee|Lee, Sun-Min|Kim, Young-Joon|Kim, Sangsoo', 'Department of Bioinformatics and Life Science, Soongsil University, Seoul, 156-743, Republic of Korea.|Department of Biochemistry, College of Life Science and Technology, Yonsei University, Seoul, 120-749, Republic of Korea.|Department of Biochemistry, College of Life Science and Technology, Yonsei University, Seoul, 120-749, Republic of Korea. yjkim@yonsei.ac.kr.|Department of Bioinformatics and Life Science, Soongsil University, Seoul, 156-743, Republic of Korea. sskimb@ssu.ac.kr.', '-|-|-|-', 4, 'Algorithms|Animals|Carcinoma, Hepatocellular|Chromatin Immunoprecipitation|CpG Islands|DNA Methylation|Data Mining|Epigenomics|Hepatitis B|Histone Code|Histones|Liver Neoplasms|Methylation|Mice|Promoter Regions, Genetic|Signal Transduction', 'Journal Article', '10.1186/s12859-016-1307-z', '28105934', 'Various chromatin modifications, identified in large-scale epigenomic analyses, are associated with distinct phenotypes of different cells and disease phases. To improve our understanding of these variations, many computational methods have been developed to discover novel sites and cell-specific chromatin modifications. Despite the availability of existing methods, there is still room for further improvement when they are applied to resolve the histone code hypothesis. Hence, we aim to investigate the development of a computational method to provide new insights into de novo combinatorial pattern discovery of chromatin modifications to characterize epigenetic variations in distinct phenotypes of different cells.'),
(97600, 2017, 'bmc', 'MC-GenomeKey: a multicloud system for the detection and annotation of genomic variants', 'Elshazly, Hatem|Souilmi, Yassine|Tonellato, Peter J|Wall, Dennis P|Abouelhoda, Mohamed', 'Center for Informatics Sciences, Nile University, Juhayna Square, Sheikh Zayed, Giza, Egypt.|Department of Biology, Mohamed Vth University in Rabat, 4 Ibn Battouta Avenue, BP: 1014RP, Rabat, Morocco.|Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA.|Department of Pediatrics and Psychiatry (by courtesy), Division of Systems Medicine & Program in Biomedical Informatics, Stanford University, Stanford, CA, 94305, USA.|Center for Informatics Sciences, Nile University, Juhayna Square, Sheikh Zayed, Giza, Egypt. mabouelhoda@yahoo.com.', 'Egypt|Morocco|United States|United States|Egypt', 7, 'Cloud Computing|Databases, Genetic|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Internet|Software|Workflow', 'Journal Article', '10.1186/s12859-016-1454-2', '28107819', 'Next Generation Genome sequencing techniques became affordable for massive sequencing efforts devoted to clinical characterization of human diseases. However, the cost of providing cloud-based data analysis of the mounting datasets remains a concerning bottleneck for providing cost-effective clinical services. To address this computational problem, it is important to optimize the variant analysis workflow and the used analysis tools to reduce the overall computational processing time, and concomitantly reduce the processing cost. Furthermore, it is important to capitalize on the use of the recent development in the cloud computing market, which have witnessed more providers competing in terms of products and prices.'),
(97601, 2017, 'bmc', 'Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy', 'Penas, David R|González, Patricia|Egea, Jose A|Doallo, Ramón|Banga, Julio R', 'BioProcess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain.|Computer Architecture Group, Universidade da Coruña, Campus de Elviña s/n, Coruña, 15071 A, Spain.|Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, c/ Dr. Fleming s/n, Cartagena, 30202, Spain.|Computer Architecture Group, Universidade da Coruña, Campus de Elviña s/n, Coruña, 15071 A, Spain.|BioProcess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain. julio@iim.csic.es.', 'Spain|Spain|Spain|Spain|Spain', 34, 'Animals|CHO Cells|Computational Biology|Cricetulus|Drosophila melanogaster|Escherichia coli|Gene Expression Regulation|Models, Theoretical|Nonlinear Dynamics|Saccharomyces cerevisiae|Signal Transduction|Systems Biology|Transcription, Genetic', 'Journal Article', '10.1186/s12859-016-1452-4', '28109249', 'The development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and therefore further increasing the computation times. Here we present a novel parallel method, self-adaptive cooperative enhanced scatter search (saCeSS), to accelerate the solution of this class of problems. The method is based on the scatter search optimization metaheuristic and incorporates several key new mechanisms: (i) asynchronous cooperation between parallel processes, (ii) coarse and fine-grained parallelism, and (iii) self-tuning strategies.'),
(97602, 2017, 'bmc', 'RNA-TVcurve: a Web server for RNA secondary structure comparison based on a multi-scale similarity of its triple vector curve representation', 'Li, Ying|Shi, Xiaohu|Liang, Yanchun|Xie, Juan|Zhang, Yu|Ma, Qin', 'College of Computer Science and Technology, Jilin University, Changchun, 130012, China.|College of Computer Science and Technology, Jilin University, Changchun, 130012, China.|College of Computer Science and Technology, Jilin University, Changchun, 130012, China.|Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, 57007, USA.|College of Computer Science and Technology, Jilin University, Changchun, 130012, China. zy26@jlu.edu.cn.|Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, 57007, USA. qin.ma@sdstate.edu.', 'China|China|China|United States|China|United States', 5, 'Algorithms|Archaea|Bacteria|Eukaryota|Internet|Nucleic Acid Conformation|Phylogeny|Point Mutation|RNA|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-017-1481-7', '28109252', 'RNAs have been found to carry diverse functionalities in nature. Inferring the similarity between two given RNAs is a fundamental step to understand and interpret their functional relationship. The majority of functional RNAs show conserved secondary structures, rather than sequence conservation. Those algorithms relying on sequence-based features usually have limitations in their prediction performance. Hence, integrating RNA structure features is very critical for RNA analysis. Existing algorithms mainly fall into two categories: alignment-based and alignment-free. The alignment-free algorithms of RNA comparison usually have lower time complexity than alignment-based algorithms.'),
(97603, 2017, 'bmc', 'Dynamic substrate preferences predict metabolic properties of a simple microbial consortium', 'Erbilgin, Onur|Bowen, Benjamin P|Kosina, Suzanne M|Jenkins, Stefan|Lau, Rebecca K|Northen, Trent R', 'Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA.|Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA.|Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA.|Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA.|Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA.|Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA. trnorthen@lbl.gov.', 'United States|United States|United States|United States|United States|United States', 8, 'Bacillus|Coculture Techniques|Culture Media|Metabolomics|Microbial Consortia|Models, Theoretical|Phylogeny|Pseudomonas|RNA, Ribosomal, 16S|Sequence Analysis, RNA|Soil Microbiology', 'Journal Article', '10.1186/s12859-017-1478-2', '28114881', 'Mixed cultures of different microbial species are increasingly being used to carry out a specific biochemical function in lieu of engineering a single microbe to do the same task. However, knowing how different species metabolisms will integrate to reach a desired outcome is a difficult problem that has been studied in great detail using steady-state models. However, many biotechnological processes, as well as natural habitats, represent a more dynamic system. Examining how individual species use resources in their growth medium or environment (exometabolomics) over time in batch culture conditions can provide rich phenotypic data that encompasses regulation and transporters, creating an opportunity to integrate the data into a predictive model of resource use by a mixed community.'),
(97604, 2017, 'bmc', 'FuGePrior: A novel gene fusion prioritization algorithm based on accurate fusion structure analysis in cancer RNA-seq samples', 'Paciello, Giulia|Ficarra, Elisa', 'Department of Control and Computer Engineering DAUIN, Politecnico di Torino, C.so Duca degli Abruzzi 24, Turin, 10129, Italy. giulia.paciello@polito.it.|Department of Control and Computer Engineering DAUIN, Politecnico di Torino, C.so Duca degli Abruzzi 24, Turin, 10129, Italy.', 'Italy|Italy', 4, 'Algorithms|Breast Neoplasms|Cell Line, Tumor|Databases, Genetic|Female|Genomics|Humans|MCF-7 Cells|Male|Prostatic Neoplasms|Recombinant Fusion Proteins|Reproducibility of Results|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-016-1450-6', '28114882', 'Latest Next Generation Sequencing technologies opened the way to a novel era of genomic studies, allowing to gain novel insights into multifactorial pathologies as cancer. In particular gene fusion detection and comprehension have been deeply enhanced by these methods. However, state of the art algorithms for gene fusion identification are still challenging. Indeed, they identify huge amounts of poorly overlapping candidates and all the reported fusions should be considered for in lab validation clearly overwhelming wet lab capabilities.'),
(97605, 2017, 'bmc', 'Processing tracking in jMRUI software for magnetic resonance spectra quantitation reproducibility assurance', 'Jabxc5x82oxc5x84ski, Michaxc5x82|Starxc4x8dukovxc3xa1, Jana|Starxc4x8duk, Zenon', 'Institute of Scientific Instruments of the CAS, Krxc3xa1lovopolskxc3xa1 147, 612 64, Brno, Czech Republic. jmj@isibrno.cz.|Institute of Scientific Instruments of the CAS, Krxc3xa1lovopolskxc3xa1 147, 612 64, Brno, Czech Republic.|Institute of Scientific Instruments of the CAS, Krxc3xa1lovopolskxc3xa1 147, 612 64, Brno, Czech Republic.', '-|-|-', 2, 'Algorithms|Databases, Factual|Electronic Data Processing|Magnetic Resonance Imaging|Magnetic Resonance Spectroscopy|Reproducibility of Results|Software', 'Journal Article', '10.1186/s12859-017-1459-5', '28114896', 'Proton magnetic resonance spectroscopy is a non-invasive measurement technique which provides information about concentrations of up to 20 metabolites participating in intracellular biochemical processes. In order to obtain any metabolic information from measured spectra a processing should be done in specialized software, like jMRUI. The processing is interactive and complex and often requires many trials before obtaining a correct result. This paper proposes a jMRUI enhancement for efficient and unambiguous history tracking and file identification.'),
(97606, 2017, 'bmc', 'A systematic comparative evaluation of biclustering techniques', 'Padilha, Victor A|Campello, Ricardo J G B', 'Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, SP, Brazil. victorpadilha@usp.br.|Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, SP, Brazil.', 'Brazil|Brazil', 38, 'Algorithms|Cluster Analysis|Computational Biology|Computer Simulation|Databases, Genetic|Gene Expression Regulation|Humans|Neoplasms|Software', 'Comparative Study', '10.1186/s12859-017-1487-1', '28114903', 'Biclustering techniques are capable of simultaneously clustering rows and columns of a data matrix. These techniques became very popular for the analysis of gene expression data, since a gene can take part of multiple biological pathways which in turn can be active only under specific experimental conditions. Several biclustering algorithms have been developed in the past recent years. In order to provide guidance regarding their choice, a few comparative studies were conducted and reported in the literature. In these studies, however, the performances of the methods were evaluated through external measures that have more recently been shown to have undesirable properties. Furthermore, they considered a limited number of algorithms and datasets.'),
(97607, 2017, 'bmc', 'Eigen-Epistasis for detecting gene-gene interactions', 'Stanislas, Virginie|Dalmasso, Cyril|Ambroise, Christophe', 'Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry Val dEssonne, UMR CNRS 8071, ENSIIE, USC INRA, 23 bvd de France, 91 037, Evry Cedex, Paris, France. virginie.stanislas@math.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry Val dEssonne, UMR CNRS 8071, ENSIIE, USC INRA, 23 bvd de France, 91 037, Evry Cedex, Paris, France.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry Val dEssonne, UMR CNRS 8071, ENSIIE, USC INRA, 23 bvd de France, 91 037, Evry Cedex, Paris, France.', 'France|France|France', 7, 'Computational Biology|Computer Simulation|Epistasis, Genetic|Genome-Wide Association Study|Genotype|Humans|Inflammatory Bowel Diseases|Models, Theoretical|Phenotype|Polymorphism, Single Nucleotide|Principal Component Analysis|Thyroid Neoplasms', 'Journal Article', '10.1186/s12859-017-1488-0', '28114904', 'A large amount of research has been devoted to the detection and investigation of epistatic interactions in genome-wide association studies (GWASs). Most of the literature focuses on low-order interactions between single-nucleotide polymorphisms (SNPs) with significant main effects.'),
(97608, 2017, 'bmc', 'GSAR: Bioconductor package for Gene Set analysis in R', 'Rahmatallah, Yasir|Zybailov, Boris|Emmert-Streib, Frank|Glazko, Galina', 'Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA. yrahmatallah@uams.edu.|Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.|Computational Medicine and Statistical Learning Laboratory, Tampere University of Technology, Korkeakoulunkatu 1, Tampere, FI-33720, Finland.|Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.', 'United States|United States|Finland|United States', 0, 'Cell Line, Tumor|Computational Biology|Databases, Genetic|Gene Expression|Gene Expression Profiling|Humans|Models, Theoretical|Multivariate Analysis|Phenotype|Sequence Analysis, RNA|Software|Tumor Suppressor Protein p53', 'Journal Article', '10.1186/s12859-017-1482-6', '28118818', 'Gene set analysis (in a form of functionally related genes or pathways) has become the method of choice for analyzing omics data in general and gene expression data in particular. There are many statistical methods that either summarize gene-level statistics for a gene set or apply a multivariate statistic that accounts for intergene correlations. Most available methods detect complex departures from the null hypothesis but lack the ability to identify the specific alternative hypothesis that rejects the null.'),
(97609, 2017, 'bmc', 'Confident gene activity prediction based on single histone modification H2BK5ac in human cell lines', 'Chitsazian, Fereshteh|Sadeghi, Mehdi|Elahi, Elahe', 'Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.|National Institute for Genetic Engineering and Biotechnology, Tehran, Iran.|School of Biology, College of Science, University of Tehran, Tehran, Iran. elaheelahi@ut.ac.ir.', '-|-|-', 0, 'Algorithms|CD4-Positive T-Lymphocytes|Cell Line|Gene Expression Regulation|Histone Code|Histones|Humans|Software|Support Vector Machine|Terminator Regions, Genetic|Transcription Initiation Site', 'Journal Article', '10.1186/s12859-016-1418-6', '28122488', 'The histones in the core of nucleosomes may be subject to covalent post-transcriptional modifications. These modifications are thought to correlate with and possibly affect various genomic functions, including transcription. Each modification may alone or in combination with other modifications influence or be influenced by transcription. We aimed to identify correlations between single modifications or combinations of modifications at specific nucleosome sized gene regions with transcription activity based on global histone modification and transcription data of human CD4+ T cells and three other human cell lines. Transcription activity was defined in a binary fashion as either on or off. The analysis was done using the Classification and Regression Tree (CART) data mining protocol, and the Multifactorial Dimensionality Reduction (MDR) method was performed to confirm the CART results. These powerful methods have not previously been used for analysis of histone modification data.'),
(97610, 2017, 'bmc', 'Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers', 'Eisinga, Rob|Heskes, Tom|Pelzer, Ben|Te Grotenhuis, Manfred', 'Department of Social Science Research Methods, Radboud University Nijmegen, PO Box 9104, , 6500 HE, Nijmegen, The Netherlands. r.eisinga@maw.ru.nl.|Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands.|Department of Social Science Research Methods, Radboud University Nijmegen, PO Box 9104, , 6500 HE, Nijmegen, The Netherlands.|Department of Social Science Research Methods, Radboud University Nijmegen, PO Box 9104, , 6500 HE, Nijmegen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Computational Biology|Humans|Internet|Statistics, Nonparametric|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1486-2', '28122501', 'The Friedman rank sum test is a widely-used nonparametric method in computational biology. In addition to examining the overall null hypothesis of no significant difference among any of the rank sums, it is typically of interest to conduct pairwise comparison tests. Current approaches to such tests rely on large-sample approximations, due to the numerical complexity of computing the exact distribution. These approximate methods lead to inaccurate estimates in the tail of the distribution, which is most relevant for p-value calculation.'),
(97611, 2017, 'bmc', 'Multiple network algorithm for epigenetic modules via the integration of genome-wide DNA methylation and gene expression data', 'Ma, Xiaoke|Liu, Zaiyi|Zhang, Zhongyuan|Huang, Xiaotai|Tang, Wanxin', 'School of Computer Science and Technology, Xidian University, No.2 South TaiBai Road, Xian, Peoples Republic of China.|Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Zhongshan Road, Guangzhou, Peoples Republic of China.|School of Statistics and Mathematics, Central University of Finance and Economics, 39 South College Road, Haidian District, Beijing, Peoples Republic of China.|School of Computer Science and Technology, Xidian University, No.2 South TaiBai Road, Xian, Peoples Republic of China.|Department of Nephrology, West China Hospital, Sichuan University, Wuhou District, Chengdu, Peoples Republic of China. tangwx@scu.edu.cn.', 'China|China|China|China|China', 25, 'Algorithms|Breast Neoplasms|DNA Methylation|Epigenesis, Genetic|Female|Gene Expression Profiling|Gene Regulatory Networks|Genomics|Humans|Transcriptome', 'Journal Article', '10.1186/s12859-017-1490-6', '28137264', 'With the increase in the amount of DNA methylation and gene expression data, the epigenetic mechanisms of cancers can be extensively investigate. Available methods integrate the DNA methylation and gene expression data into a network by specifying the anti-correlation between them. However, the correlation between methylation and expression is usually unknown and difficult to determine.'),
(97612, 2017, 'bmc', 'Unsupervised detection of regulatory gene expression information in different genomic regions enables gene expression ranking', 'Zafrir, Zohar|Tuller, Tamir', 'Department of Biomedical Engineering, Tel Aviv University, P.O. Box 39040, Tel Aviv, 6997801, Israel.|Department of Biomedical Engineering, Tel Aviv University, P.O. Box 39040, Tel Aviv, 6997801, Israel. tamirtul@post.tau.ac.il.', 'Israel|Israel', 3, 'Algorithms|Escherichia coli|Gene Expression Regulation|Introns|Open Reading Frames|RNA, Messenger|Saccharomyces cerevisiae|Schizosaccharomyces|Untranslated Regions', 'Journal Article', '10.1186/s12859-017-1497-z', '28143396', 'The regulation of all gene expression steps (e.g., Transcription, RNA processing, Translation, and mRNA Degradation) is known to be primarily encoded in different parts of genes and in genomic regions in proximity to genes (e.g., promoters, untranslated regions, coding regions, introns, etc.). However, the entire gene expression codes and the genomic regions where they are encoded are still unknown.'),
(97613, 2017, 'bmc', 'Disorder recognition in clinical texts using multi-label structured SVM', 'Lin, Wutao|Ji, Donghong|Lu, Yanan', 'School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China.|School of Computer, Wuhan University, Wuhan, 430072, China. dhji@whu.edu.cn.|School of Computer, Wuhan University, Wuhan, 430072, China.', 'China|China|China', 0, 'Data Mining|Disease|Humans|Support Vector Machine', 'Journal Article', '10.1186/s12859-017-1476-4', '28143488', 'Information extraction in clinical texts enables medical workers to find out problems of patients faster as well as makes intelligent diagnosis possible in the future. There has been a lot of work about disorder mention recognition in clinical narratives. But recognition of some more complicated disorder mentions like overlapping ones is still an open issue. This paper proposes a multi-label structured Support Vector Machine (SVM) based method for disorder mention recognition. We present a multi-label scheme which could be used in complicated entity recognition tasks.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97614, 2017, 'bmc', 'A scalable algorithm for structure identification of complex gene regulatory network from temporal expression data', 'Gui, Shupeng|Rice, Andrew P|Chen, Rui|Wu, Liang|Liu, Ji|Miao, Hongyu', 'Department of Computer Science, University of Rochester, Rochester, 14620, NY, USA.|Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, 77030, TX, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, 77030, TX, USA.|Department of Biostatistics, University of Texas Health Science Center, Houston, 77030, TX, USA.|Department of Computer Science, University of Rochester, Rochester, 14620, NY, USA.|Department of Biostatistics, University of Texas Health Science Center, Houston, 77030, TX, USA. hongyu.miao@uth.tmc.edu.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Epithelial Cells|Gene Regulatory Networks|Humans|Influenza A virus|Transcription Factors', 'Journal Article', '10.1186/s12859-017-1489-z', '28143596', 'Gene regulatory interactions are of fundamental importance to various biological functions and processes. However, only a few previous computational studies have claimed success in revealing genome-wide regulatory landscapes from temporal gene expression data, especially for complex eukaryotes like human. Moreover, recent work suggests that these methods still suffer from the curse of dimensionality if a network size increases to 100 or higher.'),
(97615, 2017, 'bmc', 'Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization', 'Nair, Govind|Jungreuthmayer, Christian|Zanghellini, Jürgen', 'Department of Biotechnology, University of Natural Resources and Life Sciences, Muthgasse 11, Vienna, 1190, Austria.|TGM - Technologisches Gewerbemuseum, Wexstraße 19-23, Vienna, 1200, Austria.|Department of Biotechnology, University of Natural Resources and Life Sciences, Muthgasse 11, Vienna, 1190, Austria. juergen.zanghellini@boku.ac.at.', 'Austria|Austria|Austria', 4, 'Algorithms|Escherichia coli|Gene Knockout Techniques|Genome, Bacterial|Metabolic Engineering|Metabolic Networks and Pathways|Models, Biological|Normal Distribution', 'Journal Article', '10.1186/s12859-017-1483-5', '28143607', 'Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives.'),
(97616, 2017, 'bmc', 'Fast and accurate mutation detection in whole genome sequences of multiple isogenic samples with IsoMut', 'Pipek, O|Ribli, D|Molnár, J|Póti, Á|Krzystanek, M|Bodor, A|Tusnády, G E|Szallasi, Z|Csabai, I|Szüts, D', 'Department of Physics of Complex Systems, Eötvös Loránd University, H-1117, Budapest, Hungary.|Department of Physics of Complex Systems, Eötvös Loránd University, H-1117, Budapest, Hungary.|Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, H-1117, Budapest, Hungary.|Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, H-1117, Budapest, Hungary.|Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, DK-2800, Lyngby, Denmark.|Department of Physics of Complex Systems, Eötvös Loránd University, H-1117, Budapest, Hungary.|Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, H-1117, Budapest, Hungary.|Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, DK-2800, Lyngby, Denmark.|Department of Physics of Complex Systems, Eötvös Loránd University, H-1117, Budapest, Hungary.|Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, H-1117, Budapest, Hungary. szuts.david@ttk.mta.hu.', 'Hungary|Hungary|Hungary|Hungary|Denmark|Hungary|Hungary|Denmark|Hungary|Hungary', 6, 'Algorithms|DNA Mutational Analysis|Genomics|High-Throughput Nucleotide Sequencing|Humans|Mutation|Sequence Deletion|Software', 'Journal Article', '10.1186/s12859-017-1492-4', '28143617', 'Detection of somatic mutations is one of the main goals of next generation DNA sequencing. A wide range of experimental systems are available for the study of spontaneous or environmentally induced mutagenic processes. However, most of the routinely used mutation calling algorithms are not optimised for the simultaneous analysis of multiple samples, or for non-human experimental model systems with no reliable databases of common genetic variations. Most standard tools either require numerous in-house post filtering steps with scarce documentation or take an unpractically long time to run. To overcome these problems, we designed the streamlined IsoMut tool which can be readily adapted to experimental scenarios where the goal is the identification of experimentally induced mutations in multiple isogenic samples.'),
(97617, 2017, 'bmc', 'A novel statistical approach for identification of the master regulator transcription factor', 'Sikdar, Sinjini|Datta, Susmita', 'Department of Biostatistics, University of Florida, Gainesville, FL, 32611, USA.|Department of Biostatistics, University of Florida, Gainesville, FL, 32611, USA. susmita.datta@ufl.edu.', 'United States|United States', 0, 'Data Interpretation, Statistical|Gene Expression Profiling|Gene Regulatory Networks|Humans|Transcription Factors', 'Journal Article', '10.1186/s12859-017-1499-x', '28148240', 'Transcription factors are known to play key roles in carcinogenesis and therefore, are gaining popularity as potential therapeutic targets in drug development. A master regulator transcription factor often appears to control most of the regulatory activities of the other transcription factors and the associated genes. This master regulator transcription factor is at the top of the hierarchy of the transcriptomic regulation. Therefore, it is important to identify and target the master regulator transcription factor for proper understanding of the associated disease process and identifying the best therapeutic option.'),
(97618, 2017, 'bmc', 'DMDtoolkit: a tool for visualizing the mutated dystrophin protein and predicting the clinical severity in DMD', 'Zhou, Jiapeng|Xin, Jing|Niu, Yayun|Wu, Shiwen', 'Department of Neurology, General Hospital of Chinese Peoples Armed Police Forces, Beijing, 100039, China.|Department of Neurology, General Hospital of Chinese Peoples Armed Police Forces, Beijing, 100039, China.|Department of Neurology, General Hospital of Chinese Peoples Armed Police Forces, Beijing, 100039, China.|Department of Neurology, General Hospital of Chinese Peoples Armed Police Forces, Beijing, 100039, China. wu_shiwen@yahoo.com.', 'China|China|China|China', 0, 'Dystrophin|Exons|Humans|Male|Muscular Dystrophy, Duchenne|Mutation|Pedigree|Phenotype|Software', 'Journal Article', '10.1186/s12859-017-1504-4', '28152980', 'Dystrophinopathy is one of the most common human monogenic diseases which results in Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD). Mutations in the dystrophin gene are responsible for both DMD and BMD. However, the clinical phenotypes and treatments are quite different in these two muscular dystrophies. Since early diagnosis and treatment results in better clinical outcome in DMD it is essential to establish accurate early diagnosis of DMD to allow efficient management. Previously, the reading-frame rule was used to predict DMD versus BMD. However, there are limitations using this traditional tool. Here, we report a novel molecular method to improve the accuracy of predicting clinical phenotypes in dystrophinopathy. We utilized several additional molecular genetic rules or patterns such as ambush hypothesis, hidden stop codons and exonic splicing enhancer (ESE) to predict the expressed clinical phenotypes as DMD versus BMD.'),
(97619, 2017, 'bmc', 'Classifying kinase conformations using a machine learning approach', 'McSkimming, Daniel Ian|Rasheed, Khaled|Kannan, Natarajan', 'Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA.|Department of Computer Science, University of Georgia, Athens, GA, 30602, USA.|Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA. nkannan@uga.edu.', 'Georgia|Georgia|Georgia', 15, 'Catalytic Domain|Machine Learning|Models, Molecular|Protein Conformation|Protein Kinases', 'Journal Article', '10.1186/s12859-017-1506-2', '28152981', 'Signaling proteins such as protein kinases adopt a diverse array of conformations to respond to regulatory signals in signaling pathways. Perhaps the most fundamental conformational change of a kinase is the transition between active and inactive states, and defining the conformational features associated with kinase activation is critical for selectively targeting abnormally regulated kinases in diseases. While manual examination of crystal structures have led to the identification of key structural features associated with kinase activation, the large number of kinase crystal structures (~3,500) and extensive conformational diversity displayed by the protein kinase superfamily poses unique challenges in fully defining the conformational features associated with kinase activation. Although some computational approaches have been proposed, they are typically based on a small subset of crystal structures using measurements biased towards the active site geometry.'),
(97620, 2017, 'bmc', 'Transductive learning as an alternative to translation initiation site identification', 'Nunes Pinto, Cristiano Lacerda|Nobre, Cristiane Neri|Zárate, Luis Enrique', 'School of Engeneering of Minas Gerais - EMGE, Belo Horizonte, 30150-250, Brazil. cristiano.lacerda@gmail.com.|Pontifical Catholic University of Minas Gerais - PUC-MG, 255, Walter Ianni Street, Belo Horizonte, 31980-110, Brazil.|Pontifical Catholic University of Minas Gerais - PUC-MG, 255, Walter Ianni Street, Belo Horizonte, 31980-110, Brazil.', 'Brazil|Brazil|Brazil', 4, 'Animals|Arabidopsis|Computational Biology|Drosophila melanogaster|Humans|Mice|Peptide Chain Initiation, Translational|Rats|Software|Support Vector Machine', 'Journal Article', '10.1186/s12859-017-1502-6', '28152994', 'The correct protein coding region identification is an important and latent problem in the molecular biology field. This problem becomes a challenge due to the lack of deep knowledge about the biological systems and unfamiliarity of conservative characteristics in the messenger RNA (mRNA). Therefore, it is fundamental to research for computational methods aiming to help the patterns discovery for identification of the Translation Initiation Sites (TIS). In the field of Bioinformatics, machine learning methods have been widely applied based on the inductive inference, as Inductive Support Vector Machine (ISVM). On the other hand, not so much attention has been given to transductive inference-based machine learning methods such as Transductive Support Vector Machine (TSVM). The transductive inference performs well for problems in which the amount of unlabeled sequences is considerably greater than the labeled ones. Similarly, the problem of predicting the TIS may take advantage of transductive methods due to the fact that the amount of new sequences grows rapidly with the progress of Genome Project that allows the study of new organisms. Consequently, this work aims to investigate the transductive learning towards TIS identification and compare the results with those obtained in inductive method.'),
(97621, 2017, 'bmc', 'Mixture model normalization for non-targeted gas chromatography/mass spectrometry metabolomics data', 'Reisetter, Anna C|Muehlbauer, Michael J|Bain, James R|Nodzenski, Michael|Stevens, Robert D|Ilkayeva, Olga|Metzger, Boyd E|Newgard, Christopher B|Lowe, William L|Scholtens, Denise M', 'Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.|Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC, 27701, USA.|Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC, 27701, USA.|Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.|Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC, 27701, USA.|Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC, 27701, USA.|Department of Medicine, Division of Endocrinology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.|Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC, 27701, USA.|Department of Medicine, Division of Endocrinology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.|Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA. dscholtens@northwestern.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 20, 'Female|Gas Chromatography-Mass Spectrometry|Humans|Infant, Newborn|Metabolomics|Models, Biological|Pregnancy|Quality Control', 'Journal Article', '10.1186/s12859-017-1501-7', '28153035', 'Metabolomics offers a unique integrative perspective for health research, reflecting genetic and environmental contributions to disease-related phenotypes. Identifying robust associations in population-based or large-scale clinical studies demands large numbers of subjects and therefore sample batching for gas-chromatography/mass spectrometry (GC/MS) non-targeted assays. When run over weeks or months, technical noise due to batch and run-order threatens data interpretability. Application of existing normalization methods to metabolomics is challenged by unsatisfied modeling assumptions and, notably, failure to address batch-specific truncation of low abundance compounds.'),
(97622, 2017, 'bmc', 'Metabolomics variable selection and classification in the presence of observations below the detection limit using an extension of ERp', 'van Reenen, Mari|Westerhuis, Johan A|Reinecke, Carolus J|Venter, J Hendrik', 'Centre for Human Metabolomics, Faculty of Natural Sciences, North-West University (Potchefstroom Campus), Private Bag X6001, Potchefstroom, South Africa. 12791733@nwu.ac.za.|Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands.|Centre for Human Metabolomics, Faculty of Natural Sciences, North-West University (Potchefstroom Campus), Private Bag X6001, Potchefstroom, South Africa.|Centre for Business Mathematics and Informatics, Faculty of Natural Sciences, North-West University (Potchefstroom Campus), Private Bag X6001, Potchefstroom, South Africa.', 'South Africa|Netherlands|South Africa|South Africa', 0, 'Bias|Child|Classification|Gas Chromatography-Mass Spectrometry|Humans|Infant|Limit of Detection|Metabolomics|Sample Size|Sensitivity and Specificity|Tuberculosis, Meningeal', 'Journal Article', '10.1186/s12859-017-1480-8', '28153039', 'ERp is a variable selection and classification method for metabolomics data. ERp uses minimized classification error rates, based on data from a control and experimental group, to test the null hypothesis of no difference between the distributions of variables over the two groups. If the associated p-values are significant they indicate discriminatory variables (i.e. informative metabolites). The p-values are calculated assuming a common continuous strictly increasing cumulative distribution under the null hypothesis. This assumption is violated when zero-valued observations can occur with positive probability, a characteristic of GC-MS metabolomics data, disqualifying ERp in this context. This paper extends ERp to address two sources of zero-valued observations: (i) zeros reflecting the complete absence of a metabolite from a sample (true zeros), and (ii) zeros reflecting a measurement below the detection limit. This is achieved by allowing the null cumulative distribution function to take the form of a mixture between a jump at zero and a continuous strictly increasing function. The extended ERp approach is referred to as XERp.'),
(97623, 2017, 'bmc', 'BicPAMS: software for biological data analysis with pattern-based biclustering', 'Henriques, Rui|Ferreira, Francisco L|Madeira, Sara C', 'INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal. rmch@tecnico.ulisboa.pt.|INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal.|INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal. sara.madeira@tecnico.ulisboa.pt.', 'Portugal|Portugal|Portugal', 0, 'Algorithms|Cell Line, Tumor|Cluster Analysis|Gene Expression|Gene Regulatory Networks|Humans|Protein Interaction Mapping|Software', 'Journal Article', '10.1186/s12859-017-1493-3', '28153040', 'Biclustering has been largely applied for the unsupervised analysis of biological data, being recognised today as a key technique to discover putative modules in both expression data (subsets of genes correlated in subsets of conditions) and network data (groups of coherently interconnected biological entities). However, given its computational complexity, only recent breakthroughs on pattern-based biclustering enabled efficient searches without the restrictions that state-of-the-art biclustering algorithms place on the structure and homogeneity of biclusters. As a result, pattern-based biclustering provides the unprecedented opportunity to discover non-trivial yet meaningful biological modules with putative functions, whose coherency and tolerance to noise can be tuned and made problem-specific.'),
(97624, 2017, 'bmc', 'Visualizing phylogenetic tree landscapes', 'Wilgenbusch, James C|Huang, Wen|Gallivan, Kyle A', 'Department of Scientific Computing, Florida State University, Tallahassee, FL, 32306, USA. jwilgenb@umn.edu.|Department of Mathematics, Florida State University, Tallahassee, FL, 32306, USA.|Department of Mathematics, Florida State University, Tallahassee, FL, 32306, USA.', 'United States|United States|United States', 8, 'DNA, Mitochondrial|Evolution, Molecular|Genome, Mitochondrial|Genomics|Phylogeny|Sequence Alignment', 'Journal Article', '10.1186/s12859-017-1479-1', '28153045', 'Genomic-scale sequence alignments are increasingly used to infer phylogenies in order to better understand the processes and patterns of evolution. Different partitions within these new alignments (e.g., genes, codon positions, and structural features) often favor hundreds if not thousands of competing phylogenies. Summarizing and comparing phylogenies obtained from multi-source data sets using current consensus tree methods discards valuable information and can disguise potential methodological problems. Discovery of efficient and accurate dimensionality reduction methods used to display at once in 2- or 3- dimensions the relationship among these competing phylogenies will help practitioners diagnose the limits of current evolutionary models and potential problems with phylogenetic reconstruction methods when analyzing large multi-source data sets. We introduce several dimensionality reduction methods to visualize in 2- and 3-dimensions the relationship among competing phylogenies obtained from gene partitions found in three mid- to large-size mitochondrial genome alignments. We test the performance of these dimensionality reduction methods by applying several goodness-of-fit measures. The intrinsic dimensionality of each data set is also estimated to determine whether projections in 2- and 3-dimensions can be expected to reveal meaningful relationships among trees from different data partitions. Several new approaches to aid in the comparison of different phylogenetic landscapes are presented.'),
(97625, 2016, 'bmc', 'GFS: fuzzy preprocessing for effective gene expression analysis', 'Belorkar, Abha|Wong, Limsoon', 'School of Computing, National University of Singapore, 13 Computing Drive, Singapore, 117417, Republic of Singapore. AbhaB@comp.nus.edu.sg.|School of Computing, National University of Singapore, 13 Computing Drive, Singapore, 117417, Republic of Singapore.', 'Singapore|Singapore', 10, 'Algorithms|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/s12859-016-1327-8', '28155629', 'Gene expression data produced on high-throughput platforms such as microarrays is susceptible to much variation that obscures useful biological information. Therefore, preprocessing data with a suitable normalization method is necessary, and has a direct and massive impact on the quality of downstream data analysis. However, it is known that standard normalization methods perform poorly, specially in the presence of substantial batch effects and heterogeneity in gene expression data.'),
(97626, 2016, 'bmc', 'LNDriver: identifying driver genes by integrating mutation and expression data based on gene-gene interaction network', 'Wei, Pi-Jing|Zhang, Di|Xia, Junfeng|Zheng, Chun-Hou', 'College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui, 230601, China.|College of Computer Science and Technology, Anhui University, Hefei, Anhui, 230601, China.|Institute of Health Sciences, Anhui University, Hefei, Anhui, 230601, China.|College of Computer Science and Technology, Anhui University, Hefei, Anhui, 230601, China. zhengch99@126.com.', 'China|China|China|China', 0, 'Algorithms|Carcinogenesis|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Genes, Neoplasm|Genomics|High-Throughput Nucleotide Sequencing|Humans|Models, Genetic|Mutation|Neoplasms|Sequence Analysis, DNA|Transcriptome', 'Journal Article', '10.1186/s12859-016-1332-y', '28155630', 'Cancer is a complex disease which is characterized by the accumulation of genetic alterations during the patients lifetime. With the development of the next-generation sequencing technology, multiple omics data, such as cancer genomic, epigenomic and transcriptomic data etc., can be measured from each individual. Correspondingly, one of the key challenges is to pinpoint functional driver mutations or pathways, which contributes to tumorigenesis, from millions of functional neutral passenger mutations.'),
(97627, 2016, 'bmc', 'A fast read alignment method based on seed-and-vote for next generation sequencing', 'Liu, Song|Wang, Yi|Wang, Fei', 'Shanghai Key Lab of Intelligent Information Processing, Shanghai, China.|School of Life Sciences, Fudan University, Shanghai, China.|Shanghai Key Lab of Intelligent Information Processing, Shanghai, China. wangfei@fudan.edu.cn.', 'China|China|China', 0, 'Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-016-1329-6', '28155631', 'The next-generation of sequencing technologies, along with the development of bioinformatics, are generating a growing number of reads every day. For the convenience of further research, these reads should be aligned to the reference genome by read alignment tools. Despite the diversity of read alignment tools, most have no comprehensive advantage in both accuracy and speed. For example, BWA has comparatively high accuracy, but its speed leaves much to be desired, becoming a bottleneck while an increasing number of reads need to be aligned every day. We believe that the speed of read alignment tools still has huge room for improvement, while maintaining little to no loss in accuracy.'),
(97628, 2016, 'bmc', 'Global inference of disease-causing single nucleotide variants from exome sequencing data', 'Wu, Mengmeng|Chen, Ting|Jiang, Rui', 'MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, Tsinghua National Laboratory for Information Science and Technology, Beijing, China.|MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, Tsinghua National Laboratory for Information Science and Technology, Beijing, China.|MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, Tsinghua National Laboratory for Information Science and Technology, Beijing, China. ruijiang@tsinghua.edu.cn.', 'China|China|China', 4, 'Computational Biology|Computer Simulation|Exome|High-Throughput Nucleotide Sequencing|Humans|Introns|Polymorphism, Single Nucleotide|Promoter Regions, Genetic|RNA Splice Sites|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-016-1325-x', '28155632', 'Whole exome sequencing (WES) has recently emerged as an effective approach for identifying genetic variants underlying human diseases. However, considerable time and labour is needed for careful investigation of candidate variants. Although filtration based on population frequencies and functional prediction scores could effectively remove common and neutral variants, hundreds or even thousands of rare deleterious variants still remain. In addition, current WES platforms also provide variant information in flanking noncoding regions, such as promoters, introns and splice sites. Despite of being recognized to harbour causal variants, these regions are usually ignored by current analysis pipelines.'),
(97629, 2016, 'bmc', 'Multi-CAR: a tool of contig scaffolding using multiple references', 'Chen, Kun-Tze|Chen, Cheih-Jung|Shen, Hsin-Ting|Liu, Chia-Liang|Huang, Shang-Hao|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.|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.', '-|-|-|-|-|-', 0, 'Bacteria|Contig Mapping|Genome, Bacterial|Genomics|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-016-1328-7', '28155633', 'A draft genome assembled by current next-generation sequencing techniques from short reads is just a collection of contigs, whose relative positions and orientations along the genome being sequenced are unknown. To further obtain its complete sequence, a contig scaffolding process is usually applied to order and orient the contigs in the draft genome. Although several single reference-based scaffolding tools have been proposed, they may produce erroneous scaffolds if there are rearrangements between the target and reference genomes or their phylogenetic relationship is distant. This may suggest that a single reference genome may not be sufficient to produce correct scaffolds of a draft genome.'),
(97630, 2016, 'bmc', 'Dynamic epigenetic mode analysis using spatial temporal clustering', 'Gan, YangLan|Tao, Han|Zou, Guobing|Yan, Cairong|Guan, Jihong', 'School of Computer Science and Technology, Donghua University, Shanghai, China.|Department of Computer Science and Technology, Tongji University, Shanghai, China.|School of Computer Engineering and Science, Shanghai University, Shanghai, China.|School of Computer Science and Technology, Donghua University, Shanghai, China.|Department of Computer Science and Technology, Tongji University, Shanghai, China. jhguan@tongji.edu.cn.', 'China|China|China|China|China', 0, 'Cell Differentiation|Cluster Analysis|DNA Methylation|Embryonic Stem Cells|Epigenesis, Genetic|Gene Expression Regulation, Developmental|Histones|Humans', 'Journal Article', '10.1186/s12859-016-1331-z', '28155634', 'Differentiation of human embryonic stem cells requires precise control of gene expression that depends on specific spatial and temporal epigenetic regulation. Recently available temporal epigenomic data derived from cellular differentiation processes provides an unprecedented opportunity for characterizing fundamental properties of epigenomic dynamics and revealing regulatory roles of epigenetic modifications.'),
(97631, 2016, 'bmc', 'Genome-wide functional analysis using the barcode sequence alignment and statistical analysis (Barcas) tool', 'Mun, Jihyeob|Kim, Dong-Uk|Hoe, Kwang-Lae|Kim, Seon-Young', 'Korea Research Institute of Bioscience and Biotechnology (KRIBB), Personalized Genomic Medicine Research Center, Daejeon, Republic of Korea.|Department of Functional Genomics, University of Science and Technology, Daejeon, Republic of Korea.|Graduate School of New Drug Discovery and Development, Chungnam National University, Yusong-gu, Daejeon, South Korea.|Korea Research Institute of Bioscience and Biotechnology (KRIBB), Personalized Genomic Medicine Research Center, Daejeon, Republic of Korea. kimsy@kribb.re.kr.', '-|-|-|-', 0, 'Algorithms|Animals|CRISPR-Cas Systems|Data Interpretation, Statistical|Genome|Humans|Mice|RNA, Small Interfering|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-016-1326-9', '28155635', 'Pooled library screen analysis using shRNAs or CRISPR-Cas9 hold great promise to genome-wide functional studies. While pooled library screens are effective tools, erroneous barcodes can potentially be generated during the production of many barcodes. However, no current tools can distinguish erroneous barcodes from PCR or sequencing errors in a data preprocessing step.'),
(97632, 2016, 'bmc', 'Spectra library assisted de novo peptide sequencing for HCD and ETD spectra pairs', 'Yan, Yan|Zhang, Kaizhong', 'Department of Cumputer Science, Faculty of Science, University of Western Ontario, London, Canada.|Department of Cumputer Science, Faculty of Science, University of Western Ontario, London, Canada. kzhang@csd.uwo.ca.', 'Canada|Canada', 0, 'Amino Acid Sequence|Humans|Peptides|Sequence Analysis, Protein|Tandem Mass Spectrometry', 'Journal Article', '10.1186/s12859-016-1330-0', '28155636', 'De novo peptide sequencing via tandem mass spectrometry (MS/MS) has been developed rapidly in recent years. With the use of spectra pairs from the same peptide under different fragmentation modes, performance of de novo sequencing is greatly improved. Currently, with large amount of spectra sequenced everyday, spectra libraries containing tens of thousands of annotated experimental MS/MS spectra become available. These libraries provide information of the spectra properties, thus have the potential to be used with de novo sequencing to improve its performance.'),
(97633, 2016, 'bmc', 'CMIP: a software package capable of reconstructing genome-wide regulatory networks using gene expression data', 'Zheng, Guangyong|Xu, Yaochen|Zhang, Xiujun|Liu, Zhi-Ping|Wang, Zhuo|Chen, Luonan|Zhu, Xin-Guang', 'CAS Key Laboratory of Computational Biology and State Key Laboratory of Hybrid Rice, CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 20031, China. zhenggy@sibs.ac.cn.|CAS Key Laboratory of Computational Biology and State Key Laboratory of Hybrid Rice, CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 20031, China.|Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China.|Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China.|College of Life Science and Biotechnology, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai, 200240, China.|Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China. lnchen@sibs.ac.cn.|CAS Key Laboratory of Computational Biology and State Key Laboratory of Hybrid Rice, CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 20031, China. zhuxinguang@picb.ac.cn.', 'China|China|China|China|China|China|China', 12, 'Algorithms|Animals|Computational Biology|Gene Expression|Gene Regulatory Networks|Genome|Humans|Software|Transcriptome', 'Journal Article', '10.1186/s12859-016-1324-y', '28155637', 'A gene regulatory network (GRN) represents interactions of genes inside a cell or tissue, in which vertexes and edges stand for genes and their regulatory interactions respectively. Reconstruction of gene regulatory networks, in particular, genome-scale networks, is essential for comparative exploration of different species and mechanistic investigation of biological processes. Currently, most of network inference methods are computationally intensive, which are usually effective for small-scale tasks (e.g., networks with a few hundred genes), but are difficult to construct GRNs at genome-scale.'),
(97634, 2016, 'bmc', 'A network-based pathway-expanding approach for pathway analysis', 'Zhang, Qiaosheng|Li, Jie|Xie, Haozhe|Xue, Hanqing|Wang, Yadong', 'School of Computer Science and Technology, Harbin Institute of Technology, West Da-Zhi Street, Harbin, China.|School of Computer Science and Technology, Harbin Institute of Technology, West Da-Zhi Street, Harbin, China. jieli@hit.edu.cn.|School of Computer Science and Technology, Harbin Institute of Technology, West Da-Zhi Street, Harbin, China.|School of Computer Science and Technology, Harbin Institute of Technology, West Da-Zhi Street, Harbin, China.|School of Computer Science and Technology, Harbin Institute of Technology, West Da-Zhi Street, Harbin, China.', 'China|China|China|China|China', 0, 'Breast Neoplasms|Databases, Factual|Female|Gene Regulatory Networks|Genomics|Humans|Metabolic Networks and Pathways|Signal Transduction|Transcriptome', 'Journal Article', '10.1186/s12859-016-1333-x', '28155638', 'Pathway analysis combining multiple types of high-throughput data, such as genomics and proteomics, has become the first choice to gain insights into the pathogenesis of complex diseases. Currently, several pathway analysis methods have been developed to study complex diseases. However, these methods did not take into account the interaction between internal and external genes of the pathway and between pathways. Hence, these approaches still face some challenges. Here, we propose a network-based pathway-expanding approach that takes the topological structures of biological networks into account.'),
(97635, 2016, 'bmc', 'Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks', 'Liu, Hui|Song, Yinglong|Guan, Jihong|Luo, Libo|Zhuang, Ziheng', 'Changzhou NO. 7 Peoples Hospital, Changzhou, Jiangsu, 213011, China.|Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, 200433, China.|Department of Computer Science and Technology, Tongji University, Shanghai, 201804, China.|Changzhou NO. 7 Peoples Hospital, Changzhou, Jiangsu, 213011, China. llb213001@163.com.|Changzhou NO. 7 Peoples Hospital, Changzhou, Jiangsu, 213011, China. cczuzzh@163.com.', 'China|China|China|China|China', 26, 'Algorithms|Computational Biology|Drug Repositioning|Humans|Metabolic Networks and Pathways|Precision Medicine', 'Journal Article', '10.1186/s12859-016-1336-7', '28155639', 'Since traditional drug research and development is often time-consuming and high-risk, there is an increasing interest in establishing new medical indications for approved drugs, referred to as drug repositioning, which provides a relatively low-cost and high-efficiency approach for drug discovery. With the explosive growth of large-scale biochemical and phenotypic data, drug repositioning holds great potential for precision medicine in the post-genomic era. It is urgent to develop rational and systematic approaches to predict new indications for approved drugs on a large scale.'),
(97636, 2016, 'bmc', 'Prediction of HIV-1 protease cleavage site using a combination of sequence, structural, and physicochemical features', 'Singh, Onkar|Su, Emily Chia-Yu', 'Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.|Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan. emilysu@tmu.edu.tw.', '-|-', 10, 'Algorithms|Amino Acid Sequence|Computational Biology|Data Accuracy|Decision Trees|HIV Infections|HIV Protease|HIV-1|Humans|Neural Networks (Computer)|Protein Conformation|Substrate Specificity', 'Journal Article', '10.1186/s12859-016-1337-6', '28155640', 'The human immunodeficiency virus type 1 (HIV-1) aspartic protease is an important enzyme owing to its imperative part in viral development and a causative agent of deadliest disease known as acquired immune deficiency syndrome (AIDS). Development of HIV-1 protease inhibitors can help understand the specificity of substrates which can restrain the replication of HIV-1, thus antagonize AIDS. However, experimental methods in identification of HIV-1 protease cleavage sites are generally time-consuming and labor-intensive. Therefore, using computational methods to predict cleavage sites has become highly desirable.'),
(97637, 2016, 'bmc', 'DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations', 'Yuan, Yuchen|Shi, Yi|Li, Changyang|Kim, Jinman|Cai, Weidong|Han, Zeguang|Feng, David Dagan', 'School of Information Technologies, The University of Sydney, Darlington, NSW, 2008, Australia.|Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiaotong University, Shanghai, 200240, China. yishi@sjtu.edu.cn.|School of Information Technologies, The University of Sydney, Darlington, NSW, 2008, Australia.|School of Information Technologies, The University of Sydney, Darlington, NSW, 2008, Australia.|School of Information Technologies, The University of Sydney, Darlington, NSW, 2008, Australia.|Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiaotong University, Shanghai, 200240, China.|School of Information Technologies, The University of Sydney, Darlington, NSW, 2008, Australia.', 'Australia|China|Australia|Australia|Australia|China|Australia', 0, 'Computational Biology|Genes, Neoplasm|Humans|Neoplasms|Neural Networks (Computer)|Point Mutation|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-016-1334-9', '28155641', 'With the developments of DNA sequencing technology, large amounts of sequencing data have become available in recent years and provide unprecedented opportunities for advanced association studies between somatic point mutations and cancer types/subtypes, which may contribute to more accurate somatic point mutation based cancer classification (SMCC). However in existing SMCC methods, issues like high data sparsity, small volume of sample size, and the application of simple linear classifiers, are major obstacles in improving the classification performance.'),
(97638, 2016, 'bmc', 'Statistical ensemble of gene regulatory networks of macrophage differentiation', 'Castiglione, Filippo|Tieri, Paolo|Palma, Alessandro|Jarrah, Abdul Salam', 'Institute for Applied Computing, National Research Council of Italy, Via dei Taurini 19, Rome, 00185, Italy. f.castiglione@iac.cnr.it.|Institute for Applied Computing, National Research Council of Italy, Via dei Taurini 19, Rome, 00185, Italy.|Department of Biology, University of Tor Vergata, Via della ricerca scientifica 1, Rome, 00133, Italy.|Department of Mathematics and Statistics, American University of Sharjah, P.O.Box 26666, Sharjah, UAE.', 'Italy|Italy|Italy|-', 10, 'Cell Differentiation|Gene Expression Regulation|Gene Regulatory Networks|Humans|Macrophages|Models, Statistical|Systems Biology', 'Journal Article', '10.1186/s12859-016-1363-4', '28155642', 'Macrophages cover a major role in the immune system, being the most plastic cell yielding several key immune functions.'),
(97639, 2016, 'bmc', 'Identification of recurrent combinatorial patterns of chromatin modifications at promoters across various tissue types', 'Meng, Nan|Machiraju, Raghu|Huang, Kun', 'Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, 43210, USA.|Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, 43210, USA. machiraju.1@osu.edu.|Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, 43210, USA. Kun.huang@osumce.edu.', 'United States|United States|United States', 0, 'Acetylation|B-Lymphocytes|Chromatin|Cluster Analysis|Computational Biology|Epigenesis, Genetic|Genome, Human|Histones|Humans|Methylation|Muscle, Skeletal|Organ Specificity|Regulatory Sequences, Nucleic Acid', 'Journal Article', '10.1186/s12859-016-1346-5', '28155643', 'Identification and analysis of recurrent combinatorial patterns of multiple chromatin modifications provide invaluable information for understanding epigenetic regulations. Furthermore, as more data becomes available, it is computationally expensive and unnecessary to study combinatorial patterns of all modifications.'),
(97640, 2016, 'bmc', 'A fast exact sequential algorithm for the partial digest problem', 'Abbas, Mostafa M|Bahig, Hazem M', 'Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar. mohamza@hbku.edu.qa.|Computer Science Division, Department of Mathematics, Faculty of Science, Ain Shams University, Cairo, 11566, Egypt. hazem.m.bahig@gmail.com.', 'Qatar|Egypt', 2, 'Algorithms|Computational Biology|DNA, Bacterial|Escherichia coli|Genome, Bacterial|Luciferases|Restriction Mapping', 'Journal Article', '10.1186/s12859-016-1365-2', '28155644', 'Restriction site analysis involves determining the locations of restriction sites after the process of digestion by reconstructing their positions based on the lengths of the cut DNA. Using different reaction times with a single enzyme to cut DNA is a technique known as a partial digestion. Determining the exact locations of restriction sites following a partial digestion is challenging due to the computational time required even with the best known practical algorithm.'),
(97641, 2016, 'bmc', 'Classification of Suncus murinus species complex (Soricidae: Crocidurinae) in Peninsular Malaysia using image analysis and machine learning approaches', 'Abu, Arpah|Leow, Lee Kien|Ramli, Rosli|Omar, Hasmahzaiti', 'Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia. arpah@um.edu.my.|Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia.|Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia.|Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia.', 'Malaysia|Malaysia|Malaysia|Malaysia', 3, 'Animals|Databases, Factual|Image Processing, Computer-Assisted|Machine Learning|Malaysia|Neural Networks (Computer)|Shrews', 'Journal Article', '10.1186/s12859-016-1362-5', '28155645', 'Taxonomists frequently identify specimen from various populations based on the morphological characteristics and molecular data. This study looks into another invasive process in identification of house shrew (Suncus murinus) using image analysis and machine learning approaches. Thus, an automated identification system is developed to assist and simplify this task. In this study, seven descriptors namely area, convex area, major axis length, minor axis length, perimeter, equivalent diameter and extent which are based on the shape are used as features to represent digital image of skull that consists of dorsal, lateral and jaw views for each specimen. An Artificial Neural Network (ANN) is used as classifier to classify the skulls of S. murinus based on region (northern and southern populations of Peninsular Malaysia) and sex (adult male and female). Thus, specimen classification using Training data set and identification using Testing data set were performed through two stages of ANNs.'),
(97642, 2016, 'bmc', 'A study on the application of topic models to motif finding algorithms', 'Basha Gutierrez, Josep|Nakai, Kenta', 'Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 277-8561, Chiba, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 277-8561, Chiba, Japan. knakai@ims.u-tokyo.ac.jp.', 'Japan|Japan', 2, 'Algorithms|Binding Sites|Computational Biology|Humans|Models, Theoretical|Monte Carlo Method|Nucleotide Motifs|Protein Binding|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/s12859-016-1364-3', '28155646', 'Topic models are statistical algorithms which try to discover the structure of a set of documents according to the abstract topics contained in them. Here we try to apply this approach to the discovery of the structure of the transcription factor binding sites (TFBS) contained in a set of biological sequences, which is a fundamental problem in molecular biology research for the understanding of transcriptional regulation. Here we present two methods that make use of topic models for motif finding. First, we developed an algorithm in which first a set of biological sequences are treated as text documents, and the k-mers contained in them as words, to then build a correlated topic model (CTM) and iteratively reduce its perplexity. We also used the perplexity measurement of CTMs to improve our previous algorithm based on a genetic algorithm and several statistical coefficients.'),
(97643, 2016, 'bmc', 'A hydrophobic spine stabilizes a surface-exposed xcexb1-helix according to analysis of the solvent-accessible surface area', 'Liou, Yi-Fan|Huang, Hui-Ling|Ho, Shinn-Ying', 'Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan. syho@mail.nctu.edu.tw.', '-|-|-', 3, 'Algorithms|Amino Acids|Computer Simulation|Data Interpretation, Statistical|Databases, Protein|Humans|Hydrophobic and Hydrophilic Interactions|Models, Molecular|Protein Conformation, alpha-Helical|Protein Folding|Protein Structure, Secondary|Proteins|Solvents', 'Journal Article', '10.1186/s12859-016-1368-z', '28155647', 'Most of hydrophilic and hydrophobic residues are thought to be exposed and buried in proteins, respectively. In contrast to the majority of the existing studies on protein folding characteristics using protein structures, in this study, our aim was to design predictors for estimating relative solvent accessibility (RSA) of amino acid residues to discover protein folding characteristics from sequences.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97644, 2016, 'bmc', 'Fast processing of microscopic images using object-based extended depth of field', 'Intarapanich, Apichart|Kaewkamnerd, Saowaluck|Pannarut, Montri|Shaw, Philip J|Tongsima, Sissades', 'National Electronics and Computer Technology Center, National Science and Technology Development Agency, Thailand Science Park, Pathum Thani, Thailand.|National Electronics and Computer Technology Center, National Science and Technology Development Agency, Thailand Science Park, Pathum Thani, Thailand.|National Electronics and Computer Technology Center, National Science and Technology Development Agency, Thailand Science Park, Pathum Thani, Thailand.|National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Thailand Science Park, Pathum Thani, Thailand.|National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Thailand Science Park, Pathum Thani, Thailand. sissades@biotec.or.th.', 'Thailand|Thailand|Thailand|Thailand|Thailand', 2, 'Algorithms|Humans|Image Processing, Computer-Assisted|Malaria, Falciparum|Microscopy|Models, Biological|Pattern Recognition, Automated|Plasmodium falciparum|Signal Processing, Computer-Assisted', 'Journal Article', '10.1186/s12859-016-1373-2', '28155648', 'Microscopic analysis requires that foreground objects of interest, e.g. cells, are in focus. In a typical microscopic specimen, the foreground objects may lie on different depths of field necessitating capture of multiple images taken at different focal planes. The extended depth of field (EDoF) technique is a computational method for merging images from different depths of field into a composite image with all foreground objects in focus. Composite images generated by EDoF can be applied in automated image processing and pattern recognition systems. However, current algorithms for EDoF are computationally intensive and impractical, especially for applications such as medical diagnosis where rapid sample turnaround is important. Since foreground objects typically constitute a minor part of an image, the EDoF technique could be made to work much faster if only foreground regions are processed to make the composite image. We propose a novel algorithm called object-based extended depths of field (OEDoF) to address this issue.'),
(97645, 2016, 'bmc', 'Ceph-X: development and evaluation of 2D cephalometric system', 'Mosleh, Mogeeb Ahmed Ahmed|Baba, Mohd Sapiyan|Malek, Sorayya|Almaktari, Rasheed A', 'Software Engineering Department, Faculty of Engineering & Information Technology, Taiz University, 6169, Taiz, Yemen. MogeebMosleh@um.edu.my.|Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia.|Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia.|Faculty of Dentistry, Orthodontic Department, University of Malaya, 50603, Kuala Lumpur, Malaysia.', 'Yemen|Malaysia|Malaysia|Malaysia', 3, 'Adult|Cephalometry|Facial Bones|Female|Humans|Image Processing, Computer-Assisted|Male|Orthodontics|Radiography, Dental, Digital|Reproducibility of Results|X-Ray Film|Young Adult', 'Evaluation Studies', '10.1186/s12859-016-1370-5', '28155649', 'Cephalometric analysis and measurements of skull parameters using X-Ray images plays an important role in predicating and monitoring orthodontic treatment. Manual analysis and measurements of cephalometric is considered tedious, time consuming, and subjected to human errors. Several cephalometric systems have been developed to automate the cephalometric procedure, however, no clear insights have been reported about reliability, performance, and usability of those systems. This study utilizes some techniques to evaluate reliability, performance, and usability metric using SUS methods of the developed cephalometric system which has not been reported in previous studies.'),
(97646, 2016, 'bmc', 'Incorporating efficient radial basis function networks and significant amino acid pairs for predicting GTP binding sites in transport proteins', 'Le, Nguyen-Quoc-Khanh|Ou, Yu-Yen', 'Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan. khanhlee87@gmail.com.|Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan. yienou@gmail.com.', '-|-', 16, 'Amino Acid Sequence|Amino Acids|Binding Sites|Carrier Proteins|Guanosine Triphosphate|Humans|Models, Theoretical|Protein Binding|ROC Curve', 'Journal Article', '10.1186/s12859-016-1369-y', '28155651', 'Guanonine-protein (G-protein) is known as molecular switches inside cells, and is very important in signals transmission from outside to inside cell. Especially in transport protein, most of G-proteins play an important role in membrane trafficking, necessary for transferring proteins and other molecules to a variety of destinations outside and inside of the cell. The function of membrane trafficking is controlled by G-proteins via Guanosine triphosphate (GTP) binding sites. The GTP binding sites active G-proteins initiated to membrane vesicles by interacting with specific effector proteins. Without the interaction from GTP binding sites, G-proteins could not be active in membrane trafficking and consequently cause many diseases, i.e., cancer, Parkinson… Thus it is very important to identify GTP binding sites in membrane trafficking, in particular, and in transport protein, in general.'),
(97647, 2016, 'bmc', 'Novel group-based QSAR and combinatorial design of CK-1xcexb4 inhibitors as neuroprotective agents', 'Joshi, Kopal|Goyal, Sukriti|Grover, Sonam|Jamal, Salma|Singh, Aditi|Dhar, Pawan|Grover, Abhinav', 'Amity School of Biotechnology, Amity University, Noida, Uttar Pradesh, 201303, India.|Department of Bioscience and Biotechnology, Banasthali University, Tonk, Rajasthan, 304022, India.|Kusuma School of Biological Sciences, Indian Institute of Technology Delhi, New Delhi, 110016, India.|Department of Bioscience and Biotechnology, Banasthali University, Tonk, Rajasthan, 304022, India.|Department of Biotechnology, TERI University, New Delhi, 110070, India.|School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India.|School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India. abhinavgr@gmail.com.', 'India|India|India|India|India|India|India', 6, 'Amyotrophic Lateral Sclerosis|Casein Kinase Idelta|DNA-Binding Proteins|Drug Design|Drug Discovery|Humans|Models, Molecular|Molecular Docking Simulation|Neuroprotective Agents|Phosphorylation|Protein Conformation|Protein Kinase Inhibitors|Quantitative Structure-Activity Relationship', 'Journal Article', '10.1186/s12859-016-1379-9', '28155653', 'Tar DNA binding protein 43 (TDP-43) hyperphosphorylation, caused by Casein kinase 1 (CK-1) protein isoforms, is associated with the onset and progression of Amyotrophic Lateral Sclerosis (ALS). Among the reported isoforms and splice variants of CK-1 protein superfamily, CK-1xcexb4 is known to phosphorylate different serine and threonine sites on TDP-43 protein in vitro and thus qualifies as a potential target for ALS treatment.'),
(97648, 2016, 'bmc', 'Grouping miRNAs of similar functions via weighted information content of gene ontology', 'Lan, Chaowang|Chen, Qingfeng|Li, Jinyan', 'School of Computer, Electronic and Information, and State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangxi University, No.100 Daxue Road, Nanning, 530004, China.|School of Computer, Electronic and Information, and State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangxi University, No.100 Daxue Road, Nanning, 530004, China. qingfeng@gxu.edu.cn.|Advanced Analytics Institute, Faculty of Engineering and IT, University of Technology Sydney, PO Box 123, Broadway, Sydney, NSW 2007, Australia. jinyan.li@uts.edu.au.', 'China|China|Australia', 8, 'Algorithms|Cluster Analysis|Computational Biology|Gene Expression Profiling|Gene Ontology|Humans|MicroRNAs|Models, Statistical', 'Journal Article', '10.1186/s12859-016-1367-0', '28155659', 'Regulation mechanisms between miRNAs and genes are complicated. To accomplish a biological function, a miRNA may regulate multiple target genes, and similarly a target gene may be regulated by multiple miRNAs. Wet-lab knowledge of co-regulating miRNAs is limited. This work introduces a computational method to group miRNAs of similar functions to identify co-regulating miRNAsfrom a similarity matrix of miRNAs.'),
(97649, 2016, 'bmc', 'SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides', 'Vasylenko, Tamara|Liou, Yi-Fan|Chiou, Po-Chin|Chu, Hsiao-Wei|Lai, Yung-Sung|Chou, Yu-Ling|Huang, Hui-Ling|Ho, Shinn-Ying', 'Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan. hlhuang.nctu@gmail.com.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan. syho@mail.nctu.edu.tw.', '-|-|-|-|-|-|-|-', 4, 'Bacteria|Bacterial Proteins|Databases, Protein|Dipeptides|Propensity Score|Protein-Tyrosine Kinases|Software|Tyrosine', 'Journal Article', '10.1186/s12859-016-1371-4', '28155663', 'Bacterial tyrosine-kinases (BY-kinases), which play an important role in numerous cellular processes, are characterized as a separate class of enzymes and share no structural similarity with their eukaryotic counterparts. However, in silico methods for predicting BY-kinases have not been developed yet. Since these enzymes are involved in key regulatory processes, and are promising targets for anti-bacterial drug design, it is desirable to develop a simple and easily interpretable predictor to gain new insights into bacterial tyrosine phosphorylation. This study proposes a novel SCMBYK method for predicting and characterizing BY-kinases.'),
(97650, 2016, 'bmc', 'RiboTagger: fast and unbiased 16S/18S profiling using whole community shotgun metagenomic or metatranscriptome surveys', 'Xie, Chao|Goi, Chin Lui Wesley|Huson, Daniel H|Little, Peter F R|Williams, Rohan B H', 'Singapore Centre for Environmental Life Sciences Engineering, National University of Singapore, Singapore, 117456, Singapore. xiechaos@gmail.com.|Singapore Centre for Environmental Life Sciences Engineering, National University of Singapore, Singapore, 117456, Singapore.|Centre for Bioinformatics, Tuebingen University, Tuebingen, 72076, Germany.|Singapore Centre for Environmental Life Sciences Engineering, National University of Singapore, Singapore, 117456, Singapore.|Singapore Centre for Environmental Life Sciences Engineering, National University of Singapore, Singapore, 117456, Singapore.', 'Singapore|Singapore|Germany|Singapore|Singapore', 14, 'Bacteria|Computational Biology|DNA, Ribosomal|High-Throughput Nucleotide Sequencing|Metagenome|Metagenomics|RNA, Ribosomal, 16S|RNA, Ribosomal, 18S|Sequence Analysis, DNA|Software|Transcriptome', 'Journal Article', '10.1186/s12859-016-1378-x', '28155666', 'Taxonomic profiling of microbial communities is often performed using small subunit ribosomal RNA (SSU) amplicon sequencing (16S or 18S), while environmental shotgun sequencing is often focused on functional analysis. Large shotgun datasets contain a significant number of SSU sequences and these can be exploited to perform an unbiased SSU--based taxonomic analysis.'),
(97651, 2016, 'bmc', 'Bioinformatics and systems biology research update from the 15th International Conference on Bioinformatics (InCoB2016)', 'Schxc3xb6nbach, Christian|Verma, Chandra|Bond, Peter J|Ranganathan, Shoba', 'International Research Center for Medical Sciences, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, 860-0811, Japan. chris@kumamoto-u.ac.jp.|Bioinformatics Institute, Agency for Science, Technology and Research (Axe2x88x97STAR), Singapore, 138671, Singapore.|Bioinformatics Institute, Agency for Science, Technology and Research (Axe2x88x97STAR), Singapore, 138671, Singapore.|Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW, 2109, Australia. shoba.ranganathan@mq.edu.au.', 'Japan|Singapore|Singapore|Australia', 2, 'Biomedical Research|Computational Biology|Congresses as Topic|Humans|Systems Biology', 'Journal Article', '10.1186/s12859-016-1409-7', '28155668', 'The International Conference on Bioinformatics (InCoB) has been publishing peer-reviewed conference papers in BMC Bioinformatics since 2006. Of the 44 articles accepted for publication in supplement issues of BMC Bioinformatics, BMC Genomics, BMC Medical Genomics and BMC Systems Biology, 24 articles with a bioinformatics or systems biology focus are reviewed in this editorial. InCoB2017 is scheduled to be held in Shenzen, China, September 20-22, 2017.'),
(97652, 2016, 'bmc', 'Drug-target interaction prediction via class imbalance-aware ensemble learning', 'Ezzat, Ali|Wu, Min|Li, Xiao-Li|Kwoh, Chee-Keong', 'School of Computer Science & Engineering, Nanyang Technological University, Nanyang Ave., Singapore, 639798, Singapore.|Institute for Infocomm Research (I2R), A*Star, Fusionopolis Way, Singapore, 138632, Singapore.|Institute for Infocomm Research (I2R), A*Star, Fusionopolis Way, Singapore, 138632, Singapore. xlli@i2r.a-star.edu.sg.|School of Computer Science & Engineering, Nanyang Technological University, Nanyang Ave., Singapore, 639798, Singapore.', 'Singapore|Singapore|Singapore|Singapore', 41, 'Algorithms|Computational Biology|Drug Discovery|Drug Interactions|Humans|Pharmaceutical Preparations|Proteins', 'Journal Article', '10.1186/s12859-016-1377-y', '28155697', 'Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types.'),
(97653, 2016, 'bmc', 'Molecular modeling and lead design of substituted zanamivir derivatives as potent anti-influenza drugs', 'Dholakia, Dhwani|Goyal, Sukriti|Jamal, Salma|Singh, Aditi|Das, Asmita|Grover, Abhinav', 'Department of Biotechnology, Delhi Technological University, New Delhi, 110042, India.|Department of Bioscience and Biotechnology, Banasthali University, Tonk, Rajasthan, 304022, India.|Department of Bioscience and Biotechnology, Banasthali University, Tonk, Rajasthan, 304022, India.|Department of Biotechnology, TERI University, New Delhi, 110070, India.|Department of Biotechnology, Delhi Technological University, New Delhi, 110042, India.|School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India. abhinavgr@gmail.com.', 'India|India|India|India|India|India', 3, 'Antiviral Agents|Catalytic Domain|Drug Design|Enzyme Inhibitors|Humans|Influenza A Virus, H1N1 Subtype|Influenza A Virus, H3N2 Subtype|Influenza, Human|Molecular Dynamics Simulation|Neuraminidase|Zanamivir', 'Journal Article', '10.1186/s12859-016-1374-1', '28155702', 'Influenza virus spreads infection by two main surface glycoproteins, namely hemagglutinin (HA) and neuraminidase (NA). NA cleaves the sialic acid receptors eventually releasing newly formed virus particles which then invade new cells. Inhibition of NA could limit the replication of virus to one round which is insufficient to cause the disease.'),
(97654, 2016, 'bmc', 'A methodological approach for using high-level Petri Nets to model the immune system response', 'Pennisi, Marzio|Cavalieri, Salvatore|Motta, Santo|Pappalardo, Francesco', 'Department of Mathematics and Computer Science, University of Catania, Catania, Italy.|Department of Electrical Electronic and Computer Engineering (DIEEI), University of Catania, Catania, Italy.|Department of Mathematics and Computer Science, University of Catania, Catania, Italy.|Department of Drug Sciences, University of Catania, Catania, Italy. francesco.pappalardo@unict.it.', 'Italy|Italy|Italy|Italy', 13, 'Animals|Computer Graphics|Computer Simulation|Humans|Immune System|Models, Biological|Signal Transduction|Software', 'Journal Article', '10.1186/s12859-016-1361-6', '28155706', 'Mathematical and computational models showed to be a very important support tool for the comprehension of the immune system response against pathogens. Models and simulations allowed to study the immune system behavior, to test biological hypotheses about diseases and infection dynamics, and to improve and optimize novel and existing drugs and vaccines. Continuous models, mainly based on differential equations, usually allow to qualitatively study the system but lack in description, conversely discrete models, such as agent based models and cellular automata, permit to describe in detail entities properties at the cost of losing most qualitative analyses. Petri Nets (PN) are a graphical modeling tool developed to model concurrency and synchronization in distributed systems. Their use has become increasingly marked also thanks to the introduction in the years of many features and extensions which lead to the born of high level PN.'),
(97655, 2016, 'bmc', 'Prioritizing biological pathways by recognizing context in time-series gene expression data', 'Lee, Jusang|Jo, Kyuri|Lee, Sunwon|Kang, Jaewoo|Kim, Sun', 'Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.|Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.|Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea.|Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea.|Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea. sunkim.bioinfo@snu.ac.kr.', '-|-|-|-|-', 5, 'Algorithms|Computational Biology|Gene Expression|Humans|Metabolic Networks and Pathways|Software|Transcriptome', 'Journal Article', '10.1186/s12859-016-1335-8', '28155707', 'The primary goal of pathway analysis using transcriptome data is to find significantly perturbed pathways. However, pathway analysis is not always successful in identifying pathways that are truly relevant to the context under study. A major reason for this difficulty is that a single gene is involved in multiple pathways. In the KEGG pathway database, there are 146 genes, each of which is involved in more than 20 pathways. Thus activation of even a single gene will result in activation of many pathways. This complex relationship often makes the pathway analysis very difficult. While we need much more powerful pathway analysis methods, a readily available alternative way is to incorporate the literature information.'),
(97656, 2016, 'bmc', 'PARRoT- a homology-based strategy to quantify and compare RNA-sequencing from non-model organisms', 'Gan, Ruei-Chi|Chen, Ting-Wen|Wu, Timothy H|Huang, Po-Jung|Lee, Chi-Ching|Yeh, Yuan-Ming|Chiu, Cheng-Hsun|Huang, Hsien-Da|Tang, Petrus', 'Department of Biological Science and Technology, National Chiao Tung University, Hsin-Chu, 300, Taiwan.|Bioinformatics Center, Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan.|Institute of Biomedical Informatics, National Yang-Ming University, Taipei City, Taiwan.|Bioinformatics Center, Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan.|Bioinformatics Center, Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan.|Bioinformatics Center, Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan.|Molecular Infectious Diseases Research Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan.|Department of Biological Science and Technology, National Chiao Tung University, Hsin-Chu, 300, Taiwan. bryan@mail.nctu.edu.tw.|Bioinformatics Center, Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan. petang@mail.cgu.edu.tw.', '-|-|-|-|-|-|-|-|-', 3, 'Animals|Cnidaria|Gene Expression Profiling|Genomics|High-Throughput Nucleotide Sequencing|Internet|Models, Biological|Molecular Sequence Annotation|Sequence Analysis, RNA|Software|Transcriptome', 'Comparative Study', '10.1186/s12859-016-1366-1', '28155708', 'Next-generation sequencing promises the de novo genomic and transcriptomic analysis of samples of interests. However, there are only a few organisms having reference genomic sequences and even fewer having well-defined or curated annotations. For transcriptome studies focusing on organisms lacking proper reference genomes, the common strategy is de novo assembly followed by functional annotation. However, things become even more complicated when multiple transcriptomes are compared.'),
(97657, 2016, 'bmc', 'Predicting MoRFs in protein sequences using HMM profiles', 'Sharma, Ronesh|Kumar, Shiu|Tsunoda, Tatsuhiko|Patil, Ashwini|Sharma, Alok', 'School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji.|School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji.|CREST, JST, Yokohama, 230-0045, Japan.|Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan. ashwini@hgc.jp.|School of Engineering and Physics, The University of the South Pacific, Suva, Fiji. alok.fj@gmail.com.', 'Fiji|Fiji|Japan|Japan|Fiji', 12, 'Algorithms|Computational Biology|Humans|Intrinsically Disordered Proteins|Markov Chains|Models, Theoretical|Support Vector Machine', 'Journal Article', '10.1186/s12859-016-1375-0', '28155710', 'Intrinsically Disordered Proteins (IDPs) lack an ordered three-dimensional structure and are enriched in various biological processes. The Molecular Recognition Features (MoRFs) are functional regions within IDPs that undergo a disorder-to-order transition on binding to a partner protein. Identifying MoRFs in IDPs using computational methods is a challenging task.'),
(97658, 2016, 'bmc', 'Automated identification of Monogeneans using digital image processing and K-nearest neighbour approaches', 'Yousef Kalafi, Elham|Tan, Wooi Boon|Town, Christopher|Dhillon, Sarinder Kaur', 'Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia.|Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia.|Computer Laboratory, University of Cambridge, Cambridge, CB3 0FD, UK.|Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia. sarinder@um.edu.my.', 'Malaysia|Malaysia|United Kingdom|Malaysia', 11, 'Animals|Cluster Analysis|Diagnosis, Computer-Assisted|Fishes|Gills|Image Processing, Computer-Assisted|Pattern Recognition, Automated|Platyhelminths|Skin', 'Journal Article', '10.1186/s12859-016-1376-z', '28155722', 'Monogeneans are flatworms (Platyhelminthes) that are primarily found on gills and skin of fishes. Monogenean parasites have attachment appendages at their haptoral regions that help them to move about the body surface and feed on skin and gill debris. Haptoral attachment organs consist of sclerotized hard parts such as hooks, anchors and marginal hooks. Monogenean species are differentiated based on their haptoral bars, anchors, marginal hooks, reproductive parts (male and female copulatory organs) morphological characters and soft anatomical parts. The complex structure of these diagnostic organs and also their overlapping in microscopic digital images are impediments for developing fully automated identification system for monogeneans (LNCS 7666:256-263, 2012), (ISDA, 457-462, 2011), (J Zoolog Syst Evol Res 52(2): 95-99. 2013,). In this study images of hard parts of the haptoral organs such as bars and anchors are used to develop a fully automated identification technique for monogenean species identification by implementing image processing techniques and machine learning methods.'),
(97659, 2016, 'bmc', 'Structural analyses of 2015-updated drug-resistant mutations in HIV-1 protease: an implication of protease inhibitor cross-resistance', 'Su, Chinh Tran-To|Ling, Wei-Li|Lua, Wai-Heng|Haw, Yu-Xuan|Gan, Samuel Ken-En', 'Bioinformatics Institute, Agency for Science, Technology, and Research (A*STAR), Singapore, 138671, Singapore. chinhsutranto@bii.a-star.edu.sg.|Bioinformatics Institute, Agency for Science, Technology, and Research (A*STAR), Singapore, 138671, Singapore.|Bioinformatics Institute, Agency for Science, Technology, and Research (A*STAR), Singapore, 138671, Singapore.|Bioinformatics Institute, Agency for Science, Technology, and Research (A*STAR), Singapore, 138671, Singapore.|Bioinformatics Institute, Agency for Science, Technology, and Research (A*STAR), Singapore, 138671, Singapore. samuelg@bii.a-star.edu.sg.', 'Singapore|Singapore|Singapore|Singapore|Singapore', 0, 'Drug Resistance, Viral|HIV Infections|HIV Protease|HIV Protease Inhibitors|HIV-1|Humans|Mutation', 'Journal Article', '10.1186/s12859-016-1372-3', '28155724', 'Strategies to control HIV for improving the quality of patient lives have been aided by the Highly Active Anti-Retroviral Therapy (HAART), which consists of a cocktail of inhibitors targeting key viral enzymes. Numerous new drugs have been developed over the past few decades but viral resistances to these drugs in the targeted viral enzymes are increasingly reported. Nonetheless the acquired mutations often reduce viral fitness and infectivity. Viral compensatory secondary-line mutations mitigate this loss of fitness, equipping the virus with a broad spectrum of resistance against these drugs. While structural understanding of the viral protease and its drug resistance mutations have been well established, the interconnectivity and development of structural cross-resistance remain unclear. This paper reports the structural analyses of recent clinical mutations on the drug cross-resistance effects from various protease and protease inhibitors (PIs) complexes.'),
(97660, 2017, 'bmc', 'Erratum to: Integrating mean and variance heterogeneities to identify differentially expressed genes', 'Ouyang, Weiwei|An, Qiang|Zhao, Jinying|Qin, Huaizhen', 'Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, Suite 2001, New Orleans, LA, 70112, USA.|Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, Suite 2001, New Orleans, LA, 70112, USA.|Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, 2004 Mowry Rd, Gainesville, FL, 32610, USA.|Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, Suite 2001, New Orleans, LA, 70112, USA. hqin2@tulane.edu.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1186/s12859-017-1507-1', '28158966', '-'),
(97661, 2017, 'bmc', 'MIDcor, an R-program for deciphering mass interferences in mass spectra of metabolites enriched in stable isotopes', 'Selivanov, Vitaly A|Benito, Adrián|Miranda, Anibal|Aguilar, Esther|Polat, Ibrahim Halil|Centelles, Josep J|Jayaraman, Anusha|Lee, Paul W N|Marin, Silvia|Cascante, Marta', 'Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain. selivanov@ub.edu.|Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain.|Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain.|Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain.|Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain.|Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain.|Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain.|Department of Pediatrics, Harbor-UCLA Medical Center, Research and Education Institute, Torrance, CA, 90502, USA.|Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain.|Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain. martacascante@ub.edu.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|United States|Spain|Spain', 0, 'Carbon Isotopes|Cell Line|Culture Media|Gas Chromatography-Mass Spectrometry|Humans|Internet|Isotope Labeling|Metabolome|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1513-3', '28158972', '-'),
(97662, 2016, 'bmc', 'Proceedings of the 15th Annual UT-KBRIN Bioinformatics Summit 2016 : Cadiz, KY, USA 8-10 April 2016', '-', '-', '-', 0, '-', 'Journal Article', '10.1186/s12859-016-1154-y', '28166713', '-'),
(97663, 2017, 'bmc', 'PyHLA: tests for the association between HLA alleles and diseases', 'Fan, Yanhui|Song, You-Qiang', 'School of Biomedical Sciences, The University of Hong Kong, 21 Sassoon Road, Pokfulam, Hong Kong, Hong Kong.|School of Biomedical Sciences, The University of Hong Kong, 21 Sassoon Road, Pokfulam, Hong Kong, Hong Kong. songy@hku.hk.', 'Hong Kong|Hong Kong', 0, 'Alleles|Computational Biology|Genome, Human|Genotyping Techniques|HLA Antigens|High-Throughput Nucleotide Sequencing|Humans|Linear Models|Logistic Models|Microsatellite Repeats|Models, Theoretical|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/s12859-017-1496-0', '28166716', 'Recently, several tools have been designed for human leukocyte antigen (HLA) typing using single nucleotide polymorphism (SNP) array and next-generation sequencing (NGS) data. These tools provide high-throughput and cost-effective approaches for identifying HLA types. Therefore, tools for downstream association analysis are highly desirable. Although several tools have been designed for multi-allelic marker association analysis, they were designed only for microsatellite markers and do not scale well with increasing data volumes, or they were designed for large-scale data but provided a limited number of tests.'),
(97664, 2017, 'bmc', 'Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis', 'Choi, Seung Hoan|Labadorf, Adam T|Myers, Richard H|Lunetta, Kathryn L|Dupuis, Josée|DeStefano, Anita L', 'Department of Biostatistics, Boston University, 801 Massachusetts Avenue, Boston, Massachusetts, USA.|Department of Neurology, Boston University, 72 East Concord Street, Boston, Massachusetts, USA.|Department of Neurology, Boston University, 72 East Concord Street, Boston, Massachusetts, USA.|Department of Biostatistics, Boston University, 801 Massachusetts Avenue, Boston, Massachusetts, USA.|Department of Biostatistics, Boston University, 801 Massachusetts Avenue, Boston, Massachusetts, USA.|Department of Biostatistics, Boston University, 801 Massachusetts Avenue, Boston, Massachusetts, USA. adestef@bu.edu.', 'United States|United States|United States|United States|United States|United States', 0, 'Bayes Theorem|Case-Control Studies|Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Huntington Disease|Logistic Models|Models, Theoretical|Reproducibility of Results|Sample Size|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-017-1498-y', '28166718', 'Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates.'),
(97665, 2017, 'bmc', 'Not all predicted CRISPR-Cas systems are equal: isolated cas genes and classes of CRISPR like elements', 'Zhang, Quan|Ye, Yuzhen', 'School of Informatics and Computing, Indiana University, 150 S. Woodlawn Ave, Bloomington, IN, 47405, USA.|School of Informatics and Computing, Indiana University, 150 S. Woodlawn Ave, Bloomington, IN, 47405, USA. yye@indiana.edu.', 'India|India', 51, 'CRISPR-Cas Systems|Clustered Regularly Interspaced Short Palindromic Repeats|Enhancer Elements, Genetic|Genetic Loci|Genome, Bacterial|Genomics|Molecular Sequence Annotation|Phylogeny|Software|Staphylococcus aureus|Streptococcus pyogenes|Streptococcus thermophilus', 'Journal Article', '10.1186/s12859-017-1512-4', '28166719', 'The CRISPR-Cas systems in prokaryotes are RNA-guided immune systems that target and deactivate foreign nucleic acids. A typical CRISPR-Cas system consists of a CRISPR array of repeat and spacer units, and a locus of cas genes. The CRISPR and the cas locus are often located next to each other in the genomes. However, there is no quantitative estimate of the co-location. In addition, ad-hoc studies have shown that some non-CRISPR genomic elements contain repeat-spacer-like structures and are mistaken as CRISPRs.'),
(97666, 2017, 'bmc', 'SPANG: a SPARQL client supporting generation and reuse of queries for distributed RDF databases', 'Chiba, Hirokazu|Uchiyama, Ikuo', 'National Institute for Basic Biology, National Institutes of Natural Sciences, Nishigonaka 38, Myodaiji, Okazaki, Aichi, 444-8585, Japan. chiba@nibb.ac.jp.|National Institute for Basic Biology, National Institutes of Natural Sciences, Nishigonaka 38, Myodaiji, Okazaki, Aichi, 444-8585, Japan.', 'Japan|Japan', 0, 'Computer Communication Networks|Databases, Factual|Internet', 'Journal Article', '10.1186/s12859-017-1531-1', '28178937', 'Toward improved interoperability of distributed biological databases, an increasing number of datasets have been published in the standardized Resource Description Framework (RDF). Although the powerful SPARQL Protocol and RDF Query Language (SPARQL) provides a basis for exploiting RDF databases, writing SPARQL code is burdensome for users including bioinformaticians. Thus, an easy-to-use interface is necessary.'),
(97667, 2017, 'bmc', 'An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data', 'Wadsworth, W Duncan|Argiento, Raffaele|Guindani, Michele|Galloway-Pena, Jessica|Shelburne, Samuel A|Vannucci, Marina', 'Department of Statistics, Rice University, Houston, TX, USA.|ESOMAS Department, University of Torino and Collegio Carlo Alberto, Torino, Italy.|Department of Statistics, University of California, Irvine, CA, USA.|Department of Infectious Disease, Infection Control, and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, 77030, TX, USA.|Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, 77030, TX, USA.|Department of Statistics, Rice University, Houston, TX, USA. marina@rice.edu.', 'United States|Italy|United States|United States|United States|United States', 0, 'Algorithms|Bacteria|Bayes Theorem|Computer Simulation|Humans|Linear Models|Markov Chains|Microbiota|Monte Carlo Method', 'Journal Article', '10.1186/s12859-017-1516-0', '28178947', 'The Human Microbiome has been variously associated with the immune-regulatory mechanisms involved in the prevention or development of many non-infectious human diseases such as autoimmunity, allergy and cancer. Integrative approaches which aim at associating the composition of the human microbiome with other available information, such as clinical covariates and environmental predictors, are paramount to develop a more complete understanding of the role of microbiome in disease development.'),
(97668, 2017, 'bmc', 'How can functional annotations be derived from profiles of phenotypic annotations?', 'Serrano-Solano, Beatriz|Díaz Ramos, Antonio|Hériché, Jean-Karim|Ranea, Juan A G', 'Department of Molecular Biology and Biochemistry, University of Málaga, Boulevard Louis Pasteur, Málaga, 29071, Spain.|Department of Algebra, Geometry and Topology, University of Málaga, Boulevard Louis Pasteur, Málaga, 29071, Spain.|European Molecular Biology Laboratory, Meyerhofstrasse 1, Heidelberg, 69117, Germany.|Department of Molecular Biology and Biochemistry, University of Málaga, Boulevard Louis Pasteur, Málaga, 29071, Spain. ranea@uma.es.', 'Spain|Spain|Germany|Spain', 0, 'Area Under Curve|Cluster Analysis|Computational Biology|Humans|Phenotype|RNA Interference|ROC Curve', 'Journal Article', '10.1186/s12859-017-1503-5', '28183267', 'Loss-of-function phenotypes are widely used to infer gene function using the principle that similar phenotypes are indicative of similar functions. However, converting phenotypic to functional annotations requires careful interpretation of phenotypic descriptions and assessment of phenotypic similarity. Understanding how functions and phenotypes are linked will be crucial for the development of methods for the automatic conversion of gene loss-of-function phenotypes to gene functional annotations.'),
(97669, 2017, 'bmc', 'SEPIa, a knowledge-driven algorithm for predicting conformational B-cell epitopes from the amino acid sequence', 'Dalkas, Georgios A|Rooman, Marianne', 'BioModeling, BioInformatics & BioProcesses (3BIO), Universitxc3xa9 Libre de Bruxelles (ULB), CP 165/61, 50 Roosevelt Ave, 1050, Brussels, Belgium.|BioModeling, BioInformatics & BioProcesses (3BIO), Universitxc3xa9 Libre de Bruxelles (ULB), CP 165/61, 50 Roosevelt Ave, 1050, Brussels, Belgium. mrooman@ulb.ac.be.', 'Belgium|Belgium', 0, 'Algorithms|Amino Acid Sequence|Antigen-Antibody Complex|Area Under Curve|Epitopes, B-Lymphocyte|Humans|Molecular Sequence Data|Protein Structure, Secondary|ROC Curve|Receptors, Adrenergic, beta-2', 'Journal Article', '10.1186/s12859-017-1528-9', '28183272', 'The identification of immunogenic regions on the surface of antigens, which are able to be recognized by antibodies and to trigger an immune response, is a major challenge for the design of new and effective vaccines. The prediction of such regions through computational immunology techniques is a challenging goal, which will ultimately lead to a drastic limitation of the experimental tests required to validate their efficiency. However, current methods are far from being sufficiently reliable and/or applicable on a large scale.'),
(97670, 2016, 'bmc', 'KAOS: a new automated computational method for the identification of overexpressed genes', 'Nuzzo, Angelo|Carapezza, Giovanni|Di Bella, Sebastiano|Pulvirenti, Alfredo|Isacchi, Antonella|Bosotti, Roberta', 'Business Unit Oncology, Nerviano Medical Sciences srl, Nerviano, MI, 20014, Italy.|Business Unit Oncology, Nerviano Medical Sciences srl, Nerviano, MI, 20014, Italy.|Business Unit Oncology, Nerviano Medical Sciences srl, Nerviano, MI, 20014, Italy.|Department of Clinical and Experimental Medicine, University of Catania, Catania, 95125, Italy.|Business Unit Oncology, Nerviano Medical Sciences srl, Nerviano, MI, 20014, Italy.|Business Unit Oncology, Nerviano Medical Sciences srl, Nerviano, MI, 20014, Italy. roberta.bosotti@nervianoms.com.', 'Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Automation|Cell Line, Tumor|Computational Biology|Gene Expression|Gene Expression Profiling|Humans|Neoplasms|Phosphotransferases', 'Evaluation Studies', '10.1186/s12859-016-1188-1', '28185541', 'Kinase over-expression and activation as a consequence of gene amplification or gene fusion events is a well-known mechanism of tumorigenesis. The search for novel rearrangements of kinases or other druggable genes may contribute to understanding the biology of cancerogenesis, as well as lead to the identification of new candidate targets for drug discovery. However this requires the ability to query large datasets to identify rare events occurring in very small fractions (1-3 %) of different tumor subtypes. This task is different from what is normally done by conventional tools that are able to find genes differentially expressed between two experimental conditions.'),
(97671, 2016, 'bmc', 'Analysis of amplicon-based NGS data from neurological disease gene panels: a new method for allele drop-out management', 'Zucca, Susanna|Villaraggia, Margherita|Gagliardi, Stella|Grieco, Gaetano Salvatore|Valente, Marialuisa|Cereda, Cristina|Magni, Paolo', 'Department of Electrical, Computer and Biomedical engineering, University of Pavia, Pavia, 27100, Italy. susanna.zucca@unipv.it.|Department of Electrical, Computer and Biomedical engineering, University of Pavia, Pavia, 27100, Italy.|Center of Genomics and post-Genomics, IRCCS National Institute of Neurology Foundation C. Mondino, Pavia, 27100, Italy.|Center of Genomics and post-Genomics, IRCCS National Institute of Neurology Foundation C. Mondino, Pavia, 27100, Italy.|Center of Genomics and post-Genomics, IRCCS National Institute of Neurology Foundation C. Mondino, Pavia, 27100, Italy.|Center of Genomics and post-Genomics, IRCCS National Institute of Neurology Foundation C. Mondino, Pavia, 27100, Italy.|Department of Electrical, Computer and Biomedical engineering, University of Pavia, Pavia, 27100, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Alleles|Cerebral Hemorrhage|Genomics|High-Throughput Nucleotide Sequencing|Humans|Migraine with Aura|Mutation|Parkinson Disease|Sensitivity and Specificity|Statistics as Topic', 'Evaluation Studies', '10.1186/s12859-016-1189-0', '28185542', 'Amplicon-based targeted resequencing is a commonly adopted solution for next-generation sequencing applications focused on specific genomic regions. The reliability of such approaches rests on the high specificity and deep coverage, although sequencing artifacts attributable to PCR-like amplification can be encountered. Between these artifacts, allele drop-out, which is the preferential amplification of one allele, causes an artificial increase in homozygosity when heterozygous mutations fall on a primer pairing region. Here, a procedure to manage such artifacts, based on a pipeline composed of two steps of alignment and variant calling, is proposed. This methodology has been compared to the Illumina Custom Amplicon workflow, available on Illumina MiSeq, on the analysis of data obtained with four newly designed TruSeq Custom Amplicon gene panels.'),
(97672, 2016, 'bmc', 'An Atlas of annotations of Hydra vulgaris transcriptome', 'Evangelista, Daniela|Tripathi, Kumar Parijat|Guarracino, Mario Rosario', 'Laboratory for Genomics, Transcriptomics and Proteomics (LAB-GTP), High Performance Computing and Networking Institute (ICAR), National Research Council of Italy (CNR) Napoli, Italy, Via Pietro Castellino, Naples, 111, Italy. daniela.evangelista@na.icar.cnr.it.|Laboratory for Genomics, Transcriptomics and Proteomics (LAB-GTP), High Performance Computing and Networking Institute (ICAR), National Research Council of Italy (CNR) Napoli, Italy, Via Pietro Castellino, Naples, 111, Italy.|Laboratory for Genomics, Transcriptomics and Proteomics (LAB-GTP), High Performance Computing and Networking Institute (ICAR), National Research Council of Italy (CNR) Napoli, Italy, Via Pietro Castellino, Naples, 111, Italy.', 'Italy|Italy|Italy', 1, 'Animals|Databases, Genetic|Genomics|High-Throughput Nucleotide Sequencing|Hydra|Internet|Molecular Sequence Annotation|Software|Transcriptome', 'Journal Article', '10.1186/s12859-016-1172-9', '28185543', 'RNA sequencing takes advantage of the Next Generation Sequencing (NGS) technologies for analyzing RNA transcript counts with an excellent accuracy. Trying to interpret this huge amount of data in biological information is still a key issue, reason for which the creation of web-resources useful for their analysis is highly desiderable.'),
(97673, 2016, 'bmc', 'PWHATSHAP: efficient haplotyping for future generation sequencing', 'Bracciali, Andrea|Aldinucci, Marco|Patterson, Murray|Marschall, Tobias|Pisanti, Nadia|Merelli, Ivan|Torquati, Massimo', 'Computer Science and Mathematics, School of Natural Sciences, Stirling University, Stirling, FK9 4LA, UK. abb@cs.stir.ac.uk.|Department of Computer Science, University of Torino, Torino, Italy.|Laboratoire de Biométrie et Biologie Evolutive, University Claude Bernard, Lyon, France.|Center for Bioinformatics, Saarland University, Saarland, Germany.|Department of Computer Science, University of Pisa, Pisa, Italy.|Institute of Biomedical Technologies, National Research Council, Milan, Italy.|Department of Computer Science, University of Pisa, Pisa, Italy.', 'United Kingdom|Italy|France|Germany|Italy|Italy|Italy', 0, 'Algorithms|Computational Biology|Genetics, Population|Genome, Human|Genomics|Haplotypes|High-Throughput Nucleotide Sequencing|Humans|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-016-1170-y', '28185544', 'Haplotype phasing is an important problem in the analysis of genomics information. Given a set of DNA fragments of an individual, it consists of determining which one of the possible alleles (alternative forms of a gene) each fragment comes from. Haplotype information is relevant to gene regulation, epigenetics, genome-wide association studies, evolutionary and population studies, and the study of mutations. Haplotyping is currently addressed as an optimisation problem aiming at solutions that minimise, for instance, error correction costs, where costs are a measure of the confidence in the accuracy of the information acquired from DNA sequencing. Solutions have typically an exponential computational complexity. WHATSHAP is a recent optimal approach which moves computational complexity from DNA fragment length to fragment overlap, i.e., coverage, and is hence of particular interest when considering sequencing technologys current trends that are producing longer fragments.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97674, 2016, 'bmc', 'MiRNATIP: a SOM-based miRNA-target interactions predictor', 'Fiannaca, Antonino|Rosa, Massimo La|Paglia, Laura La|Rizzo, Riccardo|Urso, Alfonso', 'National Research Council of Italy, ICAR-CNR, via Ugo La Malfa 153, Palermo, 90146, Italy. fiannaca@pa.icar.cnr.it.|National Research Council of Italy, ICAR-CNR, via Ugo La Malfa 153, Palermo, 90146, Italy.|National Research Council of Italy, ICAR-CNR, via Ugo La Malfa 153, Palermo, 90146, Italy.|National Research Council of Italy, ICAR-CNR, via Ugo La Malfa 153, Palermo, 90146, Italy.|National Research Council of Italy, ICAR-CNR, via Ugo La Malfa 153, Palermo, 90146, Italy.', 'Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Animals|Artificial Intelligence|Binding Sites|Caenorhabditis elegans|Computational Biology|Humans|MicroRNAs|RNA, Messenger|Software', 'Journal Article', '10.1186/s12859-016-1171-x', '28185545', 'MicroRNAs (miRNAs) are small non-coding RNA sequences with regulatory functions to post-transcriptional level for several biological processes, such as cell disease progression and metastasis. MiRNAs interact with target messenger RNA (mRNA) genes by base pairing. Experimental identification of miRNA target is one of the major challenges in cancer biology because miRNAs can act as tumour suppressors or oncogenes by targeting different type of targets. The use of machine learning methods for the prediction of the target genes is considered a valid support to investigate miRNA functions and to guide related wet-lab experiments. In this paper we propose the miRNA Target Interaction Predictor (miRNATIP) algorithm, a Self-Organizing Map (SOM) based method for the miRNA target prediction. SOM is trained with the seed region of the miRNA sequences and then the mRNA sequences are projected into the SOM lattice in order to find putative interactions with miRNAs. These interactions will be filtered considering the remaining part of the miRNA sequences and estimating the free-energy necessary for duplex stability.'),
(97675, 2016, 'bmc', 'A novel scan statistics approach for clustering identification and comparison in binary genomic data', 'Pellin, Danilo|Di Serio, Clelia', 'University Center of Statistics for the Biomedical Sciences, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy. pellin.danilo@hsr.it.|University Center of Statistics for the Biomedical Sciences, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy.', 'Italy|Italy', 1, 'Algorithms|Cluster Analysis|Data Interpretation, Statistical|Genomics|HIV|Hematopoietic Stem Cells|Humans|Leukemia Virus, Murine|Likelihood Functions|Viral Proteins|Virus Integration', 'Journal Article', '10.1186/s12859-016-1173-8', '28185547', 'In biomedical research a relevant issue is to identify time intervals or portions of a n-dimensional support where a particular event of interest is more likely to occur than expected. Algorithms that require to specify a-priori number/dimension/length of clusters assumed for the data suffer from a high degree of arbitrariness whenever no precise information are available, and this may strongly affect final estimation on parameters. Within this framework, spatial scan-statistics have been proposed in the literature, representing a valid non-parametric alternative.'),
(97676, 2016, 'bmc', 'BITS 2015: the annual meeting of the Italian Society of Bioinformatics', 'Milanesi, Luciano|Guffanti, Alessandro|Mauri, Giancarlo|Masseroli, Marco', 'CNR, Institute of Biomedical Technology, Milan, Italy. luciano.milanesi@itb.cnr.it.|Genomnia srl, Milan, Italy.|University of Milano-Bicocca, Milan, Italy.|Politecnico di Milano, Milan, Italy.', 'Italy|Italy|Italy|Italy', 0, 'Computational Biology|Humans|Italy|Peer Review|Publications', 'Editorial', '10.1186/s12859-016-1187-2', '28185548', '-'),
(97677, 2016, 'bmc', 'Correcting the impact of docking pose generation error on binding affinity prediction', 'Li, Hongjian|Leung, Kwong-Sak|Wong, Man-Hon|Ballester, Pedro J', 'Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China.|Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China.|Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China.|Cancer Research Center of Marseille, INSERM U1068, Marseille, F-13009, France. pedro.ballester@inserm.fr.', 'China|China|China|France', 16, 'Humans|Ligands|Molecular Docking Simulation|Nuclear Proteins|Protein Binding|Protein Conformation|Pyrazines|Software|Transcription Factors', 'Journal Article', '10.1186/s12859-016-1169-4', '28185549', 'Pose generation error is usually quantified as the difference between the geometry of the pose generated by the docking software and that of the same molecule co-crystallised with the considered protein. Surprisingly, the impact of this error on binding affinity prediction is yet to be systematically analysed across diverse protein-ligand complexes.'),
(97678, 2016, 'bmc', 'Protein complex prediction for large protein protein interaction networks with the Core&Peel method', 'Pellegrini, Marco|Baglioni, Miriam|Geraci, Filippo', 'Laboratory for Integrative Systems Medicine - Istituto di Informatica e Telematica and Istituto di Fisiologia Clinica del CNR, via Moruzzi 1, Pisa, 56124, Italy. m.pellegrini@iit.cnr.it.|Laboratory for Integrative Systems Medicine - Istituto di Informatica e Telematica and Istituto di Fisiologia Clinica del CNR, via Moruzzi 1, Pisa, 56124, Italy.|Laboratory for Integrative Systems Medicine - Istituto di Informatica e Telematica and Istituto di Fisiologia Clinica del CNR, via Moruzzi 1, Pisa, 56124, Italy.', 'Italy|Italy|Italy', 14, 'Algorithms|Cluster Analysis|Humans|Protein Binding|Protein Interaction Mapping|Protein Interaction Maps|Proteins|Proteome|Saccharomyces cerevisiae', 'Evaluation Studies', '10.1186/s12859-016-1191-6', '28185552', 'Biological networks play an increasingly important role in the exploration of functional modularity and cellular organization at a systemic level. Quite often the first tools used to analyze these networks are clustering algorithms. We concentrate here on the specific task of predicting protein complexes (PC) in large protein-protein interaction networks (PPIN). Currently, many state-of-the-art algorithms work well for networks of small or moderate size. However, their performance on much larger networks, which are becoming increasingly common in modern proteome-wise studies, needs to be re-assessed.'),
(97679, 2016, 'bmc', 'Removing duplicate reads using graphics processing units', 'Manconi, Andrea|Moscatelli, Marco|Armano, Giuliano|Gnocchi, Matteo|Orro, Alessandro|Milanesi, Luciano', 'Institute for Biomedical Technologies, National Research Council, Via Fratelli Cervi, 93, Segrate (Mi), 20090, Italy. andrea.manconi@itb.cnr.it.|Institute for Biomedical Technologies, National Research Council, Via Fratelli Cervi, 93, Segrate (Mi), 20090, Italy.|Department of Electrical and Electronic Engineering, University of Cagliari, P.zza DArmi, Cagliari (CA), 09123, Italy.|Institute for Biomedical Technologies, National Research Council, Via Fratelli Cervi, 93, Segrate (Mi), 20090, Italy.|Institute for Biomedical Technologies, National Research Council, Via Fratelli Cervi, 93, Segrate (Mi), 20090, Italy.|Institute for Biomedical Technologies, National Research Council, Via Fratelli Cervi, 93, Segrate (Mi), 20090, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Computational Biology|DNA|Polymerase Chain Reaction|Sequence Analysis, DNA', 'Evaluation Studies', '10.1186/s12859-016-1192-5', '28185553', 'During library construction polymerase chain reaction is used to enrich the DNA before sequencing. Typically, this process generates duplicate read sequences. Removal of these artifacts is mandatory, as they can affect the correct interpretation of data in several analyses. Ideally, duplicate reads should be characterized by identical nucleotide sequences. However, due to sequencing errors, duplicates may also be nearly-identical. Removing nearly-identical duplicates can result in a notable computational effort. To deal with this challenge, we recently proposed a GPU method aimed at removing identical and nearly-identical duplicates generated with an Illumina platform. The method implements an approach based on prefix-suffix comparison. Read sequences with identical prefix are considered potential duplicates. Then, their suffixes are compared to identify and remove those that are actually duplicated. Although the method can be efficiently used to remove duplicates, there are some limitations that need to be overcome. In particular, it cannot to detect potential duplicates in the event that prefixes are longer than 27 bases, and it does not provide support for paired-end read libraries. Moreover, large clusters of potential duplicates are split into smaller with the aim to guarantees a reasonable computing time. This heuristic may affect the accuracy of the analysis.'),
(97680, 2016, 'bmc', 'Optimized pipeline of MuTect and GATK tools to improve the detection of somatic single nucleotide polymorphisms in whole-exome sequencing data', 'do Valle, Ítalo Faria|Giampieri, Enrico|Simonetti, Giorgia|Padella, Antonella|Manfrini, Marco|Ferrari, Anna|Papayannidis, Cristina|Zironi, Isabella|Garonzi, Marianna|Bernardi, Simona|Delledonne, Massimo|Martinelli, Giovanni|Remondini, Daniel|Castellani, Gastone', 'Department of Physics and Astronomy, University of Bologna, Bologna, Italy.|Department of Physics and Astronomy, University of Bologna, Bologna, Italy.|Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy.|Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy.|Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy.|Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy.|Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy.|Department of Physics and Astronomy, University of Bologna, Bologna, Italy.|Department of Biotechnology, University of Verona, Verona, Italy.|Unit of Blood Diseases and Stem Cell Transplantation, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy.|Department of Biotechnology, University of Verona, Verona, Italy.|Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy.|Department of Physics and Astronomy, University of Bologna, Bologna, Italy. daniel.remondini@unibo.it.|Department of Physics and Astronomy, University of Bologna, Bologna, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Exome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Mutation|Neoplasms|Polymorphism, Single Nucleotide|Sensitivity and Specificity|Software', 'Evaluation Studies', '10.1186/s12859-016-1190-7', '28185561', '-'),
(97681, 2016, 'bmc', 'Comparative genomics meets topology: a novel view on genome median and halving problems', 'Alexeev, Nikita|Avdeyev, Pavel|Alekseyev, Max A', 'The George Washington University, Washington, DC, USA. nikita_alexeev@gwu.edu.|The George Washington University, Washington, DC, USA.|The George Washington University, Washington, DC, USA.', 'United States|United States|United States', 5, 'Genome|Genomics|Models, Genetic', 'Journal Article', '10.1186/s12859-016-1263-7', '28185564', 'Genome median and genome halving are combinatorial optimization problems that aim at reconstruction of ancestral genomes by minimizing the number of evolutionary events between them and genomes of the extant species. While these problems have been widely studied in past decades, their solutions are often either not efficient or not biologically adequate. These shortcomings have been recently addressed by restricting the problems solution space.'),
(97682, 2016, 'bmc', 'Reconstructing ancestral gene orders with duplications guided by synteny level genome reconstruction', 'Rajaraman, Ashok|Ma, Jian', 'Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, USA.|Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, USA. jianma@cs.cmu.edu.', 'United States|United States', 0, 'Algorithms|Animals|Evolution, Molecular|Gene Order|Genes, Duplicate|Genome|Humans|Internet|Mammals|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1262-8', '28185565', 'Reconstructing ancestral gene orders in the presence of duplications is important for a better understanding of genome evolution. Current methods for ancestral reconstruction are limited by either computational constraints or the availability of reliable gene trees, and often ignore duplications altogether. Recently, methods that consider duplications in ancestral reconstructions have been developed, but the quality of reconstruction, counted as the number of contiguous ancestral regions found, decreases rapidly with the number of duplicated genes, complicating the application of such approaches to mammalian genomes. However, such high fragmentation is not encountered when reconstructing mammalian genomes at the synteny-block level, although the relative positions of genes in such reconstruction cannot be recovered.'),
(97683, 2016, 'bmc', 'A continuous analog of run length distributions reflecting accumulated fractionation events', 'Yu, Zhe|Sankoff, David', 'Department of Mathematics and Statistics, University of Ottawa, 585 King Edward Avenue, Ottawa, Ontario, K1N 6N5, Canada.|Department of Mathematics and Statistics, University of Ottawa, 585 King Edward Avenue, Ottawa, Ontario, K1N 6N5, Canada. sankoff@uottawa.ca.', 'Canada|Canada', 0, 'Gene Deletion|Gene Duplication|Genomics|Models, Theoretical', 'Journal Article', '10.1186/s12859-016-1265-5', '28185566', 'We propose a new, continuous model of the fractionation process (duplicate gene deletion after polyploidization) on the real line. The aim is to infer how much DNA is deleted at a time, based on segment lengths for alternating deleted (invisible) and undeleted (visible) regions.'),
(97684, 2016, 'bmc', 'From comorbidities of chronic obstructive pulmonary disease to identification of shared molecular mechanisms by data integration', 'Gomez-Cabrero, David|Menche, Jörg|Vargas, Claudia|Cano, Isaac|Maier, Dieter|Barabási, Albert-László|Tegnér, Jesper|Roca, Josep|Synergy-COPD Consortia', 'Department of Medicine, Karolinska Institutet, Unit of Computational Medicine, Stockholm, 171 77, Sweden. david.gomezcabrero@ki.se.|Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, USA.|Institut dInvestigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clinic de Barcelona, Universitat de Barcelona, Barcelona, Spain.|Institut dInvestigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clinic de Barcelona, Universitat de Barcelona, Barcelona, Spain.|Biomax Informatics AG, Planegg, Germany.|Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, USA.|Department of Medicine, Karolinska Institutet, Unit of Computational Medicine, Stockholm, 171 77, Sweden.|Institut dInvestigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clinic de Barcelona, Universitat de Barcelona, Barcelona, Spain. jroca@clinic.ub.es.|Institut dInvestigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clinic de Barcelona, Universitat de Barcelona, Barcelona, Spain. jroca@clinic.ub.es.', 'Sweden|United States|Spain|Spain|Germany|United States|Sweden|Spain|Spain', 0, 'Aging|Biomarkers|Chromosome Mapping|Cluster Analysis|Comorbidity|Databases, Factual|Disease Progression|Humans|Life Style|Principal Component Analysis|Pulmonary Disease, Chronic Obstructive|Risk|Smoking', 'Journal Article', '10.1186/s12859-016-1291-3', '28185567', 'Deep mining of healthcare data has provided maps of comorbidity relationships between diseases. In parallel, integrative multi-omics investigations have generated high-resolution molecular maps of putative relevance for understanding disease initiation and progression. Yet, it is unclear how to advance an observation of comorbidity relations (one disease to others) to a molecular understanding of the driver processes and associated biomarkers.'),
(97685, 2016, 'bmc', 'Identification of expression patterns in the progression of disease stages by integration of transcriptomic data', 'Aibar, Sara|Abaigar, Maria|Campos-Laborie, Francisco Jose|Sánchez-Santos, Jose Manuel|Hernandez-Rivas, Jesus M|De Las Rivas, Javier', 'Bioinformatics and Functional Genomics research group, Cancer Research Center (IMBCC, CSIC/USAL) and Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca, Spain.|Unidad de Diagnóstico Molecular y Celular del Cáncer, Cancer Research Center (IMBCC, CSIC/USAL) and Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca, Spain.|Bioinformatics and Functional Genomics research group, Cancer Research Center (IMBCC, CSIC/USAL) and Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca, Spain.|Bioinformatics and Functional Genomics research group, Cancer Research Center (IMBCC, CSIC/USAL) and Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca, Spain.|Unidad de Diagnóstico Molecular y Celular del Cáncer, Cancer Research Center (IMBCC, CSIC/USAL) and Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca, Spain.|Bioinformatics and Functional Genomics research group, Cancer Research Center (IMBCC, CSIC/USAL) and Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca, Spain. jrivas@usal.es.', 'Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Algorithms|Alzheimer Disease|Cluster Analysis|Colorectal Neoplasms|Databases, Genetic|Disease Progression|Gene Expression Profiling|Humans|Myelodysplastic Syndromes|Neoplasm Staging|Sequence Analysis, RNA|Severity of Illness Index|Transcriptome', 'Journal Article', '10.1186/s12859-016-1290-4', '28185568', 'In the study of complex diseases using genome-wide expression data from clinical samples, a difficult case is the identification and mapping of the gene signatures associated to the stages that occur in the progression of a disease. The stages usually correspond to different subtypes or classes of the disease, and the difficulty to identify them often comes from patient heterogeneity and sample variability that can hide the biomedical relevant changes that characterize each stage, making standard differential analysis inadequate or inefficient.'),
(97686, 2016, 'bmc', 'A comprehensive collection of annotations to interpret sequence variation in human mitochondrial transfer RNAs', 'Diroma, Maria Angela|Lubisco, Paolo|Attimonelli, Marcella', 'Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, Bari, 70126, Italy.|Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, Bari, 70126, Italy.|Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, Bari, 70126, Italy. marcella.attimonelli@uniba.it.', 'Italy|Italy|Italy', 0, 'Base Sequence|Computational Biology|DNA, Mitochondrial|Databases, Genetic|Genetic Variation|Genome, Mitochondrial|Genomics|Humans|Mitochondria|Molecular Sequence Annotation|RNA|RNA, Mitochondrial|RNA, Transfer', 'Journal Article', '10.1186/s12859-016-1193-4', '28185569', 'The abundance of biological data characterizing the genomics era is contributing to a comprehensive understanding of human mitochondrial genetics. Nevertheless, many aspects are still unclear, specifically about the variability of the 22 human mitochondrial transfer RNA (tRNA) genes and their involvement in diseases. The complex enrichment and isolation of tRNAs in vitro leads to an incomplete knowledge of their post-transcriptional modifications and three-dimensional folding, essential for correct tRNA functioning. An accurate annotation of mitochondrial tRNA variants would be definitely useful and appreciated by mitochondrial researchers and clinicians since the most of bioinformatics tools for variant annotation and prioritization available so far cannot shed light on the functional role of tRNA variations.'),
(97687, 2016, 'bmc', 'An analytical upper bound on the number of loci required for all splits of a species tree to appear in a set of gene trees', 'Uricchio, Lawrence H|Warnow, Tandy|Rosenberg, Noah A', 'Department of Biology, Stanford University, Stanford, CA, USA.|Departments of Computer Science & Bioengineering, University of Illinois, Urbana-Champaign, IL, USA.|Department of Biology, Stanford University, Stanford, CA, USA. noahr@stanford.edu.', 'United States|United States|United States', 0, 'Genomics|Models, Genetic|Probability', 'Journal Article', '10.1186/s12859-016-1266-4', '28185570', 'Many methods for species tree inference require data from a sufficiently large sample of genomic loci in order to produce accurate estimates. However, few studies have attempted to use analytical theory to quantify sufficiently large.'),
(97688, 2016, 'bmc', 'In the light of deep coalescence: revisiting trees within networks', 'Zhu, Jiafan|Yu, Yun|Nakhleh, Luay', 'Department of Computer Science, Rice University, Houston, 77005, Texas, USA.|Department of Computer Science, Rice University, Houston, 77005, Texas, USA.|Department of Computer Science, Rice University, Houston, 77005, Texas, USA. nakhleh@rice.edu.', 'United States|United States|United States', 0, 'Algorithms|Biological Evolution|Models, Genetic|Phylogeny', 'Journal Article', '10.1186/s12859-016-1269-1', '28185572', 'Phylogenetic networks model reticulate evolutionary histories. The last two decades have seen an increased interest in establishing mathematical results and developing computational methods for inferring and analyzing these networks. A salient concept underlying a great majority of these developments has been the notion that a network displays a set of trees and those trees can be used to infer, analyze, and study the network.'),
(97689, 2016, 'bmc', 'RGmatch: matching genomic regions to proximal genes in omics data integration', 'Furió-Tarí, Pedro|Conesa, Ana|Tarazona, Sonia', 'Genomics of Gene Expression Laboratory, Gene Expression and Epigenomics Program, Centro de Investigación Príncipe Felipe, Eduardo Primo Yúfera 3, 46012, Valencia, Spain.|Genomics of Gene Expression Laboratory, Gene Expression and Epigenomics Program, Centro de Investigación Príncipe Felipe, Eduardo Primo Yúfera 3, 46012, Valencia, Spain. aconesa@cipf.es.|Genomics of Gene Expression Laboratory, Gene Expression and Epigenomics Program, Centro de Investigación Príncipe Felipe, Eduardo Primo Yúfera 3, 46012, Valencia, Spain. starazona@cipf.es.', 'Spain|Spain|Spain', 0, 'Algorithms|Exons|Genomics|Internet|Promoter Regions, Genetic|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1293-1', '28185573', 'The integrative analysis of multiple genomics data often requires that genome coordinates-based signals have to be associated with proximal genes. The relative location of a genomic region with respect to the gene (gene area) is important for functional data interpretation, hence algorithms that match regions to genes should be able to deliver insight into this information.'),
(97690, 2016, 'bmc', 'Integration of metabolomics, lipidomics and clinical data using a machine learning method', 'Acharjee, Animesh|Ament, Zsuzsanna|West, James A|Stanley, Elizabeth|Griffin, Julian L', 'Medical Research Council, Elsie Widdowson Laboratory, 120 Fulbourn Road, Cambridge, CB1 9NL, UK.|Medical Research Council, Elsie Widdowson Laboratory, 120 Fulbourn Road, Cambridge, CB1 9NL, UK.|Medical Research Council, Elsie Widdowson Laboratory, 120 Fulbourn Road, Cambridge, CB1 9NL, UK.|Medical Research Council, Elsie Widdowson Laboratory, 120 Fulbourn Road, Cambridge, CB1 9NL, UK.|Medical Research Council, Elsie Widdowson Laboratory, 120 Fulbourn Road, Cambridge, CB1 9NL, UK. Jules.Griffin@mrc-hnr.cam.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 24, 'Animals|Blood Chemical Analysis|Databases, Factual|Humans|Lipid Metabolism|Liver|Machine Learning|Male|Metabolic Syndrome|Metabolomics|Obesity|PPAR alpha|PPAR gamma|Rats|Rats, Sprague-Dawley', 'Journal Article', '10.1186/s12859-016-1292-2', '28185575', 'The recent pandemic of obesity and the metabolic syndrome (MetS) has led to the realisation that new drug targets are needed to either reduce obesity or the subsequent pathophysiological consequences associated with excess weight gain. Certain nuclear hormone receptors (NRs) play a pivotal role in lipid and carbohydrate metabolism and have been highlighted as potential treatments for obesity. This realisation started a search for NR agonists in order to understand and successfully treat MetS and associated conditions such as insulin resistance, dyslipidaemia, hypertension, hypertriglyceridemia, obesity and cardiovascular disease. The most studied NRs for treating metabolic diseases are the peroxisome proliferator-activated receptors (PPARs), PPAR-xcexb1, PPAR-xcexb3, and PPAR-xcexb4. However, prolonged PPAR treatment in animal models has led to adverse side effects including increased risk of a number of cancers, but how these receptors change metabolism long term in terms of pathology, despite many beneficial effects shorter term, is not fully understood. In the current study, changes in male Sprague Dawley rat liver caused by dietary treatment with a PPAR-pan (PPAR-xcexb1, -xcexb3, and -xcexb4) agonist were profiled by classical toxicology (clinical chemistry) and high throughput metabolomics and lipidomics approaches using mass spectrometry.'),
(97691, 2016, 'bmc', 'Var2GO: a web-based tool for gene variants selection', 'Granata, Ilaria|Sangiovanni, Mara|Maiorano, Francesco|Miele, Marco|Guarracino, Mario Rosario', 'High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy. ilaria.granata@na.icar.cnr.it.|High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy.|High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy.|High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy.|High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy.', 'Italy|Italy|Italy|Italy|Italy', 0, 'Computational Biology|Genetic Variation|High-Throughput Nucleotide Sequencing|Humans|Internet|Proteins|Software', 'Evaluation Studies', '10.1186/s12859-016-1197-0', '28185576', 'One of the most challenging issue in the variant calling process is handling the resulting data, and filtering the genes retaining only the ones strictly related to the topic of interest. Several tools permit to gather annotations at different levels of complexity for the detected genes and to group them according to the pathways and/or processes they belong to. However, it might be a time consuming and frustrating task. This is partly due to the size of the file, that might contain many thousands of genes, and to the search of associated variants that requires a gene-by-gene investigation and annotation approach. As a consequence, the initial gene list is often reduced exploiting the knowledge of variants effect, novelty and genotype, with the potential risk of losing meaningful pieces of information.'),
(97692, 2016, 'bmc', 'Artificial neural network classifier predicts neuroblastoma patients outcome', 'Cangelosi, Davide|Pelassa, Simone|Morini, Martina|Conte, Massimo|Bosco, Maria Carla|Eva, Alessandra|Sementa, Angela Rita|Varesio, Luigi', 'Laboratory of Molecular Biology, Gaslini Institute, Largo G. Gaslini 5, 16147, Genoa, Italy.|Laboratory of Molecular Biology, Gaslini Institute, Largo G. Gaslini 5, 16147, Genoa, Italy.|Laboratory of Molecular Biology, Gaslini Institute, Largo G. Gaslini 5, 16147, Genoa, Italy.|Department of Hematology-Oncology, Gaslini Institute, Largo G. Gaslini 5, 16147, Genoa, Italy.|Laboratory of Molecular Biology, Gaslini Institute, Largo G. Gaslini 5, 16147, Genoa, Italy.|Laboratory of Molecular Biology, Gaslini Institute, Largo G. Gaslini 5, 16147, Genoa, Italy.|Department of Pathology, Gaslini Institute, Largo G. Gaslini 5, 16147, Genoa, Italy.|Laboratory of Molecular Biology, Gaslini Institute, Largo G. Gaslini 5, 16147, Genoa, Italy. luigivaresio@gaslini.org.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Female|Gene Expression Profiling|Humans|Hypoxia|Infant|Kaplan-Meier Estimate|Male|Neural Networks (Computer)|Neuroblastoma|Oxygen', 'Journal Article', '10.1186/s12859-016-1194-3', '28185577', 'More than fifty percent of neuroblastoma (NB) patients with adverse prognosis do not benefit from treatment making the identification of new potential targets mandatory. Hypoxia is a condition of low oxygen tension, occurring in poorly vascularized tissues, which activates specific genes and contributes to the acquisition of the tumor aggressive phenotype. We defined a gene expression signature (NB-hypo), which measures the hypoxic status of the neuroblastoma tumor. We aimed at developing a classifier predicting neuroblastoma patients outcome based on the assessment of the adverse effects of tumor hypoxia on the progression of the disease.'),
(97693, 2016, 'bmc', 'Fast ancestral gene order reconstruction of genomes with unequal gene content', 'Feijão, Pedro|Araujo, Eloi', 'Technische Fakultät and CeBiTec, Universität Bielefeld, Universitätsstr. 25, Bielefeld, 33615, Germany. pfeijao@cebitec.uni-bielefeld.de.|Technische Fakultät and CeBiTec, Universität Bielefeld, Universitätsstr. 25, Bielefeld, 33615, Germany.', 'Germany|Germany', 0, 'Algorithms|Evolution, Molecular|Gene Order|Genome|Internet|Models, Genetic|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1261-9', '28185578', 'During evolution, genomes are modified by large scale structural events, such as rearrangements, deletions or insertions of large blocks of DNA. Of particular interest, in order to better understand how this type of genomic evolution happens, is the reconstruction of ancestral genomes, given a phylogenetic tree with extant genomes at its leaves. One way of solving this problem is to assume a rearrangement model, such as Double Cut and Join (DCJ), and find a set of ancestral genomes that minimizes the number of events on the input tree. Since this problem is NP-hard for most rearrangement models, exact solutions are practical only for small instances, and heuristics have to be used for larger datasets. This type of approach can be called event-based. Another common approach is based on finding conserved structures between the input genomes, such as adjacencies between genes, possibly also assigning weights that indicate a measure of confidence or probability that this particular structure is present on each ancestral genome, and then finding a set of non conflicting adjacencies that optimize some given function, usually trying to maximize total weight and minimizing character changes in the tree. We call this type of methods homology-based.'),
(97694, 2016, 'bmc', 'A fuzzy method for RNA-Seq differential expression analysis in presence of multireads', 'Consiglio, Arianna|Mencar, Corrado|Grillo, Giorgio|Marzano, Flaviana|Caratozzolo, Mariano Francesco|Liuni, Sabino', 'Institute for Biomedical Technologies of Bari - ITB, National Research Council, Bari, 70126, Italy. arianna.consiglio@ba.itb.cnr.it.|Department of Informatics, University of Bari Aldo Moro, Bari, 70121, Italy.|Institute for Biomedical Technologies of Bari - ITB, National Research Council, Bari, 70126, Italy.|Institute for Biomedical Technologies of Bari - ITB, National Research Council, Bari, 70126, Italy.|Institute for Biomedical Technologies of Bari - ITB, National Research Council, Bari, 70126, Italy.|Institute for Biomedical Technologies of Bari - ITB, National Research Council, Bari, 70126, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 9, 'Computational Biology|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|RNA|Reproducibility of Results|Sequence Analysis, RNA|Software', 'Evaluation Studies', '10.1186/s12859-016-1195-2', '28185579', 'When the reads obtained from high-throughput RNA sequencing are mapped against a reference database, a significant proportion of them - known as multireads - can map to more than one reference sequence. These multireads originate from gene duplications, repetitive regions or overlapping genes. Removing the multireads from the mapping results, in RNA-Seq analyses, causes an underestimation of the read counts, while estimating the real read count can lead to false positives during the detection of differentially expressed sequences.'),
(97695, 2016, 'bmc', 'On the consistency of orthology relationships', 'Jones, Mark|Paul, Christophe|Scornavacca, Céline', 'LIRMM, CNRS, Université de Montpellier, Montpellier, France.|ISE-M, CNRS, IRD, EPHE, Université, Montpellier, France.|ISE-M, CNRS, IRD, EPHE, Université, Montpellier, France. celine.scornavacca@umontpellier.fr.', 'France|France|France', 1, 'Algorithms|Genomics|Internet|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1267-3', '28185580', 'Orthologs inference is the starting point of most comparative genomics studies, and a plethora of methods have been designed in the last decade to address this challenging task. In this paper we focus on the problems of deciding consistency with a species tree (known or not) of a partial set of orthology/paralogy relationships [Formula: see text] on a collection of n genes.'),
(97696, 2016, 'bmc', 'Genome rearrangements with indels in intergenes restrict the scenario space', 'Bulteau, Laurent|Fertin, Guillaume|Tannier, Eric', 'Laboratoire dInformatique Gaspard Monge, CNRS UMR 8049, Université Paris-Est, 5 Bd Descartes, Marne-la-Vallée, 77454, France. laurent.bulteau@u-pem.fr.|LINA UMR CNRS 6241, Université de Nantes, 2 rue de la Houssinière, Nantes, 44322, France.|Laboratoire de Biométrie et Biologie Évolutive (LBBE), 43 boulevard du 11 novembre 1918, Villeurbanne, 69622, France.', 'France|France|France', 0, 'Algorithms|Gene Rearrangement|Genome|INDEL Mutation|Models, Genetic', 'Journal Article', '10.1186/s12859-016-1264-6', '28185582', 'Given two genomes that have diverged by a series of rearrangements, we infer minimum Double Cut-and-Join (DCJ) scenarios to explain their organization differences, coupled with indel scenarios to explain their intergene size distribution, where DCJs themselves also alter the sizes of broken intergenes.'),
(97697, 2016, 'bmc', 'Probabilistic inference of lateral gene transfer events', 'Khan, Mehmood Alam|Mahmudi, Owais|Ullah, Ikram|Arvestad, Lars|Lagergren, Jens', 'KTH Royal Institute of Technology, School of Computer Science and Communication, Box 1031, Solna, 171 21, Sweden.|KTH Royal Institute of Technology, School of Computer Science and Communication, Box 1031, Solna, 171 21, Sweden.|KTH Royal Institute of Technology, School of Computer Science and Communication, Box 1031, Solna, 171 21, Sweden.|Science for Life Laboratory, Box 1031, Solna, 171 21, Sweden.|KTH Royal Institute of Technology, School of Computer Science and Communication, Box 1031, Solna, 171 21, Sweden. jensl@kth.se.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Biological Evolution|Entomoplasmataceae|Gene Transfer, Horizontal|Models, Genetic|Phylogeny', 'Journal Article', '10.1186/s12859-016-1268-2', '28185583', 'Lateral gene transfer (LGT) is an evolutionary process that has an important role in biology. It challenges the traditional binary tree-like evolution of species and is attracting increasing attention of the molecular biologists due to its involvement in antibiotic resistance. A number of attempts have been made to model LGT in the presence of gene duplication and loss, but reliably placing LGT events in the species tree has remained a challenge.'),
(97698, 2016, 'bmc', 'How interacting pathways are regulated by miRNAs in breast cancer subtypes', 'Cava, Claudia|Colaprico, Antonio|Bertoli, Gloria|Bontempi, Gianluca|Mauri, Giancarlo|Castiglioni, Isabella', 'Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), Milan, Italy.|Interuniversity Institute of Bioinformatics in Brussels (IB), Brussels, Belgium.|Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), Milan, Italy.|Interuniversity Institute of Bioinformatics in Brussels (IB), Brussels, Belgium.|Department of Informatics, Systems and Communications, University of Milan-Bicocca, Milan, Italy.|Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), Milan, Italy. isabella.castiglioni@ibfm.cnr.it.', 'Italy|Belgium|Italy|Belgium|Italy|Italy', 15, 'Breast Neoplasms|Female|Gene Expression Profiling|Gene Regulatory Networks|Genomics|Humans|MicroRNAs', 'Evaluation Studies', '10.1186/s12859-016-1196-1', '28185585', 'An important challenge in cancer biology is to understand the complex aspects of the disease. It is increasingly evident that genes are not isolated from each other and the comprehension of how different genes are related to each other could explain biological mechanisms causing diseases. Biological pathways are important tools to reveal gene interaction and reduce the large number of genes to be studied by partitioning it into smaller paths. Furthermore, recent scientific evidence has proven that a combination of pathways, instead than a single element of the pathway or a single pathway, could be responsible for pathological changes in a cell.'),
(97699, 2017, 'bmc', 'iHMS: a database integrating human histone modification data across developmental stages and tissues', 'Gan, Yanglan|Tao, Han|Guan, Jihong|Zhou, Shuigeng', 'School of Computer Science and Technology, Donghua University, Shanghai, China.|Department of Computer Science and Technology, Tongji University, Shanghai, China.|Department of Computer Science and Technology, Tongji University, Shanghai, China. jhguan@tongji.edu.cn.|Shanghai Key Lab of Intelligent Information Processing and School of Computer Science, Fudan University, Shanghai, China.', 'China|China|China|China', 0, 'Chromatin|CpG Islands|Databases, Genetic|Histones|Humans|Internet|Protein Processing, Post-Translational|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1461-y', '28187703', 'Differences in chromatin states are critical to the multiplicity of cell states. Recently genome-wide histone modification maps of diverse human developmental stages and tissues have been charted.'),
(97700, 2017, 'bmc', 'Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO', 'Zuo, Yiming|Cui, Yi|Yu, Guoqiang|Li, Ruijiang|Ressom, Habtom W', 'Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, 22203, VA, USA.|Department of Radiation Oncology, Stanford University, Palo Alto, 94304, CA, USA.|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, 22203, VA, USA.|Department of Radiation Oncology, Stanford University, Palo Alto, 94304, CA, USA.|Lombardi Comprehensive Cancer Center, Georgetown University, Washington, 20007, DC, USA. hwr@georgetown.edu.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Area Under Curve|Biomarkers|Breast Neoplasms|Carcinoma, Hepatocellular|Female|Gene Expression Profiling|Gene Regulatory Networks|Humans|Liver Neoplasms|RNA|ROC Curve|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-017-1515-1', '28187708', 'Conventional differential gene expression analysis by methods such as students t-test, SAM, and Empirical Bayes often searches for statistically significant genes without considering the interactions among them. Network-based approaches provide a natural way to study these interactions and to investigate the rewiring interactions in disease versus control groups. In this paper, we apply weighted graphical LASSO (wgLASSO) algorithm to integrate a data-driven network model with prior biological knowledge (i.e., protein-protein interactions) for biological network inference. We propose a novel differentially weighted graphical LASSO (dwgLASSO) algorithm that builds group-specific networks and perform network-based differential gene expression analysis to select biomarker candidates by considering their topological differences between the groups.'),
(97701, 2017, 'bmc', 'The Drosophila Gene Expression Tool (DGET) for expression analyses', 'Hu, Yanhui|Comjean, Aram|Perrimon, Norbert|Mohr, Stephanie E', 'Drosophila RNAi Screening Center, Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA. yanhui_hu@hms.harvard.edu.|Drosophila RNAi Screening Center, Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.|Drosophila RNAi Screening Center, Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.|Drosophila RNAi Screening Center, Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.', 'United States|United States|United States|United States', 0, 'Animals|Digestive System|Drosophila|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Internet|RNA|Sequence Analysis, RNA|Transcriptome|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1509-z', '28187709', 'Next-generation sequencing technologies have greatly increased our ability to identify gene expression levels, including at specific developmental stages and in specific tissues. Gene expression data can help researchers understand the diverse functions of genes and gene networks, as well as help in the design of specific and efficient functional studies, such as by helping researchers choose the most appropriate tissue for a study of a group of genes, or conversely, by limiting a long list of gene candidates to the subset that are normally expressed at a given stage or in a given tissue.'),
(97702, 2017, 'bmc', 'VMCMC: a graphical and statistical analysis tool for Markov chain Monte Carlo traces', 'Ali, Raja H|Bark, Mikael|Miró, Jorge|Muhammad, Sayyed A|Sjöstrand, Joel|Zubair, Syed M|Abbas, Raja M|Arvestad, Lars', 'KTH Royal Institute of Technology, Swedish e-Science Research Centre, Science for Life Laboratory, School of Computer Science and Communication, Solna, SE-171 77, Sweden.|KTH Royal Institute of Technology, School of Information and Communication Technology, Kista, SE-164 40, Sweden.|KTH Royal Institute of Technology, School of Information and Communication Technology, Kista, SE-164 40, Sweden.|KTH Royal Institute of Technology, Swedish e-Science Research Centre, Science for Life Laboratory, School of Computer Science and Communication, Solna, SE-171 77, Sweden.|Department of Numerical Analysis and Computer Science, Swedish e-Science Research Centre, Science for Life Laboratory, Stockholm University, Stockholm, SE-100 44, Sweden.|KTH Royal Institute of Technology, Laboratory for Communication Networks, School of Electrical Engineering, Stockholm, SE-100 44, Sweden.|Department of Computer Science and Engineering, University of Gothenburg, Gothenburg, SE-411 37, Sweden.|Department of Numerical Analysis and Computer Science, Swedish e-Science Research Centre, Science for Life Laboratory, Stockholm University, Stockholm, SE-100 44, Sweden. arve@nada.su.se.', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 2, 'Markov Chains|Monte Carlo Method|Phylogeny|Software', 'Journal Article', '10.1186/s12859-017-1505-3', '28187712', 'MCMC-based methods are important for Bayesian inference of phylogeny and related parameters. Although being computationally expensive, MCMC yields estimates of posterior distributions that are useful for estimating parameter values and are easy to use in subsequent analysis. There are, however, sometimes practical difficulties with MCMC, relating to convergence assessment and determining burn-in, especially in large-scale analyses. Currently, multiple software are required to perform, e.g., convergence, mixing and interactive exploration of both continuous and tree parameters.'),
(97703, 2017, 'bmc', 'Novel methods to optimize gene and statistic test for evaluation - an application for Escherichia coli', 'Tuan-Anh, Tran|Ly, Le Thi|Viet, Ngo Quoc|Bao, Pham The', 'Faculty of Mathematics and Computer Science, VNUHCM-University of Science, 227 Nguyen Van Cu Street, District 5, Ho Chi Minh City, Vietnam.|School of Biotechnology, VNUHCM-International University, Quarter 6, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam.|Faculty of Information Technology, Ho Chi Minh City University of Pedagogy, 280 An Duong Vuong Street, Ward 4, District 5, Ho Chi Minh City, Vietnam.|Faculty of Mathematics and Computer Science, VNUHCM-University of Science, 227 Nguyen Van Cu Street, District 5, Ho Chi Minh City, Vietnam. ptbao@hcmus.edu.vn.', '-|-|-|-', 0, 'Algorithms|Base Composition|Bayes Theorem|Codon|Escherichia coli|Gene Expression|Linear Models|Recombinant Proteins', 'Journal Article', '10.1186/s12859-017-1517-z', '28187713', 'Since the recombinant protein was discovered, it has become more popular in many aspects of life science. The value of global pharmaceutical market was $87 billion in 2008 and the sales for industrial enzyme exceeded $4 billion in 2012. This is strong evidence showing the great potential of recombinant protein. However, native genes introduced into a host can cause incompatibility of codon usage bias, GC content, repeat region, Shine-Dalgarno sequence with hosts expression system, so the yields can fall down significantly. Hence, we propose novel methods for gene optimization based on neural network, Bayesian theory, and Euclidian distance.'),
(97704, 2017, 'bmc', 'TipMT: Identification of PCR-based taxon-specific markers', 'Rodrigues-Luiz, Gabriela F|Cardoso, Mariana S|Valdivia, Hugo O|Ayala, Edward V|Gontijo, Célia M F|Rodrigues, Thiago de S|Fujiwara, Ricardo T|Lopes, Robson S|Bartholomeu, Daniella C', 'Laboratório de Imunologia e Genômica de Parasitos, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.|Laboratório de Imunologia e Genômica de Parasitos, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.|Laboratório de Imunologia e Genômica de Parasitos, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.|Laboratório de Imunologia e Genômica de Parasitos, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.|Centro de Pesquisa René Rachou, Fiocruz, Belo Horizonte, Brazil.|Departamento de Computação, Centro Federal de Educação Tecnológica de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.|Laboratório de Imunologia e Genômica de Parasitos, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.|Laboratório de Imunologia e Genômica de Parasitos, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.|Laboratório de Imunologia e Genômica de Parasitos, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil. daniella@icb.ufmg.br.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 0, 'DNA Primers|Genetic Markers|Genome, Protozoan|Internet|Leishmania|Polymerase Chain Reaction|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1485-3', '28187714', 'Molecular genetic markers are one of the most informative and widely used genome features in clinical and environmental diagnostic studies. A polymerase chain reaction (PCR)-based molecular marker is very attractive because it is suitable to high throughput automation and confers high specificity. However, the design of taxon-specific primers may be difficult and time consuming due to the need to identify appropriate genomic regions for annealing primers and to evaluate primer specificity.');
