INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97705, 2017, 'bmc', 'A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies', 'Teschendorff, Andrew E|Breeze, Charles E|Zheng, Shijie C|Beck, Stephan', 'CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, 200031, Shanghai, China. a.teschendorff@ucl.ac.uk.|Medical Genomics, Paul OGorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, WC1E 6BT, London, United Kingdom.|CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, 200031, Shanghai, China.|Medical Genomics, Paul OGorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, WC1E 6BT, London, United Kingdom.', 'China|United Kingdom|China|United Kingdom', 0, 'Algorithms|Epigenomics|Genetic Markers|Genome-Wide Association Study|Humans', 'Journal Article', '10.1186/s12859-017-1511-5', '28193155', 'Intra-sample cellular heterogeneity presents numerous challenges to the identification of biomarkers in large Epigenome-Wide Association Studies (EWAS). While a number of reference-based deconvolution algorithms have emerged, their potential remains underexplored and a comparative evaluation of these algorithms beyond tissues such as blood is still lacking.'),
(97706, 2017, 'bmc', 'ECDomainMiner: discovering hidden associations between enzyme commission numbers and Pfam domains', 'Alborzi, Seyed Ziaeddin|Devignes, Marie-Dominique|Ritchie, David W', 'Université de Lorraine, LORIA, UMR, Vandœuvre-lès-Nancy, 7503, 54506, France.|Université de Lorraine, LORIA, UMR, Vandœuvre-lès-Nancy, 7503, 54506, France.|Inria Nancy Grand-Es, Villers-lès-Nancy, 54600, France. dave.ritchie@inria.fr.', 'France|France|France', 0, 'Computational Biology|Data Mining|Databases, Protein|Enzymes|Proteins', 'Journal Article', '10.1186/s12859-017-1519-x', '28193156', 'Many entries in the protein data bank (PDB) are annotated to show their component protein domains according to the Pfam classification, as well as their biological function through the enzyme commission (EC) numbering scheme. However, despite the fact that the biological activity of many proteins often arises from specific domain-domain and domain-ligand interactions, current on-line resources rarely provide a direct mapping from structure to function at the domain level. Since the PDB now contains many tens of thousands of protein chains, and since protein sequence databases can dwarf such numbers by orders of magnitude, there is a pressing need to develop automatic structure-function annotation tools which can operate at the domain level.'),
(97707, 2017, 'bmc', 'VacSol: a high throughput in silico pipeline to predict potential therapeutic targets in prokaryotic pathogens using subtractive reverse vaccinology', 'Rizwan, Muhammad|Naz, Anam|Ahmad, Jamil|Naz, Kanwal|Obaid, Ayesha|Parveen, Tamsila|Ahsan, Muhammad|Ali, Amjad', 'Research Center for Modelling and Simulation (RCMS), National University of Sciences and Technology (NUST), H-12, Islamabad, Pakistan.|Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), H-12, Islamabad, Pakistan.|Research Center for Modelling and Simulation (RCMS), National University of Sciences and Technology (NUST), H-12, Islamabad, Pakistan. dr.ahmad.jamil@gmail.com.|Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), H-12, Islamabad, Pakistan.|Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), H-12, Islamabad, Pakistan.|Biosciences Department, COMSATS Institute of Information Technology, Islamabad, Pakistan.|Research Center for Modelling and Simulation (RCMS), National University of Sciences and Technology (NUST), H-12, Islamabad, Pakistan.|Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), H-12, Islamabad, Pakistan. amjaduni@gmail.com.', 'Pakistan|Pakistan|Pakistan|Pakistan|Pakistan|Pakistan|Pakistan|Pakistan', 0, 'Computational Biology|Computer Simulation|Drug Delivery Systems|Genome, Bacterial|Helicobacter pylori|Software|Vaccines', 'Journal Article', '10.1186/s12859-017-1540-0', '28193166', 'With advances in reverse vaccinology approaches, a progressive improvement has been observed in the prediction of putative vaccine candidates. Reverse vaccinology has changed the way of discovery and provides a mean to propose target identification in reduced time and labour. In this regard, high throughput genomic sequencing technologies and supporting bioinformatics tools have greatly facilitated the prompt analysis of pathogens, where various predicted candidates have been found effective against certain infections and diseases. A pipeline, VacSol, is designed here based on a similar approach to predict putative vaccine candidates both rapidly and efficiently.'),
(97708, 2017, 'bmc', 'Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data', 'Yoshida, Kosuke|Yoshimoto, Junichiro|Doya, Kenji', 'Graduate School of Informatics, Kyoto University, Kyoto, Japan. kosuke.yoshida@oist.jp.|Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan.|Neural Computation Unit, Okinawa Institute of Science and Technology, Okinawa, Japan.', 'Japan|Japan|Japan', 14, 'Algorithms|Computational Biology|Databases, Genetic|Models, Biological|Nonlinear Dynamics', 'Journal Article', '10.1186/s12859-017-1543-x', '28196464', 'Advance in high-throughput technologies in genomics, transcriptomics, and metabolomics has created demand for bioinformatics tools to integrate high-dimensional data from different sources. Canonical correlation analysis (CCA) is a statistical tool for finding linear associations between different types of information. Previous extensions of CCA used to capture nonlinear associations, such as kernel CCA, did not allow feature selection or capturing of multiple canonical components. Here we propose a novel method, two-stage kernel CCA (TSKCCA) to select appropriate kernels in the framework of multiple kernel learning.'),
(97709, 2017, 'bmc', 'Improvement of peptide identification with considering the abundance of mRNA and peptide', 'Ma, Chunwei|Xu, Shaohang|Liu, Geng|Liu, Xin|Xu, Xun|Wen, Bo|Liu, Siqi', 'BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Shenzhen, Shenzhen, 518083, China. wenbo@genomics.cn.|BGI-Shenzhen, Shenzhen, 518083, China. siqiliu@genomics.cn.', 'China|China|China|China|China|China|China', 0, 'Algorithms|Databases, Protein|Peptides|Proteomics|RNA, Messenger|Tandem Mass Spectrometry', 'Journal Article', '10.1186/s12859-017-1491-5', '28201984', 'Tandem mass spectrometry (MS/MS) followed by database search is a main approach to identify peptides/proteins in proteomic studies. A lot of effort has been devoted to improve the identification accuracy and sensitivity for peptides/proteins, such as developing advanced algorithms and expanding protein databases.'),
(97710, 2017, 'bmc', 'repo: an R package for data-centered management of bioinformatic pipelines', 'Napolitano, Francesco', 'Telethon Institute of Genetics and Medicine (TIGEM), Via Campi Flegrei 34, Pozzuoli (NA), 80078, Italy. franapoli@gmail.com.', 'Italy', 2, 'Computational Biology|Reproducibility of Results|Software', 'Journal Article', '10.1186/s12859-017-1510-6', '28209127', 'Reproducibility in Data Analysis research has long been a significant concern, particularly in the areas of Bioinformatics and Computational Biology. Towards the aim of developing reproducible and reusable processes, Data Analysis management tools can help giving structure and coherence to complex data flows. Nonetheless, improved software quality comes at the cost of additional design and planning effort, which may become impractical in rapidly changing development environments. I propose that an adjustment of focus from processes to data in the management of Bioinformatic pipelines may help improving reproducibility with minimal impact on preexisting development practices.'),
(97711, 2017, 'bmc', 'Orthograph: a versatile tool for mapping coding nucleotide sequences to clusters of orthologous genes', 'Petersen, Malte|Meusemann, Karen|Donath, Alexander|Dowling, Daniel|Liu, Shanlin|Peters, Ralph S|Podsiadlowski, Lars|Vasilikopoulos, Alexandros|Zhou, Xin|Misof, Bernhard|Niehuis, Oliver', 'Center for Molecular Biodiversity Research, Zoological Research Museum Alexander Koenig, Adenauerallee 160, Bonn, 53113, Germany. mpetersen@uni-bonn.de.|Center for Molecular Biodiversity Research, Zoological Research Museum Alexander Koenig, Adenauerallee 160, Bonn, 53113, Germany.|Center for Molecular Biodiversity Research, Zoological Research Museum Alexander Koenig, Adenauerallee 160, Bonn, 53113, Germany.|Center for Molecular Biodiversity Research, Zoological Research Museum Alexander Koenig, Adenauerallee 160, Bonn, 53113, Germany.|China National GeneBank, BGI-Shenzhen, Shenzhen, China.|Arthropod Department, Zoological Research Museum Alexander Koenig, Adenauerallee 160, Bonn, 53113, Germany.|Institute of Evolutionary Biology and Ecology, Zoology and Evolutionary Biology, University of Bonn, An der Immenburg 1, Bonn, 53121, Germany.|Center for Molecular Biodiversity Research, Zoological Research Museum Alexander Koenig, Adenauerallee 160, Bonn, 53113, Germany.|Beijing Advanced Innovation Center for Food Nutrition and Human Health, China Agricultural University, Beijing, 100193, China.|Center for Molecular Biodiversity Research, Zoological Research Museum Alexander Koenig, Adenauerallee 160, Bonn, 53113, Germany.|Center for Molecular Biodiversity Research, Zoological Research Museum Alexander Koenig, Adenauerallee 160, Bonn, 53113, Germany. o.niehuis@zfmk.de.', 'Germany|Germany|Germany|Germany|China|Germany|Germany|Germany|China|Germany|Germany', 47, 'Animals|Genome|Genomics|Multigene Family|Open Reading Frames|Sequence Analysis, DNA|Sequence Analysis, Protein|Software|Transcriptome|Wasps', 'Journal Article', '10.1186/s12859-017-1529-8', '28209129', 'Orthology characterizes genes of different organisms that arose from a single ancestral gene via speciation, in contrast to paralogy, which is assigned to genes that arose via gene duplication. An accurate orthology assignment is a crucial step for comparative genomic studies. Orthologous genes in two organisms can be identified by applying a so-called reciprocal search strategy, given that complete information of the organisms gene repertoire is available. In many investigations, however, only a fraction of the gene content of the organisms under study is examined (e.g., RNA sequencing). Here, identification of orthologous nucleotide or amino acid sequences can be achieved using a graph-based approach that maps nucleotide sequences to genes of known orthology. Existing implementations of this approach, however, suffer from algorithmic issues that may cause problems in downstream analyses.'),
(97712, 2017, 'bmc', 'MARV: a tool for genome-wide multi-phenotype analysis of rare variants', 'Kaakinen, Marika|Mxc3xa4gi, Reedik|Fischer, Krista|Heikkinen, Jani|Jxc3xa4rvelin, Marjo-Riitta|Morris, Andrew P|Prokopenko, Inga', 'Department of Genomics of Common Disease, Imperial College London, London, W12 0NN, UK. m.kaakinen@imperial.ac.uk.|Estonian Genome Center, University of Tartu, Tartu, 51010, Estonia.|Estonian Genome Center, University of Tartu, Tartu, 51010, Estonia.|Department of Genomics of Common Disease, Imperial College London, London, W12 0NN, UK.|Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, W2 1PG, UK.|Department of Biostatistics, University of Liverpool, Liverpool, L69 3BX, UK.|Department of Genomics of Common Disease, Imperial College London, London, W12 0NN, UK. i.prokopenko@imperial.ac.uk.', 'United Kingdom|Estonia|Estonia|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 6, 'Computational Biology|Genetic Variation|Genome-Wide Association Study|Genotype|Humans|Phenotype|Software', 'Journal Article', '10.1186/s12859-017-1530-2', '28209135', 'Genome-wide association studies have enabled identification of thousands of loci for hundreds of traits. Yet, for most human traits a substantial part of the estimated heritability is unexplained. This and recent advances in technology to produce high-dimensional data cost-effectively have led to method development beyond standard common variant analysis, including single-phenotype rare variant and multi-phenotype common variant analysis, with the latter increasing power for locus discovery and providing suggestions of pleiotropic effects. However, there are currently no optimal methods and tools for the combined analysis of rare variants and multiple phenotypes.'),
(97713, 2017, 'bmc', 'Erratum to: 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', 0, '-', 'Journal Article', '10.1186/s12859-017-1508-0', '28212605', '-'),
(97714, 2017, 'bmc', 'Computing structure-based lipid accessibility of membrane proteins with mp_lipid_acc in RosettaMP', 'Koehler Leman, Julia|Lyskov, Sergey|Bonneau, Richard', 'Center for Computational Biology, Flatiron Institute, 162 Fifth Avenue, New York, NY, 10010, USA. julia.koehler.leman@gmail.com.|Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.|Center for Computational Biology, Flatiron Institute, 162 Fifth Avenue, New York, NY, 10010, USA.', 'United States|United States|United States', 8, 'Algorithms|Computational Biology|Databases, Protein|Lipids|Membrane Proteins|Protein Structure, Secondary|Software|Solvents', 'Journal Article', '10.1186/s12859-017-1541-z', '28219343', 'Membrane proteins are underrepresented in structural databases, which has led to a lack of computational tools and the corresponding inappropriate use of tools designed for soluble proteins. For membrane proteins, lipid accessibility is an essential property. Although programs are available for sequence-based prediction of lipid accessibility and structure-based identification of solvent-accessible surface area, the latter does not distinguish between water accessible and lipid accessible residues in membrane proteins.'),
(97715, 2017, 'bmc', 'Distribution based nearest neighbor imputation for truncated high dimensional data with applications to pre-clinical and clinical metabolomics studies', 'Shah, Jasmit S|Rai, Shesh N|DeFilippis, Andrew P|Hill, Bradford G|Bhatnagar, Aruni|Brock, Guy N', 'Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, 40202, USA. jasmit.shah@louisville.edu.|Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, 40202, USA.|Department of Medicine, Division of Cardiovascular Medicine, Diabetes and Obesity Center, University of Louisville, Louisville, KY, 40202, USA.|Department of Medicine, Division of Cardiovascular Medicine, Diabetes and Obesity Center, University of Louisville, Louisville, KY, 40202, USA.|Department of Medicine, Division of Cardiovascular Medicine, Diabetes and Obesity Center, University of Louisville, Louisville, KY, 40202, USA.|Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, 40202, USA. Guy.Brock@osumc.edu.', 'United States|United States|United States|United States|United States|United States', 16, 'Algorithms|Biomedical Research|Computational Biology|Humans|Metabolomics', 'Journal Article', '10.1186/s12859-017-1547-6', '28219348', 'High throughput metabolomics makes it possible to measure the relative abundances of numerous metabolites in biological samples, which is useful to many areas of biomedical research. However, missing values (MVs) in metabolomics datasets are common and can arise due to both technical and biological reasons. Typically, such MVs are substituted by a minimum value, which may lead to different results in downstream analyses.'),
(97716, 2017, 'bmc', 'Manananggal - a novel viewer for alternative splicing events', 'Barann, Matthias|Zimmer, Ralf|Birzele, Fabian', 'Roche Pharma Research & Early Development, Roche Innovation Center Munich, Nonnenwald 2, 82377, Penzberg, Germany.|Practical Informatics and Bioinformatics Group, Department of Informatics, Ludwig-Maximilians-Universität München, Amalienstrasse 17, D-80333, Munich, Germany.|Roche Pharma Research & Early Development, Roche Innovation Center Basel, Grenzacherstraße 124, 4052, Basel, Switzerland. fabian.birzele@roche.com.', 'Germany|Germany|Switzerland', 3, 'Alternative Splicing|Computational Biology|High-Throughput Nucleotide Sequencing|Internet|RNA Isoforms|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-017-1548-5', '28222683', 'Alternative splicing is an important cellular mechanism that can be analyzed by RNA sequencing. However, identification of splicing events in an automated fashion is error-prone. Thus, further validation is required to select reliable instances of alternative splicing events (ASEs). There are only few tools specifically designed for interactive inspection of ASEs and available visualization approaches can be significantly improved.'),
(97717, 2017, 'bmc', 'ATGC transcriptomics: a web-based application to integrate, explore and analyze de novo transcriptomic data', 'Gonzalez, Sergio|Clavijo, Bernardo|Rivarola, Máximo|Moreno, Patricio|Fernandez, Paula|Dopazo, Joaquín|Paniego, Norma', 'Instituto de Biotecnología, Centro Investigación en Ciencias Veterinarias y Agronómicas (CICVyA) INTA, Hurlingham, Buenos Aires, Argentina. gonzalez.sergio@inta.gob.ar.|Earlham Institute, Norwich Research Park, Norwich, NR4 7UG, UK.|Instituto de Biotecnología, Centro Investigación en Ciencias Veterinarias y Agronómicas (CICVyA) INTA, Hurlingham, Buenos Aires, Argentina. rivarola.maximo@inta.gob.ar.|Instituto de Ingeniería Biomédica, Facultad de Ingeniería, Universidad de Buenos Aires, Buenos Aires, Argentina.|Instituto de Biotecnología, Centro Investigación en Ciencias Veterinarias y Agronómicas (CICVyA) INTA, Hurlingham, Buenos Aires, Argentina.|Computational Genomics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain.|Instituto de Biotecnología, Centro Investigación en Ciencias Veterinarias y Agronómicas (CICVyA) INTA, Hurlingham, Buenos Aires, Argentina.', 'Argentina|United Kingdom|Argentina|Argentina|Argentina|Spain|Argentina', 4, 'Animals|Databases, Genetic|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Internet|Sequence Analysis, RNA|Transcriptome|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1494-2', '28222698', 'In the last years, applications based on massively parallelized RNA sequencing (RNA-seq) have become valuable approaches for studying non-model species, e.g., without a fully sequenced genome. RNA-seq is a useful tool for detecting novel transcripts and genetic variations and for evaluating differential gene expression by digital measurements. The large and complex datasets resulting from functional genomic experiments represent a challenge in data processing, management, and analysis. This problem is especially significant for small research groups working with non-model species.'),
(97718, 2017, 'bmc', 'Exploring the transcriptome of non-model oleaginous microalga Dunaliella tertiolecta through high-throughput sequencing and high performance computing', 'Yao, Lina|Tan, Kenneth Wei Min|Tan, Tin Wee|Lee, Yuan Kun', 'Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117545, Singapore.|Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117545, Singapore.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117596, Singapore.|Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117545, Singapore. yuan_kun_lee@nuhs.edu.sg.', 'Singapore|Singapore|Singapore|Singapore', 4, 'Chlorophyta|Databases, Genetic|Down-Regulation|High-Throughput Nucleotide Sequencing|Molecular Sequence Annotation|Nitrogen|Sequence Analysis, RNA|Software|Transcriptome|Triglycerides|Up-Regulation', 'Journal Article', '10.1186/s12859-017-1551-x', '28228091', 'RNA-Seq technology has received a lot of attention in recent years for microalgal global transcriptomic profiling. It is widely used in transcriptome-wide analysis of gene expression., particularly for microalgal strains with potential as biofuel sources. However, insufficient genomic or transcriptomic information of non-model microalgae has limited the understanding of their regulatory mechanisms and hampered genetic manipulation to enhance biofuel production. As such, an optimal microalgal transcriptomic database construction is a subject of urgent investigation.'),
(97719, 2017, 'bmc', 'SFREEMAP - A simulation-free tool for stochastic mapping', 'Pasqualin, Diego|Barbeitos, Marcos|Silva, Fabiano', 'Departmento de Informática, Universidade Federal do Paraná, Caixa Postal 19081, Curitiba, PR, 81531-980, Brazil. dpasqualin@inf.ufpr.br.|Departmento de Zoologia, Universidade Federal do Paraná, Caixa Postal 19020, Curitiba, PR, 81531-990, Brazil.|Departmento de Informática, Universidade Federal do Paraná, Caixa Postal 19081, Curitiba, PR, 81531-980, Brazil.', 'Brazil|Brazil|Brazil', 0, 'Algorithms|Animals|Anthozoa|Biological Evolution|Phylogeny|Software|Stochastic Processes', 'Journal Article', '10.1186/s12859-017-1554-7', '28228094', 'Stochastic mapping is frequently used in comparative biology to simulate character evolution, enabling the probabilistic computation of statistics such as number of state transitions along a tree and distribution of states in its internal nodes. Common implementations rely on Continuous-time Markov Chain simulations whose parameters are difficult to adjust and subjected to inherent inaccuracy. Thus, researchers must run a large number of simulations in order to obtain adequate estimates. Although execution time tends to be relatively small when simulations are performed on a single tree assumed to be the true topology, it may become an issue if analyses are conducted on several trees, such as the ones that make up posterior distributions obtained via Bayesian phylogenetic inference. Working with such distributions is preferable to working with a single tree, for they allow the integration of phylogenetic uncertainty into parameter estimation. In such cases, detailed character mapping becomes less important than parameter integration across topologies. Here, we present an R-based implementation (SFREEMAP) of an analytical approach to obtain accurate, per-branch expectations of numbers of state transitions and dwelling times. We also introduce an intuitive way of visualizing the results by integrating over the posterior distribution and summarizing the parameters onto a target reference topology (such as a consensus or MAP tree) provided by the user.'),
(97720, 2017, 'bmc', 'A practical exact maximum compatibility algorithm for reconstruction of recent evolutionary history', 'Cherry, Joshua L', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA. jcherry@ncbi.nlm.nih.gov.', 'United States', 7, 'Algorithms|Evolution, Molecular|Genome, Bacterial|Phylogeny|Salmonella enterica|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1520-4', '28231758', 'Maximum compatibility is a method of phylogenetic reconstruction that is seldom applied to molecular sequences. It may be ideal for certain applications, such as reconstructing phylogenies of closely-related bacteria on the basis of whole-genome sequencing.'),
(97721, 2017, 'bmc', 'SEEG assistant: a 3DSlicer extension to support epilepsy surgery', 'Narizzano, Massimo|Arnulfo, Gabriele|Ricci, Serena|Toselli, Benedetta|Tisdall, Martin|Canessa, Andrea|Fato, Marco Massimo|Cardinale, Francesco', 'Department of Informatics, Bioengineering Robotics and System engineering (DIBRIS), University of Genoa, Viale Causa 13, Genova, 16143, Italy.|Department of Informatics, Bioengineering Robotics and System engineering (DIBRIS), University of Genoa, Viale Causa 13, Genova, 16143, Italy. gabriele.arnulfo@gmail.com.|Departement of Physiology Pharmacology and Neuroscience, CUNY Medical School, New York, New York, USA.|Department of Informatics, Bioengineering Robotics and System engineering (DIBRIS), University of Genoa, Viale Causa 13, Genova, 16143, Italy.|Great Ormond Street Hospital for Children NHS Foundation Trust, Great Ormond St, WC1N 3JH, London, UK.|Department of Informatics, Bioengineering Robotics and System engineering (DIBRIS), University of Genoa, Viale Causa 13, Genova, 16143, Italy.|Department of Informatics, Bioengineering Robotics and System engineering (DIBRIS), University of Genoa, Viale Causa 13, Genova, 16143, Italy.|Claudio Munari Center for Epilepsy Surgery, Niguarda Hospital, Milan, Italy.', 'Italy|Italy|United Kingdom|Italy|United Kingdom|Italy|Italy|Italy', 19, 'Adult|Brain|Brain Mapping|Electrodes, Implanted|Electroencephalography|Epilepsy|Female|Humans|Imaging, Three-Dimensional|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1545-8', '28231759', 'In the evaluation of Stereo-Electroencephalography (SEEG) signals, the physicists workflow involves several operations, including determining the position of individual electrode contacts in terms of both relationship to grey or white matter and location in specific brain regions. These operations are (i) generally carried out manually by experts with limited computer support, (ii) hugely time consuming, and (iii) often inaccurate, incomplete, and prone to errors.'),
(97722, 2017, 'bmc', 'Meta-aligner: long-read alignment based on genome statistics', 'Nashta-Ali, Damoon|Aliyari, Ali|Ahmadian Moghadam, Ahmad|Edrisi, Mohammad Amin|Motahari, Seyed Abolfazl|Hossein Khalaj, Babak', 'Department of Electrical Engineering, Sharif University of Technology, Tehran, Azadi, Iran.|Department of Electrical Engineering, Sharif University of Technology, Tehran, Azadi, Iran.|Department of Computer Engineering, Sharif University of Technology, Azadi, Tehran, Iran.|Department of Electrical Engineering, Sharif University of Technology, Tehran, Azadi, Iran.|Department of Computer Engineering, Sharif University of Technology, Azadi, Tehran, Iran.|Department of Electrical Engineering, Sharif University of Technology, Tehran, Azadi, Iran. khalaj@sharif.ir.', '-|-|-|-|-|-', 2, 'Algorithms|DNA|DNA Copy Number Variations|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1518-y', '28231760', 'Current development of sequencing technologies is towards generating longer and noisier reads. Evidently, accurate alignment of these reads play an important role in any downstream analysis. Similarly, reducing the overall cost of sequencing is related to the time consumption of the aligner. The tradeoff between accuracy and speed is the main challenge in designing long read aligners.'),
(97723, 2017, 'bmc', 'stpm: an R package for stochastic process model', 'Zhbannikov, Ilya Y|Arbeev, Konstantin|Akushevich, Igor|Stallard, Eric|Yashin, Anatoliy I', 'Biodemography of Aging Research Unit (BARU) at Social Science Research Institute, Duke University, 2024 W. Main St., Durham, Box 90420, 27705, NC, USA. ilya.zhbannikov@duke.edu.|Biodemography of Aging Research Unit (BARU) at Social Science Research Institute, Duke University, 2024 W. Main St., Durham, Box 90420, 27705, NC, USA.|Biodemography of Aging Research Unit (BARU) at Social Science Research Institute, Duke University, 2024 W. Main St., Durham, Box 90420, 27705, NC, USA.|Biodemography of Aging Research Unit (BARU) at Social Science Research Institute, Duke University, 2024 W. Main St., Durham, Box 90420, 27705, NC, USA.|Biodemography of Aging Research Unit (BARU) at Social Science Research Institute, Duke University, 2024 W. Main St., Durham, Box 90420, 27705, NC, USA.', 'United States|United States|United States|United States|United States', 4, 'Age Factors|Blood Glucose|Heart Diseases|Humans|Internet|Kaplan-Meier Estimate|Models, Theoretical|Stochastic Processes|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1538-7', '28231764', 'The Stochastic Process Model (SPM) represents a general framework for modeling the joint evolution of repeatedly measured variables and time-to-event outcomes observed in longitudinal studies, i.e., SPM relates the stochastic dynamics of variables (e.g., physiological or biological measures) with the probabilities of end points (e.g., death or system failure). SPM is applicable for analyses of longitudinal data in many research areas, however, there are no publicly available software tools that implement this methodology.'),
(97724, 2017, 'bmc', 'BBCAnalyzer: a visual approach to facilitate variant calling', 'Sandmann, Sarah|de Graaf, Aniek O|Dugas, Martin', 'Institute of Medical Informatics, University of Münster, Albert-Schweitzer-Campus 1, Münster, 48149, Germany. sarah.sandmann@uni-muenster.de.|Laboratory Hematology, RadboudUMC, Geert Grooteplein zuid 8, Nijmegen, 6525, Netherlands.|Institute of Medical Informatics, University of Münster, Albert-Schweitzer-Campus 1, Münster, 48149, Germany.', 'Germany|Netherlands|Germany', 0, 'DNA Mutational Analysis|High-Throughput Nucleotide Sequencing|Humans|Mutation|Sequence Alignment|Software', 'Journal Article', '10.1186/s12859-017-1549-4', '28241736', 'Deriving valid variant calling results from raw next-generation sequencing data is a particularly challenging task, especially with respect to clinical diagnostics and personalized medicine. However, when using classic variant calling software, the user usually obtains nothing more than a list of variants that pass the corresponding callers internal filters. Any expected mutations (e.g. hotspot mutations), that have not been called by the software, need to be investigated manually.'),
(97725, 2017, 'bmc', 'MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms', 'Rohart, Florian|Eslami, Aida|Matigian, Nicholas|Bougeard, Stéphanie|Lê Cao, Kim-Anh', 'The University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, 4102, QLD, Australia.|Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC V6Z 1Y6, Canada.|The University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, 4102, QLD, Australia.|French agency for food, environmental and occupational health safety (Anses), Department of Epidemiology, Ploufragan, 22440, France.|The University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, 4102, QLD, Australia. k.lecao@uq.edu.au.', 'Australia|Canada|Australia|France|Australia', 21, 'Gene Expression Profiling|Humans|Multivariate Analysis|Reproducibility of Results|Sample Size', 'Journal Article', '10.1186/s12859-017-1553-8', '28241739', 'Molecular signatures identified from high-throughput transcriptomic studies often have poor reliability and fail to reproduce across studies. One solution is to combine independent studies into a single integrative analysis, additionally increasing sample size. However, the different protocols and technological platforms across transcriptomic studies produce unwanted systematic variation that strongly confounds the integrative analysis results. When studies aim to discriminate an outcome of interest, the common approach is a sequential two-step procedure, unwanted systematic variation removal techniques are applied prior to classification methods.'),
(97726, 2017, 'bmc', 'Differential correlation analysis of glioblastoma reveals immune ceRNA interactions predictive of patient survival', 'Chiu, Yu-Chiao|Wang, Li-Ju|Lu, Tzu-Pin|Hsiao, Tzu-Hung|Chuang, Eric Y|Chen, Yidong', 'Greehey Childrens Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229, USA.|Department of Medical Research, Taichung Veterans General Hospital, No. 1650, Section 4, Taiwan Blvd., Xitun District, Taichung City, 40705, Taiwan.|Department of Public Health, National Taiwan University, Taipei, 10055, Taiwan.|Department of Medical Research, Taichung Veterans General Hospital, No. 1650, Section 4, Taiwan Blvd., Xitun District, Taichung City, 40705, Taiwan. thsiao@vghtc.gov.tw.|Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1, Section 4, Roosevelt Rd., Taipei, 10617, Taiwan. chuangey@ntu.edu.tw.|Greehey Childrens Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229, USA. ChenY8@uthscsa.edu.', 'United States|United States|United States|United States|United States|United States', 9, 'Brain Neoplasms|Chemokine CCL22|Epistasis, Genetic|Glioblastoma|Humans|Interferon Regulatory Factors|Interleukin-2 Receptor beta Subunit|MicroRNAs|RNA, Neoplasm|Survival Analysis', 'Journal Article', '10.1186/s12859-017-1557-4', '28241741', 'Recent studies illuminated a novel role of microRNA (miRNA) in the competing endogenous RNA (ceRNA) interaction: two genes (ceRNAs) can achieve coexpression by competing for a pool of common targeting miRNAs. Individual biological investigations implied ceRNA interaction performs crucial oncogenic/tumor suppressive functions in glioblastoma multiforme (GBM). Yet, a systematic analysis has not been conducted to explore the functional landscape and prognostic significance of ceRNA interaction.'),
(97727, 2017, 'bmc', '3D: diversity, dynamics, differential testing - a proposed pipeline for analysis of next-generation sequencing T cell repertoire data', 'Zhang, Li|Cham, Jason|Paciorek, Alan|Trager, James|Sheikh, Nadeem|Fong, Lawrence', 'Division of Hematology and Oncology, Department of Medicine, UCSF Helen Diller Family Comprehensive Cancer Center, 550 16th Street, 6th Floor, UCSF Box 0981, San Francisco, CA, 94158, USA. li.zhang@ucsf.edu.|Division of Hematology and Oncology, Department of Medicine, University of California, Room HSE301, UCSF Box 1270, 513 Parnassus Ave, San Francisco, CA, 94143-1270, USA.|Department of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, 6th Floor, UCSF Box 0981, San Francisco, CA, 94158, USA.|Research and Development, Nkarta, Inc, 329 Oyster Point Blvd, South San Francisco, CA, 94080, USA.|Department of Research - Translational Biology, Dendreon Pharmaceuticals Inc, 1208 Eastlake Ave E, Seattle, WA, 98102, USA.|Division of Hematology and Oncology, Department of Medicine, University of California, Room HSE301, UCSF Box 1270, 513 Parnassus Ave, San Francisco, CA, 94143-1270, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'High-Throughput Nucleotide Sequencing|Humans|Immunotherapy|Reproducibility of Results|T-Lymphocytes', 'Journal Article', '10.1186/s12859-017-1544-9', '28241742', 'Cancer immunotherapy has demonstrated significant clinical activity in different cancers. T cells represent a crucial component of the adaptive immune system and are thought to mediate anti-tumoral immunity. Antigen-specific recognition by T cells is via the T cell receptor (TCR) which is unique for each T cell. Next generation sequencing (NGS) of the TCRs can be used as a platform to profile the T cell repertoire. Though there are a number of software tools available for processing repertoire data by mapping antigen receptor segments to sequencing reads and assembling the clonotypes, most of them are not designed to track and examine the dynamic nature of the TCR repertoire across multiple time points or between different biologic compartments (e.g., blood and tissue samples) in a clinical context.'),
(97728, 2017, 'bmc', 'Drug voyager: a computational platform for exploring unintended drug action', 'Oh, Min|Ahn, Jaegyoon|Lee, Taekeon|Jang, Giup|Park, Chihyun|Yoon, Youngmi', 'Department of Computer Science, Virginia Tech, Blacksburg, VA, USA.|Department of Computer Science & Engineering, Incheon National University, Incheon, South Korea.|Department of Computer Engineering, Gachon University, Seongnam, South Korea.|Department of Computer Engineering, Gachon University, Seongnam, South Korea.|Biomedical HPC Technology Research Center, Korean Institute of Science and Technology Information, Daejeon, South Korea.|Department of Computer Engineering, Gachon University, Seongnam, South Korea. ymyoon@gachon.ac.kr.', 'United States|United States|United States|United States|United States|United States', 5, 'Databases, Pharmaceutical|Drug Discovery|Drug Repositioning|Drug-Related Side Effects and Adverse Reactions|Gene Expression Profiling|Humans|Pharmacogenomic Variants|Phenotype|Signal Transduction|Software|Systems Biology', 'Journal Article', '10.1186/s12859-017-1558-3', '28241745', 'The dominant paradigm in understanding drug action focuses on the intended therapeutic effects and frequent adverse reactions. However, this approach may limit opportunities to grasp unintended drug actions, which can open up channels to repurpose existing drugs and identify rare adverse drug reactions. Advances in systems biology can be exploited to comprehensively understand pharmacodynamic actions, although proper frameworks to represent drug actions are still lacking.'),
(97729, 2017, 'bmc', 'RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach', 'Pan, Xiaoyong|Shen, Hong-Bin', 'Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, Copenhagen, Denmark. xypan172436@gmail.com.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China. hbshen@sjtu.edu.cn.', 'Denmark|China', 62, 'Algorithms|Binding Sites|Data Mining|Machine Learning|Neural Networks (Computer)|Nucleotide Motifs|Protein Binding|RNA|RNA-Binding Proteins|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-017-1561-8', '28245811', 'RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recognize the target RNAs and why they bind specific positions is still far from clear. Machine learning-based algorithms are widely acknowledged to be capable of speeding up this process. Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e.g. sequence, structure, their domain specific features and formats have posed significant computational challenges. One of current difficulties is that the cross-source shared common knowledge is at a higher abstraction level beyond the observed data, resulting in a low efficiency of direct integration of observed data across domains. The other difficulty is how to interpret the prediction results. Existing approaches tend to terminate after outputting the potential discrete binding sites on the sequences, but how to assemble them into the meaningful binding motifs is a topic worth of further investigation.'),
(97730, 2017, 'bmc', 'String kernels for protein sequence comparisons: improved fold recognition', 'Nojoomi, Saghi|Koehl, Patrice', 'Biotechnology program, University of California, Davis, 1, Shields Avenue, Davis, CA, 95616, USA.|Department of Computer Science and Genome Center, 1, Shields Avenue, Davis, CA, 95616, USA. pakoehl@ucdavis.edu.', 'United States|United States', 0, 'Algorithms|DNA-Directed RNA Polymerases|Phylogeny|Protein Folding|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/s12859-017-1560-9', '28245816', 'The amino acid sequence of a protein is the blueprint from which its structure and ultimately function can be derived. Therefore, sequence comparison methods remain essential for the determination of similarity between proteins. Traditional approaches for comparing two protein sequences begin with strings of letters (amino acids) that represent the sequences, before generating textual alignments between these strings and providing scores for each alignment. When the similitude between the two protein sequences to be compared is low however, the quality of the corresponding sequence alignment is usually poor, leading to poor performance for the recognition of similarity.'),
(97731, 2017, 'bmc', 'Reactome pathway analysis: a high-performance in-memory approach', 'Fabregat, Antonio|Sidiropoulos, Konstantinos|Viteri, Guilherme|Forner, Oscar|Marin-Garcia, Pablo|Arnau, Vicente|DEustachio, Peter|Stein, Lincoln|Hermjakob, Henning', 'European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK.|Fundación Investigación INCLIVA, Universitat de València, Valencia, Spain.|Escuela Técnica Superior de Ingenierías, Universitat de València, Valencia, Spain.|NYU Langone Medical Center, New York, USA.|Ontario Institute for Cancer Research, Toronto, Canada.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK. hhe@ebi.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|Spain|Spain|United Kingdom|Canada|United Kingdom', 94, 'Algorithms|Computational Biology|Databases, Factual|Humans|Nucleic Acids|Proteins|Software', 'Journal Article', '10.1186/s12859-017-1559-2', '28249561', 'Reactome aims to provide bioinformatics tools for visualisation, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modelling, systems biology and education. Pathway analysis methods have a broad range of applications in physiological and biomedical research, one of the main problems, from the analysis methods performance point of view, is the constantly increasing size of the data samples.'),
(97732, 2017, 'bmc', 'NucAmino: a nucleotide to amino acid alignment optimized for virus gene sequences', 'Tzou, Philip L|Huang, Xiaoqiu|Shafer, Robert W', 'Division of Infectious Diseases, Department of Medicine, Stanford University, Stanford, CA, USA.|Iowa State University, Ames, IA, USA.|Division of Infectious Diseases, Department of Medicine, Stanford University, Stanford, CA, USA. rshafer@stanford.edu.', 'United States|United States|United States', 3, 'Algorithms|Amino Acid Sequence|Base Sequence|HIV Integrase|HIV Protease|HIV Reverse Transcriptase|HIV-1|Humans|Sequence Alignment|Software', 'Journal Article', '10.1186/s12859-017-1555-6', '28249562', 'Current nucleotide-to-amino acid alignment software programs were developed primarily for detecting gene exons within eukaryotic genomes and were therefore optimized for speed across long genetic sequences. We developed a nucleotide-to-amino acid alignment program NucAmino optimized for virus sequencing.'),
(97733, 2017, 'bmc', 'Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies', 'Nettling, Martin|Treutler, Hendrik|Cerquides, Jesus|Grosse, Ivo', 'Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany. martin.nettling@informatik.uni-halle.de.|Leibniz Institute of Plant Biochemistry, Halle, Germany.|Institut dInvestigació en Intel ·ligència Artificial, IIIA-CSIC, Campus UAB, Cerdanyola, Spain.|Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany.', 'Germany|Germany|Spain|Germany', 4, 'Algorithms|Amino Acid Motifs|Binding Sites|Chromatin|DNA|Humans|Models, Molecular|Phylogeny|Protein Binding|Protein Domains|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/s12859-017-1495-1', '28249564', 'Transcriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. Approaches for de-novo motif discovery can be subdivided in phylogenetic footprinting that takes into account phylogenetic dependencies in aligned sequences of more than one species and non-phylogenetic approaches based on sequences from only one species that typically take into account intra-motif dependencies. It has been shown that modeling (i) phylogenetic dependencies as well as (ii) intra-motif dependencies separately improves de-novo motif discovery, but there is no approach capable of modeling both (i) and (ii) simultaneously.'),
(97734, 2017, 'bmc', 'Statistical method to compare massive parallel sequencing pipelines', 'Elsensohn, M H|Leblay, N|Dimassi, S|Campan-Fournier, A|Labalme, A|Roucher-Boulez, F|Sanlaville, D|Lesca, G|Bardel, C|Roy, P', 'Service de Biostatistique-Bioinformatique, Hospices Civils de Lyon, 162 avenue Lacassagne, F-69003, Lyon, France. mad-helenie.elsensohn@chu-lyon.fr.|Service de Biostatistique-Bioinformatique, Hospices Civils de Lyon, 162 avenue Lacassagne, F-69003, Lyon, France.|Universitxc3xa9 de Lyon, Lyon, France.|Universitxc3xa9 de Lyon, Lyon, France.|Service de Gxc3xa9nxc3xa9tique, Hospices Civils de Lyon, Lyon, France.|Universitxc3xa9 de Lyon, Lyon, France.|Universitxc3xa9 de Lyon, Lyon, France.|Universitxc3xa9 de Lyon, Lyon, France.|Service de Biostatistique-Bioinformatique, Hospices Civils de Lyon, 162 avenue Lacassagne, F-69003, Lyon, France.|Service de Biostatistique-Bioinformatique, Hospices Civils de Lyon, 162 avenue Lacassagne, F-69003, Lyon, France.', 'France|France|France|France|France|France|France|France|France|France', 2, 'Computational Biology|Epilepsy|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Models, Statistical|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-017-1552-9', '28249565', 'Today, sequencing is frequently carried out by Massive Parallel Sequencing (MPS) that cuts drastically sequencing time and expenses. Nevertheless, Sanger sequencing remains the main validation method to confirm the presence of variants. The analysis of MPS data involves the development of several bioinformatic tools, academic or commercial. We present here a statistical method to compare MPS pipelines and test it in a comparison between an academic (BWA-GATK) and a commercial pipeline (TMAP-NextGENexc2xae), with and without reference to a gold standard (here, Sanger sequencing), on a panel of 41 genes in 43 epileptic patients. This method used the number of variants to fit log-linear models for pairwise agreements between pipelines. To assess the heterogeneity of the margins and the odds ratios of agreement, four log-linear models were used: a full model, a homogeneous-margin model, a model with single odds ratio for all patients, and a model with single intercept. Then a log-linear mixed model was fitted considering the biological variability as a random effect.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97735, 2017, 'bmc', 'Positive-Unlabeled Learning for inferring drug interactions based on heterogeneous attributes', 'Hameed, Pathima Nusrath|Verspoor, Karin|Kusljic, Snezana|Halgamuge, Saman', 'Department of Mechanical Engineering, University of Melbourne, Parkville, Melbourne, 3010, Australia. nusrath@dcs.ruh.ac.lk.|Department of Computing and Information Systems, University of Melbourne, Parkville, Melbourne, 3010, Australia.|Department of Nursing, University of Melbourne, Parkville, Melbourne, 3010, Australia.|Research School of Engineering, College of Engineering and Computer Science, The Australian National University, Canberra, 2601, ACT, Australia.', 'Australia|Australia|Australia|Australia', 21, 'Cluster Analysis|Cytochrome P-450 Enzyme System|Databases, Factual|Drug Interactions|Humans|Pharmaceutical Preparations|Protein Isoforms|Support Vector Machine', 'Journal Article', '10.1186/s12859-017-1546-7', '28249566', 'Investigating and understanding drug-drug interactions (DDIs) is important in improving the effectiveness of clinical care. DDIs can occur when two or more drugs are administered together. Experimentally based DDI detection methods require a large cost and time. Hence, there is a great interest in developing efficient and useful computational methods for inferring potential DDIs. Standard binary classifiers require both positives and negatives for training. In a DDI context, drug pairs that are known to interact can serve as positives for predictive methods. But, the negatives or drug pairs that have been confirmed to have no interaction are scarce. To address this lack of negatives, we introduce a Positive-Unlabeled Learning method for inferring potential DDIs.'),
(97736, 2017, 'bmc', 'Generalized box-plot for root growth ensembles', 'Vad, Viktor|Cedrim, Douglas|Busch, Wolfgang|Filzmoser, Peter|Viola, Ivan', 'TU WIEN, Karlsplatz 13, Vienna, 1040, Austria. vad@cg.tuwien.ac.at.|ICMC - University of São Paulo, São Carlos, 15260, Brazil.|Gregor Mendel Institute of Molecular Plant Biology GmbH, Dr. Bohr-Gasse 3, Vienna, 1030, Austria.|TU WIEN, Karlsplatz 13, Vienna, 1040, Austria.|TU WIEN, Karlsplatz 13, Vienna, 1040, Austria.', 'Austria|Brazil|Austria|Austria|Austria', 2, 'Databases, Factual|Evaluation Studies as Topic|Image Processing, Computer-Assisted|Models, Theoretical|Phenotype|Plant Roots|Reproducibility of Results', 'Journal Article', '10.1186/s12859-016-1445-3', '28251866', 'In the field of root biology there has been a remarkable progress in root phenotyping, which is the efficient acquisition and quantitative description of root morphology. What is currently missing are means to efficiently explore, exchange and present the massive amount of acquired, and often time dependent root phenotypes.'),
(97737, 2017, 'bmc', 'Virtual reality assisted microscopy data visualization and colocalization analysis', 'Theart, Rensu P|Loos, Ben|Niesler, Thomas R', 'Stellenbosch University, Stellenbosch, 7600, South Africa.|Stellenbosch University, Stellenbosch, 7600, South Africa.|Stellenbosch University, Stellenbosch, 7600, South Africa. trn@sun.ac.za.', 'South Africa|South Africa|South Africa', 12, 'Adult|Animals|Cell Line|Computer Simulation|Female|Humans|Male|Mice|Microscopy, Confocal|Middle Aged|Mouse Embryonic Stem Cells|User-Computer Interface|Young Adult', 'Journal Article', '10.1186/s12859-016-1446-2', '28251867', 'Confocal microscopes deliver detailed three-dimensional data and are instrumental in biological analysis and research. Usually, this three-dimensional data is rendered as a projection onto a two-dimensional display. We describe a system for rendering such data using a modern virtual reality (VR) headset. Sample manipulation is possible by fully-immersive hand-tracking and also by means of a conventional gamepad. We apply this system to the specific task of colocalization analysis, an important analysis tool in biological microscopy. We evaluate our system by means of a set of user trials.'),
(97738, 2017, 'bmc', 'Unboxing cluster heatmaps', 'Engle, Sophie|Whalen, Sean|Joshi, Alark|Pollard, Katherine S', 'University of San Francisco, San Francisco, 94117, CA, USA. sjengle@usfca.edu.|Gladstone Institutes, San Francisco, 94158, CA, USA.|University of San Francisco, San Francisco, 94117, CA, USA.|Gladstone Institutes, San Francisco, 94158, CA, USA.', 'United States|United States|United States|United States', 9, 'Cell Line, Tumor|Cluster Analysis|Computational Biology|Humans|K562 Cells', 'Journal Article', '10.1186/s12859-016-1442-6', '28251868', 'Cluster heatmaps are commonly used in biology and related fields to reveal hierarchical clusters in data matrices. This visualization technique has high data density and reveal clusters better than unordered heatmaps alone. However, cluster heatmaps have known issues making them both time consuming to use and prone to error. We hypothesize that visualization techniques without the rigid grid constraint of cluster heatmaps will perform better at clustering-related tasks.'),
(97739, 2017, 'bmc', 'A taxonomy of visualization tasks for the analysis of biological pathway data', 'Murray, Paul|McGee, Fintan|Forbes, Angus G', 'Electronic Visualization Laboratory, University of Illinois at Chicago, Chicago, IL, USA. pmurra5@uic.edu.|eScience Group, Environmental Research and Innovation (ERIN) department, Luxembourg Institute of Science and Technology, Esch/Alzette, Luxembourg.|Electronic Visualization Laboratory, University of Illinois at Chicago, Chicago, IL, USA.', 'United States|Luxembourg|United States', 15, 'Algorithms|Computational Biology|Databases, Factual|Empirical Research|Humans|Proteins|Research Design|Software|Surveys and Questionnaires', 'Journal Article', '10.1186/s12859-016-1443-5', '28251869', 'Understanding complicated networks of interactions and chemical components is essential to solving contemporary problems in modern biology, especially in domains such as cancer and systems research. In these domains, biological pathway data is used to represent chains of interactions that occur within a given biological process. Visual representations can help researchers understand, interact with, and reason about these complex pathways in a number of ways. At the same time, these datasets offer unique challenges for visualization, due to their complexity and heterogeneity.'),
(97740, 2017, 'bmc', 'Bio-physically plausible visualization of highly scattering fluorescent neocortical models for in silico experimentation', 'Abdellah, Marwan|Bilgili, Ahmet|Eilemann, Stefan|Shillcock, Julian|Markram, Henry|Schxc3xbcrmann, Felix', 'Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Biotech Campus, Chemin des Mines 9, Geneva, 1202, Switzerland.|Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Biotech Campus, Chemin des Mines 9, Geneva, 1202, Switzerland.|Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Biotech Campus, Chemin des Mines 9, Geneva, 1202, Switzerland.|Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Biotech Campus, Chemin des Mines 9, Geneva, 1202, Switzerland.|Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Biotech Campus, Chemin des Mines 9, Geneva, 1202, Switzerland.|Blue Brain Project (BBP), xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Biotech Campus, Chemin des Mines 9, Geneva, 1202, Switzerland. felix.schuermann@epfl.ch.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 4, 'Animals|Biophysical Phenomena|Fluorescent Dyes|Image Processing, Computer-Assisted|Models, Biological|Monte Carlo Method|Neocortex|Rats|Reproducibility of Results|Scattering, Radiation', 'Journal Article', '10.1186/s12859-016-1444-4', '28251871', 'We present a visualization pipeline capable of accurate rendering of highly scattering fluorescent neocortical neuronal models. The pipeline is mainly developed to serve the computational neurobiology community. It allows the scientists to visualize the results of their virtual experiments that are performed in computer simulations, or in silico. The impact of the presented pipeline opens novel avenues for assisting the neuroscientists to build biologically accurate models of the brain. These models result from computer simulations of physical experiments that use fluorescence imaging to understand the structural and functional aspects of the brain. Due to the limited capabilities of the current visualization workflows to handle fluorescent volumetric datasets, we propose a physically-based optical model that can accurately simulate light interaction with fluorescent-tagged scattering media based on the basic principles of geometric optics and Monte Carlo path tracing. We also develop an automated and efficient framework for generating dense fluorescent tissue blocks from a neocortical column model that is composed of approximately 31000 neurons.'),
(97741, 2017, 'bmc', 'PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space', 'Ma, Chihua|Luciani, Timothy|Terebus, Anna|Liang, Jie|Marai, G Elisabeta', 'Electronic Visualization Laboratory, Department of Computer Science, University of Illinois at Chicago, 851 S. Morgan St (M/C 152), Room 1120 SEO, Chicago, 60607, IL, US. cma6@uic.edu.|Electronic Visualization Laboratory, Department of Computer Science, University of Illinois at Chicago, 851 S. Morgan St (M/C 152), Room 1120 SEO, Chicago, 60607, IL, US.|Department of Bioengineering, University of Illinois at Chicago, 851 S. Morgan St (M/C 063), Room 218 SEO, Chicago, 60607, IL, USA.|Department of Bioengineering, University of Illinois at Chicago, 851 S. Morgan St (M/C 063), Room 218 SEO, Chicago, 60607, IL, USA.|Electronic Visualization Laboratory, Department of Computer Science, University of Illinois at Chicago, 851 S. Morgan St (M/C 152), Room 1120 SEO, Chicago, 60607, IL, US.', 'United States|United States|United States|United States|United States', 1, 'Computational Biology|Computer Simulation|Databases, Factual|Gene Regulatory Networks|Internet|Models, Molecular|Probability|Protein Conformation|Software|Stochastic Processes|Transcription, Genetic', 'Journal Article', '10.1186/s12859-016-1447-1', '28251874', 'Visualizing the complex probability landscape of stochastic gene regulatory networks can further biologists understanding of phenotypic behavior associated with specific genes.'),
(97742, 2017, 'bmc', 'Comparative visualization of protein secondary structures', 'Kocincová, Lucia|Jarešová, Miroslava|Byška, Jan|Parulek, Július|Hauser, Helwig|Kozlíková, Barbora', 'Masaryk University, Brno, Czech Republic.|Masaryk University, Brno, Czech Republic.|Masaryk University, Brno, Czech Republic. jan.byska@gmail.com.|University of Bergen, Bergen, Norway.|University of Bergen, Bergen, Norway.|Masaryk University, Brno, Czech Republic.', '-|-|-|Norway|Norway|-', 4, 'Algorithms|Amino Acid Sequence|Models, Molecular|Molecular Dynamics Simulation|Protein Structure, Secondary|Proteins|Sequence Alignment', 'Journal Article', '10.1186/s12859-016-1449-z', '28251875', 'Protein function is determined by many factors, namely by its constitution, spatial arrangement, and dynamic behavior. Studying these factors helps the biochemists and biologists to better understand the protein behavior and to design proteins with modified properties. One of the most common approaches to these studies is to compare the protein structure with other molecules and to reveal similarities and differences in their polypeptide chains.'),
(97743, 2017, 'bmc', 'Interactive exploration of ligand transportation through protein tunnels', 'Furmanovxc3xa1, Katarxc3xadna|Jarexc5xa1ovxc3xa1, Miroslava|Byxc5xa1ka, Jan|Jurxc4x8dxc3xadk, Adam|Parulek, Jxc3xbalius|Hauser, Helwig|Kozlxc3xadkovxc3xa1, Barbora', 'Masaryk University, Brno, Czech Republic.|Masaryk University, Brno, Czech Republic.|Masaryk University, Brno, Czech Republic. jan.byska@gmail.com.|Masaryk University, Brno, Czech Republic.|University of Bergen, Bergen, Norway.|University of Bergen, Bergen, Norway.|Masaryk University, Brno, Czech Republic.', '-|-|-|-|Norway|Norway|-', 8, 'Amino Acids|Catalytic Domain|Image Processing, Computer-Assisted|Ligands|Models, Molecular|Molecular Dynamics Simulation|Protein Conformation|Proteins', 'Journal Article', '10.1186/s12859-016-1448-0', '28251878', 'Protein structures and their interaction with ligands have been in the focus of biochemistry and structural biology research for decades. The transportation of ligand into the protein active site is often complex process, driven by geometric and physico-chemical properties, which renders the ligand path full of jitter and impasses. This prevents understanding of the ligand transportation and reasoning behind its behavior along the path.'),
(97744, 2017, 'bmc', 'flippant-An R package for the automated analysis of fluorescence-based scramblase assays', 'Cotton, Richard J|Ploier, Birgit|Goren, Michael A|Menon, Anant K|Graumann, Johannes', 'Research Division, Weill Cornell Medicine-Qatar, P.O.Box 24144, Doha, State of Qatar.|Department of Biochemistry, Weill Cornell Medical College, New York, NY, 10065, USA.|Department of Biochemistry, Weill Cornell Medical College, New York, NY, 10065, USA.|Department of Biochemistry, Weill Cornell Medical College, New York, NY, 10065, USA.|Research Division, Weill Cornell Medicine-Qatar, P.O.Box 24144, Doha, State of Qatar. jog2030@qatar-med.cornell.edu.', 'Qatar|United States|United States|United States|Qatar', 0, 'Biochemistry|Fluorescence|Humans|Lipids|Phospholipid Transfer Proteins|Software', 'Journal Article', '10.1186/s12859-017-1542-y', '28253836', 'The lipid scrambling activity of protein extracts and purified scramblases is typically measured using a fluorescence-based assay. While the assay has yielded insight into the scramblase activity in crude membrane preparations, functional validation of candidate scramblases, stoichiometry of scramblase complexes as well as ATP-dependence of flippases, data analysis in its context has remained a task involving many manual steps.'),
(97745, 2017, 'bmc', 'DISMS2: A flexible algorithm for direct proteome- wide distance calculation of LC-MS/MS runs', 'Rieder, Vera|Blank-Landeshammer, Bernhard|Stuhr, Marleen|Schell, Tilman|Biß, Karsten|Kollipara, Laxmikanth|Meyer, Achim|Pfenninger, Markus|Westphal, Hildegard|Sickmann, Albert|Rahnenführer, Jörg', 'Department of Statistics, TU Dortmund University, Dortmund, Germany.|Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany.|Leibniz Center for Tropical Marine Ecology (ZMT), Bremen, Germany.|Biodiversity and Climate Research Centre, Senckenberg Gesellschaft für Naturforschung, Frankfurt, Germany.|Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany.|Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany.|Leibniz Center for Tropical Marine Ecology (ZMT), Bremen, Germany.|Biodiversity and Climate Research Centre, Senckenberg Gesellschaft für Naturforschung, Frankfurt, Germany.|Leibniz Center for Tropical Marine Ecology (ZMT), Bremen, Germany.|Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany.|Department of Statistics, TU Dortmund University, Dortmund, Germany. rahnenfuehrer@statistik.tu-dortmund.de.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 5, 'Algorithms|Amino Acid Sequence|Chromatography, Liquid|Databases, Protein|Humans|Peptides|Proteome|Proteomics|Tandem Mass Spectrometry', 'Journal Article', '10.1186/s12859-017-1514-2', '28253837', 'The classification of samples on a molecular level has manifold applications, from patient classification regarding cancer treatment to phylogenetics for identifying evolutionary relationships between species. Modern methods employ the alignment of DNA or amino acid sequences, mostly not genome-wide but only on selected parts of the genome. Recently proteomics-based approaches have become popular. An established method for the identification of peptides and proteins is liquid chromatography-tandem mass spectrometry (LC-MS/MS). First, protein sequences from MS/MS spectra are identified by means of database searches, given samples with known genome-wide sequence information, then sequence based methods are applied. Alternatively, de novo peptide sequencing algorithms annotate MS/MS spectra and deduce peptide/protein information without a database. A newer approach independent of additional information is to directly compare unidentified tandem mass spectra. The challenge then is to compute the distance between pairwise MS/MS runs consisting of thousands of spectra.'),
(97746, 2017, 'bmc', 'Model based heritability scores for high-throughput sequencing data', 'Rudra, Pratyaydipta|Shi, W Jenny|Vestal, Brian|Russell, Pamela H|Odell, Aaron|Dowell, Robin D|Radcliffe, Richard A|Saba, Laura M|Kechris, Katerina', 'Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora, CO 80045, USA.|Computational Bioscience Program, University of Colorado School of Medicine, Aurora, CO 80045, USA.|Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora, CO 80045, USA.|Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora, CO 80045, USA.|Department of Biology, University of Oregon, Eugene, OR, USA.|Department of Molecular, Cellular and Developmental Biology, University of Colorado at Boulder, Boulder, CO 80303, USA.|Department of Pharmaceutical Sciences, University of Colorado Skaggs School of Pharmaceutical Sciences, Aurora, CO 80045, USA.|Department of Pharmaceutical Sciences, University of Colorado Skaggs School of Pharmaceutical Sciences, Aurora, CO 80045, USA.|Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora, CO 80045, USA. katerina.kechris@ucdenver.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 3, 'Animals|Genotype|High-Throughput Nucleotide Sequencing|Linear Models|Mice|MicroRNAs|Models, Genetic|Phenotype|Quantitative Trait, Heritable|RNA, Messenger|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-017-1539-6', '28253840', 'Heritability of a phenotypic or molecular trait measures the proportion of variance that is attributable to genotypic variance. It is an important concept in breeding and genetics. Few methods are available for calculating heritability for traits derived from high-throughput sequencing.'),
(97747, 2017, 'bmc', 'Comparison of statistical methods for subnetwork detection in the integration of gene expression and protein interaction network', 'He, Hao|Lin, Dongdong|Zhang, Jigang|Wang, Yu-Ping|Deng, Hong-Wen', 'Department of Biostatistics and Bioinformatics, Center for Bioinformatics and Genomics, Tulane University School of Public Health and Tropical Medicine, 1440 Canal St., Suite 2001, New Orleans, LA, 70112, USA.|Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA.|Department of Biostatistics and Bioinformatics, Center for Bioinformatics and Genomics, Tulane University School of Public Health and Tropical Medicine, 1440 Canal St., Suite 2001, New Orleans, LA, 70112, USA.|Department of Biostatistics and Bioinformatics, Center for Bioinformatics and Genomics, Tulane University School of Public Health and Tropical Medicine, 1440 Canal St., Suite 2001, New Orleans, LA, 70112, USA.|Department of Biostatistics and Bioinformatics, Center for Bioinformatics and Genomics, Tulane University School of Public Health and Tropical Medicine, 1440 Canal St., Suite 2001, New Orleans, LA, 70112, USA. hdeng2@tulane.edu.', 'United States|United States|United States|United States|United States', 13, 'Algorithms|Computational Biology|Humans|Male|Prostatic Neoplasms|Protein Interaction Maps|Transcriptome', 'Journal Article', '10.1186/s12859-017-1567-2', '28253853', 'With the advancement of high-throughput technologies and enrichment of popular public databases, more and more research focuses of bioinformatics research have been on computational integration of network and gene expression profiles for extracting context-dependent active subnetworks. Many methods for subnetwork searching have been developed. Scoring and searching algorithms present a range of computational considerations and implementations. The primary goal of present study is to comprehensively evaluate the performance of different subnetwork detection methods. Eleven popular methods were selected for comprehensive comparison.'),
(97748, 2017, 'bmc', 'SeqCNV: a novel method for identification of copy number variations in targeted next-generation sequencing data', 'Chen, Yong|Zhao, Li|Wang, Yi|Cao, Ming|Gelowani, Violet|Xu, Mingchu|Agrawal, Smriti A|Li, Yumei|Daiger, Stephen P|Gibbs, Richard|Wang, Fei|Chen, Rui', 'Shanghai Key Lab of Intelligent Information Processing, Shanghai, China.|Structural and Computational Biology & Molecular Biophysics Graduate Program, Baylor College of Medicine, Houston, TX, USA.|School of Life Sciences, Fudan University, Shanghai, China.|University of Texas Health Science Center, Houston, TX, USA.|Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.|Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.|Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.|Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.|Department of Ophthalmology and Visual Sciences, University of Texas Health Science Center, Houston, TX, USA.|Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.|Shanghai Key Lab of Intelligent Information Processing, Shanghai, China. wangfei@fudan.edu.cn.|Structural and Computational Biology & Molecular Biophysics Graduate Program, Baylor College of Medicine, Houston, TX, USA. ruichen@bcm.edu.', 'China|United States|China|United States|United States|United States|United States|United States|United States|United States|China|United States', 26, 'DNA Copy Number Variations|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Likelihood Functions|Sensitivity and Specificity|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1566-3', '28253855', 'Targeted next-generation sequencing (NGS) has been widely used as a cost-effective way to identify the genetic basis of human disorders. Copy number variations (CNVs) contribute significantly to human genomic variability, some of which can lead to disease. However, effective detection of CNVs from targeted capture sequencing data remains challenging.'),
(97749, 2017, 'bmc', 'Prediction of GCRV virus-host protein interactome based on structural motif-domain interactions', 'Zhang, Aidi|He, Libo|Wang, Yaping', 'State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China.|State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China.|State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China. wangyp@ihb.ac.cn.', 'China|China|China', 0, 'Animals|Base Sequence|Carps|Cluster Analysis|Fish Diseases|Host-Pathogen Interactions|Plant Proteins|Protein Domains|Protein Interaction Maps|Reoviridae|Sequence Alignment|Transcriptome|Viral Proteins', 'Journal Article', '10.1186/s12859-017-1500-8', '28253857', 'Grass carp hemorrhagic disease, caused by grass carp reovirus (GCRV), is the most fatal causative agent in grass carp aquaculture. Protein-protein interactions between virus and host are one avenue through which GCRV can trigger infection and induce disease. Experimental approaches for the detection of host-virus interactome have many inherent limitations, and studies on protein-protein interactions between GCRV and its host remain rare.'),
(97750, 2017, 'bmc', 'Two-way learning with one-way supervision for gene expression data', 'Wong, Monica H T|Mutch, David M|McNicholas, Paul D', 'Department of Mathematics and Statistics, McMaster University, Hamilton, L8S 4L8, ON, Canada. monica.wong@math.mcmaster.ca.|Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, N1G 2W1, ON, Canada.|Department of Mathematics and Statistics, McMaster University, Hamilton, L8S 4L8, ON, Canada.', 'Canada|Canada|Canada', 2, 'Animals|Bayes Theorem|Biomarkers|Cluster Analysis|Databases, Genetic|Diabetes Mellitus, Experimental|Gene Expression|Humans|Male|Models, Theoretical|Rats|Rats, Zucker|Supervised Machine Learning|Transcriptome', 'Journal Article', '10.1186/s12859-017-1564-5', '28257645', 'A family of parsimonious Gaussian mixture models for the biclustering of gene expression data is introduced. Biclustering is accommodated by adopting a mixture of factor analyzers model with a binary, row-stochastic factor loadings matrix. This particular form of factor loadings matrix results in a block-diagonal covariance matrix, which is a useful property in gene expression analyses, specifically in biomarker discovery scenarios where blood can potentially act as a surrogate tissue for other less accessible tissues. Prior knowledge of the factor loadings matrix is useful in this application and is reflected in the one-way supervised nature of the algorithm. Additionally, the factor loadings matrix can be assumed to be constant across all components because of the relationship desired between the various types of tissue samples. Parameter estimates are obtained through a variant of the expectation-maximization algorithm and the best-fitting model is selected using the Bayesian information criterion. The family of models is demonstrated using simulated data and two real microarray data sets. The first real data set is from a rat study that investigated the influence of diabetes on gene expression in different tissues. The second real data set is from a human transcriptomics study that focused on blood and immune tissues. The microarray data sets illustrate the biclustering familys performance in biomarker discovery involving peripheral blood as surrogate biopsy material.'),
(97751, 2017, 'bmc', 'Avoiding the pitfalls of gene set enrichment analysis with SetRank', 'Simillion, Cedric|Liechti, Robin|Lischer, Heidi E L|Ioannidis, Vassilios|Bruggmann, Rémy', 'Interfaculty Bioinformatics Unit and SIB Swiss Institute of Bioinformatics, University of Bern, Baltzerstrasse 6, 3012, Berne, Switzerland. cedric.simillion@dkf.unibe.ch.|Vital-IT, SIB Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Genopode, 1015, Lausanne, Switzerland.|Interfaculty Bioinformatics Unit and SIB Swiss Institute of Bioinformatics, University of Bern, Baltzerstrasse 6, 3012, Berne, Switzerland.|Vital-IT, SIB Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Genopode, 1015, Lausanne, Switzerland.|Interfaculty Bioinformatics Unit and SIB Swiss Institute of Bioinformatics, University of Bern, Baltzerstrasse 6, 3012, Berne, Switzerland. remy.bruggmann@bioinformatics.unibe.ch.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 23, 'Algorithms|Brain|Computational Biology|Databases, Genetic|Gene Expression Profiling|Genome, Human|Genomics|Humans|Models, Theoretical|Neoplasms|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1186/s12859-017-1571-6', '28259142', 'The purpose of gene set enrichment analysis (GSEA) is to find general trends in the huge lists of genes or proteins generated by many functional genomics techniques and bioinformatics analyses.'),
(97752, 2017, 'bmc', 'SAG-QC: quality control of single amplified genome information by subtracting non-target sequences based on sequence compositions', 'Maruyama, Toru|Mori, Tetsushi|Yamagishi, Keisuke|Takeyama, Haruko', 'Department of Life Science & Medical Bioscience, Graduate School of Advanced Science & Engineering, Waseda University, 3-4-1 Okubo, Shinjuku, Tokyo, 169-8555, Japan.|Institute for Nanoscience and Nanotechnology, Waseda University, 513 Waseda-Tsurumaki-cho, Shinjuku, Tokyo, 162-0041, Japan.|Department of Life Science & Medical Bioscience, Graduate School of Advanced Science & Engineering, Waseda University, 3-4-1 Okubo, Shinjuku, Tokyo, 169-8555, Japan.|Department of Life Science & Medical Bioscience, Graduate School of Advanced Science & Engineering, Waseda University, 3-4-1 Okubo, Shinjuku, Tokyo, 169-8555, Japan. haruko-takeyama@waseda.jp.', 'Japan|Japan|Japan|Japan', 2, 'Computational Biology|Computer Simulation|Escherichia coli|Genetic Association Studies|Genome, Bacterial|Genomics|Models, Theoretical|Quality Control|RNA, Ribosomal, 16S|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1572-5', '28259144', 'Whole genome amplification techniques have enabled the analysis of unexplored genomic information by sequencing of single-amplified genomes (SAGs). Whole genome amplification of single bacteria is currently challenging because contamination often occurs in experimental processes. Thus, to increase the confidence in the analyses of sequenced SAGs, bioinformatics approaches that identify and exclude non-target sequences from SAGs are required. Since currently reported approaches utilize sequence information in public databases, they have limitations when new strains are the targets of interest. Here, we developed a software SAG-QC that identify and exclude non-target sequences independent of database.'),
(97753, 2017, 'bmc', 'ProteoModlR for functional proteomic analysis', 'Cifani, Paolo|Shakiba, Mojdeh|Chhangawala, Sagar|Kentsis, Alex', 'Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.|Physiology, Biophysics and Systems Biology Program, Weill Cornell Graduate School of Medical Sciences, New York, NY, USA.|Physiology, Biophysics and Systems Biology Program, Weill Cornell Graduate School of Medical Sciences, New York, NY, USA.|Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. kentsisresearchgroup@gmail.com.', 'United States|United States|United States|United States', 0, 'Evaluation Studies as Topic|Isotope Labeling|Mass Spectrometry|Peptides|Protein Processing, Post-Translational|Proteome|Proteomics|Software', 'Journal Article', '10.1186/s12859-017-1563-6', '28259147', 'High-accuracy mass spectrometry enables near comprehensive quantification of the components of the cellular proteomes, increasingly including their chemically modified variants. Likewise, large-scale libraries of quantified synthetic peptides are becoming available, enabling absolute quantification of chemically modified proteoforms, and therefore systems-level analyses of changes of their absolute abundance and stoichiometry. Existing computational methods provide advanced tools for mass spectral analysis and statistical inference, but lack integrated functions for quantitative analysis of post-translationally modified proteins and their modification stoichiometry.'),
(97754, 2017, 'bmc', 'The Repertoire Dissimilarity Index as a method to compare lymphocyte receptor repertoires', 'Bolen, Christopher R|Rubelt, Florian|Vander Heiden, Jason A|Davis, Mark M', 'Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, 94305, CA, USA.|Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, 94305, CA, USA.|Interdepartmental Program in Computational Biology and Bioinformatics, Department of Computational Biology & Bioinformatics, Yale University, New Haven, 06520, CT, USA.|Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, 94305, CA, USA. mmdavis@stanford.edu.', 'United States|United States|United States|United States', 13, 'Algorithms|Base Sequence|CD4-Positive T-Lymphocytes|CD8-Positive T-Lymphocytes|Gene Rearrangement, T-Lymphocyte|Genetic Variation|Humans|Receptors, Antigen, T-Cell|Sequence Analysis, RNA|V(D)J Recombination', 'Journal Article', '10.1186/s12859-017-1556-5', '28264647', 'The B and T cells of the human adaptive immune system leverage a highly diverse repertoire of antigen-specific receptors to protect the human body from pathogens. The sequencing and analysis of immune repertoires is emerging as an important tool to understand immune responses, whether beneficial or harmful (in the case of autoimmunity). However, methods for studying these repertoires, and for directly comparing different immune repertoires, are lacking.'),
(97755, 2017, 'bmc', 'Ultra-high dimensional variable selection with application to normative aging study: DNA methylation and metabolic syndrome', 'Yoon, Grace|Zheng, Yinan|Zhang, Zhou|Zhang, Haixiang|Gao, Tao|Joyce, Brian|Zhang, Wei|Guan, Weihua|Baccarelli, Andrea A|Jiang, Wenxin|Schwartz, Joel|Vokonas, Pantel S|Hou, Lifang|Liu, Lei', 'Department of Statistics, Northwestern University, 2006 Sheridan Road, Evanston, 60201, USA.|Department of Preventive Medicine, Northwestern University, 680 N Lake Shore Drive, Chicago, 60611, USA.|Department of Preventive Medicine, Northwestern University, 680 N Lake Shore Drive, Chicago, 60611, USA.|Center for Applied Mathematics, Tianjin University, 92 Weijin Road, Tianjin, 300072, China.|Department of Preventive Medicine, Northwestern University, 680 N Lake Shore Drive, Chicago, 60611, USA.|Department of Preventive Medicine, Northwestern University, 680 N Lake Shore Drive, Chicago, 60611, USA.|Department of Preventive Medicine, Northwestern University, 680 N Lake Shore Drive, Chicago, 60611, USA.|Department of Biostatistics, University of Minnesota, 420 Delaware, Minneapolis, 55455, USA.|Department of Environmental Health, Harvard University, 677 Huntington Avenue, Boston, 02115, USA.|Department of Statistics, Northwestern University, 2006 Sheridan Road, Evanston, 60201, USA.|Department of Environmental Health, Harvard University, 677 Huntington Avenue, Boston, 02115, USA.|Department of Preventive Medicine and Epidemiology, Boston University, 801 Massachusetts Avenue, Boston, 02118, USA.|Department of Preventive Medicine, Northwestern University, 680 N Lake Shore Drive, Chicago, 60611, USA.|Department of Preventive Medicine, Northwestern University, 680 N Lake Shore Drive, Chicago, 60611, USA. lei.liu@northwestern.edu.', 'United States|United States|United States|China|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'CpG Islands|DNA Methylation|Humans|Male|Metabolic Syndrome|Models, Statistical', 'Journal Article', '10.1186/s12859-017-1568-1', '28264653', 'Metabolic syndrome has become a major public health challenge worldwide. The association between metabolic syndrome and DNA methylation is of great research interest.'),
(97756, 2017, 'bmc', 'Machine learning classifier for identification of damaging missense mutations exclusive to human mitochondrial DNA-encoded polypeptides', 'Martín-Navarro, Antonio|Gaudioso-Simón, Andrés|Álvarez-Jarreta, Jorge|Montoya, Julio|Mayordomo, Elvira|Ruiz-Pesini, Eduardo', 'Departamento de Bioquímica, Biología Molecular y Celular, Universidad de Zaragoza, C/ Miguel Servet 177, Zaragoza, 50013, Spain.|Departamento de Bioquímica, Biología Molecular y Celular, Universidad de Zaragoza, C/ Miguel Servet 177, Zaragoza, 50013, Spain.|Departamento de Informática e Ingeniería de Sistemas, Universidad de Zaragoza, C/ María de Luna 1, Zaragoza, 50018, Spain.|Departamento de Bioquímica, Biología Molecular y Celular, Universidad de Zaragoza, C/ Miguel Servet 177, Zaragoza, 50013, Spain.|Departamento de Informática e Ingeniería de Sistemas, Universidad de Zaragoza, C/ María de Luna 1, Zaragoza, 50018, Spain. elvira@unizar.es.|Departamento de Bioquímica, Biología Molecular y Celular, Universidad de Zaragoza, C/ Miguel Servet 177, Zaragoza, 50013, Spain. eduruiz@unizar.es.', 'Spain|Spain|Spain|Spain|Spain|Spain', 11, 'Computational Biology|DNA Mutational Analysis|DNA, Mitochondrial|Humans|Machine Learning|Mitochondria|Mutation, Missense|Peptides|Sensitivity and Specificity', 'Journal Article', '10.1186/s12859-017-1562-7', '28270093', 'Several methods have been developed to predict the pathogenicity of missense mutations but none has been specifically designed for classification of variants in mtDNA-encoded polypeptides. Moreover, there is not available curated dataset of neutral and damaging mtDNA missense variants to test the accuracy of predictors. Because mtDNA sequencing of patients suffering mitochondrial diseases is revealing many missense mutations, it is needed to prioritize candidate substitutions for further confirmation. Predictors can be useful as screening tools but their performance must be improved.'),
(97757, 2017, 'bmc', 'Benchmarking and refining probability-based models for nucleosome-DNA interaction', 'Tompitak, Marco|Barkema, Gerard T|Schiessel, Helmut', 'Lorentz Institute, Leiden University, Niels Bohrweg 2, Leiden, 2333CA, The Netherlands. tompitak@lorentz.leidenuniv.nl.|Institute for Theoretical Physics, Utrecht University, Princetonplein 5, Utrecht, 3584CC, The Netherlands.|Lorentz Institute, Leiden University, Niels Bohrweg 2, Leiden, 2333CA, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 9, 'Animals|Base Sequence|Chromatin Assembly and Disassembly|Computer Simulation|DNA|Humans|Markov Chains|Mice|Models, Biological|Models, Statistical|Nucleosomes|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-017-1569-0', '28270095', 'In investigations of nucleosome positioning preferences, a model that assigns an affinity to a given sequence is necessary to make predictions. One important class of models, which treats a nucleosome sequence as a Markov chain, has been applied with success when informed with experimentally measured nucleosomal sequence preferences.'),
(97758, 2017, 'bmc', 'Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data', 'Conrad, Tim O F|Genzel, Martin|Cvetkovic, Nada|Wulkow, Niklas|Leichtle, Alexander|Vybiral, Jan|Kutyniok, Gitta|Schütte, Christof', 'Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany. conrad@math.fu-berlin.de.|Department of Mathematics, Technische Universität Berlin, Düsternbrooker Weg 20, Berlin, Germany.|Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany.|Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany.|Center of Laboratory Medicine, Inselspital - Bern University Hospital, Düsternbrooker Weg 20, Bern, 24105, Switzerland.|Department of Mathematical Analysis, Charles University, Düsternbrooker Weg 20, Prague, Czech Republic.|Department of Mathematics, Technische Universität Berlin, Düsternbrooker Weg 20, Berlin, Germany.|Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany.', 'Germany|Germany|Germany|Germany|Switzerland|-|Germany|Germany', 0, 'Algorithms|Case-Control Studies|Computer Simulation|Databases, Factual|Humans|Machine Learning|Models, Theoretical|Pancreatic Neoplasms|Proteomics|Reproducibility of Results|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1186/s12859-017-1565-4', '28274197', 'High-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients having a particular disease. Machine learning algorithms are needed to (a) identify these discriminating features and (b) classify unknown spectra based on this feature set. Since the acquired data is usually noisy, the algorithms should be robust against noise and outliers, while the identified feature set should be as small as possible.'),
(97759, 2017, 'bmc', 'A new parallel pipeline for DNA methylation analysis of long reads datasets', 'Olanda, Ricardo|Pérez, Mariano|Orduña, Juan M|Tárraga, Joaquín|Dopazo, Joaquín', 'Departamento de Informática, Universidad de Valencia, Avda. Universidad, s/n, Burjassot Valencia, Spain.|Departamento de Informática, Universidad de Valencia, Avda. Universidad, s/n, Burjassot Valencia, Spain.|Departamento de Informática, Universidad de Valencia, Avda. Universidad, s/n, Burjassot Valencia, Spain. Juan.Orduna@uv.es.|Department of Computational Genomics, Centro de Investigación Príncipe Felipe, Eduardo Primo Yúfera, s/n, Valencia, Spain.|Department of Computational Genomics, Centro de Investigación Príncipe Felipe, Eduardo Primo Yúfera, s/n, Valencia, Spain.', 'Spain|Spain|Spain|Spain|Spain', 0, 'DNA Methylation|Databases, Genetic|Epigenesis, Genetic|Gene Expression Regulation|Genome, Human|Humans|Mutation|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1574-3', '28274198', 'DNA methylation is an important mechanism of epigenetic regulation in development and disease. New generation sequencers allow genome-wide measurements of the methylation status by reading short stretches of the DNA sequence (Methyl-seq). Several software tools for methylation analysis have been proposed over recent years. However, the current trend is that the new sequencers and the ones expected for an upcoming future yield sequences of increasing length, making these software tools inefficient and obsolete.'),
(97760, 2017, 'bmc', 'Erratum to: 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, '-', 'Journal Article', '10.1186/s12859-017-1573-4', '28279148', '-'),
(97761, 2017, 'bmc', 'Prediction of virus-host protein-protein interactions mediated by short linear motifs', 'Becerra, Andrxc3xa9s|Bucheli, Victor A|Moreno, Pedro A', 'Escuela de ingenierxc3xada de sistemas y computacixc3xb3n, Universidad del Valle, Calle 13 # 100-00, A. A. 25360, Cali, Colombia.|Escuela de ingenierxc3xada de sistemas y computacixc3xb3n, Universidad del Valle, Calle 13 # 100-00, A. A. 25360, Cali, Colombia.|Escuela de ingenierxc3xada de sistemas y computacixc3xb3n, Universidad del Valle, Calle 13 # 100-00, A. A. 25360, Cali, Colombia. pedro.moreno@correounivalle.edu.co.', 'Colombia|Colombia|Colombia', 0, 'Amino Acid Motifs|DNA, Viral|Genes, Viral|HIV-1|Host-Pathogen Interactions|Humans|Protein Interaction Domains and Motifs|Reproducibility of Results|Sequence Alignment|Sequence Analysis, DNA|Software|Viral Proteins', 'Journal Article', '10.1186/s12859-017-1570-7', '28279163', 'Short linear motifs in host organisms proteins can be mimicked by viruses to create protein-protein interactions that disable or control metabolic pathways. Given that viral linear motif instances of host motif regular expressions can be found by chance, it is necessary to develop filtering methods of functional linear motifs. We conduct a systematic comparison of linear motifs filtering methods to develop a computational approach for predicting motif-mediated protein-protein interactions between human and the human immunodeficiency virus 1 (HIV-1).'),
(97762, 2017, 'bmc', 'The prediction of a pathogenesis-related secretome of Puccinia helianthi through high-throughput transcriptome analysis', 'Jing, Lan|Guo, Dandan|Hu, Wenjie|Niu, Xiaofan', 'Department of Plant Pathology, Inner Mongolia Agricultural University, Hohhot, 010019, China. jinglan71@126.com.|Department of Plant Pathology, Inner Mongolia Agricultural University, Hohhot, 010019, China.|Department of Plant Pathology, Inner Mongolia Agricultural University, Hohhot, 010019, China.|Division of Plant Sciences, University of Missouri, Columbia, MO, 65211, USA.', 'China|China|China|United States', 13, 'Algorithms|Aspartic Acid Endopeptidases|Bacterial Proteins|Basidiomycota|Fungal Proteins|Gene Expression Profiling|Helianthus|Open Reading Frames|Plant Diseases|Plant Leaves|Protein Sorting Signals|Transcriptome', 'Journal Article', '10.1186/s12859-017-1577-0', '28284182', 'Many plant pathogen secretory proteins are known to be elicitors or pathogenic factors,which play an important role in the host-pathogen interaction process. Bioinformatics approaches make possible the large scale prediction and analysis of secretory proteins from the Puccinia helianthi transcriptome. The internet-based software SignalP v4.1, TargetP v1.01, Big-PI predictor, TMHMM v2.0 and ProtComp v9.0 were utilized to predict the signal peptides and the signal peptide-dependent secreted proteins among the 35,286 ORFs of the P. helianthi transcriptome.'),
(97763, 2017, 'bmc', 'MOST: most-similar ligand based approach to target prediction', 'Huang, Tao|Mi, Hong|Lin, Cheng-Yuan|Zhao, Ling|Zhong, Linda L D|Liu, Feng-Bin|Zhang, Ge|Lu, Ai-Ping|Bian, Zhao-Xiang|for MZRW Group', 'Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, Peoples Republic of China.|Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, Peoples Republic of China.|Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, Peoples Republic of China.|Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, Peoples Republic of China.|Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, Peoples Republic of China.|Department of Gastroenterology, the First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, Peoples Republic of China.|Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, Peoples Republic of China.|Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, Peoples Republic of China.|Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, Peoples Republic of China. bianzxiang@gmail.com.|Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, Peoples Republic of China. bianzxiang@gmail.com.', 'China|China|China|China|China|China|China|China|China|China', 0, 'Acetylcholinesterase|Aloe|Animals|Cathartics|Databases, Chemical|Emodin|Humans|Kinetics|Ligands|Logistic Models|Machine Learning', 'Journal Article', '10.1186/s12859-017-1586-z', '28284192', 'Many computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested that chemical similarity searching may be driven by the most-similar ligand. However, the extent of bioactivity of most-similar ligands has been oversimplified or even neglected in these studies, and this has impaired the prediction power.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97764, 2017, 'bmc', 'ClonoCalc and ClonoPlot: immune repertoire analysis from raw files to publication figures with graphical user interface', 'Fähnrich, Anke|Krebbel, Moritz|Decker, Normann|Leucker, Martin|Lange, Felix D|Kalies, Kathrin|Möller, Steffen', 'Institute of Anatomy, Ratzeburger Allee, Lübeck, 160, 23562, Germany. faehnrich@anat.uni-luebeck.de.|Institute for Software Engineering and Programming Languages, Ratzeburger Allee, Lübeck, 160, 23562, Germany.|Institute for Software Engineering and Programming Languages, Ratzeburger Allee, Lübeck, 160, 23562, Germany.|Institute for Software Engineering and Programming Languages, Ratzeburger Allee, Lübeck, 160, 23562, Germany.|Institute for Software Engineering and Programming Languages, Ratzeburger Allee, Lübeck, 160, 23562, Germany.|Institute of Anatomy, Ratzeburger Allee, Lübeck, 160, 23562, Germany.|Institute for Biostatistics and Informatics in Medicine and Ageing Research, Ernst-Heydemann-Str. 8, Rostock, 18057, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Animals|Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Internet|Receptors, Antigen, B-Cell|Receptors, Antigen, T-Cell|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1575-2', '28284194', 'Next generation sequencing (NGS) technologies enable studies and analyses of the diversity of both T and B cell receptors (TCR and BCR) in human and animal systems to elucidate immune functions in health and disease. Over the last few years, several algorithms and tools have been developed to support respective analyses of raw sequencing data of the immune repertoire. These tools focus on distinct aspects of the data processing and require a strong bioinformatics background. To facilitate the analysis of T and B cell repertoires by less experienced users, software is needed that combines the most common tools for repertoire analysis.'),
(97765, 2017, 'bmc', 'Guide Picker is a comprehensive design tool for visualizing and selecting guides for CRISPR experiments', 'Hough, Soren H|Kancleris, Kris|Brody, Leigh|Humphryes-Kirilov, Neil|Wolanski, Joseph|Dunaway, Keith|Ajetunmobi, Ayokunmi|Dillard, Victor', 'Desktop Genetics, Ltd., 28 Hanbury Street, London, E1 6QR, UK. sorenh@desktopgenetics.com.|Desktop Genetics, Ltd., 28 Hanbury Street, London, E1 6QR, UK.|Desktop Genetics, Ltd., 28 Hanbury Street, London, E1 6QR, UK.|Desktop Genetics, Ltd., 28 Hanbury Street, London, E1 6QR, UK.|Desktop Genetics, Ltd., 28 Hanbury Street, London, E1 6QR, UK.|Desktop Genetics, Ltd., 28 Hanbury Street, London, E1 6QR, UK.|Desktop Genetics, Ltd., 28 Hanbury Street, London, E1 6QR, UK.|Desktop Genetics, Ltd., 28 Hanbury Street, London, E1 6QR, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 7, 'Animals|Clustered Regularly Interspaced Short Palindromic Repeats|Gene Editing|Humans|Internet|Mice|RNA, Guide|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1581-4', '28288556', 'Guide Picker ( https://www.deskgen.com/guide-picker/ ) serves as a meta tool for designing CRISPR experiments by presenting ten different guide RNA scoring functions in one simple graphical interface. It allows investigators to simultaneously visualize and sort through every guide targeting the protein-coding regions of any mouse or human gene.'),
(97766, 2017, 'bmc', 'A novel measure and significance testing in data analysis of cell image segmentation', 'Wu, Jin Chu|Halter, Michael|Kacker, Raghu N|Elliott, John T|Plant, Anne L', 'National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA. jinchu.wu@nist.gov.|National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA.|National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA.|National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA.|National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA.', 'United States|United States|United States|United States|United States', 2, 'Algorithms|Animals|Image Interpretation, Computer-Assisted|Mice|Microscopy, Fluorescence|Myocytes, Smooth Muscle', 'Journal Article', '10.1186/s12859-017-1527-x', '28292256', 'Cell image segmentation (CIS) is an essential part of quantitative imaging of biological cells. Designing a performance measure and conducting significance testing are critical for evaluating and comparing the CIS algorithms for image-based cell assays in cytometry. Many measures and methods have been proposed and implemented to evaluate segmentation methods. However, computing the standard errors (SE) of the measures and their correlation coefficient is not described, and thus the statistical significance of performance differences between CIS algorithms cannot be assessed.'),
(97767, 2017, 'bmc', 'CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests', 'Ma, Li|Fan, Suohai', 'School of Information Science and Technology, Jinan University, Guangzhou, 510632, China.|School of Information Science and Technology, Jinan University, Guangzhou, 510632, China. tfsh@jnu.edu.cn.', 'China|China', 0, 'Algorithms|Area Under Curve|Cluster Analysis|ROC Curve', 'Journal Article', '10.1186/s12859-017-1578-z', '28292263', 'The random forests algorithm is a type of classifier with prominent universality, a wide application range, and robustness for avoiding overfitting. But there are still some drawbacks to random forests. Therefore, to improve the performance of random forests, this paper seeks to improve imbalanced data processing, feature selection and parameter optimization.'),
(97768, 2017, 'bmc', 'MicroRNA categorization using sequence motifs and k-mers', 'Yousef, Malik|Khalifa, Waleed|Acar, xc4xb0lhan Erkin|Allmer, Jens', 'Community Information Systems, Zefat Academic College, Zefat, 13206, Israel. malik.yousef@gmail.com.|Computer Science, The College of Sakhnin, Sakhnin, 30810, Israel.|Biotechnology, Izmir Institute of Technology, 35430, Urla, Izmir, Turkey.|Molecular Biology and Genetics, Izmir Institute of Technology, 35430, Urla, Izmir, Turkey. jens@allmer.de.', 'Israel|Israel|Turkey|Turkey', 0, 'Animals|Base Sequence|Fabaceae|High-Throughput Nucleotide Sequencing|Humans|MicroRNAs|Phylogeny|RNA Precursors', 'Journal Article', '10.1186/s12859-017-1584-1', '28292266', 'Post-transcriptional gene dysregulation can be a hallmark of diseases like cancer and microRNAs (miRNAs) play a key role in the modulation of translation efficiency. Known pre-miRNAs are listed in miRBase, and they have been discovered in a variety of organisms ranging from viruses and microbes to eukaryotic organisms. The computational detection of pre-miRNAs is of great interest, and such approaches usually employ machine learning to discriminate between miRNAs and other sequences. Many features have been proposed describing pre-miRNAs, and we have previously introduced the use of sequence motifs and k-mers as useful ones. There have been reports of xeno-miRNAs detected via next generation sequencing. However, they may be contaminations and to aid that important decision-making process, we aimed to establish a means to differentiate pre-miRNAs from different species.'),
(97769, 2017, 'bmc', 'Bacterial protein meta-interactomes predict cross-species interactions and protein function', 'Caufield, J Harry|Wimble, Christopher|Shary, Semarjit|Wuchty, Stefan|Uetz, Peter', 'Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, USA.|Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, USA.|Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, USA.|Department of Computer Science, University of Miami, Coral Gables, Florida, USA.|Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, USA. peter@uetz.us.', 'United States|United States|United States|United States|United States', 11, 'Bacillus subtilis|Bacteria|Bacterial Proteins|Evolution, Molecular|Humans|Protein Interaction Mapping|Proteome|Salmonella enterica', 'Journal Article', '10.1186/s12859-017-1585-0', '28298180', 'Protein-protein interactions (PPIs) can offer compelling evidence for protein function, especially when viewed in the context of proteome-wide interactomes. Bacteria have been popular subjects of interactome studies: more than six different bacterial species have been the subjects of comprehensive interactome studies while several more have had substantial segments of their proteomes screened for interactions. The protein interactomes of several bacterial species have been completed, including several from prominent human pathogens. The availability of interactome data has brought challenges, as these large data sets are difficult to compare across species, limiting their usefulness for broad studies of microbial genetics and evolution.'),
(97770, 2017, 'bmc', 'microclass: an R-package for 16S taxonomy classification', 'Liland, Kristian Hovde|Vinje, Hilde|Snipen, Lars', 'Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, P.O. Box 5003, N-1432, Norway.|Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, P.O. Box 5003, N-1432, Norway.|Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, P.O. Box 5003, N-1432, Norway. lars.snipen@nmbu.no.', 'Norway|Norway|Norway', 0, 'Area Under Curve|Bacteria|High-Throughput Nucleotide Sequencing|RNA, Ribosomal, 16S|ROC Curve|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-017-1583-2', '28302051', 'Taxonomic classification based on the 16S rRNA gene sequence is important for the profiling of microbial communities. In addition to giving the best possible accuracy, it is also important to quantify uncertainties in the classifications.'),
(97771, 2017, 'bmc', 'A fast and efficient python library for interfacing with the Biological Magnetic Resonance Data Bank', 'Smelter, Andrey|Astra, Morgan|Moseley, Hunter N B', 'School of Interdisciplinary and Graduate Studies, University of Louisville, Louisville, KY, 40292, USA.|Center for Environmental and Systems Biochemistry, University of Kentucky, Lexington, KY, 40356, USA.|Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY, 40356, USA. hunter.moseley@uky.edu.', 'United States|United States|United States', 4, 'Databases, Factual|Magnetic Resonance Spectroscopy|Software', 'Journal Article', '10.1186/s12859-017-1580-5', '28302053', 'The Biological Magnetic Resonance Data Bank (BMRB) is a public repository of Nuclear Magnetic Resonance (NMR) spectroscopic data of biological macromolecules. It is an important resource for many researchers using NMR to study structural, biophysical, and biochemical properties of biological macromolecules. It is primarily maintained and accessed in a flat file ASCII format known as NMR-STAR. While the format is human readable, the size of most BMRB entries makes computer readability and explicit representation a practical requirement for almost any rigorous systematic analysis.'),
(97772, 2017, 'bmc', 'Subgroup detection in genotype data using invariant coordinate selection', 'Fischer, Daniel|Honkatukia, Mervi|Tuiskula-Haavisto, Maria|Nordhausen, Klaus|Cavero, David|Preisinger, Rudolf|Vilkki, Johanna', 'Natural Resources Institute Finland (LUKE), Myllytie 1, Jokioinen, Finland. daniel.fischer@luke.fi.|Natural Resources Institute Finland (LUKE), Myllytie 1, Jokioinen, Finland.|Natural Resources Institute Finland (LUKE), Myllytie 1, Jokioinen, Finland.|Department of Mathematics and Statistics, University of Turku, Turku, Finland.|Lohmann Tierzucht GmbH, Am Seedeich 9-11, Cuxhaven, 27454, Germany.|Lohmann Tierzucht GmbH, Am Seedeich 9-11, Cuxhaven, 27454, Germany.|Natural Resources Institute Finland (LUKE), Myllytie 1, Jokioinen, Finland.', 'Finland|Finland|Finland|Finland|Germany|Germany|Finland', 0, 'Algorithms|Animals|Chickens|Cluster Analysis|Genotype|Principal Component Analysis', 'Journal Article', '10.1186/s12859-017-1589-9', '28302061', 'The current gold standard in dimension reduction methods for high-throughput genotype data is the Principle Component Analysis (PCA). The presence of PCA is so dominant, that other methods usually cannot be found in the analysts toolbox and hence are only rarely applied.'),
(97773, 2017, 'bmc', 'Bioinformatics identification of new targets for improving low temperature stress tolerance in spring and winter wheat', 'Tchagang, Alain B|Fauteux, François|Tulpan, Dan|Pan, Youlian', 'Information and Communications Technologies, National Research Council Canada, Ottawa, ON, K1A 0R6, Canada. alain.tchagang@nrc-cnrc.gc.ca.|Information and Communications Technologies, National Research Council Canada, Ottawa, ON, K1A 0R6, Canada.|Information and Communications Technologies, National Research Council Canada, Moncton, NB, E1A 7R1, Canada.|Information and Communications Technologies, National Research Council Canada, Ottawa, ON, K1A 0R6, Canada.', 'Canada|Canada|Canada|Canada', 2, 'Adaptation, Physiological|Cold Temperature|Computational Biology|Gene Expression Regulation, Plant|Gene Regulatory Networks|Linear Models|Plant Proteins|Seasons|Stress, Physiological|Triticum', 'Journal Article', '10.1186/s12859-017-1596-x', '28302069', 'Phenotypic studies in Triticeae have shown that low temperature-induced protective mechanisms are developmentally regulated and involve dynamic acclimation processes. Understanding these mechanisms is important for breeding cold-resistant wheat cultivars. In this study, we combined three computational techniques for the analysis of gene expression data from spring and winter wheat cultivars subjected to low temperature treatments. Our main objective was to construct a comprehensive network of cold response transcriptional events in wheat, and to identify novel cold tolerance candidate genes in wheat.'),
(97774, 2017, 'bmc', 'Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms', 'Wiesmann, Veit|Bergler, Matthias|Palmisano, Ralf|Prinzen, Martin|Franz, Daniela|Wittenberg, Thomas', 'Fraunhofer Institute for Integrated Circuits IIS, Am Wolfsmantel 33, Erlangen, 91058, Germany. veit.wiesmann@iis.fraunhofer.de.|Fraunhofer Institute for Integrated Circuits IIS, Am Wolfsmantel 33, Erlangen, 91058, Germany.|Optical Imaging Centre Erlangen (OICE), Hartmannstraße 14, Erlangen, 91052, Germany.|Fraunhofer Institute for Integrated Circuits IIS, Am Wolfsmantel 33, Erlangen, 91058, Germany.|Fraunhofer Institute for Integrated Circuits IIS, Am Wolfsmantel 33, Erlangen, 91058, Germany.|Fraunhofer Institute for Integrated Circuits IIS, Am Wolfsmantel 33, Erlangen, 91058, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Animals|B-Lymphocytes|Cell Shape|Image Processing, Computer-Assisted|Macrophages|Mice|Mice, Inbred C57BL|Microscopy, Fluorescence|Protoplasts', 'Journal Article', '10.1186/s12859-017-1591-2', '28315633', 'Manual assessment and evaluation of fluorescent micrograph cell experiments is time-consuming and tedious. Automated segmentation pipelines can ensure efficient and reproducible evaluation and analysis with constant high quality for all images of an experiment. Such cell segmentation approaches are usually validated and rated in comparison to manually annotated micrographs. Nevertheless, manual annotations are prone to errors and display inter- and intra-observer variability which influence the validation results of automated cell segmentation pipelines.'),
(97775, 2017, 'bmc', 'TreeToReads - a pipeline for simulating raw reads from phylogenies', 'McTavish, Emily Jane|Pettengill, James|Davis, Steven|Rand, Hugh|Strain, Errol|Allard, Marc|Timme, Ruth E', 'University of California, Merced, Merced, CA, USA. ejmctavish@ucmerced.edu.|Center for Food Safety and Nutrition, Food and Drug Administration, College Park, MD, USA.|Center for Food Safety and Nutrition, Food and Drug Administration, College Park, MD, USA.|Center for Food Safety and Nutrition, Food and Drug Administration, College Park, MD, USA.|Center for Food Safety and Nutrition, Food and Drug Administration, College Park, MD, USA.|Center for Food Safety and Nutrition, Food and Drug Administration, College Park, MD, USA.|Center for Food Safety and Nutrition, Food and Drug Administration, College Park, MD, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Genomics|Phylogeny', 'Journal Article', '10.1186/s12859-017-1592-1', '28320310', 'Using phylogenomic analysis tools for tracking pathogens has become standard practice in academia, public health agencies, and large industries. Using the same raw read genomic data as input, there are several different approaches being used to infer phylogenetic tree. These include many different SNP pipelines, wgMLST approaches, k-mer algorithms, whole genome alignment and others, each of these has advantages and disadvantages, some have been extensively validated, some are faster, some have higher resolution. A few of these analysis approaches are well-integrated into the regulatory process of US Federal agencies (e.g. the FDAs SNP pipeline for tracking foodborne pathogens). However, despite extensive validation on benchmark datasets and comparison with other pipelines, we lack methods for fully exploring the effects of multiple parameter values in each pipeline that can potentially have an effect on whether the correct phylogenetic tree is recovered.'),
(97776, 2017, 'bmc', 'NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology', 'Wei, Qing|Khan, Ishita K|Ding, Ziyun|Yerneni, Satwica|Kihara, Daisuke', 'Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA.|Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA.|Department of Biological Science, Purdue University, West Lafayette, IN, 47907, USA.|Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA.|Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA. dkihara@purdue.edu.', 'United States|United States|United States|United States|United States', 7, 'Gene Ontology|Genomics', 'Journal Article', '10.1186/s12859-017-1600-5', '28320317', 'The number of genomics and proteomics experiments is growing rapidly, producing an ever-increasing amount of data that are awaiting functional interpretation. A number of function prediction algorithms were developed and improved to enable fast and automatic function annotation. With the well-defined structure and manual curation, Gene Ontology (GO) is the most frequently used vocabulary for representing gene functions. To understand relationship and similarity between GO annotations of genes, it is important to have a convenient pipeline that quantifies and visualizes the GO function analyses in a systematic fashion.'),
(97777, 2017, 'bmc', 'EnCOUNTer: a parsing tool to uncover the mature N-terminus of organelle-targeted proteins in complex samples', 'Bienvenut, Willy Vincent|Scarpelli, Jean-Pierre|Dumestier, Johan|Meinnel, Thierry|Giglione, Carmela', 'Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Universitxc3xa9 Paris Saclay, 91198, Gif-sur-Yvette Cedex, France. Willy.Bienvenut@i2bc.Paris-Saclay.fr.|Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Universitxc3xa9 Paris Saclay, 91198, Gif-sur-Yvette Cedex, France.|Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Universitxc3xa9 Paris Saclay, 91198, Gif-sur-Yvette Cedex, France.|Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Universitxc3xa9 Paris Saclay, 91198, Gif-sur-Yvette Cedex, France.|Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Universitxc3xa9 Paris Saclay, 91198, Gif-sur-Yvette Cedex, France.', 'France|France|France|France|France', 0, 'Acetylation|Organelles|Protein Transport|Proteomics', 'Journal Article', '10.1186/s12859-017-1595-y', '28320318', 'Characterization of mature protein N-termini by large scale proteomics is challenging. This is especially true for proteins undergoing cleavage of transit peptides when they are targeted to specific organelles, such as mitochondria or chloroplast. Protein neo-N-termini can be located up to 100-150 amino acids downstream from the initiator methionine and are not easily predictable. Although some bioinformatics tools are available, they usually require extensive manual validation to identify the exact N-terminal position. The situation becomes even more complex when post-translational modifications take place at the neo-N-terminus. Although N-terminal acetylation occurs mostly in the cytosol, it is also observed in some organelles such as chloroplast. To date, no bioinformatics tool is available to define mature protein starting positions, the associated N-terminus acetylation status and/or yield for each proteoform. In this context, we have developed the EnCOUNTer tool (i) to score all characterized peptides using discriminating parameters to identify bona fide mature protein N-termini and (ii) to determine the N-terminus acetylation yield of the most reliable ones.'),
(97778, 2017, 'bmc', 'QuickMIRSeq: a pipeline for quick and accurate quantification of both known miRNAs and isomiRs by jointly processing multiple samples from microRNA sequencing', 'Zhao, Shanrong|Gordon, William|Du, Sarah|Zhang, Chi|He, Wen|Xi, Li|Mathur, Sachin|Agostino, Michael|Paradis, Theresa|von Schack, David|Vincent, Michael|Zhang, Baohong', 'Early Clinical Development, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA. Shanrong.Zhao@pfizer.com.|Early Clinical Development, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA.|Early Clinical Development, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA.|Early Clinical Development, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA.|Early Clinical Development, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA.|Early Clinical Development, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA.|Business Technology, Pfizer Worldwide Research and Development, Andover, MA, 01810, USA.|Business Technology, Pfizer Worldwide Research and Development, Andover, MA, 01810, USA.|Early Clinical Development, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA.|Early Clinical Development, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA.|I&I Research Unit, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA.|Early Clinical Development, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA. Baohong.Zhang@pfizer.com.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 8, 'High-Throughput Nucleotide Sequencing|MicroRNAs|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-017-1601-4', '28320324', 'Genome-wide miRNA expression data can be used to study miRNA dysregulation comprehensively. Although many open-source tools for microRNA (miRNA)-seq data analyses are available, challenges remain in accurate miRNA quantification from large-scale miRNA-seq dataset. We implemented a pipeline called QuickMIRSeq for accurate quantification of known miRNAs and miRNA isoforms (isomiRs) from multiple samples simultaneously.'),
(97779, 2017, 'bmc', 'LW-FQZip 2: a parallelized reference-based compression of FASTQ files', 'Huang, Zhi-An|Wen, Zhenkun|Deng, Qingjin|Chu, Ying|Sun, Yiwen|Zhu, Zexuan', 'College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.|School of Medicine, Shenzhen University, Shenzhen, 518060, China.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.', 'China|China|China|China|China|China', 0, 'Data Compression|High-Throughput Nucleotide Sequencing|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-017-1588-x', '28320326', 'The rapid progress of high-throughput DNA sequencing techniques has dramatically reduced the costs of whole genome sequencing, which leads to revolutionary advances in gene industry. The explosively increasing volume of raw data outpaces the decreasing disk cost and the storage of huge sequencing data has become a bottleneck of downstream analyses. Data compression is considered as a solution to reduce the dependency on storage. Efficient sequencing data compression methods are highly demanded.'),
(97780, 2017, 'bmc', 'Preservation affinity in consensus modules among stages of HIV-1 progression', 'Mosaddek Hossain, Sk Md|Ray, Sumanta|Mukhopadhyay, Anirban', 'Department of Computer Science and Engineering, Aliah University, Kolkata, West Bengal, 700156, India.|Department of Computer Science and Engineering, Aliah University, Kolkata, West Bengal, 700156, India. sumantababai86@gmail.com.|Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, 741235, India.', 'India|India|India', 0, 'Disease Progression|Gene Regulatory Networks|Genes, Regulator|HIV-1|Humans', 'Journal Article', '10.1186/s12859-017-1590-3', '28320358', 'Analysis of gene expression data provides valuable insights into disease mechanism. Investigating relationship among co-expression modules of different stages is a meaningful tool to understand the way in which a disease progresses. Identifying topological preservation of modular structure also contributes to that understanding.'),
(97781, 2017, 'bmc', 'KNN-MDR: a learning approach for improving interactions mapping performances in genome wide association studies', 'Abo Alchamlat, Sinan|Farnir, Frédéric', 'Department of Biostatistics, Faculty of Veterinary Medicine, FARAH, University of Liège, Sart Tilman B43, 4000, Liege, Belgium.|Department of Biostatistics, Faculty of Veterinary Medicine, FARAH, University of Liège, Sart Tilman B43, 4000, Liege, Belgium. f.farnir@ulg.ac.be.', 'Belgium|Belgium', 7, 'Epistasis, Genetic|Genome-Wide Association Study|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-017-1599-7', '28327091', 'Finding epistatic interactions in large association studies like genome-wide association studies (GWAS) with the nowadays-available large volume of genomic data is a challenging and largely unsolved issue. Few previous studies could handle genome-wide data due to the intractable difficulties met in searching a combinatorial explosive search space and statistically evaluating epistatic interactions given a limited number of samples. Our work is a contribution to this field. We propose a novel approach combining K-Nearest Neighbors (KNN) and Multi Dimensional Reduction (MDR) methods for detecting gene-gene interactions as a possible alternative to existing algorithms, e especially in situations where the number of involved determinants is high. After describing the approach, a comparison of our method (KNN-MDR) to a set of the other most performing methods (i.e., MDR, BOOST, BHIT, MegaSNPHunter and AntEpiSeeker) is carried on to detect interactions using simulated data as well as real genome-wide data.'),
(97782, 2017, 'bmc', 'metaX: a flexible and comprehensive software for processing metabolomics data', 'Wen, Bo|Mei, Zhanlong|Zeng, Chunwei|Liu, Siqi', 'BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Shenzhen, Shenzhen, 518083, China. siqiliu@genomics.cn.', 'China|China|China|China', 0, 'Mass Spectrometry|Metabolomics|Quality Control', 'Journal Article', '10.1186/s12859-017-1579-y', '28327092', 'Non-targeted metabolomics based on mass spectrometry enables high-throughput profiling of the metabolites in a biological sample. The large amount of data generated from mass spectrometry requires intensive computational processing for annotation of mass spectra and identification of metabolites. Computational analysis tools that are fully integrated with multiple functions and are easily operated by users who lack extensive knowledge in programing are needed in this research field.'),
(97783, 2017, 'bmc', 'Erratum to: 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, TX, 77030, USA.|Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.|Department of Statistics, Rice University, Houston, TX, USA. marina@rice.edu.', 'United States|Italy|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1186/s12859-017-1606-z', '28335713', '-'),
(97784, 2017, 'bmc', 'rNAV 20: a visualization tool for bacterial sRNA-mediated regulatory networks mining', 'Bourqui, Romain|Dutour, Isabelle|Dubois, Jonathan|Benchimol, William|Thébault, Patricia', 'LaBRI, CNRS UMR5800, Université de Bordeaux, 351, cours de la Libération, Talence cedex, F-33405, France. bourqui@labri.fr.|LaBRI, CNRS UMR5800, Université de Bordeaux, 351, cours de la Libération, Talence cedex, F-33405, France.|LaBRI, CNRS UMR5800, Université de Bordeaux, 351, cours de la Libération, Talence cedex, F-33405, France.|LaBRI, CNRS UMR5800, Université de Bordeaux, 351, cours de la Libération, Talence cedex, F-33405, France.|LaBRI, CNRS UMR5800, Université de Bordeaux, 351, cours de la Libération, Talence cedex, F-33405, France.', 'France|France|France|France|France', 2, 'Computational Biology|Gene Expression Regulation, Bacterial|RNA, Bacterial', 'Journal Article', '10.1186/s12859-017-1598-8', '28335718', 'Bacterial sRNA-mediated regulatory networks has been introduced as a powerful way to analyze the fast rewiring capabilities of a bacteria in response to changing environmental conditions. The identification of mRNA targets of bacterial sRNAs is essential to investigate their functional activities. However, this step remains challenging with the lack of knowledge of the topological and biological constraints behind the formation of sRNA-mRNA duplexes. Even with the most sophisticated bioinformatics target prediction tools, the large proportion of false predictions may be prohibitive for further analyses. To deal with this issue, sRNA target analyses can be carried out from the resulting gene lists given by RNA-SEQ experiments when available. However, the number of resulting target candidates may be still huge and cannot be easily interpreted by domain experts who need to confront various biological features to prioritize the target candidates. Therefore, novel strategies have to be carried out to improve the specificity of computational prediction results, before proposing new candidates for an expensive experimental validation stage.'),
(97785, 2017, 'bmc', 'Enhanced construction of gene regulatory networks using hub gene information', 'Yu, Donghyeon|Lim, Johan|Wang, Xinlei|Liang, Faming|Xiao, Guanghua', 'Department of Statistics, Inha University, Incheon, Korea.|Department of Statistics, Seoul National University, Seoul, Korea.|Department of Statistical Science, Southern Methodist University, 6425 Boaz Lane, Dallas, TX 75205, USA.|Department of Biostatistics, University of Florida, 2004 Mowry Road, Gainesville, FL 32611, USA.|Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, USA. guanghua.xiao@utsouthwestern.edu.', 'United States|United States|United States|United States|United States', 0, 'Gene Expression Profiling|Gene Regulatory Networks|Humans', 'Journal Article', '10.1186/s12859-017-1576-1', '28335719', 'Gene regulatory networks reveal how genes work together to carry out their biological functions. Reconstructions of gene networks from gene expression data greatly facilitate our understanding of underlying biological mechanisms and provide new opportunities for biomarker and drug discoveries. In gene networks, a gene that has many interactions with other genes is called a hub gene, which usually plays an essential role in gene regulation and biological processes. In this study, we developed a method for reconstructing gene networks using a partial correlation-based approach that incorporates prior information about hub genes. Through simulation studies and two real-data examples, we compare the performance in estimating the network structures between the existing methods and the proposed method.'),
(97786, 2017, 'bmc', 'Generalizing cell segmentation and quantification', 'Wang, Zhenzhou|Li, Haixing', 'State Key Laboratory for Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China. zzwangsia@yahoo.com.|State Key Laboratory for Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China.', 'China|China', 0, 'Calibration|Cell Separation|Humans', 'Journal Article', '10.1186/s12859-017-1604-1', '28335722', 'In recent years, the microscopy technology for imaging cells has developed greatly and rapidly. The accompanying requirements for automatic segmentation and quantification of the imaged cells are becoming more and more. After studied widely in both scientific research and industrial applications for many decades, cell segmentation has achieved great progress, especially in segmenting some specific types of cells, e.g. muscle cells. However, it lacks a framework to address the cell segmentation problems generally. On the contrary, different segmentation methods were proposed to address the different types of cells, which makes the research work divergent. In addition, most of the popular segmentation and quantification tools usually require a great part of manual work.'),
(97787, 2017, 'bmc', 'Identification of long non-coding transcripts with feature selection: a comparative study', 'Ventola, Giovanna M M|Noviello, Teresa M R|DAniello, Salvatore|Spagnuolo, Antonietta|Ceccarelli, Michele|Cerulo, Luigi', 'Department of Science and Technology, University of Sannio, via PortArsa, 11, Benevento, 82100, Italy.|Department of Science and Technology, University of Sannio, via PortArsa, 11, Benevento, 82100, Italy.|Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Villa Comunale, Napoli, 80121, Italy.|Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Villa Comunale, Napoli, 80121, Italy.|Department of Science and Technology, University of Sannio, via PortArsa, 11, Benevento, 82100, Italy.|Department of Science and Technology, University of Sannio, via PortArsa, 11, Benevento, 82100, Italy. lcerulo@unisannio.it.', 'Italy|Italy|Italy|Italy|Italy|Italy', 11, 'Humans|Proteins|RNA, Long Noncoding', 'Journal Article', '10.1186/s12859-017-1594-z', '28335739', 'The unveiling of long non-coding RNAs as important gene regulators in many biological contexts has increased the demand for efficient and robust computational methods to identify novel long non-coding RNAs from transcripts assembled with high throughput RNA-seq data. Several classes of sequence-based features have been proposed to distinguish between coding and non-coding transcripts. Among them, open reading frame, conservation scores, nucleotide arrangements, and RNA secondary structure have been used with success in literature to recognize intergenic long non-coding RNAs, a particular subclass of non-coding RNAs.'),
(97788, 2017, 'bmc', 'Genomic prediction using subsampling', 'Xavier, Alencar|Xu, Shizhong|Muir, William|Rainey, Katy Martin', 'Department of Agronomy, Purdue University, 915 W. State St., Lilly Hall, West Lafayette, IN, 47907, USA.|Department of Plant Science, University of California, 3134 Batchelor Hall, Riverside, CA, 92521, USA.|Department of Animal Science, Purdue University, 915 W. State St., Lilly Hall, West Lafayette, IN, 47907, USA.|Department of Agronomy, Purdue University, 915 W. State St., Lilly Hall, West Lafayette, IN, 47907, USA. krainey@purdue.edu.', 'United States|United States|United States|United States', 0, 'Animals|Bayes Theorem|Genome-Wide Association Study|Genomics|Humans|Sampling Studies', 'Journal Article', '10.1186/s12859-017-1582-3', '28340551', 'Genome-wide assisted selection is a critical tool for the genetic improvement of plants and animals. Whole-genome regression models in Bayesian framework represent the main family of prediction methods. Fitting such models with a large number of observations involves a prohibitive computational burden. We propose the use of subsampling bootstrap Markov chain in genomic prediction. Such method consists of fitting whole-genome regression models by subsampling observations in each round of a Markov Chain Monte Carlo. We evaluated the effect of subsampling bootstrap on prediction and computational parameters.'),
(97789, 2017, 'bmc', 'StrAuto: automation and parallelization of STRUCTURE analysis', 'Chhatre, Vikram E|Emerson, Kevin J', 'Department of Plant Biology, University of Vermont, Burlington, Vermont, USA. vchhatre@uwyo.edu.|Department of Biology, St. Marys College of Maryland, St. Marys City, Maryland, USA.', 'United States|United States', 0, 'Automation|Computational Biology|Computing Methodologies|Humans', 'Journal Article', '10.1186/s12859-017-1593-0', '28340552', 'Population structure inference using the software STRUCTURE has become an integral part of population genetic studies covering a broad spectrum of taxa including humans. The ever-expanding size of genetic data sets poses computational challenges for this analysis. Although at least one tool currently implements parallel computing to reduce computational overload of this analysis, it does not fully automate the use of replicate STRUCTURE analysis runs required for downstream inference of optimal K. There is pressing need for a tool that can deploy population structure analysis on high performance computing clusters.'),
(97790, 2017, 'bmc', 'Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite', 'Peng, Hui|Lan, Chaowang|Zheng, Yi|Hutvagner, Gyorgy|Tao, Dacheng|Li, Jinyan', 'Advanced Analytics Institute, University of Technology Sydney, PO Box 123, Broadway, 2007, NSW, Australia.|Advanced Analytics Institute, University of Technology Sydney, PO Box 123, Broadway, 2007, NSW, Australia.|Advanced Analytics Institute, University of Technology Sydney, PO Box 123, Broadway, 2007, NSW, Australia.|Centre for Health Technologies, University of Technology Sydney, PO Box 123, Broadway, 2007, NSW, Australia.|School of Information Technologies and the Faculty of Engineering and Information Technologies, University of Sydney, J12/318 Cleveland St, Darlington, 2008, NSW, Australia.|Advanced Analytics Institute, University of Technology Sydney, PO Box 123, Broadway, 2007, NSW, Australia. Jinyan.Li@uts.edu.au.', 'Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Computational Biology|Humans|MicroRNAs', 'Journal Article', '10.1186/s12859-017-1605-0', '28340554', 'MicroRNAs always function cooperatively in their regulation of gene expression. Dysfunctions of these co-functional microRNAs can play significant roles in disease development. We are interested in those multi-disease associated co-functional microRNAs that regulate their common dysfunctional target genes cooperatively in the development of multiple diseases. The research is potentially useful for human disease studies at the transcriptional level and for the study of multi-purpose microRNA therapeutics.'),
(97791, 2017, 'bmc', 'DIRProt: a computational approach for discriminating insecticide resistant proteins from non-resistant proteins', 'Meher, Prabina Kumar|Sahu, Tanmaya Kumar|Banchariya, Anjali|Rao, Atmakuri Ramakrishna', 'Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India.|Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India.|Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India.|Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India. rao.cshl.work@gmail.com.', 'India|India|India|India', 3, 'Animals|Cytochrome P-450 Enzyme System|Humans|Insecticides|Proteins|gamma-Aminobutyric Acid', 'Journal Article', '10.1186/s12859-017-1587-y', '28340571', 'Insecticide resistance is a major challenge for the control program of insect pests in the fields of crop protection, human and animal health etc. Resistance to different insecticides is conferred by the proteins encoded from certain class of genes of the insects. To distinguish the insecticide resistant proteins from non-resistant proteins, no computational tool is available till date. Thus, development of such a computational tool will be helpful in predicting the insecticide resistant proteins, which can be targeted for developing appropriate insecticides.'),
(97792, 2017, 'bmc', 'The autophagy interaction network of the aging model Podospora anserina', 'Philipp, Oliver|Hamann, Andrea|Osiewacz, Heinz D|Koch, Ina', 'Molecular Bioinformatics, Institute of Computer Science, Faculty of Computer Science and Mathematics and Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Str. 11-15, Frankfurt am Main, 60325, Germany.|Molecular Developmental Biology, Institute of Molecular Biosciences, Faculty for Biosciences & Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe-University Frankfurt am Main, Max-von-Laue-Str. 9, Frankfurt am Main, 60438, Germany.|Molecular Developmental Biology, Institute of Molecular Biosciences, Faculty for Biosciences & Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe-University Frankfurt am Main, Max-von-Laue-Str. 9, Frankfurt am Main, 60438, Germany.|Molecular Bioinformatics, Institute of Computer Science, Faculty of Computer Science and Mathematics and Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Str. 11-15, Frankfurt am Main, 60325, Germany. ina.koch@bioinformatik.uni-frankfurt.de.', 'Germany|Germany|Germany|Germany', 4, 'Aging|Autophagy|Humans|Podospora', 'Journal Article', '10.1186/s12859-017-1603-2', '28347269', 'Autophagy is a conserved molecular pathway involved in the degradation and recycling of cellular components. It is active either as response to starvation or molecular damage. Evidence is emerging that autophagy plays a key role in the degradation of damaged cellular components and thereby affects aging and lifespan control. In earlier studies, it was found that autophagy in the aging model Podospora anserina acts as a longevity assurance mechanism. However, only little is known about the individual components controlling autophagy in this aging model. Here, we report a biochemical and bioinformatics study to detect the protein-protein interaction (PPI) network of P. anserina combining experimental and theoretical methods.'),
(97793, 2017, 'bmc', 'Inclusion of the fitness sharing technique in an evolutionary algorithm to analyze the fitness landscape of the genetic code adaptability', 'Santos, José|Monteagudo, Ángel', 'Department of Computer Science, University of A Coruña, Campus de Elviña s/n, A Coruña, 15071, Spain. jose.santos@udc.es.|Department of Computer Science, University of A Coruña, Campus de Elviña s/n, A Coruña, 15071, Spain.', 'Spain|Spain', 11, 'Algorithms|Biological Evolution|Codon|Evolution, Molecular|Genetic Code|Models, Genetic|Selection, Genetic', 'Journal Article', '10.1186/s12859-017-1608-x', '28347270', 'The canonical code, although prevailing in complex genomes, is not universal. It was shown the canonical genetic code superior robustness compared to random codes, but it is not clearly determined how it evolved towards its current form. The error minimization theory considers the minimization of point mutation adverse effect as the main selection factor in the evolution of the code. We have used simulated evolution in a computer to search for optimized codes, which helps to obtain information about the optimization level of the canonical code in its evolution. A genetic algorithm searches for efficient codes in a fitness landscape that corresponds with the adaptability of possible hypothetical genetic codes. The lower the effects of errors or mutations in the codon bases of a hypothetical code, the more efficient or optimal is that code. The inclusion of the fitness sharing technique in the evolutionary algorithm allows the extent to which the canonical genetic code is in an area corresponding to a deep local minimum to be easily determined, even in the high dimensional spaces considered.'),
(97794, 2017, 'bmc', 'Erratum to: How can functional annotations be derived from profiles of phenotypic annotations?', 'Serrano-Solano, Beatriz|Ramos, Antonio Díaz|Hériché, Jean-Karim|Ranea, Juan A G', 'Department of Molecular Biology and Biochemistry, University of Málaga, Boulevard Louis Pasteur, 29071, Málaga, Spain.|Department of Algebra, Geometry and Topology, University of Málaga, Boulevard Louis Pasteur, 29071, Málaga, Spain.|European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117, Heidelberg, Germany.|Department of Molecular Biology and Biochemistry, University of Málaga, Boulevard Louis Pasteur, 29071, Málaga, Spain. ranea@uma.es.', 'Spain|Spain|Germany|Spain', 0, '-', 'Journal Article', '10.1186/s12859-017-1607-y', '28347274', '-'),
(97795, 2017, 'bmc', 'M-AMST: an automatic 3D neuron tracing method based on mean shift and adapted minimum spanning tree', 'Wan, Zhijiang|He, Yishan|Hao, Ming|Yang, Jian|Zhong, Ning', 'Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China.|Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Japan.|Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Japan.|Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Japan.|Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China. zhong@maebashi-it.ac.jp.', 'China|Japan|Japan|Japan|China', 4, 'Algorithms|Animals|Automation|Brain|Drosophila|Imaging, Three-Dimensional|Microscopy, Confocal|Neurons', 'Journal Article', '10.1186/s12859-017-1597-9', '28356056', 'Understanding the working mechanism of the brain is one of the grandest challenges for modern science. Toward this end, the BigNeuron project was launched to gather a worldwide community to establish a big data resource and a set of the state-of-the-art of single neuron reconstruction algorithms. Many groups contributed their own algorithms for the project, including our mean shift and minimum spanning tree (M-MST). Although M-MST is intuitive and easy to implement, the MST just considers spatial information of single neuron and ignores the shape information, which might lead to less precise connections between some neuron segments. In this paper, we propose an improved algorithm, namely M-AMST, in which a rotating sphere model based on coordinate transformation is used to improve the weight calculation method in M-MST.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97796, 2017, 'bmc', 'A neural joint model for entity and relation extraction from biomedical text', 'Li, Fei|Zhang, Meishan|Fu, Guohong|Ji, Donghong', 'School of Computer, Wuhan University, Bayi Road, Wuhan, China.|School of Computer Science and Technology, Heilongjiang University, Xuefu Road, Harbin, China.|School of Computer Science and Technology, Heilongjiang University, Xuefu Road, Harbin, China.|School of Computer, Wuhan University, Bayi Road, Wuhan, China. dhji@whu.edu.cn.', 'China|China|China|China', 0, 'Biomedical Research|Data Mining|Databases, Factual|Models, Theoretical|Monocytes|Neural Networks (Computer)', 'Journal Article', '10.1186/s12859-017-1609-9', '28359255', 'Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above.'),
(97797, 2017, 'bmc', 'Significance evaluation in factor graphs', 'Madsen, Tobias|Hobolth, Asger|Jensen, Jens Ledet|Pedersen, Jakob Skou', 'Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, Aarhus, Denmark. tobias.madsen@clin.au.dk.|Bioinformatics Research Center, Aarhus University, C.F. Møllers Allé 8, Aarhus, Denmark.|Department of Mathematics, Aarhus University, Ny Munkegade 118, Aarhus, Denmark.|Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, Aarhus, Denmark.', 'Denmark|Denmark|Denmark|Denmark', 2, 'Algorithms|Amino Acid Sequence|CCCTC-Binding Factor|Computational Biology|Genomics|Humans|MCF-7 Cells|Models, Theoretical|Neoplasms|Phylogeny|Probability|Protein Interaction Domains and Motifs|Repressor Proteins|Sequence Alignment', 'Journal Article', '10.1186/s12859-017-1614-z', '28359297', 'Factor graphs provide a flexible and general framework for specifying probability distributions. They can capture a range of popular and recent models for analysis of both genomics data as well as data from other scientific fields. Owing to the ever larger data sets encountered in genomics and the multiple-testing issues accompanying them, accurate significance evaluation is of great importance. We here address the problem of evaluating statistical significance of observations from factor graph models.'),
(97798, 2017, 'bmc', 'Network-based analysis of transcriptional profiles from chemical perturbations experiments', 'Mulas, Francesca|Li, Amy|Sherr, David H|Monti, Stefano', 'Division of Computational Biomedicine, Boston University School of Medicine, 72 E Concord St, Boston, 02118, USA.|Division of Computational Biomedicine, Boston University School of Medicine, 72 E Concord St, Boston, 02118, USA.|Department of Environmental Health, Boston University School of Public Health, 72 E Concord St, Boston, 02118, USA.|Division of Computational Biomedicine, Boston University School of Medicine, 72 E Concord St, Boston, 02118, USA. smonti@bu.edu.', 'United States|United States|United States|United States', 7, 'Animals|Carcinogens|Computer Simulation|Gene Regulatory Networks|Humans|Metabolic Networks and Pathways|Mutagens|Toxicogenetics|Transcriptome', 'Journal Article', '10.1186/s12859-017-1536-9', '28361664', 'Methods for inference and comparison of biological networks are emerging as powerful tools for the identification of groups of tightly connected genes whose activity may be altered during disease progression or due to chemical perturbations. Connectivity-based comparisons help identify aggregate changes that would be difficult to detect with differential analysis methods comparing individual genes.'),
(97799, 2017, 'bmc', 'A computational method for estimating the PCR duplication rate in DNA and RNA-seq experiments', 'Bansal, Vikas', 'Department of Pediatrics, School of Medicine, University of California San Diego, 9500 Gilman Drive, 92093, La JollaCA, USA. vibansal@cs.ucsd.edu.', 'United States', 0, 'Alleles|Cell Line|Cluster Analysis|Computational Biology|Computer Simulation|DNA Fragmentation|Databases, Genetic|Exome|Genome, Human|Genotyping Techniques|Heterozygote|High-Throughput Nucleotide Sequencing|Humans|Models, Theoretical|Polymerase Chain Reaction|Sequence Analysis, DNA|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-017-1471-9', '28361665', 'PCR amplification is an important step in the preparation of DNA sequencing libraries prior to high-throughput sequencing. PCR amplification introduces redundant reads in the sequence data and estimating the PCR duplication rate is important to assess the frequency of such reads. Existing computational methods do not distinguish PCR duplicates from natural read duplicates that represent independent DNA fragments and therefore, over-estimate the PCR duplication rate for DNA-seq and RNA-seq experiments.'),
(97800, 2017, 'bmc', 'Towards targeted combinatorial therapy design for the treatment of castration-resistant prostate cancer', 'Arshad, Osama Ali|Datta, Aniruddha', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA. datta@ece.tamu.edu.', 'United States|United States', 8, 'Antineoplastic Combined Chemotherapy Protocols|Gene Regulatory Networks|Humans|Male|Molecular Targeted Therapy|Neoplasm Proteins|Prostatic Neoplasms, Castration-Resistant|Signal Transduction', 'Journal Article', '10.1186/s12859-017-1522-2', '28361666', 'Prostate cancer is one of the most prevalent cancers in males in the United States and amongst the leading causes of cancer related deaths. A particularly virulent form of this disease is castration-resistant prostate cancer (CRPC), where patients no longer respond to medical or surgical castration. CRPC is a complex, multifaceted and heterogeneous malady with limited standard treatment options.'),
(97801, 2017, 'bmc', 'Combination therapy design for maximizing sensitivity and minimizing toxicity', 'Matlock, Kevin|Berlow, Noah|Keller, Charles|Pal, Ranadip', 'Department of Electrical and Computer Engineering, Texas Tech University, 1012 Boston Ave, Lubbock, 79409, TX, USA.|Childrens Cancer Therapy Development Institute, Portland, 97005, OR, USA.|Childrens Cancer Therapy Development Institute, Portland, 97005, OR, USA.|Department of Electrical and Computer Engineering, Texas Tech University, 1012 Boston Ave, Lubbock, 79409, TX, USA. ranadip.pal@ttu.edu.', 'United States|United States|United States|United States', 6, 'Algorithms|Antineoplastic Combined Chemotherapy Protocols|Cell Survival|Genomics|Humans|Models, Theoretical|Neoplasms', 'Journal Article', '10.1186/s12859-017-1523-1', '28361667', 'Design of personalized targeted therapies involve modeling of patient sensitivity to various drugs and drug combinations. Majority of studies evaluate the sensitivity of tumor cells to targeted drugs without modeling the effect of the drugs on normal cells. In this article, we consider the individual modeling of drug responses to tumor and normal cells and utilize them to design targeted combination therapies that maximize sensitivity over tumor cells and minimize toxicity over normal cells.'),
(97802, 2017, 'bmc', 'GATK hard filtering: tunable parameters to improve variant calling for next generation sequencing targeted gene panel data', 'De Summa, Simona|Malerba, Giovanni|Pinto, Rosamaria|Mori, Antonio|Mijatovic, Vladan|Tommasi, Stefania', 'IRCCS-Istituto Tumori Giovanni Paolo II, Molecular Genetics Laboratory, viale Orazio Flacco, 65, 70124, Bari, Italy.|Department of Neuroscience, Biomedicine and Movement Sciences, Section of Biology and Genetics, University of Verona, Strada Le Grazie 8, 37135, Verona, Italy. giovanni.malerba@univr.it.|IRCCS-Istituto Tumori Giovanni Paolo II, Molecular Genetics Laboratory, viale Orazio Flacco, 65, 70124, Bari, Italy.|Department of Neuroscience, Biomedicine and Movement Sciences, Section of Biology and Genetics, University of Verona, Strada Le Grazie 8, 37135, Verona, Italy.|Department of Neuroscience, Biomedicine and Movement Sciences, Section of Biology and Genetics, University of Verona, Strada Le Grazie 8, 37135, Verona, Italy.|IRCCS-Istituto Tumori Giovanni Paolo II, Molecular Genetics Laboratory, viale Orazio Flacco, 65, 70124, Bari, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Computer Simulation|Exome|High-Throughput Nucleotide Sequencing|Humans|Mutation|Polymorphism, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1537-8', '28361668', 'NGS technology represents a powerful alternative to the standard Sanger sequencing in the context of clinical setting. The proprietary software that are generally used for variant calling often depend on preset parameters that may not fit in a satisfactory manner for different genes. GATK, which is widely used in the academic world, is rich in parameters for variant calling. However the self-adjusting parameter calibration of GATK requires data from a large number of exomes. When these are not available, which is the standard condition of a diagnostic laboratory, the parameters must be set by the operator (hard filtering). The aim of the present paper was to set up a procedure to assess the best parameters to be used in the hard filtering of GATK. This was pursued by using classification trees on true and false variants from simulated sequences of a real dataset data.'),
(97803, 2017, 'bmc', 'Interpretive time-frequency analysis of genomic sequences', 'Hassani Saadi, Hamed|Sameni, Reza|Zollanvari, Amin', 'School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.|School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.|Department of Electrical and Electronic Engineering, Nazarbayev University, Astana, Kazakhstan. amin.zollanvari@nu.edu.kz.', '-|-|Kazakhstan', 3, 'Algorithms|Animals|Brachyura|Genome|Genomics|Models, Statistical|Sequence Analysis, DNA|Time Factors', 'Journal Article', '10.1186/s12859-017-1524-0', '28361669', 'Time-Frequency (TF) analysis has been extensively used for the analysis of non-stationary numeric signals in the past decade. At the same time, recent studies have statistically confirmed the non-stationarity of genomic non-numeric sequences and suggested the use of non-stationary analysis for these sequences. The conventional approach to analyze non-numeric genomic sequences using techniques specific to numerical data is to convert non-numerical data into numerical values in some way and then apply time or transform domain signal processing algorithms. Nevertheless, this approach raises questions regarding the relative magnitudes under numeric transforms, which can potentially lead to spurious patterns or misinterpretation of results.'),
(97804, 2017, 'bmc', 'Prediction of virus-host infectious association by supervised learning methods', 'Zhang, Mengge|Yang, Lianping|Ren, Jie|Ahlgren, Nathan A|Fuhrman, Jed A|Sun, Fengzhu', 'Molecular and Computational Biology Program, University of Southern California, Los Angeles, California, USA.|College of Sciences, Northeastern University, Shenyang, China.|Molecular and Computational Biology Program, University of Southern California, Los Angeles, California, USA.|Department of Biological Sciences and Wrigley Institute for Environmental Studies, University of Southern California, Los Angeles, California, USA.|Department of Biological Sciences and Wrigley Institute for Environmental Studies, University of Southern California, Los Angeles, California, USA.|Molecular and Computational Biology Program, University of Southern California, Los Angeles, California, USA. fsun@usc.edu.', 'United States|China|United States|United States|United States|United States', 9, 'Bacteria|Bayes Theorem|DNA, Viral|Genome, Viral|Host-Pathogen Interactions|Likelihood Functions|Logistic Models|Metagenomics|Models, Theoretical|ROC Curve|Reproducibility of Results|Sequence Analysis, DNA|Support Vector Machine|Viruses', 'Journal Article', '10.1186/s12859-017-1473-7', '28361670', 'The study of virus-host infectious association is important for understanding the functions and dynamics of microbial communities. Both cellular and fractionated viral metagenomic data generate a large number of viral contigs with missing host information. Although relative simple methods based on the similarity between the word frequency vectors of viruses and bacterial hosts have been developed to study virus-host associations, the problem is significantly understudied. We hypothesize that machine learning methods based on word frequencies can be efficiently used to study virus-host infectious associations.'),
(97805, 2017, 'bmc', 'Improving the accuracy of high-throughput protein-protein affinity prediction may require better training data', 'Dias, Raquel|Kolaczkowski, Bryan', 'Department of Microbiology and Cell Science, University of Florida, Gainesville, FL, USA.|Department of Microbiology and Cell Science, University of Florida, Gainesville, FL, USA. bryank@ufl.edu.', 'United States|United States', 0, 'Animals|Data Accuracy|Humans|Machine Learning|Models, Molecular|Models, Statistical|Protein Binding|Protein Conformation|Proteins', 'Journal Article', '10.1186/s12859-017-1533-z', '28361672', 'One goal of structural biology is to understand how a proteins 3-dimensional conformation determines its capacity to interact with potential ligands. In the case of small chemical ligands, deconstructing a static protein-ligand complex into its constituent atom-atom interactions is typically sufficient to rapidly predict ligand affinity with high accuracy (>70% correlation between predicted and experimentally-determined affinity), a fact that is exploited to support structure-based drug design. We recently found that protein-DNA/RNA affinity can also be predicted with high accuracy using extensions of existing techniques, but protein-protein affinity could not be predicted with >60% correlation, even when the protein-protein complex was available.'),
(97806, 2017, 'bmc', 'AfterQC: automatic filtering, trimming, error removing and quality control for fastq data', 'Chen, Shifu|Huang, Tanxiao|Zhou, Yanqing|Han, Yue|Xu, Mingyan|Gu, Jia', 'Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Xueyuan Road, Shenzhen, China.|HaploX BioTechnology, Songpingshan Road, Shenzhen, China.|HaploX BioTechnology, Songpingshan Road, Shenzhen, China.|HaploX BioTechnology, Songpingshan Road, Shenzhen, China.|HaploX BioTechnology, Songpingshan Road, Shenzhen, China.|Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Xueyuan Road, Shenzhen, China. jia.gu@siat.ac.cn.', 'China|China|China|China|China|China', 64, 'Algorithms|Amino Acid Sequence|DNA|DNA Mismatch Repair|Databases, Genetic|High-Throughput Nucleotide Sequencing|Humans|Quality Control|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1469-3', '28361673', 'Some applications, especially those clinical applications requiring high accuracy of sequencing data, usually have to face the troubles caused by unavoidable sequencing errors. Several tools have been proposed to profile the sequencing quality, but few of them can quantify or correct the sequencing errors. This unmet requirement motivated us to develop AfterQC, a tool with functions to profile sequencing errors and correct most of them, plus highly automated quality control and data filtering features. Different from most tools, AfterQC analyses the overlapping of paired sequences for pair-end sequencing data. Based on overlapping analysis, AfterQC can detect and cut adapters, and furthermore it gives a novel function to correct wrong bases in the overlapping regions. Another new feature is to detect and visualise sequencing bubbles, which can be commonly found on the flowcell lanes and may raise sequencing errors. Besides normal per cycle quality and base content plotting, AfterQC also provides features like polyX (a long sub-sequence of a same base X) filtering, automatic trimming and K-MER based strand bias profiling.'),
(97807, 2017, 'bmc', 'BATVI: Fast, sensitive and accurate detection of virus integrations', 'Tennakoon, Chandana|Sung, Wing Kin', 'Department of Computational and Systems Biology, Genome Institute of Singapore, Singapore, 138672, Singapore.|Department of Computational and Systems Biology, Genome Institute of Singapore, Singapore, 138672, Singapore. ksung@comp.nus.edu.sg.', 'Singapore|Singapore', 0, 'Algorithms|Cluster Analysis|DNA, Viral|DNA-Binding Proteins|Genome, Human|High-Throughput Nucleotide Sequencing|Host-Pathogen Interactions|Humans|Liver Neoplasms|Models, Theoretical|Sequence Analysis, DNA|Software|Telomerase|Virus Integration', 'Journal Article', '10.1186/s12859-017-1470-x', '28361674', 'The study of virus integrations in human genome is important since virus integrations were shown to be associated with diseases. In the literature, few methods have been proposed that predict virus integrations using next generation sequencing datasets. Although they work, they are slow and are not very sensitive.'),
(97808, 2017, 'bmc', 'A statistical method for the conservative adjustment of false discovery rate (q-value)', 'Lai, Yinglei', 'Department of Statistics and Biostatistics Center, The George Washington University, Washington D.C., 20052, USA. ylai@gwu.edu.', 'United States', 12, 'Algorithms|Computational Biology|Data Interpretation, Statistical|Diabetes Mellitus, Type 2|False Positive Reactions|Genetic Association Studies|Genome, Human|Humans|Islets of Langerhans|Male|Models, Statistical|Prostatic Neoplasms|Reproducibility of Results|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-017-1474-6', '28361675', 'q-value is a widely used statistical method for estimating false discovery rate (FDR), which is a conventional significance measure in the analysis of genome-wide expression data. q-value is a random variable and it may underestimate FDR in practice. An underestimated FDR can lead to unexpected false discoveries in the follow-up validation experiments. This issue has not been well addressed in literature, especially in the situation when the permutation procedure is necessary for p-value calculation.'),
(97809, 2017, 'bmc', 'Identifying miRNA sponge modules using biclustering and regulatory scores', 'Zhang, Junpeng|Le, Thuc D|Liu, Lin|Li, Jiuyong', 'School of Engineering, Dali University, Dali, Yunnan, 671003, Peoples Republic of China. zhangjunpeng_411@yahoo.com.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia. jiuyong.li@unisa.edu.au.', 'China|Australia|Australia|Australia', 8, 'Animals|Breast Neoplasms|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|MicroRNAs|Models, Molecular|RNA, Messenger|Reproducibility of Results', 'Journal Article', '10.1186/s12859-017-1467-5', '28361682', 'MicroRNA (miRNA) sponges with multiple tandem miRNA binding sequences can sequester miRNAs from their endogenous target mRNAs. Therefore, miRNA sponge acting as a decoy is extremely important for long-term loss-of-function studies both in vivo and in silico. Recently, a growing number of in silico methods have been used as an effective technique to generate hypotheses for in vivo methods for studying the biological functions and regulatory mechanisms of miRNA sponges. However, most existing in silico methods only focus on studying miRNA sponge interactions or networks in cancer, the module-level properties of miRNA sponges in cancer is still largely unknown.'),
(97810, 2017, 'bmc', 'Comparative network stratification analysis for identifying functional interpretable network biomarkers', 'Zhang, Chuanchao|Liu, Juan|Shi, Qianqian|Zeng, Tao|Chen, Luonan', 'State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, 430072, China.|State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, 430072, China. liujuan@whu.edu.cn.|Key Laboratory of Systems Biology, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.|Key Laboratory of Systems Biology, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China. zengtao@sibs.ac.cn.|Key Laboratory of Systems Biology, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China. lnchen@sibs.ac.cn.', 'China|China|China|China|China', 0, 'Biomarkers|Computational Biology|Databases, Genetic|Diabetes Mellitus, Type 1|Diabetes Mellitus, Type 2|Humans|Insulin|Insulin Secretion|Models, Theoretical|Precision Medicine', 'Journal Article', '10.1186/s12859-017-1462-x', '28361683', 'A major challenge of bioinformatics in the era of precision medicine is to identify the molecular biomarkers for complex diseases. It is a general expectation that these biomarkers or signatures have not only strong discrimination ability, but also readable interpretations in a biological sense. Generally, the conventional expression-based or network-based methods mainly capture differential genes or differential networks as biomarkers, however, such biomarkers only focus on phenotypic discrimination and usually have less biological or functional interpretation. Meanwhile, the conventional function-based methods could consider the biomarkers corresponding to certain biological functions or pathways, but ignore the differential information of genes, i.e., disregard the active degree of particular genes involved in particular functions, thereby resulting in less discriminative ability on phenotypes. Hence, it is strongly demanded to develop elaborate computational methods to directly identify functional network biomarkers with both discriminative power on disease states and readable interpretation on biological functions.'),
(97811, 2017, 'bmc', 'NEArender: an R package for functional interpretation of omics data via network enrichment analysis', 'Jeggari, Ashwini|Alexeyenko, Andrey', 'Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.|National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Stockholm, Sweden. andrej.alekseenko@scilifelab.se.', 'Sweden|Sweden', 8, 'Computational Biology|DNA Methylation|Genes|Humans|Mutation|Software|Transcriptome', 'Journal Article', '10.1186/s12859-017-1534-y', '28361684', 'The statistical evaluation of pathway enrichment, i.e. of gene profiles confluence to the pathway level, allows exploring molecular landscapes using functionally annotated gene sets. However, pathway scores can also be used as predictive features in machine learning. That requires, firstly, increasing statistical power and biological relevance via a network enrichment analysis (NEA) and, secondly, a fast and convenient procedure for rendering the original data into a space of pathway scores. However, previous implementations of NEA involved multiple runs of network randomization and were therefore slow.'),
(97812, 2017, 'bmc', 'DTL reconciliation repair', 'Ma, Weiyun|Smirnov, Dmitriy|Libeskind-Hadas, Ran', 'Department of Computer Science, Harvey Mudd College, Claremont, California, USA.|Pomona College, Claremont, California, USA.|Department of Computer Science, Harvey Mudd College, Claremont, California, USA. hadas@cs.hmc.edu.', 'United States|United States|United States', 4, 'Algorithms|Computational Biology|Evolution, Molecular|Feasibility Studies|Models, Theoretical|Phylogeny|Software', 'Journal Article', '10.1186/s12859-017-1463-9', '28361686', 'Maximum parsimony phylogenetic tree reconciliation is an important technique for reconstructing the evolutionary histories of hosts and parasites, genes and species, and other interdependent pairs. Since the problem of finding temporally feasible maximum parsimony reconciliations is NP-complete, current methods use either exact algorithms with exponential worst-case running time or heuristics that do not guarantee optimal solutions.'),
(97813, 2017, 'bmc', 'Pysim-sv: a package for simulating structural variation data with GC-biases', 'Xia, Yuchao|Liu, Yun|Deng, Minghua|Xi, Ruibin', 'School of Mathematics Science and Center for Statistical Science, Peking University, Yiheyuan Road 5, Beijing, 100871, China.|School of Mathematics Science and Center for Statistical Science, Peking University, Yiheyuan Road 5, Beijing, 100871, China.|School of Mathematics Science and Center for Statistical Science, Peking University, Yiheyuan Road 5, Beijing, 100871, China. dengmh@math.pku.edu.cn.|School of Mathematics Science and Center for Statistical Science, Peking University, Yiheyuan Road 5, Beijing, 100871, China. ruibinxi@math.pku.edu.cn.', 'China|China|China|China', 8, 'Algorithms|Alleles|Aneuploidy|Base Composition|Computer Simulation|DNA Copy Number Variations|Databases, Genetic|Genetic Variation|Genome, Human|Germ-Line Mutation|High-Throughput Nucleotide Sequencing|Humans|Sensitivity and Specificity|Software', 'Journal Article', '10.1186/s12859-017-1464-8', '28361688', 'Structural variations (SVs) are wide-spread in human genomes and may have important implications in disease-related and evolutionary studies. High-throughput sequencing (HTS) has become a major platform for SV detection and simulation serves as a powerful and cost-effective approach for benchmarking SV detection algorithms. Accurate performance assessment by simulation requires the simulator capable of generating simulation data with all important features of real data, such GC biases in HTS data and various complexities in tumor data. However, no available package has systematically addressed all issues in data simulation for SV benchmarking.'),
(97814, 2017, 'bmc', 'AVC: Selecting discriminative features on basis of AUC by maximizing variable complementarity', 'Sun, Lei|Wang, Jun|Wei, Jinmao', 'Institute of Big Data, College of Computer and Control Engineering, Nankai University, 38 Tongyan Road, Tianjin, 300350, China.|Institute of Big Data, College of Computer and Control Engineering, Nankai University, 38 Tongyan Road, Tianjin, 300350, China.|Institute of Big Data, College of Computer and Control Engineering, Nankai University, 38 Tongyan Road, Tianjin, 300350, China. weijm@nankai.edu.cn.', 'China|China|China', 0, 'Algorithms|Area Under Curve|Computational Biology|Empirical Research|Models, Theoretical|Oligonucleotide Array Sequence Analysis|ROC Curve', 'Journal Article', '10.1186/s12859-017-1468-4', '28361689', 'The Receiver Operator Characteristic (ROC) curve is well-known in evaluating classification performance in biomedical field. Owing to its superiority in dealing with imbalanced and cost-sensitive data, the ROC curve has been exploited as a popular metric to evaluate and find out disease-related genes (features). The existing ROC-based feature selection approaches are simple and effective in evaluating individual features. However, these approaches may fail to find real target feature subset due to their lack of effective means to reduce the redundancy between features, which is essential in machine learning.'),
(97815, 2017, 'bmc', 'Improving prediction of burial state of residues by exploiting correlation among residues', 'Gong, Haie|Zhang, Haicang|Zhu, Jianwei|Wang, Chao|Sun, Shiwei|Zheng, Wei-Mou|Bu, Dongbo', 'Key Lab of Intelligent Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.|Key Lab of Intelligent Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.|Key Lab of Intelligent Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.|Key Lab of Intelligent Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.|Key Lab of Intelligent Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.|Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, 100190, China. zheng@itp.ac.cn.|Key Lab of Intelligent Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China. dbu@ict.ac.cn.', 'China|China|China|China|China|China|China', 1, 'Databases, Factual|Hydrophobic and Hydrophilic Interactions|Models, Theoretical|Protein Conformation|Proteins|Reproducibility of Results|Solvents', 'Journal Article', '10.1186/s12859-017-1475-5', '28361691', 'Residues in a protein might be buried inside or exposed to the solvent surrounding the protein. The buried residues usually form hydrophobic cores to maintain the structural integrity of proteins while the exposed residues are tightly related to protein functions. Thus, the accurate prediction of solvent accessibility of residues will greatly facilitate our understanding of both structure and functionalities of proteins. Most of the state-of-the-art prediction approaches consider the burial state of each residue independently, thus neglecting the correlations among residues.'),
(97816, 2017, 'bmc', 'Selected research articles from the 2016 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC)', 'Yoon, Byung-Jun|Qian, Xiaoning|Kahveci, Tamer', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. bjyoon@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA.|Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32611-6125, USA.', 'United States|United States|United States', 2, '-', 'Introductory Journal Article', '10.1186/s12859-017-1521-3', '28361697', '-'),
(97817, 2017, 'bmc', 'In silico prediction of lncRNA function using tissue specific and evolutionary conserved expression', 'Perron, Umberto|Provero, Paolo|Molineris, Ivan', 'Department of Molecular Biotechnology and Health Sciences, University of Turin, via Nizza 52, Torino, 10126, Italy.|Department of Molecular Biotechnology and Health Sciences, University of Turin, via Nizza 52, Torino, 10126, Italy.|Department of Molecular Biotechnology and Health Sciences, University of Turin, via Nizza 52, Torino, 10126, Italy. ivan.molineris@unito.it.', 'Italy|Italy|Italy', 21, 'Algorithms|Animals|Computational Biology|Computer Simulation|Evolution, Molecular|Humans|Models, Genetic|Organ Specificity|RNA, Long Noncoding|Transcriptome', 'Journal Article', '10.1186/s12859-017-1535-x', '28361701', 'In recent years long non coding RNAs (lncRNAs) have been the subject of increasing interest. Thanks to many recent functional studies, the existence of a large class of lncRNAs with potential regulatory functions is now widely accepted. Although an increasing number of lncRNAs is being characterized and shown to be involved in many biological processes, the functions of the vast majority lncRNA genes is still unknown. Therefore computational methods able to take advantage of the increasing amount of publicly available data to predict lncRNA functions could be very useful.'),
(97818, 2017, 'bmc', 'Comparison of tissue/disease specific integrated networks using directed graphlet signatures', 'Sonmez, Arzu Burcak|Can, Tolga', 'Department of Medical Informatics, Informatics Institute, Middle East Technical University, Dumlupinar Bulvari No:1, Ankara, 06800, Turkey.|Department of Computer Egineering, Middle East Technical University, Dumlupinar Bulvari No:1, Ankara, 06800, Turkey. tcan@metu.edu.tr.', 'Turkey|Turkey', 6, 'Computer Graphics|Computer Simulation|Disease|Gene Regulatory Networks|Humans|Models, Biological|Organ Specificity|Protein Interaction Mapping|Protein Interaction Maps|Proteins|Proteome|User-Computer Interface', 'Comparative Study', '10.1186/s12859-017-1525-z', '28361704', 'Analysis of integrated genome-scale networks is a challenging problem due to heterogeneity of high-throughput data. There are several topological measures, such as graphlet counts, for characterization of biological networks.'),
(97819, 2017, 'bmc', 'Comprehensive evaluation of RNA-seq quantification methods for linearity', 'Jin, Haijing|Wan, Ying-Wooi|Liu, Zhandong', 'Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, One Baylor Plaza, Houston, 77030, TX, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, 77030, TX, USA.|Department of Pediatrics-Neurology, Jan and Dan Duncan Neurological Research Institute, Baylor College of Medicine, 1250 Moursund St., Suite 1325, Houston, 77030, TX, USA. zhandonl@bcm.edu.', 'United States|United States|United States', 19, 'Computer Simulation|Gene Expression Profiling|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Linear Models|Protein Isoforms|RNA|Sequence Analysis, RNA|Transcriptome', 'Evaluation Studies', '10.1186/s12859-017-1526-y', '28361706', 'Deconvolution is a mathematical process of resolving an observed function into its constituent elements. In the field of biomedical research, deconvolution analysis is applied to obtain single cell-type or tissue specific signatures from a mixed signal and most of them follow the linearity assumption. Although recent development of next generation sequencing technology suggests RNA-seq as a fast and accurate method for obtaining transcriptomic profiles, few studies have been conducted to investigate best RNA-seq quantification methods that yield the optimum linear space for deconvolution analysis.'),
(97820, 2017, 'bmc', 'Investigation and identification of protein carbonylation sites based on position-specific amino acid composition and physicochemical features', 'Weng, Shun-Long|Huang, Kai-Yao|Kaunang, Fergie Joanda|Huang, Chien-Hsun|Kao, Hui-Ju|Chang, Tzu-Hao|Wang, Hsin-Yao|Lu, Jang-Jih|Lee, Tzong-Yi', 'Department of Obstetrics and Gynecology, Hsinchu Mackay Memorial Hospital, Hsin-Chu, 300, Taiwan.|Department of Medical Research, Hsinchu Mackay Memorial Hospital, Hsin-Chu, 300, Taiwan.|Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan.|Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan.|Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan.|Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, 110, Taiwan.|Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, 333, Taiwan.|Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, 333, Taiwan. janglu45@gmail.com.|Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan. francis@saturn.yzu.edu.tw.', '-|-|-|-|-|-|-|-|-', 16, 'Amino Acid Sequence|Amino Acids|Arginine|Chemical Phenomena|Humans|Lysine|Models, Theoretical|Position-Specific Scoring Matrices|Proline|Protein Carbonylation|Protein Processing, Post-Translational|Proteins|Reactive Oxygen Species|Substrate Specificity|Threonine', 'Journal Article', '10.1186/s12859-017-1472-8', '28361707', 'Protein carbonylation, an irreversible and non-enzymatic post-translational modification (PTM), is often used as a marker of oxidative stress. When reactive oxygen species (ROS) oxidized the amino acid side chains, carbonyl (CO) groups are produced especially on Lysine (K), Arginine (R), Threonine (T), and Proline (P). Nevertheless, due to the lack of information about the carbonylated substrate specificity, we were encouraged to develop a systematic method for a comprehensive investigation of protein carbonylation sites.'),
(97821, 2017, 'bmc', 'A framework for space-efficient read clustering in metagenomic samples', 'Alanko, Jarno|Cunial, Fabio|Belazzougui, Djamal|Mxc3xa4kinen, Veli', 'Department of Computer Science, University of Helsinki, Gustaf Hxc3xa4llstrxc3xb6min katu 2b, Helsinki, 00560, Finland. jarno.alanko@cs.helsinki.fi.|Max Planck Institute for Molecular Cell Biology and Genetics, Pfotenhauerstr. 108, Dresden, 01307, Germany.|DTISI, CERIST (Research Centre for Scientific and Technical Information), Rue des 3 Frxc3xa9res Aissou, Algiers, 16306, Algeria.|Department of Computer Science, University of Helsinki, Gustaf Hxc3xa4llstrxc3xb6min katu 2b, Helsinki, 00560, Finland.', 'Finland|Germany|Algeria|Finland', 0, 'Algorithms|Cluster Analysis|Computational Biology|DNA Fragmentation|Metagenomics|Models, Theoretical|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-017-1466-6', '28361710', 'A metagenomic sample is a set of DNA fragments, randomly extracted from multiple cells in an environment, belonging to distinct, often unknown species. Unsupervised metagenomic clustering aims at partitioning a metagenomic sample into sets that approximate taxonomic units, without using reference genomes. Since samples are large and steadily growing, space-efficient clustering algorithms are strongly needed.'),
(97822, 2017, 'bmc', 'The joint NETTAB/Integrative Bioinformatics 2015 Meeting: aims, topics and outcomes', 'Romano, Paolo|Hofestädt, Ralf|Lange, Matthias|DElia, Domenica', 'IRCCS AOU San Martino IST, Genoa, I-16132, Italy. paolo.romano@hsanmartino.it.|University of Bielefeld, Bielefeld, D-33615, Germany.|Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, D-06466, Germany.|Institute for Biomedical Technology - National Research Council, Bari, I-70126, Italy.', 'Italy|Germany|Germany|Italy', 0, 'Animals|Computational Biology|Humans|RNA, Untranslated', 'Congress', '10.1186/s12859-017-1532-0', '28361713', '-'),
(97823, 2017, 'bmc', 'SheddomeDB: the ectodomain shedding database for membrane-bound shed markers', 'Tien, Wei-Sheng|Chen, Jun-Hong|Wu, Kun-Pin', 'Institute of Biomedical Informatics, National Yang Ming University, Taipei, 112, Taiwan.|Department of Computer Science, National Taipei University of Education, Taipei, 106, Taiwan.|Institute of Biomedical Informatics, National Yang Ming University, Taipei, 112, Taiwan. kpwu@ym.edu.tw.', '-|-|-', 0, 'Amino Acid Sequence|Animals|Cell Membrane|Computational Biology|Databases, Protein|Genetic Markers|Humans|Membrane Proteins|Reproducibility of Results|Software', 'Journal Article', '10.1186/s12859-017-1465-7', '28361715', 'A number of membrane-anchored proteins are known to be released from cell surface via ectodomain shedding. The cleavage and release of membrane proteins has been shown to modulate various cellular processes and disease pathologies. Numerous studies revealed that cell membrane molecules of diverse functional groups are subjected to proteolytic cleavage, and the released soluble form of proteins may modulate various signaling processes. Therefore, in addition to the secreted protein markers that undergo secretion through the secretory pathway, the shed membrane proteins may comprise an additional resource of noninvasive and accessible biomarkers. In this context, identifying the membrane-bound proteins that will be shed has become important in the discovery of clinically noninvasive biomarkers. Nevertheless, a data repository for biological and clinical researchers to review the shedding information, which is experimentally validated, for membrane-bound protein shed markers is still lacking.'),
(97824, 2017, 'bmc', 'Using structural knowledge in the protein data bank to inform the search for potential host-microbe protein interactions in sequence space: application to Mycobacterium tuberculosis', 'Mahajan, Gaurang|Mande, Shekhar C', 'National Centre for Cell Science, Ganeshkhind, Pune, 411 007, India. gaurang@iiserpune.ac.in.|National Centre for Cell Science, Ganeshkhind, Pune, 411 007, India.', 'India|India', 9, 'Amino Acid Sequence|Bacterial Proteins|Databases, Protein|Host-Pathogen Interactions|Humans|Molecular Sequence Data|Mycobacterium tuberculosis|Protein Domains|Protein Interaction Mapping|Sequence Alignment', 'Journal Article', '10.1186/s12859-017-1550-y', '28376709', 'A comprehensive map of the human-M. tuberculosis (MTB) protein interactome would help fill the gaps in our understanding of the disease, and computational prediction can aid and complement experimental studies towards this end. Several sequence-based in silico approaches tap the existing data on experimentally validated protein-protein interactions (PPIs), these PPIs serve as templates from which novel interactions between pathogen and host are inferred. Such comparative approaches typically make use of local sequence alignment, which, in the absence of structural details about the interfaces mediating the template interactions, could lead to incorrect inferences, particularly when multi-domain proteins are involved.'),
(97825, 2017, 'bmc', 'Erratum to: Guide Picker is a comprehensive design tool for visualizing and selecting guides for CRISPR experiments', 'Hough, Soren H|Kancleris, Kris|Brody, Leigh|Humphryes-Kirilov, Neil|Wolanski, Joseph|Dunaway, Keith|Ajetunmobi, Ayokunmi|Dillard, Victor', ', Desktop Genetics, Ltd., 28 Hanbury Street, London, E1 6QR, UK. sorenh@desktopgenetics.com.|, Desktop Genetics, Ltd., 28 Hanbury Street, London, E1 6QR, UK.|, Desktop Genetics, Ltd., 28 Hanbury Street, London, E1 6QR, UK.|, Desktop Genetics, Ltd., 28 Hanbury Street, London, E1 6QR, UK.|, Desktop Genetics, Ltd., 28 Hanbury Street, London, E1 6QR, UK.|, Desktop Genetics, Ltd., 28 Hanbury Street, London, E1 6QR, UK.|, Desktop Genetics, Ltd., 28 Hanbury Street, London, E1 6QR, UK.|, Desktop Genetics, Ltd., 28 Hanbury Street, London, E1 6QR, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1186/s12859-017-1618-8', '28376719', '-'),
(97826, 2017, 'bmc', 'AD-LIBS: inferring ancestry across hybrid genomes using low-coverage sequence data', 'Schaefer, Nathan K|Shapiro, Beth|Green, Richard E', 'Department of Biomolecular Engineering, University of California, Santa Cruz, USA. nkschaefer@soe.ucsc.edu.|Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, USA.|Department of Biomolecular Engineering, University of California, Santa Cruz, USA.', 'United States|United States|United States', 0, 'Animals|Genetic Speciation|Genome|Humans|Hybridization, Genetic|Markov Chains|Polymorphism, Single Nucleotide|Software|Ursidae', 'Journal Article', '10.1186/s12859-017-1613-0', '28376731', 'Inferring the ancestry of each region of admixed individuals genomes is useful in studies ranging from disease gene mapping to speciation genetics. Current methods require high-coverage genotype data and phased reference panels, and are therefore inappropriate for many data sets. We present a software application, AD-LIBS, that uses a hidden Markov model to infer ancestry across hybrid genomes without requiring variant calling or phasing. This approach is useful for non-model organisms and in cases of low-coverage data, such as ancient DNA.'),
(97827, 2017, 'bmc', 'AHCODA-DB: a data repository with web-based mining tools for the analysis of automated high-content mouse phenomics data', 'Koopmans, Bastijn|Smit, August B|Verhage, Matthijs|Loos, Maarten', 'Sylics (Synaptologics BV), Amsterdam, The Netherlands. bastijn.koopmans@sylics.com.|Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, The Netherlands.|Department of Functional Genomics, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, The Netherlands.|Sylics (Synaptologics BV), Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 1, 'Animals|Automation|Behavior, Animal|Brain|Databases, Factual|Humans|Internet|Mice|Pharmaceutical Preparations|Phenotype|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1612-1', '28376796', 'Systematic, standardized and in-depth phenotyping and data analyses of rodent behaviour empowers gene-function studies, drug testing and therapy design. However, no data repositories are currently available for standardized quality control, data analysis and mining at the resolution of individual mice.'),
(97828, 2017, 'bmc', 'The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism', 'Birkel, Garrett W|Ghosh, Amit|Kumar, Vinay S|Weaver, Daniel|Ando, David|Backman, Tyler W H|Arkin, Adam P|Keasling, Jay D|Martín, Héctor García', 'Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.|Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.|Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.|Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.|Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.|Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.|Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.|Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.|Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. hgmartin@lbl.gov.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 11, 'Biofuels|Carbon Isotopes|Escherichia coli|Internet|Metabolic Flux Analysis|Metabolomics|Models, Biological|Principal Component Analysis|Proteomics|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1615-y', '28381205', 'Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed.'),
(97829, 2017, 'bmc', 'DisBind: A database of classified functional binding sites in disordered and structured regions of intrinsically disordered proteins', 'Yu, Jia-Feng|Dou, Xiang-Hua|Sha, Yu-Jie|Wang, Chun-Ling|Wang, Hong-Bo|Chen, Yi-Ting|Zhang, Feng|Zhou, Yaoqi|Wang, Ji-Hua', 'Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, 253023, China.|College of Physics and Electronic Information, Dezhou University, Dezhou, 253023, China.|Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, 253023, China.|College of Physics and Electronic Information, Dezhou University, Dezhou, 253023, China.|Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, 253023, China.|College of Physics and Electronic Information, Dezhou University, Dezhou, 253023, China.|College of Physics and Electronic Information, Dezhou University, Dezhou, 253023, China.|Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, 253023, China. yaoqi.zhou@griffith.edu.au.|Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, 253023, China. jhw25336@126.com.', 'China|China|China|China|China|China|China|China|China', 4, 'Binding Sites|Databases, Factual|Humans|Internet|Intrinsically Disordered Proteins|Protein Structure, Tertiary|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1620-1', '28381244', 'Intrinsically unstructured or disordered proteins function via interacting with other molecules. Annotation of these binding sites is the first step for mapping functional impact of genetic variants in coding regions of human and other genomes, considering that a significant portion of eukaryotic genomes code for intrinsically disordered regions in proteins.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97830, 2017, 'bmc', 'HALC: High throughput algorithm for long read error correction', 'Bao, Ergude|Lan, Lingxiao', 'School of Software Engineering, Beijing Jiaotong University, 3 Shangyuan Residence, Haidian District, Beijing, 100044, China. baoe@bjtu.edu.cn.|School of Software Engineering, Beijing Jiaotong University, 3 Shangyuan Residence, Haidian District, Beijing, 100044, China.', 'China|China', 0, 'Algorithms|Animals|Arabidopsis|Cichlids|Contig Mapping|Escherichia coli|Genome, Bacterial|Genome, Plant|High-Throughput Nucleotide Sequencing|Internet|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1610-3', '28381259', 'The third generation PacBio SMRT long reads can effectively address the read length issue of the second generation sequencing technology, but contain approximately 15% sequencing errors. Several error correction algorithms have been designed to efficiently reduce the error rate to 1%, but they discard large amounts of uncorrected bases and thus lead to low throughput. This loss of bases could limit the completeness of downstream assemblies and the accuracy of analysis.'),
(97831, 2017, 'bmc', 'Pgltools: a genomic arithmetic tool suite for manipulation of Hi-C peak and other chromatin interaction data', 'Greenwald, William W|Li, He|Smith, Erin N|Benaglio, Paola|Nariai, Naoki|Frazer, Kelly A', 'Bioinformatics and Systems Biology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.|Institute for Genomic Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.|Department of Pediatrics and Rady Childrens Hospital, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.|Department of Pediatrics and Rady Childrens Hospital, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.|Institute for Genomic Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.|Institute for Genomic Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA. kafrazer@ucsd.edu.', 'United States|United States|United States|United States|United States|United States', 6, 'Chromatin|Chromatin Immunoprecipitation|Genetic Loci|Genomics|High-Throughput Nucleotide Sequencing|Software', 'Journal Article', '10.1186/s12859-017-1621-0', '28388874', 'Genomic interaction studies use next-generation sequencing (NGS) to examine the interactions between two loci on the genome, with subsequent bioinformatics analyses typically including annotation, intersection, and merging of data from multiple experiments. While many file types and analysis tools exist for storing and manipulating single locus NGS data, there is currently no file standard or analysis tool suite for manipulating and storing paired-genomic-loci: the data type resulting from genomic interaction studies. As genomic interaction sequencing data are becoming prevalent, a standard file format and tools for working with these data conveniently and efficiently are needed.'),
(97832, 2017, 'bmc', 'Meta-analysis approach as a gene selection method in class prediction: does it improve model performance? A case study in acute myeloid leukemia', 'Novianti, Putri W|Jong, Victor L|Roes, Kit C B|Eijkemans, Marinus J C', 'Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508, GA, Utrecht, The Netherlands. p.novianti@vumc.nl.|Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508, GA, Utrecht, The Netherlands.|Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508, GA, Utrecht, The Netherlands.|Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508, GA, Utrecht, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genes, Neoplasm|Humans|Leukemia, Myeloid, Acute|Models, Genetic', 'Journal Article', '10.1186/s12859-017-1619-7', '28399794', 'Aggregating gene expression data across experiments via meta-analysis is expected to increase the precision of the effect estimates and to increase the statistical power to detect a certain fold change. This study evaluates the potential benefit of using a meta-analysis approach as a gene selection method prior to predictive modeling in gene expression data.'),
(97833, 2017, 'bmc', 'Pan-cancer analysis of systematic batch effects on somatic sequence variations', 'Choi, Ji-Hye|Hong, Seong-Eui|Woo, Hyun Goo', 'Department of Physiology, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon, South Korea.|Department of Physiology, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon, South Korea.|Department of Physiology, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon, South Korea. hg@ajou.ac.kr.', '-|-|-', 2, 'Databases, Genetic|Genomics|Humans|Mutation|Neoplasms|Reproducibility of Results|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-017-1627-7', '28399795', 'The Cancer Genome Atlas (TCGA) is a comprehensive database that includes multi-layered cancer genome profiles. Large-scale collection of data inevitably generates batch effects introduced by differences in processing at various stages from sample collection to data generation. However, batch effects on the sequence variation and its characteristics have not been studied extensively.'),
(97834, 2017, 'bmc', 'A machine learning approach for viral genome classification', 'Remita, Mohamed Amine|Halioui, Ahmed|Malick Diouara, Abou Abdallah|Daigle, Bruno|Kiani, Golrokh|Diallo, Abdoulaye Baniré', 'Laboratoire de bioinformatique, département dinformatique, Université du Québec à Montréal, Montreal, P.O. Box 8888 Downtown Station, H3C 3P8, Qc, Canada.|Laboratoire de bioinformatique, département dinformatique, Université du Québec à Montréal, Montreal, P.O. Box 8888 Downtown Station, H3C 3P8, Qc, Canada.|Laboratoire de bioinformatique, département dinformatique, Université du Québec à Montréal, Montreal, P.O. Box 8888 Downtown Station, H3C 3P8, Qc, Canada.|Laboratoire de bioinformatique, département dinformatique, Université du Québec à Montréal, Montreal, P.O. Box 8888 Downtown Station, H3C 3P8, Qc, Canada.|Laboratoire de bioinformatique, département dinformatique, Université du Québec à Montréal, Montreal, P.O. Box 8888 Downtown Station, H3C 3P8, Qc, Canada.|Laboratoire de bioinformatique, département dinformatique, Université du Québec à Montréal, Montreal, P.O. Box 8888 Downtown Station, H3C 3P8, Qc, Canada. diallo.abdoulaye@uqam.ca.', 'Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Classification|Computer Simulation|Genome, Viral|Genomics|HIV-1|Hepatitis B virus|Humans|Machine Learning|Papillomaviridae|Sequence Analysis, DNA|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-017-1602-3', '28399797', 'Advances in cloning and sequencing technology are yielding a massive number of viral genomes. The classification and annotation of these genomes constitute important assets in the discovery of genomic variability, taxonomic characteristics and disease mechanisms. Existing classification methods are often designed for specific well-studied family of viruses. Thus, the viral comparative genomic studies could benefit from more generic, fast and accurate tools for classifying and typing newly sequenced strains of diverse virus families.'),
(97835, 2017, 'bmc', 'Multi-scale structural community organisation of the human genome', 'Boulos, Rasha E|Tremblay, Nicolas|Arneodo, Alain|Borgnat, Pierre|Audit, Benjamin', 'Univ Lyon, Ens de Lyon, Univ Claude Bernard Lyon 1, CNRS, Laboratoire de Physique, F-69342, Lyon, France.|Univ Lyon, Ens de Lyon, Univ Claude Bernard Lyon 1, CNRS, Laboratoire de Physique, F-69342, Lyon, France.|Univ Lyon, Ens de Lyon, Univ Claude Bernard Lyon 1, CNRS, Laboratoire de Physique, F-69342, Lyon, France.|Univ Lyon, Ens de Lyon, Univ Claude Bernard Lyon 1, CNRS, Laboratoire de Physique, F-69342, Lyon, France.|Univ Lyon, Ens de Lyon, Univ Claude Bernard Lyon 1, CNRS, Laboratoire de Physique, F-69342, Lyon, France. benjamin.audit@ens-lyon.fr.', 'France|France|France|France|France', 0, 'Algorithms|Chromatin|Chromosomes, Human|Computational Biology|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-017-1616-x', '28399820', 'Structural interaction frequency matrices between all genome loci are now experimentally achievable thanks to high-throughput chromosome conformation capture technologies. This ensues a new methodological challenge for computational biology which consists in objectively extracting from these data the structural motifs characteristic of genome organisation.'),
(97836, 2017, 'bmc', 'Homology to peptide pattern for annotation of carbohydrate-active enzymes and prediction of function', 'Busk, P K|Pilgaard, B|Lezyk, M J|Meyer, A S|Lange, L', 'Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 229, 2800, Kgs. Lyngby, Denmark. pbus@kt.dtu.dk.|Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 229, 2800, Kgs. Lyngby, Denmark.|Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 229, 2800, Kgs. Lyngby, Denmark.|Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 229, 2800, Kgs. Lyngby, Denmark.|Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 229, 2800, Kgs. Lyngby, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark', 34, 'Bacteria|Carbohydrate Metabolism|Databases, Protein|Environmental Microbiology|Fungi|Genome, Bacterial|Genome, Fungal|Glycoside Hydrolases|Mixed Function Oxygenases|Molecular Sequence Annotation', 'Journal Article', '10.1186/s12859-017-1625-9', '28403817', 'Carbohydrate-active enzymes are found in all organisms and participate in key biological processes. These enzymes are classified in 274 families in the CAZy database but the sequence diversity within each family makes it a major task to identify new family members and to provide basis for prediction of enzyme function. A fast and reliable method for de novo annotation of genes encoding carbohydrate-active enzymes is to identify conserved peptides in the curated enzyme families followed by matching of the conserved peptides to the sequence of interest as demonstrated for the glycosyl hydrolase and the lytic polysaccharide monooxygenase families. This approach not only assigns the enzymes to families but also provides functional prediction of the enzymes with high accuracy.'),
(97837, 2017, 'bmc', 'Empirical Bayes method for reducing false discovery rates of correlation matrices with block diagonal structure', 'Pacini, Clare|Ajioka, James W|Micklem, Gos', 'CCBI, Department Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA, UK. cep46@cam.ac.uk.|Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QP, UK.|CCBI, Department Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Bacillus subtilis|Bayes Theorem|Computer Simulation|Sample Size|Software|Transcription, Genetic', 'Journal Article', '10.1186/s12859-017-1623-y', '28403823', 'Correlation matrices are important in inferring relationships and networks between regulatory or signalling elements in biological systems. With currently available technology sample sizes for experiments are typically small, meaning that these correlations can be difficult to estimate. At a genome-wide scale estimation of correlation matrices can also be computationally demanding.'),
(97838, 2017, 'bmc', 'An adaptive threshold determination method of feature screening for genomic selection', 'Fu, Guifang|Wang, Gang|Dai, Xiaotian', 'Department of Mathematics and Statistics, Utah State University, Logan, 84322, UT, USA. guifang.fu@usu.edu.|Department of Mathematics and Statistics, Utah State University, Logan, 84322, UT, USA.|Department of Mathematics and Statistics, Utah State University, Logan, 84322, UT, USA.', 'United States|United States|United States', 0, 'Arabidopsis|Arabidopsis Proteins|Computer Simulation|Genome, Plant|Genome-Wide Association Study|Genomics|Models, Genetic|Phenotype|Plant Breeding|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-017-1617-9', '28403836', 'Although the dimension of the entire genome can be extremely large, only a parsimonious set of influential SNPs are correlated with a particular complex trait and are important to the prediction of the trait. Efficiently and accurately selecting these influential SNPs from millions of candidates is in high demand, but poses challenges. We propose a backward elimination iterative distance correlation (BE-IDC) procedure to select the smallest subset of SNPs that guarantees sufficient prediction accuracy, while also solving the unclear threshold issue for traditional feature screening approaches.'),
(97839, 2017, 'bmc', 'Segmentum: a tool for copy number analysis of cancer genomes', 'Afyounian, Ebrahim|Annala, Matti|Nykter, Matti', 'Faculty of Medicine and Life Sciences and BioMediTech institute, University of Tampere, Tampere, Finland.|Faculty of Medicine and Life Sciences and BioMediTech institute, University of Tampere, Tampere, Finland.|Faculty of Medicine and Life Sciences and BioMediTech institute, University of Tampere, Tampere, Finland. matti.nykter@uta.fi.', 'Finland|Finland|Finland', 0, 'Astrocytoma|Chromosome Deletion|Chromosomes, Human, Pair 1|Chromosomes, Human, Pair 17|Computer Simulation|DNA Copy Number Variations|Genome-Wide Association Study|Glioma|High-Throughput Nucleotide Sequencing|Humans|Isocitrate Dehydrogenase|Loss of Heterozygosity|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1626-8', '28407731', 'Somatic alterations, including loss of heterozygosity, can affect the expression of oncogenes and tumor suppressor genes. Whole genome sequencing enables detailed characterization of such aberrations. However, due to the limitations of current high throughput sequencing technologies, this task remains challenging. Hence, accurate and reliable detection of such events is crucial for the identification of cancer-related alterations.'),
(97840, 2017, 'bmc', 'Comparison of different cell type correction methods for genome-scale epigenetics studies', 'Kaushal, Akhilesh|Zhang, Hongmei|Karmaus, Wilfried J J|Ray, Meredith|Torres, Mylin A|Smith, Alicia K|Wang, Shu-Li', 'Division of Epidemiology, Biostatistics, and Environmental Health, University of Memphis, Memphis, 38152, TN, USA.|Division of Epidemiology, Biostatistics, and Environmental Health, University of Memphis, Memphis, 38152, TN, USA. hzhang6@memphis.edu.|Division of Epidemiology, Biostatistics, and Environmental Health, University of Memphis, Memphis, 38152, TN, USA.|Division of Epidemiology, Biostatistics, and Environmental Health, University of Memphis, Memphis, 38152, TN, USA.|Winship Cancer Institute, Emory University, 1365 Clifton Rd. NE, Atlanta, 30322, GA, USA.|Winship Cancer Institute, Emory University, 1365 Clifton Rd. NE, Atlanta, 30322, GA, USA.|National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan. slwang@nhri.org.tw.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Arsenic|Blood Cells|CpG Islands|DNA Methylation|Epigenesis, Genetic|Epigenomics|Female|Fetal Blood|Genome-Wide Association Study|Humans|Maternal Exposure|Neoplasms|Software', 'Comparative Study', '10.1186/s12859-017-1611-2', '28410574', 'Whole blood is frequently utilized in genome-wide association studies of DNA methylation patterns in relation to environmental exposures or clinical outcomes. These associations can be confounded by cellular heterogeneity. Algorithms have been developed to measure or adjust for this heterogeneity, and some have been compared in the literature. However, with new methods available, it is unknown whether the findings will be consistent, if not which method(s) perform better.'),
(97841, 2017, 'bmc', 'Genetic sequence-based prediction of long-range chromatin interactions suggests a potential role of short tandem repeat sequences in genome organization', 'Nikumbh, Sarvesh|Pfeifer, Nico', 'Computational Biology & Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, Building E1.4, Saarbruecken, D-66123, Germany. snikumbh@mpi-inf.mpg.de.|Computational Biology & Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, Building E1.4, Saarbruecken, D-66123, Germany.', 'Germany|Germany', 8, 'Chromatin|Genome|Humans|Microsatellite Repeats|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid', 'Journal Article', '10.1186/s12859-017-1624-x', '28420341', 'Knowing the three-dimensional (3D) structure of the chromatin is important for obtaining a complete picture of the regulatory landscape. Changes in the 3D structure have been implicated in diseases. While there exist approaches that attempt to predict the long-range chromatin interactions, they focus only on interactions between specific genomic regions - the promoters and enhancers, neglecting other possibilities, for instance, the so-called structural interactions involving intervening chromatin.'),
(97842, 2017, 'bmc', 'On the association analysis of CNV data: a fast and robust family-based association method', 'Liu, Meiling|Moon, Sanghoon|Wang, Longfei|Kim, Sulgi|Kim, Yeon-Jung|Hwang, Mi Yeong|Kim, Young Jin|Elston, Robert C|Kim, Bong-Jo|Won, Sungho', 'Department of Applied Statistics, Chung-Ang University, Seoul, 156-756, South Korea.|Division of Structural and Functional Genomics, Center for Genome Science, National Institute of Health, Cheongju-si, Chungcheongbuk-do, 363-951, South Korea.|Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, 151-742, South Korea.|Naver Labs, 235 Pangyoyeok-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, 13494, South Korea.|Division of Structural and Functional Genomics, Center for Genome Science, National Institute of Health, Cheongju-si, Chungcheongbuk-do, 363-951, South Korea.|Division of Structural and Functional Genomics, Center for Genome Science, National Institute of Health, Cheongju-si, Chungcheongbuk-do, 363-951, South Korea.|Division of Structural and Functional Genomics, Center for Genome Science, National Institute of Health, Cheongju-si, Chungcheongbuk-do, 363-951, South Korea.|Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, 44106, USA.|Division of Structural and Functional Genomics, Center for Genome Science, National Institute of Health, Cheongju-si, Chungcheongbuk-do, 363-951, South Korea. kbj6181@cdc.go.kr.|Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, 151-742, South Korea. won1@snu.ac.kr.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'DNA Copy Number Variations|Genome-Wide Association Study|Hematocrit|Humans', 'Journal Article', '10.1186/s12859-017-1622-z', '28420343', 'Copy number variation (CNV) is known to play an important role in the genetics of complex diseases and several methods have been proposed to detect association of CNV with phenotypes of interest. Statistical methods for CNV association analysis can be categorized into two different strategies. First, the copy number is estimated by maximum likelihood and association of the expected copy number with the phenotype is tested. Second, the observed probe intensity measurements can be directly used to detect association of CNV with the phenotypes of interest.'),
(97843, 2017, 'bmc', 'Erratum to: The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism', 'Birkel, Garrett W|Ghosh, Amit|Kumar, Vinay S|Weaver, Daniel|Ando, David|Backman, Tyler W H|Arkin, Adam P|Keasling, Jay D|Martín, Héctor García', 'Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.|Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.|Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.|Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.|Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.|Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.|Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.|Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.|Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. hgmartin@lbl.gov.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1186/s12859-017-1631-y', '28420344', '-'),
(97844, 2017, 'bmc', 'AnkPlex: algorithmic structure for refinement of near-native ankyrin-protein docking', 'Wisitponchai, Tanchanok|Shoombuatong, Watshara|Lee, Vannajan Sanghiran|Kitidee, Kuntida|Tayapiwatana, Chatchai', 'Division of Clinical Immunology, Department of Medical Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, 50200, Thailand.|Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.|Thailand Center of Excellence in Physics, Commission on Higher Education, Bangkok, 10400, Thailand.|Center of Biomolecular Therapy and Diagnostic, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, 50200, Thailand. kitidee_010@hotmail.com.|Division of Clinical Immunology, Department of Medical Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, 50200, Thailand. asimi002@hotmail.com.', 'Thailand|Thailand|Thailand|Thailand|Thailand', 1, 'Algorithms|Ankyrins|Computational Biology|Models, Chemical|Protein Binding|Protein Conformation|Proteins', 'Journal Article', '10.1186/s12859-017-1628-6', '28424069', 'Computational analysis of protein-protein interaction provided the crucial information to increase the binding affinity without a change in basic conformation. Several docking programs were used to predict the near-native poses of the protein-protein complex in 10 top-rankings. The universal criteria for discriminating the near-native pose are not available since there are several classes of recognition protein. Currently, the explicit criteria for identifying the near-native pose of ankyrin-protein complexes (APKs) have not been reported yet.'),
(97845, 2017, 'bmc', 'An auditory display tool for DNA sequence analysis', 'Temple, Mark D', 'School of Science and Health, Western Sydney University, Campbelltown Campus, Locked Bag 1797, Penrith South DC, NSW, 1797, Australia. m.temple@westernsydney.edu.au.', 'Australia', 0, 'Algorithms|Audiovisual Aids|Genetic Code|Music|Reading Frames|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-017-1632-x', '28438115', 'DNA Sonification refers to the use of an auditory display to convey the information content of DNA sequence data. Six sonification algorithms are presented that each produce an auditory display. These algorithms are logically designed from the simple through to the more complex. Three of these parse individual nucleotides, nucleotide pairs or codons into musical notes to give rise to 4, 16 or 64 notes, respectively. Codons may also be parsed degenerately into 20 notes with respect to the genetic code. Lastly nucleotide pairs can be parsed as two separate frames or codons can be parsed as three reading frames giving rise to multiple streams of audio.'),
(97846, 2017, 'bmc', 'Erratum to: Genomic prediction using subsampling', 'Xavier, Alencar|Xu, Shizhong|Muir, William|Rainey, Katy Martin', 'Department of Agronomy, Purdue University, 915 W. State St., Lilly Hall, West Lafayette, IN, 47907, USA.|Department of Plant Science, University of California, 3134 Batchelor Hall, Riverside, CA, 92521, USA.|Department of Animal Science, Purdue University, 915W. State St., Lilly Hall, West Lafayette, IN, 47907, USA.|Department of Agronomy, Purdue University, 915 W. State St., Lilly Hall, West Lafayette, IN, 47907, USA. krainey@purdue.edu.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1186/s12859-017-1629-5', '28446128', '-'),
(97847, 2017, 'bmc', 'De novo assembly of highly polymorphic metagenomic data using in situ generated reference sequences and a novel BLAST-based assembly pipeline', 'Lin, You-Yu|Hsieh, Chia-Hung|Chen, Jiun-Hong|Lu, Xuemei|Kao, Jia-Horng|Chen, Pei-Jer|Chen, Ding-Shinn|Wang, Hurng-Yi', 'Department of Life Science, National Taiwan University, Taipei, 106, Taiwan. youylin@ntu.edu.tw.|Department of Forestry and Nature Conservation, Chinese Culture University, Taipei, 111, Taiwan.|Department of Life Science, National Taiwan University, Taipei, 106, Taiwan.|Laboratory of Disease Genomics and Individualized Medicine, Beijing Institute of Genomics, the Chinese Academy of Sciences, Beijing, 100101, China.|Graduate Institute of Clinical Medicine, National Taiwan University, Taipei, 100, Taiwan.|Graduate Institute of Clinical Medicine, National Taiwan University, Taipei, 100, Taiwan.|Graduate Institute of Clinical Medicine, National Taiwan University, Taipei, 100, Taiwan.|Graduate Institute of Clinical Medicine, National Taiwan University, Taipei, 100, Taiwan. hurngyi@ntu.edu.tw.', '-|-|-|China|-|-|-|-', 8, 'Algorithms|DNA, Viral|Hepatitis B virus|High-Throughput Nucleotide Sequencing|Metagenomics|Software', 'Journal Article', '10.1186/s12859-017-1630-z', '28446139', 'The accuracy of metagenomic assembly is usually compromised by high levels of polymorphism due to divergent reads from the same genomic region recognized as different loci when sequenced and assembled together. A viral quasispecies is a group of abundant and diversified genetically related viruses found in a single carrier. Current mainstream assembly methods, such as Velvet and SOAPdenovo, were not originally intended for the assembly of such metagenomics data, and therefore demands for new methods to provide accurate and informative assembly results for metagenomic data.'),
(97848, 2017, 'bmc', 'MYCbase: a database of functional sites and biochemical properties of Myc in both normal and cancer cells', 'Chakravorty, Debangana|Jana, Tanmoy|Das Mandal, Sukhen|Seth, Anuradha|Bhattacharya, Anubrata|Saha, Sudipto', 'Bioinformatics Centre, Bose Institute, P 1/12, C.I.T. Road, Scheme-VII (M), Kolkata, 700054, India.|Bioinformatics Centre, Bose Institute, P 1/12, C.I.T. Road, Scheme-VII (M), Kolkata, 700054, India.|Bioinformatics Centre, Bose Institute, P 1/12, C.I.T. Road, Scheme-VII (M), Kolkata, 700054, India.|St.Xaviers College, Kolkata, India.|Biochemistry Department, University of Calcutta, Kolkata, India.|Bioinformatics Centre, Bose Institute, P 1/12, C.I.T. Road, Scheme-VII (M), Kolkata, 700054, India. ssaha4@jcbose.ac.in.', 'India|India|India|India|India|India', 0, 'Animals|Breast Neoplasms|Cell Proliferation|Databases, Protein|Humans|Mice|MicroRNAs|Mutation|Neoplasms|Protein Processing, Post-Translational|Proto-Oncogene Proteins c-myc', 'Journal Article', '10.1186/s12859-017-1652-6', '28454513', 'Myc is an essential gene having multiple functions such as in cell growth, differentiation, apoptosis, genomic stability, angiogenesis, and disease biology. A large number of researchers dedicated to Myc biology are generating a substantial amount of data in normal and cancer cells/tissues including Burkitts lymphoma and ovarian cancer.'),
(97849, 2017, 'bmc', 'QueryOR: a comprehensive web platform for genetic variant analysis and prioritization', 'Bertoldi, Loris|Forcato, Claudio|Vitulo, Nicola|Birolo, Giovanni|De Pascale, Fabio|Feltrin, Erika|Schiavon, Riccardo|Anglani, Franca|Negrisolo, Susanna|Zanetti, Alessandra|DAvanzo, Francesca|Tomanin, Rosella|Faulkner, Georgine|Vezzi, Alessandro|Valle, Giorgio', 'CRIBI Biotechnology Centre, University of Padua, Padua, Italy.|CRIBI Biotechnology Centre, University of Padua, Padua, Italy.|CRIBI Biotechnology Centre, University of Padua, Padua, Italy.|CRIBI Biotechnology Centre, University of Padua, Padua, Italy.|CRIBI Biotechnology Centre, University of Padua, Padua, Italy.|CRIBI Biotechnology Centre, University of Padua, Padua, Italy.|Department of Biology, University of Padua, Padua, Italy.|Department of Medicine, University of Padua, Padua, Italy.|Department of Womens and Childrens Health, University of Padua, Padua, Italy.|Department of Womens and Childrens Health, University of Padua, Padua, Italy.|Department of Womens and Childrens Health, University of Padua, Padua, Italy.|Department of Womens and Childrens Health, University of Padua, Padua, Italy.|Department of Biology, University of Padua, Padua, Italy.|Department of Biology, University of Padua, Padua, Italy.|CRIBI Biotechnology Centre, University of Padua, Padua, Italy. giorgio.valle@unipd.it.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 13, 'Databases, Genetic|Disease|Exome|Genetic Variation|Genome, Human|Humans|Internet|Software', 'Journal Article', '10.1186/s12859-017-1654-4', '28454514', 'Whole genome and exome sequencing are contributing to the extraordinary progress in the study of human genetic variants. In this fast developing field, appropriate and easily accessible tools are required to facilitate data analysis.'),
(97850, 2017, 'bmc', 'A vascular image registration method based on network structure and circuit simulation', 'Chen, Li|Lian, Yuxi|Guo, Yi|Wang, Yuanyuan|Hatsukami, Thomas S|Pimentel, Kristi|Balu, Niranjan|Yuan, Chun', 'Department of Electronic Engineering, Fudan University, Shanghai, 200433, China.|Department of Electronic Engineering, Fudan University, Shanghai, 200433, China.|Department of Electronic Engineering, Fudan University, Shanghai, 200433, China. guoyi@fudan.edu.cn.|Department of Electronic Engineering, Fudan University, Shanghai, 200433, China.|Department of Surgery, University of Washington, Seattle, WA, 98195, USA.|Department of Radiology, University of Washington, Seattle, WA, 98195-7115, USA.|Department of Radiology, University of Washington, Seattle, WA, 98195-7115, USA.|Department of Radiology, University of Washington, Seattle, WA, 98195-7115, USA.', 'China|China|China|China|United States|United States|United States|United States', 4, 'Angiography|Humans|Image Processing, Computer-Assisted', 'Journal Article', '10.1186/s12859-017-1649-1', '28464789', 'Image registration is an important research topic in the field of image processing. Applying image registration to vascular image allows multiple images to be strengthened and fused, which has practical value in disease detection, clinical assisted therapy, etc. However, it is hard to register vascular structures with high noise and large difference in an efficient and effective method.'),
(97851, 2017, 'bmc', 'Automated multigroup outlier identification in molecular high-throughput data using bagplots and gemplots', 'Kruppa, Jochen|Jung, Klaus', 'Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Foundation, Bünteweg 17p, Hannover, D-30559, Germany.|Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Foundation, Bünteweg 17p, Hannover, D-30559, Germany. klaus.jung@tiho-hannover.de.', 'Germany|Germany', 9, 'Cluster Analysis|Computational Biology|Databases, Genetic|High-Throughput Nucleotide Sequencing|Humans|Principal Component Analysis', 'Journal Article', '10.1186/s12859-017-1645-5', '28464790', 'Analyses of molecular high-throughput data often lack in robustness, i.e. results are very sensitive to the addition or removal of a single observation. Therefore, the identification of extreme observations is an important step of quality control before doing further data analysis. Standard outlier detection methods for univariate data are however not applicable, since the considered data are high-dimensional, i.e. multiple hundreds or thousands of features are observed in small samples. Usually, outliers in high-dimensional data are solely detected by visual inspection of a graphical representation of the data by the analyst. Typical graphical representation for high-dimensional data are hierarchical cluster tree or principal component plots. Pure visual approaches depend, however, on the individual judgement of the analyst and are hard to automate. Existing methods for automated outlier detection are only dedicated to data of a single experimental groups.'),
(97852, 2017, 'bmc', 'ICoVeR - an interactive visualization tool for verification and refinement of metagenomic bins', 'Broeksema, Bertjan|Calusinska, Magdalena|McGee, Fintan|Winter, Klaas|Bongiovanni, Francesco|Goux, Xavier|Wilmes, Paul|Delfosse, Philippe|Ghoniem, Mohammad', 'Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology, 41 rue du Brill, L-4422, Belvaux, Luxembourg.|Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology, 41 rue du Brill, L-4422, Belvaux, Luxembourg. magdalena.calusinska@list.lu.|Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology, 41 rue du Brill, L-4422, Belvaux, Luxembourg.|Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology, 41 rue du Brill, L-4422, Belvaux, Luxembourg.|Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology, 41 rue du Brill, L-4422, Belvaux, Luxembourg.|Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology, 41 rue du Brill, L-4422, Belvaux, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7 avenue des Hauts-Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg.|Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology, 41 rue du Brill, L-4422, Belvaux, Luxembourg.|Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology, 41 rue du Brill, L-4422, Belvaux, Luxembourg.', 'Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg', 0, 'Algorithms|Databases, Genetic|Gastrointestinal Microbiome|Genome, Microbial|Humans|Infant|Metagenome|Metagenomics|Software', 'Journal Article', '10.1186/s12859-017-1653-5', '28464793', 'Recent advances in high-throughput sequencing allow for much deeper exploitation of natural and engineered microbial communities, and to unravel so-called microbial dark matter (microbes that until now have evaded cultivation). Metagenomic analyses result in a large number of genomic fragments (contigs) that need to be grouped (binned) in order to reconstruct draft microbial genomes. While several contig binning algorithms have been developed in the past 2 years, they often lack consensus. Furthermore, these software tools typically lack a provision for the visualization of data and bin characteristics.'),
(97853, 2017, 'bmc', 'Zipper plot: visualizing transcriptional activity of genomic regions', 'Avila Cobos, Francisco|Anckaert, Jasper|Volders, Pieter-Jan|Everaert, Celine|Rombaut, Dries|Vandesompele, Jo|De Preter, Katleen|Mestdagh, Pieter', 'Center for Medical Genetics, Ghent University, De Pintelaan 185, Ghent, Belgium.|Center for Medical Genetics, Ghent University, De Pintelaan 185, Ghent, Belgium.|Center for Medical Genetics, Ghent University, De Pintelaan 185, Ghent, Belgium.|Center for Medical Genetics, Ghent University, De Pintelaan 185, Ghent, Belgium.|Center for Medical Genetics, Ghent University, De Pintelaan 185, Ghent, Belgium.|Center for Medical Genetics, Ghent University, De Pintelaan 185, Ghent, Belgium.|Center for Medical Genetics, Ghent University, De Pintelaan 185, Ghent, Belgium.|Center for Medical Genetics, Ghent University, De Pintelaan 185, Ghent, Belgium. Pieter.Mestdagh@UGent.be.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 3, 'Chromatin Immunoprecipitation|Genomics|Humans|RNA, Long Noncoding|Sequence Analysis, RNA|Software|Transcription Initiation Site', 'Journal Article', '10.1186/s12859-017-1651-7', '28464823', 'Reconstructing transcript models from RNA-sequencing (RNA-seq) data and establishing these as independent transcriptional units can be a challenging task. Current state-of-the-art tools for long non-coding RNA (lncRNA) annotation are mainly based on evolutionary constraints, which may result in false negatives due to the overall limited conservation of lncRNAs.'),
(97854, 2017, 'bmc', 'microTaboo: a general and practical solution to the k-disjoint problem', 'Al-Jaff, Mohammed|Sandström, Eric|Grabherr, Manfred', 'Department of Medical Biochemistry and Microbiology, Uppsala University, 75123, Uppsala, Sweden.|Department of Medical Biochemistry and Microbiology, Uppsala University, 75123, Uppsala, Sweden.|Department of Medical Biochemistry and Microbiology, Uppsala University, 75123, Uppsala, Sweden. manfred.grabherr@imbim.uu.se.', 'Sweden|Sweden|Sweden', 0, 'Computational Biology|Genome|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1644-6', '28464826', 'A common challenge in bioinformatics is to identify short sub-sequences that are unique in a set of genomes or reference sequences, which can efficiently be achieved by k-mer (k consecutive nucleotides) counting. However, there are several areas that would benefit from a more stringent definition of unique, requiring that these sub-sequences of length W differ by more than k mismatches (i.e. a Hamming distance greater than k) from any other sub-sequence, which we term the k-disjoint problem. Examples include finding sequences unique to a pathogen for probe-based infection diagnostics, reducing off-target hits for re-sequencing or genome editing, detecting sequence (e.g. phage or viral) insertions, and multiple substitution mutations. Since both sensitivity and specificity are critical, an exhaustive, yet efficient solution is desirable.'),
(97855, 2017, 'bmc', 'Intervention in prediction measure: a new approach to assessing variable importance for random forests', 'Epifanio, Irene', 'Departament de Matemàtiques and Institut de Matemàtiques i Aplicacions de Castelló, Universitat Jaume I, Campus del Riu Sec, Castelló, 12071, Spain. epifanio@uji.es.', 'Spain', 0, 'Algorithms|Computational Biology|Decision Trees|Models, Statistical|Multivariate Analysis', 'Journal Article', '10.1186/s12859-017-1650-8', '28464827', 'Random forests are a popular method in many fields since they can be successfully applied to complex data, with a small sample size, complex interactions and correlations, mixed type predictors, etc. Furthermore, they provide variable importance measures that aid qualitative interpretation and also the selection of relevant predictors. However, most of these measures rely on the choice of a performance measure. But measures of prediction performance are not unique or there is not even a clear definition, as in the case of multivariate response random forests.'),
(97856, 2017, 'bmc', 'Orthoscape: a cytoscape application for grouping and visualization KEGG based gene networks by taxonomy and homology principles', 'Mustafin, Zakhar Sergeevich|Lashin, Sergey Alexandrovich|Matushkin, Yury Georgievich|Gunbin, Konstantin Vladimirovich|Afonnikov, Dmitry Arkadievich', 'Institute of Cytology and Genetics SB RAS, Lavrentiev Avenue 10, Novosibirsk, 630090, Russia.|Institute of Cytology and Genetics SB RAS, Lavrentiev Avenue 10, Novosibirsk, 630090, Russia. lashin@bionet.nsc.ru.|Institute of Cytology and Genetics SB RAS, Lavrentiev Avenue 10, Novosibirsk, 630090, Russia.|Institute of Cytology and Genetics SB RAS, Lavrentiev Avenue 10, Novosibirsk, 630090, Russia.|Institute of Cytology and Genetics SB RAS, Lavrentiev Avenue 10, Novosibirsk, 630090, Russia.', '-|-|-|-|-', 0, 'Computational Biology|Computer Graphics|Databases, Genetic|Evolution, Molecular|Gene Regulatory Networks|Humans|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1186/s12859-016-1427-5', '28466792', 'There are many available software tools for visualization and analysis of biological networks. Among them, Cytoscape ( http://cytoscape.org/ ) is one of the most comprehensive packages, with many plugins and applications which extends its functionality by providing analysis of protein-protein interaction, gene regulatory and gene co-expression networks, metabolic, signaling, neural as well as ecological-type networks including food webs, communities networks etc. Nevertheless, only three plugins tagged network evolution found in Cytoscape official app store and in literature. We have developed a new Cytoscape 3.0 application Orthoscape aimed to facilitate evolutionary analysis of gene networks and visualize the results.'),
(97857, 2017, 'bmc', 'Seqping: gene prediction pipeline for plant genomes using self-training gene models and transcriptomic data', 'Chan, Kuang-Lim|Rosli, Rozana|Tatarinova, Tatiana V|Hogan, Michael|Firdaus-Raih, Mohd|Low, Eng-Ti Leslie', 'Advanced Biotechnology and Breeding Center, Malaysian Palm Oil Board, 6 Persiaran Institusi, Bandar Baru Bangi, 43000, Kajang, Selangor, Malaysia. chankl@mpob.gov.my.|Advanced Biotechnology and Breeding Center, Malaysian Palm Oil Board, 6 Persiaran Institusi, Bandar Baru Bangi, 43000, Kajang, Selangor, Malaysia.|Center for Personalized Medicine and Spatial Sciences Institute, University of Southern California, Los Angeles, CA, USA.|Orion Genomics, 4041 Forest Park Avenue, St. Louis, MO, 63108, USA.|Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.|Advanced Biotechnology and Breeding Center, Malaysian Palm Oil Board, 6 Persiaran Institusi, Bandar Baru Bangi, 43000, Kajang, Selangor, Malaysia.', 'Malaysia|Malaysia|United States|United States|Malaysia|Malaysia', 15, 'Arabidopsis|Exons|Gene Expression Profiling|Genome, Plant|Genomics|Markov Chains|Oryza|Software|Transcriptome', 'Journal Article', '10.1186/s12859-016-1426-6', '28466793', 'Gene prediction is one of the most important steps in the genome annotation process. A large number of software tools and pipelines developed by various computing techniques are available for gene prediction. However, these systems have yet to accurately predict all or even most of the protein-coding regions. Furthermore, none of the currently available gene-finders has a universal Hidden Markov Model (HMM) that can perform gene prediction for all organisms equally well in an automatic fashion.'),
(97858, 2017, 'bmc', 'Power analysis for RNA-Seq differential expression studies', 'Yu, Lianbo|Fernandez, Soledad|Brock, Guy', 'Center for Biostatistics, Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Dr., Columbus, 43210, OH, USA. Lianbo.Yu@osumc.edu.|Center for Biostatistics, Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Dr., Columbus, 43210, OH, USA.|Center for Biostatistics, Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Dr., Columbus, 43210, OH, USA.', 'United States|United States|United States', 15, 'Binomial Distribution|Breast Neoplasms|False Positive Reactions|Gene Expression Profiling|Humans|Linear Models|Sequence Analysis, RNA|Statistics as Topic', 'Journal Article', '10.1186/s12859-017-1648-2', '28468606', 'Sample size calculation and power estimation are essential components of experimental designs in biomedical research. It is very challenging to estimate power for RNA-Seq differential expression under complex experimental designs. Moreover, the dependency among genes should be taken into account in order to obtain accurate results.'),
(97859, 2017, 'bmc', 'WebGIVI: a web-based gene enrichment analysis and visualization tool', 'Sun, Liang|Zhu, Yongnan|Mahmood, A S M Ashique|Tudor, Catalina O|Ren, Jia|Vijay-Shanker, K|Chen, Jian|Schmidt, Carl J', 'Department of Animal and Food Sciences, University of Delaware, Newark, DE, USA.|Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD, USA.|Department of Computer and Information Sciences, University of Delaware, Newark, DE, 19716, USA.|Department of Computer and Information Sciences, University of Delaware, Newark, DE, 19716, USA.|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, 19711, USA.|Department of Computer and Information Sciences, University of Delaware, Newark, DE, 19716, USA.|Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD, USA.|Department of Animal and Food Sciences, University of Delaware, Newark, DE, USA. schmidtc@udel.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Data Mining|Genes|Genomics|Internet|Software', 'Journal Article', '10.1186/s12859-017-1664-2', '28472919', 'A major challenge of high throughput transcriptome studies is presenting the data to researchers in an interpretable format. In many cases, the outputs of such studies are gene lists which are then examined for enriched biological concepts. One approach to help the researcher interpret large gene datasets is to associate genes and informative terms (iTerm) that are obtained from the biomedical literature using the eGIFT text-mining system. However, examining large lists of iTerm and gene pairs is a daunting task.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97860, 2017, 'bmc', 'Geminivirus data warehouse: a database enriched with machine learning approaches', 'Silva, Jose Cleydson F|Carvalho, Thales F M|Basso, Marcos F|Deguchi, Michihito|Pereira, Welison A|Sobrinho, Roberto R|Vidigal, Pedro M P|Brustolini, Otávio J B|Silva, Fabyano F|Dal-Bianco, Maximiller|Fontes, Renildes L F|Santos, Anésia A|Zerbini, Francisco Murilo|Cerqueira, Fabio R|Fontes, Elizabeth P B', 'Departamento de Informática, Universidade Federal de Viçosa, Viçosa, Brazil.|Departamento de Informática, Universidade Federal de Viçosa, Viçosa, Brazil.|National Institute of Science and Technology in Plant-Pest Interactions/BIOAGRO, Universidade Federal de Viçosa, Viçosa, Brazil.|National Institute of Science and Technology in Plant-Pest Interactions/BIOAGRO, Universidade Federal de Viçosa, Viçosa, Brazil.|National Institute of Science and Technology in Plant-Pest Interactions/BIOAGRO, Universidade Federal de Viçosa, Viçosa, Brazil.|National Institute of Science and Technology in Plant-Pest Interactions/BIOAGRO, Universidade Federal de Viçosa, Viçosa, Brazil.|Núcleo de Biomoléculas, Universidade Federal de Viçosa, Viçosa, MG, Brazil.|National Institute of Science and Technology in Plant-Pest Interactions/BIOAGRO, Universidade Federal de Viçosa, Viçosa, Brazil.|Departamento de Zootecnia, Universidade Federal de Viçosa, Viçosa, Brazil.|National Institute of Science and Technology in Plant-Pest Interactions/BIOAGRO, Universidade Federal de Viçosa, Viçosa, Brazil.|Departamento de Solos, Universidade Federal de Viçosa, Viçosa, Brazil.|National Institute of Science and Technology in Plant-Pest Interactions/BIOAGRO, Universidade Federal de Viçosa, Viçosa, Brazil.|National Institute of Science and Technology in Plant-Pest Interactions/BIOAGRO, Universidade Federal de Viçosa, Viçosa, Brazil.|Departamento de Informática, Universidade Federal de Viçosa, Viçosa, Brazil.|National Institute of Science and Technology in Plant-Pest Interactions/BIOAGRO, Universidade Federal de Viçosa, Viçosa, Brazil. bbfontes@ufv.br.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Br', 11, 'Algorithms|Computational Biology|DNA, Single-Stranded|DNA, Viral|Databases, Genetic|Geminiviridae|Machine Learning|Open Reading Frames|Phylogeny|Plants', 'Journal Article', '10.1186/s12859-017-1646-4', '28476106', 'The Geminiviridae family encompasses a group of single-stranded DNA viruses with twinned and quasi-isometric virions, which infect a wide range of dicotyledonous and monocotyledonous plants and are responsible for significant economic losses worldwide. Geminiviruses are divided into nine genera, according to their insect vector, host range, genome organization, and phylogeny reconstruction. Using rolling-circle amplification approaches along with high-throughput sequencing technologies, thousands of full-length geminivirus and satellite genome sequences were amplified and have become available in public databases. As a consequence, many important challenges have emerged, namely, how to classify, store, and analyze massive datasets as well as how to extract information or new knowledge. Data mining approaches, mainly supported by machine learning (ML) techniques, are a natural means for high-throughput data analysis in the context of genomics, transcriptomics, proteomics, and metabolomics.'),
(97861, 2017, 'bmc', 'Inferring miRNA sponge co-regulation of protein-protein interactions in human breast cancer', 'Zhang, Junpeng|Le, Thuc Duy|Liu, Lin|Li, Jiuyong', 'School of Engineering, Dali University, Dali, Yunnan, 671003, Peoples Republic of China. zhangjunpeng_411@yahoo.com.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia. jiuyong.li@unisa.edu.au.', 'China|Australia|Australia|Australia', 16, 'Breast Neoplasms|Female|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Genes, Neoplasm|Humans|MicroRNAs|Nucleotide Motifs|Protein Interaction Mapping|RNA, Messenger|Reproducibility of Results|Signal Transduction|Software', 'Journal Article', '10.1186/s12859-017-1672-2', '28482794', 'Recent studies have shown that the crosstalk between microRNA (miRNA) sponges plays an important role in human cancers. However, the co-regulation roles of miRNA sponges in protein-protein interactions (PPIs) are still unknown.'),
(97862, 2017, 'bmc', 'A time series driven decomposed evolutionary optimization approach for reconstructing large-scale gene regulatory networks based on fuzzy cognitive maps', 'Liu, Jing|Chi, Yaxiong|Zhu, Chen|Jin, Yaochu', 'Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xian, 710071, China. neouma@mail.xidian.edu.cn.|Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xian, 710071, China.|Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xian, 710071, China.|Department of Computer Science, University of Surrey, Guildford, GU2 7XH, UK.', 'China|China|China|United Kingdom', 0, 'Algorithms|Biological Evolution|Computer Simulation|Fuzzy Logic|Gene Regulatory Networks|Models, Theoretical|Time Factors', 'Journal Article', '10.1186/s12859-017-1657-1', '28482795', 'Reconstructing gene regulatory networks (GRNs) from expression data plays an important role in understanding the fundamental cellular processes and revealing the underlying relations among genes. Although many algorithms have been proposed to reconstruct GRNs, more rapid and efficient methods which can handle large-scale problems still need to be developed. The process of reconstructing GRNs can be formulated as an optimization problem, which is actually reconstructing GRNs from time series data, and the reconstructed GRNs have good ability to simulate the observed time series. This is a typical big optimization problem, since the number of variables needs to be optimized increases quadratically with the scale of GRNs, resulting an exponential increase in the number of candidate solutions. Thus, there is a legitimate need to devise methods capable of automatically reconstructing large-scale GRNs.'),
(97863, 2017, 'bmc', 'ImmunoNodes - graphical development of complex immunoinformatics workflows', 'Schubert, Benjamin|de la Garza, Luis|Mohr, Christopher|Walzer, Mathias|Kohlbacher, Oliver', 'Center for Bioinformatics, University of Tübingen, Tübingen, 72076, Germany. benjamin_schubert@hms.harvard.edu.|Center for Bioinformatics, University of Tübingen, Tübingen, 72076, Germany.|Center for Bioinformatics, University of Tübingen, Tübingen, 72076, Germany.|Center for Bioinformatics, University of Tübingen, Tübingen, 72076, Germany.|Center for Bioinformatics, University of Tübingen, Tübingen, 72076, Germany.', 'Germany|Germany|Germany|Germany|Germany', 6, 'Allergy and Immunology|Amino Acid Sequence|Computational Biology|Computer Graphics|Epitopes|HLA Antigens|Humans|Ligands|Software|Viral Vaccines|Workflow|Zika Virus', 'Journal Article', '10.1186/s12859-017-1667-z', '28482806', 'Immunoinformatics has become a crucial part in biomedical research. Yet many immunoinformatics tools have command line interfaces only and can be difficult to install. Web-based immunoinformatics tools, on the other hand, are difficult to integrate with other tools, which is typically required for the complex analysis and prediction pipelines required for advanced applications.'),
(97864, 2017, 'bmc', 'A Bayesian taxonomic classification method for 16S rRNA gene sequences with improved species-level accuracy', 'Gao, Xiang|Lin, Huaiying|Revanna, Kashi|Dong, Qunfeng', 'Department of Public Health Sciences, Loyola University Chicago Health Sciences Division, Maywood, IL, 60153, USA.|Department of Public Health Sciences, Loyola University Chicago Health Sciences Division, Maywood, IL, 60153, USA.|Department of Public Health Sciences, Loyola University Chicago Health Sciences Division, Maywood, IL, 60153, USA.|Department of Public Health Sciences, Loyola University Chicago Health Sciences Division, Maywood, IL, 60153, USA. qdong@luc.edu.', 'United States|United States|United States|United States', 34, 'Algorithms|Base Sequence|Bayes Theorem|Classification|Computer Simulation|Phylogeny|RNA, Ribosomal, 16S|Reproducibility of Results|Sequence Alignment|Software|Species Specificity', 'Journal Article', '10.1186/s12859-017-1670-4', '28486927', 'Species-level classification for 16S rRNA gene sequences remains a serious challenge for microbiome researchers, because existing taxonomic classification tools for 16S rRNA gene sequences either do not provide species-level classification, or their classification results are unreliable. The unreliable results are due to the limitations in the existing methods which either lack solid probabilistic-based criteria to evaluate the confidence of their taxonomic assignments, or use nucleotide k-mer frequency as the proxy for sequence similarity measurement.'),
(97865, 2017, 'bmc', 'LASSIE: simulating large-scale models of biochemical systems on GPUs', 'Tangherloni, Andrea|Nobile, Marco S|Besozzi, Daniela|Mauri, Giancarlo|Cazzaniga, Paolo', 'Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, Milano, 20126, Italy.|Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, Milano, 20126, Italy.|Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, Milano, 20126, Italy.|Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, Milano, 20126, Italy.|Department of Human and Social Sciences, University of Bergamo, Piazzale SantAgostino 2, Bergamo, 24129, Italy. paolo.cazzaniga@unibg.it.', 'Italy|Italy|Italy|Italy|Italy', 14, 'Algorithms|Biochemical Phenomena|Computer Graphics|Computer Simulation|Kinetics|Time Factors|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1666-0', '28486952', 'Mathematical modeling and in silico analysis are widely acknowledged as complementary tools to biological laboratory methods, to achieve a thorough understanding of emergent behaviors of cellular processes in both physiological and perturbed conditions. Though, the simulation of large-scale models-consisting in hundreds or thousands of reactions and molecular species-can rapidly overtake the capabilities of Central Processing Units (CPUs). The purpose of this work is to exploit alternative high-performance computing solutions, such as Graphics Processing Units (GPUs), to allow the investigation of these models at reduced computational costs.'),
(97866, 2017, 'bmc', 'Solving the master equation for Indels', 'Holmes, Ian H', 'Dept of Bioengineering, University of California, Berkeley, 94720, USA. ihh@berkeley.edu.', 'United States', 0, 'Algorithms|Computer Simulation|INDEL Mutation|Markov Chains|Models, Theoretical|Monte Carlo Method|Phylogeny|Sequence Alignment', 'Editorial', '10.1186/s12859-017-1665-1', '28494756', 'Despite the long-anticipated possibility of putting sequence alignment on the same footing as statistical phylogenetics, theorists have struggled to develop time-dependent evolutionary models for indels that are as tractable as the analogous models for substitution events.'),
(97867, 2017, 'bmc', 'Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order', 'Aron, Miles|Browning, Richard|Carugo, Dario|Sezgin, Erdinc|Bernardino de la Serna, Jorge|Eggeling, Christian|Stride, Eleanor', 'Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK.|Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK.|Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK.|MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Headley Way, Oxford, OX3 9DS, UK.|MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Headley Way, Oxford, OX3 9DS, UK.|MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Headley Way, Oxford, OX3 9DS, UK.|Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK. eleanor.stride@eng.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 9, '2-Naphthylamine|A549 Cells|Cell Membrane|Fluorescent Dyes|Humans|Image Processing, Computer-Assisted|Laurates|Membrane Lipids|Microbubbles|Microscopy, Confocal|Pyridinium Compounds|Spectrometry, Fluorescence', 'Journal Article', '10.1186/s12859-017-1656-2', '28494801', 'Spectral imaging with polarity-sensitive fluorescent probes enables the quantification of cell and model membrane physical properties, including local hydration, fluidity, and lateral lipid packing, usually characterized by the generalized polarization (GP) parameter. With the development of commercial microscopes equipped with spectral detectors, spectral imaging has become a convenient and powerful technique for measuring GP and other membrane properties. The existing tools for spectral image processing, however, are insufficient for processing the large data sets afforded by this technological advancement, and are unsuitable for processing images acquired with rapidly internalized fluorescent probes.'),
(97868, 2017, 'bmc', 'Epimetheus - a multi-profile normalizer for epigenomic sequencing data', 'Saleem, Mohamed-Ashick M|Mendoza-Parra, Marco-Antonio|Cholley, Pierre-Etienne|Blum, Matthias|Gronemeyer, Hinrich', 'Equipe Labellisée Ligue Contre le Cancer, Department of Functional Genomics and Cancer, Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France.|Equipe Labellisée Ligue Contre le Cancer, Department of Functional Genomics and Cancer, Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France. marco@igbmc.u-strasbg.fr.|Equipe Labellisée Ligue Contre le Cancer, Department of Functional Genomics and Cancer, Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France.|Equipe Labellisée Ligue Contre le Cancer, Department of Functional Genomics and Cancer, Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France.|Equipe Labellisée Ligue Contre le Cancer, Department of Functional Genomics and Cancer, Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France. hg@igbmc.u-strasbg.fr.', 'France|France|France|France|France', 1, 'Cell Differentiation|Cell Line|Chromatin|Chromatin Immunoprecipitation|Epigenomics|Hep G2 Cells|High-Throughput Nucleotide Sequencing|Histones|Humans|Sequence Analysis, DNA|Tretinoin', 'Journal Article', '10.1186/s12859-017-1655-3', '28499349', 'Exponentially increasing numbers of NGS-based epigenomic datasets in public repositories like GEO constitute an enormous source of information that is invaluable for integrative and comparative studies of gene regulatory mechanisms. One of todays challenges for such studies is to identify functionally informative local and global patterns of chromatin states in order to describe the regulatory impact of the epigenome in normal cell physiology and in case of pathological aberrations. Critically, the most preferred Chromatin ImmunoPrecipitation-Sequencing (ChIP-Seq) is inherently prone to significant variability between assays, which poses significant challenge on comparative studies. One challenge concerns data normalization to adjust sequencing depth variation.'),
(97869, 2017, 'bmc', 'Ranking metrics in gene set enrichment analysis: do they matter?', 'Zyla, Joanna|Marczyk, Michal|Weiner, January|Polanska, Joanna', 'Data Mining Group, Institute of Automatic Control, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, Gliwice, 44-100, Poland.|Data Mining Group, Institute of Automatic Control, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, Gliwice, 44-100, Poland. michal.marczyk@polsl.pl.|Max Planck Institute for Infection Biology, Charitéplatz 1, Berlin, 10117, Germany.|Data Mining Group, Institute of Automatic Control, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, Gliwice, 44-100, Poland.', 'Poland|Poland|Germany|Poland', 14, 'Algorithms|Oligonucleotide Array Sequence Analysis|Sample Size|Signal-To-Noise Ratio', 'Journal Article', '10.1186/s12859-017-1674-0', '28499413', 'There exist many methods for describing the complex relation between changes of gene expression in molecular pathways or gene ontologies under different experimental conditions. Among them, Gene Set Enrichment Analysis seems to be one of the most commonly used (over 10,000 citations). An important parameter, which could affect the final result, is the choice of a metric for the ranking of genes. Applying a default ranking metric may lead to poor results.'),
(97870, 2017, 'bmc', 'HLAscan: genotyping of the HLA region using next-generation sequencing data', 'Ka, Sojeong|Lee, Sunho|Hong, Jonghee|Cho, Yangrae|Sung, Joohon|Kim, Han-Na|Kim, Hyung-Lae|Jung, Jongsun', 'R&D center, Syntekabio, Inc., 5 Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, South Korea.|Main office, Syntekabio, Inc., 187 Techno 2-ro, Yuseong-gu, Daejeon, 34025, South Korea.|R&D center, Syntekabio, Inc., 5 Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, South Korea.|Main office, Syntekabio, Inc., 187 Techno 2-ro, Yuseong-gu, Daejeon, 34025, South Korea.|Complex Disease and Genome Epidemiology Branch, Department of Epidemiology, School of Public Health, Seoul National University, Seoul, 08826, South Korea.|Department of Biochemistry, School of Medicine, Ewha Womans University, Seoul, 07985, South Korea.|Department of Biochemistry, School of Medicine, Ewha Womans University, Seoul, 07985, South Korea. hyung@ewha.ac.kr.|Main office, Syntekabio, Inc., 187 Techno 2-ro, Yuseong-gu, Daejeon, 34025, South Korea. jung@syntekabio.com.', '-|-|-|-|-|-|-|-', 0, 'Alleles|Area Under Curve|Exons|Genotype|HLA Antigens|HLA-A Antigens|HLA-B Antigens|HLA-DRB1 Chains|HapMap Project|High-Throughput Nucleotide Sequencing|Histocompatibility Testing|Humans|ROC Curve|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-017-1671-3', '28499414', 'Several recent studies showed that next-generation sequencing (NGS)-based human leukocyte antigen (HLA) typing is a feasible and promising technique for variant calling of highly polymorphic regions. To date, however, no method with sufficient read depth has completely solved the allele phasing issue. In this study, we developed a new method (HLAscan) for HLA genotyping using NGS data.'),
(97871, 2017, 'bmc', 'Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks', 'Maheshwari, Surabhi|Brylinski, Michal', 'Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA.|Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA. michal@brylinski.org.', 'United States|United States', 0, 'Area Under Curve|Dimerization|Escherichia coli|Escherichia coli Proteins|Humans|Immune System Diseases|Machine Learning|Molecular Docking Simulation|Protein Interaction Maps|Protein Structure, Tertiary|Proteins|Proteome|ROC Curve', 'Journal Article', '10.1186/s12859-017-1675-z', '28499419', 'Deciphering complete networks of interactions between proteins is the key to comprehend cellular regulatory mechanisms. A significant effort has been devoted to expanding the coverage of the proteome-wide interaction space at molecular level. Although a growing body of research shows that protein docking can, in principle, be used to predict biologically relevant interactions, the accuracy of the across-proteome identification of interacting partners and the selection of near-native complex structures still need to be improved.'),
(97872, 2017, 'bmc', 'Clone temporal centrality measures for incomplete sequences of graph snapshots', 'Hanke, Moritz|Foraita, Ronja', 'Leibniz Institute for Prevention Research and Epidemiology - BIPS, Department of Biometry and Data Management, Achterstr. 30, Bremen, Germany. hanke@leibniz-bips.de.|Leibniz Institute for Prevention Research and Epidemiology - BIPS, Department of Biometry and Data Management, Achterstr. 30, Bremen, Germany.', 'Germany|Germany', 3, 'Algorithms|Brain|Humans|Metabolic Networks and Pathways|Protein Interaction Maps', 'Journal Article', '10.1186/s12859-017-1677-x', '28511665', 'Different phenomena like the spread of a disease, social interactions or the biological relation between genes can be thought of as dynamic networks. These can be represented as a sequence of static graphs (so called graph snapshots). Based on this graph sequences, classical vertex centrality measures like closeness and betweenness centrality have been extended to quantify the importance of single vertices within a dynamic network. An implicit assumption for the calculation of temporal centrality measures is that the graph sequence contains all information about the network dynamics over time. This assumption is unlikely to be justified in many real world applications due to limited access to fully observed network data. Incompletely observed graph sequences lack important information about duration or existence of edges and may result in biased temporal centrality values.'),
(97873, 2017, 'bmc', 'NFPscanner: a webtool for knowledge-based deciphering of biomedical networks', 'Xu, Wenjian|Cao, Yang|Xie, Ziwei|He, Haochen|He, Song|Hong, Hao|Bo, Xiaochen|Li, Fei', 'Department of Biotechnology, Beijing Institute of Radiation Medicine, 27 Taiping Street, Haidian District, Beijing, 100850, China.|Tianjin Institute of Health & Environmental Medicine, 1 Dali Road, Heping District, Tianjin, 300050, China.|Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, Hubei, China.|Department of Biotechnology, Beijing Institute of Radiation Medicine, 27 Taiping Street, Haidian District, Beijing, 100850, China.|Department of Biotechnology, Beijing Institute of Radiation Medicine, 27 Taiping Street, Haidian District, Beijing, 100850, China.|Department of Biomedical Engineering, National University of Defense Technology, 109 Deya Road, Kaifu District, Changsha, 410073, Hunan, China.|Department of Biotechnology, Beijing Institute of Radiation Medicine, 27 Taiping Street, Haidian District, Beijing, 100850, China. boxc@bmi.ac.cn.|Department of Biotechnology, Beijing Institute of Radiation Medicine, 27 Taiping Street, Haidian District, Beijing, 100850, China. pittacus@gmail.com.', 'China|China|China|China|China|China|China|China', 0, 'Algorithms|Cluster Analysis|Gene Regulatory Networks|Internet|Knowledge Bases|Reproducibility of Results|Sequence Alignment|Software', 'Journal Article', '10.1186/s12859-017-1673-1', '28521733', 'Many biological pathways have been created to represent different types of knowledge, such as genetic interactions, metabolic reactions, and gene-regulating and physical-binding relationships. Biologists are using a wide range of omics data to elaborately construct various context-specific differential molecular networks. However, they cannot easily gain insight into unfamiliar gene networks with the tools that are currently available for pathways resource and network analysis. They would benefit from the development of a standardized tool to compare functions of multiple biological networks quantitatively and promptly.'),
(97874, 2017, 'bmc', 'The rainfall plot: its motivation, characteristics and pitfalls', 'Domanska, Diana|Vodák, Daniel|Lund-Andersen, Christin|Salvatore, Stefania|Hovig, Eivind|Sandve, Geir Kjetil', 'Department of Informatics, University of Oslo, Oslo, Norway. dianadom@ifi.uio.no.|Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.|Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.|Department of Informatics, University of Oslo, Oslo, Norway.|Department of Informatics, University of Oslo, Oslo, Norway.|Department of Informatics, University of Oslo, Oslo, Norway.', 'Norway|Norway|Norway|Norway|Norway|Norway', 3, 'Genome, Human|Guidelines as Topic|Humans|Motivation|Mutation|Pancreatic Neoplasms|Software', 'Journal Article', '10.1186/s12859-017-1679-8', '28521741', 'A visualization referred to as rainfall plot has recently gained popularity in genome data analysis. The plot is mostly used for illustrating the distribution of somatic cancer mutations along a reference genome, typically aiming to identify mutation hotspots. In general terms, the rainfall plot can be seen as a scatter plot showing the location of events on the x-axis versus the distance between consecutive events on the y-axis. Despite its frequent use, the motivation for applying this particular visualization and the appropriateness of its usage have never been critically addressed in detail.'),
(97875, 2017, 'bmc', 'Coordinates and intervals in graph-based reference genomes', 'Rand, Knut D|Grytten, Ivar|Nederbragt, Alexander J|Storvik, Geir O|Glad, Ingrid K|Sandve, Geir K', 'Department of Mathematics, University of Oslo, Moltke Moes vei 35, Oslo, 0851, Norway. knutdr@math.uio.no.|Department of informatics, University of Oslo, Gaustadalleen 23 B, Oslo, 0371, Norway.|Department of informatics, University of Oslo, Gaustadalleen 23 B, Oslo, 0371, Norway.|Department of Mathematics, University of Oslo, Moltke Moes vei 35, Oslo, 0851, Norway.|Department of Mathematics, University of Oslo, Moltke Moes vei 35, Oslo, 0851, Norway.|Department of informatics, University of Oslo, Gaustadalleen 23 B, Oslo, 0371, Norway.', 'Norway|Norway|Norway|Norway|Norway|Norway', 6, 'Algorithms|Computer Graphics|Genetic Loci|Genome, Human|Genomics|Humans|Internet|RNA, Messenger|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1678-9', '28521770', 'It has been proposed that future reference genomes should be graph structures in order to better represent the sequence diversity present in a species. However, there is currently no standard method to represent genomic intervals, such as the positions of genes or transcription factor binding sites, on graph-based reference genomes.'),
(97876, 2017, 'bmc', 'SurvivalGWAS_SV: software for the analysis of genome-wide association studies of imputed genotypes 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', 4, 'Computer Simulation|Genome-Wide Association Study|Genotype|Humans|Polymorphism, Single Nucleotide|Proportional Hazards Models|Software|Time Factors', 'Journal Article', '10.1186/s12859-017-1683-z', '28525968', 'Analysis of genome-wide association studies (GWAS) with time to event outcomes have become increasingly popular, predominantly in the context of pharmacogenetics, where the survival endpoint could be death, disease remission or the occurrence of an adverse drug reaction. However, methodology and software that can efficiently handle the scale and complexity of genetic data from GWAS with time to event outcomes has not been extensively developed.'),
(97877, 2017, 'bmc', 'In silico approach to designing rational metagenomic libraries for functional studies', 'Kusnezowa, Anna|Leichert, Lars I', 'Institute of Biochemistry and Pathobiochemistry - Microbial Biochemistry, Ruhr University Bochum, Universitätsstr. 150, 44780, Bochum, Germany.|Institute of Biochemistry and Pathobiochemistry - Microbial Biochemistry, Ruhr University Bochum, Universitätsstr. 150, 44780, Bochum, Germany. lars.leichert@ruhr-uni-bochum.de.', 'Germany|Germany', 2, 'Amino Acid Sequence|Bacteria|Computer Simulation|Databases, Protein|Gene Library|Lipolysis|Markov Chains|Metagenome|Metagenomics|Oceans and Seas|Proteins', 'Journal Article', '10.1186/s12859-017-1668-y', '28532384', 'With the development of Next Generation Sequencing technologies, the number of predicted proteins from entire (meta-) genomes has risen exponentially. While for some of these sequences protein functions can be inferred from homology, an experimental characterization is still a requirement for the determination of protein function. However, functional characterization of proteins cannot keep pace with our capabilities to generate more and more sequence data.'),
(97878, 2017, 'bmc', 'CircularLogo: A lightweight web application to visualize intra-motif dependencies', 'Ye, Zhenqing|Ma, Tao|Kalmbach, Michael T|Dasari, Surendra|Kocher, Jean-Pierre A|Wang, Liguo', 'Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.|Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA.|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA. Wang.Liguo@mayo.edu.', 'United States|United States|United States|United States|United States|United States', 2, 'Base Sequence|Binding Sites|Eukaryotic Cells|Internet|Introns|Nucleic Acid Conformation|Nucleotide Motifs|RNA Splice Sites|RNA, Transfer|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1680-2', '28532394', 'The sequence logo has been widely used to represent DNA or RNA motifs for more than three decades. Despite its intelligibility and intuitiveness, the traditional sequence logo is unable to display the intra-motif dependencies and therefore is insufficient to fully characterize nucleotide motifs. Many methods have been developed to quantify the intra-motif dependencies, but fewer tools are available for visualization.'),
(97879, 2017, 'bmc', 'An integrated enhancement and reconstruction strategy for the quantitative extraction of actin stress fibers from fluorescence micrographs', 'Zhang, Zhen|Xia, Shumin|Kanchanawong, Pakorn', 'Mechanobiology Institute, Singapore, 117411, Republic of Singapore.|Mechanobiology Institute, Singapore, 117411, Republic of Singapore.|Mechanobiology Institute, Singapore, 117411, Republic of Singapore. biekp@nus.edu.sg.', 'Singapore|Singapore|Singapore', 4, 'Actins|Algorithms|Anisotropy|Automation|Cell Line, Tumor|Cell Shape|Humans|Image Enhancement|Microscopy, Fluorescence|Software|Stress Fibers', 'Journal Article', '10.1186/s12859-017-1684-y', '28532442', 'The stress fibers are prominent organization of actin filaments that perform important functions in cellular processes such as migration, polarization, and traction force generation, and whose collective organization reflects the physiological and mechanical activities of the cells. Easily visualized by fluorescence microscopy, the stress fibers are widely used as qualitative descriptors of cell phenotypes. However, due to the complexity of the stress fibers and the presence of other actin-containing cellular features, images of stress fibers are relatively challenging to quantitatively analyze using previously developed approaches, requiring significant user intervention. This poses a challenge for the automation of their detection, segmentation, and quantitative analysis.'),
(97880, 2017, 'bmc', 'DTWscore: differential expression and cell clustering analysis for time-series single-cell RNA-seq data', 'Wang, Zhuo|Jin, Shuilin|Liu, Guiyou|Zhang, Xiurui|Wang, Nan|Wu, Deliang|Hu, Yang|Zhang, Chiping|Jiang, Qinghua|Xu, Li|Wang, Yadong', 'Department of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, West Dazhi Street, China.|Department of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, West Dazhi Street, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, West Dazhi Street, China.|Department of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, West Dazhi Street, China.|Department of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, West Dazhi Street, China.|Department of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, West Dazhi Street, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, West Dazhi Street, China.|Department of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, West Dazhi Street, China. cpz@hit.edu.cn.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, West Dazhi Street, China. jiangqinghua@hit.edu.cn.|College of Computer Science and Technology, Harbin Engineering University, Harbin, Nantong Street, Heilongjiang, 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Nantong Street, Heilongjiang, 150001, China.', 'China|China|China|China|China|China|China|China|China|China|China', 4, 'Algorithms|Cluster Analysis|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Humans|Muscle, Skeletal|Myoblasts|RNA|ROC Curve|Sequence Analysis, RNA|Single-Cell Analysis|Statistics as Topic|Time Factors', 'Journal Article', '10.1186/s12859-017-1647-3', '28535748', 'The development of single-cell RNA sequencing has enabled profound discoveries in biology, ranging from the dissection of the composition of complex tissues to the identification of novel cell types and dynamics in some specialized cellular environments. However, the large-scale generation of single-cell RNA-seq (scRNA-seq) data collected at multiple time points remains a challenge to effective measurement gene expression patterns in transcriptome analysis.'),
(97881, 2017, 'bmc', 'MQAPRank: improved global protein model quality assessment by learning-to-rank', 'Jing, Xiaoyang|Dong, Qiwen', 'School of Computer Science, Fudan University, Shanghai, 200433, Peoples Republic of China.|School of Data Science and Engineering, East China Normal University, Shanghai, 200062, Peoples Republic of China. qwdong@sei.ecnu.edu.cn.', 'China|China', 6, 'Algorithms|Cluster Analysis|Internet|Models, Molecular|Protein Conformation|Proteins|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1691-z', '28545390', 'Protein structure prediction has achieved a lot of progress during the last few decades and a greater number of models for a certain sequence can be predicted. Consequently, assessing the qualities of predicted protein models in perspective is one of the key components of successful protein structure prediction. Over the past years, a number of methods have been developed to address this issue, which could be roughly divided into three categories: single methods, quasi-single methods and clustering (or consensus) methods. Although these methods achieve much success at different levels, accurate protein model quality assessment is still an open problem.'),
(97882, 2017, 'bmc', 'A random effects model for the identification of differential splicing (REIDS) using exon and HTA arrays', 'Van Moerbeke, Marijke|Kasim, Adetayo|Talloen, Willem|Reumers, Joke|Göhlmann, Hinrick W H|Shkedy, Ziv', 'Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Hasselt, 3500, Belgium. marijke.vanmoerbeke@uhasselt.be.|Wolfson Research Institute for Health and Wellbeing, Durham University, Durham, UK.|Janssen Pharmaceutica, Beerse, 2340, Belgium.|Janssen Pharmaceutica, Beerse, 2340, Belgium.|Janssen Pharmaceutica, Beerse, 2340, Belgium.|Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Hasselt, 3500, Belgium.', 'Belgium|United Kingdom|Belgium|Belgium|Belgium|Belgium', 2, 'Alternative Splicing|Area Under Curve|Colonic Neoplasms|Exons|Humans|LIM Domain Proteins|Microfilament Proteins|Models, Genetic|Oligonucleotide Array Sequence Analysis|Protein Isoforms|ROC Curve|Transcriptome', 'Journal Article', '10.1186/s12859-017-1687-8', '28545391', 'Alternative gene splicing is a common phenomenon in which a single gene gives rise to multiple transcript isoforms. The process is strictly guided and involves a multitude of proteins and regulatory complexes. Unfortunately, aberrant splicing events do occur which have been linked to genetic disorders, such as several types of cancer and neurodegenerative diseases (Fan et al., Theor Biol Med Model 3:19, 2006). Therefore, understanding the mechanism of alternative splicing and identifying the difference in splicing events between diseased and healthy tissue is crucial in biomedical research with the potential of applications in personalized medicine as well as in drug development.'),
(97883, 2017, 'bmc', 'quantGenius: implementation of a decision support system for qPCR-based gene quantification', 'Baebler, Špela|Svalina, Miha|Petek, Marko|Stare, Katja|Rotter, Ana|Pompe-Novak, Maruša|Gruden, Kristina', 'Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, 1000, SI, Slovenia. spela.baebler@nib.si.|Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, 1000, SI, Slovenia.|Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, 1000, SI, Slovenia.|Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, 1000, SI, Slovenia.|Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, 1000, SI, Slovenia.|Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, 1000, SI, Slovenia.|Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, 1000, SI, Slovenia.', 'Slovenia|Slovenia|Slovenia|Slovenia|Slovenia|Slovenia|Slovenia', 14, 'Internet|Limit of Detection|Nucleic Acids|Quality Control|Real-Time Polymerase Chain Reaction|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1688-7', '28545393', 'Quantitative molecular biology remains a challenge for researchers due to inconsistent approaches for control of errors in the final results. Due to several factors that can influence the final result, quantitative analysis and interpretation of qPCR data are still not trivial. Together with the development of high-throughput qPCR platforms, there is a need for a tool allowing for robust, reliable and fast nucleic acid quantification.'),
(97884, 2017, 'bmc', 'AGeNNT: annotation of enzyme families by means of refined neighborhood networks', 'Kandlinger, Florian|Plach, Maximilian G|Merkl, Rainer', 'Institute of Biophysics and Physical Biochemistry, University of Regensburg, D-93040, Regensburg, Germany.|Institute of Biophysics and Physical Biochemistry, University of Regensburg, D-93040, Regensburg, Germany.|Institute of Biophysics and Physical Biochemistry, University of Regensburg, D-93040, Regensburg, Germany. Rainer.Merkl@ur.de.', 'Germany|Germany|Germany', 2, 'Algorithms|Anthranilate Phosphoribosyltransferase|Biocatalysis|Cluster Analysis|Enzymes|Gene Regulatory Networks|Histidine|Internet|Molecular Sequence Annotation|Thymidine Phosphorylase|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1689-6', '28545394', 'Large enzyme families may contain functionally diverse members that give rise to clusters in a sequence similarity network (SSN). In prokaryotes, the genome neighborhood of a gene-product is indicative of its function and thus, a genome neighborhood network (GNN) deduced for an SSN provides strong clues to the specific function of enzymes constituting the different clusters. The Enzyme Function Initiative ( http://enzymefunction.org/ ) offers services that compute SSNs and GNNs.'),
(97885, 2017, 'bmc', 'JED: a Java Essential Dynamics Program for comparative analysis of protein trajectories', 'David, Charles C|Singam, Ettayapuram Ramaprasad Azhagiya|Jacobs, Donald J', 'Department of Bioinformatics and Genomics, University of North Carolina, Charlotte, USA. charles.david@plantandfood.co.nz.|Department of Physics and Optical Science, University of North Carolina, Charlotte, USA.|Department of Physics and Optical Science, University of North Carolina, Charlotte, USA. djacobs1@uncc.edu.', 'United States|United States|United States', 5, 'Algorithms|Principal Component Analysis|Proteins|Software', 'Journal Article', '10.1186/s12859-017-1676-y', '28545397', 'Essential Dynamics (ED) is a common application of principal component analysis (PCA) to extract biologically relevant motions from atomic trajectories of proteins. Covariance and correlation based PCA are two common approaches to determine PCA modes (eigenvectors) and their eigenvalues. Protein dynamics can be characterized in terms of Cartesian coordinates or internal distance pairs. In understanding protein dynamics, a comparison of trajectories taken from a set of proteins for similarity assessment provides insight into conserved mechanisms. Comprehensive software is needed to facilitate comparative-analysis with user-friendly features that are rooted in best practices from multivariate statistics.'),
(97886, 2017, 'bmc', 'Limitations of a metabolic network-based reverse ecology method for inferring host-pathogen interactions', 'Takemoto, Kazuhiro|Aie, Kazuki', 'Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, 820-8502, Japan. takemoto@bio.kyutech.ac.jp.|Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, 820-8502, Japan.', 'Japan|Japan', 2, 'Animals|Area Under Curve|Bacteria|Fungi|Host-Pathogen Interactions|Humans|Insecta|Internet|Logistic Models|Metabolic Networks and Pathways|Phylogeny|Plants|RNA, Ribosomal, 16S|ROC Curve|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1696-7', '28545448', 'Host-pathogen interactions are important in a wide range of research fields. Given the importance of metabolic crosstalk between hosts and pathogens, a metabolic network-based reverse ecology method was proposed to infer these interactions. However, the validity of this method remains unclear because of the various explanations presented and the influence of potentially confounding factors that have thus far been neglected.'),
(97887, 2017, 'bmc', 'Sequence-based prediction of protein protein interaction using a deep-learning algorithm', 'Sun, Tanlin|Zhou, Bo|Lai, Luhua|Pei, Jianfeng', 'Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China.|Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China.|Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China.|Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China. jfpei@pku.edu.cn.', 'China|China|China|China', 82, 'Algorithms|Amino Acid Sequence|Animals|Caenorhabditis elegans|Drosophila|Escherichia coli|High-Throughput Screening Assays|Humans|Internet|Protein Interaction Mapping|Proteins|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1700-2', '28545462', 'Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested.'),
(97888, 2017, 'bmc', 'An unsupervised learning approach for tracking mice in an enclosed area', 'Unger, Jakob|Mansour, Mike|Kopaczka, Marcin|Gronloh, Nina|Spehr, Marc|Merhof, Dorit', 'Institute of Imaging and Computer Vision, RWTH Aachen University, Kopernikusstr. 16, Aachen, 52056, Germany. jaunger@ucdavis.edu.|Institute of Imaging and Computer Vision, RWTH Aachen University, Kopernikusstr. 16, Aachen, 52056, Germany.|Institute of Imaging and Computer Vision, RWTH Aachen University, Kopernikusstr. 16, Aachen, 52056, Germany.|Department of Chemosensation, Institute of Biology II, RWTH Aachen University, Worringer Weg 3, Aachen, 52074, Germany.|Department of Chemosensation, Institute of Biology II, RWTH Aachen University, Worringer Weg 3, Aachen, 52074, Germany.|Institute of Imaging and Computer Vision, RWTH Aachen University, Kopernikusstr. 16, Aachen, 52056, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 5, 'Algorithms|Animals|Behavior, Animal|Electronic Data Processing|Female|Mice|Mice, Inbred C57BL|Models, Animal|Social Behavior|Unsupervised Machine Learning', 'Journal Article', '10.1186/s12859-017-1681-1', '28545524', 'In neuroscience research, mouse models are valuable tools to understand the genetic mechanisms that advance evidence-based discovery. In this context, large-scale studies emphasize the need for automated high-throughput systems providing a reproducible behavioral assessment of mutant mice with only a minimum level of manual intervention. Basic element of such systems is a robust tracking algorithm. However, common tracking algorithms are either limited by too specific model assumptions or have to be trained in an elaborate preprocessing step, which drastically limits their applicability for behavioral analysis.'),
(97889, 2017, 'bmc', 'Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features', 'Xu, Yan|Jia, Zhipeng|Wang, Liang-Bo|Ai, Yuqing|Zhang, Fang|Lai, Maode|Chang, Eric I-Chao', 'State Key Laboratory of Software Development Environment and Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and Research Institute of Beihang University in Shenzhen, Beijing, China. xuyan04@gmail.com.|Microsoft Research, Beijing, China.|Microsoft Research, Beijing, China.|Microsoft Research, Beijing, China.|Microsoft Research, Beijing, China.|Department of Pathology, School of Medicine, Zhejiang University, Hangzhou, China.|Microsoft Research, Beijing, China.', 'China|China|China|China|China|China|China', 112, 'Algorithms|Brain Neoplasms|Carcinoma|Colonic Neoplasms|Humans|Image Processing, Computer-Assisted|Neural Networks (Computer)|Support Vector Machine', 'Journal Article', '10.1186/s12859-017-1685-x', '28549410', 'Histopathology image analysis is a gold standard for cancer recognition and diagnosis. Automatic analysis of histopathology images can help pathologists diagnose tumor and cancer subtypes, alleviating the workload of pathologists. There are two basic types of tasks in digital histopathology image analysis: image classification and image segmentation. Typical problems with histopathology images that hamper automatic analysis include complex clinical representations, limited quantities of training images in a dataset, and the extremely large size of singular images (usually up to gigapixels). The property of extremely large size for a single image also makes a histopathology image dataset be considered large-scale, even if the number of images in the dataset is limited.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97890, 2017, 'bmc', 'FluoRender: joint freehand segmentation and visualization for many-channel fluorescence data analysis', 'Wan, Yong|Otsuna, Hideo|Holman, Holly A|Bagley, Brig|Ito, Masayoshi|Lewis, A Kelsey|Colasanto, Mary|Kardon, Gabrielle|Ito, Kei|Hansen, Charles', 'Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA. wanyong@cs.utah.edu.|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, USA.|Department of Bioengineering, University of Utah, Salt Lake City, USA.|Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA.|Institute of Molecular and Cellular Biosciences, University of Tokyo, Tokyo, Japan.|Department of Biology, University of Florida, Gainesville, USA.|Department of Human Genetics, University of Utah, Salt Lake City, USA.|Department of Human Genetics, University of Utah, Salt Lake City, USA.|Institute of Molecular and Cellular Biosciences, University of Tokyo, Tokyo, Japan.|Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA.', 'United States|United States|United States|United States|Japan|United States|United States|United States|Japan|United States', 0, 'Algorithms|Animals|Batrachoidiformes|Extremities|Eye|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Mice|Microscopy, Fluorescence|Software|Zebrafish', 'Journal Article', '10.1186/s12859-017-1694-9', '28549411', 'Image segmentation and registration techniques have enabled biologists to place large amounts of volume data from fluorescence microscopy, morphed three-dimensionally, onto a common spatial frame. Existing tools built on volume visualization pipelines for single channel or red-green-blue (RGB) channels have become inadequate for the new challenges of fluorescence microscopy. For a three-dimensional atlas of the insect nervous system, hundreds of volume channels are rendered simultaneously, whereas fluorescence intensity values from each channel need to be preserved for versatile adjustment and analysis. Although several existing tools have incorporated support of multichannel data using various strategies, the lack of a flexible design has made true many-channel visualization and analysis unavailable. The most common practice for many-channel volume data presentation is still converting and rendering pseudosurfaces, which are inaccurate for both qualitative and quantitative evaluations.'),
(97891, 2017, 'bmc', 'gnparser: a powerful parser for scientific names based on Parsing Expression Grammar', 'Mozzherin, Dmitry Y|Myltsev, Alexander A|Patterson, David J', 'University of Illinois, Illinois Natural History Survey, Species File Group, 1816 South Oak St., Champaign, 61820, IL, USA. mozzheri@illinois.edu.|IP Myltsev, Kaslinskaya St., Chelyabinsk, 454084, Russia.|University of Sydney, Sydney, Australia.', 'United States|United States|Australia', 2, 'Biodiversity|Informatics|Internet|Terminology as Topic|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1663-3', '28549446', 'Scientific names in biology act as universal links. They allow us to cross-reference information about organisms globally. However variations in spelling of scientific names greatly diminish their ability to interconnect data. Such variations may include abbreviations, annotations, misspellings, etc. Authorship is a part of a scientific name and may also differ significantly. To match all possible variations of a name we need to divide them into their elements and classify each element according to its role. We refer to this as parsing the name. Parsing categorizes names elements into those that are stable and those that are prone to change. Names are matched first by combining them according to their stable elements. Matches are then refined by examining their varying elements. This two stage process dramatically improves the number and quality of matches. It is especially useful for the automatic data exchange within the context of Big Data in biology.'),
(97892, 2017, 'bmc', 'Hapl-o-Mat: open-source software for HLA haplotype frequency estimation from ambiguous and heterogeneous data', 'Schäfer, Christian|Schmidt, Alexander H|Sauter, Jürgen', 'DKMS gemeinnützige GmbH, Kressbach 1, 72072, Tübingen, Germany.|DKMS gemeinnützige GmbH, Kressbach 1, 72072, Tübingen, Germany.|DKMS gemeinnützige GmbH, Kressbach 1, 72072, Tübingen, Germany. sauter@dkms.de.', 'Germany|Germany|Germany', 0, 'Algorithms|Alleles|Gene Frequency|Genotype|HLA Antigens|Haplotypes|Histocompatibility Testing|Humans|Software', 'Journal Article', '10.1186/s12859-017-1692-y', '28558647', 'Knowledge of HLA haplotypes is helpful in many settings as disease association studies, population genetics, or hematopoietic stem cell transplantation. Regarding the recruitment of unrelated hematopoietic stem cell donors, HLA haplotype frequencies of specific populations are used to optimize both donor searches for individual patients and strategic donor registry planning. However, the estimation of haplotype frequencies from HLA genotyping data is challenged by the large amount of genotype data, the complex HLA nomenclature, and the heterogeneous and ambiguous nature of typing records.'),
(97893, 2017, 'bmc', 'Numerical integration methods and layout improvements in the context of dynamic RNA visualization', 'Shabash, Boris|Wiese, Kay C', 'School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada.|School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada. wiese@sfu.ca.', 'Canada|Canada', 0, 'Algorithms|Base Sequence|Nucleic Acid Conformation|RNA|Software', 'Journal Article', '10.1186/s12859-017-1682-0', '28558664', 'RNA visualization software tools have traditionally presented a static visualization of RNA molecules with limited ability for users to interact with the resulting image once it is complete. Only a few tools allowed for dynamic structures. One such tool is jViz.RNA. Currently, jViz.RNA employs a unique method for the creation of the RNA molecule layout by mapping the RNA nucleotides into vertexes in a graph, which we call the detailed graph, and then utilizes a Newtonian mechanics inspired system of forces to calculate a layout for the RNA molecule. The work presented here focuses on improvements to jViz.RNA that allow the drawing of RNA secondary structures according to common drawing conventions, as well as dramatic run-time performance improvements. This is done first by presenting an alternative method for mapping the RNA molecule into a graph, which we call the compressed graph, and then employing advanced numerical integration methods for the compressed graph representation.'),
(97894, 2017, 'bmc', 'Learning rule sets from survival data', 'Wrxc3xb3bel, xc5x81ukasz|Gudyxc5x9b, Adam|Sikora, Marek', 'Institute of Informatics, Silesian Univ. of Technology, Akademicka 16, Gliwice, 44-100, Poland. lukasz.wrobel@polsl.pl.|Institute of Informatics, Silesian Univ. of Technology, Akademicka 16, Gliwice, 44-100, Poland.|Institute of Innovative Technologies, EMAG, Leopolda 31, Katowice, 40-189, Poland.', 'Poland|Poland|Poland', 5, 'Algorithms|Breast Neoplasms|DNA Copy Number Variations|DNA Methylation|Female|Humans|Kaplan-Meier Estimate|MicroRNAs|Polymorphism, Single Nucleotide|Transcriptome', 'Journal Article', '10.1186/s12859-017-1693-x', '28558674', 'Survival analysis is an important element of reasoning from data. Applied in a number of fields, it has become particularly useful in medicine to estimate the survival rate of patients on the basis of their condition, examination results, and undergoing treatment. The recent developments in the next generation sequencing open new opportunities in survival study as they allow vast amount of genome-, transcriptome-, and proteome-related features to be investigated. These include single nucleotide and structural variants, expressions of genes and microRNAs, DNA methylation, and many others.'),
(97895, 2017, 'bmc', 'Evaluating the accuracy of amplicon-based microbiome computational pipelines on simulated human gut microbial communities', 'Golob, Jonathan L|Margolis, Elisa|Hoffman, Noah G|Fredricks, David N', 'Vaccine and Infectious Disease Division, Fred Hutch, 1100 Eastlake Ave E, E4-100, Seattle, WA, 98109, USA. jgolob@fredhutch.org.|Vaccine and Infectious Disease Division, Fred Hutch, 1100 Eastlake Ave E, E4-100, Seattle, WA, 98109, USA.|Department Laboratory Medicine, University of Washington, Seattle, WA, USA.|Vaccine and Infectious Disease Division, Fred Hutch, 1100 Eastlake Ave E, E4-100, Seattle, WA, 98109, USA.', 'United States|United States|United States|United States', 23, 'Algorithms|Bacteria|Humans|Intestines|Microbiota|Polymerase Chain Reaction|RNA, Ribosomal, 16S', 'Journal Article', '10.1186/s12859-017-1690-0', '28558684', 'Microbiome studies commonly use 16S rRNA gene amplicon sequencing to characterize microbial communities. Errors introduced at multiple steps in this process can affect the interpretation of the data. Here we evaluate the accuracy of operational taxonomic unit (OTU) generation, taxonomic classification, alpha- and beta-diversity measures for different settings in QIIME, MOTHUR and a pplacer-based classification pipeline, using a novel software package: DECARD.'),
(97896, 2017, 'bmc', 'Intervene: a tool for intersection and visualization of multiple gene or genomic region sets', 'Khan, Aziz|Mathelier, Anthony', 'Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318, Oslo, Norway. aziz.khan@ncmm.uio.no.|Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318, Oslo, Norway. anthony.mathelier@ncmm.uio.no.', 'Norway|Norway', 53, 'Animals|Computer Graphics|Gene Dosage|Genome|High-Throughput Nucleotide Sequencing|Humans|Mice|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1708-7', '28569135', 'A common task for scientists relies on comparing lists of genes or genomic regions derived from high-throughput sequencing experiments. While several tools exist to intersect and visualize sets of genes, similar tools dedicated to the visualization of genomic region sets are currently limited.'),
(97897, 2017, 'bmc', 'An evaluation of copy number variation detection tools for cancer using whole exome sequencing data', 'Zare, Fatima|Dow, Michelle|Monteleone, Nicholas|Hosny, Abdelrahman|Nabavi, Sheida', 'Computer Science and Engineering Department, University of Connecticut, Storrs, CT, USA.|Biomedical Informatics Department, University of California San Diego, San Diego, CA, USA.|Computer Science and Engineering Department, University of Connecticut, Storrs, CT, USA.|Computer Science and Engineering Department, University of Connecticut, Storrs, CT, USA.|Computer Science and Engineering Department and Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA. sheida.nabavi@uconn.edu.', 'United States|United States|United States|United States|United States', 53, 'Algorithms|DNA Copy Number Variations|Exome|Female|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1705-x', '28569140', 'Recently copy number variation (CNV) has gained considerable interest as a type of genomic/genetic variation that plays an important role in disease susceptibility. Advances in sequencing technology have created an opportunity for detecting CNVs more accurately. Recently whole exome sequencing (WES) has become primary strategy for sequencing patient samples and study their genomics aberrations. However, compared to whole genome sequencing, WES introduces more biases and noise that make CNV detection very challenging. Additionally, tumors complexity makes the detection of cancer specific CNVs even more difficult. Although many CNV detection tools have been developed since introducing NGS data, there are few tools for somatic CNV detection for WES data in cancer.'),
(97898, 2017, 'bmc', 'Identification of the sequence determinants of protein N-terminal acetylation through a decision tree approach', 'Yamada, Kazunori D|Omori, Satoshi|Nishi, Hafumi|Miyagi, Masaru', 'Graduate School of Information Sciences, Tohoku University, Sendai, 980-8579, Japan. kyamada@ecei.tohoku.ac.jp.|Graduate School of Information Sciences, Tohoku University, Sendai, 980-8579, Japan.|Graduate School of Information Sciences, Tohoku University, Sendai, 980-8579, Japan.|Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH, 44106, USA. masaru.miyagi@case.edu.', 'Japan|Japan|Japan|United States', 5, 'Acetylation|Algorithms|Amino Acid Sequence|Databases, Factual|Proteins|Static Electricity', 'Journal Article', '10.1186/s12859-017-1699-4', '28578658', 'N-terminal acetylation is one of the most common protein modifications in eukaryotes and occurs co-translationally when the N-terminus of the nascent polypeptide is still attached to the ribosome. This modification has been shown to be involved in a wide range of biological phenomena such as protein half-life regulation, protein-protein and protein-membrane interactions, and protein subcellular localization. Thus, accurately predicting which proteins receive an acetyl group based on their protein sequence is expected to facilitate the functional study of this modification. As the occurrence of N-terminal acetylation strongly depends on the context of protein sequences, attempts to understand the sequence determinants of N-terminal acetylation were conducted initially by simply examining the N-terminal sequences of many acetylated and unacetylated proteins and more recently by machine learning approaches. However, a complete understanding of the sequence determinants of this modification remains to be elucidated.'),
(97899, 2017, 'bmc', 'Simple adjustment of the sequence weight algorithm remarkably enhances PSI-BLAST performance', 'Oda, Toshiyuki|Lim, Kyungtaek|Tomii, Kentaro', 'Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan. laevis@xenopus.sakura.ne.jp.|Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.|Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan. k-tomii@aist.go.jp.', 'Japan|Japan|Japan', 2, 'Algorithms|Amino Acid Sequence|Area Under Curve|Databases, Factual|Position-Specific Scoring Matrices|Proteins|ROC Curve|Sequence Alignment', 'Journal Article', '10.1186/s12859-017-1686-9', '28578660', 'PSI-BLAST, an extremely popular tool for sequence similarity search, features the utilization of Position-Specific Scoring Matrix (PSSM) constructed from a multiple sequence alignment (MSA). PSSM allows the detection of more distant homologs than a general amino acid substitution matrix does. An accurate estimation of the weights for sequences in an MSA is crucially important for PSSM construction. PSI-BLAST divides a given MSA into multiple blocks, for which sequence weights are calculated. When the block width becomes very narrow, the sequence weight calculation can be odd.'),
(97900, 2017, 'bmc', 'Parallel tiled Nussinov RNA folding loop nest generated using both dependence graph transitive closure and loop skewing', 'Palkowski, Marek|Bielecki, Wlodzimierz', 'West Pomeranian University of Technology, Faculty of Computer Science, Szczecin, 71-210, Poland. mpalkowski@wi.zut.edu.pl.|West Pomeranian University of Technology, Faculty of Computer Science, Szczecin, 71-210, Poland.', 'Poland|Poland', 9, 'Algorithms|Humans|Nucleic Acid Conformation|RNA|RNA Folding', 'Journal Article', '10.1186/s12859-017-1707-8', '28578661', 'RNA secondary structure prediction is a compute intensive task that lies at the core of several search algorithms in bioinformatics. Fortunately, the RNA folding approaches, such as the Nussinov base pair maximization, involve mathematical operations over affine control loops whose iteration space can be represented by the polyhedral model. Polyhedral compilation techniques have proven to be a powerful tool for optimization of dense array codes. However, classical affine loop nest transformations used with these techniques do not optimize effectively codes of dynamic programming of RNA structure predictions.'),
(97901, 2017, 'bmc', 'dictyExpress: a web-based platform for sequence data management and analytics in Dictyostelium and beyond', 'Stajdohar, Miha|Rosengarten, Rafael D|Kokosar, Janez|Jeran, Luka|Blenkus, Domen|Shaulsky, Gad|Zupan, Blaz', 'Genialis d.o.o., Trzaska cesta 315, Ljubljana, 1000, Slovenia.|Genialis Inc., 2726 Bissonnett Street, Suite 240-374, Houston, 77005, TX, USA. rafael@genialis.com.|Genialis d.o.o., Trzaska cesta 315, Ljubljana, 1000, Slovenia.|Genialis d.o.o., Trzaska cesta 315, Ljubljana, 1000, Slovenia.|Genialis d.o.o., Trzaska cesta 315, Ljubljana, 1000, Slovenia.|Department of Molecular and Human Genetics, Baylor College of Medicine, 1 Baylor Plaza, Houston, 77030, TX, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, 1 Baylor Plaza, Houston, 77030, TX, USA.', 'Slovenia|United States|Slovenia|Slovenia|Slovenia|United States|United States', 5, 'Chromatin Immunoprecipitation|Cluster Analysis|Dictyostelium|High-Throughput Nucleotide Sequencing|Internet|Sequence Analysis, RNA|Transcriptome|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1706-9', '28578698', 'Dictyostelium discoideum, a soil-dwelling social amoeba, is a model for the study of numerous biological processes. Research in the field has benefited mightily from the adoption of next-generation sequencing for genomics and transcriptomics. Dictyostelium biologists now face the widespread challenges of analyzing and exploring high dimensional data sets to generate hypotheses and discovering novel insights.'),
(97902, 2017, 'bmc', 'PFASUM: a substitution matrix from Pfam structural alignments', 'Keul, Frank|Hess, Martin|Goesele, Michael|Hamacher, Kay', 'Computational Biology and Simulation, Department of Biology, Technische Universität Darmstadt, Schnittspahnstraße 2, Darmstadt, 64287, Germany.|Graphics, Capture and Massively Parallel Computing, Department of Computer Science, Technische Universität Darmstadt, Rundeturmstraße 12, Darmstadt, 64283, Germany. martin.hess@gcc.tu-darmstadt.de.|Graphics, Capture and Massively Parallel Computing, Department of Computer Science, Technische Universität Darmstadt, Rundeturmstraße 12, Darmstadt, 64283, Germany.|Computational Biology and Simulation, Department of Biology, Technische Universität Darmstadt, Schnittspahnstraße 2, Darmstadt, 64287, Germany.', 'Germany|Germany|Germany|Germany', 3, 'Algorithms|Amino Acid Sequence|Amino Acid Substitution|Databases, Protein|Entropy|Proteins|Sequence Alignment|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/s12859-017-1703-z', '28583067', 'Detecting homologous protein sequences and computing multiple sequence alignments (MSA) are fundamental tasks in molecular bioinformatics. These tasks usually require a substitution matrix for modeling evolutionary substitution events derived from a set of aligned sequences. Over the last years, the known sequence space increased drastically and several publications demonstrated that this can lead to significantly better performing matrices. Interestingly, matrices based on dated sequence datasets are still the de facto standard for both tasks even though their data basis may limit their capabilities. We address these aspects by presenting a new substitution matrix series called PFASUM. These matrices are derived from Pfam seed MSAs using a novel algorithm and thus build upon expert ground truth data covering a large and diverse sequence space.'),
(97903, 2017, 'bmc', 'Prediction of bioluminescent proteins by using sequence-derived features and lineage-specific scheme', 'Zhang, Jian|Chai, Haiting|Yang, Guifu|Ma, Zhiqiang', 'School of Computer Science and Information Technology, Northeast Normal University, Changchun, Jilin Province, 130117, Peoples Republic of China.|School of Computer Science and Information Technology, Northeast Normal University, Changchun, Jilin Province, 130117, Peoples Republic of China.|School of Computer Science and Information Technology, Northeast Normal University, Changchun, Jilin Province, 130117, Peoples Republic of China.|School of Computer Science and Information Technology, Northeast Normal University, Changchun, Jilin Province, 130117, Peoples Republic of China. zhiqiangma.nenu@gmail.com.', 'China|China|China|China', 4, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Amino Acids|Databases, Protein|Luminescent Proteins|Peptides|Phylogeny', 'Journal Article', '10.1186/s12859-017-1709-6', '28583090', 'Bioluminescent proteins (BLPs) widely exist in many living organisms. As BLPs are featured by the capability of emitting lights, they can be served as biomarkers and easily detected in biomedical research, such as gene expression analysis and signal transduction pathways. Therefore, accurate identification of BLPs is important for disease diagnosis and biomedical engineering. In this paper, we propose a novel accurate sequence-based method named PredBLP (Prediction of BioLuminescent Proteins) to predict BLPs.'),
(97904, 2017, 'bmc', 'Reconciliation feasibility in the presence of gene duplication, loss, and coalescence with multiple individuals per species', 'Rogers, Jennifer|Fishberg, Andrew|Youngs, Nora|Wu, Yi-Chieh', 'Department of Computer Science, Harvey Mudd College, Claremont, 91711, California, USA.|Department of Computer Science, Harvey Mudd College, Claremont, 91711, California, USA.|Department of Mathematics, Harvey Mudd College, Claremont, 91711, California, USA.|Department of Computer Science, Harvey Mudd College, Claremont, 91711, California, USA. yjw@cs.hmc.edu.', 'United States|United States|United States|United States', 3, 'Algorithms|Alleles|Animals|Computer Simulation|Databases, Genetic|Evolution, Molecular|Feasibility Studies|Gene Duplication|Genome|Hominidae|Models, Genetic|Phylogeny|Species Specificity', 'Journal Article', '10.1186/s12859-017-1701-1', '28583091', 'In phylogenetics, we often seek to reconcile gene trees with species trees within the framework of an evolutionary model. While the most popular models for eukaryotic species allow for only gene duplication and gene loss or only multispecies coalescence, recent work has combined these phenomena through a reconciliation structure, the labeled coalescent tree (LCT), that simultaneously describes the duplication-loss and coalescent history of a gene family. However, the LCT makes the simplifying assumption that only one individual is sampled per species whereas, with advances in gene sequencing, we now have access to multiple samples per species.'),
(97905, 2017, 'bmc', 'A systematic evaluation of nucleotide properties for CRISPR sgRNA design', 'Kuan, Pei Fen|Powers, Scott|He, Shuyao|Li, Kaiqiao|Zhao, Xiaoyu|Huang, Bo', 'Department of Applied Mathematics and Statistics, Stony Brook University, 100 Nicolls Road, Stony Brook, 11794, USA. peifen.kuan@stonybrook.edu.|Department of Pathology, Stony Brook University, 100 Nicolls Road, Stony Brook, 11794, USA.|Department of Applied Mathematics and Statistics, Stony Brook University, 100 Nicolls Road, Stony Brook, 11794, USA.|Department of Applied Mathematics and Statistics, Stony Brook University, 100 Nicolls Road, Stony Brook, 11794, USA.|Department of Pathology, Stony Brook University, 100 Nicolls Road, Stony Brook, 11794, USA.|Oncology Business Unit, Pfizer Inc., 558 Eastern Point Rd, Groton, 06340, USA.', 'United States|United States|United States|United States|United States|United States', 12, 'Animals|Area Under Curve|Clustered Regularly Interspaced Short Palindromic Repeats|Gene Editing|Internet|Machine Learning|Nucleotides|ROC Curve|Thermodynamics|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1697-6', '28587596', 'CRISPR is a versatile gene editing tool which has revolutionized genetic research in the past few years. Optimizing sgRNA design to improve the efficiency of target/DNA cleavage is critical to ensure the success of CRISPR screens.'),
(97906, 2017, 'bmc', 'Meta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes', 'Khan, Atif|Katanic, Dejan|Thakar, Juilee', 'Department of Microbiology and Immunology, University of Rochester, Rochester, NY, 14642, USA.|Department of Microbiology and Immunology, University of Rochester, Rochester, NY, 14642, USA.|Department of Microbiology and Immunology, University of Rochester, Rochester, NY, 14642, USA. juilee_thakar@urmc.rochester.edu.', 'United States|United States|United States', 5, 'A549 Cells|Cluster Analysis|Databases, Factual|Dendritic Cells|Fuzzy Logic|Humans|Influenza, Human|Internet|Transcriptome|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1669-x', '28587632', 'Despite advances in the gene-set enrichment analysis methods, inadequate definitions of gene-sets cause a major limitation in the discovery of novel biological processes from the transcriptomic datasets. Typically, gene-sets are obtained from publicly available pathway databases, which contain generalized definitions frequently derived by manual curation. Recently unsupervised clustering algorithms have been proposed to identify gene-sets from transcriptomics datasets deposited in public domain. These data-driven definitions of the gene-sets can be context-specific revealing novel biological mechanisms. However, the previously proposed algorithms for identification of data-driven gene-sets are based on hard clustering which do not allow overlap across clusters, a characteristic that is predominantly observed across biological pathways.'),
(97907, 2017, 'bmc', 'Recognizing RNA structural motifs in HT-SELEX data for ribosomal protein S15', 'Pei, Shermin|Slinger, Betty L|Meyer, Michelle M', 'Boston College, 140 Commonwealth Ave., 02467, Chestnut Hill, USA.|Boston College, 140 Commonwealth Ave., 02467, Chestnut Hill, USA.|Boston College, 140 Commonwealth Ave., 02467, Chestnut Hill, USA. m.meyer@bc.edu.', 'United States|United States|United States', 8, 'Algorithms|Base Pairing|Base Sequence|Cluster Analysis|Geobacillus|High-Throughput Nucleotide Sequencing|Logistic Models|Nucleic Acid Conformation|Protein Binding|RNA-Binding Proteins|Ribosomal Proteins|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-017-1704-y', '28587636', 'Proteins recognize many different aspects of RNA ranging from single stranded regions to discrete secondary or tertiary structures. High-throughput sequencing (HTS) of in vitro selected populations offers a large scale method to study RNA-proteins interactions. However, most existing analysis methods require that the binding motifs are enriched in the population relative to earlier rounds, and that motifs are found in a loop or single stranded region of the potential RNA secondary structure. Such methods do not generalize to all RNA-protein interaction as some RNA binding proteins specifically recognize more complex structures such as double stranded RNA.'),
(97908, 2017, 'bmc', 'NaviSE: superenhancer navigator integrating epigenomics signal algebra', 'Ascensión, Alex M|Arrospide-Elgarresta, Mikel|Izeta, Ander|Araúzo-Bravo, Marcos J', 'Computational Biology and Systems Biomedicine, Biodonostia Health Research Institute, San Sebastián, 20014, Spain.|Computational Biology and Systems Biomedicine, Biodonostia Health Research Institute, San Sebastián, 20014, Spain.|Tissue Engineering Laboratory, Bioengineering Area, Biodonostia Health Research Institute, San Sebastián, 20014, Spain. ander.izeta@biodonostia.org.|Computational Biology and Systems Biomedicine, Biodonostia Health Research Institute, San Sebastián, 20014, Spain. mararabra@yahoo.co.uk.', 'Spain|Spain|Spain|Spain', 0, 'Biomarkers|Chromosomes|Epigenomics|High-Throughput Nucleotide Sequencing|Human Embryonic Stem Cells|Humans|Internet|Monocytes|Neurons|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1698-5', '28587674', 'Superenhancers are crucial structural genomic elements determining cell fate, and they are also involved in the determination of several diseases, such as cancer or neurodegeneration. Although there are pipelines which use independent pieces of software to predict the presence of superenhancers from genome-wide chromatin marks or DNA-interaction protein binding sites, there is not yet an integrated software tool that processes automatically algebra combinations of raw data sequencing into a comprehensive final annotated report of predicted superenhancers.'),
(97909, 2017, 'bmc', 'Variable-order sequence modeling improves bacterial strain discrimination for Ion Torrent DNA reads', 'Poulsen, Thomas M|Frith, Martin', 'Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26 Aomi, Koto-ku, Tokyo, 135-0064, Japan. thomasaistjp@gmail.com.|Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26 Aomi, Koto-ku, Tokyo, 135-0064, Japan.', 'Japan|Japan', 1, 'DNA, Bacterial|Genome, Bacterial|Genomics|Markov Chains|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-017-1710-0', '28606054', 'Genome sequencing provides a powerful tool for pathogen detection and can help resolve outbreaks that pose public safety and health risks. Mapping of DNA reads to genomes plays a fundamental role in this approach, where accurate alignment and classification of sequencing data is crucial. Standard mapping methods crudely treat bases as independent from their neighbors. Accuracy might be improved by using higher order paired hidden Markov models (HMMs), which model neighbor effects, but introduce design and implementation issues that have typically made them impractical for read mapping applications. We present a variable-order paired HMM that we term VarHMM, which addresses central issues involved with higher order modeling for sequence alignment.'),
(97910, 2017, 'bmc', 'Analysis and prediction of single-stranded and double-stranded DNA binding proteins based on protein sequences', 'Wang, Wei|Sun, Lin|Zhang, Shiguang|Zhang, Hongjun|Shi, Jinling|Xu, Tianhe|Li, Keliang', 'College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan Province, 453007, China. weiwang@htu.edu.cn.|College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan Province, 453007, China.|College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan Province, 453007, China.|School of Aviation Engineering, Anyang University, Anyang, Henan Province, 455000, China.|School of International Education, Xuchang University, Xuchang, Henan Province, 461000, China.|College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan Province, 453007, China.|College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan Province, 453007, China.', 'China|China|China|China|China|China|China', 6, 'Amino Acid Sequence|Computational Biology|DNA|DNA, Single-Stranded|DNA-Binding Proteins|Protein Binding|Sequence Analysis, Protein|Support Vector Machine', 'Journal Article', '10.1186/s12859-017-1715-8', '28606086', 'DNA-binding proteins perform important functions in a great number of biological activities. DNA-binding proteins can interact with ssDNA (single-stranded DNA) or dsDNA (double-stranded DNA), and DNA-binding proteins can be categorized as single-stranded DNA-binding proteins (SSBs) and double-stranded DNA-binding proteins (DSBs). The identification of DNA-binding proteins from amino acid sequences can help to annotate protein functions and understand the binding specificity. In this study, we systematically consider a variety of schemes to represent protein sequences: OAAC (overall amino acid composition) features, dipeptide compositions, PSSM (position-specific scoring matrix profiles) and split amino acid composition (SAA), and then we adopt SVM (support vector machine) and RF (random forest) classification model to distinguish SSBs from DSBs.'),
(97911, 2017, 'bmc', 'Integration of quantitated expression estimates from polyA-selected and rRNA-depleted RNA-seq libraries', 'Bush, Stephen J|McCulloch, Mary E B|Summers, Kim M|Hume, David A|Clark, Emily L', 'The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK. stephen.bush@roslin.ed.ac.uk.|The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK.|The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK.|The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK. david.hume@roslin.ed.ac.uk.|The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 9, 'Animals|Female|Gene Expression Profiling|Gene Library|Lipopolysaccharides|Macrophages|Male|Poly A|RNA|RNA, Ribosomal|Sequence Analysis, RNA|Sheep|Transcriptome', 'Journal Article', '10.1186/s12859-017-1714-9', '28610557', 'The availability of fast alignment-free algorithms has greatly reduced the computational burden of RNA-seq processing, especially for relatively poorly assembled genomes. Using these approaches, previous RNA-seq datasets could potentially be processed and integrated with newly sequenced libraries. Confounding factors in such integration include sequencing depth and methods of RNA extraction and selection. Different selection methods (typically, either polyA-selection or rRNA-depletion) omit different RNAs, resulting in different fractions of the transcriptome being sequenced. In particular, rRNA-depleted libraries sample a broader fraction of the transcriptome than polyA-selected libraries. This study aimed to develop a systematic means of accounting for library type that allows data from these two methods to be compared.'),
(97912, 2017, 'bmc', '3D deep convolutional neural networks for amino acid environment similarity analysis', 'Torng, Wen|Altman, Russ B', 'Deparment of Bioengineering, Stanford University, Stanford, CA, 94305, USA.|Deparment of Bioengineering, Stanford University, Stanford, CA, 94305, USA. russ.altman@stanford.edu.', 'United States|United States', 29, 'Amino Acids|Animals|Area Under Curve|Brain|Embryonic Development|In Situ Hybridization|Mice|Nerve Net|ROC Curve|Transcriptome', 'Journal Article', '10.1186/s12859-017-1702-0', '28615003', 'Central to protein biology is the understanding of how structural elements give rise to observed function. The surfeit of protein structural data enables development of computational methods to systematically derive rules governing structural-functional relationships. However, performance of these methods depends critically on the choice of protein structural representation. Most current methods rely on features that are manually selected based on knowledge about protein structures. These are often general-purpose but not optimized for the specific application of interest. In this paper, we present a general framework that applies 3D convolutional neural network (3DCNN) technology to structure-based protein analysis. The framework automatically extracts task-specific features from the raw atom distribution, driven by supervised labels. As a pilot study, we use our network to analyze local protein microenvironments surrounding the 20 amino acids, and predict the amino acids most compatible with environments within a protein structure. To further validate the power of our method, we construct two amino acid substitution matrices from the prediction statistics and use them to predict effects of mutations in T4 lysozyme structures.'),
(97913, 2017, 'bmc', 'Quantifying and filtering knowledge generated by literature based discovery', 'Preiss, Judita|Stevenson, Mark', 'Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield, UK. j.preiss@sheffield.ac.uk.|Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield, UK.', 'United Kingdom|United Kingdom', 2, 'Algorithms|Data Mining|Humans|Knowledge|Literature Based Discovery', 'Journal Article', '10.1186/s12859-017-1641-9', '28617217', 'Literature based discovery (LBD) automatically infers missed connections between concepts in literature. It is often assumed that LBD generates more information than can be reasonably examined.'),
(97914, 2017, 'bmc', 'Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions', 'Murugesan, Sugeerth|Bouchard, Kristofer|Chang, Edward|Dougherty, Max|Hamann, Bernd|Weber, Gunther H', 'Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, 94720, CA, USA. sugeerth@gmail.com.|Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, 94720, CA, USA.|Department of Neurological Surgery, UCSF, 505 Parnassus Ave, San Francisco, 94143, CA, USA.|Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, 94720, CA, USA.|Department of Computer Science, University of California, One Shields Avenue, Davis, 95616, CA, USA.|Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, 94720, CA, USA.', 'United States|United States|United States|United States|United States|United States', 6, 'Algorithms|Brain|Cluster Analysis|Computational Biology|Electrocorticography|Epilepsy|Humans|Software', 'Journal Article', '10.1186/s12859-017-1633-9', '28617218', 'There exists a need for effective and easy-to-use software tools supporting the analysis of complex Electrocorticography (ECoG) data. Understanding how epileptic seizures develop or identifying diagnostic indicators for neurological diseases require the in-depth analysis of neural activity data from ECoG. Such data is multi-scale and is of high spatio-temporal resolution. Comprehensive analysis of this data should be supported by interactive visual analysis methods that allow a scientist to understand functional patterns at varying levels of granularity and comprehend its time-varying behavior.'),
(97915, 2017, 'bmc', 'Drug repositioning for enzyme modulator based on human metabolite-likeness', 'Lee, Yoon Hyeok|Choi, Hojae|Park, Seongyong|Lee, Boah|Yi, Gwan-Su', 'Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea.|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea.|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea.|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea.|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea. gsyi@kaist.ac.kr.', '-|-|-|-|-', 0, 'Antimetabolites|Area Under Curve|Databases, Factual|Drug Repositioning|Enzymes|Gaucher Disease|Glucosylceramidase|Humans|ROC Curve', 'Journal Article', '10.1186/s12859-017-1637-5', '28617219', 'Recently, the metabolite-likeness of the drug space has emerged and has opened a new possibility for exploring human metabolite-like candidates in drug discovery. However, the applicability of metabolite-likeness in drug discovery has been largely unexplored. Moreover, there are no reports on its applications for the repositioning of drugs to possible enzyme modulators, although enzyme-drug relations could be directly inferred from the similarity relationships between enzymes metabolites and drugs.'),
(97916, 2017, 'bmc', 'A theorem proving approach for automatically synthesizing visualizations of flow cytometry data', 'Raj, Sunny|Hussain, Faraz|Husein, Zubir|Torosdagli, Neslisah|Turgut, Damla|Deo, Narsingh|Pattanaik, Sumanta|Chang, Chung-Che Jeff|Jha, Sumit Kumar', 'Computer Science Department, University of Central Florida, Orlando, 32816, Florida, USA. sraj@cs.ucf.edu.|School of Computing, University of Utah, Salt Lake City, Utah, USA.|Computer Science Department, University of Central Florida, Orlando, 32816, Florida, USA.|Computer Science Department, University of Central Florida, Orlando, 32816, Florida, USA.|Computer Science Department, University of Central Florida, Orlando, 32816, Florida, USA.|Computer Science Department, University of Central Florida, Orlando, 32816, Florida, USA.|Computer Science Department, University of Central Florida, Orlando, 32816, Florida, USA.|Department of Pathology, Florida Hospital, Orlando, Florida, USA.|Computer Science Department, University of Central Florida, Orlando, 32816, Florida, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 1, 'Algorithms|Computational Biology|Flow Cytometry', 'Journal Article', '10.1186/s12859-017-1662-4', '28617220', 'Polychromatic flow cytometry is a popular technique that has wide usage in the medical sciences, especially for studying phenotypic properties of cells. The high-dimensionality of data generated by flow cytometry usually makes it difficult to visualize. The naive solution of simply plotting two-dimensional graphs for every combination of observables becomes impractical as the number of dimensions increases. A natural solution is to project the data from the original high dimensional space to a lower dimensional space while approximately preserving the overall relationship between the data points. The expert can then easily visualize and analyze this low-dimensional embedding of the original dataset.'),
(97917, 2017, 'bmc', 'Identification of cancer-specific motifs in mimotope profiles of serum antibody repertoire', 'Gerasimov, Ekaterina|Zelikovsky, Alex|Mxc4x83ndoiu, Ion|Ionov, Yurij', 'Department of Computer Science, Georgia State University, 25 Park Place, Atlanta, 30303, GA, USA. enenastyeva1@student.gsu.edu.|Department of Computer Science, Georgia State University, 25 Park Place, Atlanta, 30303, GA, USA.|Department of Computer Science and Engineering, University of Connecticut, Storrs, 06269, CT, USA.|Department of Cancer Genetics, Roswell Park Cancer Institute, Buffalo, 14263, NY, USA.', 'Georgia|Georgia|United States|United States', 4, 'Autoantibodies|Biomarkers, Tumor|Computational Biology|Early Detection of Cancer|Epitopes|Humans|Neoplasms|Peptide Library', 'Journal Article', '10.1186/s12859-017-1661-5', '28617221', 'For fighting cancer, earlier detection is crucial. Circulating auto-antibodies produced by the patients own immune system after exposure to cancer proteins are promising bio-markers for the early detection of cancer. Since an antibody recognizes not the whole antigen but 4-7 critical amino acids within the antigenic determinant (epitope), the whole proteome can be represented by a random peptide phage display library. This opens the possibility to develop an early cancer detection test based on a set of peptide sequences identified by comparing cancer patients and healthy donors global peptide profiles of antibody specificities.'),
(97918, 2017, 'bmc', 'An extensive assessment of network alignment algorithms for comparison of brain connectomes', 'Milano, Marianna|Guzzi, Pietro Hiram|Tymofieva, Olga|Xu, Duan|Hess, Christofer|Veltri, Pierangelo|Cannataro, Mario', 'Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Italy.|Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Italy. hguzzi@unicz.it.|Department of Radiology University of California, San Francisco, USA.|Department of Radiology University of California, San Francisco, USA.|Department of Radiology University of California, San Francisco, USA.|Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Italy.|Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Italy.', 'Italy|Italy|United States|United States|United States|Italy|Italy', 0, 'Algorithms|Brain|Connectome|Humans|Image Processing, Computer-Assisted|Magnetic Resonance Imaging', 'Journal Article', '10.1186/s12859-017-1635-7', '28617222', 'Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages, the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. The atlas-based parcellations present some known limitations in cases of early brain development and abnormal anatomy. Consequently, it has been recently proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space, as a way to deal with the unknown correspondences of the parcels. Such process requires modeling of the brain using graph theory and the subsequent comparison of the structure of graphs. The latter step may be modeled as a network alignment (NA) problem.'),
(97919, 2017, 'bmc', 'Coupling effects on turning points of infectious diseases epidemics in scale-free networks', 'Kim, Kiseong|Lee, Sangyeon|Lee, Doheon|Lee, Kwang Hyung', 'Department of Bio and Brain Engineering, KAIST, Daejeon, South Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon, South Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon, South Korea. dhlee@biosoft.kaist.ac.kr.|Department of Bio and Brain Engineering, KAIST, Daejeon, South Korea. khlee@biosoft.kaist.ac.kr.', '-|-|-|-', 0, 'Algorithms|Communicable Diseases|Humans|Models, Theoretical|Monte Carlo Method', 'Journal Article', '10.1186/s12859-017-1643-7', '28617223', 'Pandemic is a typical spreading phenomenon that can be observed in the human society and is dependent on the structure of the social network. The Susceptible-Infective-Recovered (SIR) model describes spreading phenomena using two spreading factors, contagiousness (xcexb2) and recovery rate (xcexb3). Some network models are trying to reflect the social network, but the real structure is difficult to uncover.'),
(97920, 2017, 'bmc', 'ENIGMA-Viewer: interactive visualization strategies for conveying effect sizes in meta-analysis', 'Zhang, Guohao|Kochunov, Peter|Hong, Elliot|Kelly, Sinead|Whelan, Christopher|Jahanshad, Neda|Thompson, Paul|Chen, Jian', 'Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, 21250, MD, USA.|Maryland Psychiatric Research Center, University of Maryland, Baltimore, 55 Wade Ave, Baltimore, 21228, MD, USA. pkochunov@mprc.umaryland.edu.|Maryland Psychiatric Research Center, University of Maryland, Baltimore, 55 Wade Ave, Baltimore, 21228, MD, USA.|Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, 02215, MA, USA.|Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, 1975 Zonal Ave, Los Angeles, 90033, LA, USA.|Keck School of Medicine, University of Southern California, 1975 Zonal Ave, Los Angeles, 90033, LA, USA.|Keck School of Medicine, University of Southern California, 1975 Zonal Ave, Los Angeles, 90033, LA, USA.|Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, 21250, MD, USA. jichen@umbc.edu.', 'United States|United States|United States|Israel|United States|United States|United States|United States', 0, 'Brain|Computational Biology|Humans|Image Processing, Computer-Assisted|Meta-Analysis as Topic|Neuroimaging|Software', 'Journal Article', '10.1186/s12859-017-1634-8', '28617224', 'Global scale brain research collaborations such as the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium are beginning to collect data in large quantity and to conduct meta-analyses using uniformed protocols. It becomes strategically important that the results can be communicated among brain scientists effectively. Traditional graphs and charts failed to convey the complex shapes of brain structures which are essential to the understanding of the result statistics from the analyses. These problems could be addressed using interactive visualization strategies that can link those statistics with brain structures in order to provide a better interface to understand brain research results.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97921, 2017, 'bmc', 'A greedy alignment-free distance estimator for phylogenetic inference', 'Thankachan, Sharma V|Chockalingam, Sriram P|Liu, Yongchao|Krishnan, Ambujam|Aluru, Srinivas', 'Department of Computer Science, University of Central Florida, Orlando, 32816, FL, USA.|Institute for Data Engineering and Science, Georgia Institute of Technology, Atlanta, 30332, GA, USA.|School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, 30332, GA, USA.|School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, 70703, LA, USA.|Institute for Data Engineering and Science, Georgia Institute of Technology, Atlanta, 30332, GA, USA. aluru@cc.gatech.edu.', 'United States|Georgia|Georgia|United States|Georgia', 17, 'Algorithms|Computational Biology|Phylogeny|Sequence Analysis', 'Journal Article', '10.1186/s12859-017-1658-0', '28617225', 'Alignment-free sequence comparison approaches have been garnering increasing interest in various data- and compute-intensive applications such as phylogenetic inference for large-scale sequences. While k-mer based methods are predominantly used in real applications, the average common substring (ACS) approach is emerging as one of the prominent alignment-free approaches. This ACS approach has been further generalized by some recent work, either greedily or exactly, by allowing a bounded number of mismatches in the common substrings.'),
(97922, 2017, 'bmc', 'Contextualization of drug-mediator relations using evidence networks', 'Tran, Hai Joey|Speyer, Gil|Kiefer, Jeff|Kim, Seungchan', 'Integrated Cancer Genomics Division, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA.|Integrated Cancer Genomics Division, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA.|Integrated Cancer Genomics Division, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA.|Integrated Cancer Genomics Division, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA. sekim@pvamu.edu.', 'United States|United States|United States|United States', 0, 'Cell Line|Cyclin H|DNA Repair|Databases, Factual|Death-Associated Protein Kinases|Gene Regulatory Networks|Humans|Imidazoles|Pharmaceutical Preparations|Protein Kinase Inhibitors|Protein-Serine-Threonine Kinases|Proteins|Triazines', 'Journal Article', '10.1186/s12859-017-1642-8', '28617226', 'Genomic analysis of drug response can provide unique insights into therapies that can be used to match the right drug to the right patient. However, the process of discovering such therapeutic insights using genomic data is not straightforward and represents an area of active investigation. EDDY (Evaluation of Differential DependencY), a statistical test to detect differential statistical dependencies, is one method that leverages genomic data to identify differential genetic dependencies. EDDY has been used in conjunction with the Cancer Therapeutics Response Portal (CTRP), a dataset with drug-response measurements for more than 400 small molecules, and RNAseq data of cell lines in the Cancer Cell Line Encyclopedia (CCLE) to find potential drug-mediator pairs. Mediators were identified as genes that showed significant change in genetic statistical dependencies within annotated pathways between drug sensitive and drug non-sensitive cell lines, and the results are presented as a public web-portal (EDDY-CTRP). However, the interpretability of drug-mediator pairs currently hinders further exploration of these potentially valuable results.'),
(97923, 2017, 'bmc', 'In silico re-identification of properties of drug target proteins', 'Kim, Baeksoo|Jo, Jihoon|Han, Jonghyun|Park, Chungoo|Lee, Hyunju', 'Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro,Buk-gu, Gwangju, 61005, Republic of Korea.|Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju, 24105, Republic of Korea.|Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro,Buk-gu, Gwangju, 61005, Republic of Korea.|Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju, 24105, Republic of Korea. chungoo@jnu.ac.kr.|Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro,Buk-gu, Gwangju, 61005, Republic of Korea. hyunjulee@gist.ac.kr.', '-|-|-|-|-', 0, 'Databases, Factual|Genomics|Humans|Protein Processing, Post-Translational|Proteins|Support Vector Machine', 'Journal Article', '10.1186/s12859-017-1639-3', '28617227', 'Computational approaches in the identification of drug targets are expected to reduce time and effort in drug development. Advances in genomics and proteomics provide the opportunity to uncover properties of druggable genomes. Although several studies have been conducted for distinguishing drug targets from non-drug targets, they mainly focus on the sequences and functional roles of proteins. Many other properties of proteins have not been fully investigated.'),
(97924, 2017, 'bmc', 'Prediction models for drug-induced hepatotoxicity by using weighted molecular fingerprints', 'Kim, Eunyoung|Nam, Hojung', 'School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea.|School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea. hjnam@gist.ac.kr.', '-|-', 16, 'Bayes Theorem|Biological Products|Chemical and Drug Induced Liver Injury|Databases, Factual|Humans|Models, Theoretical|Support Vector Machine', 'Journal Article', '10.1186/s12859-017-1638-4', '28617228', 'Drug-induced liver injury (DILI) is a critical issue in drug development because DILI causes failures in clinical trials and the withdrawal of approved drugs from the market. There have been many attempts to predict the risk of DILI based on in vivo and in silico identification of hepatotoxic compounds. In the current study, we propose the in silico prediction model predicting DILI using weighted molecular fingerprints.'),
(97925, 2017, 'bmc', 'Analyzing the field of bioinformatics with the multi-faceted topic modeling technique', 'Heo, Go Eun|Kang, Keun Young|Song, Min|Lee, Jeong-Hoon', 'Department of Library and Information Science, Yonsei University, 50 Yonsei-ro Seodaemun-gu, Seoul, 03722, Republic of Korea.|Department of Library and Information Science, Yonsei University, 50 Yonsei-ro Seodaemun-gu, Seoul, 03722, Republic of Korea.|Department of Library and Information Science, Yonsei University, 50 Yonsei-ro Seodaemun-gu, Seoul, 03722, Republic of Korea. min.song@yonsei.ac.kr.|Department of Creative IT Engineering, POSTECH, 77 Cheongam-ro Nam-gu, Pohang, Gyeongbuk, 37673, Republic of Korea.', '-|-|-|-', 0, 'Bibliometrics|Computational Biology|Data Mining|Databases, Factual|Humans|Models, Theoretical', 'Journal Article', '10.1186/s12859-017-1640-x', '28617229', 'Bioinformatics is an interdisciplinary field at the intersection of molecular biology and computing technology. To characterize the field as convergent domain, researchers have used bibliometrics, augmented with text-mining techniques for content analysis. In previous studies, Latent Dirichlet Allocation (LDA) was the most representative topic modeling technique for identifying topic structure of subject areas. However, as opposed to revealing the topic structure in relation to metadata such as authors, publication date, and journals, LDA only displays the simple topic structure.'),
(97926, 2017, 'bmc', 'Predicting anatomic therapeutic chemical classification codes using tiered learning', 'Olson, Thomas|Singh, Rahul', 'Department of Computer Science, San Francisco State University, San Francisco, CA, USA.|Department of Computer Science, San Francisco State University, San Francisco, CA, USA. rahul@sfsu.edu.', 'United States|United States', 2, 'Algorithms|Computational Biology|Drug Discovery|Drug Evaluation, Preclinical|Machine Learning|Reproducibility of Results', 'Journal Article', '10.1186/s12859-017-1660-6', '28617230', 'The low success rate and high cost of drug discovery requires the development of new paradigms to identify molecules of therapeutic value. The Anatomical Therapeutic Chemical (ATC) Code System is a World Health Organization (WHO) proposed classification that assigns multi-level codes to compounds based on their therapeutic, pharmacological and chemical characteristics as well as the in-vivo sites(s) of activity. The ability to predict ATC codes of compounds can assist in creation of high-quality chemical libraries for drug screening and in applications such as drug repositioning. We propose a machine learning architecture called tiered learning for prediction of ATC codes that relies on the prediction results of the higher levels of the ATC code to simplify the predictions of the lower levels.'),
(97927, 2017, 'bmc', 'ATria: a novel centrality algorithm applied to biological networks', 'Cickovski, Trevor|Peake, Eli|Aguiar-Pulido, Vanessa|Narasimhan, Giri', 'Bioinformatics Research Group (BioRG) & Biomolecular Sciences Institute, School of Computing & Information Sciences, Florida International University, 11200 SW 8th St, Miami, 33196, FL, USA. tcickovs@fiu.edu.|Department of Computer Science, Eckerd College, 4200 54th Avenue South, Saint Petersburg, 33711, FL, USA.|Bioinformatics Research Group (BioRG) & Biomolecular Sciences Institute, School of Computing & Information Sciences, Florida International University, 11200 SW 8th St, Miami, 33196, FL, USA.|Bioinformatics Research Group (BioRG) & Biomolecular Sciences Institute, School of Computing & Information Sciences, Florida International University, 11200 SW 8th St, Miami, 33196, FL, USA.', 'United States|United States|United States|United States', 6, 'Algorithms|Bacterial Physiological Phenomena|Computational Biology|Models, Biological|Software', 'Journal Article', '10.1186/s12859-017-1659-z', '28617231', 'The notion of centrality is used to identify important nodes in social networks. Importance of nodes is not well-defined, and many different notions exist in the literature. The challenge of defining centrality in meaningful ways when network edges can be positively or negatively weighted has not been adequately addressed in the literature. Existing centrality algorithms also have a second shortcoming, i.e., the list of the most central nodes are often clustered in a specific region of the network and are not well represented across the network.'),
(97928, 2017, 'bmc', 'TIGERi: modeling and visualizing the responses to perturbation of a transcription factor network', 'Han, Namshik|Noyes, Harry A|Brass, Andy', 'Gurdon Institute, University of Cambridge, Cambridge, UK. namshik.han@gurdon.cam.ac.uk.|School of Biological Sciences, University of Liverpool, Liverpool, UK.|School of Computer Science and School of Health Sciences, University of Manchester, Manchester, UK. andy.brass@manchester.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Binding Sites|Gene Regulatory Networks|Machine Learning|Mice|Mice, Knockout|Mitogen-Activated Protein Kinase 14|Transcription Factors|Transcriptome', 'Journal Article', '10.1186/s12859-017-1636-6', '28617232', 'Transcription factor (TF) networks play a key role in controlling the transfer of genetic information from gene to mRNA. Much progress has been made on understanding and reverse-engineering TF network topologies using a range of experimental and theoretical methodologies. Less work has focused on using these models to examine how TF networks respond to changes in the cellular environment.'),
(97929, 2017, 'bmc', 'EPSILON-CP: using deep learning to combine information from multiple sources for protein contact prediction', 'Stahl, Kolja|Schneider, Michael|Brock, Oliver', 'Robotics and Biology Laboratory, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Marchstraße 23, Berlin, 10587, Germany.|Robotics and Biology Laboratory, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Marchstraße 23, Berlin, 10587, Germany.|Robotics and Biology Laboratory, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Marchstraße 23, Berlin, 10587, Germany. oliver.brock@tu-berlin.de.', 'Germany|Germany|Germany', 15, 'Computational Biology|Protein Binding|Protein Conformation|Proteins|Software', 'Journal Article', '10.1186/s12859-017-1713-x', '28623886', 'Accurately predicted contacts allow to compute the 3D structure of a protein. Since the solution space of native residue-residue contact pairs is very large, it is necessary to leverage information to identify relevant regions of the solution space, i.e. correct contacts. Every additional source of information can contribute to narrowing down candidate regions. Therefore, recent methods combined evolutionary and sequence-based information as well as evolutionary and physicochemical information. We develop a new contact predictor (EPSILON-CP) that goes beyond current methods by combining evolutionary, physicochemical, and sequence-based information. The problems resulting from the increased dimensionality and complexity of the learning problem are combated with a careful feature analysis, which results in a drastically reduced feature set. The different information sources are combined using deep neural networks.'),
(97930, 2017, 'bmc', 'Ub-ISAP: a streamlined UNIX pipeline for mining unique viral vector integration sites from next generation sequencing data', 'Kamboj, Atul|Hallwirth, Claus V|Alexander, Ian E|McCowage, Geoffrey B|Kramer, Belinda', 'Childrens Cancer Research Unit, Kids Research Institute, The Childrens Hospital at Westmead, Locked Bag 4001, Westmead, NSW, 2145, Australia. atul.kamboj@health.nsw.gov.au.|Gene Therapy Research Unit, Childrens Medical Research Institute and The Childrens Hospital at Westmead, Westmead, NSW, Australia.|Gene Therapy Research Unit, Childrens Medical Research Institute and The Childrens Hospital at Westmead, Westmead, NSW, Australia.|Cancer Centre for Children, The Childrens Hospital, Westmead, NSW, Australia.|Childrens Cancer Research Unit, Kids Research Institute, The Childrens Hospital at Westmead, Locked Bag 4001, Westmead, NSW, 2145, Australia.', 'Australia|Australia|Australia|Australia|Australia', 0, 'Chromosome Mapping|Computational Biology|Genetic Therapy|Genetic Vectors|High-Throughput Nucleotide Sequencing|Humans|Software|Transcription Initiation Site|Virus Integration', 'Journal Article', '10.1186/s12859-017-1719-4', '28623888', 'The analysis of viral vector genomic integration sites is an important component in assessing the safety and efficiency of patient treatment using gene therapy. Alongside this clinical application, integration site identification is a key step in the genetic mapping of viral elements in mutagenesis screens that aim to elucidate gene function.'),
(97931, 2017, 'bmc', 'Biomarker correlation network in colorectal carcinoma by tumor anatomic location', 'Nishihara, Reiko|Glass, Kimberly|Mima, Kosuke|Hamada, Tsuyoshi|Nowak, Jonathan A|Qian, Zhi Rong|Kraft, Peter|Giovannucci, Edward L|Fuchs, Charles S|Chan, Andrew T|Quackenbush, John|Ogino, Shuji|Onnela, Jukka-Pekka', 'Program of MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA. rnishiha@hsph.harvard.edu.|Channing Division of Network Medicine, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA.|Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.|Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.|Program of MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA.|Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.|Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.|Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.|Channing Division of Network Medicine, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA.|Channing Division of Network Medicine, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA.|Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.|Program of MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA. shuji_ogino@dfci.harvard.edu.|Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. onnela@hsph.harvard.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', 0, 'Biomarkers, Tumor|Colonic Neoplasms|Colorectal Neoplasms|Computational Biology|DNA Mutational Analysis|Female|Humans|Male|Microsatellite Instability|Protein Interaction Mapping', 'Journal Article', '10.1186/s12859-017-1718-5', '28623901', 'Colorectal carcinoma evolves through a multitude of molecular events including somatic mutations, epigenetic alterations, and aberrant protein expression, influenced by host immune reactions. One way to interrogate the complex carcinogenic process and interactions between aberrant events is to model a biomarker correlation network. Such a network analysis integrates multidimensional tumor biomarker data to identify key molecular events and pathways that are central to an underlying biological process. Due to embryological, physiological, and microbial differences, proximal and distal colorectal cancers have distinct sets of molecular pathological signatures. Given these differences, we hypothesized that a biomarker correlation network might vary by tumor location.'),
(97932, 2017, 'bmc', 'A multiplex primer design algorithm for target amplification of continuous genomic regions', 'Ozturk, Ahmet Rasit|Can, Tolga', 'Middle East Technical University, Informatics Institute, Ankara, Turkey. ahmetrasit@gmail.com.|Department of Computer Engineering, Middle East Technical University, Ankara, Turkey.', 'Turkey|Turkey', 0, 'Algorithms|DNA|DNA Primers|Exons|High-Throughput Nucleotide Sequencing|Humans|Multiplex Polymerase Chain Reaction|Pyrin', 'Journal Article', '10.1186/s12859-017-1716-7', '28629316', 'Targeted Next Generation Sequencing (NGS) assays are cost-efficient and reliable alternatives to Sanger sequencing. For sequencing of very large set of genes, the target enrichment approach is suitable. However, for smaller genomic regions, the target amplification method is more efficient than both the target enrichment method and Sanger sequencing. The major difficulty of the target amplification method is the preparation of amplicons, regarding required time, equipment, and labor. Multiplex PCR (MPCR) is a good solution for the mentioned problems.'),
(97933, 2017, 'bmc', 'Quasi-linear score for capturing heterogeneous structure in biomarkers', 'Omae, Katsuhiro|Komori, Osamu|Eguchi, Shinto', 'Department of Statistical Science, The Graduate University for Advanced Studies, 10-3, Midoricho, Tachikawa, Tokyo, 190-8562, Japan. omae.katsuhiro@ism.ac.jp.|Department of Electrical, Electronic and Computer Engineering, University of Fukui, Fukui, Japan.|Department of Statistical Science, The Graduate University for Advanced Studies, 10-3, Midoricho, Tachikawa, Tokyo, 190-8562, Japan.', 'Japan|Japan|Japan', 4, 'Algorithms|Biomarkers|Breast Neoplasms|Cluster Analysis|Discriminant Analysis|Female|Humans|Likelihood Functions|Logistic Models|Neoplasm Metastasis|Transcriptome', 'Journal Article', '10.1186/s12859-017-1721-x', '28629325', 'Linear scores are widely used to predict dichotomous outcomes in biomedical studies because of their learnability and understandability. Such approaches, however, cannot be used to elucidate biodiversity when there is heterogeneous structure in target population.'),
(97934, 2017, 'bmc', 'Biologically constrained optimization based cell membrane segmentation in C elegans embryos', 'Azuma, Yusuke|Onami, Shuichi', 'Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo, 650-0047, Japan.|Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo, 650-0047, Japan. sonami@riken.jp.', 'Japan|Japan', 0, 'Algorithms|Animals|Automation|Caenorhabditis elegans|Cell Membrane|Embryo, Nonmammalian|Embryonic Development|Image Processing, Computer-Assisted', 'Journal Article', '10.1186/s12859-017-1717-6', '28629355', 'Recent advances in bioimaging and automated analysis methods have enabled the large-scale systematic analysis of cellular dynamics during the embryonic development of Caenorhabditis elegans. Most of these analyses have focused on cell lineage tracing rather than cell shape dynamics. Cell shape analysis requires cell membrane segmentation, which is challenging because of insufficient resolution and image quality. This problem is currently solved by complicated segmentation methods requiring laborious and time consuming parameter adjustments.'),
(97935, 2017, 'bmc', 'OrthoReD: a rapid and accurate orthology prediction tool with low computational requirement', 'Battenberg, Kai|Lee, Ernest K|Chiu, Joanna C|Berry, Alison M|Potter, Daniel', 'Department of Plant Sciences, University of California, Davis, CA, USA. kbattenberg@ucdavis.edu.|Department of Entomology and Nematology, University of California, Davis, CA, USA.|Department of Entomology and Nematology, University of California, Davis, CA, USA.|Department of Plant Sciences, University of California, Davis, CA, USA.|Department of Plant Sciences, University of California, Davis, CA, USA.', 'United States|United States|United States|United States|United States', 0, 'Actinobacteria|Animals|Databases, Factual|Drosophila|Genetics|Genome|High-Throughput Nucleotide Sequencing|Magnoliopsida|Software|Transcriptome', 'Journal Article', '10.1186/s12859-017-1726-5', '28633662', 'Identifying orthologous genes is an initial step required for phylogenetics, and it is also a common strategy employed in functional genetics to find candidates for functionally equivalent genes across multiple species. At the same time, in silico orthology prediction tools often require large computational resources only available on computing clusters. Here we present OrthoReD, an open-source orthology prediction tool with accuracy comparable to published tools that requires only a desktop computer. The low computational resource requirement of OrthoReD is achieved by repeating orthology searches on one gene of interest at a time, thereby generating a reduced dataset to limit the scope of orthology search for each gene of interest.'),
(97936, 2017, 'bmc', 'A novel pathway-based distance score enhances assessment of disease heterogeneity in gene expression', 'Yan, Xiting|Liang, Anqi|Gomez, Jose|Cohn, Lauren|Zhao, Hongyu|Chupp, Geoffrey L', 'Center for Pulmonary Personalized Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, 06520, USA. xiting.yan@yale.edu.|Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06520, USA.|Center for Pulmonary Personalized Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, 06520, USA.|Center for Pulmonary Personalized Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, 06520, USA.|Center for Pulmonary Personalized Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, 06520, USA.|Center for Pulmonary Personalized Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, 06520, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Asthma|Cluster Analysis|Gene Expression|Humans|Metabolic Networks and Pathways|Normal Distribution|Phenotype', 'Journal Article', '10.1186/s12859-017-1727-4', '28637421', 'Distance based unsupervised clustering of gene expression data is commonly used to identify heterogeneity in biologic samples. However, high noise levels in gene expression data and relatively high correlation between genes are often encountered, so traditional distances such as Euclidean distance may not be effective at discriminating the biological differences between samples. An alternative method to examine disease phenotypes is to use pre-defined biological pathways. These pathways have been shown to be perturbed in different ways in different subjects who have similar clinical features. We hypothesize that differences in the expressions of genes in a given pathway are more predictive of differences in biological differences compared to standard approaches and if integrated into clustering analysis will enhance the robustness and accuracy of the clustering method. To examine this hypothesis, we developed a novel computational method to assess the biological differences between samples using gene expression data by assuming that ontologically defined biological pathways in biologically similar samples have similar behavior.'),
(97937, 2017, 'bmc', 'Optimal alpha reduces error rates in gene expression studies: a meta-analysis approach', 'Mudge, J F|Martyniuk, C J|Houlahan, J E', 'Department of Biology, Canadian Rivers Institute, University of New Brunswick, Saint John, NB, E2L 4L5, Canada.|Center for Environmental and Human Toxicology & Department of Physiological Sciences, UF Genetics Institute, University of Florida, Gainesville, Florida, 32611, USA.|Department of Biology, Canadian Rivers Institute, University of New Brunswick, Saint John, NB, E2L 4L5, Canada. jeffhoul@unb.ca.', 'Canada|United States|Canada', 15, 'Databases, Factual|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Oligonucleotide Array Sequence Analysis|RNA|Reproducibility of Results|Research Design|Sample Size|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1186/s12859-017-1728-3', '28637422', 'Transcriptomic approaches (microarray and RNA-seq) have been a tremendous advance for molecular science in all disciplines, but they have made interpretation of hypothesis testing more difficult because of the large number of comparisons that are done within an experiment. The result has been a proliferation of techniques aimed at solving the multiple comparisons problem, techniques that have focused primarily on minimizing Type I error with little or no concern about concomitant increases in Type II errors. We have previously proposed a novel approach for setting statistical thresholds with applications for high throughput omics-data, optimal xcexb1, which minimizes the probability of making either error (i.e. Type I or II) and eliminates the need for post-hoc adjustments.'),
(97938, 2017, 'bmc', 'Bioinformatics: indispensable, yet hidden in plain sight?', 'Bartlett, Andrew|Penders, Bart|Lewis, Jamie', 'Department of Sociological Studies, University of Sheffield, Elmfield, Northumberland Road, Sheffield, S10 2TU, UK. A.J.Bartlett@sheffield.ac.uk.|Department of Health, Ethics & Society, Care and Public Health Research Institute (Caphri), Maastricht University, PO Box 616, Maastricht, 6200MD, the Netherlands.|School of Social Sciences, Cardiff University, Glamorgan Building, King Edward VII Avenue, Cardiff, CF10 3WT, UK.', 'United Kingdom|Netherlands|United Kingdom', 0, 'Algorithms|Computational Biology|Humans|Research|Software', 'Journal Article', '10.1186/s12859-017-1730-9', '28637426', 'Bioinformatics has multitudinous identities, organisational alignments and disciplinary links. This variety allows bioinformaticians and bioinformatic work to contribute to much (if not most) of life science research in profound ways. The multitude of bioinformatic work also translates into a multitude of credit-distribution arrangements, apparently dismissing that work.'),
(97939, 2017, 'bmc', 'Early response index: a statistic to discover potential early stage disease biomarkers', 'Salekin, Sirajul|Bari, Mehrab Ghanat|Raphael, Itay|Forsthuber, Thomas G|Zhang, Jianqiu Michelle', 'Department of Electrical and Computer Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78207, USA. Sirajul.Salekin@utsa.edu.|Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, 200 First Street SW, MN, Rochester, 55905, USA.|Department of Biology, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78207, USA.|Department of Biology, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78207, USA.|Department of Electrical and Computer Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78207, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Biomarkers|Central Nervous System|Early Diagnosis|Mice|Multiple Sclerosis|Proteome|Proteomics|Time Factors', 'Journal Article', '10.1186/s12859-017-1712-y', '28645323', 'Identifying disease correlated features early before large number of molecules are impacted by disease progression with significant abundance change is very advantageous to biologists for developing early disease diagnosis biomarkers. Disease correlated features have relatively low level of abundance change at early stages. Finding them using existing bioinformatic tools in high throughput data is a challenging task since the technology suffers from limited dynamic range and significant noise. Most existing biomarker discovery algorithms can only detect molecules with high abundance changes, frequently missing early disease diagnostic markers.'),
(97940, 2017, 'bmc', 'CMSA: a heterogeneous CPU/GPU computing system for multiple similar RNA/DNA sequence alignment', 'Chen, Xi|Wang, Chen|Tang, Shanjiang|Yu, Ce|Zou, Quan', 'School of Computer Science and Technology, Tianjin University, Yaguan Road, Tianjin, China.|School of Computer Science and Technology, Tianjin University, Yaguan Road, Tianjin, China.|School of Computer Science and Technology, Tianjin University, Yaguan Road, Tianjin, China.|School of Computer Science and Technology, Tianjin University, Yaguan Road, Tianjin, China. yuce@tju.edu.cn.|School of Computer Science and Technology, Tianjin University, Yaguan Road, Tianjin, China.', 'China|China|China|China|China', 0, 'Algorithms|Computer Systems|DNA|RNA|RNA, Ribosomal, 16S|Sequence Alignment|Software', 'Journal Article', '10.1186/s12859-017-1725-6', '28646874', 'The multiple sequence alignment (MSA) is a classic and powerful technique for sequence analysis in bioinformatics. With the rapid growth of biological datasets, MSA parallelization becomes necessary to keep its running time in an acceptable level. Although there are a lot of work on MSA problems, their approaches are either insufficient or contain some implicit assumptions that limit the generality of usage. First, the information of users sequences, including the sizes of datasets and the lengths of sequences, can be of arbitrary values and are generally unknown before submitted, which are unfortunately ignored by previous work. Second, the center star strategy is suited for aligning similar sequences. But its first stage, center sequence selection, is highly time-consuming and requires further optimization. Moreover, given the heterogeneous CPU/GPU platform, prior studies consider the MSA parallelization on GPU devices only, making the CPUs idle during the computation. Co-run computation, however, can maximize the utilization of the computing resources by enabling the workload computation on both CPU and GPU simultaneously.'),
(97941, 2017, 'bmc', 'SBMLmod: a Python-based web application and web service for efficient data integration and model simulation', 'Schäuble, Sascha|Stavrum, Anne-Kristin|Bockwoldt, Mathias|Puntervoll, Pål|Heiland, Ines', 'Jena University Language & Information Engineering (JULIE) Lab, Friedrich-Schiller-University Jena, Jena, Germany.|Department of Informatics, University of Bergen, Bergen, Norway.|Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Tromsø, Norway.|Centre for Applied Biotechnology, Uni Research Environment, Bergen, Norway.|Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Tromsø, Norway. ines.heiland@uit.no.', 'Germany|Norway|Norway|Norway|Norway', 0, 'Cell Line, Tumor|Humans|Internet|Kynurenine|Models, Theoretical|Neoplasms|Serotonin|Tryptophan|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1722-9', '28646877', 'Systems Biology Markup Language (SBML) is the standard model representation and description language in systems biology. Enriching and analysing systems biology models by integrating the multitude of available data, increases the predictive power of these models. This may be a daunting task, which commonly requires bioinformatic competence and scripting.'),
(97942, 2017, 'bmc', 'GRAPE: a pathway template method to characterize tissue-specific functionality from gene expression profiles', 'Klein, Michael I|Stern, David F|Zhao, Hongyu', 'Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.|Department of Pathology, Yale University, New Haven, CT, USA.|Department of Biostatistics, Yale University, 60 College Street, P.O. Box 208034, New Haven, 06520-8034, CT, USA. hongyu.zhao@yale.edu.', 'United States|United States|United States', 1, 'Gene Expression Profiling|Humans|Internet|Neoplasms|Support Vector Machine|Survival Analysis|Transcriptome|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1711-z', '28651562', 'Personalizing treatment regimes based on gene expression profiles of individual tumors will facilitate management of cancer. Although many methods have been developed to identify pathways perturbed in tumors, the results are often not generalizable across independent datasets due to the presence of platform/batch effects. There is a need to develop methods that are robust to platform/batch effects and able to identify perturbed pathways in individual samples.'),
(97943, 2017, 'bmc', 'ReTrOS: a MATLAB toolbox for reconstructing transcriptional activity from gene and protein expression data', 'Minas, Giorgos|Momiji, Hiroshi|Jenkins, Dafyd J|Costa, Maria J|Rand, David A|Finkenstädt, Bärbel', 'Systems Biology Centre, University of Warwick, Coventry, CV34 4HD, UK. Giorgos.Minas@warwick.ac.uk.|Systems Biology Centre, University of Warwick, Coventry, CV34 4HD, UK.|Systems Biology Centre, University of Warwick, Coventry, CV34 4HD, UK.|Systems Biology Centre, University of Warwick, Coventry, CV34 4HD, UK.|Systems Biology Centre, University of Warwick, Coventry, CV34 4HD, UK.|Department of Statistics, University of Warwick, Coventry, CV34 4HD, UK. B.F.Finkenstadt@warwick.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Arabidopsis|Arabidopsis Proteins|Circadian Clocks|Proteins|RNA, Messenger|Software|Transcription, Genetic', 'Journal Article', '10.1186/s12859-017-1695-8', '28651569', 'Given the development of high-throughput experimental techniques, an increasing number of whole genome transcription profiling time series data sets, with good temporal resolution, are becoming available to researchers. The ReTrOS toolbox (Reconstructing Transcription Open Software) provides MATLAB-based implementations of two related methods, namely ReTrOS-Smooth and ReTrOS-Switch, for reconstructing the temporal transcriptional activity profile of a gene from given mRNA expression time series or protein reporter time series. The methods are based on fitting a differential equation model incorporating the processes of transcription, translation and degradation.'),
(97944, 2017, 'bmc', 'SparkBLAST: scalable BLAST processing using in-memory operations', 'de Castro, Marcelo Rodrigo|Tostes, Catherine Dos Santos|Dávila, Alberto M R|Senger, Hermes|da Silva, Fabricio A B', 'Computer Science Department, Federal University of São Carlos, Rod. Washington Luís, Km 235, São Carlos, 21040-900, Brazil.|LBCS-IOC, Oswaldo Cruz Foundation, Av Brasil 4365, Rio de Janeiro, 21040-900, Brazil.|LBCS-IOC, Oswaldo Cruz Foundation, Av Brasil 4365, Rio de Janeiro, 21040-900, Brazil.|Computer Science Department, Federal University of São Carlos, Rod. Washington Luís, Km 235, São Carlos, 21040-900, Brazil.|PROCC, Oswaldo Cruz Foundation, Av. Brasil 4365, Rio de Janeiro, 21040-900, Brazil. fabricio.silva@fiocruz.br.', 'Brazil|Brazil|Brazil|Brazil|Brazil', 11, 'Algorithms|Cloud Computing|Comparative Genomic Hybridization|Databases, Factual|Sequence Alignment|Software', 'Journal Article', '10.1186/s12859-017-1723-8', '28655296', 'The demand for processing ever increasing amounts of genomic data has raised new challenges for the implementation of highly scalable and efficient computational systems. In this paper we propose SparkBLAST, a parallelization of a sequence alignment application (BLAST) that employs cloud computing for the provisioning of computational resources and Apache Spark as the coordination framework. As a proof of concept, some radionuclide-resistant bacterial genomes were selected for similarity analysis.'),
(97945, 2017, 'bmc', 'Semi-automated quantitative Drosophila wings measurements', 'Loh, Sheng Yang Michael|Ogawa, Yoshitaka|Kawana, Sara|Tamura, Koichiro|Lee, Hwee Kuan', 'Imaging Informatics Division, Bioinformatics Institute, 30 Biopolis Street, 07-01, Matrix, Singapore, Singapore, 138671, Singapore.|Department of Biological Sciences, Tokyo Metropolitan University, Hachioji, Tokyo, 192-0397, Japan.|Department of Biological Sciences, Tokyo Metropolitan University, Hachioji, Tokyo, 192-0397, Japan.|Department of Biological Sciences, Tokyo Metropolitan University, Hachioji, Tokyo, 192-0397, Japan.|Imaging Informatics Division, Bioinformatics Institute, 30 Biopolis Street, 07-01, Matrix, Singapore, Singapore, 138671, Singapore. leehk@bii.a-star.edu.sg.', 'Singapore|Japan|Japan|Japan|Singapore', 0, 'Algorithms|Animals|Automation|Drosophila|Female|Image Processing, Computer-Assisted|Male|Software|Wings, Animal', 'Journal Article', '10.1186/s12859-017-1720-y', '28659123', 'Drosophila melanogaster is an important organism used in many fields of biological research such as genetics and developmental biology. Drosophila wings have been widely used to study the genetics of development, morphometrics and evolution. Therefore there is much interest in quantifying wing structures of Drosophila. Advancement in technology has increased the ease in which images of Drosophila can be acquired. However such studies have been limited by the slow and tedious process of acquiring phenotypic data.'),
(97946, 2017, 'bmc', 'SLMSuite: a suite of algorithms for segmenting genomic profiles', 'Orlandini, Valerio|Provenzano, Aldesia|Giglio, Sabrina|Magi, Alberto', 'Medical Genetics Unit, Meyer Childrens University Hospital, Florence, Italy.|Medical Genetics Unit, Meyer Childrens University Hospital, Florence, Italy.|Medical Genetics Unit, Meyer Childrens University Hospital, Florence, Italy.|Department of Experimental and Clinical Medicine, University of Florence, Viale Pieraccini 6, Florence, 50139, Italy. alberto.magi@gmail.com.', 'Italy|Italy|Italy|Italy', 0, 'Algorithms|DNA|DNA Copy Number Variations|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Internet|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1734-5', '28659129', 'The identification of copy number variants (CNVs) is essential to study human genetic variation and to understand the genetic basis of mendelian disorders and cancers. At present, genome-wide detection of CNVs can be achieved using microarray or second generation sequencing (SGS) data. Although these technologies are very different, the genomic profiles that they generate are mathematically very similar and consist of noisy signals in which a decrease or increase of consecutive data represent deletions or duplication of DNA. In this framework, the most important step of the analysis consists of segmenting genomic profiles for the identification of the boundaries of genomic regions with increased or decreased signal.'),
(97947, 2017, 'bmc', 'ISVASE: identification of sequence variant associated with splicing event using RNA-seq data', 'Aljohi, Hasan Awad|Liu, Wanfei|Lin, Qiang|Yu, Jun|Hu, Songnian', 'Joint Center for Genomics Research (JCGR), King Abdulaziz City for Science and Technology and Chinese Academy of Sciences, Prince Turki Road, Riyadh, 11442, Saudi Arabia.|Joint Center for Genomics Research (JCGR), King Abdulaziz City for Science and Technology and Chinese Academy of Sciences, Prince Turki Road, Riyadh, 11442, Saudi Arabia.|Joint Center for Genomics Research (JCGR), King Abdulaziz City for Science and Technology and Chinese Academy of Sciences, Prince Turki Road, Riyadh, 11442, Saudi Arabia.|Joint Center for Genomics Research (JCGR), King Abdulaziz City for Science and Technology and Chinese Academy of Sciences, Prince Turki Road, Riyadh, 11442, Saudi Arabia. junyu@big.ac.cn.|Joint Center for Genomics Research (JCGR), King Abdulaziz City for Science and Technology and Chinese Academy of Sciences, Prince Turki Road, Riyadh, 11442, Saudi Arabia. husn@big.ac.cn.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia', 0, 'Base Sequence|Humans|Internet|RNA|RNA Editing|RNA Splicing|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1732-7', '28659141', 'Exon recognition and splicing precisely and efficiently by spliceosome is the key to generate mature mRNAs. About one third or a half of disease-related mutations affect RNA splicing. Software PVAAS has been developed to identify variants associated with aberrant splicing by directly using RNA-seq data. However, it bases on the assumption that annotated splicing site is normal splicing, which is not true in fact.'),
(97948, 2017, 'bmc', 'BioNanoAnalyst: a visualisation tool to assess genome assembly quality using BioNano data', 'Yuan, Yuxuan|Bayer, Philipp E|Scheben, Armin|Chan, Chon-Kit Kenneth|Edwards, David', 'School of Biological Sciences, the University of Western Australia, Perth, WA, Australia.|School of Biological Sciences, the University of Western Australia, Perth, WA, Australia.|School of Biological Sciences, the University of Western Australia, Perth, WA, Australia.|School of Biological Sciences, the University of Western Australia, Perth, WA, Australia.|School of Biological Sciences, the University of Western Australia, Perth, WA, Australia. dave.edwards@uwa.edu.au.', 'Australia|Australia|Australia|Australia|Australia', 1, 'Chromosomes, Human, Pair 1|DNA Restriction Enzymes|Genome, Human|Genomics|Humans|Internet|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1735-4', '28666410', 'Reference genome assemblies are valuable, as they provide insights into gene content, genetic evolution and domestication. The higher the quality of a reference genome assembly the more accurate the downstream analysis will be. During the last few years, major efforts have been made towards improving the quality of genome assemblies. However, erroneous and incomplete assemblies are still common. Complementary to DNA sequencing technologies, optical mapping has advanced genomic studies by facilitating the production of genome scaffolds and assessing structural variation. However, there are few tools available to comprehensively examine misassemblies in reference genome sequences using optical map data.'),
(97949, 2017, 'bmc', 'RGIFE: a ranked guided iterative feature elimination heuristic for the identification of biomarkers', 'Lazzarini, Nicola|Bacardit, Jaume', 'ICOS research group, School of Computing Science, Newcastle-upon-Tyne, UK.|ICOS research group, School of Computing Science, Newcastle-upon-Tyne, UK. jaume.bacardit@newcastle.ac.uk.', 'United Kingdom|United Kingdom', 0, 'Algorithms|Biomarkers|Databases, Factual|Humans|Internet|Neoplasms|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1729-2', '28666416', 'Current -omics technologies are able to sense the state of a biological sample in a very wide variety of ways. Given the high dimensionality that typically characterises these data, relevant knowledge is often hidden and hard to identify. Machine learning methods, and particularly feature selection algorithms, have proven very effective over the years at identifying small but relevant subsets of variables from a variety of application domains, including -omics data. Many methods exist with varying trade-off between the size of the identified variable subsets and the predictive power of such subsets. In this paper we focus on an heuristic for the identification of biomarkers called RGIFE: Rank Guided Iterative Feature Elimination. RGIFE is guided in its biomarker identification process by the information extracted from machine learning models and incorporates several mechanisms to ensure that it creates minimal and highly predictive features sets.'),
(97950, 2017, 'bmc', 'An improved filtering algorithm for big read datasets and its application to single-cell assembly', 'Wedemeyer, Axel|Kliemann, Lasse|Srivastav, Anand|Schielke, Christian|Reusch, Thorsten B|Rosenstiel, Philip', 'Department of Computer Science, Kiel University, Christian-Albrechts-Platz 4, Kiel, 24118, Germany. axw@informatik.uni-kiel.de.|Department of Computer Science, Kiel University, Christian-Albrechts-Platz 4, Kiel, 24118, Germany.|Department of Computer Science, Kiel University, Christian-Albrechts-Platz 4, Kiel, 24118, Germany.|Department of Computer Science, Kiel University, Christian-Albrechts-Platz 4, Kiel, 24118, Germany.|Marine Ecology, GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, Kiel, 24105, Germany.|Institute of Clinical Molecular Biology, Kiel University, Schittenhelmstr. 12, Kiel, 24105, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 2, 'Algorithms|Bacteria|Datasets as Topic|Metagenomics|Sequence Analysis, DNA|Single-Cell Analysis', 'Journal Article', '10.1186/s12859-017-1724-7', '28673253', 'For single-cell or metagenomic sequencing projects, it is necessary to sequence with a very high mean coverage in order to make sure that all parts of the sample DNA get covered by the reads produced. This leads to huge datasets with lots of redundant data. A filtering of this data prior to assembly is advisable. Brown et al. (2012) presented the algorithm Diginorm for this purpose, which filters reads based on the abundance of their k-mers.'),
(97951, 2017, 'bmc', 'sgnesR: An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters', 'Tripathi, Shailesh|Lloyd-Price, Jason|Ribeiro, Andre|Yli-Harja, Olli|Dehmer, Matthias|Emmert-Streib, Frank', 'Predictive Medicine and Data Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland.|Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, USA.|Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, Tampere, Finland.|Institute of Biosciences and Medical Technology, Tampere, Finland.|Institute for Theoretical Informatics, Mathematics and Operations Research, Department of Computer Science, Universität der Bundeswehr München, Munich, Germany.|Predictive Medicine and Data Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland. v@bio-complexity.com.', 'Finland|United States|Finland|Finland|Germany|Finland', 6, 'Algorithms|Gene Expression|Gene Regulatory Networks|Internet|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1731-8', '28676075', 'sgnesR (Stochastic Gene Network Expression Simulator in R) is an R package that provides an interface to simulate gene expression data from a given gene network using the stochastic simulation algorithm (SSA). The package allows various options for delay parameters and can easily included in reactions for promoter delay, RNA delay and Protein delay. A user can tune these parameters to model various types of reactions within a cell. As examples, we present two network models to generate expression profiles. We also demonstrated the inference of networks and the evaluation of association measure of edge and non-edge components from the generated expression profiles.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97952, 2017, 'bmc', 'TnseqDiff: identification of conditionally essential genes in transposon sequencing studies', 'Zhao, Lili|Anderson, Mark T|Wu, Weisheng|T Mobley, Harry L|Bachman, Michael A', 'Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, USA. zhaolili@umich.edu.|Department of Microbiology and Immunology, School of medicine, University of Michigan, Ann Arbor, USA.|BRCF Bioinformatics Core, University of Michigan, Ann Arbor, USA.|Department of Microbiology and Immunology, School of medicine, University of Michigan, Ann Arbor, USA.|Department of Pathology, School of medicine, University of Michigan, Ann Arbor, USA.', 'United States|United States|United States|United States|United States', 0, 'Area Under Curve|DNA Transposable Elements|Genes, Essential|Genome, Bacterial|Genomics|Mutagenesis, Insertional|ROC Curve|Serratia marcescens', 'Journal Article', '10.1186/s12859-017-1745-2', '28683752', 'Tn-Seq is a high throughput technique for analysis of transposon mutant libraries to determine conditional essentiality of a gene under an experimental condition. A special feature of the Tn-seq data is that multiple mutants in a gene provides independent evidence to prioritize that gene as being essential. The existing methods do not account for this feature or rely on a high-density transposon library. Moreover, these methods are unable to accommodate complex designs.'),
(97953, 2017, 'bmc', 'ACE: an efficient and sensitive tool to detect insecticide resistance-associated mutations in insect acetylcholinesterase from RNA-Seq data', 'Guo, Dianhao|Luo, Jiapeng|Zhou, Yuenan|Xiao, Huamei|He, Kang|Yin, Chuanlin|Xu, Jianhua|Li, Fei', 'Ministry of Agriculture Key Lab of Molecular Biology of Crop Pathogens and Insects, Institute of Insect Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.|Ministry of Agriculture Key Lab of Molecular Biology of Crop Pathogens and Insects, Institute of Insect Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.|Ministry of Agriculture Key Lab of Molecular Biology of Crop Pathogens and Insects, Institute of Insect Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.|College of Life Sciences and Resource Environment, Yichun University, Yichun, 336000, China.|Ministry of Agriculture Key Lab of Molecular Biology of Crop Pathogens and Insects, Institute of Insect Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.|Ministry of Agriculture Key Lab of Molecular Biology of Crop Pathogens and Insects, Institute of Insect Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.|College of Life Sciences and Resource Environment, Yichun University, Yichun, 336000, China.|Ministry of Agriculture Key Lab of Molecular Biology of Crop Pathogens and Insects, Institute of Insect Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China. lifei18@zju.edu.cn.', 'China|China|China|China|China|China|China|China', 10, 'Acetylcholinesterase|Animals|Insecticide Resistance|Insecticides|Moths|Mutation|Organophosphates|Phylogeny|RNA|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-017-1741-6', '28693417', 'Insecticide resistance is a substantial problem in controlling agricultural and medical pests. Detecting target site mutations is crucial to manage insecticide resistance. Though PCR-based methods have been widely used in this field, they are time-consuming and inefficient, and typically have a high false positive rate. Acetylcholinesterases (Ace) is the neural target of the widely used organophosphate (OP) and carbamate insecticides. However, there is not any software available to detect insecticide resistance associated mutations in RNA-Seq data at present.'),
(97954, 2017, 'bmc', 'LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data', 'Koelmel, Jeremy P|Kroeger, Nicholas M|Ulmer, Candice Z|Bowden, John A|Patterson, Rainey E|Cochran, Jason A|Beecher, Christopher W W|Garrett, Timothy J|Yost, Richard A', 'Department of Chemistry, University of Florida, 214 Leigh Hall, Gainesville, Florida, 32611, USA.|College of Engineering, University of Florida, 412, Newell Dr, Gainesville, FL, 32611, USA.|Department of Chemistry, University of Florida, 214 Leigh Hall, Gainesville, Florida, 32611, USA.|National Institute of Standards and Technology, Hollings Marine Laboratory, 331 Ft. Johnson Road, Charleston, SC, 29412, USA.|Department of Chemistry, University of Florida, 214 Leigh Hall, Gainesville, Florida, 32611, USA.|College of Engineering, University of Florida, 412, Newell Dr, Gainesville, FL, 32611, USA.|Clinical and Translational Science Institute, University of Florida, 2004 Mowry Road, Gainesville, FL, 32610, USA.|Department of Chemistry, University of Florida, 214 Leigh Hall, Gainesville, Florida, 32611, USA.|Department of Chemistry, University of Florida, 214 Leigh Hall, Gainesville, Florida, 32611, USA. ryost@chem.ufl.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 74, 'Algorithms|Automation|Chromatography, High Pressure Liquid|Humans|Lipids|Molecular Weight|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1186/s12859-017-1744-3', '28693421', 'Lipids are ubiquitous and serve numerous biological functions, thus lipids have been shown to have great potential as candidates for elucidating biomarkers and pathway perturbations associated with disease. Methods expanding coverage of the lipidome increase the likelihood of biomarker discovery and could lead to more comprehensive understanding of disease etiology.'),
(97955, 2017, 'bmc', 'Using the multi-objective optimization replica exchange Monte Carlo enhanced sampling method for protein-small molecule docking', 'Wang, Hongrui|Liu, Hongwei|Cai, Leixin|Wang, Caixia|Lv, Qiang', 'School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, 215006, Peoples Republic of China. riihon@yeah.net.|School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, 215006, Peoples Republic of China.|School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, 215006, Peoples Republic of China.|School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, 215006, Peoples Republic of China.|School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, 215006, Peoples Republic of China.', 'China|China|China|China|China', 1, 'Algorithms|Ligands|Molecular Docking Simulation|Monte Carlo Method|Protein Binding|Proteins|Small Molecule Libraries', 'Journal Article', '10.1186/s12859-017-1733-6', '28693470', 'In this study, we extended the replica exchange Monte Carlo (REMC) sampling method to protein-small molecule docking conformational prediction using RosettaLigand. In contrast to the traditional Monte Carlo (MC) and REMC sampling methods, these methods use multi-objective optimization Pareto front information to facilitate the selection of replicas for exchange.'),
(97956, 2017, 'bmc', 'Spatial pattern analysis of nuclear migration in remodelled muscles during Drosophila metamorphosis', '-', '-', '-', 0, 'Algorithms|Animals|Autophagy|Autophagy-Related Proteins|Cell Nucleus|Cysteine Endopeptidases|Drosophila|Drosophila Proteins|Humans|Larva|Membrane Proteins|Metamorphosis, Biological|Muscles|RNA Interference|RNA, Small Interfering|Time-Lapse Imaging', 'Journal Article', '10.1186/s12859-017-1739-0', '28693471', 'Many human muscle wasting diseases are associated with abnormal nuclear localization. During metamorphosis in Drosophila melanogaster, multi-nucleated larval dorsal abdominal muscles either undergo cell death or are remodeled to temporary adult muscles. Muscle remodeling is associated with anti-polar nuclear migration and atrophy during early pupation followed by polar migration and muscle growth during late pupation. Muscle remodeling is a useful model to study genes involved in myonuclear migration. Previously, we showed that loss of Cathepsin-L inhibited anti-polar movements, while knockdown of autophagy-related genes affected nuclear positioning along the medial axis in late metamorphosis.'),
(97957, 2017, 'bmc', 'Reliable Biomarker discovery from Metagenomic data via RegLRSD algorithm', 'Alshawaqfeh, Mustafa|Bashaireh, Ahmad|Serpedin, Erchin|Suchodolski, Jan', 'Bioinformatics and Genomic Signal Processing Lab, ECEN Dept., Texas A&M University, College Station, 77843-3128, TX, USA.|Bioinformatics and Genomic Signal Processing Lab, ECEN Dept., Texas A&M University, College Station, 77843-3128, TX, USA.|Bioinformatics and Genomic Signal Processing Lab, ECEN Dept., Texas A&M University, College Station, 77843-3128, TX, USA. eserpedin@tamu.edu.|College of Veterinary Medicine and Biomedical Sciences, Gastrointestinal Laboratory, Texas A&M University, College Station, 77843-3128, TX, USA.', 'United States|United States|United States|United States', 3, 'Algorithms|Animals|Biomarkers|Colitis, Ulcerative|Dogs|Exocrine Pancreatic Insufficiency|High-Throughput Nucleotide Sequencing|Inflammatory Bowel Diseases|Metagenomics|Mice|Reproducibility of Results', 'Journal Article', '10.1186/s12859-017-1738-1', '28693478', 'Biomarker detection presents itself as a major means of translating biological data into clinical applications. Due to the recent advances in high throughput sequencing technologies, an increased number of metagenomics studies have suggested the dysbiosis in microbial communities as potential biomarker for certain diseases. The reproducibility of the results drawn from metagenomic data is crucial for clinical applications and to prevent incorrect biological conclusions. The variability in the sample size and the subjects participating in the experiments induce diversity, which may drastically change the outcome of biomarker detection algorithms. Therefore, a robust biomarker detection algorithm that ensures the consistency of the results irrespective of the natural diversity present in the samples is needed.'),
(97958, 2017, 'bmc', 'Incorporating biological information in sparse principal component analysis with application to genomic data', 'Li, Ziyi|Safo, Sandra E|Long, Qi', 'Department of Biostatistics and Bioinformatics, Emory University, 1518 Clifton Road, Atlanta, 30322, GA, USA.|Department of Biostatistics and Bioinformatics, Emory University, 1518 Clifton Road, Atlanta, 30322, GA, USA.|Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, 19104, PA, USA. qlong@mail.med.upenn.edu.', 'United States|United States|United States', 0, 'Algorithms|Genomics|Humans|Principal Component Analysis', 'Journal Article', '10.1186/s12859-017-1740-7', '28697740', 'Sparse principal component analysis (PCA) is a popular tool for dimensionality reduction, pattern recognition, and visualization of high dimensional data. It has been recognized that complex biological mechanisms occur through concerted relationships of multiple genes working in networks that are often represented by graphs. Recent work has shown that incorporating such biological information improves feature selection and prediction performance in regression analysis, but there has been limited work on extending this approach to PCA. In this article, we propose two new sparse PCA methods called Fused and Grouped sparse PCA that enable incorporation of prior biological information in variable selection.'),
(97959, 2017, 'bmc', 'Integrated genomic analysis of biological gene sets with applications in lung cancer prognosis', 'Chu, Su Hee|Huang, Yen-Tsung', 'Department of Epidemiology, School of Public Health, Brown University, 121 S Main St, Providence, RI, USA.|Department of Epidemiology, School of Public Health, Brown University, 121 S Main St, Providence, RI, USA. ythuang@stat.sinica.edu.tw.', 'United States|United States', 6, 'DNA Methylation|Epigenesis, Genetic|Gene Expression Profiling|Genomics|Humans|Lung Neoplasms|Prognosis', 'Journal Article', '10.1186/s12859-017-1737-2', '28697753', 'Burgeoning interest in integrative analyses has produced a rise in studies which incorporate data from multiple genomic platforms. Literature for conducting formal hypothesis testing on an integrative gene set level is considerably sparse. This paper is biologically motivated by our interest in the joint effects of epigenetic methylation loci and their associated mRNA gene expressions on lung cancer survival status.'),
(97960, 2017, 'bmc', 'Estimating Phred scores of Illumina base calls by logistic regression and sparse modeling', 'Zhang, Sheng|Wang, Bo|Wan, Lin|Li, Lei M', 'National Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.|National Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.|National Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.|National Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China. lilei@amss.ac.cn.', 'China|China|China|China', 0, 'High-Throughput Nucleotide Sequencing|Logistic Models|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1743-4', '28697757', 'Phred quality scores are essential for downstream DNA analysis such as SNP detection and DNA assembly. Thus a valid model to define them is indispensable for any base-calling software. Recently, we developed the base-caller 3Dec for Illumina sequencing platforms, which reduces base-calling errors by 44-69% compared to the existing ones. However, the model to predict its quality scores has not been fully investigated yet.'),
(97961, 2017, 'bmc', 'HLA-check: evaluating HLA data from SNP information', 'Jeanmougin, Marc|Noirel, Josselin|Coulonges, Cédric|Zagury, Jean-François', 'Laboratoire Génomique, Bioinformatique et Applications, EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint-Martin, Paris, 75003, France.|Laboratoire Génomique, Bioinformatique et Applications, EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint-Martin, Paris, 75003, France.|Laboratoire Génomique, Bioinformatique et Applications, EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint-Martin, Paris, 75003, France.|Laboratoire Génomique, Bioinformatique et Applications, EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint-Martin, Paris, 75003, France. zagury@cnam.fr.', 'France|France|France|France', 0, 'Alleles|European Continental Ancestry Group|Genotyping Techniques|HLA Antigens|Histocompatibility Antigens Class I|Histocompatibility Testing|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/s12859-017-1746-1', '28697761', 'The major histocompatibility complex (MHC) region of the human genome, and specifically the human leukocyte antigen (HLA) genes, play a major role in numerous human diseases. With the recent progress of sequencing methods (eg, Next-Generation Sequencing, NGS), the accurate genotyping of this region has become possible but remains relatively costly. In order to obtain the HLA information for the millions of samples already genotyped by chips in the past ten years, efficient bioinformatics tools, such as SNP2HLA or HIBAG, have been developed that infer HLA information from the linkage disequilibrium existing between HLA alleles and SNP markers in the MHC region.'),
(97962, 2017, 'bmc', 'SegCorr a statistical procedure for the detection of genomic regions of correlated expression', 'Delatola, Eleni Ioanna|Lebarbier, Emilie|Mary-Huard, Tristan|Radvanyi, François|Robin, Stéphane|Wong, Jennifer', 'AgroParisTech UMR518, Paris, 75005, France. eldelatola@yahoo.gr.|AgroParisTech UMR518, Paris, 75005, France.|AgroParisTech UMR518, Paris, 75005, France.|Institut Curie, PSL Research University, Cedex 05, Paris, 75248, France.|AgroParisTech UMR518, Paris, 75005, France.|Institut Curie, PSL Research University, Cedex 05, Paris, 75248, France.', 'France|France|France|France|France|France', 1, 'Algorithms|DNA Copy Number Variations|DNA Methylation|Epigenesis, Genetic|Gene Expression|Gene Expression Regulation, Neoplastic|Genomics|Humans|Models, Statistical|Neoplasms', 'Journal Article', '10.1186/s12859-017-1742-5', '28697800', 'Detecting local correlations in expression between neighboring genes along the genome has proved to be an effective strategy to identify possible causes of transcriptional deregulation in cancer. It has been successfully used to illustrate the role of mechanisms such as copy number variation (CNV) or epigenetic alterations as factors that may significantly alter expression in large chromosomal regions (gene silencing or gene activation).'),
(97963, 2017, 'bmc', 'NucDiff: in-depth characterization and annotation of differences between two sets of DNA sequences', 'Khelik, Ksenia|Lagesen, Karin|Sandve, Geir Kjetil|Rognes, Torbjørn|Nederbragt, Alexander Johan', 'Biomedical Informatics Research Group, Department of Informatics, University of Oslo, PO Box 1080, 0316, Oslo, Norway.|Biomedical Informatics Research Group, Department of Informatics, University of Oslo, PO Box 1080, 0316, Oslo, Norway.|Biomedical Informatics Research Group, Department of Informatics, University of Oslo, PO Box 1080, 0316, Oslo, Norway.|Biomedical Informatics Research Group, Department of Informatics, University of Oslo, PO Box 1080, 0316, Oslo, Norway.|Biomedical Informatics Research Group, Department of Informatics, University of Oslo, PO Box 1080, 0316, Oslo, Norway. lex.nederbragt@ibv.uio.no.', 'Norway|Norway|Norway|Norway|Norway', 0, 'Base Sequence|DNA|Genomics|Internet|Sequence Alignment|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1748-z', '28701187', 'Comparing sets of sequences is a situation frequently encountered in bioinformatics, examples being comparing an assembly to a reference genome, or two genomes to each other. The purpose of the comparison is usually to find where the two sets differ, e.g. to find where a subsequence is repeated or deleted, or where insertions have been introduced. Such comparisons can be done using whole-genome alignments. Several tools for making such alignments exist, but none of them 1) provides detailed information about the types and locations of all differences between the two sets of sequences, 2) enables visualisation of alignment results at different levels of detail, and 3) carefully takes genomic repeats into consideration.'),
(97964, 2017, 'bmc', 'Investigating reproducibility and tracking provenance - A genomic workflow case study', 'Kanwal, Sehrish|Khan, Farah Zaib|Lonie, Andrew|Sinnott, Richard O', 'Department of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, 3010, Australia. kanwals@unimelb.edu.au.|Department of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, 3010, Australia. khanf1@unimelb.edu.au.|Melbourne Bioinformatics, The University of Melbourne, Melbourne, VIC, 3010, Australia.|Department of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, 3010, Australia.', 'Australia|Australia|Australia|Australia', 24, 'Computational Biology|Genomics|High-Throughput Nucleotide Sequencing|Internet|Reproducibility of Results|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1747-0', '28701218', 'Computational bioinformatics workflows are extensively used to analyse genomics data, with different approaches available to support implementation and execution of these workflows. Reproducibility is one of the core principles for any scientific workflow and remains a challenge, which is not fully addressed. This is due to incomplete understanding of reproducibility requirements and assumptions of workflow definition approaches. Provenance information should be tracked and used to capture all these requirements supporting reusability of existing workflows.'),
(97965, 2017, 'bmc', 'An efficient algorithm for improving structure-based prediction of transcription factor binding sites', 'Farrel, Alvin|Guo, Jun-Tao', 'Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA. jguo4@uncc.edu.', 'United States|United States', 0, 'Algorithms|Binding Sites|DNA|Nucleotide Motifs|Position-Specific Scoring Matrices|Protein Binding|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/s12859-017-1755-0', '28715997', 'Gene expression is regulated by transcription factors binding to specific target DNA sites. Understanding how and where transcription factors bind at genome scale represents an essential step toward our understanding of gene regulation networks. Previously we developed a structure-based method for prediction of transcription factor binding sites using an integrative energy function that combines a knowledge-based multibody potential and two atomic energy terms. While the method performs well, it is not computationally efficient due to the exponential increase in the number of binding sequences to be evaluated for longer binding sites. In this paper, we present an efficient pentamer algorithm by splitting DNA binding sequences into overlapping fragments along with a simplified integrative energy function for transcription factor binding site prediction.'),
(97966, 2017, 'bmc', 'Factors affecting interactome-based prediction of human genes associated with clinical signs', 'González-Pérez, Sara|Pazos, Florencio|Chagoyen, Mónica', 'Computational Systems Biology Group, National Center for Biotechnology (CNB-CSIC), Darwin 3, 28049, Madrid, Spain.|Computational Systems Biology Group, National Center for Biotechnology (CNB-CSIC), Darwin 3, 28049, Madrid, Spain.|Computational Systems Biology Group, National Center for Biotechnology (CNB-CSIC), Darwin 3, 28049, Madrid, Spain. monica.chagoyen@cnb.csic.es.', 'Spain|Spain|Spain', 0, 'Diagnosis|Disease|Genomics|Humans|Inheritance Patterns|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/s12859-017-1754-1', '28715999', 'Clinical signs are a fundamental aspect of human pathologies. While disease diagnosis is problematic or impossible in many cases, signs are easier to perceive and categorize. Clinical signs are increasingly used, together with molecular networks, to prioritize detected variants in clinical genomics pipelines, even if the patient is still undiagnosed. Here we analyze the ability of these network-based methods to predict genes that underlie clinical signs from the human interactome.'),
(97967, 2017, 'bmc', 'Quantiprot - a Python package for quantitative analysis of protein sequences', 'Konopka, Bogumixc5x82 M|Marciniak, Marta|Dyrka, Witold', 'Katedra Inxc5xbbynierii Biomedycznej, Wydziaxc5x82 Podstawowych Problemxc3xb3w Techniki, Politechnika Wrocxc5x82awska, Wybrzexc5xbbe Wyspiaxc5x84skiego 27, Wroclaw, 50-370, Poland.|Katedra Inxc5xbbynierii Biomedycznej, Wydziaxc5x82 Podstawowych Problemxc3xb3w Techniki, Politechnika Wrocxc5x82awska, Wybrzexc5xbbe Wyspiaxc5x84skiego 27, Wroclaw, 50-370, Poland.|Katedra Inxc5xbbynierii Biomedycznej, Wydziaxc5x82 Podstawowych Problemxc3xb3w Techniki, Politechnika Wrocxc5x82awska, Wybrzexc5xbbe Wyspiaxc5x84skiego 27, Wroclaw, 50-370, Poland. witold.dyrka@pwr.edu.pl.', 'Poland|Poland|Poland', 0, 'Amino Acids|Cluster Analysis|Humans|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/s12859-017-1751-4', '28716000', 'The field of protein sequence analysis is dominated by tools rooted in substitution matrices and alignments. A complementary approach is provided by methods of quantitative characterization. A major advantage of the approach is that quantitative properties defines a multidimensional solution space, where sequences can be related to each other and differences can be meaningfully interpreted.'),
(97968, 2017, 'bmc', 'Variant Ranker: a web-tool to rank genomic data according to functional significance', 'Alexander, John|Mantzaris, Dimitris|Georgitsi, Marianthi|Drineas, Petros|Paschou, Peristera', 'Department of Molecular Biology and Genetics, Democritus University of Thrace, Panepistimioupoli, Dragana, Alexandroupolis, 68100, Greece. jalexand@mbg.duth.gr.|Department of Molecular Biology and Genetics, Democritus University of Thrace, Panepistimioupoli, Dragana, Alexandroupolis, 68100, Greece.|Department of Molecular Biology and Genetics, Democritus University of Thrace, Panepistimioupoli, Dragana, Alexandroupolis, 68100, Greece.|Department of Computer Science, Purdue University, West Lafayette, 47907, Indiana, United States.|Department of Molecular Biology and Genetics, Democritus University of Thrace, Panepistimioupoli, Dragana, Alexandroupolis, 68100, Greece.', 'Greece|Greece|Greece|India|Greece', 0, 'Algorithms|Gene Frequency|Gene Ontology|Genetic Variation|Genomics|Genotype|Humans|Internet|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1752-3', '28716001', 'The increasing volume and complexity of high-throughput genomic data make analysis and prioritization of variants difficult for researchers with limited bioinformatics skills. Variant Ranker allows researchers to rank identified variants and determine the most confident variants for experimental validation.'),
(97969, 2017, 'bmc', 'Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints', 'Liu, Jie|Su, Minyi|Liu, Zhihai|Li, Jie|Li, Yan|Wang, Renxiao', 'State Key Laboratory of Bioorganic and Natural Products Chemistry, Collaborative Innovation Center of Chemistry for Life Sciences, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai, 200032, China.|State Key Laboratory of Bioorganic and Natural Products Chemistry, Collaborative Innovation Center of Chemistry for Life Sciences, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai, 200032, China.|State Key Laboratory of Bioorganic and Natural Products Chemistry, Collaborative Innovation Center of Chemistry for Life Sciences, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai, 200032, China.|State Key Laboratory of Bioorganic and Natural Products Chemistry, Collaborative Innovation Center of Chemistry for Life Sciences, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai, 200032, China.|State Key Laboratory of Bioorganic and Natural Products Chemistry, Collaborative Innovation Center of Chemistry for Life Sciences, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai, 200032, China. kathyli@mail.sioc.ac.cn.|State Key Laboratory of Bioorganic and Natural Products Chemistry, Collaborative Innovation Center of Chemistry for Life Sciences, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai, 200032, China. wangrx@mail.sioc.ac.cn.', 'China|China|China|China|China|China', 0, 'Amyloid Precursor Protein Secretases|Carbonic Anhydrase II|Checkpoint Kinase 1|Computational Biology|HIV Protease|Humans|Ligands|Molecular Docking Simulation|Pharmaceutical Preparations|Protein Binding|Proteins|Software', 'Journal Article', '10.1186/s12859-017-1750-5', '28720122', 'In structure-based drug design, binding affinity prediction remains as a challenging goal for current scoring functions. Development of target-biased scoring functions provides a new possibility for tackling this problem, but this approach is also associated with certain technical difficulties. We previously reported the Knowledge-Guided Scoring (KGS) method as an alternative approach (BMC Bioinformatics, 2010, 11, 193-208). The key idea is to compute the binding affinity of a given protein-ligand complex based on the known binding data of an appropriate reference complex, so the error in binding affinity prediction can be reduced effectively.'),
(97970, 2017, 'bmc', 'Assessment of genome annotation using gene function similarity within the gene neighborhood', 'Jun, Se-Ran|Nookaew, Intawat|Hauser, Loren|Gorin, Andrey', 'Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA. sjun@uams.edu.|Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.|Comparative Genomics Group, Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.|Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Bayes Theorem|Clostridium thermocellum|Databases, Genetic|Escherichia coli|Genome, Bacterial|Genomics', 'Journal Article', '10.1186/s12859-017-1761-2', '28724412', 'Functional annotation of bacterial genomes is an obligatory and crucially important step of information processing from the genome sequences into cellular mechanisms. However, there is a lack of computational methods to evaluate the quality of functional assignments.'),
(97971, 2017, 'bmc', 'Examining the role of unmeasured confounding in mediation analysis with genetic and genomic applications', 'Lutz, Sharon M|Thwing, Annie|Schmiege, Sarah|Kroehl, Miranda|Baker, Christopher D|Starling, Anne P|Hokanson, John E|Ghosh, Debashis', 'Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, 13001 E. 17th Place, B119 Bldg. 500 W3128, Aurora, CO, 80045, USA. sharon.lutz@ucdenver.edu.|Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, 13001 E. 17th Place, B119 Bldg. 500 W3128, Aurora, CO, 80045, USA.|Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, 13001 E. 17th Place, B119 Bldg. 500 W3128, Aurora, CO, 80045, USA.|Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, 13001 E. 17th Place, B119 Bldg. 500 W3128, Aurora, CO, 80045, USA.|Department of Pediatrics and Pulmonary Medicine, Childrens Hospital Colorado, Aurora, CO, USA.|Department of Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.|Department of Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.|Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, 13001 E. 17th Place, B119 Bldg. 500 W3128, Aurora, CO, 80045, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 4, 'Confounding Factors (Epidemiology)|Genome-Wide Association Study|Humans|Principal Component Analysis|Pulmonary Disease, Chronic Obstructive|Software', 'Journal Article', '10.1186/s12859-017-1749-y', '28724417', 'In mediation analysis if unmeasured confounding is present, the estimates for the direct and mediated effects may be over or under estimated. Most methods for the sensitivity analysis of unmeasured confounding in mediation have focused on the mediator-outcome relationship.'),
(97972, 2017, 'bmc', 'CLOVE: classification of genomic fusions into structural variation events', 'Schröder, Jan|Wirawan, Adrianto|Schmidt, Bertil|Papenfuss, Anthony T', 'Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC, 3052, Australia. schroeder@wehi.edu.au.|Institut für Informatik, Johannes Gutenberg Universität Mainz, Mainz, Germany.|Institut für Informatik, Johannes Gutenberg Universität Mainz, Mainz, Germany.|Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC, 3052, Australia. papenfuss@wehi.edu.au.', 'Australia|Germany|Germany|Australia', 1, 'Algorithms|Chromosomes|DNA|Escherichia coli|Genomics|Humans|Internet|Nucleic Acid Conformation|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1760-3', '28728542', 'A precise understanding of structural variants (SVs) in DNA is important in the study of cancer and population diversity. Many methods have been designed to identify SVs from DNA sequencing data. However, the problem remains challenging because existing approaches suffer from low sensitivity, precision, and positional accuracy. Furthermore, many existing tools only identify breakpoints, and so not collect related breakpoints and classify them as a particular type of SV. Due to the rapidly increasing usage of high throughput sequencing technologies in this area, there is an urgent need for algorithms that can accurately classify complex genomic rearrangements (involving more than one breakpoint or fusion).'),
(97973, 2017, 'bmc', 'CRISPulator: a discrete simulation tool for pooled genetic screens', 'Nagy, Tamas|Kampmann, Martin', 'Graduate program in Bioinformatics, University of California, San Francisco, CA, 94158, USA.|Department of Biochemistry and Biophysics, Institute for Neurodegenerative Diseases and California Institute for Quantitative Biomedical Research, University of California, San Francisco, CA, 94158, USA. Martin.Kampmann@ucsf.edu.', 'United States|United States', 6, 'Animals|Area Under Curve|CRISPR-Cas Systems|Flow Cytometry|Gene Library|Genetic Testing|Humans|Internet|Monte Carlo Method|RNA, Guide|ROC Curve|User-Computer Interface', 'Journal Article', '10.1186/s12859-017-1759-9', '28732459', 'The rapid adoption of CRISPR technology has enabled biomedical researchers to conduct CRISPR-based genetic screens in a pooled format. The quality of results from such screens is heavily dependent on the selection of optimal screen design parameters, which also affects cost and scalability. However, the cost and effort of implementing pooled screens prohibits experimental testing of a large number of parameters.'),
(97974, 2017, 'bmc', 'A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy', 'Zhu, Yanan|Ouyang, Qi|Mao, Youdong', 'Center for Quantitative Biology, Peking University, Beijing, 100871, China.|Center for Quantitative Biology, Peking University, Beijing, 100871, China.|Center for Quantitative Biology, Peking University, Beijing, 100871, China. youdong_mao@dfci.harvard.edu.', 'China|China|China', 40, 'Algorithms|Cryoelectron Microscopy|Hemocyanins|Macromolecular Substances|Neural Networks (Computer)|Signal-To-Noise Ratio', 'Journal Article', '10.1186/s12859-017-1757-y', '28732461', 'Single-particle cryo-electron microscopy (cryo-EM) has become a mainstream tool for the structural determination of biological macromolecular complexes. However, high-resolution cryo-EM reconstruction often requires hundreds of thousands of single-particle images. Particle extraction from experimental micrographs thus can be laborious and presents a major practical bottleneck in cryo-EM structural determination. Existing computational methods for particle picking often use low-resolution templates for particle matching, making them susceptible to reference-dependent bias. It is critical to develop a highly efficient template-free method for the automatic recognition of particle images from cryo-EM micrographs.'),
(97975, 2017, 'bmc', 'Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone', 'Ruiz-Blanco, Yasser B|Agüero-Chapin, Guillermin|García-Hernández, Enrique|Álvarez, Orlando|Antunes, Agostinho|Green, James', 'Facultad de Química y Farmacia, Universidad Central Marta Abreu de Las Villas, 54830, Santa Clara, Cuba.|CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208, Porto, Portugal. gchapin@ciimar.up.pt.|Instituto de Química, Universidad Nacional Autónoma de México (UNAM), 04360, D.F, México, Mexico.|Centro de Bioactivos Químicos (CBQ), Universidad Central ¨Marta Abreu¨ de Las Villas (UCLV), 54830, Santa Clara, Cuba.|CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208, Porto, Portugal.|Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada.', 'Cuba|Portugal|Mexico|Cuba|Portugal|Canada', 6, 'Amino Acid Sequence|Bacterial Proteins|Enzymes|Proteins|Proteome|Sequence Alignment|Sequence Analysis, Protein|Shewanella|Support Vector Machine', 'Journal Article', '10.1186/s12859-017-1758-x', '28732462', '-'),
(97976, 2017, 'bmc', 'NoGOA: predicting noisy GO annotations using evidences and sparse representation', 'Yu, Guoxian|Lu, Chang|Wang, Jun', 'College of Computer and Information Sciences, Southwest University, Chongqing, China. gxyu@swu.edu.cn.|College of Computer and Information Sciences, Southwest University, Chongqing, China.|College of Computer and Information Sciences, Southwest University, Chongqing, China.', 'China|China|China', 3, 'Animals|Arabidopsis|Cattle|Chickens|Computational Biology|Gene Ontology|Humans|Mice|Molecular Sequence Annotation|Saccharomyces cerevisiae', 'Journal Article', '10.1186/s12859-017-1764-z', '28732468', 'Gene Ontology (GO) is a community effort to represent functional features of gene products. GO annotations (GOA) provide functional associations between GO terms and gene products. Due to resources limitation, only a small portion of annotations are manually checked by curators, and the others are electronically inferred. Although quality control techniques have been applied to ensure the quality of annotations, the community consistently report that there are still considerable noisy (or incorrect) annotations. Given the wide application of annotations, however, how to identify noisy annotations is an important but yet seldom studied open problem.'),
(97977, 2017, 'bmc', 'An extended model of vesicle fusion at the plasma membrane to estimate protein lateral diffusion from TIRF microscopy images', 'Basset, Antoine|Bouthemy, Patrick|Boulanger, Jérôme|Waharte, François|Salamero, Jean|Kervrann, Charles', 'Inria, Campus de Beaulieu, Rennes, 35042, France.|Inria, Campus de Beaulieu, Rennes, 35042, France. Patrick.Bouthemy@inria.fr.|Institut Curie, PSL Research University, CNRS UMR 144 and PICT-Cell and Tissue Imaging Facility, 12 rue Lhomond, Paris, 75005, France.|Institut Curie, PSL Research University, CNRS UMR 144 and PICT-Cell and Tissue Imaging Facility, 12 rue Lhomond, Paris, 75005, France.|Institut Curie, PSL Research University, CNRS UMR 144 and PICT-Cell and Tissue Imaging Facility, 12 rue Lhomond, Paris, 75005, France.|Inria, Campus de Beaulieu, Rennes, 35042, France.', 'France|France|France|France|France|France', 0, 'Algorithms|Animals|Antigens, CD|Cell Membrane|Diffusion|Exocytosis|Microscopy, Fluorescence|Models, Molecular|Receptors, Transferrin', 'Journal Article', '10.1186/s12859-017-1765-y', '28738814', 'Characterizing membrane dynamics is a key issue to understand cell exchanges with the extra-cellular medium. Total internal reflection fluorescence microscopy (TIRFM) is well suited to focus on the late steps of exocytosis at the plasma membrane. However, it is still a challenging task to quantify (lateral) diffusion and estimate local dynamics of proteins.'),
(97978, 2017, 'bmc', 'Visibiome: an efficient microbiome search engine based on a scalable, distributed architecture', 'Azman, Syafiq Kamarul|Anwar, Muhammad Zohaib|Henschel, Andreas', 'Department of Electrical Engineering and Computer Science, Masdar Institute of Science and Technology, Masdar City, Abu Dhabi, UAE.|Department of Environmental Science, Aarhus University, Frederiksborgvej 399, Roskilde, Denmark.|Department of Electrical Engineering and Computer Science, Masdar Institute of Science and Technology, Masdar City, Abu Dhabi, UAE. ahenschel@masdar.ac.ae.', '-|Denmark|-', 0, 'Algorithms|Databases, Factual|Microbiota|Phylogeny|Principal Component Analysis|RNA, Ribosomal, 16S|Search Engine', 'Journal Article', '10.1186/s12859-017-1763-0', '28738824', 'Given the current influx of 16S rRNA profiles of microbiota samples, it is conceivable that large amounts of them eventually are available for search, comparison and contextualization with respect to novel samples. This process facilitates the identification of similar compositional features in microbiota elsewhere and therefore can help to understand driving factors for microbial community assembly.'),
(97979, 2017, 'bmc', 'Identifying and mitigating batch effects in whole genome sequencing data', 'Tom, Jennifer A|Reeder, Jens|Forrest, William F|Graham, Robert R|Hunkapiller, Julie|Behrens, Timothy W|Bhangale, Tushar R', 'Bioinformatics and Computational Biology Department, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA. tom.jennifer@gene.com.|Bioinformatics and Computational Biology Department, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.|Bioinformatics and Computational Biology Department, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.|Human Genetics Department, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.|Human Genetics Department, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.|Human Genetics Department, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.|Bioinformatics and Computational Biology Department, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.', 'United States|United States|United States|United States|United States|United States|United States', 10, 'Genome-Wide Association Study|Genotype|High-Throughput Nucleotide Sequencing|Humans|Macular Degeneration|Phenotype|Polymorphism, Single Nucleotide|Principal Component Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-017-1756-z', '28738841', 'Large sample sets of whole genome sequencing with deep coverage are being generated, however assembling datasets from different sources inevitably introduces batch effects. These batch effects are not well understood and can be due to changes in the sequencing protocol or bioinformatics tools used to process the data. No systematic algorithms or heuristics exist to detect and filter batch effects or remove associations impacted by batch effects in whole genome sequencing data.'),
(97980, 2017, 'bmc', 'Quantification of tumour evolution and heterogeneity via Bayesian epiallele detection', 'Barrett, James E|Feber, Andrew|Herrero, Javier|Tanic, Miljana|Wilson, Gareth A|Swanton, Charles|Beck, Stephan', 'UCL Cancer Institute, University College London, London, UK. regmjeb@ucl.ac.uk.|UCL Cancer Institute, University College London, London, UK.|UCL Cancer Institute, University College London, London, UK.|UCL Cancer Institute, University College London, London, UK.|UCL Cancer Institute, University College London, London, UK.|UCL Cancer Institute, University College London, London, UK.|UCL Cancer Institute, University College London, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Algorithms|Alleles|Bayes Theorem|CpG Islands|DNA Methylation|Epigenomics|Humans|Lung Neoplasms', 'Journal Article', '10.1186/s12859-017-1753-2', '28743252', 'Epigenetic heterogeneity within a tumour can play an important role in tumour evolution and the emergence of resistance to treatment. It is increasingly recognised that the study of DNA methylation (DNAm) patterns along the genome - so-called epialleles - offers greater insight into epigenetic dynamics than conventional analyses which examine DNAm marks individually.'),
(97981, 2017, 'bmc', 'Assessing the model transferability for prediction of transcription factor binding sites based on chromatin accessibility', 'Liu, Sheng|Zibetti, Cristina|Wan, Jun|Wang, Guohua|Blackshaw, Seth|Qian, Jiang', 'Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, 21287, MD, USA.|Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, 21287, MD, USA.|Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, 21287, MD, USA.|Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, 21287, MD, USA.|Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, 21287, MD, USA.|Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, 21287, MD, USA. jiang.qian@jhmi.edu.', 'United States|United States|United States|United States|United States|United States', 7, 'Algorithms|Area Under Curve|Binding Sites|Cell Line, Tumor|Chromatin|Chromatin Assembly and Disassembly|DNA|Humans|Models, Genetic|Protein Binding|ROC Curve|Transcription Factors', 'Journal Article', '10.1186/s12859-017-1769-7', '28750606', 'Computational prediction of transcription factor (TF) binding sites in different cell types is challenging. Recent technology development allows us to determine the genome-wide chromatin accessibility in various cellular and developmental contexts. The chromatin accessibility profiles provide useful information in prediction of TF binding events in various physiological conditions. Furthermore, ChIP-Seq analysis was used to determine genome-wide binding sites for a range of different TFs in multiple cell types. Integration of these two types of genomic information can improve the prediction of TF binding events.'),
(97982, 2017, 'bmc', 'l1kdeconv: an R package for peak calling analysis with LINCS L1000 data', 'Li, Zhao|Li, Jin|Yu, Peng', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. pengyu.bio@gmail.com.', 'United States|United States|United States', 4, 'Algorithms|Cell Line, Tumor|Humans|Normal Distribution|Software|Transcriptome', 'Journal Article', '10.1186/s12859-017-1767-9', '28750623', 'LINCS L1000 is a high-throughput technology that allows gene expression measurement in a large number of assays. However, to fit the measurements of ~1000 genes in the ~500 color channels of LINCS L1000, every two landmark genes are designed to share a single channel. Thus, a deconvolution step is required to infer the expression values of each gene. Any errors in this step can be propagated adversely to the downstream analyses.'),
(97983, 2017, 'bmc', 'Variable selection for disease progression models: methods for oncogenetic trees and application to cancer and HIV', 'Hainke, Katrin|Szugat, Sebastian|Fried, Roland|Rahnenführer, Jörg', 'Department of Statistics, TU Dortmund University, Dortmund, 44221, Germany.|Department of Statistics, TU Dortmund University, Dortmund, 44221, Germany.|Department of Statistics, TU Dortmund University, Dortmund, 44221, Germany.|Department of Statistics, TU Dortmund University, Dortmund, 44221, Germany. rahnenfuehrer@statistik.tu-dortmund.de.', 'Germany|Germany|Germany|Germany', 2, 'Algorithms|Carcinogenesis|Cell Transformation, Neoplastic|Computer Simulation|Disease Progression|Glioblastoma|HIV Infections|Humans|Models, Biological|Models, Statistical|Neoplasms|Probability', 'Journal Article', '10.1186/s12859-017-1762-1', '28764644', 'Disease progression models are important for understanding the critical steps during the development of diseases. The models are imbedded in a statistical framework to deal with random variations due to biology and the sampling process when observing only a finite population. Conditional probabilities are used to describe dependencies between events that characterise the critical steps in the disease process. Many different model classes have been proposed in the literature, from simple path models to complex Bayesian networks. A popular and easy to understand but yet flexible model class are oncogenetic trees. These have been applied to describe the accumulation of genetic aberrations in cancer and HIV data. However, the number of potentially relevant aberrations is often by far larger than the maximal number of events that can be used for reliably estimating the progression models. Still, there are only a few approaches to variable selection, which have not yet been investigated in detail.');
