INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98252, 2018, 'bmc', 'Evaluation of reaction gap-filling accuracy by randomization', 'Latendresse, Mario|Karp, Peter D', 'SRI International/Artificial Intelligence Center, 333 Ravenswood Ave, Menlo Park, 94025, USA. latendre@ai.sri.com.|SRI International/Artificial Intelligence Center, 333 Ravenswood Ave, Menlo Park, 94025, USA.', 'United States|United States', 0, 'Algorithms|Genomics|Metabolic Flux Analysis|Models, Biological|Programming, Linear|Software', 'Journal Article', '10.1186/s12859-018-2050-4', '29444634', 'Completion of genome-scale flux-balance models using computational reaction gap-filling is a widely used approach, but its accuracy is not well known.'),
(98253, 2018, 'bmc', 'Oasis 2: improved online analysis of small RNA-seq data', 'Rahman, Raza-Ur|Gautam, Abhivyakti|Bethune, Jörn|Sattar, Abdul|Fiosins, Maksims|Magruder, Daniel Sumner|Capece, Vincenzo|Shomroni, Orr|Bonn, Stefan', 'Laboratory of Computational Systems Biology, German Center for Neurodegenerative Diseases, Göttingen, Germany.|Laboratory of Computational Systems Biology, German Center for Neurodegenerative Diseases, Göttingen, Germany.|Laboratory of Computational Systems Biology, German Center for Neurodegenerative Diseases, Göttingen, Germany.|Laboratory of Computational Systems Biology, German Center for Neurodegenerative Diseases, Göttingen, Germany.|Laboratory of Computational Systems Biology, German Center for Neurodegenerative Diseases, Göttingen, Germany.|Laboratory of Computational Systems Biology, German Center for Neurodegenerative Diseases, Göttingen, Germany.|Laboratory of Computational Systems Biology, German Center for Neurodegenerative Diseases, Göttingen, Germany.|Laboratory of Computational Systems Biology, German Center for Neurodegenerative Diseases, Göttingen, Germany.|Laboratory of Computational Systems Biology, German Center for Neurodegenerative Diseases, Göttingen, Germany. sbonn@uke.de.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 21, 'Base Sequence|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|MicroRNAs|RNA, Small Untranslated|Sequence Analysis, RNA|Software|Statistics as Topic', 'Journal Article', '10.1186/s12859-018-2047-z', '29444641', 'Small RNA molecules play important roles in many biological processes and their dysregulation or dysfunction can cause disease. The current method of choice for genome-wide sRNA expression profiling is deep sequencing.'),
(98254, 2018, 'bmc', 'StructRNAfinder: an automated pipeline and web server for RNA families prediction', 'Arias-Carrasco, Raúl|Vásquez-Morán, Yessenia|Nakaya, Helder I|Maracaja-Coutinho, Vinicius', 'Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, 8580745, Santiago, Chile.|Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, 8580745, Santiago, Chile.|Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, 05508-900, Brazil. hnakaya@usp.br.|Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, 8580745, Santiago, Chile. viniciusmaracaja@integrativebioinformatics.me.', 'Chile|Chile|Brazil|Chile', 6, 'Automation|Computational Biology|Internet|RNA|Software|Workflow', 'Journal Article', '10.1186/s12859-018-2052-2', '29454313', 'The function of many noncoding RNAs (ncRNAs) depend upon their secondary structures. Over the last decades, several methodologies have been developed to predict such structures or to use them to functionally annotate RNAs into RNA families. However, to fully perform this analysis, researchers should utilize multiple tools, which require the constant parsing and processing of several intermediate files. This makes the large-scale prediction and annotation of RNAs a daunting task even to researchers with good computational or bioinformatics skills.'),
(98255, 2018, 'bmc', 'CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses', 'Russo, Pedro S T|Ferreira, Gustavo R|Cardozo, Lucas E|Bürger, Matheus C|Arias-Carrasco, Raul|Maruyama, Sandra R|Hirata, Thiago D C|Lima, Diógenes S|Passos, Fernando M|Fukutani, Kiyoshi F|Lever, Melissa|Silva, João S|Maracaja-Coutinho, Vinicius|Nakaya, Helder I', 'Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, SP, 05508-900, Brazil.|Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, SP, 05508-900, Brazil.|Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, SP, 05508-900, Brazil.|Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, SP, 05508-900, Brazil.|Advanced Center for Chronic Diseases (ACCDiS), Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago, Chile.|Department of Biochemistry, Immunology, and Cell Biology, University of São Paulo, Ribeirão Preto, São Paulo, Brazil.|Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, SP, 05508-900, Brazil.|Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, SP, 05508-900, Brazil.|Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, SP, 05508-900, Brazil.|Department of Biochemistry, Immunology, and Cell Biology, University of São Paulo, Ribeirão Preto, São Paulo, Brazil.|Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, SP, 05508-900, Brazil.|Department of Biochemistry, Immunology, and Cell Biology, University of São Paulo, Ribeirão Preto, São Paulo, Brazil.|Advanced Center for Chronic Diseases (ACCDiS), Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago, Chile.|Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, SP, 05508-900, Brazil. hnakaya@usp.br.', 'Brazil|Brazil|Brazil|Brazil|Chile|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Chile|Brazil', 0, 'Automation|Databases, Genetic|Dengue|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Humans|Leishmaniasis, Visceral|Psoriasis|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1186/s12859-018-2053-1', '29458351', 'The analysis of modular gene co-expression networks is a well-established method commonly used for discovering the systems-level functionality of genes. In addition, these studies provide a basis for the discovery of clinically relevant molecular pathways underlying different diseases and conditions.'),
(98256, 2018, 'bmc', 'The VAAST Variant Prioritizer (VVP): ultrafast, easy to use whole genome variant prioritization tool', 'Flygare, Steven|Hernandez, Edgar Javier|Phan, Lon|Moore, Barry|Li, Man|Fejes, Anthony|Hu, Hao|Eilbeck, Karen|Huff, Chad|Jorde, Lynn|G Reese, Martin|Yandell, Mark', 'Department of Human Genetics, University of Utah, Salt Lake City, UT, USA.|Department of Human Genetics, University of Utah, Salt Lake City, UT, USA.|National Center for Biotechnology Information, Bethesda, MD, USA.|Department of Human Genetics, University of Utah, Salt Lake City, UT, USA.|Department of Human Genetics, University of Utah, Salt Lake City, UT, USA.|Fabric Genomics, Oakland, CA, USA.|Department of Epidemiology, M.D. Anderson Cancer Center, Houston, TX, USA.|USTAR Center for Genetic Discovery, Salt Lake City, UT, USA.|Department of Epidemiology, M.D. Anderson Cancer Center, Houston, TX, USA.|Department of Human Genetics, University of Utah, Salt Lake City, UT, USA.|Fabric Genomics, Oakland, CA, USA.|Department of Human Genetics, University of Utah, Salt Lake City, UT, USA. myandell@genetics.utah.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', 0, 'Computational Biology|Databases, Genetic|Genetic Variation|Genome, Human|Humans|Polymorphism, Single Nucleotide|ROC Curve|Software|Time Factors|Whole Genome Sequencing|Zygote', 'Journal Article', '10.1186/s12859-018-2056-y', '29463208', 'Prioritization of sequence variants for diagnosis and discovery of Mendelian diseases is challenging, especially in large collections of whole genome sequences (WGS). Fast, scalable solutions are needed for discovery research, for clinical applications, and for curation of massive public variant repositories such as dbSNP and gnomAD. In response, we have developed VVP, the VAAST Variant Prioritizer. VVP is ultrafast, scales to even the largest variant repositories and genome collections, and its outputs are designed to simplify clinical interpretation of variants of uncertain significance.'),
(98257, 2018, 'bmc', 'How large B-factors can be in protein crystal structures', 'Carugo, Oliviero', 'Department of Structural and Computational Biology, University of Vienna, Campus Vienna Biocenter 5, A-1030, Vienna, Austria. oliviero.carugo@univie.ac.at.', 'Austria', 0, 'Amino Acid Sequence|Amino Acids|Crystallography, X-Ray|Databases, Protein|Models, Molecular|Protein Conformation|Proteins|Solvents', 'Journal Article', '10.1186/s12859-018-2083-8', '29471780', 'Protein crystal structures are potentially over-interpreted since they are routinely refined without any restraint on the upper limit of atomic B-factors. Consequently, some of their atoms, undetected in the electron density maps, are allowed to reach extremely large B-factors, even above 100 square Angstroms, and their final positions are purely speculative and not based on any experimental evidence.'),
(98258, 2018, 'bmc', 'NoLogo: a new statistical model highlights the diversity and suggests new classes of Crm1-dependent nuclear export signals', 'Liku, Muluye E|Legere, Elizabeth-Ann|Moses, Alan M', 'Department of Cell & Systems Biology, University of Toronto, Toronto, Canada.|Department of Cell & Systems Biology, University of Toronto, Toronto, Canada.|Department of Cell & Systems Biology, University of Toronto, Toronto, Canada. alan.moses@utoronto.ca.', 'Canada|Canada|Canada', 4, 'Amino Acid Sequence|Cluster Analysis|Humans|Hydrophobic and Hydrophilic Interactions|Karyopherins|Markov Chains|Models, Statistical|Nuclear Export Signals|Position-Specific Scoring Matrices|Probability|Receptors, Cytoplasmic and Nuclear|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1186/s12859-018-2076-7', '29482494', 'Crm1-dependent Nuclear Export Signals (NESs) are clusters of alternating hydrophobic and non-hydrophobic amino acid residues between 10 to 15 amino acids in length. NESs were largely thought to follow simple consensus patterns, based on which they were categorized into 6-10 classes. However, newly discovered NESs often deviate from the established consensus patterns. Thus, identifying NESs within protein sequences remains a bioinformatics challenge.'),
(98259, 2018, 'bmc', 'Microvessel prediction in H&E Stained Pathology Images using fully convolutional neural networks', 'Yi, Faliu|Yang, Lin|Wang, Shidan|Guo, Lei|Huang, Chenglong|Xie, Yang|Xiao, Guanghua', 'Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX, 75390, USA.|Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX, 75390, USA.|Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX, 75390, USA.|Department of Pathology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100023, P. R. China.|Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX, 75390, USA.|Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX, 75390, USA.|Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX, 75390, USA. guanghua.xiao@utsouthwestern.edu.', 'United States|United States|United States|China|United States|United States|United States', 0, 'Algorithms|Humans|Image Processing, Computer-Assisted|Microvessels|Neoplasms|Neural Networks (Computer)|Survival Analysis|Time Factors', 'Journal Article', '10.1186/s12859-018-2055-z', '29482496', 'Pathological angiogenesis has been identified in many malignancies as a potential prognostic factor and target for therapy. In most cases, angiogenic analysis is based on the measurement of microvessel density (MVD) detected by immunostaining of CD31 or CD34. However, most retrievable public data is generally composed of Hematoxylin and Eosin (H&E)-stained pathology images, for which is difficult to get the corresponding immunohistochemistry images. The role of microvessels in H&E stained images has not been widely studied due to their complexity and heterogeneity. Furthermore, identifying microvessels manually for study is a labor-intensive task for pathologists, with high inter- and intra-observer variation. Therefore, it is important to develop automated microvessel-detection algorithms in H&E stained pathology images for clinical association analysis.'),
(98260, 2018, 'bmc', 'Effect of the sequence data deluge on the performance of methods for detecting protein functional residues', 'Garrido-Martín, Diego|Pazos, Florencio', 'Present address: Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, c/ Dr. Aiguader, 88, 08003, Barcelona, Spain.|Computational Systems Biology Group, Systems Biology Program, National Centre for Biotechnology (CNB-CSIC), c/ Darwin, 3, 28049, Madrid, Spain. pazos@cnb.csic.es.', 'Spain|Spain', 1, 'Algorithms|Amino Acid Sequence|Amino Acids|Binding Sites|Conserved Sequence|Molecular Sequence Annotation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Time Factors', 'Journal Article', '10.1186/s12859-018-2084-7', '29482506', 'The exponential accumulation of new sequences in public databases is expected to improve the performance of all the approaches for predicting protein structural and functional features. Nevertheless, this was never assessed or quantified for some widely used methodologies, such as those aimed at detecting functional sites and functional subfamilies in protein multiple sequence alignments. Using raw protein sequences as only input, these approaches can detect fully conserved positions, as well as those with a family-dependent conservation pattern. Both types of residues are routinely used as predictors of functional sites and, consequently, understanding how the sequence content of the databases affects them is relevant and timely.'),
(98261, 2018, 'bmc', 'DecoFungi: a web application for automatic characterisation of dye decolorisation in fungal strains', 'Domínguez, César|Heras, Jónathan|Mata, Eloy|Pascual, Vico', 'Department of Mathematics and Computer Science, University of La Rioja, Ed. CCT. C/ Madre de Dios 53, Logroño, 26006, Spain.|Department of Mathematics and Computer Science, University of La Rioja, Ed. CCT. C/ Madre de Dios 53, Logroño, 26006, Spain. jonathan.heras@unirioja.es.|Department of Mathematics and Computer Science, University of La Rioja, Ed. CCT. C/ Madre de Dios 53, Logroño, 26006, Spain.|Department of Mathematics and Computer Science, University of La Rioja, Ed. CCT. C/ Madre de Dios 53, Logroño, 26006, Spain.', 'Spain|Spain|Spain|Spain', 0, 'Coloring Agents|Fungi|Internet|Models, Theoretical|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-018-2082-9', '29482515', 'Fungi have diverse biotechnological applications in, among others, agriculture, bioenergy generation, or remediation of polluted soil and water. In this context, culture media based on color change in response to degradation of dyes are particularly relevant, but measuring dye decolorisation of fungal strains mainly relies on a visual and semiquantitative classification of color intensity changes. Such a classification is a subjective, time-consuming and difficult to reproduce process.'),
(98262, 2018, 'bmc', 'lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals', 'Ziyatdinov, Andrey|Vázquez-Santiago, Miquel|Brunel, Helena|Martinez-Perez, Angel|Aschard, Hugues|Soria, Jose Manuel', 'Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America. ziyatdinov@hsph.harvard.edu.|Unitat de Genòmica de Malalties Complexes, Institut dInvestigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona, Spain.|Unitat de Genòmica de Malalties Complexes, Institut dInvestigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona, Spain.|Unitat de Genòmica de Malalties Complexes, Institut dInvestigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona, Spain.|Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.|Unitat de Genòmica de Malalties Complexes, Institut dInvestigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona, Spain.', 'United States|Spain|Spain|Spain|United States|Spain', 16, 'Genome-Wide Association Study|Humans|Linear Models|Models, Genetic|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Software|Thrombophilia', 'Journal Article', '10.1186/s12859-018-2057-x', '29486711', 'Quantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods, however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices, and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software.'),
(98263, 2018, 'bmc', 'A randomized approach to speed up the analysis of large-scale read-count data in the application of CNV detection', 'Wang, WeiBo|Sun, Wei|Wang, Wei|Szatkiewicz, Jin', 'Department of Computer Science, University of North Carolina at Chapel Hill, 201 S. Columbia St., Chapel Hill, 27599-3175, USA.|Biostatistics Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, 19024, USA.|Department of Computer Science, University of California, Los Angeles, 580 Portola Plaza, Los Angeles, 90095-1596, USA.|Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, 27599-7264, USA. jin_szatkiewicz@med.unc.edu.', 'United States|United States|United States|United States', 1, 'Algorithms|DNA Copy Number Variations|Genomics|High-Throughput Nucleotide Sequencing|Humans|Linear Models|Models, Statistical', 'Journal Article', '10.1186/s12859-018-2077-6', '29490610', 'The application of high-throughput sequencing in a broad range of quantitative genomic assays (e.g., DNA-seq, ChIP-seq) has created a high demand for the analysis of large-scale read-count data. Typically, the genome is divided into tiling windows and windowed read-count data is generated for the entire genome from which genomic signals are detected (e.g. copy number changes in DNA-seq, enrichment peaks in ChIP-seq). For accurate analysis of read-count data, many state-of-the-art statistical methods use generalized linear models (GLM) coupled with the negative-binomial (NB) distribution by leveraging its ability for simultaneous bias correction and signal detection. However, although statistically powerful, the GLM+NB method has a quadratic computational complexity and therefore suffers from slow running time when applied to large-scale windowed read-count data. In this study, we aimed to speed up substantially the GLM+NB method by using a randomized algorithm and we demonstrate here the utility of our approach in the application of detecting copy number variants (CNVs) using a real example.'),
(98264, 2018, 'bmc', 'Variance component analysis to assess protein quantification in biomarker validation: application to selected reaction monitoring-mass spectrometry', 'Klich, Amna|Mercier, Catherine|Gerfault, Laurent|Grangeat, Pierre|Beaulieu, Corinne|Degout-Charmette, Elodie|Fortin, Tanguy|Mahé, Pierre|Giovannelli, Jean-François|Charrier, Jean-Philippe|Giremus, Audrey|Maucort-Boulch, Delphine|Roy, Pascal', 'Service de Biostatistique-Bioinformatique, Hospices Civils de Lyon, 162, avenue Lacassagne, F-69003, Lyon, France. amna.klich@chu-lyon.fr.|Service de Biostatistique-Bioinformatique, Hospices Civils de Lyon, 162, avenue Lacassagne, F-69003, Lyon, France.|Université Grenoble-Alpes, F-38000, Grenoble, France.|Université Grenoble-Alpes, F-38000, Grenoble, France.|Innovation Unit, Technology Research Department, bioMérieux, F-69280, Marcy lÉtoile, France.|Innovation Unit, Technology Research Department, bioMérieux, F-69280, Marcy lÉtoile, France.|Innovation Unit, Technology Research Department, bioMérieux, F-69280, Marcy lÉtoile, France.|Innovation Unit, Technology Research Department, bioMérieux, F-38000, Grenoble, France.|Intégration du Matériau au Système (Université de Bordeaux, CNRS, Bordeaux Aquitaine INP), F-33400, Talence, France.|Innovation Unit, Technology Research Department, bioMérieux, F-69280, Marcy lÉtoile, France.|Intégration du Matériau au Système (Université de Bordeaux, CNRS, Bordeaux Aquitaine INP), F-33400, Talence, 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|France|France|France', 0, 'Algorithms|Analysis of Variance|Biomarkers|Enzyme-Linked Immunosorbent Assay|Humans|Mass Spectrometry|Proteins|Reproducibility of Results', 'Journal Article', '10.1186/s12859-018-2075-8', '29490628', 'In the field of biomarker validation with mass spectrometry, controlling the technical variability is a critical issue. In selected reaction monitoring (SRM) measurements, this issue provides the opportunity of using variance component analysis to distinguish various sources of variability. However, in case of unbalanced data (unequal number of observations in all factor combinations), the classical methods cannot correctly estimate the various sources of variability, particularly in presence of interaction. The present paper proposes an extension of the variance component analysis to estimate the various components of the variance, including an interaction component in case of unbalanced data.'),
(98265, 2018, 'bmc', 'Control procedures and estimators of the false discovery rate and their application in low-dimensional settings: an empirical investigation', 'Brinster, Regina|Köttgen, Anna|Tayo, Bamidele O|Schumacher, Martin|Sekula, Peggy|CKDGen Consortium', 'Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany. brinster@imbi.uni-heidelberg.de.|Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Hugstetter Str. 49, 79106, Freiburg, Germany.|Department of Public Health Sciences, Loyola University Chicago Stritch School of Medicine, Maywood, IL, USA.|Institute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Stefan-Meier-Str. 26, 79104, Freiburg, Germany.|Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Hugstetter Str. 49, 79106, Freiburg, Germany.|Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Hugstetter Str. 49, 79106, Freiburg, Germany.', 'Germany|Germany|United States|Germany|Germany|Germany', 4, 'Algorithms|Computer Simulation|Empirical Research|False Positive Reactions|Genome-Wide Association Study|Humans|Polymorphism, Single Nucleotide|Renal Insufficiency, Chronic', 'Journal Article', '10.1186/s12859-018-2081-x', '29499647', 'When many (up to millions) of statistical tests are conducted in discovery set analyses such as genome-wide association studies (GWAS), approaches controlling family-wise error rate (FWER) or false discovery rate (FDR) are required to reduce the number of false positive decisions. Some methods were specifically developed in the context of high-dimensional settings and partially rely on the estimation of the proportion of true null hypotheses. However, these approaches are also applied in low-dimensional settings such as replication set analyses that might be restricted to a small number of specific hypotheses. The aim of this study was to compare different approaches in low-dimensional settings using (a) real data from the CKDGen Consortium and (b) a simulation study.'),
(98266, 2018, 'bmc', 'BpWrapper: BioPerl-based sequence and tree utilities for rapid prototyping of bioinformatics pipelines', 'Hernández, Yözen|Bernstein, Rocky|Pagan, Pedro|Vargas, Levy|McCaig, William|Ramrattan, Girish|Akther, Saymon|Larracuente, Amanda|Di, Lia|Vieira, Filipe G|Qiu, Wei-Gang', 'Department of Biological Sciences, Hunter College, City University of New York, New York, 10065, USA.|Department of Biological Sciences, Hunter College, City University of New York, New York, 10065, USA.|Department of Biological Sciences, Hunter College, City University of New York, New York, 10065, USA.|Department of Biological Sciences, Hunter College, City University of New York, New York, 10065, USA.|Department of Biological Sciences, Hunter College, City University of New York, New York, 10065, USA.|Department of Biological Sciences, Hunter College, City University of New York, New York, 10065, USA.|Graduate Center, City University of New York, New York, 10016, USA.|Department of Biological Sciences, Hunter College, City University of New York, New York, 10065, USA.|Department of Biological Sciences, Hunter College, City University of New York, New York, 10065, USA.|Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark.|Department of Biological Sciences, Hunter College, City University of New York, New York, 10065, USA. weigang@genectr.hunter.cuny.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|Denmark|United States', 0, 'Base Sequence|Computational Biology|Phylogeny|Sequence Alignment|Software', 'Journal Article', '10.1186/s12859-018-2074-9', '29499649', 'Automated bioinformatics workflows are more robust, easier to maintain, and results more reproducible when built with command-line utilities than with custom-coded scripts. Command-line utilities further benefit by relieving bioinformatics developers to learn the use of, or to interact directly with, biological software libraries. There is however a lack of command-line utilities that leverage popular Open Source biological software toolkits such as BioPerl ( http://bioperl.org ) to make many of the well-designed, robust, and routinely used biological classes available for a wider base of end users.'),
(98267, 2018, 'bmc', 'Better ILP models for haplotype assembly', 'Etemadi, Maryam|Bagherian, Mehri|Chen, Zhi-Zhong|Wang, Lusheng', 'Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Rasht, 41938-33697, Iran.|Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Rasht, 41938-33697, Iran. mbagherian@guilan.ac.ir.|Division of Information System Design, Tokyo Denki University, Saitama, 350-0394, Japan. zzchen@mail.dendai.ac.jp.|Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.', '-|-|Japan|Hong Kong', 2, 'Algorithms|Haplotypes|Heterozygote|Humans|Models, Genetic|Polymorphism, Single Nucleotide|Programming, Linear', 'Journal Article', '10.1186/s12859-018-2012-x', '29504891', 'The haplotype assembly problem for diploid is to find a pair of haplotypes from a given set of aligned Single Nucleotide Polymorphism (SNP) fragments (reads). It has many applications in association studies, drug design, and genetic research. Since this problem is computationally hard, both heuristic and exact algorithms have been designed for it. Although exact algorithms are much slower, they are still of great interest because they usually output significantly better solutions than heuristic algorithms in terms of popular measures such as the Minimum Error Correction (MEC) score, the number of switch errors, and the QAN50 score. Exact algorithms are also valuable because they can be used to witness how good a heuristic algorithm is. The best known exact algorithm is based on integer linear programming (ILP) and it is known that ILP can also be used to improve the output quality of every heuristic algorithm with a little decline in speed. Therefore, faster ILP models for the problem are highly demanded.'),
(98268, 2018, 'bmc', 'Isoform specific gene expression analysis of KRAS in the prognosis of lung adenocarcinoma patients', 'Yang, In Seok|Kim, Sangwoo', 'Severance Biomedical Science Institute, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seoul, 03722, South Korea.|Severance Biomedical Science Institute, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seoul, 03722, South Korea. swkim@yuhs.ac.', '-|-', 2, 'Adenocarcinoma of Lung|Aged|Disease-Free Survival|Female|Gene Expression|Humans|Lung Neoplasms|Male|Middle Aged|Mutation|Prognosis|Proportional Hazards Models|Protein Isoforms|Proto-Oncogene Proteins p21(ras)', 'Journal Article', '10.1186/s12859-018-2011-y', '29504894', 'Aberrant mutations in KRAS play a critical role in tumor initiation and progression, and are a negative prognosis factor in lung adenocarcinoma (LUAD).'),
(98269, 2018, 'bmc', 'Improving prediction of heterodimeric protein complexes using combination with pairwise kernel', 'Ruan, Peiying|Hayashida, Morihiro|Akutsu, Tatsuya|Vert, Jean-Philippe', 'Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan.|Department of Electrical Engineering and Computer Science, National Institute of Technology, Matsue College, 14-4, Nishiikumacho, Matsue, 690-8518, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 6110011, Japan.|MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, 75006, France. jean-philippe.vert@mines-paristech.fr.', 'Japan|Japan|Japan|France', 3, 'Algorithms|Dimerization|Multiprotein Complexes|Phylogeny|Protein Domains|Protein Interaction Maps|Protein Multimerization|Support Vector Machine', 'Journal Article', '10.1186/s12859-018-2017-5', '29504897', 'Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers.'),
(98270, 2018, 'bmc', 'TERIUS: accurate prediction of lncRNA via high-throughput sequencing data representing RNA-binding protein association', 'Choi, Seo-Won|Nam, Jin-Wu', 'Department of Life Science, College of Natural Sciences, Hanyang University, Seoul, 04763, Republic of Korea.|Department of Life Science, College of Natural Sciences, Hanyang University, Seoul, 04763, Republic of Korea. jwnam@hanyang.ac.kr.', '-|-', 0, '3 Untranslated Regions|Animals|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Mice|Molecular Sequence Annotation|RNA Helicases|RNA, Long Noncoding|RNA-Binding Proteins|Sequence Analysis, RNA|Software|Trans-Activators', 'Journal Article', '10.1186/s12859-018-2013-9', '29504902', 'LncRNAs are long regulatory non-coding RNAs, some of which are arguably predicted to have coding potential. Despite coding potential classifiers that utilize ribosome profiling data successfully detected actively translated regions, they are less sensitive to lncRNAs. Furthermore, lncRNA annotation can be susceptible to false positives obtained from 3 untranslated region (UTR) fragments of mRNAs.'),
(98271, 2018, 'bmc', 'HiComet: a high-throughput comet analysis tool for large-scale DNA damage assessment', 'Lee, Taehoon|Lee, Sungmin|Sim, Woo Young|Jung, Yu Mi|Han, Sunmi|Won, Joong-Ho|Min, Hyeyoung|Yoon, Sungroh', 'Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea.|Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea.|R&D Center, Wearable Healthcare, Gyeonggi-do, 16954, Korea.|Research Division, NanoEnTek, Seoul, 08389, Korea.|Research Division, NanoEnTek, Seoul, 08389, Korea.|Department of Statistics, Seoul National University, Seoul, 08826, Korea.|College of Pharmachy, Chung-Ang University, Seoul, 06974, Korea.|Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea. sryoon@snu.ac.kr.', '-|-|-|-|-|-|-|-', 6, 'Algorithms|Comet Assay|DNA Damage|Image Processing, Computer-Assisted|Software', 'Journal Article', '10.1186/s12859-018-2015-7', '29504903', 'DNA damage causes aging, cancer, and other serious diseases. The comet assay can detect multiple types of DNA lesions with high sensitivity, and it has been widely applied. Although comet assay platforms have improved the limited throughput and reproducibility of traditional assays in recent times, analyzing large quantities of comet data often requires a tremendous human effort. To overcome this challenge, we proposed HiComet, a computational tool that can rapidly recognize and characterize a large number of comets, using little user intervention.'),
(98272, 2018, 'bmc', 'Closha: bioinformatics workflow system for the analysis of massive sequencing data', 'Ko, GunHwan|Kim, Pan-Gyu|Yoon, Jongcheol|Han, Gukhee|Park, Seong-Jin|Song, Wangho|Lee, Byungwook', 'Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea. sryoon@snu.ac.kr.|Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea. sryoon@snu.ac.kr.|Korean BioInformation Center (KOBIC), KRIBB, 125 Gwahangno, Yuseong-gu, Daejeon, 34141, South Korea.|Korean BioInformation Center (KOBIC), KRIBB, 125 Gwahangno, Yuseong-gu, Daejeon, 34141, South Korea.|Korean BioInformation Center (KOBIC), KRIBB, 125 Gwahangno, Yuseong-gu, Daejeon, 34141, South Korea.|Korean BioInformation Center (KOBIC), KRIBB, 125 Gwahangno, Yuseong-gu, Daejeon, 34141, South Korea.|Korean BioInformation Center (KOBIC), KRIBB, 125 Gwahangno, Yuseong-gu, Daejeon, 34141, South Korea. bulee@kribb.re.kr.', '-|-|-|-|-|-|-', 0, 'Algorithms|Cloud Computing|Genomics|High-Throughput Nucleotide Sequencing|Software|Workflow', 'Journal Article', '10.1186/s12859-018-2019-3', '29504905', 'While next-generation sequencing (NGS) costs have fallen in recent years, the cost and complexity of computation remain substantial obstacles to the use of NGS in bio-medical care and genomic research. The rapidly increasing amounts of data available from the new high-throughput methods have made data processing infeasible without automated pipelines. The integration of data and analytic resources into workflow systems provides a solution to the problem by simplifying the task of data analysis.'),
(98273, 2018, 'bmc', 'Introducing difference recurrence relations for faster semi-global alignment of long sequences', 'Suzuki, Hajime|Kasahara, Masahiro', 'Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa City, Chiba, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa City, Chiba, Japan. mkasa@k.u-tokyo.ac.jp.', 'Japan|Japan', 12, 'Algorithms|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-018-2014-8', '29504909', 'The read length of single-molecule DNA sequencers is reaching 1 Mb. Popular alignment software tools widely used for analyzing such long reads often take advantage of single-instruction multiple-data (SIMD) operations to accelerate calculation of dynamic programming (DP) matrices in the Smith-Waterman-Gotoh (SWG) algorithm with a fixed alignment start position at the origin. Nonetheless, 16-bit or 32-bit integers are necessary for storing the values in a DP matrix when sequences to be aligned are long, this situation hampers the use of the full SIMD width of modern processors.'),
(98274, 2018, 'bmc', 'BRCA-Pathway: a structural integration and visualization system of TCGA breast cancer data on KEGG pathways', 'Kim, Inyoung|Choi, Saemi|Kim, Sun', 'Department of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Seoul, 08826, Korea.|Department of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Seoul, 08826, Korea.|Department of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Seoul, 08826, Korea. sunkim.bioinfo@snu.ac.kr.', '-|-|-', 0, 'Breast Neoplasms|Computer Graphics|DNA Copy Number Variations|Databases, Genetic|Female|Gene Expression Profiling|Genomics|Humans|Mutation|Software|Transcription Factors', 'Journal Article', '10.1186/s12859-018-2016-6', '29504910', 'Bioinformatics research for finding biological mechanisms can be done by analysis of transcriptome data with pathway based interpretation. Therefore, researchers have tried to develop tools to analyze transcriptome data with pathway based interpretation. Over the years, the amount of omics data has become huge, e.g., TCGA, and the data types to be analyzed have come in many varieties, including mutations, copy number variations, and transcriptome. We also need to consider a complex relationship with regulators of genes, particularly Transcription Factors(TF). However, there has not been a system for pathway based exploration and analysis of TCGA multi-omics data. In this reason, We have developed a web based system BRCA-Pathway to fulfill the need for pathway based analysis of TCGA multi-omics data.'),
(98275, 2018, 'bmc', 'Capturing alternative secondary structures of RNA by decomposition of base-pairing probabilities', 'Hagio, Taichi|Sakuraba, Shun|Iwakiri, Junichi|Mori, Ryota|Asai, Kiyoshi', 'Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, 277-8561, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, 277-8561, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, 277-8561, Japan.|Unique Co. Ltd., 6-6-20 Daita, Setagaya-ku, Tokyo, 155-0033, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, 277-8561, Japan. asai@k.u-tokyo.ac.jp.', 'Japan|Japan|Japan|Japan|Japan', 6, 'Algorithms|Base Pairing|Computational Biology|Nucleic Acid Conformation|Probability|Protein Structure, Secondary|RNA|Riboswitch|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-018-2018-4', '29504917', 'It is known that functional RNAs often switch their functions by forming different secondary structures. Popular tools for RNA secondary structures prediction, however, predict the single best structures, and do not produce alternative structures. There are bioinformatics tools to predict suboptimal structures, but it is difficult to detect which alternative secondary structures are essential.'),
(98276, 2018, 'bmc', 'Natural language processing in text mining for structural modeling of protein complexes', 'Badal, Varsha D|Kundrotas, Petras J|Vakser, Ilya A', 'Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047, USA.|Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047, USA. pkundro@ku.edu.|Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047, USA. vakser@ku.edu.', 'United States|United States|United States', 8, 'Data Mining|Machine Learning|Models, Molecular|Natural Language Processing|Protein Binding|Proteins|Semantics|Support Vector Machine', 'Journal Article', '10.1186/s12859-018-2079-4', '29506465', 'Structural modeling of protein-protein interactions produces a large number of putative configurations of the protein complexes. Identification of the near-native models among them is a serious challenge. Publicly available results of biomedical research may provide constraints on the binding mode, which can be essential for the docking. Our text-mining (TM) tool, which extracts binding site residues from the PubMed abstracts, was successfully applied to protein docking (Badal et al., PLoS Comput Biol, 2015, 11: e1004630). Still, many extracted residues were not relevant to the docking.'),
(98277, 2018, 'bmc', 'Characterization and visualization of RNA secondary structure Boltzmann ensemble via information theory', 'Lin, Luan|McKerrow, Wilson H|Richards, Bryce|Phonsom, Chukiat|Lawrence, Charles E', 'Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, 20993, MD, USA.|Division of Applied Mathematics, Brown University, Providence, 02912, RI, USA.|Software Engineer, Google, New York, 10011, NY, USA.|Department of Mathematics, University of Southern California, Los Angeles, 90089, CA, USA.|Division of Applied Mathematics, Brown University, Providence, 02912, RI, USA. charles_lawrence@brown.edu.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Base Pairing|Base Sequence|Cluster Analysis|Entropy|Information Theory|Mutation|Nucleic Acid Conformation|RNA', 'Journal Article', '10.1186/s12859-018-2078-5', '29506466', 'The nearest neighbor model and associated dynamic programming algorithms allow for the efficient estimation of the RNA secondary structure Boltzmann ensemble. However because a given RNA secondary structure only contains a fraction of the possible helices that could form from a given sequence, the Boltzmann ensemble is multimodal. Several methods exist for clustering structures and finding those modes. However less focus is given to exploring the underlying reasons for this multimodality: the presence of conflicting basepairs. Information theory, or more specifically mutual information, provides a method to identify those basepairs that are key to the secondary structure.'),
(98278, 2018, 'bmc', 'ToxPi Graphical User Interface 20: Dynamic exploration, visualization, and sharing of integrated data models', 'Marvel, Skylar W|To, Kimberly|Grimm, Fabian A|Wright, Fred A|Rusyn, Ivan|Reif, David M', 'Bioinformatics Research Center, Center for Human Health and the Environment, Department of Biological Sciences, North Carolina State University, Box 7566, 1 Lampe Drive, Raleigh, NC, 27695, USA.|Bioinformatics Research Center, Center for Human Health and the Environment, Department of Biological Sciences, North Carolina State University, Box 7566, 1 Lampe Drive, Raleigh, NC, 27695, USA.|Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA.|Bioinformatics Research Center, Center for Human Health and the Environment, Department of Biological Sciences, North Carolina State University, Box 7566, 1 Lampe Drive, Raleigh, NC, 27695, USA.|Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA.|Bioinformatics Research Center, Center for Human Health and the Environment, Department of Biological Sciences, North Carolina State University, Box 7566, 1 Lampe Drive, Raleigh, NC, 27695, USA. dmreif@ncsu.edu.', 'United States|United States|United States|United States|United States|United States', 0, 'Cluster Analysis|Information Dissemination|Information Storage and Retrieval|Models, Theoretical|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-018-2089-2', '29506467', 'Drawing integrated conclusions from diverse source data requires synthesis across multiple types of information. The ToxPi (Toxicological Prioritization Index) is an analytical framework that was developed to enable integration of multiple sources of evidence by transforming data into integrated, visual profiles. Methodological improvements have advanced ToxPi and expanded its applicability, necessitating a new, consolidated software platform to provide functionality, while preserving flexibility for future updates.'),
(98279, 2018, 'bmc', 'Alignment-free clustering of large data sets of unannotated protein conserved regions using minhashing', 'Abnousi, Armen|Broschat, Shira L|Kalyanaraman, Ananth', 'School of EECS, Washington State University, 355 NE Spokane St, Pullman, 99164, USA. aabnousi@eecs.wsu.edu.|School of EECS, Washington State University, 355 NE Spokane St, Pullman, 99164, USA.|School of EECS, Washington State University, 355 NE Spokane St, Pullman, 99164, USA.', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Cluster Analysis|Databases, Protein|Molecular Sequence Annotation|Phylogeny|Protein Domains|Rickettsia|Sequence Alignment', 'Journal Article', '10.1186/s12859-018-2080-y', '29506470', 'Clustering of protein sequences is of key importance in predicting the structure and function of newly sequenced proteins and is also of use for their annotation. With the advent of multiple high-throughput sequencing technologies, new protein sequences are becoming available at an extraordinary rate. The rapid growth rate has impeded deployment of existing protein clustering/annotation tools which depend largely on pairwise sequence alignment.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98280, 2018, 'bmc', 'IntLIM: integration using linear models of metabolomics and gene expression data', 'Siddiqui, Jalal K|Baskin, Elizabeth|Liu, Mingrui|Cantemir-Stone, Carmen Z|Zhang, Bofei|Bonneville, Russell|McElroy, Joseph P|Coombes, Kevin R|Mathxc3xa9, Ewy A', 'Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.|Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.|Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.|Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.|Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.|Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH, USA.|Center for Biostatistics, The Ohio State University, Columbus, OH, USA.|Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.|Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA. ewy.mathe@osumc.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 11, 'Breast Neoplasms|Cell Line, Tumor|Databases, Genetic|Female|Gene Expression Regulation|Humans|Linear Models|Metabolome|Metabolomics|Phenotype|Software|Transcriptome', 'Journal Article', '10.1186/s12859-018-2085-6', '29506475', 'Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (>xe2x80x89100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e.g. one time point) gene and metabolite profiles and, oftentimes, most metabolites measured are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of transcript-metabolite relationships may neither be applicable nor readily uncover novel relationships. With this in mind, we propose a simple linear modeling approach to capture disease-(or other phenotype) specific gene-metabolite associations, with the assumption that co-regulation patterns reflect functionally related genes and metabolites.'),
(98281, 2018, 'bmc', 'Building protein-protein interaction networks for Leishmania species through protein structural information', 'Dos Santos Vasconcelos, Crhisllane Rafaele|de Lima Campos, Txc3xbalio|Rezende, Antonio Mauro', 'Microbiology Department of Instituto Aggeu Magalhxc3xa3es - FIOCRUZ, Recife, PE, Brazil. crhisllane@gmail.com.|Microbiology Department of Instituto Aggeu Magalhxc3xa3es - FIOCRUZ, Recife, PE, Brazil.|Microbiology Department of Instituto Aggeu Magalhxc3xa3es - FIOCRUZ, Recife, PE, Brazil. antonio.rezende@cpqam.fiocruz.br.', 'Brazil|Brazil|Brazil', 0, 'Area Under Curve|Leishmania|Leishmaniasis|Machine Learning|Protein Interaction Maps|Proteome|Protozoan Proteins|Thermodynamics', 'Journal Article', '10.1186/s12859-018-2105-6', '29510668', 'Systematic analysis of a parasite interactome is a key approach to understand different biological processes. It makes possible to elucidate disease mechanisms, to predict protein functions and to select promising targets for drug development. Currently, several approaches for protein interaction prediction for non-model species incorporate only small fractions of the entire proteomes and their interactions. Based on this perspective, this study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum. These parasites cause Leishmaniasis, a worldwide distributed and neglected disease, with limited treatment options using currently available drugs.'),
(98282, 2018, 'bmc', 'Comparison of discriminative motif optimization using matrix and DNA shape-based models', 'Ruan, Shuxiang|Stormo, Gary D', 'Department of Genetics and Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, 63110, USA.|Department of Genetics and Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, 63110, USA. stormo@wustl.edu.', 'United States|United States', 2, 'Algorithms|Area Under Curve|Binding Sites|DNA|Databases, Nucleic Acid|Humans|Models, Molecular|Nucleotide Motifs|Position-Specific Scoring Matrices|Protein Binding', 'Comparative Study', '10.1186/s12859-018-2104-7', '29510689', 'Transcription factor (TF) binding site specificity is commonly represented by some form of matrix model in which the positions in the binding site are assumed to contribute independently to the sites activity. The independence assumption is known to be an approximation, often a good one but sometimes poor. Alternative approaches have been developed that use k-mers (DNA words of length k) to account for the non-independence, and more recently DNA structural parameters have been incorporated into the models. ChIP-seq data are often used to assess the discriminatory power of motifs and to compare different models. However, to measure the improvement due to using more complex models, one must compare to optimized matrix models.'),
(98283, 2018, 'bmc', 'Prediction of sensitivity to gefitinib/erlotinib for EGFR mutations in NSCLC based on structural interaction fingerprints and multilinear principal component analysis', 'Zou, Bin|Lee, Victor H F|Yan, Hong', 'Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong, China. binzou2-c@my.cityu.edu.hk.|Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, China.|Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong, China.', 'China|China|China', 0, 'Carcinoma, Non-Small-Cell Lung|Computer Simulation|ErbB Receptors|Erlotinib Hydrochloride|Exons|Gefitinib|Humans|Lung Neoplasms|Mutation|Principal Component Analysis|Protein Kinase Inhibitors|Quinazolines', 'Journal Article', '10.1186/s12859-018-2093-6', '29514601', 'Non-small cell lung cancer (NSCLC) with activating EGFR mutations, especially exon 19 deletions and the L858R point mutation, is particularly responsive to gefitinib and erlotinib. However, the sensitivity varies for less common and rare EGFR mutations. There are various explanations for the low sensitivity of EGFR exon 20 insertions and the exon 20 T790 M point mutation to gefitinib/erlotinib. However, few studies discuss, from a structural perspective, why less common mutations, like G719X and L861Q, have moderate sensitivity to gefitinib/erlotinib.'),
(98284, 2018, 'bmc', 'An information-theoretic approach to the modeling and analysis of whole-genome bisulfite sequencing data', 'Jenkinson, Garrett|Abante, Jordi|Feinberg, Andrew P|Goutsias, John', 'Whitaker Biomedical Engineering Institute, Johns Hopkins University, Baltimore, MD, USA.|Whitaker Biomedical Engineering Institute, Johns Hopkins University, Baltimore, MD, USA.|Center for Epigenetics, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Whitaker Biomedical Engineering Institute, Johns Hopkins University, Baltimore, MD, USA. goutsias@jhu.edu.', 'United States|United States|United States|United States', 0, 'Base Sequence|Computer Simulation|CpG Islands|DNA Methylation|Entropy|Epigenesis, Genetic|Gene Ontology|Genome, Human|Humans|Information Theory|Lung Neoplasms|Models, Theoretical|Probability|Statistics as Topic|Sulfites|Web Browser|Whole Genome Sequencing', 'Journal Article', '10.1186/s12859-018-2086-5', '29514626', 'DNA methylation is a stable form of epigenetic memory used by cells to control gene expression. Whole genome bisulfite sequencing (WGBS) has emerged as a gold-standard experimental technique for studying DNA methylation by producing high resolution genome-wide methylation profiles. Statistical modeling and analysis is employed to computationally extract and quantify information from these profiles in an effort to identify regions of the genome that demonstrate crucial or aberrant epigenetic behavior. However, the performance of most currently available methods for methylation analysis is hampered by their inability to directly account for statistical dependencies between neighboring methylation sites, thus ignoring significant information available in WGBS reads.'),
(98285, 2018, 'bmc', 'Textpresso Central: a customizable platform for searching, text mining, viewing, and curating biomedical literature', 'Müller, H-M|Van Auken, K M|Li, Y|Sternberg, P W', 'Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA. mueller@caltech.edu.|Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.|Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.|Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.', 'United States|United States|United States|United States', 9, 'Algorithms|Animals|Caenorhabditis elegans|Data Mining|Gene Ontology|Humans|Internet|Molecular Sequence Annotation|PubMed|Publications|Search Engine|User-Computer Interface', 'Journal Article', '10.1186/s12859-018-2103-8', '29523070', 'The biomedical literature continues to grow at a rapid pace, making the challenge of knowledge retrieval and extraction ever greater. Tools that provide a means to search and mine the full text of literature thus represent an important way by which the efficiency of these processes can be improved.'),
(98286, 2018, 'bmc', 'An interpretable framework for clustering single-cell RNA-Seq datasets', 'Zhang, Jesse M|Fan, Jue|Fan, H Christina|Rosenfeld, David|Tse, David N', 'Department of Electrical Engineering, Stanford, Stanford, 94305, California, USA.|BD Genomics, California, 94025, Menlo Park, USA.|BD Genomics, California, 94025, Menlo Park, USA.|BD Genomics, California, 94025, Menlo Park, USA.|Department of Electrical Engineering, Stanford, Stanford, 94305, California, USA. dntse@stanford.edu.', 'United States|United States|United States|United States|United States', 12, 'Algorithms|Cluster Analysis|Databases, Genetic|Humans|Leukocytes, Mononuclear|Reference Standards|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1186/s12859-018-2092-7', '29523077', 'With the recent proliferation of single-cell RNA-Seq experiments, several methods have been developed for unsupervised analysis of the resulting datasets. These methods often rely on unintuitive hyperparameters and do not explicitly address the subjectivity associated with clustering.'),
(98287, 2018, 'bmc', 'HT-eQTL: integrative expression quantitative trait loci analysis in a large number of human tissues', 'Li, Gen|Jima, Dereje|Wright, Fred A|Nobel, Andrew B', 'Department of Biostatistics, Mailman School of Public Health, Columbia University, 722 W 168 Street, New York, USA. gl2521@cumc.columbia.edu.|Center for Human Health and the Environment and Bioinformatics Research Center, North Carolina State University, 850 Main Campus Drive, Raleigh, 27695, USA.|Center for Human Health and the Environment and Bioinformatics Research Center, North Carolina State University, 850 Main Campus Drive, Raleigh, 27695, USA.|Department of Statistics and Operations Research and Department of Biostatistics, University of North Carolina at Chapel Hill, 318 E Cameron Avenue, Chapel Hill, 27599, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Bayes Theorem|Computer Simulation|Gene Expression Regulation|Genome-Wide Association Study|Genotype|Humans|Organ Specificity|Polymorphism, Single Nucleotide|Quantitative Trait Loci|ROC Curve', 'Journal Article', '10.1186/s12859-018-2088-3', '29523079', 'Expression quantitative trait loci (eQTL) analysis identifies genetic markers associated with the expression of a gene. Most existing eQTL analyses and methods investigate association in a single, readily available tissue, such as blood. Joint analysis of eQTL in multiple tissues has the potential to improve, and expand the scope of, single-tissue analyses. Large-scale collaborative efforts such as the Genotype-Tissue Expression (GTEx) program are currently generating high quality data in a large number of tissues. However, computational constraints limit genome-wide multi-tissue eQTL analysis.'),
(98288, 2018, 'bmc', 'Fast and efficient short read mapping based on a succinct hash index', 'Zhang, Haowen|Chan, Yuandong|Fan, Kaichao|Schmidt, Bertil|Liu, Weiguo', 'School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, 30332, GA, USA.|School of Software, Shandong University, Shunhua Road 1500, Jinan, Shandong, China.|School of Software, Shandong University, Shunhua Road 1500, Jinan, Shandong, China.|Johannes Gutenberg University, Mainz, Germany.|School of Software, Shandong University, Shunhua Road 1500, Jinan, Shandong, China. weiguo.liu@sdu.edu.cn.', 'Georgia|China|China|Germany|China', 5, 'Algorithms|Base Pairing|Base Sequence|Computer Simulation|Databases, Genetic|Genome, Human|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-018-2094-5', '29523083', 'Various indexing techniques have been applied by next generation sequencing read mapping tools. The choice of a particular data structure is a trade-off between memory consumption, mapping throughput, and construction time.'),
(98289, 2018, 'bmc', 'Comparative assessment of strategies to identify similar ligand-binding pockets in proteins', 'Govindaraj, Rajiv Gandhi|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, 'Algorithms|Amino Acid Sequence|Area Under Curve|Binding Sites|Databases, Protein|Drug Discovery|Ligands|Models, Molecular|Protein Binding|Protein Conformation|Proteins|ROC Curve|Sequence Alignment', 'Comparative Study', '10.1186/s12859-018-2109-2', '29523085', 'Detecting similar ligand-binding sites in globally unrelated proteins has a wide range of applications in modern drug discovery, including drug repurposing, the prediction of side effects, and drug-target interactions. Although a number of techniques to compare binding pockets have been developed, this problem still poses significant challenges.'),
(98290, 2018, 'bmc', 'Inferring RNA sequence preferences for poorly studied RNA-binding proteins based on co-evolution', 'Yang, Shu|Wang, Junwen|Ng, Raymond T', 'Department of Computer Science, University of British Columbia, Vancouver, Canada. syang11@cs.ubc.ca.|Department of Health Sciences Research, Mayo Clinic Arizona, Scottsdale, USA.|Department of Computer Science, University of British Columbia, Vancouver, Canada.', 'Canada|United States|Canada', 1, 'Algorithms|Binding Sites|Evolution, Molecular|RNA|RNA-Binding Proteins', 'Journal Article', '10.1186/s12859-018-2091-8', '29529991', 'Characterizing the binding preference of RNA-binding proteins (RBP) is essential for us to understand the interaction between an RBP and its RNA targets, and to decipher the mechanism of post-transcriptional regulation. Experimental methods have been used to generate protein-RNA binding data for a number of RBPs in vivo and in vitro. Utilizing the binding data, a couple of computational methods have been developed to detect the RNA sequence or structure preferences of the RBPs. However, the majority of RBPs have not yet been experimentally characterized and lack RNA binding data. For these poorly studied RBPs, the identification of their binding preferences cannot be performed by most existing computational methods because the experimental binding data are prerequisite to these methods.'),
(98291, 2018, 'bmc', 'Watchdog - a workflow management system for the distributed analysis of large-scale experimental data', 'Kluge, Michael|Friedel, Caroline C', 'Institute for Informatics, Ludwig-Maximilians-Universität München, Amalienstraße 17, München, 80333, Germany.|Institute for Informatics, Ludwig-Maximilians-Universität München, Amalienstraße 17, München, 80333, Germany. caroline.friedel@bio.ifi.lmu.de.', 'Germany|Germany', 0, 'Computational Biology|Herpes Simplex|Herpesvirus 1, Human|High-Throughput Nucleotide Sequencing|Humans|RNA|Software|User-Computer Interface|Virus Replication|Workflow', 'Journal Article', '10.1186/s12859-018-2107-4', '29534677', 'The development of high-throughput experimental technologies, such as next-generation sequencing, have led to new challenges for handling, analyzing and integrating the resulting large and diverse datasets. Bioinformatical analysis of these data commonly requires a number of mutually dependent steps applied to numerous samples for multiple conditions and replicates. To support these analyses, a number of workflow management systems (WMSs) have been developed to allow automated execution of corresponding analysis workflows. Major advantages of WMSs are the easy reproducibility of results as well as the reusability of workflows or their components.'),
(98292, 2018, 'bmc', 'Phylogenetic convolutional neural networks in metagenomics', 'Fioravanti, Diego|Giarratano, Ylenia|Maggio, Valerio|Agostinelli, Claudio|Chierici, Marco|Jurman, Giuseppe|Furlanello, Cesare', 'Fondazione Bruno Kessler (FBK), Via Sommarive 18 Povo, Trento, I-38123, Italy.|Centre for Medical Informatics, Usher Institute, University of Edinburgh, 9 Little France Road, Edinburgh, EH16 4UX, UK.|Fondazione Bruno Kessler (FBK), Via Sommarive 18 Povo, Trento, I-38123, Italy.|Department of Mathematics, University of Trento, Via Sommarive 14 Povo, Trento, I-38123, Italy.|Fondazione Bruno Kessler (FBK), Via Sommarive 18 Povo, Trento, I-38123, Italy.|Fondazione Bruno Kessler (FBK), Via Sommarive 18 Povo, Trento, I-38123, Italy. jurman@fbk.eu.|Fondazione Bruno Kessler (FBK), Via Sommarive 18 Povo, Trento, I-38123, Italy.', 'Italy|France|Italy|Italy|Italy|Italy|Italy', 20, 'Algorithms|Data Analysis|Databases, Genetic|Humans|Inflammatory Bowel Diseases|Metagenomics|Neural Networks (Computer)|Phylogeny|Principal Component Analysis|Reproducibility of Results|Support Vector Machine', 'Journal Article', '10.1186/s12859-018-2033-5', '29536822', 'Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space.'),
(98293, 2018, 'bmc', 'Ensemble of rankers for efficient gene signature extraction in smoke exposure classification', 'Giordano, Maurizio|Tripathi, Kumar Parijat|Guarracino, Mario Rosario', 'High Performance Computing and Networking Institute (ICAR), National Council of Research (CNR), Naples, Italy. maurizio.giordano@cnr.it.|High Performance Computing and Networking Institute (ICAR), National Council of Research (CNR), Naples, Italy.|High Performance Computing and Networking Institute (ICAR), National Council of Research (CNR), Naples, Italy.', 'Italy|Italy|Italy', 3, 'Algorithms|Disease|Gene Expression Profiling|Gene Ontology|Humans|Models, Genetic|Neural Networks (Computer)|Smoking|Support Vector Machine', 'Journal Article', '10.1186/s12859-018-2035-3', '29536823', 'System toxicology aims at understanding the mechanisms used by biological systems to respond to toxicants. Such understanding can be leveraged to assess the risk of chemicals, drugs, and consumer products in living organisms. In system toxicology, machine learning techniques and methodologies are applied to develop prediction models for classification of toxicant exposure of biological systems. Gene expression data (RNA/DNA microarray) are often used to develop such prediction models.'),
(98294, 2018, 'bmc', 'Discriminate the response of Acute Myeloid Leukemia patients to treatment by using proteomics data and Answer Set Programming', 'Chebouba, Lokmane|Miannay, Bertrand|Boughaci, Dalila|Guziolowski, Carito', 'Department of Computer Science, LRIA Laboratory, Electrical Engineering and Computer Science Faculty, University of Science and Technology Houari Boumediene (USTHB), El-Alia BP 32 Bab-Ezzouar, Algiers, 16111, Algeria.|LS2N, UMR 6004, École Centrale de Nantes, Nantes, France.|Department of Computer Science, LRIA Laboratory, Electrical Engineering and Computer Science Faculty, University of Science and Technology Houari Boumediene (USTHB), El-Alia BP 32 Bab-Ezzouar, Algiers, 16111, Algeria.|LS2N, UMR 6004, École Centrale de Nantes, Nantes, France. carito.guziolowski@ls2n.fr.', 'Algeria|France|Algeria|France', 3, 'Algorithms|Databases, Protein|Humans|Leukemia, Myeloid, Acute|Logic|Protein Interaction Maps|Proteomics|Reproducibility of Results', 'Journal Article', '10.1186/s12859-018-2034-4', '29536824', 'During the last years, several approaches were applied on biomedical data to detect disease specific proteins and genes in order to better target drugs. It was shown that statistical and machine learning based methods use mainly clinical data and improve later their results by adding omics data. This work proposes a new method to discriminate the response of Acute Myeloid Leukemia (AML) patients to treatment. The proposed approach uses proteomics data and prior regulatory knowledge in the form of networks to predict cancer treatment outcomes by finding out the different Boolean networks specific to each type of response to drugs. To show its effectiveness we evaluate our method on a dataset from the DREAM 9 challenge.'),
(98295, 2018, 'bmc', 'An integrated approach to infer cross-talks between intracellular protein transport and signaling pathways', 'Tripathi, Kumar Parijat|Piccirillo, Marina|Guarracino, Mario Rosario', 'Lab-GTP, High Performance Computing and Networking Institute, National Research Council, Via Pietro Castellino, 111, Naples, 80131, Italy. parijat.tripathi@icar.cnr.it.|Lab-GTP, High Performance Computing and Networking Institute, National Research Council, Via Pietro Castellino, 111, Naples, 80131, Italy.|Lab-GTP, High Performance Computing and Networking Institute, National Research Council, Via Pietro Castellino, 111, Naples, 80131, Italy.', 'Italy|Italy|Italy', 2, 'Cluster Analysis|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Intracellular Space|MCF-7 Cells|Phosphatidylinositol 3-Kinases|Protein Transport|Proto-Oncogene Proteins c-akt|Signal Transduction|Transcriptome|ras Proteins', 'Journal Article', '10.1186/s12859-018-2036-2', '29536825', 'The endomembrane system, known as secretory pathway, is responsible for the synthesis and transport of protein molecules in cells. Therefore, genes involved in the secretory pathway are essential for the cellular development and function. Recent scientific investigations show that ER and Golgi apparatus may provide a convenient drug target for cancer therapy. On the other hand, it is known that abundantly expressed genes in different cellular organelles share interconnected pathways and co-regulate each other activities. The cross-talks among these genes play an important role in signaling pathways, associated to the regulation of intracellular protein transport.'),
(98296, 2018, 'bmc', 'Assessing microscope image focus quality with deep learning', 'Yang, Samuel J|Berndl, Marc|Michael Ando, D|Barch, Mariya|Narayanaswamy, Arunachalam|Christiansen, Eric|Hoyer, Stephan|Roat, Chris|Hung, Jane|Rueden, Curtis T|Shankar, Asim|Finkbeiner, Steven|Nelson, Philip', 'Google Inc, Mountain View, CA, USA. samuely@google.com.|Google Inc, Mountain View, CA, USA.|Google Inc, Mountain View, CA, USA.|Taube/Koret Center for Neurodegenerative Disease Research and DaedalusBio, Gladstone, USA.|Google Inc, Mountain View, CA, USA.|Google Inc, Mountain View, CA, USA.|Google Inc, Mountain View, CA, USA.|Google Inc, Mountain View, CA, USA.|Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA.|Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, WI, USA.|Google Inc, Mountain View, CA, USA.|Taube/Koret Center for Neurodegenerative Disease Research and DaedalusBio, Gladstone, USA.|Google Inc, Mountain View, CA, USA. pqnelson@google.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|United States', 35, 'Bone Neoplasms|Diagnostic Imaging|Humans|Image Processing, Computer-Assisted|Machine Learning|Microscopy|Osteosarcoma|Software|Tumor Cells, Cultured', 'Journal Article', '10.1186/s12859-018-2087-4', '29540156', 'Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality.'),
(98297, 2018, 'bmc', 'Heterogeneous computing for epidemiological model fitting and simulation', 'Kovac, Thomas|Haber, Tom|Reeth, Frank Van|Hens, Niel', 'Center for Statistics, I-BioStat, Hasselt University, Agoralaan building D, Diepenbeek, 3590, Belgium. thomas.kovac@uhasselt.be.|Expertise Centre for Digital Media, Hasselt University, Wetenschapspark 2, Diepenbeek, 3590, Belgium.|Expertise Centre for Digital Media, Hasselt University, Wetenschapspark 2, Diepenbeek, 3590, Belgium.|Center for Statistics, I-BioStat, Hasselt University, Agoralaan building D, Diepenbeek, 3590, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 3, 'Algorithms|Communicable Diseases|Computer Graphics|Computer Simulation|Disease Susceptibility|Epidemics|Models, Biological', 'Journal Article', '10.1186/s12859-018-2108-3', '29548279', 'Over the last years, substantial effort has been put into enhancing our arsenal in fighting epidemics from both technological and theoretical perspectives with scientists from different fields teaming up for rapid assessment of potentially urgent situations. This paper focusses on the computational aspects of infectious disease models and applies commonly available graphics processing units (GPUs) for the simulation of these models. However, fully utilizing the resources of both CPUs and GPUs requires a carefully balanced heterogeneous approach.'),
(98298, 2018, 'bmc', 'IDPM: an online database for ion distribution in protein molecules', 'Xiang, Xilun|Liu, Haiguang', 'Complex Systems Division, Beijing Computational Science Research Center, Beijing, 100193, China.|Complex Systems Division, Beijing Computational Science Research Center, Beijing, 100193, China. hgliu@csrc.ac.cn.', 'China|China', 2, 'Amino Acids|Binding Sites|Databases, Protein|Internet|Ions|Probability|Proteins', 'Journal Article', '10.1186/s12859-018-2110-9', '29548284', 'Interactions between ions and proteins have been extensively studied, yet most of the studies focus on the ion binding site. The binding mechanism for many ion binding sites can be clearly described from high resolution structures. Although knowledge accumulated on a case-by-case basis is valuable, it is also important to study the ion-protein interaction statistically. From experimentally determined structures, it is possible to examine the ion distribution around each amino acid. Such distributions can reveal relation between ions and amino acids, so it is desirable to carry out a systematic survey of ion-amino acid pairing interaction and share the information with a publicly available database.'),
(98299, 2018, 'bmc', 'Completing sparse and disconnected protein-protein network by deep learning', 'Huang, Lei|Liao, Li|Wu, Cathy H', 'Department of Computer and Information Sciences, University of Delaware, 18 Amstel Avenue, Newark, 19716, Delaware, USA.|Department of Computer and Information Sciences, University of Delaware, 18 Amstel Avenue, Newark, 19716, Delaware, USA. lliao@cis.udel.edu.|Department of Computer and Information Sciences, University of Delaware, 18 Amstel Avenue, Newark, 19716, Delaware, USA.', 'United States|United States|United States', 3, 'Algorithms|Area Under Curve|Bayes Theorem|Humans|Machine Learning|Neural Networks (Computer)|Protein Interaction Maps|Proteins|ROC Curve|Saccharomyces cerevisiae', 'Journal Article', '10.1186/s12859-018-2112-7', '29566671', 'Protein-protein interaction (PPI) prediction remains a central task in systems biology to achieve a better and holistic understanding of cellular and intracellular processes. Recently, an increasing number of computational methods have shifted from pair-wise prediction to network level prediction. Many of the existing network level methods predict PPIs under the assumption that the training network should be connected. However, this assumption greatly affects the prediction power and limits the application area because the current golden standard PPI networks are usually very sparse and disconnected. Therefore, how to effectively predict PPIs based on a training network that is sparse and disconnected remains a challenge.'),
(98300, 2018, 'bmc', 'Diabetes classification model based on boosting algorithms', 'Chen, Peihua|Pan, Chuandi', 'Institute of Biopharmaceutical Informatics and Technologies, Wenzhou Medical University, Wenzhou, China. chenphwmu666@163.com.|Department of Computer Technology and Information Management, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou City, China.', 'China|China', 17, 'Algorithms|Diabetes Mellitus|Humans|Models, Theoretical|ROC Curve', 'Journal Article', '10.1186/s12859-018-2090-9', '29587624', 'Diabetes mellitus is a common and complicated chronic lifelong disease. Hence, it is of high clinical significance to find the most relevant clinical indexes and to perform efficient computer-aided pre-diagnoses and diagnoses.'),
(98301, 2018, 'bmc', 'Obtaining insights from high-dimensional data: sparse principal covariates regression', 'Van Deun, Katrijn|Crompvoets, Elise A V|Ceulemans, Eva', 'Department of Methodology & Statistics, Tilburg University, Warandelaan 2, Tilburg, 5000 LE, The Netherlands. k.vandeun@uvt.nl.|Department of Methodology & Statistics, Tilburg University, Warandelaan 2, Tilburg, 5000 LE, The Netherlands.|Department of Psychology, KU Leuven, Tiensestraat 102, Leuven, 3000, Belgium.', 'Netherlands|Netherlands|Belgium', 2, 'Algorithms|Computer Simulation|Gene Ontology|Humans|Influenza Vaccines|Least-Squares Analysis|Principal Component Analysis|Regression Analysis|Selection, Genetic|Systems Biology', 'Journal Article', '10.1186/s12859-018-2114-5', '29587627', 'Data analysis methods are usually subdivided in two distinct classes: There are methods for prediction and there are methods for exploration. In practice, however, there often is a need to learn from the data in both ways. For example, when predicting the antibody titers a few weeks after vaccination on the basis of genomewide mRNA transcription rates, also mechanistic insights about the effect of vaccinations on the immune system are sought. Principal covariates regression (PCovR) is a method that combines both purposes. Yet, it misses insightful representations of the data as these include all the variables.'),
(98302, 2018, 'bmc', 'A method combining a random forest-based technique with the modeling of linkage disequilibrium through latent variables, to run multilocus genome-wide association studies', 'Sinoquet, Christine', 'LS2N, UMR CNRS 6004, Université de Nantes, 2 rue de la Houssinière, BP 92208, Nantes Cedex, 44322, France. christine.sinoquet@univ-nantes.fr.', 'France', 4, 'Algorithms|Bayes Theorem|Chromosomes, Human, Pair 22|Computer Simulation|Databases, Genetic|Genetic Loci|Genome-Wide Association Study|Humans|Linkage Disequilibrium|Models, Genetic|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-018-2054-0', '29587628', 'Genome-wide association studies (GWASs) have been widely used to discover the genetic basis of complex phenotypes. However, standard single-SNP GWASs suffer from lack of power. In particular, they do not directly account for linkage disequilibrium, that is the dependences between SNPs (Single Nucleotide Polymorphisms).'),
(98303, 2018, 'bmc', 'Notos - a galaxy tool to analyze CpN observed expected ratios for inferring DNA methylation types', 'Bulla, Ingo|Aliaga, Benoît|Lacal, Virginia|Bulla, Jan|Grunau, Christoph|Chaparro, Cristian', 'Institut für Mathematik und Informatik, Universität Greifswald, Walther-Rathenau-Str. 47, Greifswald, 17487, Germany.|Univ. Perpignan Via Domitia, IHPE UMR 5244, CNRS, IFREMER, Univ. Montpellier, 58 Avenue Paul Alduy, Perpignan, 66860, France.|Department of Mathematics, University of Bergen, P.O. Box 7803, Bergen, 5020, Norway.|Department of Mathematics, University of Bergen, P.O. Box 7803, Bergen, 5020, Norway. Jan.Bulla@uib.no.|Univ. Perpignan Via Domitia, IHPE UMR 5244, CNRS, IFREMER, Univ. Montpellier, 58 Avenue Paul Alduy, Perpignan, 66860, France.|Univ. Perpignan Via Domitia, IHPE UMR 5244, CNRS, IFREMER, Univ. Montpellier, 58 Avenue Paul Alduy, Perpignan, 66860, France.', 'Germany|France|Norway|Norway|France|France', 2, 'Alligators and Crocodiles|Animals|Citrus|Cluster Analysis|CpG Islands|DNA Methylation|Grasshoppers|Humans|Moths|Neurospora crassa|Software', 'Journal Article', '10.1186/s12859-018-2115-4', '29587630', 'DNA methylation patterns store epigenetic information in the vast majority of eukaryotic species. The relatively high costs and technical challenges associated with the detection of DNA methylation however have created a bias in the number of methylation studies towards model organisms. Consequently, it remains challenging to infer kingdom-wide general rules about the functions and evolutionary conservation of DNA methylation. Methylated cytosine is often found in specific CpN dinucleotides, and the frequency distributions of, for instance, CpG observed/expected (CpG o/e) ratios have been used to infer DNA methylation types based on higher mutability of methylated CpG.'),
(98304, 2018, 'bmc', 'CarrierSeq: a sequence analysis workflow for low-input nanopore sequencing', 'Mojarro, Angel|Hachey, Julie|Ruvkun, Gary|Zuber, Maria T|Carr, Christopher E', 'Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Ave, E25-610, Cambridge, MA, 02139, USA. mojarro@mit.edu.|ReadCoor, Cambridge, MA, USA.|Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA.|Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Ave, E25-610, Cambridge, MA, 02139, USA.|Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Ave, E25-610, Cambridge, MA, 02139, USA.', 'United States|United States|United States|United States|United States', 6, 'Bacillus subtilis|Nanopores|ROC Curve|Sequence Analysis, DNA|Software|Workflow', 'Journal Article', '10.1186/s12859-018-2124-3', '29587645', 'Long-read nanopore sequencing technology is of particular significance for taxonomic identification at or below the species level. For many environmental samples, the total extractable DNA is far below the current input requirements of nanopore sequencing, preventing sample to sequence metagenomics from low-biomass or recalcitrant samples.'),
(98305, 2018, 'bmc', 'A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets', 'Liang, Kun|Du, Chuanlong|You, Hankun|Nettleton, Dan', 'Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, N2L 3G1, Canada. kun.liang@uwaterloo.ca.|Department of Statistics, Iowa State University, Ames, 50011, USA.|Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, N2L 3G1, Canada.|Department of Statistics, Iowa State University, Ames, 50011, USA.', 'Canada|United States|Canada|United States', 1, 'Algorithms|Bayes Theorem|Computer Simulation|Gene Ontology|Humans|Markov Chains|Models, Genetic|Models, Theoretical|Quantitative Trait Loci|ROC Curve|Transcriptome', 'Journal Article', '10.1186/s12859-018-2106-5', '29587646', 'Testing predefined gene categories has become a common practice for scientists analyzing high throughput transcriptome data. A systematic way of testing gene categories leads to testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph. The relationships among gene categories induce logical restrictions among the corresponding null hypotheses. An existing fully Bayesian method is powerful but computationally demanding.'),
(98306, 2018, 'bmc', 'Bayesian graphical models for computational network biology', 'Ni, Yang|Müller, Peter|Wei, Lin|Ji, Yuan', 'Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, 78712, TX, USA. yangni87@gmail.com.|Department of Mathematics, The University of Texas at Austin, Austin, 78712, TX, USA.|NorthShore University HealthSystem, Evanston, 60201, IL, USA.|NorthShore University HealthSystem, Evanston, 60201, IL, USA.', 'United States|United States|United States|United States', 4, 'Algorithms|Bayes Theorem|Computational Biology|DNA Methylation|Female|Gene Regulatory Networks|Genome|Humans|Markov Chains|Models, Theoretical|Ovarian Neoplasms|Phosphatidylinositol 3-Kinases|Signal Transduction', 'Journal Article', '10.1186/s12859-018-2063-z', '29589555', 'Computational network biology is an emerging interdisciplinary research area. Among many other network approaches, probabilistic graphical models provide a comprehensive probabilistic characterization of interaction patterns between molecules and the associated uncertainties.'),
(98307, 2018, 'bmc', 'A Bayesian approach to determine the composition of heterogeneous cancer tissue', 'Katiyar, Ashish|Mohanty, Anwoy|Hua, Jianping|Chao, Sima|Lopes, Rosana|Datta, Aniruddha|Bittner, Michael L', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843-3128, USA. ashish.katiyar13@tamu.edu.|Division of Genetics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, P.O. Box 1212, Boston, MA, 02115, USA.|Center for Bioinformatics and Genomic Systems Engineering, TEES/Texas A&M University, College Station, TX, USA.|Center for Bioinformatics and Genomic Systems Engineering, TEES/Texas A&M University, College Station, TX, USA.|Center for Bioinformatics and Genomic Systems Engineering, TEES/Texas A&M University, College Station, TX, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843-3128, USA.|Translational Genomics Research Institute, Phoenix, AZ, USA.', 'United States|United States|United States|United States|United States|United States|United States', 1, 'Algorithms|Antineoplastic Agents|Bayes Theorem|Cell Count|Cell Line, Tumor|Computer Simulation|Humans|Lapatinib|Neoplasms|Probability|Sirolimus', 'Journal Article', '10.1186/s12859-018-2062-0', '29589556', 'Cancer Tissue Heterogeneity is an important consideration in cancer research as it can give insights into the causes and progression of cancer. It is known to play a significant role in cancer cell survival, growth and metastasis. Determining the compositional breakup of a heterogeneous cancer tissue can also help address the therapeutic challenges posed by heterogeneity. This necessitates a low cost, scalable algorithm to address the challenge of accurate estimation of the composition of a heterogeneous cancer tissue.'),
(98308, 2018, 'bmc', 'Selected research articles from the 2017 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC)', 'Yoon, Byung-Jun|Qian, Xiaoning|Kahveci, Tamer|Pal, Ranadip', '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.|Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409-3102, USA.', 'United States|United States|United States|United States', 1, '-', 'Introductory Journal Article', '10.1186/s12859-018-2058-9', '29589557', '-'),
(98309, 2018, 'bmc', 'Heuristic algorithms for feature selection under Bayesian models with block-diagonal covariance structure', 'Foroughi Pour, Ali|Dalton, Lori A', 'Department of Electrical and Computer Engineering, The Ohio State University, 2015 Neil Avenue, Columbus, Ohio, 43210, USA. foroughipour.1@osu.edu.|Department of Electrical and Computer Engineering, The Ohio State University, 2015 Neil Avenue, Columbus, Ohio, 43210, USA.', 'United States|United States', 0, 'Algorithms|Bayes Theorem|Heuristics|Humans|Neoplasms|Normal Distribution', 'Journal Article', '10.1186/s12859-018-2059-8', '29589558', 'Many bioinformatics studies aim to identify markers, or features, that can be used to discriminate between distinct groups. In problems where strong individual markers are not available, or where interactions between gene products are of primary interest, it may be necessary to consider combinations of features as a marker family. To this end, recent work proposes a hierarchical Bayesian framework for feature selection that places a prior on the set of features we wish to select and on the label-conditioned feature distribution. While an analytical posterior under Gaussian models with block covariance structures is available, the optimal feature selection algorithm for this model remains intractable since it requires evaluating the posterior over the space of all possible covariance block structures and feature-block assignments. To address this computational barrier, in prior work we proposed a simple suboptimal algorithm, 2MNC-Robust, with robust performance across the space of block structures. Here, we present three new heuristic feature selection algorithms.'),
(98310, 2018, 'bmc', 'Investigation of model stacking for drug sensitivity prediction', 'Matlock, Kevin|De Niz, Carlos|Rahman, Raziur|Ghosh, Souparno|Pal, Ranadip', 'Department of Electrical and Computer Engineering, Texas Tech University, 1012 Boston Ave, Lubbock, 79409, TX, USA.|Department of Electrical and Computer Engineering, Texas Tech University, 1012 Boston Ave, Lubbock, 79409, TX, USA.|Department of Electrical and Computer Engineering, Texas Tech University, 1012 Boston Ave, Lubbock, 79409, TX, USA.|Department of Mathematics and Statistics, Texas Tech University, 1108 Memorial Circle, Lubbock, 79409, TX, 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|United States', 8, 'Algorithms|Area Under Curve|Bias|Cell Line, Tumor|Deep Learning|Drug Screening Assays, Antitumor|Humans|Models, Biological|Neoplasms|Precision Medicine', 'Journal Article', '10.1186/s12859-018-2060-2', '29589559', 'A significant problem in precision medicine is the prediction of drug sensitivity for individual cancer cell lines. Predictive models such as Random Forests have shown promising performance while predicting from individual genomic features such as gene expressions. However, accessibility of various other forms of data types including information on multiple tested drugs necessitates the examination of designing predictive models incorporating the various data types.'),
(98311, 2018, 'bmc', 'Simulating variance heterogeneity in quantitative genome wide association studies', 'Al Kawam, Ahmad|Alshawaqfeh, Mustafa|Cai, James J|Serpedin, Erchin|Datta, Aniruddha', 'Electrical & Computer Engineering Dept., Texas A&M University, College Station, TX, USA. ahmad.alkawam@tamu.edu.|Electrical & Computer Engineering Dept., German Jordanian University, Amman, Jordan.|Veterinary Integrative Biosciences Dept., Texas A&M University, College Station, TX, USA.|Electrical & Computer Engineering Dept., Texas A&M University, College Station, TX, USA.|Electrical & Computer Engineering Dept., Texas A&M University, College Station, TX, USA.', 'United States|Jordan|United States|United States|United States', 0, 'Algorithms|Computer Simulation|Diploidy|Genetic Loci|Genome-Wide Association Study|Genotype|Humans|Linkage Disequilibrium|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-018-2061-1', '29589560', 'Analyzing Variance heterogeneity in genome wide association studies (vGWAS) is an emerging approach for detecting genetic loci involved in gene-gene and gene-environment interactions. vGWAS analysis detects variability in phenotype values across genotypes, as opposed to typical GWAS analysis, which detects variations in the mean phenotype value.'),
(98312, 2018, 'bmc', 'A statistical approach to identify, monitor, and manage incomplete curated data sets', 'Howe, Douglas G', 'The Institute of Neuroscience, University of Oregon, Eugene, OR, USA. dhowe@zfin.org.', 'United States', 1, 'Animals|Animals, Genetically Modified|Data Mining|Databases, Genetic|Gene Expression Regulation|Molecular Sequence Annotation|Regression Analysis|Reproducibility of Results|Statistics as Topic|Zebrafish', 'Journal Article', '10.1186/s12859-018-2121-6', '29609549', 'Many biological knowledge bases gather data through expert curation of published literature. High data volume, selective partial curation, delays in access, and publication of data prior to the ability to curate it can result in incomplete curation of published data. Knowing which data sets are incomplete and how incomplete they are remains a challenge. Awareness that a data set may be incomplete is important for proper interpretation, to avoiding flawed hypothesis generation, and can justify further exploration of published literature for additional relevant data. Computational methods to assess data set completeness are needed. One such method is presented here.'),
(98313, 2018, 'bmc', 'BS-Seeker3: ultrafast pipeline for bisulfite sequencing', 'Huang, Kevin Yu Yuan|Huang, Yan-Jiun|Chen, Pao-Yang', 'Institute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan.|Institute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan.|Institute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan. paoyang@gate.sinica.edu.tw.', '-|-|-', 0, 'Computational Biology|DNA Methylation|Epigenesis, Genetic|Epigenomics|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1186/s12859-018-2120-7', '29614954', 'DNA methylation is an important epigenetic modification critical in regulation and transgenerational inheritance. The methylation level can be estimated at single-nucleotide resolution by whole-genome bisulfite sequencing (BS-seq, WGBS). Current bisulfite aligners provide pipelines for processing the reads by WGBS, however, few are able to analyze the BS-seqs in a reasonable timeframe that meets the needs of the rapid expansion of epigenome sequencing in biomedical research.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98314, 2018, 'bmc', 'AceTree: a major update and case study in the long term maintenance of open-source scientific software', 'Katzman, Braden|Tang, Doris|Santella, Anthony|Bao, Zhirong', 'Developmental Biology Program, Sloan-Kettering Institute, New York, NY, USA.|Developmental Biology Program, Sloan-Kettering Institute, New York, NY, USA.|Developmental Biology Program, Sloan-Kettering Institute, New York, NY, USA.|Developmental Biology Program, Sloan-Kettering Institute, New York, NY, USA. baoz@mskcc.org.', 'United States|United States|United States|United States', 0, 'Animals|Caenorhabditis elegans|Cell Lineage|Computational Biology|Drosophila|Embryo, Nonmammalian|Microscopy, Fluorescence|Phenotype|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-018-2127-0', '29618316', 'AceTree, a software application first released in 2006, facilitates exploration, curation and editing of tracked C. elegans nuclei in 4-dimensional (4D) fluorescence microscopy datasets. Since its initial release, AceTree has been continuously used to interact with, edit and interpret C. elegans lineage data. In its 11 year lifetime, AceTree has been periodically updated to meet the technical and research demands of its community of users. This paper presents the newest iteration of AceTree which contains extensive updates, demonstrates the new applicability of AceTree in other developmental contexts, and presents its evolutionary software development paradigm as a viable model for maintaining scientific software.'),
(98315, 2018, 'bmc', 'A simulation study investigating power estimates in phenome-wide association studies', 'Verma, Anurag|Bradford, Yuki|Dudek, Scott|Lucas, Anastasia M|Verma, Shefali S|Pendergrass, Sarah A|Ritchie, Marylyn D', 'Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.|Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.|Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.|Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.|Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.|Biomedical and Translational Informatics, Geisinger, Danville, PA, USA.|Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. marylyn@pennmedicine.upenn.edu.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Computer Simulation|Disease|Genetic Association Studies|Genome-Wide Association Study|Humans|Phenotype|Polymorphism, Single Nucleotide|Quantitative Trait Loci', 'Journal Article', '10.1186/s12859-018-2135-0', '29618318', 'Phenome-wide association studies (PheWAS) are a high-throughput approach to evaluate comprehensive associations between genetic variants and a wide range of phenotypic measures. PheWAS has varying sample sizes for quantitative traits, and variable numbers of cases and controls for binary traits across the many phenotypes of interest, which can affect the statistical power to detect associations. The motivation of this study is to investigate the various parameters which affect the estimation of statistical power in PheWAS, including sample size, case-control ratio, minor allele frequency, and disease penetrance.'),
(98316, 2018, 'bmc', 'nQuire: a statistical framework for ploidy estimation using next generation sequencing', 'Weiß, Clemens L|Pais, Marina|Cano, Liliana M|Kamoun, Sophien|Burbano, Hernán A', 'Research Group for Ancient Genomics and Evolution, Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tuebingen, Germany.|The Sainsbury Laboratory, Norwich, UK.|The Sainsbury Laboratory, Norwich, UK.|The Sainsbury Laboratory, Norwich, UK.|Research Group for Ancient Genomics and Evolution, Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tuebingen, Germany. hernan.burbano@tuebingen.mpg.de.', 'Germany|United Kingdom|United Kingdom|United Kingdom|Germany', 17, 'Genome, Fungal|High-Throughput Nucleotide Sequencing|Ploidies|Saccharomyces cerevisiae|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-018-2128-z', '29618319', 'Intraspecific variation in ploidy occurs in a wide range of species including pathogenic and nonpathogenic eukaryotes such as yeasts and oomycetes. Ploidy can be inferred indirectly - without measuring DNA content - from experiments using next-generation sequencing (NGS). We present nQuire, a statistical framework that distinguishes between diploids, triploids and tetraploids using NGS. The command-line tool models the distribution of base frequencies at variable sites using a Gaussian Mixture Model, and uses maximum likelihood to select the most plausible ploidy model. nQuire handles large genomes at high coverage efficiently and uses standard input file formats.'),
(98317, 2018, 'bmc', 'Linear MALDI-ToF simultaneous spectrum deconvolution and baseline removal', 'Picaud, Vincent|Giovannelli, Jean-Francois|Truntzer, Caroline|Charrier, Jean-Philippe|Giremus, Audrey|Grangeat, Pierre|Mercier, Catherine', 'University of Bordeaux, IMS, UMR 5218, Talence, 33400, France. vincent.picaud@cea.fr.|University of Bordeaux, IMS, UMR 5218, Talence, 33400, France.|CLIPP, Pôle de Recherche Université de Bourgogne, Dijon, 21000, France.|Technology Research Department, Innovation Unit, Marcy lÉtoile, bioMérieux SA, Marcy lÉtoile, 69280, France.|University of Bordeaux, IMS, UMR 5218, Talence, 33400, France.|Université Grenoble Alpes, Grenoble, 38000, France.|Service de Biostatistique, Hospices Civils de Lyon, Lyon, 69000, France.', 'France|France|France|France|France|France|France', 3, 'Algorithms|Artifacts|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1186/s12859-018-2116-3', '29621971', 'Thanks to a reasonable cost and simple sample preparation procedure, linear MALDI-ToF spectrometry is a growing technology for clinical microbiology. With appropriate spectrum databases, this technology can be used for early identification of pathogens in body fluids. However, due to the low resolution of linear MALDI-ToF instruments, robust and accurate peak picking remains a challenging task. In this context we propose a new peak extraction algorithm from raw spectrum. With this method the spectrum baseline and spectrum peaks are processed jointly. The approach relies on an additive model constituted by a smooth baseline part plus a sparse peak list convolved with a known peak shape. The model is then fitted under a Gaussian noise model. The proposed method is well suited to process low resolution spectra with important baseline and unresolved peaks.'),
(98318, 2018, 'bmc', 'A simple method for exploring adverse drug events in patients with different primary diseases using spontaneous reporting system', 'Noguchi, Yoshihiro|Ueno, Anri|Otsubo, Manami|Katsuno, Hayato|Sugita, Ikuto|Kanematsu, Yuta|Yoshida, Aki|Esaki, Hiroki|Tachi, Tomoya|Teramachi, Hitomi', 'Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan. noguchiy@gifu-pu.ac.jp.|Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan.|Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan.|Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan.|Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan.|Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan.|Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan.|Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan.|Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan.|Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan. teramachih@gifu-pu.ac.jp.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Adverse Drug Reaction Reporting Systems|Area Under Curve|Chemical and Drug Induced Liver Injury|Databases, Factual|Humans|Japan|Kidney Diseases|Pharmacovigilance|ROC Curve', 'Journal Article', '10.1186/s12859-018-2137-y', '29621976', 'Patient background (e.g. age, sex, and primary disease) is an important factor to consider when monitoring adverse drug events (ADEs) for the purpose of pharmacovigilance. However, in disproportionality methods, when additional factors are considered, the number of combinations that have to be computed increases, and it becomes very difficult to explore the whole spontaneous reporting system (SRS). Since the signals need to be detected quickly in pharmacovigilance, a simple exploration method is required. Although association rule mining (AR) is commonly used for the analysis of large data, its application to pharmacovigilance is rare and there are almost no studies comparing AR with conventional signal detection methods.'),
(98319, 2018, 'bmc', 'COZOID: contact zone identifier for visual analysis of protein-protein interactions', 'Furmanovxc3xa1, Katarxc3xadna|Byxc5xa1ka, Jan|Grxc3xb6ller, Eduard M|Viola, Ivan|Palexc4x8dek, Jan J|Kozlxc3xadkovxc3xa1, Barbora', 'Faculty of Informatics, Masaryk University, Brno, Czech Republic.|Department of Informatics, University of Bergen, Bergen, Norway.|Institute of Visual Computing & Human-Centered Technology, TU Wien, Wien, Austria.|Institute of Visual Computing & Human-Centered Technology, TU Wien, Wien, Austria.|National Centre for Biomolecular Research, Masaryk University, Brno, Czech Republic.|Faculty of Informatics, Masaryk University, Brno, Czech Republic. kozlikova@fi.muni.cz.', '-|Norway|Austria|Austria|-|-', 0, 'Protein Interaction Domains and Motifs|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1186/s12859-018-2113-6', '29625561', 'Studying the patterns of protein-protein interactions (PPIs) is fundamental for understanding the structure and function of protein complexes. The exploration of the vast space of possible mutual configurations of interacting proteins and their contact zones is very time consuming and requires the proteomic expert knowledge.'),
(98320, 2018, 'bmc', 'Robust volcano plot: identification of differential metabolites in the presence of outliers', 'Kumar, Nishith|Hoque, Md Aminul|Sugimoto, Masahiro', 'Department of Statistics, Rajshahi University, Rajshahi, Bangladesh. nk.bru09@gmail.com.|Department of Statistics, Rajshahi University, Rajshahi, Bangladesh.|Health Promotion and Preemptive Medicine, Research and Development Center for Minimally Invasive Therapies, Tokyo Medical University, Shinjuku, Tokyo, 160-8402, Japan.', 'Bangladesh|Bangladesh|Japan', 6, 'Algorithms|Biomarkers|Down-Regulation|Female|Humans|Metabolome|Metabolomics|ROC Curve|Up-Regulation', 'Journal Article', '10.1186/s12859-018-2117-2', '29642836', 'The identification of differential metabolites in metabolomics is still a big challenge and plays a prominent role in metabolomics data analyses. Metabolomics datasets often contain outliers because of analytical, experimental, and biological ambiguity, but the currently available differential metabolite identification techniques are sensitive to outliers.'),
(98321, 2018, 'bmc', 'Approximate inference of gene regulatory network models from RNA-Seq time series data', 'Thorne, Thomas', 'Department of Computer Science, University of Reading, Reading, UK. t.thorne@reading.ac.uk.', 'United Kingdom', 6, 'Algorithms|Area Under Curve|Bayes Theorem|Cell Differentiation|Gene Regulatory Networks|Humans|Models, Genetic|Neural Stem Cells|ROC Curve|Saccharomyces cerevisiae|Sequence Analysis, RNA|Time Factors', 'Journal Article', '10.1186/s12859-018-2125-2', '29642837', 'Inference of gene regulatory network structures from RNA-Seq data is challenging due to the nature of the data, as measurements take the form of counts of reads mapped to a given gene. Here we present a model for RNA-Seq time series data that applies a negative binomial distribution for the observations, and uses sparse regression with a horseshoe prior to learn a dynamic Bayesian network of interactions between genes. We use a variational inference scheme to learn approximate posterior distributions for the model parameters.'),
(98322, 2018, 'bmc', 'TIA: algorithms for development of identity-linked SNP islands for analysis by massively parallel DNA sequencing', 'Farris, M Heath|Scott, Andrew R|Texter, Pamela A|Bartlett, Marta|Coleman, Patricia|Masters, David', 'The Homeland Security Systems Engineering and Development Institute (HSSEDI), operated by The MITRE Corporation, McLean, Virginia, USA. hfarris@mitre.org.|The Homeland Security Systems Engineering and Development Institute (HSSEDI), operated by The MITRE Corporation, McLean, Virginia, USA.|The Homeland Security Systems Engineering and Development Institute (HSSEDI), operated by The MITRE Corporation, McLean, Virginia, USA.|The Homeland Security Systems Engineering and Development Institute (HSSEDI), operated by The MITRE Corporation, McLean, Virginia, USA.|The Department of Homeland Security, U.S. Customs and Border Protection, Washington, DC, USA.|The Department of Homeland Security, Science and Technology Directorate, Washington, DC, USA.', 'United States|United States|United States|United States|United States|United States', 1, 'Algorithms|DNA|Genome, Human|Genomic Islands|Haplotypes|High-Throughput Nucleotide Sequencing|Humans|Polymorphism, Single Nucleotide|Reproducibility of Results|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2133-2', '29642839', 'Single nucleotide polymorphisms (SNPs) located within the human genome have been shown to have utility as markers of identity in the differentiation of DNA from individual contributors. Massively parallel DNA sequencing (MPS) technologies and human genome SNP databases allow for the design of suites of identity-linked target regions, amenable to sequencing in a multiplexed and massively parallel manner. Therefore, tools are needed for leveraging the genotypic information found within SNP databases for the discovery of genomic targets that can be evaluated on MPS platforms.'),
(98323, 2018, 'bmc', 'iSeg: an efficient algorithm for segmentation of genomic and epigenomic data', 'Girimurugan, Senthil B|Liu, Yuhang|Lung, Pei-Yau|Vera, Daniel L|Dennis, Jonathan H|Bass, Hank W|Zhang, Jinfeng', 'Department of Mathematics, Florida Gulf Coast University, Fort Myers, FL, USA.|Department of Statistics, Florida State University, Tallahassee, FL, USA.|Department of Statistics, Florida State University, Tallahassee, FL, USA.|Center for Genomics and Personalized Medicine, Florida State University, Tallahassee, FL, USA.|Department of Biological Science, Florida State University, Tallahassee, FL, USA.|Department of Biological Science, Florida State University, Tallahassee, FL, USA.|Department of Statistics, Florida State University, Tallahassee, FL, USA. jinfeng@stat.fsu.edu.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Computer Simulation|DNA Copy Number Variations|Databases, Genetic|Deoxyribonucleases|Epigenomics|Genome|Humans|Models, Statistical|Neoplasms|Zea mays', 'Journal Article', '10.1186/s12859-018-2140-3', '29642840', 'Identification of functional elements of a genome often requires dividing a sequence of measurements along a genome into segments where adjacent segments have different properties, such as different mean values. Despite dozens of algorithms developed to address this problem in genomics research, methods with improved accuracy and speed are still needed to effectively tackle both existing and emerging genomic and epigenomic segmentation problems.'),
(98324, 2018, 'bmc', 'Encompassing new use cases - level 30 of the HUPO-PSI format for molecular interactions', 'Sivade Dumousseau, M|Alonso-López, D|Ammari, M|Bradley, G|Campbell, N H|Ceol, A|Cesareni, G|Combe, C|De Las Rivas, J|Del-Toro, N|Heimbach, J|Hermjakob, H|Jurisica, I|Koch, M|Licata, L|Lovering, R C|Lynn, D J|Meldal, B H M|Micklem, G|Panni, S|Porras, P|Ricard-Blum, S|Roechert, B|Salwinski, L|Shrivastava, A|Sullivan, J|Thierry-Mieg, N|Yehudi, Y|Van Roey, K|Orchard, S', 'European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK.|Cancer Research Center (CiC-IBMCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC) and Universidad de Salamanca (USAL), 37007, Salamanca, Spain.|School of Animal and Comparative Biomedical Sciences, University of Arizona, Tucson, USA.|Target Sciences, GSK, Stevenage, UK.|Institute of Cardiovascular Science, University College London, Rayne Building, 5 University Street, London, WC1E 6JF, UK.|Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, I-20139, Milan, Italy.|Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, Italy.|Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3BF, UK.|Cancer Research Center (CiC-IBMCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC) and Universidad de Salamanca (USAL), 37007, Salamanca, Spain.|European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK.|Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK.|European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK.|Krembil Research Institute, University Health Network, Toronto, ON, M5T 2S8, Canada.|European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK.|Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, Italy.|Institute of Cardiovascular Science, University College London, Rayne Building, 5 University Street, London, WC1E 6JF, UK.|EMBL Australia Group, South Australian Health and Medical Research Institute, Adelaide, Australia.|European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK.|Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK.|Department of Biology, Ecology and Earth Sciences, Università della Calabria, Rende, Italy.|European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK.|Univ Lyon, University Claude Bernard Lyon 1, INSA Lyon, CPE, Institute of Molecular and Supramolecular Chemistry and Biochemistry (ICBMS), UMR 5246, F-69622, Villeurbanne, France.|SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, 1211, Geneva 4, Switzerland.|UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, USA.|European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK.|Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK.|TIMC-IMAG, CNRS, Univ. Grenoble Alpes, F-38000, Grenoble, France.|Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK.|Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, D-69117, Heidelberg, Germany.|European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK. orchard@ebi.ac.uk.', 'United Kingdom|Spain|United Kingdom|United Kingdom|United Kingdom|Italy|Italy|United Kingdom|Spain|United Kingdom|United Kingdom|United Kingdom|Canada|United Kingdom|Italy|United Kingdom|Australia|United Kingdom|United Kingdom|Italy|United Kingdom|France|', 13, 'Databases, Protein|Humans|Mutation|Protein Interaction Maps|Proteome|Proteomics|Systems Biology', 'Journal Article', '10.1186/s12859-018-2118-1', '29642841', 'Systems biologists study interaction data to understand the behaviour of whole cell systems, and their environment, at a molecular level. In order to effectively achieve this goal, it is critical that researchers have high quality interaction datasets available to them, in a standard data format, and also a suite of tools with which to analyse such data and form experimentally testable hypotheses from them. The PSI-MI XML standard interchange format was initially published in 2004, and expanded in 2007 to enable the download and interchange of molecular interaction data. PSI-XML2.5 was designed to describe experimental data and to date has fulfilled this basic requirement. However, new use cases have arisen that the format cannot properly accommodate. These include data abstracted from more than one publication such as allosteric/cooperative interactions and protein complexes, dynamic interactions and the need to link kinetic and affinity data to specific mutational changes.'),
(98325, 2018, 'bmc', 'GROOLS: reactive graph reasoning for genome annotation through biological processes', 'Mercier, Jonathan|Josso, Adrien|Médigue, Claudine|Vallenet, David', 'LABGeM, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Université dEvry, Université Paris-Saclay, 2 rue Gaston Crémieux, Evry, 91057, France. jonathan.mercier.fr@gmail.com.|LABGeM, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Université dEvry, Université Paris-Saclay, 2 rue Gaston Crémieux, Evry, 91057, France.|LABGeM, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Université dEvry, Université Paris-Saclay, 2 rue Gaston Crémieux, Evry, 91057, France.|LABGeM, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Université dEvry, Université Paris-Saclay, 2 rue Gaston Crémieux, Evry, 91057, France.', 'France|France|France|France', 0, 'Acinetobacter|Algorithms|Biological Phenomena|Biosynthetic Pathways|Computational Biology|Cysteine|Databases, Factual|Genome|Molecular Sequence Annotation|Software', 'Journal Article', '10.1186/s12859-018-2126-1', '29642842', 'High quality functional annotation is essential for understanding the phenotypic consequences encoded in a genome. Despite improvements in bioinformatics methods, millions of sequences in databanks are not assigned reliable functions. The curation of protein functions in the context of biological processes is a way to evaluate and improve their annotation.'),
(98326, 2018, 'bmc', 'IntEREst: intron-exon retention estimator', 'Oghabian, Ali|Greco, Dario|Frilander, Mikko J', 'Institute of Biotechnology, University of Helsinki, P.O. Box 56 (Viikinkaari 5), FI-00014, Helsinki, Finland.|Institute of Biotechnology, University of Helsinki, P.O. Box 56 (Viikinkaari 5), FI-00014, Helsinki, Finland.|Institute of Biotechnology, University of Helsinki, P.O. Box 56 (Viikinkaari 5), FI-00014, Helsinki, Finland. Mikko.Frilander@Helsinki.Fi.', 'Finland|Finland|Finland', 0, 'Base Pairing|Computational Biology|Down-Regulation|Exons|Genome, Human|Humans|Introns|Myelodysplastic Syndromes|Sample Size|Software|Up-Regulation', 'Journal Article', '10.1186/s12859-018-2122-5', '29642843', 'In-depth study of the intron retention levels of transcripts provide insights on the mechanisms regulating pre-mRNA splicing efficiency. Additionally, detailed analysis of retained introns can link these introns to post-transcriptional regulation or identify aberrant splicing events in human diseases.'),
(98327, 2018, 'bmc', 'JAMI: a Java library for molecular interactions and data interoperability', 'Sivade Dumousseau, M|Koch, M|Shrivastava, A|Alonso-López, D|De Las Rivas, J|Del-Toro, N|Combe, C W|Meldal, B H M|Heimbach, J|Rappsilber, J|Sullivan, J|Yehudi, Y|Orchard, S', 'European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK.|European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK.|European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK.|Cancer Research Center (CiC-IBMCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC) and Universidad de Salamanca (USAL), 37007, Salamanca, Spain.|Cancer Research Center (CiC-IBMCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC) and Universidad de Salamanca (USAL), 37007, Salamanca, Spain.|European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK.|Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3BF, UK.|European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK.|Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK.|Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3BF, UK.|Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK.|Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK.|European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK. orchard@ebi.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|Spain|Spain|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Databases, Protein|Humans|Programming Languages|Protein Interaction Maps|Proteomics|Software|Statistics as Topic', 'Journal Article', '10.1186/s12859-018-2119-0', '29642846', 'A number of different molecular interactions data download formats now exist, designed to allow access to these valuable data by diverse user groups. These formats include the PSI-XML and MITAB standard interchange formats developed by Molecular Interaction workgroup of the HUPO-PSI in addition to other, use-specific downloads produced by other resources. The onus is currently on the user to ensure that a piece of software is capable of read/writing all necessary versions of each format. This problem may increase, as data providers strive to meet ever more sophisticated user demands and data types.'),
(98328, 2018, 'bmc', 'A two-tiered unsupervised clustering approach for drug repositioning through heterogeneous data integration', '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 & Computer Science, The Australian National University, Canberra, ACT, 2601, Australia.', 'Australia|Australia|Australia|Australia', 4, 'Algorithms|Cluster Analysis|Computational Biology|Drug Repositioning|Humans|Pharmaceutical Preparations|Statistics as Topic', 'Journal Article', '10.1186/s12859-018-2123-4', '29642848', 'Drug repositioning is the process of identifying new uses for existing drugs. Computational drug repositioning methods can reduce the time, costs and risks of drug development by automating the analysis of the relationships in pharmacology networks. Pharmacology networks are large and heterogeneous. Clustering drugs into small groups can simplify large pharmacology networks, these subgroups can also be used as a starting point for repositioning drugs. In this paper, we propose a two-tiered drug-centric unsupervised clustering approach for drug repositioning, integrating heterogeneous drug data profiles: drug-chemical, drug-disease, drug-gene, drug-protein and drug-side effect relationships.'),
(98329, 2018, 'bmc', 'Realizing drug repositioning by adapting a recommendation system to handle the process', 'Ozsoy, Makbule Gulcin|Özyer, Tansel|Polat, Faruk|Alhajj, Reda', 'Department of Computer Engineering, Middle East Technical University, Ankara, Turkey.|Department of Computer Engineering, TOBB University, Ankara, Turkey.|Department of Computer Engineering, Middle East Technical University, Ankara, Turkey.|Department of Computer Science, University of Calgary, Calgary, AB, Canada. alhajj@ucalgary.ca.', 'Turkey|Turkey|Turkey|Canada', 5, 'Area Under Curve|Computational Biology|Databases as Topic|Drug Discovery|Drug Repositioning|Humans', 'Journal Article', '10.1186/s12859-018-2142-1', '29649971', 'Drug repositioning is the process of identifying new targets for known drugs. It can be used to overcome problems associated with traditional drug discovery by adapting existing drugs to treat new discovered diseases. Thus, it may reduce associated risk, cost and time required to identify and verify new drugs. Nowadays, drug repositioning has received more attention from industry and academia. To tackle this problem, researchers have applied many different computational methods and have used various features of drugs and diseases.'),
(98330, 2018, 'bmc', 'VIPER: Visualization Pipeline for RNA-seq, a Snakemake workflow for efficient and complete RNA-seq analysis', 'Cornwell, MacIntosh|Vangala, Mahesh|Taing, Len|Herbert, Zachary|Köster, Johannes|Li, Bo|Sun, Hanfei|Li, Taiwen|Zhang, Jian|Qiu, Xintao|Pun, Matthew|Jeselsohn, Rinath|Brown, Myles|Liu, X Shirley|Long, Henry W', 'Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.|University of Massachusetts Medical School, Worcester, MA, 01655, USA.|Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.|Molecular Biology Core Facilities, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.|Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA, 02215, USA.|Department of Bioinformatics, School of Life Sciences, Tongji University, Shanghai, 200092, China.|State Key Laboratory of Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China.|Beijing Institute of Basic Medical Sciences, Beijing, China.|Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.|Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.|Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.|Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.|Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.|Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA. henry_long@dfci.harvard.edu.', 'United States|United States|United States|United States|United States|United States|China|China|China|United States|United States|United States|United States|United States|United States', 24, 'Base Sequence|Cluster Analysis|Down-Regulation|Gene Expression Profiling|Gene Ontology|High-Throughput Nucleotide Sequencing|RNA, Messenger|Sequence Alignment|Sequence Analysis, RNA|Signal Transduction|Software|Up-Regulation|Workflow', 'Journal Article', '10.1186/s12859-018-2139-9', '29649993', 'RNA sequencing has become a ubiquitous technology used throughout life sciences as an effective method of measuring RNA abundance quantitatively in tissues and cells. The increase in use of RNA-seq technology has led to the continuous development of new tools for every step of analysis from alignment to downstream pathway analysis. However, effectively using these analysis tools in a scalable and reproducible way can be challenging, especially for non-experts.'),
(98331, 2018, 'bmc', 'Genexpi: a toolset for identifying regulons and validating gene regulatory networks using time-course expression data', 'Modrxc3xa1k, Martin|Vohradskxc3xbd, Jixc5x99xc3xad', 'Institute of Microbiology of the Czech Academy of Sciences, Vxc3xaddexc5x88skxc3xa1, 1083, Prague, Czech Republic. martin.modrak@biomed.cas.cz.|Institute of Microbiology of the Czech Academy of Sciences, Vxc3xaddexc5x88skxc3xa1, 1083, Prague, Czech Republic.', '-|-', 4, 'Bacteria|Databases, Genetic|Eukaryota|Gene Expression Regulation|Gene Regulatory Networks|Humans|Regulon|Reproducibility of Results|Saccharomyces cerevisiae|Software|Time Factors', 'Journal Article', '10.1186/s12859-018-2138-x', '29653518', 'Identifying regulons of sigma factors is a vital subtask of gene network inference. Integrating multiple sources of data is essential for correct identification of regulons and complete gene regulatory networks. Time series of expression data measured with microarrays or RNA-seq combined with static binding experiments (e.g., ChIP-seq) or literature mining may be used for inference of sigma factor regulatory networks.'),
(98332, 2018, 'bmc', 'SPIDR: small-molecule peptide-influenced drug repurposing', 'King, Matthew D|Long, Thomas|Pfalmer, Daniel L|Andersen, Timothy L|McDougal, Owen M', 'Department of Chemistry and Biochemistry, Boise State University, Boise, USA.|Department of Computer Science, Boise State University, Boise, USA.|Biomolecular Sciences Ph.D. Program, Boise State University, Boise, USA.|Department of Computer Science, Boise State University, Boise, USA.|Department of Chemistry and Biochemistry, Boise State University, Boise, USA. owenmcdougal@boisestate.edu.', 'United States|United States|United States|United States|United States', 4, 'Amino Acid Sequence|Conotoxins|Drug Repositioning|Ligands|Models, Molecular|Molecular Conformation|Peptides|Receptors, Nicotinic|Small Molecule Libraries|Software', 'Journal Article', '10.1186/s12859-018-2153-y', '29661129', 'Conventional de novo drug design is costly and time consuming, making it accessible to only the best resourced research organizations. An emergent approach to new drug development is drug repurposing, in which compounds that have already gone through some level of clinical testing are examined for efficacy against diseases divergent than their original application. Repurposing of existing drugs circumvents the time and considerable cost of early stages of drug development, and can be accelerated by using software to screen existing chemical databases to identify suitable drug candidates.'),
(98333, 2018, 'bmc', 'Identification of missing variants by combining multiple analytic pipelines', 'Ren, Yingxue|Reddy, Joseph S|Pottier, Cyril|Sarangi, Vivekananda|Tian, Shulan|Sinnwell, Jason P|McDonnell, Shannon K|Biernacka, Joanna M|Carrasquillo, Minerva M|Ross, Owen A|Ertekin-Taner, Nilxc3xbcfer|Rademakers, Rosa|Hudson, Matthew|Mainzer, Liudmila Sergeevna|Asmann, Yan W', 'Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, 32224, USA.|Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, 32224, USA.|Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA.|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.|Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA.|Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA.|Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA.|Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA.|National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.|National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.|Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, 32224, USA. Asmann.Yan@mayo.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 3, 'Alzheimer Disease|Base Composition|Computational Biology|Drug Discovery|Genetic Variation|Genome|Genotype|Genotyping Techniques|Humans|Sample Size|Sequence Alignment', 'Journal Article', '10.1186/s12859-018-2151-0', '29661148', 'After decades of identifying risk factors using array-based genome-wide association studies (GWAS), genetic research of complex diseases has shifted to sequencing-based rare variants discovery. This requires large sample sizes for statistical power and has brought up questions about whether the current variant calling practices are adequate for large cohorts. It is well-known that there are discrepancies between variants called by different pipelines, and that using a single pipeline always misses true variants exclusively identifiable by other pipelines. Nonetheless, it is common practice today to call variants by one pipeline due to computational cost and assume that false negative calls are a small percent of total.'),
(98334, 2018, 'bmc', 'Prediction of microRNA-disease associations based on distance correlation set', 'Zhao, Haochen|Kuang, Linai|Wang, Lei|Ping, Pengyao|Xuan, Zhanwei|Pei, Tingrui|Wu, Zhelun', 'Key Laboratory of Intelligent Computing & Information Processing (Xiangtan University), Ministry of Education, China, Xiangtan, 411105, Hunan, Peoples Republic of China.|College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410001, Hunan, Peoples Republic of China.|College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410001, Hunan, Peoples Republic of China. wanglei@xtu.edu.cn.|Key Laboratory of Intelligent Computing & Information Processing (Xiangtan University), Ministry of Education, China, Xiangtan, 411105, Hunan, Peoples Republic of China.|Key Laboratory of Intelligent Computing & Information Processing (Xiangtan University), Ministry of Education, China, Xiangtan, 411105, Hunan, Peoples Republic of China.|Key Laboratory of Intelligent Computing & Information Processing (Xiangtan University), Ministry of Education, China, Xiangtan, 411105, Hunan, Peoples Republic of China.|Department of Computer Science, Princeton University, Princeton, New Jersey, USA.', 'China|China|China|China|China|China|Jersey', 13, 'Algorithms|Area Under Curve|Computational Biology|Databases, Genetic|Genetic Predisposition to Disease|Humans|Male|MicroRNAs|Models, Genetic|Neoplasms|RNA, Long Noncoding', 'Journal Article', '10.1186/s12859-018-2146-x', '29665774', 'Recently, numerous laboratory studies have indicated that many microRNAs (miRNAs) are involved in and associated with human diseases and can serve as potential biomarkers and drug targets. Therefore, developing effective computational models for the prediction of novel associations between diseases and miRNAs could be beneficial for achieving an understanding of disease mechanisms at the miRNA level and the interactions between diseases and miRNAs at the disease level. Thus far, only a few miRNA-disease association pairs are known, and models analyzing miRNA-disease associations based on lncRNA are limited.'),
(98335, 2018, 'bmc', 'IRProfiler - a software toolbox for high throughput immune receptor profiling', 'Maramis, Christos|Gkoufas, Athanasios|Vardi, Anna|Stalika, Evangelia|Stamatopoulos, Kostas|Hatzidimitriou, Anastasia|Maglaveras, Nicos|Chouvarda, Ioanna', 'Lab of Computing, Medical Informatics & Biomedical-Imaging Technologies, Department of Medicine, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece. chmaramis@med.auth.gr.|Lab of Computing, Medical Informatics & Biomedical-Imaging Technologies, Department of Medicine, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece.|Institute of Applied Biosiences, Centre for Research & Technology Hellas, 57001, Thermi, Greece.|Institute of Applied Biosiences, Centre for Research & Technology Hellas, 57001, Thermi, Greece.|Institute of Applied Biosiences, Centre for Research & Technology Hellas, 57001, Thermi, Greece.|Institute of Applied Biosiences, Centre for Research & Technology Hellas, 57001, Thermi, Greece.|Lab of Computing, Medical Informatics & Biomedical-Imaging Technologies, Department of Medicine, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece.|Lab of Computing, Medical Informatics & Biomedical-Imaging Technologies, Department of Medicine, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece.', 'Greece|Greece|Greece|Greece|Greece|Greece|Greece|Greece', 3, 'Algorithms|Hematologic Diseases|High-Throughput Nucleotide Sequencing|Humans|Receptors, Antigen, B-Cell|Receptors, Antigen, T-Cell|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-018-2144-z', '29669518', 'The study of the huge diversity of immune receptors, often referred to as immune repertoire profiling, is a prerequisite for diagnosis, prognostication and monitoring of hematological disorders. In the era of high-throughput sequencing (HTS), the abundance of immunogenetic data has revealed unprecedented opportunities for the thorough profiling of T-cell receptors (TR) and B-cell receptors (BcR). However, the volume of the data to be analyzed mandates for efficient and ease-to-use immune repertoire profiling software applications.'),
(98336, 2018, 'bmc', 'Spatial registration of neuron morphologies based on maximization of volume overlap', 'Kumaraswamy, Ajayrama|Kai, Kazuki|Ai, Hiroyuki|Ikeno, Hidetoshi|Wachtler, Thomas', 'Department of Biology II, Ludwig-Maximilians-Universität München, Grosshadernerstr, 2, Planegg-Martinsried, 82152, Germany. ajayramak@bio.lmu.de.|Department of Earth System Science, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka-shi, Fukuoka, 814-0180, Japan.|Department of Earth System Science, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka-shi, Fukuoka, 814-0180, Japan.|School of Human Science and Environment, University of Hyogo, 1-1-12 Shinazaike-Honcho, Himeji, 670-0092, Hyogo, Japan.|Department of Biology II, Ludwig-Maximilians-Universität München, Grosshadernerstr, 2, Planegg-Martinsried, 82152, Germany.', 'Germany|Japan|Japan|Japan|Germany', 2, 'Algorithms|Animals|Brain|Drosophila melanogaster|Neurons|Principal Component Analysis', 'Journal Article', '10.1186/s12859-018-2136-z', '29669537', 'Morphological features are widely used in the study of neuronal function and pathology. Invertebrate neurons are often structurally stereotypical, showing little variance in gross spatial features but larger variance in their fine features. Such variability can be quantified using detailed spatial analysis, which however requires the morphologies to be registered to a common frame of reference.'),
(98337, 2018, 'bmc', 'Modeling and correct the GC bias of tumor and normal WGS data for SCNA based tumor subclonal population inferring', 'Chu, Yanshuo|Teng, Mingxiang|Wang, Yadong', 'Center for Bioinformatics, Harbin Institute of Technology, Harbin, China.|Center for Bioinformatics, Harbin Institute of Technology, Harbin, China.|Center for Bioinformatics, Harbin Institute of Technology, Harbin, China. ydwang@hit.edu.cn.', 'China|China|China', 1, 'Base Composition|Bias|Cell Line, Tumor|Clone Cells|DNA Copy Number Variations|Heterozygote|Humans|Markov Chains|Models, Genetic|Monte Carlo Method|Neoplasms|Polymorphism, Single Nucleotide|Whole Genome Sequencing', 'Journal Article', '10.1186/s12859-018-2099-0', '29671389', 'Somatic copy number alternations (SCNAs) can be utilized to infer tumor subclonal populations in whole genome seuqncing studies, where usually their read count ratios between tumor-normal paired samples serve as the inferring proxy. Existing SCNA based subclonal population inferring tools consider the GC bias of tumor and normal sample is of the same fature, and could be fully offset by read count ratio. However, we found that, the read count ratio on SCNA segments presents a Log linear biased pattern, which influence existing read count ratios based subclonal inferring tools performance. Currently no correction tools take into account the read ratio bias.'),
(98338, 2018, 'bmc', 'BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data', 'Guo, Yang|Liu, Shuhui|Li, Zhanhuai|Shang, Xuequn', 'School of Computer Science and Engineering, Northwestern Polytechnical University, Xian, 710072, Peoples Republic of China.|School of Computer Science and Engineering, Northwestern Polytechnical University, Xian, 710072, Peoples Republic of China.|School of Computer Science and Engineering, Northwestern Polytechnical University, Xian, 710072, Peoples Republic of China.|School of Computer Science and Engineering, Northwestern Polytechnical University, Xian, 710072, Peoples Republic of China. shang@nwpu.edu.cn.', 'China|China|China|China', 18, 'Algorithms|Databases as Topic|Gene Expression Regulation, Neoplastic|Humans|Neoplasms|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-018-2095-4', '29671390', 'The classification of cancer subtypes is of great importance to cancer disease diagnosis and therapy. Many supervised learning approaches have been applied to cancer subtype classification in the past few years, especially of deep learning based approaches. Recently, the deep forest model has been proposed as an alternative of deep neural networks to learn hyper-representations by using cascade ensemble decision trees. It has been proved that the deep forest model has competitive or even better performance than deep neural networks in some extent. However, the standard deep forest model may face overfitting and ensemble diversity challenges when dealing with small sample size and high-dimensional biology data.'),
(98339, 2018, 'bmc', 'Effective norm emergence in cell systems under limited communication', 'Hao, Xiaotian|Hao, Jianye|Wang, Li|Hou, Hanxu', 'School of Computer Science and Software, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350, China.|School of Computer Science and Software, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350, China.|School of Computer Science and Software, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350, China. wangli@tju.edu.cn.|School of Electrical Engineering and Intelligentization, Dongguan University of Technology, No. 1, university road, songshan lake district, dongguan, 221116, China. houhanxu@163.com.', 'China|China|China|China', 0, 'Algorithms|Cell Communication|Cells|Humans|Models, Biological', 'Journal Article', '10.1186/s12859-018-2097-2', '29671391', 'The cooperation of cells in biological systems is similar to that of agents in cooperative multi-agent systems. Research findings in multi-agent systems literature can provide valuable inspirations to biological research. The well-coordinated states in cell systems can be viewed as desirable social norms in cooperative multi-agent systems. One important research question is how a norm can rapidly emerge with limited communication resources.'),
(98340, 2018, 'bmc', 'Higher-order partial least squares for predicting gene expression levels from chromatin states', 'Sun, Shiquan|Sun, Xifang|Zheng, Yan', 'School of Computer Science, Northwestern Polytechnical University, Xian, 710072, Shaanxi, Peoples Republic of China. sqsun@nwpu.edu.cn.|School of Science, Xian Shiyou University, Xian, 710065, Shaanxi, Peoples Republic of China.|School of Computer Science, Northwestern Polytechnical University, Xian, 710072, Shaanxi, Peoples Republic of China.', 'China|China|China', 0, 'Algorithms|Animals|Cell Line|Chromatin|Computer Simulation|Gene Expression Regulation|Humans|Least-Squares Analysis|Macaca mulatta|Pan troglodytes|Transcription Initiation Site', 'Journal Article', '10.1186/s12859-018-2100-y', '29671394', 'Extensive studies have shown that gene expression levels are strongly affected by chromatin mark combinations via at least two mechanisms, i.e., activation or repression. But their combinatorial patterns are still unclear. To further understand the relationship between histone modifications and gene expression levels, here in this paper, we introduce a purely geometric higher-order representation, tensor (also called multidimensional array), which might borrow more unknown interactions in chromatin states to predicting gene expression levels.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98341, 2018, 'bmc', 'A framework for analyzing DNA methylation data from Illumina Infinium HumanMethylation450 BeadChip', 'Wang, Zhenxing|Wu, XiaoLiang|Wang, Yadong', 'School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China. ydwang@hit.edu.cn.', 'China|China|China', 8, 'CpG Islands|DNA Methylation|Databases, Genetic|Gene Ontology|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/s12859-018-2096-3', '29671397', 'DNA methylation has been identified to be widely associated to complex diseases. Among biological platforms to profile DNA methylation in human, the Illumina Infinium HumanMethylation450 BeadChip (450K) has been accepted as one of the most efficient technologies. However, challenges exist in analysis of DNA methylation data generated by this technology due to widespread biases.'),
(98342, 2018, 'bmc', 'Identifying diseases-related metabolites using random walk', 'Hu, Yang|Zhao, Tianyi|Zhang, Ningyi|Zang, Tianyi|Zhang, Jun|Cheng, Liang', 'School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Peoples Republic of China.|School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Peoples Republic of China.|School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Peoples Republic of China.|School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Peoples Republic of China. tianyi.zang@hit.edu.cn.|Department of rehabilitation, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, 150001, Peoples Republic of China. zhangjun13902003@163.com.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150001, China. liangcheng@hrbmu.edu.cn.', 'China|China|China|China|China|China', 0, 'Algorithms|Data Analysis|Databases, Factual|Disease|Humans|Metabolome|Probability|Reproducibility of Results', 'Journal Article', '10.1186/s12859-018-2098-1', '29671398', 'Metabolites disrupted by abnormal state of human body are deemed as the effect of diseases. In comparison with the cause of diseases like genes, these markers are easier to be captured for the prevention and diagnosis of metabolic diseases. Currently, a large number of metabolic markers of diseases need to be explored, which drive us to do this work.'),
(98343, 2018, 'bmc', 'Automatic infection detection based on electronic medical records', 'Tou, Huaixiao|Yao, Lu|Wei, Zhongyu|Zhuang, Xiahai|Zhang, Bo', 'School of Data Science, Fudan University, Shanghai, China.|Zhongshan Hospital Affiliated to Fudan University, Shanghai, China.|School of Data Science, Fudan University, Shanghai, China. zywei@fudan.edu.cn.|School of Data Science, Fudan University, Shanghai, China.|Zhongshan Hospital Affiliated to Fudan University, Shanghai, China. zhang.bo@zs-hospital.sh.cn.', 'China|China|China|China|China', 10, 'Age Factors|Algorithms|Automation|Communicable Diseases|Electronic Health Records|Humans|Machine Learning', 'Journal Article', '10.1186/s12859-018-2101-x', '29671399', 'Making accurate patient care decision, as early as possible, is a constant challenge, especially for physicians in the emergency department. The increasing volumes of electronic medical records (EMRs) open new horizons for automatic diagnosis. In this paper, we propose to use machine learning approaches for automatic infection detection based on EMRs. Five categories of information are utilized for prediction, including personal information, admission note, vital signs, diagnose test results and medical image diagnose.'),
(98344, 2018, 'bmc', 'Measuring phenotype-phenotype similarity through the interactome', 'Peng, Jiajie|Hui, Weiwei|Shang, Xuequn', 'School of Computer Science, Northwestern Polytechnical University, Xian, China.|School of Computer Science, Northwestern Polytechnical University, Xian, China.|School of Computer Science, Northwestern Polytechnical University, Xian, China. shang@nwpu.edu.cn.', 'China|China|China', 20, 'Algorithms|Databases, Genetic|Gene Expression Regulation|Gene Ontology|Humans|Phenotype|Protein Interaction Maps|Proteins', 'Journal Article', '10.1186/s12859-018-2102-9', '29671400', 'Recently, measuring phenotype similarity began to play an important role in disease diagnosis. Researchers have begun to pay attention to develop phenotype similarity measurement. However, existing methods ignore the interactions between phenotype-associated proteins, which may lead to inaccurate phenotype similarity.'),
(98345, 2018, 'bmc', 'ATMAD: robust image analysis for Automatic Tissue MicroArray De-arraying', 'Nguyen, Hoai Nam|Paveau, Vincent|Cauchois, Cyril|Kervrann, Charles', 'Inria Rennes - Bretagne Atlantique, Campus universitaire de Beaulieu, Rennes, 35042, France. hoai-nam.nguyen@inria.fr.|Innopsys, Parc dActivités Activestre, Carbonne, 31390, France.|Innopsys, Parc dActivités Activestre, Carbonne, 31390, France.|Inria Rennes - Bretagne Atlantique, Campus universitaire de Beaulieu, Rennes, 35042, France.', 'France|France|France|France', 0, 'Algorithms|Automation|Computer Simulation|Databases, Genetic|Humans|Image Processing, Computer-Assisted|Microscopy, Fluorescence|Oligonucleotide Array Sequence Analysis|Software|Tissue Array Analysis|Workflow', 'Journal Article', '10.1186/s12859-018-2111-8', '29673310', 'Over the last two decades, an innovative technology called Tissue Microarray (TMA), which combines multi-tissue and DNA microarray concepts, has been widely used in the field of histology. It consists of a collection of several (up to 1000 or more) tissue samples that are assembled onto a single support - typically a glass slide - according to a design grid (array) layout, in order to allow multiplex analysis by treating numerous samples under identical and standardized conditions. However, during the TMA manufacturing process, the sample positions can be highly distorted from the design grid due to the imprecision when assembling tissue samples and the deformation of the embedding waxes. Consequently, these distortions may lead to severe errors of (histological) assay results when the sample identities are mismatched between the design and its manufactured output. The development of a robust method for de-arraying TMA, which localizes and matches TMA samples with their design grid, is therefore crucial to overcome the bottleneck of this prominent technology.'),
(98346, 2018, 'bmc', 'Identification of residue pairing in interacting xcexb2-strands from a predicted residue contact map', 'Mao, Wenzhi|Wang, Tong|Zhang, Wenxuan|Gong, Haipeng', 'MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China.|MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China.|MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China.|MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China. hgong@tsinghua.edu.cn.', 'China|China|China|China', 4, 'Algorithms|Amino Acids|Computational Biology|Models, Molecular|Protein Conformation, beta-Strand|Protein Structure, Tertiary|Proteins|Reproducibility of Results', 'Journal Article', '10.1186/s12859-018-2150-1', '29673311', 'Despite the rapid progress of protein residue contact prediction, predicted residue contact maps frequently contain many errors. However, information of residue pairing in xcexb2 strands could be extracted from a noisy contact map, due to the presence of characteristic contact patterns in xcexb2-xcexb2 interactions. This information may benefit the tertiary structure prediction of mainly xcexb2 proteins. In this work, we propose a novel ridge-detection-based xcexb2-xcexb2 contact predictor to identify residue pairing in xcexb2 strands from any predicted residue contact map.'),
(98347, 2018, 'bmc', 'A site specific model and analysis of the neutral somatic mutation rate in whole-genome cancer data', 'Bertl, Johanna|Guo, Qianyun|Juul, Malene|Besenbacher, Søren|Nielsen, Morten Muhlig|Hornshøj, Henrik|Pedersen, Jakob Skou|Hobolth, Asger', 'Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, Aarhus N, DK-8200, Denmark. johanna.bertl@clin.au.dk.|Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, Aarhus N, DK-8200, Denmark.|Bioinformatics Research Centre, Aarhus University, C.F. Mollers Alle 8, Aarhus C, DK-8000, Denmark.|Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, Aarhus N, DK-8200, Denmark.|Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, Aarhus N, DK-8200, Denmark.|Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, Aarhus N, DK-8200, Denmark.|Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, Aarhus N, DK-8200, Denmark.|Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, Aarhus N, DK-8200, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark|Denmark|Denmark|Denmark', 1, 'Databases, Genetic|Epigenomics|Genome, Human|Humans|Models, Genetic|Mutation|Mutation Rate|Neoplasms|Polymorphism, Single Nucleotide|Regression Analysis', 'Journal Article', '10.1186/s12859-018-2141-2', '29673314', 'Detailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration.'),
(98348, 2018, 'bmc', 'A study on fast calling variants from next-generation sequencing data using decision tree', 'Li, Zhentang|Wang, Yi|Wang, Fei', 'Shanghai Key Lab of Intelligent Information Processing, Shanghai, China.|MOE Key Laboratory of Contemporary Anthropology and State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Developmental Biology and School of Life Sciences, Fudan University, Shanghai, 200438, China.|Shanghai Key Lab of Intelligent Information Processing, Shanghai, China. wangfei@fudan.edu.cn.', 'China|China|China', 5, 'Algorithms|Asian Continental Ancestry Group|Decision Trees|Exome|Genetic Variation|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Polymorphism, Single Nucleotide|Software|Whole Genome Sequencing', 'Journal Article', '10.1186/s12859-018-2147-9', '29673316', 'The rapid development of next-generation sequencing (NGS) technology has continuously been refreshing the throughput of sequencing data. However, due to the lack of a smart tool that is both fast and accurate, the analysis task for NGS data, especially those with low-coverage, remains challenging.'),
(98349, 2018, 'bmc', 'SpirPep: an in silico digestion-based platform to assist bioactive peptides discovery from a genome-wide database', 'Anekthanakul, Krittima|Hongsthong, Apiradee|Senachak, Jittisak|Ruengjitchatchawalya, Marasri', 'Biotechnology Program, School of Bioresources and Technology, King Mongkuts University of Technology Thonburi (Bang Khun Thian), 49 Soi Thian Thale 25, Bang Khun Thian Chai Thale Rd., Tha Kham, Bang Khun Thian, Bangkok, 10150, Thailand.|Biochemical Engineering and Pilot Plant Research and Development Unit, National Center for Genetic Engineering and Biotechnology at King Mongkuts University of Technology Thonburi, 49 Soi Thian Thale 25, Bang Khun Thian Chai Thale Rd., Tha Kham, Bang Khun Thian, Bangkok, 10150, Thailand.|Biochemical Engineering and Pilot Plant Research and Development Unit, National Center for Genetic Engineering and Biotechnology at King Mongkuts University of Technology Thonburi, 49 Soi Thian Thale 25, Bang Khun Thian Chai Thale Rd., Tha Kham, Bang Khun Thian, Bangkok, 10150, Thailand.|Biotechnology Program, School of Bioresources and Technology, King Mongkuts University of Technology Thonburi (Bang Khun Thian), 49 Soi Thian Thale 25, Bang Khun Thian Chai Thale Rd., Tha Kham, Bang Khun Thian, Bangkok, 10150, Thailand. marasri.rue@kmutt.ac.th.', 'Thailand|Thailand|Thailand|Thailand', 8, 'Amino Acid Sequence|Animals|Computational Biology|Computer Simulation|Databases, Protein|Genome|Humans|Peptides|Software|Workflow', 'Journal Article', '10.1186/s12859-018-2143-0', '29678128', 'Bioactive peptides, including biological sources-derived peptides with different biological activities, are protein fragments that influence the functions or conditions of organisms, in particular humans and animals. Conventional methods of identifying bioactive peptides are time-consuming and costly. To quicken the processes, several bioinformatics tools are recently used to facilitate screening of the potential peptides prior their activity assessment in vitro and/or in vivo. In this study, we developed an efficient computational method, SpirPep, which offers many advantages over the currently available tools.'),
(98350, 2018, 'bmc', 'ClustAGE: a tool for clustering and distribution analysis of bacterial accessory genomic elements', 'Ozer, Egon A', 'Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA. e-ozer@northwestern.edu.', 'United States', 12, 'Algorithms|Base Sequence|Cluster Analysis|Genes, Bacterial|Genome, Bacterial|Genomic Islands|Phylogeny|Pseudomonas aeruginosa|Software', 'Journal Article', '10.1186/s12859-018-2154-x', '29678129', 'The non-conserved accessory genome of bacteria can be associated with important adaptive characteristics that can contribute to niche specificity or pathogenicity of strains. High degrees of structural and compositional diversity in genomic islands and other elements of the accessory genome can complicate characterization of accessory genome contents among populations of strains. Methods for easily and effectively defining the distributions of discrete elements of the accessory genome among bacterial strains in a population are needed to explore the relationships between the flexible genome and bacterial adaptive traits.'),
(98351, 2018, 'bmc', 'SECIMTools: a suite of metabolomics data analysis tools', 'Kirpich, Alexander S|Ibarra, Miguel|Moskalenko, Oleksandr|Fear, Justin M|Gerken, Joseph|Mi, Xinlei|Ashrafi, Ali|Morse, Alison M|McIntyre, Lauren M', 'Southeast Center for Integrated Metabolomics (SECIM), University of Florida, Gainesville, FL, 32611, USA.|Southeast Center for Integrated Metabolomics (SECIM), University of Florida, Gainesville, FL, 32611, USA.|University of Florida Research Computing, University of Florida, Gainesville, FL, 32611, USA.|Southeast Center for Integrated Metabolomics (SECIM), University of Florida, Gainesville, FL, 32611, USA.|Southeast Center for Integrated Metabolomics (SECIM), University of Florida, Gainesville, FL, 32611, USA.|Department of Biostatistics, University of Florida, Gainesville, FL, 32611, USA.|Southeast Center for Integrated Metabolomics (SECIM), University of Florida, Gainesville, FL, 32611, USA.|Southeast Center for Integrated Metabolomics (SECIM), University of Florida, Gainesville, FL, 32611, USA.|Southeast Center for Integrated Metabolomics (SECIM), University of Florida, Gainesville, FL, 32611, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 11, 'Analysis of Variance|Cluster Analysis|Discriminant Analysis|Humans|Least-Squares Analysis|Metabolomics|Principal Component Analysis|Quality Control|Reproducibility of Results|Software|Statistics as Topic|Support Vector Machine|Workflow', 'Journal Article', '10.1186/s12859-018-2134-1', '29678131', 'Metabolomics has the promise to transform the area of personalized medicine with the rapid development of high throughput technology for untargeted analysis of metabolites. Open access, easy to use, analytic tools that are broadly accessible to the biological community need to be developed. While technology used in metabolomics varies, most metabolomics studies have a set of features identified. Galaxy is an open access platform that enables scientists at all levels to interact with big data. Galaxy promotes reproducibility by saving histories and enabling the sharing workflows among scientists.'),
(98352, 2018, 'bmc', 'An improved approach to infer protein-protein interaction based on a hierarchical vector space model', 'Zhang, Jiongmin|Jia, Ke|Jia, Jinmeng|Qian, Ying', 'Department of Computer Science & Technology, East China Normal University, North Zhongshan Road, Shanghai, 200062, China.|Department of Computer Science & Technology, East China Normal University, North Zhongshan Road, Shanghai, 200062, China.|School of life science, East China Normal University, Dongchuan Road, Shanghai, 200241, China.|Department of Computer Science & Technology, East China Normal University, North Zhongshan Road, Shanghai, 200062, China. yqian@cs.ecnu.edu.cn.', 'China|China|China|China', 2, 'Algorithms|Computational Biology|Humans|Models, Theoretical|Protein Interaction Mapping|Proteins|Saccharomyces cerevisiae|Semantics|Software', 'Journal Article', '10.1186/s12859-018-2152-z', '29699476', 'Comparing and classifying functions of gene products are important in todays biomedical research. The semantic similarity derived from the Gene Ontology (GO) annotation has been regarded as one of the most widely used indicators for protein interaction. Among the various approaches proposed, those based on the vector space model are relatively simple, but their effectiveness is far from satisfying.'),
(98353, 2018, 'bmc', 'Enhanced JBrowse plugins for epigenomics data visualization', 'Hofmeister, Brigitte T|Schmitz, Robert J', 'Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA. bth29393@uga.edu.|Department of Genetics, University of Georgia, Athens, GA, 30602, USA. schmitz@uga.edu.', 'Georgia|Georgia', 5, 'DNA|Databases, Genetic|Epigenomics|Humans|Nucleotide Motifs|RNA|Software', 'Journal Article', '10.1186/s12859-018-2160-z', '29699480', 'New sequencing techniques require new visualization strategies, as is the case for epigenomics data such as DNA base modifications, small non-coding RNAs, and histone modifications.'),
(98354, 2018, 'bmc', 'Computer simulations of the signalling network in FLT3 +-acute myeloid leukaemia – indications for an optimal dosage of inhibitors against FLT3 and CDK6', 'Buetti-Dinh, Antoine|Friedman, Ran', 'Department of Chemistry and Biomedical Sciences, Linnæus University, Norra vägen 49, Kalmar, SE-391 82, Sweden.|Department of Chemistry and Biomedical Sciences, Linnæus University, Norra vägen 49, Kalmar, SE-391 82, Sweden. ran.friedman@lnu.se.', 'Sweden|Sweden', 4, 'Apoptosis|Cell Proliferation|Computer Simulation|Cyclin-Dependent Kinase 6|Gene Expression Regulation, Neoplastic|Humans|Leukemia, Myeloid, Acute|Mutation|Protein Kinase Inhibitors|Signal Transduction|fms-Like Tyrosine Kinase 3', 'Journal Article', '10.1186/s12859-018-2145-y', '29699481', 'Mutations in the FMS-like tyrosine kinase 3 (FLT3) are associated with uncontrolled cellular functions that contribute to the development of acute myeloid leukaemia (AML). We performed computer simulations of the FLT3-dependent signalling network in order to study the pathways that are involved in AML development and resistance to targeted therapies.'),
(98355, 2018, 'bmc', 'Structural disorder of plasmid-encoded proteins in Bacteria and Archaea', 'Mitixc4x87, Nenad S|Malkov, Saxc5xa1a N|Kovaxc4x8devixc4x87, Jovana J|Pavlovixc4x87-Laxc5xbeetixc4x87, Gordana M|Beljanski, Miloxc5xa1 V', 'Department of Computer Science, Faculty of Mathematics, University of Belgrade, P.O.B. 550 Studentski trg 16, Belgrade, 11001, Serbia. nenad@matf.bg.ac.rs.|Department of Computer Science, Faculty of Mathematics, University of Belgrade, P.O.B. 550 Studentski trg 16, Belgrade, 11001, Serbia.|Department of Computer Science, Faculty of Mathematics, University of Belgrade, P.O.B. 550 Studentski trg 16, Belgrade, 11001, Serbia.|Department of Computer Science, Faculty of Mathematics, University of Belgrade, P.O.B. 550 Studentski trg 16, Belgrade, 11001, Serbia.|Bio-lab, Institute of General and Physical Chemistry, P.O.B. 45, Studentski trg 12/V, Belgrade, 11001, Serbia.', 'Serbia|Serbia|Serbia|Serbia|Serbia', 0, 'Archaea|Archaeal Proteins|Bacteria|Bacterial Proteins|Chromosomes, Archaeal|Chromosomes, Bacterial|Intrinsically Disordered Proteins|Plasmids|Proteome|Toxins, Biological', 'Journal Article', '10.1186/s12859-018-2158-6', '29699482', 'In the last decade and a half it has been firmly established that a large number of proteins do not adopt a well-defined (ordered) structure under physiological conditions. Such intrinsically disordered proteins (IDPs) and intrinsically disordered (protein) regions (IDRs) are involved in essential cell processes through two basic mechanisms: the entropic chain mechanism which is responsible for rapid fluctuations among many alternative conformations, and molecular recognition via short recognition elements that bind to other molecules. IDPs possess a high adaptive potential and there is special interest in investigating their involvement in organism evolution.'),
(98356, 2018, 'bmc', 'PDZscape: a comprehensive PDZ-protein database', 'Doshi, Jitesh|Kuppili, Raja Reddy|Gurdasani, Siddharth|Venkatakrishnan, Navneet|Saxena, Amit|Bose, Kakoli', 'Integrated Biophysics and Structural Biology Lab, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Kharghar, Navi Mumbai, 410210, India.|Integrated Biophysics and Structural Biology Lab, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Kharghar, Navi Mumbai, 410210, India.|Integrated Biophysics and Structural Biology Lab, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Kharghar, Navi Mumbai, 410210, India.|Integrated Biophysics and Structural Biology Lab, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Kharghar, Navi Mumbai, 410210, India.|Centre for Development of Advanced Computing (C-DAC), Pune University Campus, Ganesh Khind, Pune, Maharashtra, 411007, India.|Integrated Biophysics and Structural Biology Lab, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Kharghar, Navi Mumbai, 410210, India. kbose@actrec.gov.in.', 'India|India|India|India|India|India', 0, 'Databases, Protein|Humans|PDZ Domains|Software|Statistics as Topic|User-Computer Interface', 'Journal Article', '10.1186/s12859-018-2156-8', '29699484', 'PDZ-containing proteins comprise one of the most widely distributed protein families playing major role in localization and membrane receptor clustering. They are hence important regulators of signal transduction in cellular pathways. Although knowledge on these proteins has increased exponentially, the existing database PDZBase is limited by presence of only 339 proteins as it dates back to 2004 when very little data was available. Thus, lack of exclusive information on this protein family led us to develop PDZscape. PDZscape encompasses the complete available information on 58,648 PDZ-containing proteins with their known and putative binding partners on one platform. It has a user-friendly web interface that can be easily queried with external protein identifiers. With unique integration of prominent databases including NCBI, UniProtKB, Swiss-Prot, Pubmed, PDB, STRING, IntAct, KEGG, Pfam and Protein Mutant Database, it provides detailed information on PDZ interactome apart from the customized BLAST option. Most importantly, this database encompasses the mutations and diseases associated with PDZ containing proteins manually curated by our group, thus making it a comprehensive compilation. It also features tools to query the database using sequence (PDZ-Blast) and to find if protein of interest is a PDZ-binding protein. PDZscape is freely available at http://www.actrec.gov.in:8080/pdzscape .'),
(98357, 2018, 'bmc', 'Integrative analysis and machine learning on cancer genomics data using the Cancer Systems Biology Database (CancerSysDB)', 'Krempel, Rasmus|Kulkarni, Pranav|Yim, Annie|Lang, Ulrich|Habermann, Bianca|Frommolt, Peter', 'Regional Computing Center of the University of Cologne (RRZK), Cologne, Germany.|Bioinformatics Facility, CECAD Research Center, University of Cologne, Cologne, Germany.|Institut de Biologie du Développement, Aix-Marseille University, Marseille, France.|Regional Computing Center of the University of Cologne (RRZK), Cologne, Germany.|Institut de Biologie du Développement, Aix-Marseille University, Marseille, France.|Bioinformatics Facility, CECAD Research Center, University of Cologne, Cologne, Germany. peter.frommolt@uni-koeln.de.', 'Germany|Germany|France|Germany|France|Germany', 8, 'Databases, Factual|Exome|Genomics|Humans|Machine Learning|Neoplasms|Software|Systems Biology', 'Journal Article', '10.1186/s12859-018-2157-7', '29699486', 'Recent cancer genome studies on many human cancer types have relied on multiple molecular high-throughput technologies. Given the vast amount of data that has been generated, there are surprisingly few databases which facilitate access to these data and make them available for flexible analysis queries in the broad research community. If used in their entirety and provided at a high structural level, these data can be directed into constantly increasing databases which bear an enormous potential to serve as a basis for machine learning technologies with the goal to support research and healthcare with predictions of clinically relevant traits.'),
(98358, 2018, 'bmc', 'Ioncopy: an R Shiny app to call copy number alterations in targeted NGS data', 'Budczies, Jan|Pfarr, Nicole|Romanovsky, Eva|Endris, Volker|Stenzinger, Albrecht|Denkert, Carsten', 'Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany. jan.budczies@charite.de.|Institute of Pathology, Technical University Munich (TUM), Munich, Germany.|Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany.|Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.|Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.|Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 1, 'DNA Copy Number Variations|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Software', 'Journal Article', '10.1186/s12859-018-2159-5', '29699497', 'Somatic copy number alterations (CNAs) contribute to the clinically targetable aberrations in the tumor genome. For both routine diagnostics and biomarkers research, CNA analysis in a single assay together with somatic mutations is highly desirable.'),
(98359, 2018, 'bmc', 'Treemmer: a tool to reduce large phylogenetic datasets with minimal loss of diversity', 'Menardo, Fabrizio|Loiseau, Chloé|Brites, Daniela|Coscolla, Mireia|Gygli, Sebastian M|Rutaihwa, Liliana K|Trauner, Andrej|Beisel, Christian|Borrell, Sonia|Gagneux, Sebastien', 'Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland. fabrizio.menardo@swisstph.ch.|Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland.|Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland.|Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland.|Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland.|Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland.|Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zürich, 4058, Basel, Switzerland.|Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland.|Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland. sebastien.gagneux@swisstph.ch.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Swit', 0, 'Algorithms|Computational Biology|Databases, Genetic|Humans|Influenza A virus|Information Storage and Retrieval|Mycobacterium tuberculosis|Phylogeny|Software', 'Journal Article', '10.1186/s12859-018-2164-8', '29716518', 'Large sequence datasets are difficult to visualize and handle. Additionally, they often do not represent a random subset of the natural diversity, but the result of uncoordinated and convenience sampling. Consequently, they can suffer from redundancy and sampling biases.'),
(98360, 2018, 'bmc', 'The cytidine deaminase under-representation reporter (CDUR) as a tool to study evolution of sequences under deaminase mutational pressure', 'Shapiro, Maxwell|Meier, Stephen|MacCarthy, Thomas', 'Department of Applied Mathematics and Statistics, Stony Brook University, 100 Nicolls Road, Stony Brook, NY, USA.|Department of Applied Mathematics and Statistics, Stony Brook University, 100 Nicolls Road, Stony Brook, NY, USA.|Department of Applied Mathematics and Statistics, Stony Brook University, 100 Nicolls Road, Stony Brook, NY, USA. thomas.maccarthy@stonybrook.edu.', 'United States|United States|United States', 5, 'APOBEC-3G Deaminase|B-Lymphocytes|Biological Evolution|DNA-Binding Proteins|Genes, Reporter|Germinal Center|Humans|Mutation|Oncogene Proteins, Viral|Software|Viral Proteins', 'Journal Article', '10.1186/s12859-018-2161-y', '29716522', 'Activation induced deaminase (AID) and apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3 (APOBEC3) are deaminases that mutate C to U on single-stranded DNA (ssDNA). AID is expressed primarily in germinal center B-cells, where it facilitates affinity maturation and class-switch recombination. APOBEC3 are a family of anti-viral proteins that act as part of the intrinsic immune response. In both cases, there are particular sequence motifs, also known as mutation motifs, to which these deaminases prefer to bind and mutate.'),
(98361, 2018, 'bmc', 'SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform', 'Lin, Jie|Wei, Jing|Adjeroh, Donald|Jiang, Bing-Hua|Jiang, Yue', 'College of Mathematics and Informatics, Fujian Normal University, Fuzhou, 350108, Peoples Republic of China.|College of Mathematics and Informatics, Fujian Normal University, Fuzhou, 350108, Peoples Republic of China.|Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, 26506, WV, USA.|Department of Pathology, University of Iowa, Iowa city, 52242, Iowa, USA.|College of Mathematics and Informatics, Fujian Normal University, Fuzhou, 350108, Peoples Republic of China. yueljiang@163.com.', 'China|China|United States|United States|China', 5, 'Algorithms|Cluster Analysis|Computational Biology|Humans|Sequence Analysis, DNA|Sequence Analysis, Protein|Sequence Homology|Wavelet Analysis', 'Journal Article', '10.1186/s12859-018-2155-9', '29720081', 'Alignment-free sequence similarity analysis methods often lead to significant savings in computational time over alignment-based counterparts.'),
(98362, 2018, 'bmc', 'Surveying alignment-free features for Ortholog detection in related yeast proteomes by using supervised big data classifiers', 'Galpert, Deborah|Fernández, Alberto|Herrera, Francisco|Antunes, Agostinho|Molina-Ruiz, Reinaldo|Agüero-Chapin, Guillermin', 'Departamento de Ciencia de la Computación, Universidad Central ¨Marta Abreu¨ de Las Villas (UCLV), 54830, Santa Clara, Cuba.|Department of Computer Science and Artificial Intelligence, Research Center on Information and Communications Technology (CITIC-UGR), University of Granada, 18071, Granada, Spain.|Department of Computer Science and Artificial Intelligence, Research Center on Information and Communications Technology (CITIC-UGR), University of Granada, 18071, Granada, Spain.|CIIMAR/CIMAR, 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 Matosinhos, Porto, Portugal.|Centro de Bioactivos Químicos (CBQ), Universidad Central ¨Marta Abreu¨ de Las Villas (UCLV), 54830, Santa Clara, Cuba.|CIIMAR/CIMAR, 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 Matosinhos, Porto, Portugal. gchapin@ciimar.up.pt.', 'Cuba|Spain|Spain|Portugal|Cuba|Portugal', 0, 'Algorithms|Databases, Protein|Decision Trees|Proteome|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/s12859-018-2148-8', '29724166', 'The development of new ortholog detection algorithms and the improvement of existing ones are of major importance in functional genomics. We have previously introduced a successful supervised pairwise ortholog classification approach implemented in a big data platform that considered several pairwise protein features and the low ortholog pair ratios found between two annotated proteomes (Galpert, D et al., BioMed Research International, 2015). The supervised models were built and tested using a Saccharomycete yeast benchmark dataset proposed by Salichos and Rokas (2011). Despite several pairwise protein features being combined in a supervised big data approach, they all, to some extent were alignment-based features and the proposed algorithms were evaluated on a unique test set. Here, we aim to evaluate the impact of alignment-free features on the performance of supervised models implemented in the Spark big data platform for pairwise ortholog detection in several related yeast proteomes.'),
(98363, 2018, 'bmc', 'ProLego: tool for extracting and visualizing topological modules in protein structures', 'Khan, Taushif|Panday, Shailesh Kumar|Ghosh, Indira', 'School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India. taushifkhan@gmail.com.|School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India.|School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India.', 'India|India|India', 0, 'Neural Networks (Computer)|Proteins', 'Journal Article', '10.1186/s12859-018-2171-9', '29728050', 'In protein design, correct use of topology is among the initial and most critical feature. Meticulous selection of backbone topology aids in drastically reducing the structure search space. With ProLego, we present a server application to explore the component aspect of protein structures and provide an intuitive and efficient way to scan the protein topology space.'),
(98364, 2018, 'bmc', 'Ensemble outlier detection and gene selection in triple-negative breast cancer data', 'Lopes, Marta B|Veríssimo, André|Carrasquinha, Eunice|Casimiro, Sandra|Beerenwinkel, Niko|Vinga, Susana', 'IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal.|IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal.|IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal.|Luis Costa Lab, Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Avenida Professor Egas Moniz, Lisboa, 1649-028, Portugal.|Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, Basel, 4058, Switzerland.|IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal. susanavinga@tecnico.ulisboa.pt.', 'Portugal|Portugal|Portugal|Portugal|Switzerland|Portugal', 0, 'Female|Humans|Sample Size|Triple Negative Breast Neoplasms|Whole Genome Sequencing', 'Journal Article', '10.1186/s12859-018-2149-7', '29728051', 'Learning accurate models from omics data is bringing many challenges due to their inherent high-dimensionality, e.g. the number of gene expression variables, and comparatively lower sample sizes, which leads to ill-posed inverse problems. Furthermore, the presence of outliers, either experimental errors or interesting abnormal clinical cases, may severely hamper a correct classification of patients and the identification of reliable biomarkers for a particular disease. We propose to address this problem through an ensemble classification setting based on distinct feature selection and modeling strategies, including logistic regression with elastic net regularization, Sparse Partial Least Squares - Discriminant Analysis (SPLS-DA) and Sparse Generalized PLS (SGPLS), coupled with an evaluation of the individuals outlierness based on the Cooks distance. The consensus is achieved with the Rank Product statistics corrected for multiple testing, which gives a final list of sorted observations by their outlierness level.'),
(98365, 2018, 'bmc', 'An automatic method to calculate heart rate from zebrafish larval cardiac videos', 'Kang, Chia-Pin|Tu, Hung-Chi|Fu, Tzu-Fun|Wu, Jhe-Ming|Chu, Po-Hsun|Chang, Darby Tien-Hao', 'Department of Electronic Engineering, National Cheng Kung University, Tainan, 70101, Taiwan.|The Institute of Basic Medical Sciences, National Cheng Kung University, Tainan, 70101, Taiwan.|The Institute of Basic Medical Sciences, National Cheng Kung University, Tainan, 70101, Taiwan.|Department of Electronic Engineering, National Cheng Kung University, Tainan, 70101, Taiwan.|Department of Electronic Engineering, National Cheng Kung University, Tainan, 70101, Taiwan.|Department of Electronic Engineering, National Cheng Kung University, Tainan, 70101, Taiwan. darby@mail.ncku.edu.tw.', '-|-|-|-|-|-', 0, 'Animals|Heart Rate|Larva|Videotape Recording|Zebrafish', 'Journal Article', '10.1186/s12859-018-2166-6', '29743010', 'Zebrafish is a widely used model organism for studying heart development and cardiac-related pathogenesis. With the ability of surviving without a functional circulation at larval stages, strong genetic similarity between zebrafish and mammals, prolific reproduction and optically transparent embryos, zebrafish is powerful in modeling mammalian cardiac physiology and pathology as well as in large-scale high throughput screening. However, an economical and convenient tool for rapid evaluation of fish cardiac function is still in need. There have been several image analysis methods to assess cardiac functions in zebrafish embryos/larvae, but they are still improvable to reduce manual intervention in the entire process. This work developed a fully automatic method to calculate heart rate, an important parameter to analyze cardiac function, from videos. It contains several filters to identify the heart region, to reduce video noise and to calculate heart rates.'),
(98366, 2018, 'bmc', 'Tensor decomposition-based and principal-component-analysis-based unsupervised feature extraction applied to the gene expression and methylation profiles in the brains of social insects with multiple castes', 'Taguchi, Y-H', 'Department of Physics, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan. tag@granular.com.', 'Japan', 0, 'Algorithms|Animals|Brain|DNA Methylation|Down-Regulation|Finite Element Analysis|Gene Expression Profiling|Gene Expression Regulation|Gene Ontology|Hierarchy, Social|Principal Component Analysis|Reproducibility of Results|Social Behavior|Wasps', 'Journal Article', '10.1186/s12859-018-2068-7', '29745827', 'Even though coexistence of multiple phenotypes sharing the same genomic background is interesting, it remains incompletely understood. Epigenomic profiles may represent key factors, with unknown contributions to the development of multiple phenotypes, and social-insect castes are a good model for elucidation of the underlying mechanisms. Nonetheless, previous studies have failed to identify genes associated with aberrant gene expression and methylation profiles because of the lack of suitable methodology that can address this problem properly.'),
(98367, 2018, 'bmc', 'RaptorX-Angle: real-value prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning', 'Gao, Yujuan|Wang, Sheng|Deng, Minghua|Xu, Jinbo', 'Center for Quantitative Biology, Peking University, Beijing, China.|Toyota Technological Institute at Chicago, 6045 S Kenwood Ave., Chicago, USA.|Center for Quantitative Biology, Peking University, Beijing, China. dengmh@pku.edu.cn.|Toyota Technological Institute at Chicago, 6045 S Kenwood Ave., Chicago, USA. jinbo.xu@gmail.com.', 'China|United States|China|United States', 0, 'Amino Acids|Cluster Analysis|Costs and Cost Analysis|Databases, Protein|Deep Learning|Entropy|Protein Structure, Tertiary|Proteins|Software', 'Journal Article', '10.1186/s12859-018-2065-x', '29745828', 'Protein dihedral angles provide a detailed description of protein local conformation. Predicted dihedral angles can be used to narrow down the conformational space of the whole polypeptide chain significantly, thus aiding protein tertiary structure prediction. However, direct angle prediction from sequence alone is challenging.'),
(98368, 2018, 'bmc', 'A gene profiling deconvolution approach to estimating immune cell composition from complex tissues', 'Chen, Shu-Hwa|Kuo, Wen-Yu|Su, Sheng-Yao|Chung, Wei-Chun|Ho, Jen-Ming|Lu, Henry Horng-Shing|Lin, Chung-Yen', 'Institute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan. sophia@iis.sinica.edu.tw.|Institute of Statistics, National Chiao Tung University, Assembly Building I, 1001 Ta Hsueh Road, Hsinchu, 30010, Taiwan.|Institute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan.|Institute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan.|Institute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan.|Institute of Statistics, National Chiao Tung University, Assembly Building I, 1001 Ta Hsueh Road, Hsinchu, 30010, Taiwan. hslu@stat.nctu.edu.tw.|Institute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan. cylin@iis.sinica.edu.tw.', '-|-|-|-|-|-|-', 0, 'Cluster Analysis|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Humans|Leukocytes|Phenotype', 'Journal Article', '10.1186/s12859-018-2069-6', '29745829', 'A new emerged cancer treatment utilizes intrinsic immune surveillance mechanism that is silenced by those malicious cells. Hence, studies of tumor infiltrating lymphocyte populations (TILs) are key to the success of advanced treatments. In addition to laboratory methods such as immunohistochemistry and flow cytometry, in silico gene expression deconvolution methods are available for analyses of relative proportions of immune cell types.'),
(98369, 2018, 'bmc', 'MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions', 'Hayashi, Takanori|Matsuzaki, Yuri|Yanagisawa, Keisuke|Ohue, Masahito|Akiyama, Yutaka', 'Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.|Education Academy of Computational Life Sciences, Tokyo Institute of Technology, 2-12-1 W8-93 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.|Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.|Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan. ohue@c.titech.ac.jp.|Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan. akiyama@c.titech.ac.jp.', 'Japan|Japan|Japan|Japan|Japan', 4, 'Databases, Protein|Internet|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1186/s12859-018-2073-x', '29745830', 'Protein-protein interactions (PPIs) play several roles in living cells, and computational PPI prediction is a major focus of many researchers. The three-dimensional (3D) structure and binding surface are important for the design of PPI inhibitors. Therefore, rigid body protein-protein docking calculations for two protein structures are expected to allow elucidation of PPIs different from known complexes in terms of 3D structures because known PPI information is not explicitly required. We have developed rapid PPI prediction software based on protein-protein docking, called MEGADOCK. In order to fully utilize the benefits of computational PPI predictions, it is necessary to construct a comprehensive database to gather prediction results and their predicted 3D complex structures and to make them easily accessible. Although several databases exist that provide predicted PPIs, the previous databases do not contain a sufficient number of entries for the purpose of discovering novel PPIs.'),
(98370, 2018, 'bmc', 'cmFSM: a scalable CPU-MIC coordinated drug-finding tool by frequent subgraph mining', 'Yang, Shunyun|Guo, Runxin|Liu, Rui|Liao, Xiangke|Zou, Quan|Shi, Benyun|Peng, Shaoliang', 'School of Computer Science, National University of Defense Technology, Changsha, 410073, China.|School of Computer Science, National University of Defense Technology, Changsha, 410073, China.|Department of Oncology, The Second Xiangya Hospital of Central South University, Changsha, 410011, China.|School of Computer Science, National University of Defense Technology, Changsha, 410073, China.|School of Computer Science and Technology, Tianjin University, Tianjin, 300072, China. zouquan@nclab.net.|School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China. benyunshi@outlook.com.|College of Computer Science and Electronic Engineering & National Supercomputer Centre in Changsha, Hunan University, Changsha, 410082, China. pengshaoliang@nudt.edu.cn.', 'China|China|China|China|China|China|China', 4, 'Algorithms|Data Mining|Databases as Topic|Drug Evaluation, Preclinical|Software', 'Journal Article', '10.1186/s12859-018-2071-z', '29745832', 'Frequent subgraphs mining is a significant problem in many practical domains. The solution of this kind of problem can particularly used in some large-scale drug molecular or biological libraries to help us find drugs or core biological structures rapidly and predict toxicity of some unknown compounds. The main challenge is its efficiency, as (i) it is computationally intensive to test for graph isomorphisms, and (ii) the graph collection to be mined and mining results can be very large. Existing solutions often require days to derive mining results from biological networks even with relative low support threshold. Also, the whole mining results always cannot be stored in single node memory.'),
(98371, 2018, 'bmc', 'CNNH_PSS: protein 8-class secondary structure prediction by convolutional neural network with highway', 'Zhou, Jiyun|Wang, Hongpeng|Zhao, Zhishan|Xu, Ruifeng|Lu, Qin', 'School Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, Guangdong, 518055, China.|School Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, Guangdong, 518055, China.|School Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, Guangdong, 518055, China.|School Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, Guangdong, 518055, China. xuruifeng@hit.edu.cn.|Department of Computing, the Hong Kong Polytechnic University, Hung Hom, Hong Kong.', 'China|China|China|China|Hong Kong', 0, 'Databases, Protein|Deep Learning|Neural Networks (Computer)|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1186/s12859-018-2067-8', '29745837', 'Protein secondary structure is the three dimensional form of local segments of proteins and its prediction is an important problem in protein tertiary structure prediction. Developing computational approaches for protein secondary structure prediction is becoming increasingly urgent.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98372, 2018, 'bmc', 'Hierarchical structural component modeling of microRNA-mRNA integration analysis', 'Kim, Yongkang|Lee, Sungyoung|Choi, Sungkyoung|Jang, Jin-Young|Park, Taesung', 'Department of Statistics, Seoul National University, Seoul, Korea.|Interdisciplinary program in Bioinformatics, Seoul National University, Seoul, Korea.|Interdisciplinary program in Bioinformatics, Seoul National University, Seoul, Korea.|Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.|Department of Statistics, Seoul National University, Seoul, Korea. tspark@stats.snu.ac.kr.', '-|-|-|-|-', 0, 'Algorithms|Biomarkers, Tumor|Carcinoma, Pancreatic Ductal|Computer Simulation|Gene Expression Profiling|Gene Regulatory Networks|Humans|MicroRNAs|Models, Genetic|Pancreatic Neoplasms|RNA, Messenger', 'Journal Article', '10.1186/s12859-018-2070-0', '29745843', 'Identification of multi-markers is one of the most challenging issues in personalized medicine era. Nowadays, many different types of omics data are generated from the same subject. Although many methods endeavor to identify candidate markers, for each type of omics data, few or none can facilitate such identification.'),
(98373, 2018, 'bmc', 'Automated transition analysis of activated gene regulation during diauxic nutrient shift in Escherichia coli and adipocyte differentiation in mouse cells', 'Takenaka, Yoichi|Mikami, Kazuma|Seno, Shigeto|Matsuda, Hideo', 'Faculty of Informatics, Kansai University, Ryousenji 2-1-1, Takatsuki, Osaka, Japan. takenaka@kansai-u.ac.jp.|Recruit Holdings Co. Ltd., Marunouchi 1-9-2, Chiyoda, Tokyo, Japan.|Graduate School of Information Science and Technology, Osaka University, Yamadaoka 1-5, Suita, Osaka, Japan.|Graduate School of Information Science and Technology, Osaka University, Yamadaoka 1-5, Suita, Osaka, Japan.', 'Japan|Japan|Japan|Japan', 0, 'Adipocytes|Algorithms|Animals|Automation|Cell Differentiation|Escherichia coli|Gene Expression Regulation|Gene Expression Regulation, Bacterial|Gene Regulatory Networks|Genes, Bacterial|Mice|Time Factors', 'Journal Article', '10.1186/s12859-018-2072-y', '29745848', 'Comprehensively understanding the dynamics of biological systems is among the biggest current challenges in biology and medicine. To acquire this understanding, researchers have measured the time-series expression profiles of cell lines of various organisms. Biological technologies have also drastically improved, providing a huge amount of information with support from bioinformatics and systems biology. However, the transitions between the activation and inactivation of gene regulations, at the temporal resolution of single time points, are difficult to extract from time-course gene expression profiles.'),
(98374, 2018, 'bmc', 'Pathway-based approach using hierarchical components of rare variants to analyze multiple phenotypes', 'Lee, Sungyoung|Kim, Yongkang|Choi, Sungkyoung|Hwang, Heungsun|Park, Taesung', 'Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea.|Department of Statistics, Seoul National University, 1 Gwanak-ro Gwanak-gu, Seoul, 08826, Korea.|Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea.|Department of Psychology, McGill University, Montreal, Canada.|Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea. tspark@stats.snu.ac.kr.', '-|-|-|Canada|-', 4, 'Algorithms|Computer Simulation|Databases, Genetic|Diabetes Mellitus, Type 2|Exome|Genetic Variation|Humans|Models, Genetic|Multivariate Analysis|Phenotype|Signal Transduction', 'Journal Article', '10.1186/s12859-018-2066-9', '29745849', 'As one possible solution to the missing heritability problem, many methods have been proposed that apply pathway-based analyses, using rare variants that are detected by next generation sequencing technology. However, while a number of methods for pathway-based rare-variant analysis of multiple phenotypes have been proposed, no method considers a unified model that incorporate multiple pathways.'),
(98375, 2018, 'bmc', 'A new method to measure the semantic similarity from query phenotypic abnormalities to diseases based on the human phenotype ontology', 'Gong, Xiaofeng|Jiang, Jianping|Duan, Zhongqu|Lu, Hui', 'Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.|Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.|Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.|Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China. huilu@sjtu.edu.cn.', 'China|China|China|China', 6, 'Computer Simulation|Databases as Topic|Disease|Humans|Phenotype|Semantics', 'Journal Article', '10.1186/s12859-018-2064-y', '29745853', 'Although rapid developed sequencing technologies make it possible for genotype data to be used in clinical diagnosis, it is still challenging for clinicians to understand the results of sequencing and make correct judgement based on them. Before this, diagnosis based on clinical features held a leading position. With the establishment of the Human Phenotype Ontology (HPO) and the enrichment of phenotype-disease annotations, there throws much more attention to the improvement of phenotype-based diagnosis.'),
(98376, 2018, 'bmc', 'Computing the family-free DCJ similarity', 'Rubert, Diego P|Hoshino, Edna A|Braga, Marília D V|Stoye, Jens|Martinez, Fábio V', 'Faculdade de Computação, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil.|Faculdade de Computação, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil.|Faculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany.|Faculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany.|Faculdade de Computação, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil. fhvm@facom.ufms.br.', 'Brazil|Brazil|Germany|Germany|Brazil', 0, 'Algorithms|Animals|Computer Simulation|Databases, Genetic|Genome|Genomics|Heuristics|Humans|Mice|Models, Genetic|Phylogeny|Rats', 'Journal Article', '10.1186/s12859-018-2130-5', '29745861', 'The genomic similarity is a large-scale measure for comparing two given genomes. In this work we study the (NP-hard) problem of computing the genomic similarity under the DCJ model in a setting that does not assume that the genes of the compared genomes are grouped into gene families. This problem is called family-free DCJ similarity.'),
(98377, 2018, 'bmc', 'Generation and comparative genomics of synthetic dengue viruses', 'Goz, Eli|Tsalenchuck, Yael|Benaroya, Rony Oren|Zafrir, Zohar|Atar, Shimshi|Altman, Tahel|Julander, Justin|Tuller, Tamir', 'Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel.|SynVaccineLtd. Ramat Hachayal, Tel Aviv, Israel.|SynVaccineLtd. Ramat Hachayal, Tel Aviv, Israel.|Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel.|Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel.|SynVaccineLtd. Ramat Hachayal, Tel Aviv, Israel.|Institute for Antiviral Research, Utah State University, Logan, UT, USA.|Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel. tamirtul@post.tau.ac.il.', 'Israel|Israel|Israel|Israel|Israel|Israel|United States|Israel', 1, 'Animals|Base Sequence|Cercopithecus aethiops|Dengue|Dengue Virus|Genomics|High-Throughput Nucleotide Sequencing|Humans|Mice|Oligodeoxyribonucleotides|Polymorphism, Single Nucleotide|Serogroup|Vero Cells', 'Journal Article', '10.1186/s12859-018-2132-3', '29745863', 'Synthetic virology is an important multidisciplinary scientific field, with emerging applications in biotechnology and medicine, aiming at developing methods to generate and engineer synthetic viruses. In particular, many of the RNA viruses, including among others the Dengue and Zika, are widespread pathogens of significant importance to human health. The ability to design and synthesize such viruses may contribute to exploring novel approaches for developing vaccines and virus based therapies.'),
(98378, 2018, 'bmc', 'On the rank-distance median of 3 permutations', 'Chindelevitch, Leonid|Pereira Zanetti, João Paulo|Meidanis, João', 'Simon Fraser University, Burnaby, Canada. leonid@sfu.ca.|University of Campinas, Campinas, Brazil.|University of Campinas, Campinas, Brazil.', 'Canada|Brazil|Brazil', 1, 'Algorithms|Computer Simulation|Databases, Genetic|Gene Rearrangement|Genome|Genomics|Models, Genetic|Mutation', 'Journal Article', '10.1186/s12859-018-2131-4', '29745865', 'Recently, Pereira Zanetti, Biller and Meidanis have proposed a new definition of a rearrangement distance between genomes. In this formulation, each genome is represented as a matrix, and the distance d is the rank distance between these matrices. Although defined in terms of matrices, the rank distance is equal to the minimum total weight of a series of weighted operations that leads from one genome to the other, including inversions, translocations, transpositions, and others. The computational complexity of the median-of-three problem according to this distance is currently unknown. The genome matrices are a special kind of permutation matrices, which we study in this paper. In their paper, the authors provide an [Formula: see text] algorithm for determining three candidate medians, prove the tight approximation ratio [Formula: see text], and provide a sufficient condition for their candidates to be true medians. They also conduct some experiments that suggest that their method is accurate on simulated and real data.'),
(98379, 2018, 'bmc', 'ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees', 'Zhang, Chao|Rabiee, Maryam|Sayyari, Erfan|Mirarab, Siavash', 'Bioinformatics and Systems Biology, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093-0021, CA, USA.|Department of Computer Science and Engineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093-0021, CA, USA.|Department of Electrical and Computer Engineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093-0021, CA, USA.|Department of Electrical and Computer Engineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093-0021, CA, USA. smirarab@ucsd.edu.', 'United States|United States|United States|United States', 0, 'Algorithms|Animals|Birds|Computer Simulation|Databases, Genetic|Models, Genetic|Phylogeny|Species Specificity|Time Factors', 'Journal Article', '10.1186/s12859-018-2129-y', '29745866', 'Evolutionary histories can be discordant across the genome, and such discordances need to be considered in reconstructing the species phylogeny. ASTRAL is one of the leading methods for inferring species trees from gene trees while accounting for gene tree discordance. ASTRAL uses dynamic programming to search for the tree that shares the maximum number of quartet topologies with input gene trees, restricting itself to a predefined set of bipartitions.'),
(98380, 2018, 'bmc', 'Correction to: HiComet: a high-throughput comet analysis tool for large-scale DNA damage assessment', 'Lee, Taehoon|Lee, Sungmin|Sim, Woo Young|Jung, Yu Mi|Han, Sunmi|Won, Joong-Ho|Min, Hyeyoung|Yoon, Sungroh', 'Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea.|Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea.|R&D Center, Wearable Healthcare, Gyeonggi-do, 16954, Korea.|Research Division, NanoEnTek, Seoul, 08389, Korea.|Research Division, NanoEnTek, Seoul, 08389, Korea.|Department of Statistics, Seoul National University, Seoul, 08826, Korea.|College of Pharmachy, Chung-Ang University, Seoul, 06974, Korea.|Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea. sryoon@snu.ac.kr.', '-|-|-|-|-|-|-|-', 0, '-', 'Journal Article', '10.1186/s12859-018-2186-2', '29751737', 'After publication of the original article [1], it has been found that the author affiliations have been accidentally left out in the PDF. The full affiliations can be found in this correction.'),
(98381, 2018, 'bmc', 'GPU-accelerated iterative reconstruction for limited-data tomography in CBCT systems', 'de Molina, Claudia|Serrano, Estefania|Garcia-Blas, Javier|Carretero, Jesus|Desco, Manuel|Abella, Monica', 'Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.|Computer Architecture and Technology Area, Universidad Carlos III de Madrid, Madrid, Spain.|Computer Architecture and Technology Area, Universidad Carlos III de Madrid, Madrid, Spain.|Computer Architecture and Technology Area, Universidad Carlos III de Madrid, Madrid, Spain.|Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain. desco@hggm.es.|Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Cone-Beam Computed Tomography|Humans|Image Processing, Computer-Assisted|Tomography', 'Journal Article', '10.1186/s12859-018-2169-3', '29764362', 'Standard cone-beam computed tomography (CBCT) involves the acquisition of at least 360 projections rotating through 360 degrees. Nevertheless, there are cases in which only a few projections can be taken in a limited angular span, such as during surgery, where rotation of the source-detector pair is limited to less than 180 degrees. Reconstruction of limited data with the conventional method proposed by Feldkamp, Davis and Kress (FDK) results in severe artifacts. Iterative methods may compensate for the lack of data by including additional prior information, although they imply a high computational burden and memory consumption.'),
(98382, 2018, 'bmc', 'Live neighbor-joining', 'Telles, Guilherme P|Araújo, Graziela S|Walter, Maria E M T|Brigido, Marcelo M|Almeida, Nalvo F', 'Instituto de Computação, Universidade Estadual de Campinas, Cidade Universitária, Campinas, 13083-852, Brazil.|Faculdade de Computação, Universidade Federal de Mato Grosso do Sul, Av. Costa e Silva, s/n, Campo Grande, 79070-900, Brazil.|Departamento de Ciência da Computação, Universidade de Brasília, Campus Darcy Ribeiro, Brasília, 70910-900, Brazil.|Instituto de Ciências Biológicas, Universidade de Brasília, Campus Darcy Ribeiro, Brasília, 70910-900, Brazil.|Faculdade de Computação, Universidade Federal de Mato Grosso do Sul, Av. Costa e Silva, s/n, Campo Grande, 79070-900, Brazil. nalvo@facom.ufms.br.', 'Brazil|Brazil|Brazil|Brazil|Brazil', 0, 'Models, Genetic|Phylogeny|Plants', 'Journal Article', '10.1186/s12859-018-2162-x', '29769032', 'In phylogenetic reconstruction the result is a tree where all taxa are leaves and internal nodes are hypothetical ancestors. In a live phylogeny, both ancestral and living taxa may coexist, leading to a tree where internal nodes may be living taxa. The well-known Neighbor-Joining heuristic is largely used for phylogenetic reconstruction.'),
(98383, 2018, 'bmc', 'Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction', 'Faust, Kevin|Xie, Quin|Han, Dominick|Goyle, Kartikay|Volynskaya, Zoya|Djuric, Ugljesa|Diamandis, Phedias', 'Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada.|Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, M5S 1A8, Canada.|Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada.|The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada.|Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, M5S 1A8, Canada.|Laboratory Medicine Program, Department of Pathology, University Health Network, 200 Elizabeth Street, Toronto, ON, M5G 2C4, Canada.|Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, M5S 1A8, Canada. p.diamandis@mail.utoronto.ca.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 11, 'Artificial Intelligence|Deep Learning|Humans|Machine Learning|Neural Networks (Computer)', 'Journal Article', '10.1186/s12859-018-2184-4', '29769044', 'There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce.'),
(98384, 2018, 'bmc', 'Robust joint score tests in the application of DNA methylation data analysis', 'Li, Xuan|Fu, Yuejiao|Wang, Xiaogang|Qiu, Weiliang', 'Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, M3J1P3, Canada.|Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, M3J1P3, Canada. yuejiao@mathstat.yorku.ca.|Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, M3J1P3, Canada.|Channing Division of Network Medicine, Brigham and Womens Hospital, Harvard Medical School, 181 Longwood Avenue, Boston, 02115, USA.', 'Canada|Canada|Canada|United States', 0, 'Algorithms|Computer Simulation|DNA Methylation|Data Analysis|Disease|Humans|Statistics, Nonparametric', 'Journal Article', '10.1186/s12859-018-2185-3', '29776330', 'Recently differential variability has been showed to be valuable in evaluating the association of DNA methylation to the risks of complex human diseases. The statistical tests based on both differential methylation level and differential variability can be more powerful than those based only on differential methylation level. Anh and Wang (2013) proposed a joint score test (AW) to simultaneously detect for differential methylation and differential variability. However, AWs method seems to be quite conservative and has not been fully compared with existing joint tests.'),
(98385, 2018, 'bmc', 'Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches', 'Crichton, Gamal|Guo, Yufan|Pyysalo, Sampo|Korhonen, Anna', 'Language Technology Laboratory, TAL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK. gkoc2@cam.ac.uk.|Language Technology Laboratory, TAL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK.|Language Technology Laboratory, TAL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK.|Language Technology Laboratory, TAL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Drug Discovery|Literature Based Discovery|Neural Networks (Computer)|Protein Interaction Mapping', 'Journal Article', '10.1186/s12859-018-2163-9', '29783926', 'Link prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Protein Interaction (PPI) prediction and Literature-Based Discovery (LBD). It can be done using a classifier to output the probability of link formation between nodes. Recently several works have used neural networks to create node representations which allow rich inputs to neural classifiers. Preliminary works were done on this and report promising results. However they did not use realistic settings like time-slicing, evaluate performances with comprehensive metrics or explain when or why neural network methods outperform. We investigated how inputs from four node representation algorithms affect performance of a neural link predictor on random- and time-sliced biomedical graphs of real-world sizes (xe2x88xbcxe2x80x896 million edges) containing information relevant to DTI, PPI and LBD. We compared the performance of the neural link predictor to those of established baselines and report performance across five metrics.'),
(98386, 2018, 'bmc', 'SAMSA2: a standalone metatranscriptome analysis pipeline', 'Westreich, Samuel T|Treiber, Michelle L|Mills, David A|Korf, Ian|Lemay, Danielle G', 'Genome Center, University of California, Davis, California, USA.|Genome Center, University of California, Davis, California, USA.|Department of Food Science and Technology, University of California, Davis, California, USA.|Genome Center, University of California, Davis, California, USA.|Genome Center, University of California, Davis, California, USA. danielle.lemay@ars.usda.gov.', 'United States|United States|United States|United States|United States', 17, 'Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Metagenomics|Microbiota|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-018-2189-z', '29783945', 'Complex microbial communities are an area of growing interest in biology. Metatranscriptomics allows researchers to quantify microbial gene expression in an environmental sample via high-throughput sequencing. Metatranscriptomic experiments are computationally intensive because the experiments generate a large volume of sequence data and each sequence must be compared with reference sequences from thousands of organisms.'),
(98387, 2018, 'bmc', 'NPBSS: a new PacBio sequencing simulator for generating the continuous long reads with an empirical model', 'Wei, Ze-Gang|Zhang, Shao-Wu', 'Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xian, 710072, China.|Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xian, 710072, China. zhangsw@nwpu.edu.cn.', 'China|China', 8, 'Genome|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2208-0', '29788930', 'PacBio sequencing platform offers longer read lengths than the second-generation sequencing technologies. It has revolutionized de novo genome assembly and enabled the automated reconstruction of reference-quality genomes. Due to its extremely wide range of application areas, fast sequencing simulation systems with high fidelity are in great demand to facilitate the development and comparison of subsequent analysis tools. Although there are several available simulators (e.g., PBSIM, SimLoRD and FASTQSim) that target the specific generation of PacBio libraries, the error rate of simulated sequences is not well matched to the quality value of raw PacBio datasets, especially for PacBios continuous long reads (CLR).'),
(98388, 2018, 'bmc', 'NextSV: a meta-caller for structural variants from low-coverage long-read sequencing data', 'Fang, Li|Hu, Jiang|Wang, Depeng|Wang, Kai', 'Grandomics Biosciences, Beijing, 102206, China.|Grandomics Biosciences, Beijing, 102206, China.|Grandomics Biosciences, Beijing, 102206, China.|Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Childrens Hospital of Philadelphia, Philadelphia, PA, 19104, USA. wangk@email.chop.edu.', 'China|China|China|United States', 11, 'Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2207-1', '29792160', 'Structural variants (SVs) in human genomes are implicated in a variety of human diseases. Long-read sequencing delivers much longer read lengths than short-read sequencing and may greatly improve SV detection. However, due to the relatively high cost of long-read sequencing, it is unclear what coverage is needed and how to optimally use the aligners and SV callers.'),
(98389, 2018, 'bmc', 'A website to identify shared genes in Saccharomyces cerevisiae homozygous deletion library screens', '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, 'Gene Deletion|Gene Library|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Sequence Deletion', 'Journal Article', '10.1186/s12859-018-2212-4', '29792161', 'The homozygous yeast deletion library includes approximately 4800 diploid strains each containing one deleted non-essential gene. Hundreds of publications have arisen through experimentation using this genome-wide biological resource. As part of this work over 677 genesets have been collated from these experiments representing the phenotypic responses of the library to a diverse set of chemical and physical challenges.'),
(98390, 2018, 'bmc', 'Correction to: Identification and characterization of conserved lncRNAs in human and rat brain', 'Li, Dan|Yang, Mary Qu', 'MidSouth Bioinformatics Center and Joint Bioinformatics Ph.D. Program, University of Arkansas at Little Rock and University of Arkansas for Medical Sciences, 2801 S. University Avenue, Little Rock, AR, 72204, USA.|MidSouth Bioinformatics Center and Joint Bioinformatics Ph.D. Program, University of Arkansas at Little Rock and University of Arkansas for Medical Sciences, 2801 S. University Avenue, Little Rock, AR, 72204, USA. mqyang@ualr.edu.', 'United States|United States', 0, '-', 'Journal Article', '10.1186/s12859-018-2202-6', '29793423', 'After publication of the original article [1], it was noticed that the Acknowledgement statement was incorrect. The original statement reads.'),
(98391, 2018, 'bmc', 'polyClustR: defining communities of reconciled cancer subtypes with biological and prognostic significance', 'Eason, Katherine|Nyamundanda, Gift|Sadanandam, Anguraj', 'Division of Molecular Pathology, The Institute of Cancer Research (ICR), London, UK.|Division of Molecular Pathology, The Institute of Cancer Research (ICR), London, UK.|Division of Molecular Pathology, The Institute of Cancer Research (ICR), London, UK. Anguraj.Sadanandam@icr.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Breast Neoplasms|Cluster Analysis|Female|Humans|Melanoma|Neoplasms|Prognosis|Software|Uveal Neoplasms', 'Journal Article', '10.1186/s12859-018-2204-4', '29801433', 'To ensure cancer patients are stratified towards treatments that are optimally beneficial, it is a priority to define robust molecular subtypes using clustering methods applied to high-dimensional biological data. If each of these methods produces different numbers of clusters for the same data, it is difficult to achieve an optimal solution. Here, we introduce polyClustR, a tool that reconciles clusters identified by different methods into subtype communities using a hypergeometric test or a measure of relative proportion of common samples.'),
(98392, 2018, 'bmc', 'QTLTableMiner++: semantic mining of QTL tables in scientific articles', 'Singh, Gurnoor|Kuzniar, Arnold|van Mulligen, Erik M|Gavai, Anand|Bachem, Christian W|Visser, Richard G F|Finkers, Richard', 'Plant Breeding, Wageningen University and Research, Wageningen, The Netherlands.|Netherlands eScience Center (NLeSC), Amsterdam, The Netherlands.|Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands.|Netherlands eScience Center (NLeSC), Amsterdam, The Netherlands.|Plant Breeding, Wageningen University and Research, Wageningen, The Netherlands.|Plant Breeding, Wageningen University and Research, Wageningen, The Netherlands.|Plant Breeding, Wageningen University and Research, Wageningen, The Netherlands. richard.finkers@wur.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 3, 'Algorithms|Computer Graphics|Data Mining|Databases, Factual|Lycopersicon esculentum|Publications|Quantitative Trait Loci|Semantics|Software|Solanum tuberosum', 'Journal Article', '10.1186/s12859-018-2165-7', '29801439', 'Background A quantitative trait locus (QTL) is a genomic region that correlates with a phenotype. Most of the experimental information about QTL mapping studies is described in tables of scientific publications. Traditional text mining techniques aim to extract information from unstructured text rather than from tables. We present QTLTableMiner++ (QTM), a table mining tool that extracts and semantically annotates QTL information buried in (heterogeneous) tables of plant science literature.  QTM is a command line tool written in the Java programming language. This tool takes scientific articles from the Europe PMC repository as input, extracts QTL tables using keyword matching and ontology-based concept identification. The tables are further normalized using rules derived from table properties such as captions, column headers and table footers. Furthermore, table columns are classified into three categories namely column descriptors, properties and values based on column headers and data types of cell entries. Abbreviations found in the tables are expanded using the Schwartz and Hearst algorithm. Finally, the content of QTL tables is semantically enriched with domain-specific ontologies (e.g. Crop Ontology, Plant Ontology and Trait Ontology) using the Apache Solr search platform and the results are stored in a relational database and a text file.  Results The performance of the QTM tool was assessed by precision and recall based on the information retrieved from two manually annotated corpora of open access articles, i.e. QTL mapping studies in tomato (Solanum lycopersicum) and in potato (S. tuberosum). In summary, QTM detected QTL statements in tomato with 74.53% precision and 92.56% recall and in potato with 82.82% precision and 98.94% recall.  Conclusion QTM is a unique tool that aids in providing QTL information in machine-readable and semantically interoperable formats.'),
(98393, 2018, 'bmc', 'Recommending plant taxa for supporting on-site species identification', 'Wittich, Hans Christian|Seeland, Marco|Wäldchen, Jana|Rzanny, Michael|Mäder, Patrick', 'Institute for Computer and Systems Engineering, Technische Universität Ilmenau, Helmholtzplatz 5, Ilmenau, 98693, Germany. hans-christian.wittich@tu-ilmenau.de.|Institute for Computer and Systems Engineering, Technische Universität Ilmenau, Helmholtzplatz 5, Ilmenau, 98693, Germany.|Department Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Hans-Knöll-Str. 10, Jena, 07745, Germany.|Department Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Hans-Knöll-Str. 10, Jena, 07745, Germany.|Institute for Computer and Systems Engineering, Technische Universität Ilmenau, Helmholtzplatz 5, Ilmenau, 98693, Germany. patrick.maeder@tu-ilmenau.de.', 'Germany|Germany|Germany|Germany|Germany', 7, 'Biodiversity|Germany|Plants', 'Journal Article', '10.1186/s12859-018-2201-7', '29843588', 'Predicting a list of plant taxa most likely to be observed at a given geographical location and time is useful for many scenarios in biodiversity informatics. Since efficient plant species identification is impeded mainly by the large number of possible candidate species, providing a shortlist of likely candidates can help significantly expedite the task. Whereas species distribution models heavily rely on geo-referenced occurrence data, such information still remains largely unused for plant taxa identification tools.'),
(98394, 2018, 'bmc', 'RnaSeqSampleSize: real data based sample size estimation for RNA sequencing', 'Zhao, Shilin|Li, Chung-I|Guo, Yan|Sheng, Quanhu|Shyr, Yu', 'Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.|Department of Statistics, National Cheng Kung University, Tainan, 70101, Taiwan.|Department of Internal Medicine, University of New Mexico, Albuquerque, NM, 87131, USA.|Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.|Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. shyr.yu@vanderbilt.edu.', 'United States|United States|Mexico|United States|United States', 0, 'Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Models, Statistical|Sample Size|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-018-2191-5', '29843589', 'One of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. A few negative binomial model-based methods have been developed to estimate sample size based on the parameters of a single gene. However, thousands of genes are quantified and tested for differential expression simultaneously in RNA-Seq experiments. Thus, additional issues should be carefully addressed, including the false discovery rate for multiple statistic tests, widely distributed read counts and dispersions for different genes.'),
(98395, 2018, 'bmc', 'SemaTyP: a knowledge graph based literature mining method for drug discovery', 'Sang, Shengtian|Yang, Zhihao|Wang, Lei|Liu, Xiaoxia|Lin, Hongfei|Wang, Jian', 'College of Computer Science and Technology, Dalian University of Technology, Hongling Road, Dalian, 116023, China.|College of Computer Science and Technology, Dalian University of Technology, Hongling Road, Dalian, 116023, China. yangzh@dlut.edu.cn.|Beijing Institute of Health Administration and Medical Information, Beijing, 100850, China.|College of Computer Science and Technology, Dalian University of Technology, Hongling Road, Dalian, 116023, China.|College of Computer Science and Technology, Dalian University of Technology, Hongling Road, Dalian, 116023, China.|College of Computer Science and Technology, Dalian University of Technology, Hongling Road, Dalian, 116023, China.', 'China|China|China|China|China|China', 17, 'Data Mining|Drug Discovery|Drug Therapy|Humans|Knowledge Bases|Logistic Models|Publications', 'Journal Article', '10.1186/s12859-018-2167-5', '29843590', 'Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries.'),
(98396, 2018, 'bmc', 'JCDSA: a joint covariate detection tool for survival analysis on tumor expression profiles', 'Wu, Yiming|Liu, Yanan|Wang, Yueming|Shi, Yan|Zhao, Xudong', 'College of Information and Computer Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin, 150001, China.|College of Information and Computer Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin, 150001, China.|College of Information and Computer Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin, 150001, China.|College of Information and Computer Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin, 150001, China.|College of Information and Computer Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin, 150001, China. zhaoxudong@nefu.edu.cn.', 'China|China|China|China|China', 2, 'Brain Neoplasms|Glioblastoma|Humans|MicroRNAs|Prognosis|Survival Analysis|Transcriptome', 'Journal Article', '10.1186/s12859-018-2213-3', '29843599', 'Survival analysis on tumor expression profiles has always been a key issue for subsequent biological experimental validation. It is crucial how to select features which closely correspond to survival time. Furthermore, it is important how to select features which best discriminate between low-risk and high-risk group of patients. Common features derived from the two aspects may provide variable candidates for prognosis of cancer.'),
(98397, 2018, 'bmc', 'Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi', 'Keilwagen, Jens|Hartung, Frank|Paulini, Michael|Twardziok, Sven O|Grau, Jan', 'Institute for Biosafety in Plant Biotechnology, Julius Kühn-Institut (JKI) - Federal Research Centre for Cultivated Plants, Quedlinburg, D-06484, Germany. jens.keilwagen@julius-kuehn.de.|Institute for Biosafety in Plant Biotechnology, Julius Kühn-Institut (JKI) - Federal Research Centre for Cultivated Plants, Quedlinburg, D-06484, Germany.|European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.|Plant Genome and Systems Biology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, D-85764, Germany.|Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), D-06120, Germany.', 'Germany|Germany|United Kingdom|Germany|Germany', 15, 'Animals|Gene Expression Profiling|Genes, Fungal|Genes, Plant|Genomics|Hordeum|Introns|Molecular Sequence Annotation|Nematoda|Sequence Analysis, RNA|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/s12859-018-2203-5', '29843602', 'Genome annotation is of key importance in many research questions. The identification of protein-coding genes is often based on transcriptome sequencing data, ab-initio or homology-based prediction. Recently, it was demonstrated that intron position conservation improves homology-based gene prediction, and that experimental data improves ab-initio gene prediction.'),
(98398, 2018, 'bmc', 'EqualTDRL: illustrating equivalent tandem duplication random loss rearrangements', 'Hartmann, Tom|Bernt, Matthias|Middendorf, Martin', 'Swarm Intelligence and Complex Systems Group, Faculty of Mathematics and Computer Science, Leipzig University, Augustusplatz 10, Leipzig, D-04109, Germany. thartmann@informatik.uni-leipzig.de.|Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, Leipzig, D-04318, Germany.|Swarm Intelligence and Complex Systems Group, Faculty of Mathematics and Computer Science, Leipzig University, Augustusplatz 10, Leipzig, D-04109, Germany.', 'Germany|Germany|Germany', 0, 'DNA, Intergenic|Evolution, Molecular|Gene Duplication|Gene Order|Genes, Duplicate|Genome, Mitochondrial|Software', 'Journal Article', '10.1186/s12859-018-2170-x', '29843612', 'To study the differences between two unichromosomal circular genomes, e.g., mitochondrial genomes, under the tandem duplication random loss (TDRL) rearrangement it is important to consider the whole set of potential TDRL rearrangement events that could have taken place. The reason is that for two given circular gene orders there can exist different TDRL rearrangements that transform one of the gene orders into the other. Hence, a TDRL event cannot always be reconstructed only from the knowledge of the circular gene order before a TDRL event and the circular gene order after it.'),
(98399, 2018, 'bmc', 'Improved accuracy assessment for 3D genome reconstructions', 'Segal, Mark R|Bengtsson, Henrik L', 'Division of Bioinformatics, Department of Epidemiology and Biostatistics, UCSF, 16th Street, San Francisco, 94158, USA. mark.segal@ucsf.edu.|Division of Bioinformatics, Department of Epidemiology and Biostatistics, UCSF, 16th Street, San Francisco, 94158, USA.', 'United States|United States', 2, 'Animals|Chromosomes, Mammalian|Data Accuracy|Genome|Genomics|In Situ Hybridization, Fluorescence|Mice|Molecular Conformation', 'Journal Article', '10.1186/s12859-018-2214-2', '29848293', 'Three dimensional (3D) genome spatial organization is critical for numerous cellular functions, including transcription, while certain conformation-driven structural alterations are frequently oncogenic. Genome conformation had been difficult to elucidate but the advent chromatin conformation capture assays, notably Hi-C, has transformed understanding of chromatin architecture and yielded numerous biological insights. Although most of these findings have flowed from analysis of proximity data produced by these assays, added value in generating 3D reconstructions has been demonstrated, deriving, in part, from superposing genomic features on the reconstruction. However, advantages of 3D structure-based analyses are clearly conditional on the accuracy of the attendant reconstructions, which is difficult to assess. Proponents of competing reconstruction algorithms have evaluated their accuracy by recourse to simulation of toy structures and/or limited fluorescence in situ hybridization (FISH) imaging that features a handful of low resolution probes. Accordingly, new methods of reconstruction accuracy assessment are needed.'),
(98400, 2018, 'bmc', 'Rigorous optimisation of multilinear discriminant analysis with Tucker and PARAFAC structures', 'Frølich, Laura|Andersen, Tobias Søren|Mørup, Morten', 'Department of Applied Mathematics and Computer Science, Technical University of Denmark, Building 324, Kongens Lyngby, 2800, Denmark. laura.frolich@gmail.com.|Department of Applied Mathematics and Computer Science, Technical University of Denmark, Building 324, Kongens Lyngby, 2800, Denmark.|Department of Applied Mathematics and Computer Science, Technical University of Denmark, Building 324, Kongens Lyngby, 2800, Denmark.', 'Denmark|Denmark|Denmark', 1, 'Brain-Computer Interfaces|Discriminant Analysis|Electroencephalography|Humans', 'Evaluation Studies', '10.1186/s12859-018-2188-0', '29848301', 'We propose rigorously optimised supervised feature extraction methods for multilinear data based on Multilinear Discriminant Analysis (MDA) and demonstrate their usage on Electroencephalography (EEG) and simulated data. While existing MDA methods use heuristic optimisation procedures based on an ambiguous Tucker structure, we propose a rigorous approach via optimisation on the cross-product of Stiefel manifolds. We also introduce MDA methods with the PARAFAC structure. We compare the proposed approaches to existing MDA methods and unsupervised multilinear decompositions.'),
(98401, 2018, 'bmc', 'Core Hunter 3: flexible core subset selection', 'De Beukelaer, Herman|Davenport, Guy F|Fack, Veerle', 'Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 S9, Gent, 9000, Belgium. herman.debeukelaer@ugent.be.|New Zealand Institute for Plant & Food Research Limited, 412 No1 Rd RD2, Te Puke, New Zealand.|Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 S9, Gent, 9000, Belgium.', 'Belgium|New Zealand|Belgium', 11, 'Algorithms|Genetic Variation|Humans', 'Journal Article', '10.1186/s12859-018-2209-z', '29855322', 'Core collections provide genebank curators and plant breeders a way to reduce size of their collections and populations, while minimizing impact on genetic diversity and allele frequency. Many methods have been proposed to generate core collections, often using distance metrics to quantify the similarity of two accessions, based on genetic marker data or phenotypic traits. Core Hunter is a multi-purpose core subset selection tool that uses local search algorithms to generate subsets relying on one or more metrics, including several distance metrics and allelic richness.'),
(98402, 2018, 'bmc', 'Genome-wide prediction of cis-regulatory regions using supervised deep learning methods', 'Li, Yifeng|Shi, Wenqiang|Wasserman, Wyeth W', 'Centre for Molecular Medicine and Therapeutics, BC Childrens Hospital Research Institute, Department of Medical Genetics, University of British Columbia, Rm 3109, 950 West 28th Avenue, Vancouver, V5Z 4H4, Canada.|Centre for Molecular Medicine and Therapeutics, BC Childrens Hospital Research Institute, Department of Medical Genetics, University of British Columbia, Rm 3109, 950 West 28th Avenue, Vancouver, V5Z 4H4, Canada.|Centre for Molecular Medicine and Therapeutics, BC Childrens Hospital Research Institute, Department of Medical Genetics, University of British Columbia, Rm 3109, 950 West 28th Avenue, Vancouver, V5Z 4H4, Canada. wyeth@cmmt.ubc.ca.', 'Canada|Canada|Canada', 22, 'Deep Learning|Gene Expression Regulation|Genomics|Humans|Machine Learning|Neural Networks (Computer)|Regulatory Sequences, Nucleic Acid', 'Journal Article', '10.1186/s12859-018-2187-1', '29855387', 'In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide.'),
(98403, 2018, 'bmc', 'Sequence-based prediction of physicochemical interactions at protein functional sites using a function-and-interaction-annotated domain profile database', 'Han, Min|Song, Yifan|Qian, Jiaqiang|Ming, Dengming', 'Department of Physiology and Biophysics, School of Life Science, Fudan University, Shanghai, 200438, Peoples Republic of China.|Department of Physiology and Biophysics, School of Life Science, Fudan University, Shanghai, 200438, Peoples Republic of China.|Department of Physiology and Biophysics, School of Life Science, Fudan University, Shanghai, 200438, Peoples Republic of China.|College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Biotech Building Room B1-404, 30 South Puzhu Road, Jiangsu, 211816, Nanjing, Peoples Republic of China. dming@njtech.edu.cn.', 'China|China|China|China', 1, 'Databases, Protein|Humans|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/s12859-018-2206-2', '29859055', 'Identifying protein functional sites (PFSs) and, particularly, the physicochemical interactions at these sites is critical to understanding protein functions and the biochemical reactions involved. Several knowledge-based methods have been developed for the prediction of PFSs, however, accurate methods for predicting the physicochemical interactions associated with PFSs are still lacking.'),
(98404, 2018, 'bmc', 'mySyntenyPortal: an application package to construct websites for synteny block analysis', 'Lee, Jongin|Lee, Daehwan|Sim, Mikang|Kwon, Daehong|Kim, Juyeon|Ko, Younhee|Kim, Jaebum', 'Department of Biomedical Science and Engineering, Konkuk University, Seoul, 05029, South Korea.|Department of Biomedical Science and Engineering, Konkuk University, Seoul, 05029, South Korea.|Department of Biomedical Science and Engineering, Konkuk University, Seoul, 05029, South Korea.|Department of Biomedical Science and Engineering, Konkuk University, Seoul, 05029, South Korea.|Department of Biomedical Science and Engineering, Konkuk University, Seoul, 05029, South Korea.|Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, 17035, South Korea.|Department of Biomedical Science and Engineering, Konkuk University, Seoul, 05029, South Korea. jbkim@konkuk.ac.kr.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, 'Animals|Cattle|Female|Genome|Genomics|Humans|Internet|Mice|Software|Synteny|Whole Genome Sequencing', 'Journal Article', '10.1186/s12859-018-2219-x', '29871588', 'Advances in sequencing technologies have facilitated large-scale comparative genomics based on whole genome sequencing. Constructing and investigating conserved genomic regions among multiple species (called synteny blocks) are essential in the comparative genomics. However, they require significant amounts of computational resources and time in addition to bioinformatics skills. Many web interfaces have been developed to make such tasks easier. However, these web interfaces cannot be customized for users who want to use their own set of genome sequences or definition of synteny blocks.'),
(98405, 2018, 'bmc', 'MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization', 'Su, Lingtao|Liu, Guixia|Bai, Tian|Meng, Xiangyu|Ma, Qingshan', 'College of Computer Science and Technology, Jilin University, Changchun, 130012, China.|College of Computer Science and Technology, Jilin University, Changchun, 130012, China. lgx1034@163.com.|College of Computer Science and Technology, Jilin University, Changchun, 130012, China. baitian@jlu.edu.cn.|College of Computer Science and Technology, Jilin University, Changchun, 130012, China. 413224445@qq.com.|The First Clinical Hospital of Jilin University, Changchun, 130021, China.', 'China|China|China|China|China', 0, 'Algorithms|Breast Neoplasms|Female|Gene Ontology|Gene Regulatory Networks|Genes, Neoplasm|Heuristics|Humans|Male|Prostatic Neoplasms', 'Journal Article', '10.1186/s12859-018-2216-0', '29871590', 'Prioritizing genes according to their associations with a cancer allows researchers to explore genes in more informed ways. By far, Gene-centric or network-centric gene prioritization methods are predominated. Genes and their protein products carry out cellular processes in the context of functional modules. Dysfunctional gene modules have been previously reported to have associations with cancer. However, gene module information has seldom been considered in cancer-related gene prioritization.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98406, 2018, 'bmc', 'Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network', 'Xi, Jianing|Wang, Minghui|Li, Ao', 'School of Information Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, 230027, China.|School of Information Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, 230027, China. mhwang@ustc.edu.cn.|School of Information Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, 230027, China.', 'China|China|China', 7, 'Algorithms|Gene Regulatory Networks|Genes, Neoplasm|Humans|Mutation|RNA, Messenger', 'Journal Article', '10.1186/s12859-018-2218-y', '29871594', 'Discovery of mutated driver genes is one of the primary objective for studying tumorigenesis. To discover some relatively low frequently mutated driver genes from somatic mutation data, many existing methods incorporate interaction network as prior information. However, the prior information of mRNA expression patterns are not exploited by these existing network-based methods, which is also proven to be highly informative of cancer progressions.'),
(98407, 2018, 'bmc', 'DrImpute: imputing dropout events in single cell RNA sequencing data', 'Gong, Wuming|Kwak, Il-Youp|Pota, Pruthvi|Koyano-Nakagawa, Naoko|Garry, Daniel J', 'Lillehei Heart Institute, University of Minnesota, 2231 6th St S.E, 4-165 CCRB, Minneapolis, MN, 55114, USA.|Lillehei Heart Institute, University of Minnesota, 2231 6th St S.E, 4-165 CCRB, Minneapolis, MN, 55114, USA.|Lillehei Heart Institute, University of Minnesota, 2231 6th St S.E, 4-165 CCRB, Minneapolis, MN, 55114, USA.|Lillehei Heart Institute, University of Minnesota, 2231 6th St S.E, 4-165 CCRB, Minneapolis, MN, 55114, USA.|Lillehei Heart Institute, University of Minnesota, 2231 6th St S.E, 4-165 CCRB, Minneapolis, MN, 55114, USA. garry@umn.edu.', 'United States|United States|United States|United States|United States', 55, 'Humans|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-018-2226-y', '29884114', 'The single cell RNA sequencing (scRNA-seq) technique begin a new era by allowing the observation of gene expression at the single cell level. However, there is also a large amount of technical and biological noise. Because of the low number of RNA transcriptomes and the stochastic nature of the gene expression pattern, there is a high chance of missing nonzero entries as zero, which are called dropout events.'),
(98408, 2018, 'bmc', 'MERIT reveals the impact of genomic context on sequencing error rate in ultra-deep applications', 'Hadigol, Mohammad|Khiabanian, Hossein', 'Center for Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA.|Center for Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA. h.khiabanian@rutgers.edu.', 'Jersey|Jersey', 2, 'Genomics|High-Throughput Nucleotide Sequencing|Humans', 'Journal Article', '10.1186/s12859-018-2223-1', '29884116', 'Rapid progress in high-throughput sequencing (HTS) and the development of novel library preparation methods have improved the sensitivity of detecting mutations in heterogeneous samples, specifically in high-depth (>xe2x80x89500xc3x97) clinical applications. However, HTS methods are bounded by their technical and theoretical limitations and sequencing errors cannot be completely eliminated. Comprehensive quantification of the background noise can highlight both the efficiency and the limitations of any HTS methodology, and help differentiate true mutations at low abundance from artifacts.'),
(98409, 2018, 'bmc', 'Variant site strain typer (VaST): efficient strain typing using a minimal number of variant genomic sites', 'Furstenau, Tara N|Cocking, Jill H|Sahl, Jason W|Fofanov, Viacheslav Y', 'The School of Informatics, Computing, and Cyber Systems, Northern Arizona University, 1295 S Knoles Dr., Flagstaff, Arizona, 86001, USA.|The School of Informatics, Computing, and Cyber Systems, Northern Arizona University, 1295 S Knoles Dr., Flagstaff, Arizona, 86001, USA.|Pathogen and Microbiome Institute, Northern Arizona University, 1395 S Knoles Dr., Flagstaff, Arizona, 86001, USA.|The School of Informatics, Computing, and Cyber Systems, Northern Arizona University, 1295 S Knoles Dr., Flagstaff, Arizona, 86001, USA. Viacheslav.Fofanov@nau.edu.', 'United States|United States|United States|United States', 1, 'Bacteria|Bacterial Typing Techniques|Genes, Bacterial|Genome, Bacterial|Genomics|Genotype|Humans|Multilocus Sequence Typing|Phylogeny|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-018-2225-z', '29890941', 'Targeted PCR amplicon sequencing (TAS) techniques provide a sensitive, scalable, and cost-effective way to query and identify closely related bacterial species and strains. Typically, this is accomplished by targeting housekeeping genes that provide resolution down to the family, genera, and sometimes species level. Unfortunately, this level of resolution is not sufficient in many applications where strain-level identification of bacteria is required (biodefense, forensics, clinical diagnostics, and outbreak investigations). Adding more genomic targets will increase the resolution, but the challenge is identifying the appropriate targets. VaST was developed to address this challenge by finding the minimum number of targets that, in combination, achieve maximum strain-level resolution for any strain complex. The final combination of target regions identified by the algorithm produce a unique haplotype for each strain which can be used as a fingerprint for identifying unknown samples in a TAS assay. VaST ensures that the targets have conserved primer regions so that the targets can be amplified in all of the known strains and it also favors the inclusion of targets with basal variants which makes the set more robust when identifying previously unseen strains.'),
(98410, 2018, 'bmc', 'DiNAMO: highly sensitive DNA motif discovery in high-throughput sequencing data', 'Saad, Chadi|Noé, Laurent|Richard, Hugues|Leclerc, Julie|Buisine, Marie-Pierre|Touzet, Hélène|Figeac, Martin', 'Univ. Lille, CNRS, Inria, UMR 9189 - CRIStAL - Centre de Recherche en Informatique Signal et Automatique de Lille, Lille, France. chadi.saad@univ-lille1.fr.|Univ. Lille, CNRS, Inria, UMR 9189 - CRIStAL - Centre de Recherche en Informatique Signal et Automatique de Lille, Lille, France.|Sorbonne Université, UMR7238, Laboratory Computational and Quantitative Biology, LCQB, Paris, F-75005, France.|Univ. Lille, Inserm, Lille University Hospital, UMR-S 1172 - JPARC - Centre de Recherche Jean-Pierre AUBERT, Lille, F-59000, France.|Univ. Lille, Inserm, Lille University Hospital, UMR-S 1172 - JPARC - Centre de Recherche Jean-Pierre AUBERT, Lille, F-59000, France.|Univ. Lille, CNRS, Inria, UMR 9189 - CRIStAL - Centre de Recherche en Informatique Signal et Automatique de Lille, Lille, France.|Univ. Lille. Plateau de génomique fonctionnelle et structurale, Lille, F-59000, France.', 'France|France|France|France|France|France|France', 0, 'Algorithms|Chromatin Immunoprecipitation|Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Nucleotide Motifs|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-018-2215-1', '29890948', 'Discovering over-represented approximate motifs in DNA sequences is an essential part of bioinformatics. This topic has been studied extensively because of the increasing number of potential applications. However, it remains a difficult challenge, especially with the huge quantity of data generated by high throughput sequencing technologies. To overcome this problem, existing tools use greedy algorithms and probabilistic approaches to find motifs in reasonable time. Nevertheless these approaches lack sensitivity and have difficulties coping with rare and subtle motifs.'),
(98411, 2018, 'bmc', 'Blazing Signature Filter: a library for fast pairwise similarity comparisons', 'Lee, Joon-Yong|Fujimoto, Grant M|Wilson, Ryan|Wiley, H Steven|Payne, Samuel H', 'Integrative Omics, Pacific Northwest National Laboratory, Richland, 99352, WA, USA.|Integrative Omics, Pacific Northwest National Laboratory, Richland, 99352, WA, USA.|Integrative Omics, Pacific Northwest National Laboratory, Richland, 99352, WA, USA.|Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, 99352, WA, USA.|Integrative Omics, Pacific Northwest National Laboratory, Richland, 99352, WA, USA. Samuel.Payne@pnnl.gov.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-018-2210-6', '29890950', 'Identifying similarities between datasets is a fundamental task in data mining and has become an integral part of modern scientific investigation. Whether the task is to identify co-expressed genes in large-scale expression surveys or to predict combinations of gene knockouts which would elicit a similar phenotype, the underlying computational task is often a multi-dimensional similarity test. As datasets continue to grow, improvements to the efficiency, sensitivity or specificity of such computation will have broad impacts as it allows scientists to more completely explore the wealth of scientific data.'),
(98412, 2018, 'bmc', 'Evaluation of pooling operations in convolutional architectures for drug-drug interaction extraction', 'Suárez-Paniagua, Víctor|Segura-Bedmar, Isabel', 'Computer Science Department, Carlos III University of Madrid, 28911, Leganés, Spain. vspaniag@inf.uc3m.es.|Computer Science Department, Carlos III University of Madrid, 28911, Leganés, Spain.', 'Spain|Spain', 15, 'Algorithms|Databases as Topic|Deep Learning|Drug Interactions|Humans|Information Storage and Retrieval|Neural Networks (Computer)', 'Journal Article', '10.1186/s12859-018-2195-1', '29897318', 'Deep Neural Networks (DNN), in particular, Convolutional Neural Networks (CNN), has recently achieved state-of-art results for the task of Drug-Drug Interaction (DDI) extraction. Most CNN architectures incorporate a pooling layer to reduce the dimensionality of the convolution layer output, preserving relevant features and removing irrelevant details. All the previous CNN based systems for DDI extraction used max-pooling layers.'),
(98413, 2018, 'bmc', 'Automatic extraction of informal topics from online suicidal ideation', 'Grant, Reilly N|Kucher, David|León, Ana M|Gemmell, Jonathan F|Raicu, Daniela S|Fodeh, Samah J', 'Grinnell College, Grinnell, IA, USA.|University of Michigan, Ann Arbor, MI, USA.|University of Juárez Autónoma de Tabasco, Villahermosa, Tab., 86040, Mexico.|School of Computing, DePaul University, Chicago, IL, USA. jgemmell@cdm.depaul.edu.|School of Computing, DePaul University, Chicago, IL, USA.|Yale Center for Medical Informatics, Yale University, New Haven, CT, USA.', 'United States|United States|Mexico|United States|United States|United States', 3, 'Adolescent|Algorithms|Automation|Female|Humans|Information Storage and Retrieval|Internet|Language|Male|Middle Aged|Risk Factors|Social Media|Suicidal Ideation', 'Journal Article', '10.1186/s12859-018-2197-z', '29897319', 'Suicide is an alarming public health problem accounting for a considerable number of deaths each year worldwide. Many more individuals contemplate suicide. Understanding the attributes, characteristics, and exposures correlated with suicide remains an urgent and significant problem. As social networking sites have become more common, users have adopted these sites to talk about intensely personal topics, among them their thoughts about suicide. Such data has previously been evaluated by analyzing the language features of social media posts and using factors derived by domain experts to identify at-risk users.'),
(98414, 2018, 'bmc', 'Identification of common coexpression modules based on quantitative network comparison', 'Jo, Yousang|Kim, Sanghyeon|Lee, Doheon', 'Bio-Synergy Research Center, Daejeon, 34141, South Korea.|Brain Research Laboratory, Stanley Medical Research Institute, Rockville, MD, 20850, USA. kims@stanleyresearch.org.|Bio-Synergy Research Center, Daejeon, 34141, South Korea. dhlee@kaist.ac.kr.', 'United States|United States|United States', 2, 'Aging|Brain|Gene Expression Profiling|Gene Expression Regulation|Gene Ontology|Gene Regulatory Networks|Humans|Huntington Disease|Reproducibility of Results', 'Journal Article', '10.1186/s12859-018-2193-3', '29897320', 'Finding common molecular interactions from different samples is essential work to understanding diseases and other biological processes. Coexpression networks and their modules directly reflect sample-specific interactions among genes. Therefore, identification of common coexpression network or modules may reveal the molecular mechanism of complex disease or the relationship between biological processes. However, there has been no quantitative network comparison method for coexpression networks and we examined previous methods for other networks that cannot be applied to coexpression network. Therefore, we aimed to propose quantitative comparison methods for coexpression networks and to find common biological mechanisms between Huntingtons disease and brain aging by the new method.'),
(98415, 2018, 'bmc', 'Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction mention extraction', 'Gupta, Shashank|Pawar, Sachin|Ramrakhiyani, Nitin|Palshikar, Girish Keshav|Varma, Vasudeva', 'Information Retrieval and Extraction Laboratory, Kohli Center for Intelligent Systems, International Institute of Information Technology, Hyderabad, India. shashank.gupta@research.iiit.ac.in.|Tata Consultancy Services (TCS) Research, 54-B, Hadapsar Industrial Area, Pune, India.|Information Retrieval and Extraction Laboratory, Kohli Center for Intelligent Systems, International Institute of Information Technology, Hyderabad, India.|Tata Consultancy Services (TCS) Research, 54-B, Hadapsar Industrial Area, Pune, India.|Information Retrieval and Extraction Laboratory, Kohli Center for Intelligent Systems, International Institute of Information Technology, Hyderabad, India.', 'India|India|India|India|India', 11, 'Databases as Topic|Drug-Related Side Effects and Adverse Reactions|Humans|Information Storage and Retrieval|Neural Networks (Computer)|Social Media|Supervised Machine Learning', 'Journal Article', '10.1186/s12859-018-2192-4', '29897321', 'Social media is a useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from Twitter. Medical information extraction from social media is challenging, mainly due to short and highly informal nature of text, as compared to more technical and formal medical reports.'),
(98416, 2018, 'bmc', 'A systematic approach to identify therapeutic effects of natural products based on human metabolite information', 'Noh, Kyungrin|Yoo, Sunyong|Lee, Doheon', 'Bio-Synergy Research Center, Daejeon, 34141, South Korea.|Bio-Synergy Research Center, Daejeon, 34141, South Korea.|Bio-Synergy Research Center, Daejeon, 34141, South Korea. dhlee@kaist.ac.kr.', '-|-|-', 0, 'Area Under Curve|Biological Products|Classification|Computer Simulation|Humans|Metabolome|Phenotype|ROC Curve|Reproducibility of Results', 'Journal Article', '10.1186/s12859-018-2196-0', '29897322', 'Natural products have been widely investigated in the drug development field. Their traditional use cases as medicinal agents and their resemblance of our endogenous compounds show the possibility of new drug development. Many researchers have focused on identifying therapeutic effects of natural products, yet the resemblance of natural products and human metabolites has been rarely touched.'),
(98417, 2018, 'bmc', 'Identifying tweets of personal health experience through word embedding and LSTM neural network', 'Jiang, Keyuan|Feng, Shichao|Song, Qunhao|Calix, Ricardo A|Gupta, Matrika|Bernard, Gordon R', 'Department of Computer Information Technology and Graphics, Purdue University Northwest, Hammond, IN, USA. kjiang@pnw.edu.|Department of Computer Information Technology and Graphics, Purdue University Northwest, Hammond, IN, USA.|Department of Computer Information Technology and Graphics, Purdue University Northwest, Hammond, IN, USA.|Department of Computer Information Technology and Graphics, Purdue University Northwest, Hammond, IN, USA.|Department of Computer Information Technology and Graphics, Purdue University Northwest, Hammond, IN, USA.|Department of Medicine, Vanderbilt University, Nashville, TN, USA.', 'United States|United States|United States|United States|United States|United States', 10, 'Algorithms|Health|Humans|Neural Networks (Computer)|Social Media|Vocabulary', 'Journal Article', '10.1186/s12859-018-2198-y', '29897323', 'As Twitter has become an active data source for health surveillance research, it is important that efficient and effective methods are developed to identify tweets related to personal health experience. Conventional classification algorithms rely on features engineered by human domain experts, and engineering such features is a challenging task and requires much human intelligence. The resultant features may not be optimal for the classification problem, and can make it challenging for conventional classifiers to correctly predict personal experience tweets (PETs) due to the various ways to express and/or describe personal experience in tweets. In this study, we developed a method that combines word embedding and long short-term memory (LSTM) model without the need to engineer any specific features. Through word embedding, tweet texts were represented as dense vectors which in turn were fed to the LSTM neural network as sequences.'),
(98418, 2018, 'bmc', 'In silico prediction of potential chemical reactions mediated by human enzymes', 'Yu, Myeong-Sang|Lee, Hyang-Mi|Park, Aaron|Park, Chungoo|Ceong, Hyithaek|Rhee, Ki-Hyeong|Na, Dokyun', 'School of Integrative Engineering, Chung-Ang University, Seoul, Republic of Korea.|School of Integrative Engineering, Chung-Ang University, Seoul, Republic of Korea.|School of Biological Sciences, Chonnam National University, Gwangju, Republic of Korea.|School of Biological Sciences, Chonnam National University, Gwangju, Republic of Korea.|Department of Multimedia, Chonnam National University, Yeosu, Republic of Korea.|College of Industrial Sciences, Kongju National University, Yesan, Republic of Korea.|School of Integrative Engineering, Chung-Ang University, Seoul, Republic of Korea. blisszen@cau.ac.kr.', '-|-|-|-|-|-|-', 5, 'Algorithms|Area Under Curve|Computer Simulation|Databases, Protein|Enzymes|Humans|Models, Biological', 'Journal Article', '10.1186/s12859-018-2194-2', '29897324', 'Administered drugs are often converted into an ineffective or activated form by enzymes in our body. Conventional in silico prediction approaches focused on therapeutically important enzymes such as CYP450. However, there are more than thousands of different cellular enzymes that potentially convert administered drug into other forms.'),
(98419, 2018, 'bmc', 'Relation extraction for biological pathway construction using node2vec', 'Kim, Munui|Baek, Seung Han|Song, Min', 'Department of Library and Information Science, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, Republic of Korea.|Department of Library and Information Science, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, Republic of Korea.|Department of Library and Information Science, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, Republic of Korea. min.song@yonsei.ac.kr.', '-|-|-', 6, 'Algorithms|Data Mining|Diabetes Mellitus, Type 2|Epistasis, Genetic|Gene Regulatory Networks|Humans|Insulin Resistance|Signal Transduction', 'Journal Article', '10.1186/s12859-018-2200-8', '29897325', 'Systems biology is an important field for understanding whole biological mechanisms composed of interactions between biological components. One approach for understanding complex and diverse mechanisms is to analyze biological pathways. However, because these pathways consist of important interactions and information on these interactions is disseminated in a large number of biomedical reports, text-mining techniques are essential for extracting these relationships automatically.'),
(98420, 2018, 'bmc', 'Identification of drug-target interaction by a random walk with restart method on an interactome network', 'Lee, Ingoo|Nam, Hojung', 'School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 500-712, Republic of Korea.|School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 500-712, Republic of Korea. hjnam@gist.ac.kr.', '-|-', 14, 'Algorithms|Databases as Topic|Drug Interactions|Humans|Machine Learning|Probability', 'Journal Article', '10.1186/s12859-018-2199-x', '29897326', 'Identification of drug-target interactions acts as a key role in drug discovery. However, identifying drug-target interactions via in-vitro, in-vivo experiments are very laborious, time-consuming. Thus, predicting drug-target interactions by using computational approaches is a good alternative. In recent studies, many feature-based and similarity-based machine learning approaches have shown promising results in drug-target interaction predictions. A previous study showed that accounting connectivity information of drug-drug and protein-protein interactions increase performances of prediction by the concept of guilt-by-association. However, the approach that only considers directly connected nodes often misses the information that could be derived from distance nodes. Therefore, in this study, we yield global network topology information by using a random walk with restart algorithm and apply the global topology information to the prediction model.'),
(98421, 2018, 'bmc', 'Mutational Signatures in Cancer (MuSiCa): a web application to implement mutational signatures analysis in cancer samples', 'Díaz-Gay, Marcos|Vila-Casadesús, Maria|Franch-Expósito, Sebastià|Hernández-Illán, Eva|Lozano, Juan José|Castellví-Bel, Sergi', 'Gastroenterology Department, Hospital Clínic, Institut dInvestigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD, University of Barcelona, Barcelona, Spain.|Bioinformatics Platform, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain.|Gastroenterology Department, Hospital Clínic, Institut dInvestigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD, University of Barcelona, Barcelona, Spain.|Gastroenterology Department, Hospital Clínic, Institut dInvestigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD, University of Barcelona, Barcelona, Spain.|Bioinformatics Platform, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain.|Gastroenterology Department, Hospital Clínic, Institut dInvestigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD, University of Barcelona, Barcelona, Spain. sbel@clinic.cat.', 'Spain|Spain|Spain|Spain|Spain|Spain', 24, 'Computational Biology|Databases, Genetic|Genes, Neoplasm|Genetic Variation|Genome, Human|Humans|Mutation|Neoplasms|Software|Transcriptome|Web Browser', 'Journal Article', '10.1186/s12859-018-2234-y', '29898651', 'Mutational signatures have been proved as a valuable pattern in somatic genomics, mainly regarding cancer, with a potential application as a biomarker in clinical practice. Up to now, several bioinformatic packages to address this topic have been developed in different languages/platforms. MutationalPatterns has arisen as the most efficient tool for the comparison with the signatures currently reported in the Catalogue of Somatic Mutations in Cancer (COSMIC) database. However, the analysis of mutational signatures is nowadays restricted to a small community of bioinformatic experts.'),
(98422, 2018, 'bmc', 'A short note on dynamic programming in a band', 'Gibrat, Jean-François', 'MaIAGE, INRA, Université Paris-Saclay, Jouy-en-Josas, 78350, France. jean-francois.gibrat@inra.fr.', 'France', 0, 'Algorithms|Computational Biology|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Programming Languages|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-018-2228-9', '29902968', 'Third generation sequencing technologies generate long reads that exhibit high error rates, in particular for insertions and deletions which are usually the most difficult errors to cope with. The only exact algorithm capable of aligning sequences with insertions and deletions is a dynamic programming algorithm.'),
(98423, 2018, 'bmc', 'Taxonomy-aware feature engineering for microbiome classification', 'Oudah, Mai|Henschel, Andreas', 'Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.|Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates. andreas.henschel@ku.ac.ae.', 'United Arab Emirates|United Arab Emirates', 11, 'Algorithms|Bacteria|Ecology|Humans|Metagenome|Microbiota|Phylogeny|Terminology as Topic', 'Journal Article', '10.1186/s12859-018-2205-3', '29907097', 'What is a healthy microbiome? The pursuit of this and many related questions, especially in light of the recently recognized microbial component in a wide range of diseases has sparked a surge in metagenomic studies. They are often not simply attributable to a single pathogen but rather are the result of complex ecological processes. Relatedly, the increasing DNA sequencing depth and number of samples in metagenomic case-control studies enabled the applicability of powerful statistical methods, e.g. Machine Learning approaches. For the latter, the feature space is typically shaped by the relative abundances of operational taxonomic units, as determined by cost-effective phylogenetic marker gene profiles. While a substantial body of microbiome/microbiota research involves unsupervised and supervised Machine Learning, very little attention has been put on feature selection and engineering.'),
(98424, 2018, 'bmc', 'Predicting drug-disease associations by using similarity constrained matrix factorization', 'Zhang, Wen|Yue, Xiang|Lin, Weiran|Wu, Wenjian|Liu, Ruoqi|Huang, Feng|Liu, Feng', 'School of Computer Science, Wuhan University, Wuhan, 430072, China. zhangwen@whu.edu.cn.|School of Computer Science, Wuhan University, Wuhan, 430072, China.|School of Computer Science, Wuhan University, Wuhan, 430072, China.|School of Electronic Information, Wuhan University, Wuhan, 430072, China.|School of Computer Science, Wuhan University, Wuhan, 430072, China.|School of Computer Science, Wuhan University, Wuhan, 430072, China.|School of Computer Science, Wuhan University, Wuhan, 430072, China. fliuwhu@whu.edu.cn.', 'China|China|China|China|China|China|China', 46, 'Computational Biology|Disease|Drug Discovery|Humans|Models, Theoretical|Pharmaceutical Preparations|Research Design', 'Journal Article', '10.1186/s12859-018-2220-4', '29914348', 'Drug-disease associations provide important information for the drug discovery. Wet experiments that identify drug-disease associations are time-consuming and expensive. However, many drug-disease associations are still unobserved or unknown. The development of computational methods for predicting unobserved drug-disease associations is an important and urgent task.'),
(98425, 2018, 'bmc', 'Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data', 'Chen, Shuonan|Mar, Jessica C', 'Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, USA.|Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, USA. jessica.mar@einstein.yu.edu.', 'United States|United States', 31, 'Algorithms|Computational Biology|Embryonic Stem Cells|Gene Expression Profiling|Gene Regulatory Networks|Hematopoietic Stem Cells|Humans|Single-Cell Analysis', 'Evaluation Studies', '10.1186/s12859-018-2217-z', '29914350', 'A fundamental fact in biology states that genes do not operate in isolation, and yet, methods that infer regulatory networks for single cell gene expression data have been slow to emerge. With single cell sequencing methods now becoming accessible, general network inference algorithms that were initially developed for data collected from bulk samples may not be suitable for single cells. Meanwhile, although methods that are specific for single cell data are now emerging, whether they have improved performance over general methods is unknown. In this study, we evaluate the applicability of five general methods and three single cell methods for inferring gene regulatory networks from both experimental single cell gene expression data and in silico simulated data.'),
(98426, 2018, 'bmc', 'Algorithms designed for compressed-gene-data transformation among gene banks with different references', 'Luo, Qiuming|Guo, Chao|Zhang, Yi Jun|Cai, Ye|Liu, Gang', 'NHPCC/Guangdong Key Laboratory of popular HPC and College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.|NHPCC/Guangdong Key Laboratory of popular HPC and College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. 2150230422@email.szu.edu.cn.|NHPCC/Guangdong Key Laboratory of popular HPC and College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.|NHPCC/Guangdong Key Laboratory of popular HPC and College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.|NHPCC/Guangdong Key Laboratory of popular HPC and College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.', 'China|China|China|China|China', 1, 'Algorithms|Data Compression|Databases, Factual|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Reference Standards|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2230-2', '29914357', 'With the reduction of gene sequencing cost and demand for emerging technologies such as precision medical treatment and deep learning in genome, it is an era of gene data outbreaks today. How to store, transmit and analyze these data has become a hotspot in the current research. Now the compression algorithm based on reference is widely used due to its high compression ratio. There exists a big problem that the data from different gene banks cant merge directly and share information efficiently, because these data are usually compressed with different references. The traditional workflow is decompression-and-recompression, which is too simple and time-consuming. We should improve it and speed it up.'),
(98427, 2018, 'bmc', 'SamSelect: a sample sequence selection algorithm for quorum planted motif search on large DNA datasets', 'Yu, Qiang|Wei, Dingbang|Huo, Hongwei', 'School of Computer Science and Technology, Xidian University, Xian, 710071, China.|School of Computer Science and Technology, Xidian University, Xian, 710071, China.|School of Computer Science and Technology, Xidian University, Xian, 710071, China. hwhuo@mail.xidian.edu.cn.', 'China|China|China', 4, 'Algorithms|Computational Biology|DNA|Humans|Nucleotide Motifs|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2242-y', '29914360', 'Given a set of t n-length DNA sequences, q satisfying 0xe2x80x89<xe2x80x89qxe2x80x89xe2x89xa4xe2x80x891, and l and d satisfying 0xe2x80x89xe2x89xa4xe2x80x89dxe2x80x89<xe2x80x89lxe2x80x89<xe2x80x89n, the quorum planted motif search (qPMS) finds l-length strings that occur in at least qt input sequences with up to d mismatches and is mainly used to locate transcription factor binding sites in DNA sequences. Existing qPMS algorithms have been able to efficiently process small standard datasets (e.g., txe2x80x89=xe2x80x8920 and nxe2x80x89=xe2x80x89600), but they are too time consuming to process large DNA datasets, such as ChIP-seq datasets that contain thousands of sequences or more.'),
(98428, 2018, 'bmc', 'Performance of epistasis detection methods in semi-simulated GWAS', 'Chatelain, Clément|Durand, Guillermo|Thuillier, Vincent|Augé, Franck', 'SANOFI R&D, Translational Sciences, Chilly Mazarin, 91385, France. clement.chatelain@sanofi.com.|Laboratoire de Probabilités et Modèles Aléatoires, Université Pierre et Marie Curie, 4, place Jussieu, Paris Cedex 05, 75252, France.|SANOFI R&D, Biostatistics & Programming, Chilly Mazarin, 91385, France.|SANOFI R&D, Translational Sciences, Chilly Mazarin, 91385, France.', 'France|France|France|France', 3, 'Algorithms|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Diabetes Mellitus, Type 2|Epistasis, Genetic|Genome-Wide Association Study|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-018-2229-8', '29914375', 'Part of the missing heritability in Genome Wide Association Studies (GWAS) is expected to be explained by interactions between genetic variants, also called epistasis. Various statistical methods have been developed to detect epistasis in case-control GWAS. These methods face major statistical challenges due to the number of tests required, the complexity of the Linkage Disequilibrium (LD) structure, and the lack of consensus regarding the definition of epistasis. Their limited impact in terms of uncovering new biological knowledge might be explained in part by the limited amount of experimental data available to validate their statistical performances in a realistic GWAS context. In this paper, we introduce a simulation pipeline for generating real scale GWAS data, including epistasis and realistic LD structure. We evaluate five exhaustive bivariate interaction methods, fastepi, GBOOST, SHEsisEpi, DSS, and IndOR. Two hundred thirty four different disease scenarios are considered in extensive simulations. We report the performances of each method in terms of false positive rate control, power, area under the ROC curve (AUC), and computation time using a GPU. Finally we compare the result of each methods on a real GWAS of type 2 diabetes from the Welcome Trust Case Control Consortium.'),
(98429, 2018, 'bmc', 'Feature extraction method for proteins based on Markov tripeptide by compressive sensing', 'Gao, C F|Wu, X Y', 'School of Science, Jiangnan University, Wuxi, 214122, China. cuifang_gao@163.com.|School of Science, Jiangnan University, Wuxi, 214122, China.', 'China|China', 0, 'Algorithms|Cell Membrane|Cell Nucleus|Cytoplasm|Data Compression|Humans|Markov Chains|Peptide Fragments|Protein Interaction Maps|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/s12859-018-2235-x', '29914376', 'In order to capture the vital structural information of the original protein, the symbol sequence was transformed into the Markov frequency matrix according to the consecutive three residues throughout the chain. A three-dimensional sparse matrix sized 20xe2x80x89xc3x97xe2x80x8920xe2x80x89xc3x97xe2x80x8920 was obtained and expanded to one-dimensional vector. Then, an appropriate measurement matrix was selected for the vector to obtain a compressed feature set by random projection. Consequently, the new compressive sensing feature extraction technology was proposed.'),
(98430, 2018, 'bmc', 'Epigenetic machine learning: utilizing DNA methylation patterns to predict spastic cerebral palsy', 'Crowgey, Erin L|Marsh, Adam G|Robinson, Karyn G|Yeager, Stephanie K|Akins, Robert E', 'Nemours Biomedical Research, Nemours - Alfred I. duPont Hospital for Children, 1600 Rockland Rd, Wilmington, DE, 19803, USA.|Genome Profiling LLC, 4701 Ogletown Stanton Rd #4300, Newark, DE, 19713, USA.|Nemours Biomedical Research, Nemours - Alfred I. duPont Hospital for Children, 1600 Rockland Rd, Wilmington, DE, 19803, USA.|Nemours Biomedical Research, Nemours - Alfred I. duPont Hospital for Children, 1600 Rockland Rd, Wilmington, DE, 19803, USA.|Nemours Biomedical Research, Nemours - Alfred I. duPont Hospital for Children, 1600 Rockland Rd, Wilmington, DE, 19803, USA. robert.akins@nemours.org.', 'United States|United States|United States|United States|United States', 9, 'Adolescent|Biomarkers|Case-Control Studies|Cerebral Palsy|DNA Methylation|Epigenomics|Female|Genetic Predisposition to Disease|Genome, Human|Humans|Machine Learning|Male|Pattern Recognition, Automated|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2224-0', '29925314', 'Spastic cerebral palsy (CP) is a leading cause of physical disability. Most people with spastic CP are born with it, but early diagnosis is challenging, and no current biomarker platform readily identifies affected individuals. The aim of this study was to evaluate epigenetic profiles as biomarkers for spastic CP. A novel analysis pipeline was employed to assess DNA methylation patterns between peripheral blood cells of adolescent subjects (14.9xe2x80x89xc2xb1xe2x80x890.3xc2xa0years old) with spastic CP and controls at single CpG site resolution.'),
(98431, 2018, 'bmc', 'ARKS: chromosome-scale scaffolding of human genome drafts with linked read kmers', 'Coombe, Lauren|Zhang, Jessica|Vandervalk, Benjamin P|Chu, Justin|Jackman, Shaun D|Birol, Inanc|Warren, Renxc3xa9 L', 'BC Cancer Genome Sciences Centre, Vancouver, BC, V5Z 4S6, Canada.|BC Cancer Genome Sciences Centre, Vancouver, BC, V5Z 4S6, Canada.|BC Cancer Genome Sciences Centre, Vancouver, BC, V5Z 4S6, Canada.|BC Cancer Genome Sciences Centre, Vancouver, BC, V5Z 4S6, Canada.|BC Cancer Genome Sciences Centre, Vancouver, BC, V5Z 4S6, Canada.|BC Cancer Genome Sciences Centre, Vancouver, BC, V5Z 4S6, Canada.|BC Cancer Genome Sciences Centre, Vancouver, BC, V5Z 4S6, Canada. rwarren@bcgsc.ca.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 6, 'Chromosomes, Human|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-018-2243-x', '29925315', 'The long-range sequencing information captured by linked reads, such as those available from 10xc3x97 Genomics (10xG), helps resolve genome sequence repeats, and yields accurate and contiguous draft genome assemblies. We introduce ARKS, an alignment-free linked read genome scaffolding methodology that uses linked reads to organize genome assemblies further into contiguous drafts. Our approach departs from other read alignment-dependent linked read scaffolders, including our own (ARCS), and uses a kmer-based mapping approach. The kmer mapping strategy has several advantages over read alignment methods, including better usability and faster processing, as it precludes the need for input sequence formatting and draft sequence assembly indexing. The reliance on kmers instead of read alignments for pairing sequences relaxes the workflow requirements, and drastically reduces the run time.'),
(98432, 2018, 'bmc', 'A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model', 'De Paris, Renata|Vahl Quevedo, Christian|Ruiz, Duncan D|Gargano, Furia|de Souza, Osmar Norberto', 'Business Intelligence and Machine Learning Research Group-GPIN, School of Technology, PUCRS, Av. Ipiranga, 6681, Building 32, Room 628, Porto Alegre, RS, Brazil.|Business Intelligence and Machine Learning Research Group-GPIN, School of Technology, PUCRS, Av. Ipiranga, 6681, Building 32, Room 628, Porto Alegre, RS, Brazil.|Business Intelligence and Machine Learning Research Group-GPIN, School of Technology, PUCRS, Av. Ipiranga, 6681, Building 32, Room 628, Porto Alegre, RS, Brazil. duncan.ruiz@pucrs.br.|Bioinformatics and Biossystems Modeling and Simulation Lab-LABIO, School of Technology, PUCRS, Av. Ipiranga, 6681, Building 32, Room 602, Porto Alegre, RS, Brazil.|Bioinformatics and Biossystems Modeling and Simulation Lab-LABIO, School of Technology, PUCRS, Av. Ipiranga, 6681, Building 32, Room 602, Porto Alegre, RS, Brazil.', 'Brazil|Brazil|Brazil|Brazil|Brazil', 3, 'Drug Design|Molecular Docking Simulation', 'Journal Article', '10.1186/s12859-018-2222-2', '29929475', 'In the rational drug design process, an ensemble of conformations obtained from a molecular dynamics simulation plays a crucial role in docking experiments. Some studies have found that Fully-Flexible Receptor (FFR) models predict realistic binding energy accurately and improve scoring to enhance selectiveness. At the same time, methods have been proposed to reduce the high computational costs involved in considering the explicit flexibility of proteins in receptor-ligand docking. This study introduces a novel method to optimize ensemble docking-based experiments by reducing the size of an InhA FFR model at docking runtime and scaling docking workflow invocations on cloud virtual machines.'),
(98433, 2018, 'bmc', 'Gene length corrected trimmed mean of M-values (GeTMM) processing of RNA-seq data performs similarly in intersample analyses while improving intrasample comparisons', 'Smid, Marcel|Coebergh van den Braak, Robert R J|van de Werken, Harmen J G|van Riet, Job|van Galen, Anne|de Weerd, Vanja|van der Vlugt-Daane, Michelle|Bril, Sandra I|Lalmahomed, Zarina S|Kloosterman, Wigard P|Wilting, Saskia M|Foekens, John A|IJzermans, Jan N M|MATCH study group|Martens, John W M|Sieuwerts, Anieta M', 'Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus MC University Medical Center, 3015 CE, Rotterdam, The Netherlands. m.smid@erasmusmc.nl.|Department of Surgery, Erasmus MC University Medical Center, 3015 CE, Rotterdam, The Netherlands.|Cancer Computational Biology Center, Erasmus MC Cancer Institute, Erasmus MC University Medical Center, 3015 CE, Rotterdam, The Netherlands.|Cancer Computational Biology Center, Erasmus MC Cancer Institute, Erasmus MC University Medical Center, 3015 CE, Rotterdam, The Netherlands.|Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus MC University Medical Center, 3015 CE, Rotterdam, The Netherlands.|Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus MC University Medical Center, 3015 CE, Rotterdam, The Netherlands.|Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus MC University Medical Center, 3015 CE, Rotterdam, The Netherlands.|Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus MC University Medical Center, 3015 CE, Rotterdam, The Netherlands.|Department of Surgery, Erasmus MC University Medical Center, 3015 CE, Rotterdam, The Netherlands.|Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, 3584 CX, Utrecht, The Netherlands.|Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus MC University Medical Center, 3015 CE, Rotterdam, The Netherlands.|Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus MC University Medical Center, 3015 CE, Rotterdam, The Netherlands.|Department of Surgery, Erasmus MC University Medical Center, 3015 CE, Rotterdam, The Netherlands.|Department of Surgery, Erasmus MC University Medical Center, 3015 CE, Rotterdam, The Netherlands.|Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus MC University Medical Center, 3015 CE, Rotterdam, The Netherlands.|Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus MC University Medical Center, 3015 CE, Rotterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 0, 'Gene Expression Profiling|Humans|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-018-2246-7', '29929481', 'Current normalization methods for RNA-sequencing data allow either for intersample comparison to identify differentially expressed (DE) genes or for intrasample comparison for the discovery and validation of gene signatures. Most studies on optimization of normalization methods typically use simulated data to validate methodologies. We describe a new method, GeTMM, which allows for both inter- and intrasample analyses with the same normalized data set. We used actual (i.e. not simulated) RNA-seq data from 263 colon cancers (no biological replicates) and used the same read count data to compare GeTMM with the most commonly used normalization methods (i.e. TMM (used by edgeR), RLE (used by DESeq2) and TPM) with respect to distributions, effect of RNA quality, subtype-classification, recurrence score, recall of DE genes and correlation to RT-qPCR data.'),
(98434, 2018, 'bmc', 'AUCTSP: an improved biomarker gene pair class predictor', 'Kagaris, Dimitri|Khamesipour, Alireza|Yiannoutsos, Constantin T', 'Department of Electrical and Computer Engineering, Southern Illinois University, 1230 Lincoln Drive, Carbondale, 62901, IL, USA. kagaris@engr.siu.edu.|Department of Electrical and Computer Engineering, Southern Illinois University, 1230 Lincoln Drive, Carbondale, 62901, IL, USA.|Department of Biostatistics, Indiana University School of Public Health, 410 West 10th Street, Suite 3000, Indianapolis, 46202, IN, USA.', 'United States|United States|India', 7, 'Algorithms|Biomarkers|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/s12859-018-2231-1', '29940833', 'The Top Scoring Pair (TSP) classifier, based on the concept of relative ranking reversals in the expressions of pairs of genes, has been proposed as a simple, accurate, and easily interpretable decision rule for classification and class prediction of gene expression profiles. The idea that differences in gene expression ranking are associated with presence or absence of disease is compelling and has strong biological plausibility. Nevertheless, the TSP formulation ignores significant available information which can improve classification accuracy and is vulnerable to selecting genes which do not have differential expression in the two conditions (pivot genes).'),
(98435, 2018, 'bmc', 'Multiobjective grammar-based genetic programming applied to the study of asthma and allergy epidemiology', 'Veiga, Rafael V|Barbosa, Helio J C|Bernardino, Heder S|Freitas, João M|Feitosa, Caroline A|Matos, Sheila M A|Alcântara-Neves, Neuza M|Barreto, Maurício L', 'Center of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Muniz, Fundação Oswaldo Cruz, Salvador, Brazil. rafaelvalenteveiga@gmail.com.|Universidade Federal de Juiz de Fora, Juiz de Fora, Minas Gerais, Brazil.|Universidade Federal de Juiz de Fora, Juiz de Fora, Minas Gerais, Brazil.|Universidade Federal de Juiz de Fora, Juiz de Fora, Minas Gerais, Brazil.|Instituto de Saúde Coletiva, Universidade Federal da Bahia, Savador, Bahia, Brazil.|Instituto de Saúde Coletiva, Universidade Federal da Bahia, Savador, Bahia, Brazil.|Instituto de Ciências da Saúde, Universidade Federal da Bahia, Savador, Bahia, Brazil.|Center of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Muniz, Fundação Oswaldo Cruz, Salvador, Brazil.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 3, 'Algorithms|Allergens|Asthma|Humans|Models, Genetic', 'Journal Article', '10.1186/s12859-018-2233-z', '29940834', 'Asthma and allergies prevalence increased in recent decades, being a serious global health problem. They are complex diseases with strong contextual influence, so that the use of advanced machine learning tools such as genetic programming could be important for the understanding the causal mechanisms explaining those conditions. Here, we applied a multiobjective grammar-based genetic programming (MGGP) to a dataset composed by 1047 subjects. The dataset contains information on the environmental, psychosocial, socioeconomics, nutritional and infectious factors collected from participating children. The objective of this work is to generate models that explain the occurrence of asthma, and two markers of allergy: presence of IgE antibody against common allergens, and skin prick test positivity for common allergens (SPT).');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98436, 2018, 'bmc', 'Detecting Succinylation sites from protein sequences using ensemble support vector machine', 'Ning, Qiao|Zhao, Xiaosa|Bao, Lingling|Ma, Zhiqiang|Zhao, Xiaowei', 'School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China.|School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China.|School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China.|School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China. zhiqiang.ma967@gmail.com.|Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun, 130117, China. zhaoxw303@nenu.edu.cn.', 'China|China|China|China|China', 7, 'Algorithms|Computational Biology|Support Vector Machine', 'Journal Article', '10.1186/s12859-018-2249-4', '29940836', 'Lysine succinylation is a new kind of post-translational modification which plays a key role in protein conformation regulation and cellular function control. To understand the mechanism of succinylation profoundly, it is necessary to identify succinylation sites in proteins accurately. However, traditional methods, experimental approaches, are labor-intensive and time-consuming. Computational prediction methods have been proposed recent years, and they are popular because of their convenience and high speed. In this study, we developed a new method to predict succinylation sites in protein combining multiple features, including amino acid composition, binary encoding, physicochemical property and grey pseudo amino acid composition, with a feature selection scheme (information gain). And then, it was trained using SVM (Support Vector Machine) and an ensemble learning algorithm.'),
(98437, 2018, 'bmc', 'ProMotE: an efficient algorithm for counting independent motifs in uncertain network topologies', 'Ren, Yuanfang|Sarkar, Aisharjya|Kahveci, Tamer', 'Department of Computer & Information Science & Engineering, University of Florida, Gainesville, 32611, FL, USA. yuanfang@cise.ufl.edu.|Department of Computer & Information Science & Engineering, University of Florida, Gainesville, 32611, FL, USA.|Department of Computer & Information Science & Engineering, University of Florida, Gainesville, 32611, FL, USA.', 'United States|United States|United States', 1, 'Algorithms|Amino Acid Motifs', 'Journal Article', '10.1186/s12859-018-2236-9', '29940838', 'Identifying motifs in biological networks is essential in uncovering key functions served by these networks. Finding non-overlapping motif instances is however a computationally challenging task. The fact that biological interactions are uncertain events further complicates the problem, as it makes the existence of an embedding of a given motif an uncertain event as well.'),
(98438, 2018, 'bmc', 'Benchmarking the HLA typing performance of Polysolver and Optitype in 50 Danish parental trios', 'Matey-Hernandez, Maria Luisa|Danish Pan Genome Consortium|Brunak, Søren|Izarzugaza, Jose M G', 'Center for Biological Sequence Analysis, Department of Bio and Health Informatics, Technical University of Denmark, DK-2800, Lyngby, Denmark.|Center for Biological Sequence Analysis, Department of Bio and Health Informatics, Technical University of Denmark, DK-2800, Lyngby, Denmark.|Center for Biological Sequence Analysis, Department of Bio and Health Informatics, Technical University of Denmark, DK-2800, Lyngby, Denmark.|Center for Biological Sequence Analysis, Department of Bio and Health Informatics, Technical University of Denmark, DK-2800, Lyngby, Denmark. josemgizarzugaza@gmail.com.', 'Denmark|Denmark|Denmark|Denmark', 2, 'Benchmarking|Family|Genomics|Genotyping Techniques|HLA Antigens|Histocompatibility Testing|Humans|Parents|Sweden', 'Journal Article', '10.1186/s12859-018-2239-6', '29940840', 'The adaptive immune response intrinsically depends on hypervariable human leukocyte antigen (HLA) genes. Concomitantly, correct HLA phenotyping is crucial for successful donor-patient matching in organ transplantation. The cost and technical limitations of current laboratory techniques, together with advances in next-generation sequencing (NGS) methodologies, have increased the need for precise computational typing methods.'),
(98439, 2018, 'bmc', 'Proteome-wide analysis of human motif-domain interactions mapped on influenza a virus', 'García-Pérez, Carlos A|Guo, Xianwu|Navarro, Juan García|Aguilar, Diego Alonso Gómez|Lara-Ramírez, Edgar E', 'Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Reynosa, Tamaulipas, Mexico.|Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Reynosa, Tamaulipas, Mexico.|Oracle MDC, Guadalajara, Mexico.|Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Reynosa, Tamaulipas, Mexico.|Unidad de Investigación Biomédica de Zacatecas, Instituto Mexicano del Seguro Social, Interior Alameda # 45, Colonia Centro, CP. 98000, Zacatecas, Zac, Mexico. doc_lara_ram@hotmail.com.', 'Mexico|Mexico|Mexico|Mexico|Mexico', 1, 'Host-Pathogen Interactions|Humans|Influenza A virus|Proteome', 'Journal Article', '10.1186/s12859-018-2237-8', '29940841', 'The influenza A virus (IAV) is a constant threat for humans worldwide. The understanding of motif-domain protein participation is essential to combat the pathogen.'),
(98440, 2018, 'bmc', 'BAMSI: a multi-cloud service for scalable distributed filtering of massive genome data', 'Ausmees, Kristiina|John, Aji|Toor, Salman Z|Hellander, Andreas|Nettelblad, Carl', 'Department of Information Technology, Uppsala University, Box 377, Uppsala, Sweden.|Department of Biology, University of Washington, Box 351800, Seattle, 98195, USA.|Department of Information Technology, Uppsala University, Box 377, Uppsala, Sweden.|Department of Information Technology, Uppsala University, Box 377, Uppsala, Sweden.|Department of Information Technology, Uppsala University, Box 377, Uppsala, Sweden. carl.nettelblad@it.uu.se.', 'Sweden|United States|Sweden|Sweden|Sweden', 3, 'Cloud Computing|Genomics|High-Throughput Nucleotide Sequencing|Humans', 'Journal Article', '10.1186/s12859-018-2241-z', '29940842', 'The advent of next-generation sequencing (NGS) has made whole-genome sequencing of cohorts of individuals a reality. Primary datasets of raw or aligned reads of this sort can get very large. For scientific questions where curated called variants are not sufficient, the sheer size of the datasets makes analysis prohibitively expensive. In order to make re-analysis of such data feasible without the need to have access to a large-scale computing facility, we have developed a highly scalable, storage-agnostic framework, an associated API and an easy-to-use web user interface to execute custom filters on large genomic datasets.'),
(98441, 2018, 'bmc', 'Semi-supervised machine learning for automated species identification by collagen peptide mass fingerprinting', 'Gu, Muxin|Buckley, Michael', 'Michael Smith Building, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK.|Manchester Institute of Biotechnology, School of Earth and Environmental Sciences, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. m.buckley@manchester.ac.uk.', 'United Kingdom|United Kingdom', 2, 'Collagen|DNA Fingerprinting|Humans|Peptides|Species Specificity|Supervised Machine Learning', 'Journal Article', '10.1186/s12859-018-2221-3', '29940843', 'Biomolecular methods for species identification are increasingly being utilised in the study of changing environments, both at the microscopic and macroscopic levels. High-throughput peptide mass fingerprinting has been largely applied to bacterial identification, but increasingly used to identify archaeological and palaeontological skeletal material to yield information on past environments and human-animal interaction. However, as applications move away from predominantly domesticate and the more abundant wild fauna to a much wider range of less common taxa that do not yet have genetically-derived sequence information, robust methods of species identification and biomarker selection need to be determined.'),
(98442, 2018, 'bmc', 'A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks', 'Jambusaria, Ankit|Klomp, Jeff|Hong, Zhigang|Rafii, Shahin|Dai, Yang|Malik, Asrar B|Rehman, Jalees', 'Department of Pharmacology, The University of Illinois College of Medicine, 835 S. Wolcott Ave. Rm. E403, Chicago, IL, 60612, USA.|Department of Pharmacology, The University of Illinois College of Medicine, 835 S. Wolcott Ave. Rm. E403, Chicago, IL, 60612, USA.|Department of Pharmacology, The University of Illinois College of Medicine, 835 S. Wolcott Ave. Rm. E403, Chicago, IL, 60612, USA.|Division of Regenerative Medicine, Department of Medicine, Ansary Stem Cell Institute, Weill Cornell Medicine, New York, NY, USA.|Department of Bioengineering, The University of Illinois at Chicago, Chicago, IL, USA.|Department of Pharmacology, The University of Illinois College of Medicine, 835 S. Wolcott Ave. Rm. E403, Chicago, IL, 60612, USA. abmalik@uic.edu.|Department of Pharmacology, The University of Illinois College of Medicine, 835 S. Wolcott Ave. Rm. E403, Chicago, IL, 60612, USA. jalees@uic.edu.', 'United States|United States|United States|United States|United States|United States|United States', 4, 'Cells|Computational Biology|Databases, Factual|Gene Expression Profiling|Gene Regulatory Networks|Genetic Heterogeneity|Humans|Organ Specificity|Phenotype|Transcription Factors|Transcriptome', 'Journal Article', '10.1186/s12859-018-2190-6', '29940845', 'The heterogeneity of cells across tissue types represents a major challenge for studying biological mechanisms as well as for therapeutic targeting of distinct tissues. Computational prediction of tissue-specific gene regulatory networks may provide important insights into the mechanisms underlying the cellular heterogeneity of cells in distinct organs and tissues.'),
(98443, 2018, 'bmc', 'ToTem: a tool for variant calling pipeline optimization', 'Tom, Nikola|Tom, Ondrej|Malcikova, Jitka|Pavlova, Sarka|Kubesova, Blanka|Rausch, Tobias|Kolarik, Miroslav|Benes, Vladimir|Bystry, Vojtech|Pospisilova, Sarka', 'Center of Molecular Medicine, Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|Department of Computer Science, Faculty of Science, Palacky University, Olomouc, Czech Republic.|Center of Molecular Medicine, Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|Center of Molecular Medicine, Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|Department of Internal Medicine - Hematology and Oncology, Medical Faculty, Masaryk University and University Hospital Brno, Brno, Czech Republic.|Genomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany.|Department of Computer Science, Faculty of Science, Palacky University, Olomouc, Czech Republic.|Genomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany.|Center of Molecular Medicine, Central European Institute of Technology, Masaryk University, Brno, Czech Republic. vojtech.bystry@ceitec.muni.cz.|Center of Molecular Medicine, Central European Institute of Technology, Masaryk University, Brno, Czech Republic. pospisilova.sarka@fnbrno.cz.', '-|-|-|-|-|Germany|-|Germany|-|-', 0, 'Computational Biology|High-Throughput Nucleotide Sequencing|Reproducibility of Results|Research Design|Software', 'Journal Article', '10.1186/s12859-018-2227-x', '29940847', 'High-throughput bioinformatics analyses of next generation sequencing (NGS) data often require challenging pipeline optimization. The key problem is choosing appropriate tools and selecting the best parameters for optimal precision and recall.'),
(98444, 2018, 'bmc', 'GenoGAM 20: scalable and efficient implementation of genome-wide generalized additive models for gigabase-scale genomes', 'Stricker, Georg|Galinier, Mathilde|Gagneur, Julien', 'Department of Informatics, Technical University Munich, Boltzmannstr. 3, Garching, Germany.|Department of Informatics, Technical University Munich, Boltzmannstr. 3, Garching, Germany.|Department of Informatics, Technical University Munich, Boltzmannstr. 3, Garching, Germany. gagneur@in.tum.de.', 'Germany|Germany|Germany', 0, 'Genomics|Humans', 'Journal Article', '10.1186/s12859-018-2238-7', '29945559', 'GenoGAM (Genome-wide generalized additive models) is a powerful statistical modeling tool for the analysis of ChIP-Seq data with flexible factorial design experiments. However large runtime and memory requirements of its current implementation prohibit its application to gigabase-scale genomes such as mammalian genomes.'),
(98445, 2018, 'bmc', 'PanACEA: a bioinformatics tool for the exploration and visualization of bacterial pan-chromosomes', 'Clarke, Thomas H|Brinkac, Lauren M|Inman, Jason M|Sutton, Granger|Fouts, Derrick E', 'J. Craig Venter Institute, Rockville, MD, 20850, USA. tclarke@jcvi.org.|J. Craig Venter Institute, Rockville, MD, 20850, USA.|J. Craig Venter Institute, Rockville, MD, 20850, USA.|J. Craig Venter Institute, Rockville, MD, 20850, USA.|J. Craig Venter Institute, Rockville, MD, 20850, USA.', 'United States|United States|United States|United States|United States', 3, 'Chromosomes|Computational Biology|Genomics|Humans', 'Journal Article', '10.1186/s12859-018-2250-y', '29945570', 'Bacterial pan-genomes, comprised of conserved and variable genes across multiple sequenced bacterial genomes, allow for identification of genomic regions that are phylogenetically discriminating or functionally important. Pan-genomes consist of large amounts of data, which can restrict researchers ability to locate and analyze these regions. Multiple software packages are available to visualize pan-genomes, but currently their ability to address these concerns are limited by using only pre-computed data sets, prioritizing core over variable gene clusters, or by not accounting for pan-chromosome positioning in the viewer.'),
(98446, 2018, 'bmc', 'MICOP: Maximal information coefficient-based oscillation prediction to detect biological rhythms in proteomics data', 'Iuchi, Hitoshi|Sugimoto, Masahiro|Tomita, Masaru', 'Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, 252-8520, Japan.|Institute for Advanced Biosciences, Keio University, Tsuruoka, 997-0052, Japan. msugi@sfc.keio.ac.jp.|Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, 252-8520, Japan.', 'Japan|Japan|Japan', 2, 'Circadian Rhythm|Humans|Proteomics', 'Journal Article', '10.1186/s12859-018-2257-4', '29954316', 'Circadian rhythms comprise oscillating molecular interactions, the disruption of the homeostasis of which would cause various disorders. To understand this phenomenon systematically, an accurate technique to identify oscillating molecules among omics datasets must be developed, however, this is still impeded by many difficulties, such as experimental noise and attenuated amplitude.'),
(98447, 2018, 'bmc', 'FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining', 'Bachman, John A|Gyori, Benjamin M|Sorger, Peter K', 'Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Ave, Boston, MA, 02115, USA.|Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Ave, Boston, MA, 02115, USA.|Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Ave, Boston, MA, 02115, USA. peter_sorger@hms.harvard.edu.', 'United States|United States|United States', 7, 'Data Mining|Humans|Proteins', 'Journal Article', '10.1186/s12859-018-2211-5', '29954318', 'For automated reading of scientific publications to extract useful information about molecular mechanisms it is critical that genes, proteins and other entities be correctly associated with uniform identifiers, a process known as named entity linking or grounding. Correct grounding is essential for resolving relationships among mined information, curated interaction databases, and biological datasets. The accuracy of this process is largely dependent on the availability of machine-readable resources associating synonyms and abbreviations commonly found in biomedical literature with uniform identifiers.'),
(98448, 2018, 'bmc', 'Correction to: Realizing drug repositioning by adapting a recommendation system to handle the process', 'Ozsoy, Makbule Gulcin|Özyer, Tansel|Polat, Faruk|Alhajj, Reda', 'Department of Computer Engineering, Middle East Technical University, Ankara, Turkey.|Department of Computer Engineering, TOBB University, Ankara, Turkey.|Department of Computer Engineering, Middle East Technical University, Ankara, Turkey.|Department of Computer Science, University of Calgary, Calgary, AB, Canada. alhajj@ucalgary.ca.', 'Turkey|Turkey|Turkey|Canada', 0, '-', 'Journal Article', '10.1186/s12859-018-2258-3', '29966513', 'Following publication of the original article [1], the authors reported that there was an error in the spelling of the name of one of the authors.'),
(98449, 2018, 'bmc', 'SimCAL: a flexible tool to compute biochemical reaction similarity', 'Sivakumar, Tadi Venkata|Bhaduri, Anirban|Duvvuru Muni, Rajasekhara Reddy|Park, Jin Hwan|Kim, Tae Yong', 'Bioinformatics Lab, Samsung Advanced Institute of Technology, Bangalore, 560037, India.|Bioinformatics Lab, Samsung Advanced Institute of Technology, Bangalore, 560037, India.|Bioinformatics Lab, Samsung Advanced Institute of Technology, Bangalore, 560037, India.|Biomaterials Lab, Materials Center, Samsung Advanced Institute of Technology, Gyeonggi-do, 443803, South Korea.|Biomaterials Lab, Materials Center, Samsung Advanced Institute of Technology, Gyeonggi-do, 443803, South Korea. ty76.kim@samsung.com.', 'India|India|India|-|-', 0, 'Biochemical Phenomena|Computational Biology|Humans', 'Journal Article', '10.1186/s12859-018-2248-5', '29969981', 'Computation of reaction similarity is a pre-requisite for several bioinformatics applications including enzyme identification for specific biochemical reactions, enzyme classification and mining for specific inhibitors. Reaction similarity is often assessed at either two levels: (i) comparison across all the constituent substrates and products of a reaction, reaction level similarity, (ii) comparison at the transformation center with various degrees of neighborhood, transformation level similarity. Existing reaction similarity computation tools are designed for specific applications and use different features and similarity measures. A single system integrating these diverse features enables comparison of the impact of different molecular properties on similarity score computation.'),
(98450, 2018, 'bmc', 'Plant organelle RNA editing and its specificity factors: enhancements of analyses and new database features in PREPACT 30', 'Lenz, Henning|Hein, Anke|Knoop, Volker', 'IZMB - Institut für Zelluläre und Molekulare Botanik, Abteilung Molekulare Evolution, Universität Bonn, Kirschallee 1, 53115, Bonn, Germany.|IZMB - Institut für Zelluläre und Molekulare Botanik, Abteilung Molekulare Evolution, Universität Bonn, Kirschallee 1, 53115, Bonn, Germany.|IZMB - Institut für Zelluläre und Molekulare Botanik, Abteilung Molekulare Evolution, Universität Bonn, Kirschallee 1, 53115, Bonn, Germany. volker.knoop@uni-bonn.de.', 'Germany|Germany|Germany', 0, 'Computational Biology|Plant Proteins|RNA Editing|RNA, Plant', 'Journal Article', '10.1186/s12859-018-2244-9', '29970001', 'Gene expression in plant chloroplasts and mitochondria is affected by RNA editing. Numerous C-to-U conversions, accompanied by reverse U-to-C exchanges in some plant clades, alter the genetic information encoded in the organelle genomes. Predicting and analyzing RNA editing, which ranges from only few sites in some species to thousands in other taxa, is bioinformatically demanding.'),
(98451, 2018, 'bmc', 'HapCHAT: adaptive haplotype assembly for efficiently leveraging high coverage in long reads', 'Beretta, Stefano|Patterson, Murray D|Zaccaria, Simone|Della Vedova, Gianluca|Bonizzoni, Paola', 'Department of Informatics, Systems, and Communication, University of Milano-Bicocca, Milan, Italy.|Department of Informatics, Systems, and Communication, University of Milano-Bicocca, Milan, Italy. murray.patterson@unimib.it.|Department of Computer Science, Princeton University, Princeton, New Jersey, USA.|Department of Informatics, Systems, and Communication, University of Milano-Bicocca, Milan, Italy.|Department of Informatics, Systems, and Communication, University of Milano-Bicocca, Milan, Italy.', 'Italy|Italy|Jersey|Italy|Italy', 0, 'Algorithms|Haplotypes|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2253-8', '29970002', 'Haplotype assembly is the process of assigning the different alleles of the variants covered by mapped sequencing reads to the two haplotypes of the genome of a human individual. Long reads, which are nowadays cheaper to produce and more widely available than ever before, have been used to reduce the fragmentation of the assembled haplotypes since their ability to span several variants along the genome. These long reads are also characterized by a high error rate, an issue which may be mitigated, however, with larger sets of reads, when this error rate is uniform across genome positions. Unfortunately, current state-of-the-art dynamic programming approaches designed for long reads deal only with limited coverages.'),
(98452, 2018, 'bmc', 'BROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization', 'de Boer, Carl G|Regev, Aviv', 'Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.|Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA. carlgdeboer@gmail.com.', 'United States|United States', 10, 'Binding Sites|Epigenomics|Humans|Transcription Factors', 'Journal Article', '10.1186/s12859-018-2255-6', '29970004', 'Variation in chromatin organization across single cells can help shed important light on the mechanisms controlling gene expression, but scale, noise, and sparsity pose significant challenges for interpretation of single cell chromatin data. Here, we develop BROCKMAN (Brockman Representation Of Chromatin by K-mers in Mark-Associated Nucleotides), an approach to infer variation in transcription factor (TF) activity across samples through unsupervised analysis of the variation in DNA sequences associated with an epigenomic mark.'),
(98453, 2018, 'bmc', 'Correction to: The cytidine deaminase under-representation reporter (CDUR) as a tool to study evolution of sequences under deaminase mutational pressure', 'Shapiro, Maxwell|Meier, Stephen|MacCarthy, Thomas', 'Department of Applied Mathematics and Statistics, Stony Brook University, 100 Nicolls Road, Stony Brook, NY, USA.|Department of Applied Mathematics and Statistics, Stony Brook University, 100 Nicolls Road, Stony Brook, NY, USA.|Department of Applied Mathematics and Statistics, Stony Brook University, 100 Nicolls Road, Stony Brook, NY, USA. thomas.maccarthy@stonybrook.edu.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1186/s12859-018-2259-2', '29973133', 'Following publication of the original article [1], the authors reported that Figs. 1 and 3 were interchanged. The original article has been corrected.'),
(98454, 2018, 'bmc', 'FC_analysis: a tool for investigating atomic force microscopy maps of force curves', 'Dinarelli, Simone|Girasole, Marco|Longo, Giovanni', 'Istituto di Struttura della Materia, CNR, Rome, Italy. simone.dinarelli@ism.cnr.it.|Istituto di Struttura della Materia, CNR, Rome, Italy.|Istituto di Struttura della Materia, CNR, Rome, Italy.', 'Italy|Italy|Italy', 0, 'Humans|Microscopy, Atomic Force', 'Journal Article', '10.1186/s12859-018-2265-4', '29976136', 'The collection and analysis of Atomic Force Microscopy force curves is a well-established procedure to obtain high-resolution information of non-topographic data from any kind of sample, including biological specimens. In particular, these analyses are commonly employed to study elasticity, stiffness or adhesion properties of the samples. Furthermore, the collection of several force curves over an extended area of the specimens allows reconstructing maps, called force volume maps, of the spatial distribution of the mechanical properties. Coupling these maps with the conventional high-resolution topographic reconstruction of the samples surface, provides a deeper insight on the sample composition from the structural and nanomechanical point of view.'),
(98455, 2018, 'bmc', 'BARCOSEL: a tool for selecting an optimal barcode set for high-throughput sequencing', 'Somervuo, Panu|Koskinen, Patrik|Mei, Peng|Holm, Liisa|Auvinen, Petri|Paulin, Lars', 'Mathematical Biology Group, Department of Biosciences, FIN-00014 University of Helsinki, P.O.Box 65, Finland. panu.somervuo@helsinki.fi.|Holm Group, Institute of Biotechnology, FIN-00014 University of Helsinki, P.O.Box 56, Finland.|DNA Sequencing and Genomics Laboratory, Institute of Biotechnology, FIN-00014 University of Helsinki, P.O.Box 56, Finland.|Holm Group, Institute of Biotechnology, FIN-00014 University of Helsinki, P.O.Box 56, Finland.|DNA Sequencing and Genomics Laboratory, Institute of Biotechnology, FIN-00014 University of Helsinki, P.O.Box 56, Finland.|DNA Sequencing and Genomics Laboratory, Institute of Biotechnology, FIN-00014 University of Helsinki, P.O.Box 56, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland', 6, 'DNA Barcoding, Taxonomic|High-Throughput Screening Assays|Humans', 'Journal Article', '10.1186/s12859-018-2262-7', '29976145', 'Current high-throughput sequencing platforms provide capacity to sequence multiple samples in parallel. Different samples are labeled by attaching a short sample specific nucleotide sequence, barcode, to each DNA molecule prior pooling them into a mix containing a number of libraries to be sequenced simultaneously. After sequencing, the samples are binned by identifying the barcode sequence within each sequence read. In order to tolerate sequencing errors, barcodes should be sufficiently apart from each other in sequence space. An additional constraint due to both nucleotide usage and basecalling accuracy is that the proportion of different nucleotides should be in balance in each barcode position. The number of samples to be mixed in each sequencing run may vary and this introduces a problem how to select the best subset of available barcodes at sequencing core facility for each sequencing run. There are plenty of tools available for de novo barcode design, but they are not suitable for subset selection.'),
(98456, 2018, 'bmc', 'A deep learning approach to bilingual lexicon induction in the biomedical domain', 'Heyman, Geert|Vulixc4x87, Ivan|Moens, Marie-Francine', 'LIIR, Department of Computer Science, Celestijnenlaan 200A, Leuven, Belgium. geert.heyman@cs.kuleuven.be.|Language Technology Lab, DTAL, University of Cambridge, 9 West Road, Cambridge, UK.|LIIR, Department of Computer Science, Celestijnenlaan 200A, Leuven, Belgium.', 'Belgium|United Kingdom|Belgium', 1, 'Data Mining|Deep Learning|Humans|Knowledge Bases|Multilingualism|Natural Language Processing|Semantics', 'Journal Article', '10.1186/s12859-018-2245-8', '29986664', 'Bilingual lexicon induction (BLI) is an important task in the biomedical domain as translation resources are usually available for general language usage, but are often lacking in domain-specific settings. In this article we consider BLI as a classification problem and train a neural network composed of a combination of recurrent long short-term memory and deep feed-forward networks in order to obtain word-level and character-level representations.'),
(98457, 2018, 'bmc', 'trumpet: transcriptome-guided quality assessment of m6A-seq data', 'Zhang, Teng|Zhang, Shao-Wu|Zhang, Lin|Meng, Jia', 'Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xian, 710027, Shaanxi, China.|Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xian, 710027, Shaanxi, China. zhangsw@nwpu.edu.cn.|School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China.|Department of Biological Sciences, Research Center for Precision Medicine, Xian Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, China. jia.meng@xjtlu.edu.cn.', 'China|China|China|China', 5, 'High-Throughput Nucleotide Sequencing|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1186/s12859-018-2266-3', '30001693', 'Background Methylated RNA immunoprecipitation sequencing (MeRIP-seq or m6A-seq) has been extensively used for profiling transcriptome-wide distribution of RNA N6-Methyl-Adnosine methylation. However, due to the intrinsic properties of RNA molecules and the intricate procedures of this technique, m6A-seq data often suffer from various flaws. A convenient and comprehensive tool is needed to assess the quality of m6A-seq data to ensure that they are suitable for subsequent analysis.  Results From a technical perspective, m6A-seq can be considered as a combination of ChIP-seq and RNA-seq; hence, by effectively combing the data quality assessment metrics of the two techniques, we developed the trumpet R package for evaluation of m6A-seq data quality. The trumpet package takes the aligned BAM files from m6A-seq data together with the transcriptome information as the inputs to generate a quality assessment report in the HTML format.  Conclusions The trumpet R package makes a valuable tool for assessing the data quality of m6A-seq, and it is also applicable to other fragmented RNA immunoprecipitation sequencing techniques, including m1A-seq, CeU-Seq, ?-seq, etc.'),
(98458, 2018, 'bmc', 'Alternative empirical Bayes models for adjusting for batch effects in genomic studies', 'Zhang, Yuqing|Jenkins, David F|Manimaran, Solaiappan|Johnson, W Evan', 'Division of Computational Biomedicine, Boston University School of Medicine, 72 East Concord Street, Boston, 02118, MA, USA.|Division of Computational Biomedicine, Boston University School of Medicine, 72 East Concord Street, Boston, 02118, MA, USA.|Division of Computational Biomedicine, Boston University School of Medicine, 72 East Concord Street, Boston, 02118, MA, USA.|Division of Computational Biomedicine, Boston University School of Medicine, 72 East Concord Street, Boston, 02118, MA, USA. wej@bu.edu.', 'United States|United States|United States|United States', 0, 'Bayes Theorem|Genomics|Humans|Research Design', 'Journal Article', '10.1186/s12859-018-2263-6', '30001694', 'Combining genomic data sets from multiple studies is advantageous to increase statistical power in studies where logistical considerations restrict sample size or require the sequential generation of data. However, significant technical heterogeneity is commonly observed across multiple batches of data that are generated from different processing or reagent batches, experimenters, protocols, or profiling platforms. These so-called batch effects often confound true biological relationships in the data, reducing the power benefits of combining multiple batches, and may even lead to spurious results in some combined studies. Therefore there is significant need for effective methods and software tools that account for batch effects in high-throughput genomic studies.'),
(98459, 2018, 'bmc', 'npInv: accurate detection and genotyping of inversions using long read sub-alignment', 'Shao, Haojing|Ganesamoorthy, Devika|Duarte, Tania|Cao, Minh Duc|Hoggart, Clive J|Coin, Lachlan J M', 'Genomics of Development and Disease Division, Institute for Molecular Bioscience, University of Queensland, 306 Carmody Rd, St Lucia, Brisbane, 4067, Australia.|Genomics of Development and Disease Division, Institute for Molecular Bioscience, University of Queensland, 306 Carmody Rd, St Lucia, Brisbane, 4067, Australia.|Genomics of Development and Disease Division, Institute for Molecular Bioscience, University of Queensland, 306 Carmody Rd, St Lucia, Brisbane, 4067, Australia.|Genomics of Development and Disease Division, Institute for Molecular Bioscience, University of Queensland, 306 Carmody Rd, St Lucia, Brisbane, 4067, Australia.|Department of Medicine, Imperial College London, Level 2, Faculty Building South Kensington Campus, London, SW7 2AZ, United Kingdom.|Genomics of Development and Disease Division, Institute for Molecular Bioscience, University of Queensland, 306 Carmody Rd, St Lucia, Brisbane, 4067, Australia. l.coin@imb.uq.edu.au.', 'Australia|Australia|Australia|Australia|United Kingdom|Australia', 0, 'Chromosome Inversion|Genotype|Humans', 'Journal Article', '10.1186/s12859-018-2252-9', '30001702', 'Detection of genomic inversions remains challenging. Many existing methods primarily target inzversions with a non repetitive breakpoint, leaving inverted repeat (IR) mediated non-allelic homologous recombination (NAHR) inversions largely unexplored.'),
(98460, 2018, 'bmc', 'Effective automated pipeline for 3D reconstruction of synapses based on deep learning', 'Xiao, Chi|Li, Weifu|Deng, Hao|Chen, Xi|Yang, Yang|Xie, Qiwei|Han, Hua', 'Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing, 100190, China.|Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing, 100190, China.|Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China.|Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing, 100190, China.|Institute of Neuroscience, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China.|Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing, 100190, China. qiwei.xie@ia.ac.cn.|Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing, 100190, China. hua.han@ia.ac.cn.', 'China|China|China|China|China|China|China', 4, 'Deep Learning|Humans|Imaging, Three-Dimensional|Synapses', 'Journal Article', '10.1186/s12859-018-2232-0', '30005590', 'The locations and shapes of synapses are important in reconstructing connectomes and analyzing synaptic plasticity. However, current synapse detection and segmentation methods are still not adequate for accurately acquiring the synaptic connectivity, and they cannot effectively alleviate the burden of synapse validation.'),
(98461, 2018, 'bmc', 'An integrative network-based approach to identify novel disease genes and pathways: a case study in the context of inflammatory bowel disease', 'Eguchi, Ryohei|Karim, Mohammand Bozlul|Hu, Pingzhao|Sato, Tetsuo|Ono, Naoaki|Kanaya, Shigehiko|Altaf-Ul-Amin, Md', 'Graduate School of Science and Technology & NAIST Data Science Center, Nara Institute of Science and Technology, Nara, Japan.|Graduate School of Science and Technology & NAIST Data Science Center, Nara Institute of Science and Technology, Nara, Japan.|Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Canada.|Graduate School of Science and Technology & NAIST Data Science Center, Nara Institute of Science and Technology, Nara, Japan.|Graduate School of Science and Technology & NAIST Data Science Center, Nara Institute of Science and Technology, Nara, Japan.|Graduate School of Science and Technology & NAIST Data Science Center, Nara Institute of Science and Technology, Nara, Japan.|Graduate School of Science and Technology & NAIST Data Science Center, Nara Institute of Science and Technology, Nara, Japan. amin-m@is.naist.jp.', 'Japan|Japan|Canada|Japan|Japan|Japan|Japan', 0, 'Algorithms|Area Under Curve|Databases, Genetic|Gene Ontology|Gene Regulatory Networks|Humans|Inflammatory Bowel Diseases|Protein Interaction Maps|ROC Curve|Reproducibility of Results', 'Journal Article', '10.1186/s12859-018-2251-x', '30005591', 'There are different and complicated associations between genes and diseases. Finding the causal associations between genes and specific diseases is still challenging. In this work we present a method to predict novel associations of genes and pathways with inflammatory bowel disease (IBD) by integrating information of differential gene expression, protein-protein interaction and known disease genes related to IBD.'),
(98462, 2018, 'bmc', 'Discovering themes in biomedical literature using a projection-based algorithm', 'Yeganova, Lana|Kim, Sun|Balasanov, Grigory|Wilbur, W John', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA. lana.yeganova@nih.gov.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Databases, Genetic|Humans|Polymorphism, Single Nucleotide|Publications', 'Journal Article', '10.1186/s12859-018-2240-0', '30012087', 'The need to organize any large document collection in a manner that facilitates human comprehension has become crucial with the increasing volume of information available. Two common approaches to provide a broad overview of the information space are document clustering and topic modeling. Clustering aims to group documents or terms into meaningful clusters. Topic modeling, on the other hand, focuses on finding coherent keywords for describing topics appearing in a set of documents. In addition, there have been efforts for clustering documents and finding keywords simultaneously.'),
(98463, 2018, 'bmc', 'Matataki: an ultrafast mRNA quantification method for large-scale reanalysis of RNA-Seq data', 'Okamura, Yasunobu|Kinoshita, Kengo', 'Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan.|Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan. kengo@ecei.tohoku.ac.jp.', 'Japan|Japan', 0, 'Animals|Base Sequence|Computational Biology|Computer Simulation|Genome|Humans|Mice|RNA, Messenger|Sequence Analysis, RNA|Time Factors', 'Journal Article', '10.1186/s12859-018-2279-y', '30012088', 'Data generated by RNA sequencing (RNA-Seq) is now accumulating in vast amounts in public repositories, especially for human and mouse genomes. Reanalyzing these data has emerged as a promising approach to identify gene modules or pathways. Although meta-analyses of gene expression data are frequently performed using microarray data, meta-analyses using RNA-Seq data are still rare. This lag is partly due to the limitations in reanalyzing RNA-Seq data, which requires extensive computational resources. Moreover, it is nearly impossible to calculate the gene expression levels of all samples in a public repository using currently available methods. Here, we propose a novel method, Matataki, for rapidly estimating gene expression levels from RNA-Seq data.'),
(98464, 2018, 'bmc', 'GtTR: Bayesian estimation of absolute tandem repeat copy number using sequence capture and high throughput sequencing', 'Ganesamoorthy, Devika|Cao, Minh Duc|Duarte, Tania|Chen, Wenhan|Coin, Lachlan', 'Institute for Molecular Biosciences, University of Queensland, Brisbane, Australia.|Institute for Molecular Biosciences, University of Queensland, Brisbane, Australia.|Institute for Molecular Biosciences, University of Queensland, Brisbane, Australia.|Institute for Molecular Biosciences, University of Queensland, Brisbane, Australia.|Institute for Molecular Biosciences, University of Queensland, Brisbane, Australia. l.coin@imb.uq.edu.au.', 'Australia|Australia|Australia|Australia|Australia', 0, 'Algorithms|Alleles|Base Sequence|Bayes Theorem|Computer Simulation|Gene Dosage|Genome, Human|Genotype|High-Throughput Nucleotide Sequencing|Humans|Minisatellite Repeats|Tandem Repeat Sequences|Whole Genome Sequencing', 'Journal Article', '10.1186/s12859-018-2282-3', '30012093', 'Tandem repeats comprise significant proportion of the human genome including coding and regulatory regions. They are highly prone to repeat number variation and nucleotide mutation due to their repetitive and unstable nature, making them a major source of genomic variation between individuals. Despite recent advances in high throughput sequencing, analysis of tandem repeats in the context of complex diseases is still hindered by technical limitations. We report a novel targeted sequencing approach, which allows simultaneous analysis of hundreds of repeats. We developed a Bayesian algorithm, namely - GtTR - which combines information from a reference long-read dataset with a short read counting approach to genotype tandem repeats at population scale. PCR sizing analysis was used for validation.'),
(98465, 2018, 'bmc', 'Netpredictor: R and Shiny package to perform drug-target network analysis and prediction of missing links', 'Seal, Abhik|Wild, David J', 'School of Informatics and Computing, Indiana University Bloomington, Informatics West, Bloomington, 47408, Indiana, USA.|School of Informatics and Computing, Indiana University Bloomington, Informatics West, Bloomington, 47408, Indiana, USA. djwild@indiana.edu.', 'India|India', 2, 'Algorithms|Drug Delivery Systems|Gene Ontology|Protein Interaction Maps|Software', 'Journal Article', '10.1186/s12859-018-2254-7', '30012095', 'Netpredictor is an R package for prediction of missing links in any given unipartite or bipartite network. The package provides utilities to compute missing links in a bipartite and well as unipartite networks using Random Walk with Restart and Network inference algorithm and a combination of both. The package also allows computation of Bipartite network properties, visualization of communities for two different sets of nodes, and calculation of significant interactions between two sets of nodes using permutation based testing. The application can also be used to search for top-K shortest paths between interactome and use enrichment analysis for disease, pathway and ontology. The R standalone package (including detailed introductory vignettes) and associated R Shiny web application is available under the GPL-2 Open Source license and is freely available to download.'),
(98466, 2018, 'bmc', 'CEDAR OnDemand: a browser extension to generate ontology-based scientific metadata', 'Bukhari, Syed Ahmad Chan|Martínez-Romero, Marcos|O Connor, Martin J|Egyedi, Attila L|Willrett, Debra|Graybeal, John|Musen, Mark A|Cheung, Kei-Hoi|Kleinstein, Steven H', 'Department of Pathology, Yale School of Medicine, New Haven, CT, USA. ahmad.chan@yale.edu.|Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.|Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.|Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.|Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.|Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.|Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.|Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA. kei.cheung@yale.edu.|Department of Pathology, Yale School of Medicine, New Haven, CT, USA. steven.kleinstein@yale.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Biological Ontologies|Humans|Internet|Metadata|Software', 'Journal Article', '10.1186/s12859-018-2247-6', '30012108', 'Public biomedical data repositories often provide web-based interfaces to collect experimental metadata. However, these interfaces typically reflect the ad hoc metadata specification practices of the associated repositories, leading to a lack of standardization in the collected metadata. This lack of standardization limits the ability of the source datasets to be broadly discovered, reused, and integrated with other datasets. To increase reuse, discoverability, and reproducibility of the described experiments, datasets should be appropriately annotated by using agreed-upon terms, ideally from ontologies or other controlled term sources.'),
(98467, 2018, 'bmc', 'HGT-ID: an efficient and sensitive workflow to detect human-viral insertion sites using next-generation sequencing data', 'Baheti, Saurabh|Tang, Xiaojia|OBrien, Daniel R|Chia, Nicholas|Roberts, Lewis R|Nelson, Heidi|Boughey, Judy C|Wang, Liewei|Goetz, Matthew P|Kocher, Jean-Pierre A|Kalari, Krishna R', '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.|Department of Surgery, Mayo Clinic, Rochester, MN, USA.|Division of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA.|Department of Surgery, Mayo Clinic, Rochester, MN, USA.|Department of Surgery, Mayo Clinic, Rochester, MN, USA.|Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.|Department of Molecular Pharmacology and Experimental Therapeutics, 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. kalari.krishna@mayo.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 2, 'Algorithms|Base Sequence|Breast Neoplasms|Cell Line, Tumor|Computer Simulation|Female|Gene Transfer, Horizontal|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|ROC Curve|Software|Virus Integration|Whole Genome Sequencing|Workflow', 'Journal Article', '10.1186/s12859-018-2260-9', '30016933', 'Transfer of genetic material from microbes or viruses into the host genome is known as horizontal gene transfer (HGT). The integration of viruses into the human genome is associated with multiple cancers, and these can now be detected using next-generation sequencing methods such as whole genome sequencing and RNA-sequencing.'),
(98468, 2018, 'bmc', 'Random forest versus logistic regression: a large-scale benchmark experiment', 'Couronnxc3xa9, Raphael|Probst, Philipp|Boulesteix, Anne-Laure', 'Department of Medical Information Processing, Biometry and Epidemiology, LMU Munich, Marchioninistr. 15, Munich, 81377, Germany. raphael.couronne@gmail.com.|Department of Medical Information Processing, Biometry and Epidemiology, LMU Munich, Marchioninistr. 15, Munich, 81377, Germany.|Department of Medical Information Processing, Biometry and Epidemiology, LMU Munich, Marchioninistr. 15, Munich, 81377, Germany.', 'Germany|Germany|Germany', 53, 'Algorithms|Benchmarking|Computer Simulation|Databases as Topic|Humans|Logistic Models', 'Journal Article', '10.1186/s12859-018-2264-5', '30016950', 'The Random Forest (RF) algorithm for regression and classification has considerably gained popularity since its introduction in 2001. Meanwhile, it has grown to a standard classification approach competing with logistic regression in many innovation-friendly scientific fields.'),
(98469, 2018, 'bmc', 'De novo assembly of bacterial genomes with repetitive DNA regions by dnaasm application', 'Kuxc5x9bmirek, Wiktor|Nowak, Robert', 'Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, Warsaw, 00-665, Poland. W.Kusmirek@ii.pw.edu.pl.|Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, Warsaw, 00-665, Poland.', 'Poland|Poland', 0, 'Algorithms|Bacteria|Base Sequence|Computer Simulation|DNA|Databases, Genetic|Genome, Bacterial|High-Throughput Nucleotide Sequencing|Repetitive Sequences, Nucleic Acid|Tandem Repeat Sequences', 'Journal Article', '10.1186/s12859-018-2281-4', '30021513', 'Many organisms, in particular bacteria, contain repetitive DNA fragments called tandem repeats. These structures are restored by DNA assemblers by mapping paired-end tags to unitigs, estimating the distance between them and filling the gap with the specified DNA motif, which could be repeated many times. However, some of the tandem repeats are longer than the distance between the paired-end tags.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98470, 2018, 'bmc', 'De novo profile generation based on sequence context specificity with the long short-term memory network', 'Yamada, Kazunori D|Kinoshita, Kengo', 'Graduate School of Information Sciences, Tohoku University, Sendai, Japan.|Graduate School of Information Sciences, Tohoku University, Sendai, Japan. kengo@ecei.tohoku.ac.jp.', 'Japan|Japan', 0, 'Amino Acid Sequence|Computational Biology|Databases as Topic|Deep Learning|Neural Networks (Computer)|Proteins|ROC Curve|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/s12859-018-2284-1', '30021530', 'Long short-term memory (LSTM) is one of the most attractive deep learning methods to learn time series or contexts of input data. Increasing studies, including biological sequence analyses in bioinformatics, utilize this architecture. Amino acid sequence profiles are widely used for bioinformatics studies, such as sequence similarity searches, multiple alignments, and evolutionary analyses. Currently, many biological sequences are becoming available, and the rapidly increasing amount of sequence data emphasizes the importance of scalable generators of amino acid sequence profiles.'),
(98471, 2018, 'bmc', 'Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs log-ratio transformation-based methods', 'Quinn, Thomas P|Crowley, Tamsyn M|Richardson, Mark F', 'Centre for Molecular and Medical Research, School of Medicine, Deakin University, Geelong, 3220, Australia. contacttomquinn@gmail.com.|Centre for Molecular and Medical Research, School of Medicine, Deakin University, Geelong, 3220, Australia.|Bioinformatics Core Research Group, Deakin University, Geelong, 3220, Australia.', 'Australia|Australia|Australia', 9, 'Base Sequence|Benchmarking|Computer Simulation|Databases, Genetic|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|RNA, Messenger|RNA, Ribosomal, 16S|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-018-2261-8', '30021534', 'Count data generated by next-generation sequencing assays do not measure absolute transcript abundances. Instead, the data are constrained to an arbitrary library size by the sequencing depth of the assay, and typically must be normalized prior to statistical analysis. The constrained nature of these data means one could alternatively use a log-ratio transformation in lieu of normalization, as often done when testing for differential abundance (DA) of operational taxonomic units (OTUs) in 16S rRNA data. Therefore, we benchmark how well the ALDEx2 package, a transformation-based DA tool, detects differential expression in high-throughput RNA-sequencing data (RNA-Seq), compared to conventional RNA-Seq methods such as edgeR and DESeq2.'),
(98472, 2018, 'bmc', 'phylotreejs - a JavaScript library for application development and interactive data visualization in phylogenetics', 'Shank, Stephen D|Weaver, Steven|Kosakovsky Pond, Sergei L', 'Department of Biology and Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, 19122, USA. sshank@temple.edu.|Department of Biology and Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, 19122, USA.|Department of Biology and Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, 19122, USA.', 'United States|United States|United States', 6, 'Computational Biology|Phylogeny|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-018-2283-2', '30045713', 'While several JavaScript packages for visualizing phylogenetic trees exist, most are best characterized as frameworks that are designed with a specific set of tasks in mind. Extending such packages to use cases that are not available as features often ends up being difficult. Moreover, existing packages tend to produce standalone widgets that are not designed to serve as middleware, as opposed to flexible tools that can integrate with other components of an application.'),
(98473, 2018, 'bmc', 'HiCcompare: an R-package for joint normalization and comparison of HI-C datasets', 'Stansfield, John C|Cresswell, Kellen G|Vladimirov, Vladimir I|Dozmorov, Mikhail G', 'Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, 23298, USA.|Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, 23298, USA.|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, 23219, USA.|Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, 23298, USA. mikhail.dozmorov@vcuhealth.org.', 'United States|United States|United States|United States', 21, 'Animals|CCCTC-Binding Factor|Cell Differentiation|Chromatin|Computational Biology|Databases, Genetic|Genome|Humans|Mice|Neurons|Software', 'Journal Article', '10.1186/s12859-018-2288-x', '30064362', 'Changes in spatial chromatin interactions are now emerging as a unifying mechanism orchestrating the regulation of gene expression. Hi-C sequencing technology allows insight into chromatin interactions on a genome-wide scale. However, Hi-C data contains many DNA sequence- and technology-driven biases. These biases prevent effective comparison of chromatin interactions aimed at identifying genomic regions differentially interacting between, e.g., disease-normal states or different cell types. Several methods have been developed for normalizing individual Hi-C datasets. However, they fail to account for biases between two or more Hi-C datasets, hindering comparative analysis of chromatin interactions.'),
(98474, 2018, 'bmc', 'TOPAZ: asymmetric suffix array neighbourhood search for massive protein databases', 'Medlar, Alan|Holm, Liisa', 'Institute of Biotechnology, University of Helsinki, Helsinki, 00014, Finland. alan.j.medlar@helsinki.fi.|Institute of Biotechnology, University of Helsinki, Helsinki, 00014, Finland.', 'Finland|Finland', 1, 'Algorithms|Amino Acid Sequence|Bacterial Proteins|Databases, Protein|Enterobacteriaceae|Sequence Alignment|Software', 'Journal Article', '10.1186/s12859-018-2290-3', '30064374', 'Protein homology search is an important, yet time-consuming, step in everything from protein annotation to metagenomics. Its application, however, has become increasingly challenging, due to the exponential growth of protein databases. In order to perform homology search at the required scale, many methods have been proposed as alternatives to BLAST that make an explicit trade-off between sensitivity and speed. One such method, SANSparallel, uses a parallel implementation of the suffix array neighbourhood search (SANS) technique to achieve high speed and provides several modes to allow for greater sensitivity at the expense of performance.'),
(98475, 2018, 'bmc', 'WISH-R- a fast and efficient tool for construction of epistatic networks for complex traits and diseases', 'Carmelo, Victor A O|Kogelman, Lisette J A|Madsen, Majbritt Busk|Kadarmideen, Haja N', 'Quantitative and Systems Genomics Group, Department of Bio and Health Informatics, Technical University of Denmark, Kemitorvet, Building 208, 2800, Kgs. Lyngby, Denmark.|Animal Breeding, Quantitative Genetics and Systems Biology group, Department of Large Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark.|Institute of Biological Psychiatry, Mental Health Centre, Sct. Hans, Roskilde, Capital Region of Denmark, Denmark.|Quantitative and Systems Genomics Group, Department of Bio and Health Informatics, Technical University of Denmark, Kemitorvet, Building 208, 2800, Kgs. Lyngby, Denmark. hajak@dtu.dk.', 'Denmark|Denmark|Denmark|Denmark', 6, 'Computational Biology|Disease|Epistasis, Genetic|Gene Regulatory Networks|Genetic Predisposition to Disease|Genotype|Humans|Phenotype|Polymorphism, Single Nucleotide|Quantitative Trait, Heritable|Software', 'Journal Article', '10.1186/s12859-018-2291-2', '30064383', 'Genetic epistasis is an often-overlooked area in the study of the genomics of complex traits. Genome-wide association studies are a useful tool for revealing potential causal genetic variants, but in this context, epistasis is generally ignored. Data complexity and interpretation issues make it difficult to process and interpret epistasis. As the number of interaction grows exponentially with the number of variants, computational limitation is a bottleneck. Gene Network based strategies have been successful in integrating biological data and identifying relevant hub genes and pathways related to complex traits. In this study, epistatic interactions and network-based analysis are combined in the Weighted Interaction SNP hub (WISH) method and implemented in an efficient and easy to use R package.'),
(98476, 2018, 'bmc', 'Deep learning models for bacteria taxonomic classification of metagenomic data', 'Fiannaca, Antonino|La Paglia, Laura|La Rosa, Massimo|Lo Bosco, Giosue|Renda, Giovanni|Rizzo, Riccardo|Gaglio, Salvatore|Urso, Alfonso', 'CNR-ICAR, National Research Council of Italy, Via Ugo La Malfa, 153, Palermo, Italy. antonino.fiannaca@icar.cnr.it.|CNR-ICAR, National Research Council of Italy, Via Ugo La Malfa, 153, Palermo, Italy.|CNR-ICAR, National Research Council of Italy, Via Ugo La Malfa, 153, Palermo, Italy.|Dipartimento di Matematica e Informatica, Università degli studi di Palermo, Via Archirafi, 34, Palermo, Italy.|Dipartimento dellInnovazione Industriale e Digitale, Università degli studi di Palermo, Viale Delle Scienze, ed.6, Palermo, Italy.|CNR-ICAR, National Research Council of Italy, Via Ugo La Malfa, 153, Palermo, Italy.|CNR-ICAR, National Research Council of Italy, Via Ugo La Malfa, 153, Palermo, Italy.|CNR-ICAR, National Research Council of Italy, Via Ugo La Malfa, 153, Palermo, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 19, 'Algorithms|Bacteria|Databases, Genetic|Deep Learning|Metagenome|Metagenomics|Models, Genetic|Neural Networks (Computer)|RNA, Ribosomal, 16S|Reproducibility of Results|Time Factors', 'Journal Article', '10.1186/s12859-018-2182-6', '30066629', 'An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria classification at the genus level, but till now it is hard to identify the correct composition of metagenomic data from RNA-seq short-read data. 16S short-read data are generated using two next generation sequencing technologies, i.e. whole genome shotgun (WGS) and amplicon (AMP), typically, the former is filtered to obtain short-reads belonging to a 16S shotgun (SG), whereas the latter take into account only some specific 16S hypervariable regions. The above mentioned two sequencing technologies, SG and AMP, are used alternatively, for this reason in this work we propose a deep learning approach for taxonomic classification of metagenomic data, that can be employed for both of them.'),
(98477, 2018, 'bmc', 'STAble: a novel approach to de novo assembly of RNA-seq data and its application in a metabolic model network based metatranscriptomic workflow', 'Saggese, Igor|Bona, Elisa|Conway, Max|Favero, Francesco|Ladetto, Marco|Liò, Pietro|Manzini, Giovanni|Mignone, Flavio', 'Dipartimento di Scienze e Innovazione Tecnologica, Università degli Studi del Piemonte Orientale, 15121, Alessandria, Italy.|Dipartimento di Scienze e Innovazione Tecnologica, Università degli Studi del Piemonte Orientale, 15121, Alessandria, Italy.|Computer Laboratory, University of Cambridge, Cambridge, CB2 1TN, UK.|Dipartimento di Scienze della Salute, Università degli Studi del Piemonte Orientale, 28100, Novara, Italy.|AO SS Antonio e Biagio e Cesare Arrigo, 15121, Alessandria, Italy.|Computer Laboratory, University of Cambridge, Cambridge, CB2 1TN, UK.|Dipartimento di Scienze e Innovazione Tecnologica, Università degli Studi del Piemonte Orientale, 15121, Alessandria, Italy.|Dipartimento di Scienze e Innovazione Tecnologica, Università degli Studi del Piemonte Orientale, 15121, Alessandria, Italy. flavio.mignone@uniupo.it.', 'Italy|Italy|United Kingdom|Italy|Italy|United Kingdom|Italy|Italy', 1, 'Algorithms|Animals|Humans|Metabolic Networks and Pathways|Methane|Models, Genetic|RNA, Messenger|Ruminants|Sequence Analysis, RNA|Software|Transcriptome|Workflow', 'Journal Article', '10.1186/s12859-018-2174-6', '30066630', 'De novo assembly of RNA-seq data allows the study of transcriptome in absence of a reference genome either if data is obtained from a single organism or from a mixed sample as in metatranscriptomics studies. Given the high number of sequences obtained from NGS approaches, a critical step in any analysis workflow is the assembly of reads to reconstruct transcripts thus reducing the complexity of the analysis. Despite many available tools show a good sensitivity, there is a high percentage of false positives due to the high number of assemblies considered and it is likely that the high frequency of false positive is underestimated by currently used benchmarks. The reconstruction of not existing transcripts may false the biological interpretation of results as - for example - may overestimate the identification of novel transcripts. Moreover, benchmarks performed are usually based on RNA-seq data from annotated genomes and assembled transcripts are compared to annotations and genomes to identify putative good and wrong reconstructions, but these tests alone may lead to accept a particular type of false positive as true, as better described below.'),
(98478, 2018, 'bmc', 'NetControl4BioMed: a pipeline for biomedical data acquisition and analysis of network controllability', 'Kanhaiya, Krishna|Rogojin, Vladimir|Kazemi, Keivan|Czeizler, Eugen|Petre, Ion', 'Computational Biomodeling Laboratory, Turku Centre for Computer Science, and Department of Computer Science, Å bo Akademi University, Domkyrkotorget 3, Turku, 20500, Finland.|Computational Biomodeling Laboratory, Turku Centre for Computer Science, and Department of Computer Science, Å bo Akademi University, Domkyrkotorget 3, Turku, 20500, Finland.|Computational Biomodeling Laboratory, Turku Centre for Computer Science, and Department of Computer Science, Å bo Akademi University, Domkyrkotorget 3, Turku, 20500, Finland.|Computational Biomodeling Laboratory, Turku Centre for Computer Science, and Department of Computer Science, Å bo Akademi University, Domkyrkotorget 3, Turku, 20500, Finland.|Computational Biomodeling Laboratory, Turku Centre for Computer Science, and Department of Computer Science, Å bo Akademi University, Domkyrkotorget 3, Turku, 20500, Finland. ipetre@abo.fi.', 'Finland|Finland|Finland|Finland|Finland', 0, 'Algorithms|Computational Biology|Databases, Factual|Gene Regulatory Networks|Humans|Software', 'Journal Article', '10.1186/s12859-018-2177-3', '30066633', 'Network controllability focuses on discovering combinations of external interventions that can drive a biological system to a desired configuration. In practice, this approach translates into finding a combined multi-drug therapy in order to induce a desired response from a cell, this can lead to developments of novel therapeutic approaches for systemic diseases like cancer.'),
(98479, 2018, 'bmc', 'Mendelian randomisation analysis of clustered causal effects of body mass on cardiometabolic biomarkers', 'Conde, Susana|Xu, Xiaoguang|Guo, Hui|Perola, Markus|Fazia, Teresa|Bernardinelli, Luisa|Berzuini, Carlo', 'Centre for Biostatistics, University of Manchester, University Place, Oxford Road, Manchester, M13 9PL, UK.|Centre for Biostatistics, University of Manchester, University Place, Oxford Road, Manchester, M13 9PL, UK.|Centre for Biostatistics, University of Manchester, University Place, Oxford Road, Manchester, M13 9PL, UK.|National Institute for Health and Welfare (THL), P.O. Box 30 (Mannerheimintie 166), Helsinki, FI-00271, Finland.|Department of Brain and Behavioural Sciences, University of Pavia, Via Bassi 21, Pavia, 27100, Italy.|Department of Brain and Behavioural Sciences, University of Pavia, Via Bassi 21, Pavia, 27100, Italy.|Centre for Biostatistics, University of Manchester, University Place, Oxford Road, Manchester, M13 9PL, UK. carlo.berzuini@manchester.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|Finland|Italy|Italy|United Kingdom', 0, 'Biomarkers|Body Mass Index|Body Weight|Cardiovascular Diseases|Female|Gene Frequency|Humans|Mendelian Randomization Analysis|Metabolic Diseases|Metabolome|Polymorphism, Single Nucleotide|Regression Analysis|Risk Factors', 'Journal Article', '10.1186/s12859-018-2178-2', '30066639', 'Recent advances in data analysis methods based on principles of Mendelian Randomisation, such as Egger regression and the weighted median estimator, add to the researchers ability to infer cause-effect links from observational data. Now is the time to gauge the potential of these methods within specific areas of biomedical research. In this paper, we choose a study in metabolomics as an illustrative testbed. We apply Mendelian Randomisation methods in the analysis of data from the DILGOM (Dietary, Lifestyle and Genetic determinants of Obesity and Metabolic syndrome) study, in the context of an effort to identify molecular pathways of cardiovascular disease. In particular, our illustrative analysis addresses the question whether body mass, as measured by body mass index (BMI), exerts a causal effect on the concentrations of a collection of 137 cardiometabolic markers with different degrees of atherogenic power, such as the (highly atherogenic) lipoprotein metabolites with very low density (VLDLs) and the (protective) high density lipoprotein metabolites.'),
(98480, 2018, 'bmc', 'A study on multi-omic oscillations in Escherichia coli metabolic networks', 'Bardozzo, Francesco|Lió, Pietro|Tagliaferri, Roberto', 'NeuRoNe Lab, DISA-MIS, University of Salerno, Via Giovanni Paolo II 132, Salerno, 84084 Fisciano, Italy.|Computer Laboratory, Department of Computer Science, University of Cambridge, 15 JJ Thomson Ave, Cambridge, CB3 0FD, UK.|NeuRoNe Lab, DISA-MIS, University of Salerno, Via Giovanni Paolo II 132, Salerno, 84084 Fisciano, Italy. robtag@unisa.it.', 'Italy|United Kingdom|Italy', 2, 'Algorithms|Escherichia coli|Genome, Bacterial|Metabolic Networks and Pathways|Metabolomics|Operon|Phenotype|Software', 'Journal Article', '10.1186/s12859-018-2175-5', '30066640', 'Two important challenges in the analysis of molecular biology information are data (multi-omic information) integration and the detection of patterns across large scale molecular networks and sequences. They are are actually coupled beause the integration of omic information may provide better means to detect multi-omic patterns that could reveal multi-scale or emerging properties at the phenotype levels.'),
(98481, 2018, 'bmc', 'Effect of the number of removed lymph nodes on prostate cancer recurrence and survival: evidence from an observational study', 'Gigliarano, Chiara|Nonis, Alessandro|Briganti, Alberto|Bonetti, Marco|Serio, Clelia Di', 'University of Insubria, Varese, Italy. chiara.gigliarano@uninsubria.it.|CUSSB, Vita-Salute San Raffaele University, Milan, Italy.|Ospedale San Raffaele, Milan, Italy.|Carlo F. Dondena Research Centre, Bocconi University, Milan, Italy.|CUSSB, Vita-Salute San Raffaele University, Milan, Italy.', 'Italy|Italy|Italy|Italy|Italy', 0, 'Adult|Aged|Humans|Lymph Node Excision|Lymph Nodes|Male|Middle Aged|Neoplasm Recurrence, Local|Neoplasm Staging|Probability|Prognosis|Prostatic Neoplasms|Regression Analysis|Survival Analysis|Time Factors', 'Journal Article', '10.1186/s12859-018-2180-8', '30066642', 'The aim of this article is to analyze the effect on biochemical recurrence and on overall survival of removing an extensive number of pelvic lymph nodes during prostate cancer surgery. The lack of evidence from randomized clinical trials to address this specific question has hampered the ability to determine the true effect of the number of nodes removed.'),
(98482, 2018, 'bmc', 'Beyond the one-way ANOVA for omics data', 'Hassall, Kirsty L|Mead, Andrew', 'Computational and Analytical Sciences, Rothamsted Research, Harpenden, AL5 2JQ, UK. kirsty.hassall@rothamsted.ac.uk.|Computational and Analytical Sciences, Rothamsted Research, Harpenden, AL5 2JQ, UK.', 'United Kingdom|United Kingdom', 2, 'Analysis of Variance|Arabidopsis|Computer Simulation|Genomics|Humans|Lipids|Metabolome|Metabolomics|Sodium Chloride', 'Journal Article', '10.1186/s12859-018-2173-7', '30066646', 'With ever increasing accessibility to high throughput technologies, more complex treatment structures can be assessed in a variety of omics applications. This adds an extra layer of complexity to the analysis and interpretation, in particular when inferential univariate methods are applied en masse. It is well-known that mass univariate testing suffers from multiplicity issues and although this has been well documented for simple comparative tests, few approaches have focussed on more complex explanatory structures.'),
(98483, 2018, 'bmc', 'INBIA: a boosting methodology for proteomic network inference', 'Sardina, Davide S|Micale, Giovanni|Ferro, Alfredo|Pulvirenti, Alfredo|Giugno, Rosalba', 'Department of Computer Science, University of Verona, Strada le Grazie 15, Verona, 37134, Italy.|Department of Mathematics and Computer Science, University of Catania, Viale A. Doria 6, Catania, 95125, Italy.|Department of Clinical and Experimental Medicine, University of Catania, c/o Dept. of Math. and Comp. Science, Viale A. Doria 6, Catania, 95125, Italy.|Department of Clinical and Experimental Medicine, University of Catania, c/o Dept. of Math. and Comp. Science, Viale A. Doria 6, Catania, 95125, Italy.|Department of Computer Science, University of Verona, Strada le Grazie 15, Verona, 37134, Italy. rosalba.giugno@univr.it.', 'Italy|Italy|Italy|Italy|Italy', 1, 'Algorithms|Humans|Mutation|Neoplasms|Organ Specificity|Protein Interaction Mapping|Protein Interaction Maps|Proteomics|Reproducibility of Results', 'Journal Article', '10.1186/s12859-018-2183-5', '30066650', 'The analysis of tissue-specific protein interaction networks and their functional enrichment in pathological and normal tissues provides insights on the etiology of diseases. The Pan-cancer proteomic project, in The Cancer Genome Atlas, collects protein expressions in human cancers and it is a reference resource for the functional study of cancers. However, established protocols to infer interaction networks from protein expressions are still missing.'),
(98484, 2018, 'bmc', 'Modeling the covariates effects on the hazard function by piecewise exponential artificial neural networks: an application to a controlled clinical trial on renal carcinoma', 'Fornili, Marco|Boracchi, Patrizia|Ambrogi, Federico|Biganzoli, Elia', 'Department of Clinical Sciences and Community Health, University of Milan, via Venezian 1, 20133, Milan, Italy. marco.fornili@unimi.it.|Department of Clinical Sciences and Community Health, University of Milan, via Venezian 1, 20133, Milan, Italy.|Department of Clinical Sciences and Community Health, University of Milan, via Venezian 1, 20133, Milan, Italy.|Department of Clinical Sciences and Community Health, University of Milan, via Venezian 1, 20133, Milan, Italy.', 'Italy|Italy|Italy|Italy', 0, 'Controlled Clinical Trials as Topic|Humans|Kidney Neoplasms|Lymph Nodes|Models, Biological|Neoplasm Staging|Neural Networks (Computer)|Regression Analysis|Reproducibility of Results', 'Journal Article', '10.1186/s12859-018-2179-1', '30066658', 'In exploring the time course of a disease to support or generate biological hypotheses, the shape of the hazard function provides relevant information. For long follow-ups the shape of hazard function may be complex, with the presence of multiple peaks. In this paper we present the use of a neural network extension of the piecewise exponential model to study the shape of the hazard function in time in dependence of covariates. The technique is applied to a dataset of 247 renal cell carcinoma patients from a randomized clinical trial.'),
(98485, 2018, 'bmc', 'Preface to the BMC-CIBB 2015-16 special issue', 'Angelini, Claudia|Bracciali, Andrea|Gilbert, David|Rizzo, Riccardo', 'Istituto per le Applicazioni del Calcolo - Consiglio Nazionale delle Ricerche, Via Pietro Castellino 111, Napoli, 80131, Italy. claudia.angelini@cnr.it.|Computing Sciences and Mathematics University of Stirling, FK9 4LA, Stirling, UK. abb@cs.stir.ac.uk.|Department of Computer Science, St Johns Building, Brunel University London, Uxbridge, Middlesex, London, UB8 3PH, UK.|Istituto di Calcolo e Reti ad Alte Prestazioni, Consiglio Nazionale delle Ricerche, Via Ugo La Malfa, 153, Palermo, 90146, Italy.', 'Italy|United Kingdom|United Kingdom|Italy', 0, '-', 'Introductory Journal Article', '10.1186/s12859-018-2176-4', '30066660', '-'),
(98486, 2018, 'bmc', 'Emerging ensembles of kinetic parameters to characterize observed metabolic phenotypes', 'Colombo, Riccardo|Damiani, Chiara|Gilbert, David|Heiner, Monika|Mauri, Giancarlo|Pescini, Dario', 'Department of Informatics, Systems and Communication, University of Milan - Bicocca, Viale Sarca, 336, Milan, 20126, Italy. riccardo.colombo@disco.unimib.it.|Department of Informatics, Systems and Communication, University of Milan - Bicocca, Viale Sarca, 336, Milan, 20126, Italy.|College of Engineering, Design and Physical Sciences, Brunel University, Middlesex, London, Uxbridg, UB8 3PH, UK.|Computer Science Department, Brandenburg University of Technology Cottbus-Senftenberg, Walther-Pauer-Str. 2, Cottbus, D-03046, Germany.|Department of Informatics, Systems and Communication, University of Milan - Bicocca, Viale Sarca, 336, Milan, 20126, Italy.|SYSBIO - Centre of Systems Biology, Piazza della Scienza, 2, Milan, 20126, Italy.', 'Italy|Italy|United Kingdom|Germany|Italy|Italy', 1, 'Biomass|Computer Simulation|Escherichia coli|Escherichia coli Proteins|Kinetics|Metabolic Networks and Pathways|Phenotype|Time Factors', 'Journal Article', '10.1186/s12859-018-2181-7', '30066662', 'Determining the value of kinetic constants for a metabolic system in the exact physiological conditions is an extremely hard task. However, this kind of information is of pivotal relevance to effectively simulate a biological phenomenon as complex as metabolism.'),
(98487, 2018, 'bmc', 'Comparison of metaheuristics to measure gene effects on phylogenetic supports and topologies', 'Garnier, Régis|Guyeux, Christophe|Couchot, Jean-François|Salomon, Michel|Al-Nuaimi, Bashar|AlKindy, Bassam', 'FEMTO-ST Institute, UMR 6174 CNRS, DISC Computer Science Department, Univ. Bourgogne Franche-Comté (UBFC), 16 Route de Gray, Besançon, 25000, France.|FEMTO-ST Institute, UMR 6174 CNRS, DISC Computer Science Department, Univ. Bourgogne Franche-Comté (UBFC), 16 Route de Gray, Besançon, 25000, France. christophe.guyeux@univ-fcomte.fr.|FEMTO-ST Institute, UMR 6174 CNRS, DISC Computer Science Department, Univ. Bourgogne Franche-Comté (UBFC), 16 Route de Gray, Besançon, 25000, France.|FEMTO-ST Institute, UMR 6174 CNRS, DISC Computer Science Department, Univ. Bourgogne Franche-Comté (UBFC), 16 Route de Gray, Besançon, 25000, France.|FEMTO-ST Institute, UMR 6174 CNRS, DISC Computer Science Department, Univ. Bourgogne Franche-Comté (UBFC), 16 Route de Gray, Besançon, 25000, France.|Department of Computer Science, Mustansiriyah University, Baghdad, 10052, Iraq.', 'France|France|France|France|France|Iraq', 0, 'Algorithms|Computer Simulation|Genes, Plant|Heuristics|Molecular Sequence Annotation|Phylogeny|Plants', 'Comparative Study', '10.1186/s12859-018-2172-8', '30066663', 'A huge and continuous increase in the number of completely sequenced chloroplast genomes, available for evolutionary and functional studies in plants, has been observed during the past years. Consequently, it appears possible to build large-scale phylogenetic trees of plant species. However, building such a tree that is well-supported can be a difficult task, even when a subset of close plant species is considered. Usually, the difficulty raises from a few core genes disturbing the phylogenetic information, due for example from problems of homoplasy. Fortunately, a reliable phylogenetic tree can be obtained once these problematic genes are identified and removed from the analysis.Therefore, in this paper we address the problem of finding the largest subset of core genomes which allows to build the best supported tree.'),
(98488, 2018, 'bmc', 'CircPrimer: a software for annotating circRNAs and determining the specificity of circRNA primers', 'Zhong, Shanliang|Wang, Jinyan|Zhang, Qian|Xu, Hanzi|Feng, Jifeng', 'Center of Clinical Laboratory Science, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, China.|Department of General Surgery, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, China.|Department of General Surgery, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, China.|Department of Radiation Oncology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, China.|Department of Medical Oncology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Baiziting 42, Nanjing, 210009, China. feng_jifeng@sina.com.', 'China|China|China|China|China', 21, 'Base Sequence|Genome|Humans|Molecular Sequence Annotation|RNA|RNA Splicing|Software', 'Journal Article', '10.1186/s12859-018-2304-1', '30075703', 'Since circular RNAs (circRNAs) post-transcriptionally regulate gene expression, they have attracted increasing attention. However, there is no existing tool to annotate and extract spliced sequences for circRNAs and no tool to determine the specificity of circRNA primers.'),
(98489, 2018, 'bmc', 'Prediction of 8-state protein secondary structures by a novel deep learning architecture', 'Zhang, Buzhong|Li, Jinyan|Lü, Qiang', 'School of Computer Science and Technology, Soochow University, Suzhou, China.|Advanced Analytics Institute, Faculty of Engineering and IT, University of Technology Sydney, Broadway, NSW 2007, Sydney, PO Box 123, Australia.|School of Computer Science and Technology, Soochow University, Suzhou, China. qiang@suda.edu.cn.', 'China|Australia|China', 14, 'Algorithms|Computational Biology|Databases, Protein|Deep Learning|Neural Networks (Computer)|Protein Structure, Secondary', 'Journal Article', '10.1186/s12859-018-2280-5', '30075707', 'Protein secondary structure can be regarded as an information bridge that links the primary sequence and tertiary structure. Accurate 8-state secondary structure prediction can significantly give more precise and high resolution on structure-based properties analysis.'),
(98490, 2018, 'bmc', 'Fusion of encoder-decoder deep networks improves delineation of multiple nuclear phenotypes', 'Khoshdeli, Mina|Winkelmaier, Garrett|Parvin, Bahram', 'Electrical and Biomedical Department, University of Nevada, Reno, 1664 N. Virginia, Reno, USA.|Electrical and Biomedical Department, University of Nevada, Reno, 1664 N. Virginia, Reno, USA.|Electrical and Biomedical Department, University of Nevada, Reno, 1664 N. Virginia, Reno, USA. bparvin@unr.edu.', 'United States|United States|United States', 7, 'Algorithms|Cell Nucleus|Deep Learning|Humans|Image Processing, Computer-Assisted|Neural Networks (Computer)|Phenotype', 'Journal Article', '10.1186/s12859-018-2285-0', '30086715', 'Nuclear segmentation is an important step for profiling aberrant regions of histology sections. If nuclear segmentation can be resolved, then new biomarkers of nuclear phenotypes and their organization can be predicted for the application of precision medicine. However, segmentation is a complex problem as a result of variations in nuclear geometry (e.g., size, shape), nuclear type (e.g., epithelial, fibroblast), nuclear phenotypes (e.g., vesicular, aneuploidy), and overlapping nuclei. The problem is further complicated as a result of variations in sample preparation (e.g., fixation, staining). Our hypothesis is that (i) deep learning techniques can learn complex phenotypic signatures that rise in tumor sections, and (ii) fusion of different representations (e.g., regions, boundaries) contributes to improved nuclear segmentation.'),
(98491, 2018, 'bmc', 'PRS-on-Spark (PRSoS): a novel, efficient and flexible approach for generating polygenic risk scores', 'Chen, Lawrence M|Yao, Nelson|Garg, Elika|Zhu, Yuecai|Nguyen, Thao T T|Pokhvisneva, Irina|Hari Dass, Shantala A|Unternaehrer, Eva|Gaudreau, Hxc3xa9lxc3xa8ne|Forest, Marie|McEwen, Lisa M|MacIsaac, Julia L|Kobor, Michael S|Greenwood, Celia M T|Silveira, Patricia P|Meaney, Michael J|ODonnell, Kieran J', 'Douglas Hospital Research Centre, McGill University, H4H1R3, Montreal, Quebec, Canada.|Douglas Hospital Research Centre, McGill University, H4H1R3, Montreal, Quebec, Canada.|Douglas Hospital Research Centre, McGill University, H4H1R3, Montreal, Quebec, Canada.|Douglas Hospital Research Centre, McGill University, H4H1R3, Montreal, Quebec, Canada.|Douglas Hospital Research Centre, McGill University, H4H1R3, Montreal, Quebec, Canada.|Douglas Hospital Research Centre, McGill University, H4H1R3, Montreal, Quebec, Canada.|Douglas Hospital Research Centre, McGill University, H4H1R3, Montreal, Quebec, Canada.|Douglas Hospital Research Centre, McGill University, H4H1R3, Montreal, Quebec, Canada.|Douglas Hospital Research Centre, McGill University, H4H1R3, Montreal, Quebec, Canada.|Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Montreal, QC, Canada.|Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada.|Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada.|Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada.|Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Montreal, QC, Canada.|Douglas Hospital Research Centre, McGill University, H4H1R3, Montreal, Quebec, Canada.|Douglas Hospital Research Centre, McGill University, H4H1R3, Montreal, Quebec, Canada.|Douglas Hospital Research Centre, McGill University, H4H1R3, Montreal, Quebec, Canada. kieran.odonnell@mcgill.ca.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Ca', 2, 'Adult|Alleles|Cohort Studies|Depressive Disorder, Major|Genomics|Genotype|Humans|Models, Genetic|Multifactorial Inheritance|Phenotype|Polymorphism, Single Nucleotide|Risk Factors|Software', 'Journal Article', '10.1186/s12859-018-2289-9', '30089455', 'Polygenic risk scores (PRS) describe the genomic contribution to complex phenotypes and consistently account for a larger proportion of variance in outcome than single nucleotide polymorphisms (SNPs) alone. However, there is little consensus on the optimal data input for generating PRS, and existing approaches largely preclude the use of imputed posterior probabilities and strand-ambiguous SNPs i.e., A/T or C/G polymorphisms. Our ability to predict complex traits that arise from the additive effects of a large number of SNPs would likely benefit from a more inclusive approach.'),
(98492, 2018, 'bmc', 'BART: bioinformatics array research tool', 'Amaral, Maria Luisa|Erikson, Galina A|Shokhirev, Maxim N', 'The Razavi Newman Integrative Genomics and Bioinformatics Core, Salk Institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA.|The Razavi Newman Integrative Genomics and Bioinformatics Core, Salk Institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA.|The Razavi Newman Integrative Genomics and Bioinformatics Core, Salk Institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA. mshokhirev@salk.edu.', 'United States|United States|United States', 0, 'Breast Neoplasms|Computational Biology|Databases, Genetic|Female|Humans|Oligonucleotide Array Sequence Analysis|Principal Component Analysis|RNA|Software', 'Journal Article', '10.1186/s12859-018-2308-x', '30089462', 'Microarray experiments comprise more than half of all series in the Gene Expression Omnibus (GEO). However, downloading and analyzing raw or semi-processed microarray data from GEO is not intuitive and requires manual error-prone analysis and a bioinformatics background. This is due to a lack of standardization in array platform fabrication as well as the lack of a simple interactive tool for clustering, plotting, differential expression testing, and testing for functional enrichment.'),
(98493, 2018, 'bmc', 'PIPEBAR and OverlapPER: tools for a fast and accurate DNA barcoding analysis and paired-end assembly', 'Oliveira, Renato Renison Moreira|Nunes, Gisele Lopes|de Lima, Talvâne Glauber Lopes|Oliveira, Guilherme|Alves, Ronnie', 'Instituto Tecnológico Vale, Belém, Pará, Brazil. renato.renison@gmail.com.|Instituto Tecnológico Vale, Belém, Pará, Brazil.|Instituto Tecnológico Vale, Belém, Pará, Brazil.|Instituto Tecnológico Vale, Belém, Pará, Brazil.|Instituto Tecnológico Vale, Belém, Pará, Brazil. Ronnie.alves@itv.org.', 'Brazil|Brazil|Brazil|Brazil|Brazil', 1, 'Base Sequence|Codon, Terminator|Consensus Sequence|DNA Barcoding, Taxonomic|Frameshift Mutation|Software', 'Journal Article', '10.1186/s12859-018-2307-y', '30089465', 'Taxonomic identification of plants and insects is a hard process that demands expert taxonomists and time, and its often difficult to distinguish on morphology only. DNA barcodes allow a rapid species discovery and identification and have been widely used for taxonomic identification by targeting known gene regions that permit to discriminate these species. DNA barcode sequence analysis is usually carried out with processes and tools that still demand a high interaction with the user or researcher. To reduce at most such interaction, we proposed PIPEBAR, a pipeline for DNA chromatograms analysis of Sanger platform sequencing, ensuring high quality consensus sequences along with efficient running time. We also proposed a paired-end reads assembly tool, OverlapPER, which is used in sequence or independently of PIPEBAR.'),
(98494, 2018, 'bmc', 'REHUNT: a reliable and open source package for restriction enzyme hunting', 'Cheng, Yu-Huei|Liaw, Jiun-Jian|Kuo, Che-Nan', 'Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung, Taiwan.|Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung, Taiwan.|Department of Business Administration, CTBC Business School, Tainan, Taiwan. fkikimo@hotmail.com.', '-|-|-', 0, 'DNA Restriction Enzymes|Humans|Restriction Mapping|Software', 'Journal Article', '10.1186/s12859-018-2168-4', '30092755', 'Restriction enzymes are used frequently in biotechnology. However, manual mining of restriction enzymes is challenging. Furthermore, integrating available restriction enzymes into different bioinformatics systems is necessary for many biotechnological applications, such as polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Thus, in the present study, we developed the package REHUNT (Restriction Enzymes HUNTing), which mines restriction enzymes from the public database REBASE using a series of search operations.'),
(98495, 2018, 'bmc', 'Multi-class segmentation of neuronal structures in electron microscopy images', 'Cetina, Kendrick|Buenaposada, José M|Baumela, Luis', 'Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Campus de Montegancedo s/n, Boadilla del Monte, España, Madrid, 28660, Spain.|ETSII, Universidad Rey Juan Carlos, C/ Tulipán, s/n, Móstoles, 28933, Spain. josemiguel.buenaposada@urjc.es.|Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Campus de Montegancedo s/n, Boadilla del Monte, España, Madrid, 28660, Spain.', 'Spain|Spain|Spain', 0, 'Animals|Hippocampus|Image Processing, Computer-Assisted|Mitochondria|Neurons|Rats|Synapses', 'Journal Article', '10.1186/s12859-018-2305-0', '30092759', 'Serial block face scanning electron microscopy (SBFEM) is becoming a popular technology in neuroscience. We have seen in the last years an increasing number of works addressing the problem of segmenting cellular structures in SBFEM images of brain tissue. The vast majority of them is designed to segment one specific structure, typically membranes, synapses and mitochondria. Our hypothesis is that the performance of these algorithms can be improved by concurrently segmenting more than one structure using image descriptions obtained at different scales.'),
(98496, 2018, 'bmc', 'miREM: an expectation-maximization approach for prioritizing miRNAs associated with gene-set', 'Abdul Hadi, Luqman Hakim|Xuan Lin, Quy Xiao|Minh, Tri Tran|Loh, Marie|Ng, Hong Kiat|Salim, Agus|Soong, Richie|Benoukraf, Touati', 'Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore, 117599, Singapore.|Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore, 117599, Singapore.|Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore, 117599, Singapore.|Translational Laboratory in Genetic Medicine, Agency for Science, Technology and Research, Singapore, Singapore.|Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore, 117599, Singapore.|Department of Mathematics and Statistics, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, Victoria, Australia.|Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore, 117599, Singapore. richiesoong@hotmail.com.|Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore, 117599, Singapore. benoukraf@nus.edu.sg.', 'Singapore|Singapore|Singapore|Singapore|Singapore|Australia|Singapore|Singapore', 0, 'Algorithms|Animals|Computational Biology|Databases, Nucleic Acid|Humans|Mice|MicroRNAs|RNA, Messenger', 'Journal Article', '10.1186/s12859-018-2292-1', '30097004', 'The knowledge of miRNAs regulating the expression of sets of mRNAs has led to novel insights into numerous and diverse cellular mechanisms. While a single miRNA may regulate many genes, one gene can be regulated by multiple miRNAs, presenting a complex relationship to model for accurate predictions.'),
(98497, 2018, 'bmc', 'BALCONY: an R package for MSA and functional compartments of protein variability analysis', 'Pxc5x82uciennik, Alicja|Stolarczyk, Michaxc5x82|Bzxc3xb3wka, Maria|Raczyxc5x84ska, Agata|Magdziarz, Tomasz|Gxc3xb3ra, Artur', 'Tunneling Group, Biotechnology Centre, Silesian University of Technology, ul. Krzywoustego 8, 44-100, Gliwice, Poland.|Tunneling Group, Biotechnology Centre, Silesian University of Technology, ul. Krzywoustego 8, 44-100, Gliwice, Poland.|Tunneling Group, Biotechnology Centre, Silesian University of Technology, ul. Krzywoustego 8, 44-100, Gliwice, Poland.|Tunneling Group, Biotechnology Centre, Silesian University of Technology, ul. Krzywoustego 8, 44-100, Gliwice, Poland.|Tunneling Group, Biotechnology Centre, Silesian University of Technology, ul. Krzywoustego 8, 44-100, Gliwice, Poland.|Tunneling Group, Biotechnology Centre, Silesian University of Technology, ul. Krzywoustego 8, 44-100, Gliwice, Poland. a.gora@tunnelinggroup.pl.', 'Poland|Poland|Poland|Poland|Poland|Poland', 0, 'Amino Acid Sequence|Entropy|Evolution, Molecular|Humans|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1186/s12859-018-2294-z', '30107777', 'Here, we present an R package for entropy/variability analysis that facilitates prompt and convenient data extraction, manipulation and visualization of protein features from multiple sequence alignments. BALCONY can work with residues dispersed across a protein sequence and map them on the corresponding alignment of homologous protein sequences. Additionally, it provides several entropy and variability scores that indicate the conservation of each residue.'),
(98498, 2018, 'bmc', 'Clustering of circular consensus sequences: accurate error correction and assembly of single molecule real-time reads from multiplexed amplicon libraries', 'Francis, Felix|Dumas, Michael D|Davis, Scott B|Wisser, Randall J', 'Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware, 19716, USA.|Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware, 19716, USA.|Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware, 19716, USA.|Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware, 19716, USA. rjw@udel.edu.', 'United States|United States|United States|United States', 3, 'Genetic Testing|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2293-0', '30126356', 'Targeted resequencing with high-throughput sequencing (HTS) platforms can be used to efficiently interrogate the genomes of large numbers of individuals. A critical issue for research and applications using HTS data, especially from long-read platforms, is error in base calling arising from technological limits and bioinformatic algorithms. We found that the community standard long amplicon analysis (LAA) module from Pacific Biosciences is prone to substantial bioinformatic errors that raise concerns about findings based on this pipeline, prompting the need for a new method.'),
(98499, 2018, 'bmc', 'Rscreenorm: normalization of CRISPR and siRNA screen data for more reproducible hit selection', 'Bachas, Costa|Hodzic, Jasmina|van der Mijn, Johannes C|Stoepker, Chantal|Verheul, Henk M W|Wolthuis, Rob M F|Felley-Bosco, Emanuela|van Wieringen, Wessel N|van Beusechem, Victor W|Brakenhoff, Ruud H|de Menezes, Renée X', 'Department of Otolaryngology - Head and Neck Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, 1081, HV, The Netherlands.|Department of Medical Oncology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, 1081, HV, The Netherlands.|Department of Medical Oncology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, 1081, HV, The Netherlands.|Division of Tumor Biology and Immunology, Netherlands Cancer Institute, Amsterdam, The Netherlands.|Department of Medical Oncology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, 1081, HV, The Netherlands.|Section of Oncogenetics, Department of Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1118, Amsterdam, 1081, HV, The Netherlands.|Laboratory of Molecular Oncology, University Hospital Zürich, Zürich, Switzerland.|Department of Epidemiology and Biostatistics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, 1007, MB, The Netherlands.|Department of Medical Oncology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, 1081, HV, The Netherlands.|Department of Otolaryngology - Head and Neck Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, 1081, HV, The Netherlands.|Department of Epidemiology and Biostatistics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, 1007, MB, The Netherlands. r.menezes@vumc.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Switzerland|Netherlands|Neth', 0, 'Clustered Regularly Interspaced Short Palindromic Repeats|Genetic Testing|Genomics|Humans|RNA, Small Interfering|Reproducibility of Results', 'Journal Article', '10.1186/s12859-018-2306-z', '30126372', 'Reproducibility of hits from independent CRISPR or siRNA screens is poor. This is partly due to data normalization primarily addressing technical variability within independent screens, and not the technical differences between them.');
