INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97184, 2016, 'bmc', 'MetICA: independent component analysis for high-resolution mass-spectrometry based non-targeted metabolomics', 'Liu, Youzhong|Smirnov, Kirill|Lucio, Marianna|Gougeon, Régis D|Alexandre, Hervé|Schmitt-Kopplin, Philippe', 'Research Unit Analytical BioGeoChemistry, Department of Environmental Sciences, Helmholtz Zentrum München, Ingolstädter Landstr.1, 85758, Neuherberg, Germany. youzhong.liu@u-bourgogne.fr.|Research Unit Analytical BioGeoChemistry, Department of Environmental Sciences, Helmholtz Zentrum München, Ingolstädter Landstr.1, 85758, Neuherberg, Germany. kirill.smirnov@helmholtz-muenchen.de.|Research Unit Analytical BioGeoChemistry, Department of Environmental Sciences, Helmholtz Zentrum München, Ingolstädter Landstr.1, 85758, Neuherberg, Germany. marianna.lucio@helmholtz-muenchen.de.|UMR PAM Université de Bourgogne/Agrosup Dijon, Institut Universitaire de la Vigne et du Vin, Jules Guyot, Rue Claude Ladrey, BP 27877, Dijon, Cedex, France. regis.gougeon@u-bourgogne.fr.|UMR PAM Université de Bourgogne/Agrosup Dijon, Institut Universitaire de la Vigne et du Vin, Jules Guyot, Rue Claude Ladrey, BP 27877, Dijon, Cedex, France. rvalex@u-bourgogne.fr.|Research Unit Analytical BioGeoChemistry, Department of Environmental Sciences, Helmholtz Zentrum München, Ingolstädter Landstr.1, 85758, Neuherberg, Germany. schmitt-kopplin@helmholtz-muenchen.de.', 'Germany|Germany|Germany|France|France|Germany', 0, 'Algorithms|Computer Simulation|Humans|Mass Spectrometry|Metabolomics|Principal Component Analysis|Reproducibility of Results', 'Journal Article', '10.1186/s12859-016-0970-4', '26936354', 'Interpreting non-targeted metabolomics data remains a challenging task. Signals from non-targeted metabolomics studies stem from a combination of biological causes, complex interactions between them and experimental bias/noise. The resulting data matrix usually contain huge number of variables and only few samples, and classical techniques using nonlinear mapping could result in computational complexity and overfitting. Independent Component Analysis (ICA) as a linear method could potentially bring more meaningful results than Principal Component Analysis (PCA). However, a major problem with most ICA algorithms is the output variations between different runs and the result of a single ICA run should be interpreted with reserve.'),
(97185, 2016, 'bmc', 'Computational methods for ubiquitination site prediction using physicochemical properties of protein sequences', 'Cai, Binghuang|Jiang, Xia', 'Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15206-3701, USA. bic9@pitt.edu.|Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15206-3701, USA. xij6@pitt.edu.', 'United States|United States', 9, 'Amino Acids|Bayes Theorem|Chemical Phenomena|Computational Biology|Humans|Protein Processing, Post-Translational|Proteins|ROC Curve|Sequence Analysis, Protein|Software|Support Vector Machine|Ubiquitination', 'Journal Article', '10.1186/s12859-016-0959-z', '26940649', 'Ubiquitination is a very important process in protein post-translational modification, which has been widely investigated by biology scientists and researchers. Different experimental and computational methods have been developed to identify the ubiquitination sites in protein sequences. This paper aims at exploring computational machine learning methods for the prediction of ubiquitination sites using the physicochemical properties (PCPs) of amino acids in the protein sequences.'),
(97186, 2016, 'bmc', 'Stepwise approach for combining many sources of evidence for site-recognition in genomic sequences', 'Pérez-Rodríguez, Javier|García-Pedrajas, Nicolás', 'Department of Computing and Numerical Analysis, University of Córdoba, Córdoba, 14071, Campus de Rabanales, Spain. javier.perez@uco.es.|Department of Computing and Numerical Analysis, University of Córdoba, Córdoba, 14071, Campus de Rabanales, Spain. npedrajas@uco.es.', 'Spain|Spain', 0, 'Codon, Terminator|Computational Biology|Genome, Human|Genomics|Humans|Protein Biosynthesis|ROC Curve|Sensitivity and Specificity|Software|Support Vector Machine', 'Journal Article', '10.1186/s12859-016-0968-y', '26945666', 'Recognizing the different functional parts of genes, such as promoters, translation initiation sites, donors, acceptors and stop codons, is a fundamental task of many current studies in Bioinformatics. Currently, the most successful methods use powerful classifiers, such as support vector machines with various string kernels. However, with the rapid evolution of our ability to collect genomic information, it has been shown that combining many sources of evidence is fundamental to the success of any recognition task. With the advent of next-generation sequencing, the number of available genomes is increasing very rapidly. Thus, methods for making use of such large amounts of information are needed.'),
(97187, 2016, 'bmc', 'Testing for association between RNA-Seq and high-dimensional data', 'Rauschenberger, Armin|Jonker, Marianne A|van de Wiel, Mark A|Menezes, Renée X', 'Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, 1007, MB, The Netherlands. a.rauschenberger@vumc.nl.|Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, 1007, MB, The Netherlands. m.jonker2@vumc.nl.|Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, 1007, MB, The Netherlands. mark.vdwiel@vumc.nl.|Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, 1007, MB, The Netherlands. r.menezes@vumc.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Algorithms|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Male|Prostatic Neoplasms|RNA|Regression Analysis|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-016-0961-5', '26951498', 'Testing for association between RNA-Seq and other genomic data is challenging due to high variability of the former and high dimensionality of the latter.'),
(97188, 2016, 'bmc', 'Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL)', 'Koestler, Devin C|Jones, Meaghan J|Usset, Joseph|Christensen, Brock C|Butler, Rondi A|Kobor, Michael S|Wiencke, John K|Kelsey, Karl T', 'Department of Biostatistics, University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, 66160, KS, USA. dkoestler@kumc.edu.|Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, The University of British Columbia, 950 West 28th Ave., Vancouver, V5Z 4H4, BC, Canada. mjones@cmmt.ubc.ca.|Department of Biostatistics, University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, 66160, KS, USA. jusset@kumc.edu.|Department of Epidemiology, Geisel School of Medicine, Dartmouth College, 1 Medical Center Dr., Lebanon, 03756, NH, USA. brock.c.christensen@dartmouth.edu.|Department of Pathology and Laboratory Medicine, Brown University, 70 Ship St., Providence, 02912, RI, USA. rondi_butler@brown.edu.|Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, The University of British Columbia, 950 West 28th Ave., Vancouver, V5Z 4H4, BC, Canada. msk@cmmt.ubc.ca.|Department of Neurological Surgery, University of California San Francisco, 505 Parnassus Ave., San Francisco, 94143, CA, USA. john.wiencke@ucsf.edu.|Department of Pathology and Laboratory Medicine, Brown University, 70 Ship St., Providence, 02912, RI, USA. karl_kelsey@brown.edu.', 'United States|Canada|United States|Lebanon|United States|Canada|United States|United States', 40, 'Adult|Algorithms|CpG Islands|DNA Methylation|Flow Cytometry|Gene Library|Genome, Human|Humans|Leukocytes', 'Journal Article', '10.1186/s12859-016-0943-7', '26956433', 'Confounding due to cellular heterogeneity represents one of the foremost challenges currently facing Epigenome-Wide Association Studies (EWAS). Statistical methods leveraging the tissue-specificity of DNA methylation for deconvoluting the cellular mixture of heterogenous biospecimens offer a promising solution, however the performance of such methods depends entirely on the library of methylation markers being used for deconvolution. Here, we introduce a novel algorithm for Identifying Optimal Libraries (IDOL) that dynamically scans a candidate set of cell-specific methylation markers to find libraries that optimize the accuracy of cell fraction estimates obtained from cell mixture deconvolution.'),
(97189, 2016, 'bmc', 'A NMF based approach for integrating multiple data sources to predict HIV-1-human PPIs', 'Ray, Sumanta|Bandyopadhyay, Sanghamitra', 'Department of Computer Science and Engineering, Aliah University, Kolkata-700156, West Bengal, India. sumantababai86@gmail.com.|Machine Intelligence Unit, Indian Statistical Institute, Kolkata-700108, West Bengal, India. sanghami@isical.ac.in.', 'India|India', 0, 'Algorithms|Computational Biology|HIV Infections|HIV-1|Humans|Information Storage and Retrieval|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/s12859-016-0952-6', '26956556', 'Predicting novel interactions between HIV-1 and human proteins contributes most promising area in HIV research. Prediction is generally guided by some classification and inference based methods using single biological source of information.'),
(97190, 2016, 'bmc', 'CoeViz: a web-based tool for coevolution analysis of protein residues', 'Baker, Frazier N|Porollo, Aleksey', 'Department of Electrical Engineering and Computing Systems, University of Cincinnati, 2901 Woodside Drive, Cincinnati, OH, 45221, USA. bakerfn@mail.uc.edu.|Center for Autoimmune Genomics and Etiology, Cincinnati Childrens Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA. alexey.porollo@cchmc.org.', 'United States|United States', 21, 'Amino Acids|Catalytic Domain|Computational Biology|Evolution, Molecular|Humans|Internet|Protein Conformation|Proteins', 'Journal Article', '10.1186/s12859-016-0975-z', '26956673', 'Proteins generally perform their function in a folded state. Residues forming an active site, whether it is a catalytic center or interaction interface, are frequently distant in a protein sequence. Hence, traditional sequence-based prediction methods focusing on a single residue (or a short window of residues) at a time may have difficulties in identifying and clustering the residues constituting a functional site, especially when a protein has multiple functions. Evolutionary information encoded in multiple sequence alignments is known to greatly improve sequence-based predictions. Identification of coevolving residues further advances the protein structure and function annotation by revealing cooperative pairs and higher order groupings of residues.'),
(97191, 2016, 'bmc', 'NETTAB 2014: From high-throughput structural bioinformatics to integrative systems biology', 'Romano, Paolo|Cordero, Francesca', 'Bioinformatics, IRCCS AOU San Martino - IST, c/o Centro Biotecnologie Avanzate (CBA), Largo Rosanna Benzi 10, I-16132, Genoa, Italy. paolo.romano@hsanmartino.it.|Department of Computer Science, University of Torino, Torino, I-10149, Italy. francesca.cordero@unito.it.', 'Italy|Italy', 2, 'Computational Biology|Data Mining|Drug Discovery|Genomics|Humans|Proteomics|Systems Biology', 'Editorial', '10.1186/s12859-016-0907-y', '26960985', 'The fourteenth NETTAB workshop, NETTAB 2014, was devoted to a range of disciplines going from structural bioinformatics, to proteomics and to integrative systems biology. The topics of the workshop were centred around bioinformatics methods, tools, applications, and perspectives for models, standards and management of high-throughput biological data, structural bioinformatics, functional proteomics, mass spectrometry, drug discovery, and systems biology.43 scientific contributions were presented at NETTAB 2014, including keynote, special guest and tutorial talks, oral communications, and posters. Full papers from some of the best contributions presented at the workshop were later submitted to a special Call for this Supplement.Here, we provide an overview of the workshop and introduce manuscripts that have been accepted for publication in this Supplement. '),
(97192, 2016, 'bmc', 'Genome and network visualization facilitates the analyses of the effects of drugs and mutations on protein-protein and drug-protein networks', 'Céol, Arnaud|Verhoef, Lisette G G C|Wade, Mark|Muller, Heiko', 'Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, Milan, I-20139, Italy. arnaud.ceol@iit.it.|Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, Milan, I-20139, Italy. lisette.verhoef@iit.it.|Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, Milan, I-20139, Italy. mark.wade@iit.it.|Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, Milan, I-20139, Italy. heiko.muller@iit.it.', 'Italy|Italy|Italy|Italy', 0, 'Computer Graphics|Databases, Factual|Gene Regulatory Networks|Genome, Human|Genomics|Humans|Mutation|Pharmaceutical Preparations|Protein Interaction Maps|Proteins', 'Journal Article', '10.1186/s12859-016-0908-x', '26961139', 'Biologists generally interrogate genomics data using web-based genome browsers that have limited analytical potential. New generation genome browsers such as the Integrated Genome Browser (IGB) have largely overcome this limitation and permit customized analyses to be implemented using plugins. We illustrate the use of a plugin for IGB that exploits advanced visualization techniques to integrate the analysis of genomics data with network and structural approaches.'),
(97193, 2016, 'bmc', 'A novel molecular dynamics approach to evaluate the effect of phosphorylation on multimeric protein interface: the xcexb1B-Crystallin case study', 'Chiappori, Federica|Mattiazzi, Luca|Milanesi, Luciano|Merelli, Ivan', 'Institute for Biomedical Technologies, National Research Council (ITB-CNR), Segrate, MI, 20090, Italy. federica.chiappori@itb.cnr.it.|Institute for Biomedical Technologies, National Research Council (ITB-CNR), Segrate, MI, 20090, Italy. l.mattiazzi@campus.unimib.it.|Institute for Biomedical Technologies, National Research Council (ITB-CNR), Segrate, MI, 20090, Italy. luciano.milanesi@itb.cnr.it.|Institute for Biomedical Technologies, National Research Council (ITB-CNR), Segrate, MI, 20090, Italy. ivan.merelli@itb.cnr.it.', 'Italy|Italy|Italy|Italy', 0, 'Humans|Models, Molecular|Molecular Dynamics Simulation|Phosphorylation|Protein Conformation|Protein Multimerization|Protein Processing, Post-Translational|Serine|alpha-Crystallin B Chain', 'Journal Article', '10.1186/s12859-016-0909-9', '26961246', 'Phosphorylation is one of the most important post-translational modifications (PTM) employed by cells to regulate several cellular processes. Studying the effects of phosphorylations on protein structures allows to investigate the modulation mechanisms of several proteins including chaperones, like the small HSPs, which display different multimeric structures according to the phosphorylation of a few serine residues. In this context, the proposed study is aimed at finding a method to correlate different PTM patterns (in particular phosphorylations at the monomers interface of multimeric complexes) with the dynamic behaviour of the complex, using physicochemical parameters derived from molecular dynamics simulations in the timescale of nanoseconds.'),
(97194, 2016, 'bmc', 'Fast, accurate, and lightweight analysis of BS-treated reads with ERNE 2', 'Prezza, Nicola|Vezzi, Francesco|Käller, Max|Policriti, Alberto', 'Department of Mathematics and Informatics, University of Udine, via delle Scienze, Udine, 33100, Italy. prezza.nicola@spes.uniud.it.|Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden. francesco.vezzi@scilifelab.se.|Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden. max.kaller@scilifelab.se.|Department of Mathematics and Informatics, University of Udine, via delle Scienze, Udine, 33100, Italy. policriti@dimi.uniud.it.', 'Italy|Sweden|Sweden|Italy', 3, 'CpG Islands|DNA|DNA Methylation|Data Compression|Epigenomics|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1186/s12859-016-0910-3', '26961371', 'Bisulfite treatment of DNA followed by sequencing (BS-seq) has become a standard technique in epigenetic studies, providing researchers with tools for generating single-base resolution maps of whole methylomes. Aligning bisulfite-treated reads, however, is a computationally difficult task: bisulfite treatment decreases the (lexical) complexity of low-methylated genomic regions, and C-to-T mismatches may reflect cytosine unmethylation rather than SNPs or sequencing errors. Further challenges arise both during and after the alignment phase: data structures used by the aligner should be fast and should fit into main memory, and the methylation-caller output should be somehow compressed, due to its significant size.'),
(97195, 2016, 'bmc', 'Geena 2, improved automated analysis of MALDI/TOF mass spectra', 'Romano, Paolo|Profumo, Aldo|Rocco, Mattia|Mangerini, Rosa|Ferri, Fabio|Facchiano, Angelo', 'IRCCS AOU San Martino IST, Genoa, Italy. paolo.romano@hsanmartino.it.|IRCCS AOU San Martino IST, Genoa, Italy. aldo.profumo@hsanmartino.it.|IRCCS AOU San Martino IST, Genoa, Italy. mattia.rocco@hsanmartino.it.|IRCCS AOU San Martino IST, Genoa, Italy. rosa.mangerini@hsanmartino.it.|Dipartimento di Scienza e Alta Tecnologia and To.Sca.Lab, Università dellInsubria, Como, Italy. fabio.ferri@uninsubria.it.|CNR - Istituto di Scienze dellAlimentazione, Avellino, Italy. angelo.facchiano@isa.cnr.it.', 'Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Automation|Blood Proteins|Breast Neoplasms|Databases, Factual|Female|Humans|Proteome|Proteomics|Retrospective Studies|Software|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1186/s12859-016-0911-2', '26961516', 'Mass spectrometry (MS) is producing high volumes of data supporting oncological sciences, especially for translational research. Most of related elaborations can be carried out by combining existing tools at different levels, but little is currently available for the automation of the fundamental steps. For the analysis of MALDI/TOF spectra, a number of pre-processing steps are required, including joining of isotopic abundances for a given molecular species, normalization of signals against an internal standard, background noise removal, averaging multiple spectra from the same sample, and aligning spectra from different samples. In this paper, we present Geena 2, a public software tool for the automated execution of these pre-processing steps for MALDI/TOF spectra.'),
(97196, 2016, 'bmc', 'Multiplex methods provide effective integration of multi-omic data in genome-scale models', 'Angione, Claudio|Conway, Max|Lió, Pietro', 'School of Computing - Teesside University, Middlesbrough, UK. c.angione@tees.ac.uk.|Computer Laboratory - University of Cambridge, Cambridge, UK. max.conway@cl.cam.ac.uk.|Computer Laboratory - University of Cambridge, Cambridge, UK. pietro.lio@cl.cam.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 27, 'Adaptation, Physiological|Bacterial Proteins|Environment|Escherichia coli|Gene Regulatory Networks|Genome, Bacterial|Genomics|Metabolic Networks and Pathways|Models, Biological|Systems Biology', 'Journal Article', '10.1186/s12859-016-0912-1', '26961692', 'Genomic, transcriptomic, and metabolic variations shape the complex adaptation landscape of bacteria to varying environmental conditions. Elucidating the genotype-phenotype relation paves the way for the prediction of such effects, but methods for characterizing the relationship between multiple environmental factors are still lacking. Here, we tackle the problem of extracting network-level information from collections of environmental conditions, by integrating the multiple omic levels at which the bacterial response is measured.'),
(97197, 2016, 'bmc', 'Consequential considerations when mapping tRNA fragments', 'Telonis, Aristeidis G|Loher, Phillipe|Kirino, Yohei|Rigoutsos, Isidore', 'Computational Medicine Center, Sidney Kimmel Medical College, Thomas Jefferson University, 1020 Locust Street, Philadelphia, PA, 19107, USA.|Computational Medicine Center, Sidney Kimmel Medical College, Thomas Jefferson University, 1020 Locust Street, Philadelphia, PA, 19107, USA.|Computational Medicine Center, Sidney Kimmel Medical College, Thomas Jefferson University, 1020 Locust Street, Philadelphia, PA, 19107, USA.|Computational Medicine Center, Sidney Kimmel Medical College, Thomas Jefferson University, 1020 Locust Street, Philadelphia, PA, 19107, USA. isidore.rigoutsos@jefferson.edu.', 'United States|United States|United States|United States', 16, 'Databases, Nucleic Acid|High-Throughput Nucleotide Sequencing|Molecular Sequence Annotation|RNA, Transfer', 'Journal Article', '10.1186/s12859-016-0921-0', '26961774', 'We examine several of the choices that went into the design of tDRmapper, a recently reported tool for identifying transfer RNA (tRNA) fragments in deep sequencing data, evaluate them in the context of currently available knowledge, and discuss their potential impact on the output that the tool generates. '),
(97198, 2016, 'bmc', 'FastPop: a rapid principal component derived method to infer intercontinental ancestry using genetic data', 'Li, Yafang|Byun, Jinyoung|Cai, Guoshuai|Xiao, Xiangjun|Han, Younghun|Cornelis, Olivier|Dinulos, James E|Dennis, Joe|Easton, Douglas|Gorlov, Ivan|Seldin, Michael F|Amos, Christopher I', 'Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA. yafang.li@dartmouth.edu.|Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA. jinyoung.byun@dartmouth.edu.|Department of Genetics, Dartmouth College, Hanover, NH, USA. guoshuai.cai@dartmouth.edu.|Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA. xiangjun.xiao@dartmouth.edu.|Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA. Younghun.Han@dartmouth.edu.|Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA. silenroc.olivier@gmail.com.|Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA. jedinulos@gmail.com.|Centre for Cancer Genetic Epidemiology, Cambridge University, Cambridge, UK. jgd29@cam.ac.uk.|Centre for Cancer Genetic Epidemiology, Cambridge University, Cambridge, UK. dfe20@medscho.cam.ac.uk.|Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA. ivan.p.gorlov@dartmouth.edu.|Rowe Program in Genetics, U.C. Davis, Davis, CA, USA. mfseldin@ucdavis.edu.|Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA. christopher.i.amos@dartmouth.edu.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Bayes Theorem|Continental Population Groups|Ethnic Groups|Genetics, Population|Genome-Wide Association Study|Genotype|HapMap Project|Humans|Polymorphism, Single Nucleotide|Principal Component Analysis|Software', 'Journal Article', '10.1186/s12859-016-0965-1', '26961892', 'Identifying subpopulations within a study and inferring intercontinental ancestry of the samples are important steps in genome wide association studies. Two software packages are widely used in analysis of substructure: Structure and Eigenstrat. Structure assigns each individual to a population by using a Bayesian method with multiple tuning parameters. It requires considerable computational time when dealing with thousands of samples and lacks the ability to create scores that could be used as covariates. Eigenstrat uses a principal component analysis method to model all sources of sampling variation. However, it does not readily provide information directly relevant to ancestral origin, the eigenvectors generated by Eigenstrat are sample specific and thus cannot be generalized to other individuals.'),
(97199, 2016, 'bmc', 'PeTTSy: a computational tool for perturbation analysis of complex systems biology models', 'Domijan, Mirela|Brown, Paul E|Shulgin, Boris V|Rand, David A', 'Current address: The Sainsbury Laboratory, University of Cambridge, Bateman Street, Cambridge, CB2 1LR, UK. mirela.domijan@slcu.cam.ac.uk.|Warwick Systems Biology Centre, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UK.|M&S Decisions LLC, Black Swan villa, Naryshkinskaya al., Moscow, 125167, Russia.|Warwick Systems Biology Centre, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Models, Biological|Signal Transduction|Software|Systems Biology', 'Journal Article', '10.1186/s12859-016-0972-2', '26964749', 'Over the last decade sensitivity analysis techniques have been shown to be very useful to analyse complex and high dimensional Systems Biology models. However, many of the currently available toolboxes have either used parameter sampling, been focused on a restricted set of model observables of interest, studied optimisation of a objective function, or have not dealt with multiple simultaneous model parameter changes where the changes can be permanent or temporary.'),
(97200, 2016, 'bmc', 'GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data', 'Rue-Albrecht, Kévin|McGettigan, Paul A|Hernández, Belinda|Nalpas, Nicolas C|Magee, David A|Parnell, Andrew C|Gordon, Stephen V|MacHugh, David E', 'Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland.|Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland.|UCD School of Mathematics and Statistics, Insight Centre for Data Analytics, University College Dublin, Dublin 4, Ireland.|Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland.|Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland.|UCD School of Mathematics and Statistics, Insight Centre for Data Analytics, University College Dublin, Dublin 4, Ireland.|UCD School of Veterinary Medicine, University College Dublin, Dublin 4, Ireland.|Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland. david.machugh@ucd.ie.', 'Ireland|Ireland|Ireland|Ireland|Ireland|Ireland|Ireland|Ireland', 9, 'Computational Biology|Gene Ontology|RNA, Messenger|Software|Supervised Machine Learning|Transcriptome', 'Journal Article', '10.1186/s12859-016-0971-3', '26968614', 'Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine gene set enrichment analyses, such approaches are typically limited to single gene lists resulting from simple two-group comparisons or time-series analyses. In contrast, functional class scoring and machine learning approaches provide powerful alternative methods to leverage molecular measurements for pathway analyses, and to compare continuous and multi-level categorical factors.'),
(97201, 2016, 'bmc', 'Illumina error profiles: resolving fine-scale variation in metagenomic sequencing data', 'Schirmer, Melanie|DAmore, Rosalinda|Ijaz, Umer Z|Hall, Neil|Quince, Christopher', 'The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA. melanie@broadinstitute.org.|University of Liverpool, Centre for Genomic Research, Crown Street, Liverpool, L69 7ZB, UK.|University of Glasgow, School of Engineering, Oakfield Avenue, Glasgow, G12 8LT, UK.|University of Liverpool, Centre for Genomic Research, Crown Street, Liverpool, L69 7ZB, UK.|University of Warwick, Warwick Medical School, Coventry, CV4 7AL, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Archaea|Bacteria|GC Rich Sequence|Genome, Archaeal|Genome, Bacterial|High-Throughput Nucleotide Sequencing|Metagenomics|Nucleotide Motifs|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Signal-To-Noise Ratio', 'Journal Article', '10.1186/s12859-016-0976-y', '26968756', 'Illuminas sequencing platforms are currently the most utilised sequencing systems worldwide. The technology has rapidly evolved over recent years and provides high throughput at low costs with increasing read-lengths and true paired-end reads. However, data from any sequencing technology contains noise and our understanding of the peculiarities and sequencing errors encountered in Illumina data has lagged behind this rapid development.'),
(97202, 2016, 'bmc', 'From the desktop to the grid: scalable bioinformatics via workflow conversion', 'de la Garza, Luis|Veit, Johannes|Szolek, Andras|Röttig, Marc|Aiche, Stephan|Gesing, Sandra|Reinert, Knut|Kohlbacher, Oliver', 'Center for Bioinformatics and Dept. of Computer Science, University of Tübingen, Sand 14, Tübingen, 72070, Germany. delagarza@informatik.uni-tuebingen.de.|Center for Bioinformatics and Dept. of Computer Science, University of Tübingen, Sand 14, Tübingen, 72070, Germany.|Center for Bioinformatics and Dept. of Computer Science, University of Tübingen, Sand 14, Tübingen, 72070, Germany.|Center for Bioinformatics and Dept. of Computer Science, University of Tübingen, Sand 14, Tübingen, 72070, Germany.|Algorithmic Bioinformatics, Computer Science Institute, Freie Universität Berlin, Takustr. 9, Berlin, 14195, Germany.|College of Engineering, University of Notre Dame, 257 Fitzpatrick Hall, Notre Dame, 46556, IN, United States.|Algorithmic Bioinformatics, Computer Science Institute, Freie Universität Berlin, Takustr. 9, Berlin, 14195, Germany.|Center for Bioinformatics and Dept. of Computer Science, University of Tübingen, Sand 14, Tübingen, 72070, Germany.', 'Germany|Germany|Germany|Germany|Germany|United States|Germany|Germany', 8, 'Computational Biology|Computer Communication Networks|Microcomputers|Reproducibility of Results|Software|Workflow', 'Journal Article', '10.1186/s12859-016-0978-9', '26968893', 'Reproducibility is one of the tenets of the scientific method. Scientific experiments often comprise complex data flows, selection of adequate parameters, and analysis and visualization of intermediate and end results. Breaking down the complexity of such experiments into the joint collaboration of small, repeatable, well defined tasks, each with well defined inputs, parameters, and outputs, offers the immediate benefit of identifying bottlenecks, pinpoint sections which could benefit from parallelization, among others. Workflows rest upon the notion of splitting complex work into the joint effort of several manageable tasks. There are several engines that give users the ability to design and execute workflows. Each engine was created to address certain problems of a specific community, therefore each one has its advantages and shortcomings. Furthermore, not all features of all workflow engines are royalty-free -an aspect that could potentially drive away members of the scientific community.'),
(97203, 2016, 'bmc', 'Highlights from the 11th ISCB Student Council Symposium 2015 Dublin, Ireland 10 July 2015', '-', '-', '-', 0, '-', 'Journal Article', '10.1186/s12859-016-0901-4', '26986007', '-'),
(97204, 2016, 'bmc', 'A comparative study of SMILES-based compound similarity functions for drug-target interaction prediction', 'Öztürk, Hakime|Ozkirimli, Elif|Özgür, Arzucan', 'Department of Computer Engineering, Bogazici University, Bebek, Istanbul, 34342, Turkey. hakime.ozturk@boun.edu.tr.|Department of Computer Engineering, Bogazici University, Bebek, Istanbul, 34342, Turkey. elif.ozkirimli@boun.edu.tr.|Department of Computer Engineering, Bogazici University, Bebek, Istanbul, 34342, Turkey. arzucan.ozgur@boun.edu.tr.', 'Turkey|Turkey|Turkey', 0, 'Enzymes|Ion Channels|Ligands|Models, Molecular|Molecular Structure|Pharmaceutical Preparations|Protein Binding|Receptors, Cytoplasmic and Nuclear|Receptors, G-Protein-Coupled', 'Journal Article', '10.1186/s12859-016-0977-x', '26987649', 'Molecular structures can be represented as strings of special characters using SMILES. Since each molecule is represented as a string, the similarity between compounds can be computed using SMILES-based string similarity functions. Most previous studies on drug-target interaction prediction use 2D-based compound similarity kernels such as SIMCOMP. To the best of our knowledge, using SMILES-based similarity functions, which are computationally more efficient than the 2D-based kernels, has not been investigated for this task before.'),
(97205, 2016, 'bmc', 'Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules', 'Tripathi, Shailesh|Moutari, Salissou|Dehmer, Matthias|Emmert-Streib, Frank', 'Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland.|Centre for Statistical Science and Operational Research, School of Mathematics and Physics, Queens University Belfast, Belfast, UK.|Institute for Theoretical Informatics, Mathematics and Operations Research, Department of Computer Science, Universität der Bundeswehr München, Munich, Germany.|Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland. v@bio-complexity.com.', 'Finland|United Kingdom|Germany|Finland', 0, 'Algorithms|Animals|Cluster Analysis|Eukaryota|Gene Ontology|Humans|Protein Interaction Mapping', 'Comparative Study', '10.1186/s12859-016-0979-8', '26987731', 'It is generally acknowledged that a functional understanding of a biological system can only be obtained by an understanding of the collective of molecular interactions in form of biological networks. Protein networks are one particular network type of special importance, because proteins form the functional base units of every biological cell. On a mesoscopic level of protein networks, modules are of significant importance because these building blocks may be the next elementary functional level above individual proteins allowing to gain insight into fundamental organizational principles of biological cells.'),
(97206, 2016, 'bmc', 'On the comparison of regulatory sequences with multiple resolution Entropic Profiles', 'Comin, Matteo|Antonello, Morris', 'Department of Information Engineering, University of Padova, Padova, Italy. comin@dei.unipd.it.|Department of Information Engineering, University of Padova, Padova, Italy.', 'Italy|Italy', 10, 'Animals|Drosophila|Enhancer Elements, Genetic|Entropy|Genomics|Mice|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-016-0980-2', '26987840', 'Enhancers are stretches of DNA (100-1000 bp) that play a major role in development gene expression, evolution and disease. It has been recently shown that in high-level eukaryotes enhancers rarely work alone, instead they collaborate by forming clusters of cis-regulatory modules (CRMs). Although the binding of transcription factors is sequence-specific, the identification of functionally similar enhancers is very difficult and it cannot be carried out with traditional alignment-based techniques.'),
(97207, 2016, 'bmc', 'Three-dimensional protein model similarity analysis based on salient shape index', 'Yao, Bo|Li, Zhong|Ding, Meng|Chen, Minhong', 'Departments of Mathematical Sciences, Zhejiang Sci-Tech University, Hangzhou, 310018, China.|Departments of Mathematical Sciences, Zhejiang Sci-Tech University, Hangzhou, 310018, China. lizhong@zstu.edu.cn.|Departments of Mathematical Sciences, Zhejiang Sci-Tech University, Hangzhou, 310018, China.|Departments of Mathematical Sciences, Zhejiang Sci-Tech University, Hangzhou, 310018, China.', 'China|China|China|China', 0, 'Computational Biology|Models, Molecular|Proteins|Reproducibility of Results|Structural Homology, Protein', 'Journal Article', '10.1186/s12859-016-0983-z', '26987968', 'Proteins play a special role in bioinformatics. The surface shape of a protein, which is an important characteristic of the protein, defines a geometric and biochemical domain where the protein interacts with other proteins. The similarity analysis among protein models has become an important topic of protein analysis, by which it can reveal the structure and the function of proteins.'),
(97208, 2016, 'bmc', 'Characterization of multiple sequence alignment errors using complete-likelihood score and position-shift map', 'Ezawa, Kiyoshi', 'Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, 820-8502, Japan. kezawa.ezawa3@gmail.com.', 'Japan', 5, 'Animals|Computational Biology|Computer Simulation|DNA|Evolution, Molecular|INDEL Mutation|Likelihood Functions|Mammals|Phylogeny|Sequence Alignment|Sequence Homology, Nucleic Acid|Statistical Distributions', 'Journal Article', '10.1186/s12859-016-0945-5', '26992851', 'Reconstruction of multiple sequence alignments (MSAs) is a crucial step in most homology-based sequence analyses, which constitute an integral part of computational biology. To improve the accuracy of this crucial step, it is essential to better characterize errors that state-of-the-art aligners typically make. For this purpose, we here introduce two tools: the complete-likelihood score and the position-shift map.'),
(97209, 2016, 'bmc', 'Bottom-up GGM algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways or processes', 'Kumari, Sapna|Deng, Wenping|Gunasekara, Chathura|Chiang, Vincent|Chen, Huann-Sheng|Ma, Hao|Davis, Xin|Wei, Hairong', 'School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, 49931, USA.|School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, 49931, USA.|School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, 49931, USA.|Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, 27695, USA.|Statistical Methodology and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD, 20850, USA.|NCCWA, USDA ARS, Kearneysville, WV, 25430, USA.|Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, 27695, USA.|School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, 49931, USA. hairong@mtu.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Arabidopsis|Arabidopsis Proteins|Computational Biology|Gene Regulatory Networks|Humans|Metabolic Networks and Pathways|Signal Transduction|Transcriptome', 'Journal Article', '10.1186/s12859-016-0981-1', '26993098', 'Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways.'),
(97210, 2016, 'bmc', 'Sparse regression models for unraveling group and individual associations in eQTL mapping', 'Cheng, Wei|Shi, Yu|Zhang, Xiang|Wang, Wei', 'Department of Computer Science, UNC at Chapel Hill, 201 S Columbia St., Chapel Hill, NC 27599, USA. chengw02@gmail.com.|Computer Science at the University of Illinois at Urbana-Champaign, 201 North Goodwin Avenue, Urbana, IL 61801, USA.|Department of Elect. Eng. and Computer Science, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA.|Department of Computer Science, University of California, Los Angeles, 3531-G Boelter Hall, Los Angeles, CA 90095, USA.', 'United States|United States|United States|United States', 1, 'Algorithms|Chromosome Mapping|Phenotype|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Regression Analysis|Saccharomyces cerevisiae', 'Journal Article', '10.1186/s12859-016-0986-9', '27000043', 'As a promising tool for dissecting the genetic basis of common diseases, expression quantitative trait loci (eQTL) study has attracted increasing research interest. Traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to biological pathways.'),
(97211, 2016, 'bmc', 'A benchmark for microRNA quantification algorithms using the OpenArray platform', 'McCall, Matthew N|Baras, Alexander S|Crits-Christoph, Alexander|Ingersoll, Roxann|McAlexander, Melissa A|Witwer, Kenneth W|Halushka, Marc K', 'Department of Biostatistics and Computational Biology, University of Rochester, 265 Crittenden Blvd, Rochester, 14642, NY, USA. mccallm@gmail.com.|Department of Pathology, The Johns Hopkins University, 720 Rutland Ave, Baltimore, 21205, MD, USA.|Biology Department, The Johns Hopkins University, 3400 N Charles St, Baltimore, 21218, MD, USA.|Genetic Resources Core Facility, The Johns Hopkins University, 600 N Wolfe St, Baltimore, 21287, MD, USA.|Department of Molecular and Comparative Pathobiology, The Johns Hopkins University, 733 N Broadway, Baltimore, 21205, MD, USA.|Department of Molecular and Comparative Pathobiology, The Johns Hopkins University, 733 N Broadway, Baltimore, 21205, MD, USA.|Department of Pathology, The Johns Hopkins University, 720 Rutland Ave, Baltimore, 21205, MD, USA.', 'United States|United States|United States|United States|United States|United States|United States', 3, 'Algorithms|Benchmarking|Humans|Limit of Detection|MicroRNAs|Real-Time Polymerase Chain Reaction|Software', 'Journal Article', '10.1186/s12859-016-0987-8', '27000067', 'Several techniques have been tailored to the quantification of microRNA expression, including hybridization arrays, quantitative PCR (qPCR), and high-throughput sequencing. Each of these has certain strengths and limitations depending both on the technology itself and the algorithm used to convert raw data into expression estimates. Reliable quantification of microRNA expression is challenging in part due to the relatively low abundance and short length of the miRNAs. While substantial research has been devoted to the development of methods to quantify mRNA expression, relatively little effort has been spent on microRNA expression.'),
(97212, 2016, 'bmc', 'Common molecular mechanism of the hepatic lesion and the cardiac parasympathetic regulation in chronic hepatitis C infection: a critical role for the muscarinic receptor type 3', 'Glixc5xa1ixc4x87, Sanja|Cavanaugh, David P|Chittur, Krishnan K|Sencanski, Milan|Perovic, Vladimir|Bojixc4x87, Tijana', 'Institute of Nuclear Sciences Vinxc4x8da, University of Belgrade, Center for Multidisciplinary Research, PO Box 522, Belgrade, Serbia.|Benchmark Electronics, Bradford Dr., Huntsville, AL, 35805, USA.|Chemical and Materials Engineering, University of Alabama Huntsville, Huntsville, AL, 35899, USA.|Institute of Nuclear Sciences Vinxc4x8da, University of Belgrade, Center for Multidisciplinary Research, PO Box 522, Belgrade, Serbia.|Institute of Nuclear Sciences Vinxc4x8da, University of Belgrade, Center for Multidisciplinary Research, PO Box 522, Belgrade, Serbia.|Institute of Nuclear Sciences Vinxc4x8da, University of Belgrade, Laboratory of Radiobiology and Molecular Genetics, PO Box 522, 11000, Belgrade, Serbia. tijanabojic@vinca.rs.', 'Serbia|United States|United States|Serbia|Serbia|Serbia', 4, 'Algorithms|Amino Acids|Autoantibodies|Binding Sites|Hepacivirus|Hepatitis C, Chronic|Humans|Hydrophobic and Hydrophilic Interactions|Male|Molecular Docking Simulation|Protein Binding|Protein Structure, Tertiary|Receptor, Muscarinic M3|Viral Nonstructural Proteins', 'Journal Article', '10.1186/s12859-016-0988-7', '27000565', 'The pathophysiological overlapping between Sjorgens Syndrome (SS) and HCV, presence of anti- muscarinic receptor type 3 (M3R) antibodies in SS, the role that M3R plays in the regulation of the heart rate, has led to the assumption that cardiovagal dysfunction in HCV patients is caused by anti-M3R antibodies elicited by HCV proteins or by their direct interaction with M3R.'),
(97213, 2016, 'bmc', 'Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis', 'Yang, Bo|Wang, Yong|Qian, Pei-Yuan', 'Division of Life Sciences, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.|Sanya Institute of Deep Sea Science and Engineering, Chinese Academy of Sciences, San Ya, Hai Nan, China.|Division of Life Sciences, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. boqianpy@ust.hk.', 'Hong Kong|China|Hong Kong', 0, 'Bacteria|Cluster Analysis|DNA Primers|Databases, Genetic|Evolution, Molecular|Nucleic Acid Conformation|Phylogeny|RNA, Ribosomal, 16S', 'Journal Article', '10.1186/s12859-016-0992-y', '27000765', 'Prokaryotic 16S ribosomal RNA (rRNA) sequences are widely used in environmental microbiology and molecular evolution as reliable markers for the taxonomic classification and phylogenetic analysis of microbes. Restricted by current sequencing techniques, the massive sequencing of 16S rRNA gene amplicons encompassing the full length of genes is not yet feasible. Thus, the selection of the most efficient hypervariable regions for phylogenetic analysis and taxonomic classification is still debated. In the present study, several bioinformatics tools were integrated to build an in silico pipeline to evaluate the phylogenetic sensitivity of the hypervariable regions compared with the corresponding full-length sequences.'),
(97214, 2016, 'bmc', 'Leveraging protein quaternary structure to identify oncogenic driver mutations', 'Ryslik, Gregory A|Cheng, Yuwei|Modis, Yorgo|Zhao, Hongyu', 'Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA. gregory.ryslik@yale.edu.|Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.|Department of Medicine, University of Cambridge, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH, UK.|Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 3, 'Algorithms|Cluster Analysis|Databases, Protein|Humans|Internet|Mutation|Neoplasms|Protein Structure, Quaternary|Proteins|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-0963-3', '27001666', 'Identifying key driver mutations which are responsible for tumorigenesis is critical in the development of new oncology drugs. Due to multiple pharmacological successes in treating cancers that are caused by such driver mutations, a large body of methods have been developed to differentiate these mutations from the benign passenger mutations which occur in the tumor but do not further progress the disease. Under the hypothesis that driver mutations tend to cluster in key regions of the protein, the development of algorithms that identify these clusters has become a critical area of research.'),
(97215, 2016, 'bmc', 'Modeling the integration of bacterial rRNA fragments into the human cancer genome', 'Sieber, Karsten B|Gajer, Pawel|Dunning Hotopp, Julie C', 'Institute for Genome Science, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.|Institute for Genome Science, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.|Institute for Genome Science, University of Maryland School of Medicine, Baltimore, MD, 21201, USA. jdhotopp@som.umaryland.edu.', 'United States|United States|United States', 0, '5 Untranslated Regions|Antigens, CD|Antigens, Differentiation, B-Lymphocyte|Carcinoembryonic Antigen|Cell Adhesion Molecules|GPI-Linked Proteins|Genome, Human|Histocompatibility Antigens Class II|Host-Parasite Interactions|Humans|Neoplasms|Nucleic Acid Conformation|Pseudomonas|RNA, Ribosomal|Recombination, Genetic', 'Journal Article', '10.1186/s12859-016-0982-0', '27001685', 'Cancer is a disease driven by the accumulation of genomic alterations, including the integration of exogenous DNA into the human somatic genome. We previously identified in silico evidence of DNA fragments from a Pseudomonas-like bacteria integrating into the 5-UTR of four proto-oncogenes in stomach cancer sequencing data. The functional and biological consequences of these bacterial DNA integrations remain unknown.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97216, 2016, 'bmc', 'pcaReduce: hierarchical clustering of single cell transcriptional profiles', 'xc5xbdurauskienxc4x97, Justina|Yau, Christopher', 'Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK.|Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK. cyau@well.ox.ac.uk.', 'United Kingdom|United Kingdom', 85, 'Algorithms|Animals|Cell Line|Cluster Analysis|Humans|Principal Component Analysis|RNA|Sequence Analysis, RNA|Single-Cell Analysis|Transcriptome', 'Journal Article', '10.1186/s12859-016-0984-y', '27005807', 'Advances in single cell genomics provide a way of routinely generating transcriptomics data at the single cell level. A frequent requirement of single cell expression analysis is the identification of novel patterns of heterogeneity across single cells that might explain complex cellular states or tissue composition. To date, classical statistical analysis tools have being routinely applied, but there is considerable scope for the development of novel statistical approaches that are better adapted to the challenges of inferring cellular hierarchies.'),
(97217, 2016, 'bmc', 'caOmicsV: an R package for visualizing multidimensional cancer genomic data', 'Zhang, Hongen|Meltzer, Paul S|Davis, Sean R', 'Genetics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 37, Room 6138, 37 Convent Drive, Bethesda, MD, 20892-4265, USA.|Genetics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 37, Room 6138, 37 Convent Drive, Bethesda, MD, 20892-4265, USA.|Genetics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 37, Room 6138, 37 Convent Drive, Bethesda, MD, 20892-4265, USA. sdavis2@mail.nih.gov.', 'United States|United States|United States', 2, 'DNA Copy Number Variations|DNA Methylation|Genomics|Humans|MicroRNAs|Neoplasms|RNA, Messenger|Software', 'Journal Article', '10.1186/s12859-016-0989-6', '27005934', 'Translational genomics research in cancers, e.g., International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), has generated large multidimensional datasets from high-throughput technologies. Data analysis at multidimensional level will greatly benefit clinical applications of genomic information in diagnosis, prognosis and therapeutics of cancers. To help, tools to effectively visualize integrated multidimensional data are important for understanding and describing the relationship between genomic variations and cancers.'),
(97218, 2016, 'bmc', 'McTwo: a two-step feature selection algorithm based on maximal information coefficient', 'Ge, Ruiquan|Zhou, Manli|Luo, Youxi|Meng, Qinghan|Mai, Guoqin|Ma, Dongli|Wang, Guoqing|Zhou, Fengfeng', 'Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, Guangdong, 518055, P.R. China.|Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, Guangdong, 518055, P.R. China.|Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, Guangdong, 518055, P.R. China.|Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, Guangdong, 518055, P.R. China.|Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, Guangdong, 518055, P.R. China.|Shenzhen Childrens Hospital, Shenzhen, Guangdong, 518026, P.R. China. madl1234@126.com.|Department of Pathogenobiology, Basic Medical College of Jilin University, Changchun, Jilin, China. qing@jlu.edu.cn.|Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, Guangdong, 518055, P.R. China. FengfengZhou@gmail.com.', 'China|China|China|China|China|China|China|China', 43, 'Algorithms|Databases, Factual|Humans|Software', 'Journal Article', '10.1186/s12859-016-0990-0', '27006077', 'High-throughput bio-OMIC technologies are producing high-dimension data from bio-samples at an ever increasing rate, whereas the training sample number in a traditional experiment remains small due to various difficulties. This large p, small n paradigm in the area of biomedical big data may be at least partly solved by feature selection algorithms, which select only features significantly associated with phenotypes. Feature selection is an NP-hard problem. Due to the exponentially increased time requirement for finding the globally optimal solution, all the existing feature selection algorithms employ heuristic rules to find locally optimal solutions, and their solutions achieve different performances on different datasets.'),
(97219, 2016, 'bmc', 'Does the choice of nucleotide substitution models matter topologically?', 'Hoff, Michael|Orf, Stefan|Riehm, Benedikt|Darriba, Diego|Stamatakis, Alexandros', 'Karlsruhe Institute of Technology, Department of Informatics, Kaiserstraße 12, Karlsruhe, 76131, Germany.|Karlsruhe Institute of Technology, Department of Informatics, Kaiserstraße 12, Karlsruhe, 76131, Germany.|Karlsruhe Institute of Technology, Department of Informatics, Kaiserstraße 12, Karlsruhe, 76131, Germany.|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, Heidelberg, 69118, Germany.|Karlsruhe Institute of Technology, Department of Informatics, Kaiserstraße 12, Karlsruhe, 76131, Germany. Alexandros.Stamatakis@h-its.org.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Bayes Theorem|DNA|Likelihood Functions|Models, Genetic', 'Journal Article', '10.1186/s12859-016-0985-x', '27009141', 'In the context of a master level programming practical at the computer science department of the Karlsruhe Institute of Technology, we developed and make available an open-source code for testing all 203 possible nucleotide substitution models in the Maximum Likelihood (ML) setting under the common Akaike, corrected Akaike, and Bayesian information criteria. We address the question if model selection matters topologically, that is, if conducting ML inferences under the optimal, instead of a standard General Time Reversible model, yields different tree topologies. We also assess, to which degree models selected and trees inferred under the three standard criteria (AIC, AICc, BIC) differ. Finally, we assess if the definition of the sample size (#sites versus #sites × #taxa) yields different models and, as a consequence, different tree topologies.'),
(97220, 2016, 'bmc', 'Detecting broad domains and narrow peaks in ChIP-seq data with hiddenDomains', 'Starmer, Joshua|Magnuson, Terry', 'Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA. starmer@unc.edu.|Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.', 'United States|United States', 12, 'Algorithms|Chromatin Immunoprecipitation|Estrogen Receptor alpha|GA-Binding Protein Transcription Factor|Histones|Humans|Markov Chains', 'Journal Article', '10.1186/s12859-016-0991-z', '27009150', 'Correctly identifying genomic regions enriched with histone modifications and transcription factors is key to understanding their regulatory and developmental roles. Conceptually, these regions are divided into two categories, narrow peaks and broad domains, and different algorithms are used to identify each one. Datasets that span these two categories are often analyzed with a single program for peak calling combined with an ad hoc method for domains.'),
(97221, 2016, 'bmc', 'The effect of noise-induced variance on parameter recovery from reaction times', 'Vadillo, Miguel A|Garaizar, Pablo', 'Primary Care and Public Health Sciences, Kings College London, Addison House, Guys Campus, London, SE1 1UL, UK. miguel.vadillo@kcl.ac.uk.|Faculty of Engineering, Universidad de Deusto, Avda. Universidades 24, Bilbao, 48007, Spain.', 'United Kingdom|Spain', 1, 'Artifacts|Diagnostic Imaging|Models, Theoretical|Normal Distribution|Reaction Time', 'Journal Article', '10.1186/s12859-016-0993-x', '27029377', 'Technical noise can compromise the precision and accuracy of the reaction times collected in psychological experiments, especially in the case of Internet-based studies. Although this noise seems to have only a small impact on traditional statistical analyses, its effects on model fit to reaction-time distributions remains unexplored.'),
(97222, 2016, 'bmc', 'Sample size calculation while controlling false discovery rate for differential expression analysis with RNA-sequencing experiments', 'Bi, Ran|Liu, Peng', 'Department of Statistics, Iowa State University, Snedecor Hall, Ames, Iowa, 50011, USA.|Department of Statistics, Iowa State University, Snedecor Hall, Ames, Iowa, 50011, USA. pliu@iastate.edu.', 'United States|United States', 28, 'Algorithms|Internet|RNA|Sample Size|Sequence Analysis, RNA|Transcriptome|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-0994-9', '27029470', 'RNA-Sequencing (RNA-seq) experiments have been popularly applied to transcriptome studies in recent years. Such experiments are still relatively costly. As a result, RNA-seq experiments often employ a small number of replicates. Power analysis and sample size calculation are challenging in the context of differential expression analysis with RNA-seq data. One challenge is that there are no closed-form formulae to calculate power for the popularly applied tests for differential expression analysis. In addition, false discovery rate (FDR), instead of family-wise type I error rate, is controlled for the multiple testing error in RNA-seq data analysis. So far, there are very few proposals on sample size calculation for RNA-seq experiments.'),
(97223, 2016, 'bmc', 'Do little interactions get lost in dark random forests?', 'Wright, Marvin N|Ziegler, Andreas|König, Inke R', 'Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, Lübeck, 23562, Germany.|Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, Lübeck, 23562, Germany.|Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, Lübeck, 23562, Germany. inke.koenig@imbs.uni-luebeck.de.', 'Germany|Germany|Germany', 0, 'Epistasis, Genetic|Linkage Disequilibrium|Models, Genetic|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-016-0995-8', '27029549', 'Random forests have often been claimed to uncover interaction effects. However, if and how interaction effects can be differentiated from marginal effects remains unclear. In extensive simulation studies, we investigate whether random forest variable importance measures capture or detect gene-gene interactions. With capturing interactions, we define the ability to identify a variable that acts through an interaction with another one, while detection is the ability to identify an interaction effect as such.'),
(97224, 2016, 'bmc', 'isomiR-SEA: an RNA-Seq analysis tool for miRNAs/isomiRs expression level profiling and miRNA-mRNA interaction sites evaluation', 'Urgese, Gianvito|Paciello, Giulia|Acquaviva, Andrea|Ficarra, Elisa', 'Department of Control and Computer Engineering DAUIN, Politecnico di Torino,, C.so Duca degli Abruzzi 24, Turin, 10129, IT, Italy. gianvito.urgese@polito.it.|Department of Control and Computer Engineering DAUIN, Politecnico di Torino,, C.so Duca degli Abruzzi 24, Turin, 10129, IT, Italy.|Department of Control and Computer Engineering DAUIN, Politecnico di Torino,, C.so Duca degli Abruzzi 24, Turin, 10129, IT, Italy.|Department of Control and Computer Engineering DAUIN, Politecnico di Torino,, C.so Duca degli Abruzzi 24, Turin, 10129, IT, Italy.', 'Italy|Italy|Italy|Italy', 19, 'Algorithms|Base Sequence|High-Throughput Nucleotide Sequencing|Humans|Internet|MicroRNAs|Oligonucleotides, Antisense|RNA, Messenger|Sequence Analysis, RNA|Transcriptome|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-0958-0', '27036505', 'Massive parallel sequencing of transcriptomes, revealed the presence of many miRNAs and miRNAs variants named isomiRs with a potential role in several cellular processes through their interaction with a target mRNA. Many methods and tools have been recently devised to detect and quantify miRNAs from sequencing data. However, all of them are implemented on top of general purpose alignment methods, thus providing poorly accurate results and no information concerning isomiRs and conserved miRNA-mRNA interaction sites.'),
(97225, 2016, 'bmc', 'Erratum to: BayesFlow: latent modeling of flow cytometry cell populations', 'Johnsson, Kerstin|Wallin, Jonas|Fontes, Magnus', 'Centre for Mathematical Sciences, Lund University, Box 118, S-221 00, Lund, Sweden. johnsson@maths.lth.se.|Mathematical Sciences, Chalmers and University of Gothenburg, S-412 58, Gothenburg, Sweden.|Centre for Mathematical Sciences, Lund University, Box 118, S-221 00, Lund, Sweden.', 'Sweden|Sweden|Sweden', 0, '-', 'Published Erratum', '10.1186/s12859-016-0973-1', '27036556', '-'),
(97226, 2016, 'bmc', 'Reproducibility of Illumina platform deep sequencing errors allows accurate determination of DNA barcodes in cells', 'Beltman, Joost B|Urbanus, Jos|Velds, Arno|van Rooij, Nienke|Rohr, Jan C|Naik, Shalin H|Schumacher, Ton N', 'Division of Immunology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands. j.b.beltman@lacdr.leidenuniv.nl.|Division of Immunology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.|Genomics Core Facility, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.|Division of Immunology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.|Division of Immunology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.|Division of Immunology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.|Division of Immunology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands. t.schumacher@nki.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Animals|Base Sequence|DNA|DNA Barcoding, Taxonomic|High-Throughput Nucleotide Sequencing|Mice|Polymerase Chain Reaction|Sequence Analysis, DNA|Stem Cells', 'Journal Article', '10.1186/s12859-016-0999-4', '27038897', 'Next generation sequencing (NGS) of amplified DNA is a powerful tool to describe genetic heterogeneity within cell populations that can both be used to investigate the clonal structure of cell populations and to perform genetic lineage tracing. For applications in which both abundant and rare sequences are biologically relevant, the relatively high error rate of NGS techniques complicates data analysis, as it is difficult to distinguish rare true sequences from spurious sequences that are generated by PCR or sequencing errors. This issue, for instance, applies to cellular barcoding strategies that aim to follow the amount and type of offspring of single cells, by supplying these with unique heritable DNA tags.'),
(97227, 2016, 'bmc', 'PAT: predictor for structured units and its application for the optimization of target molecules for the generation of synthetic antibodies', 'Jeon, Jouhyun|Arnold, Roland|Singh, Fateh|Teyra, Joan|Braun, Tatjana|Kim, Philip M', 'Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S 3E1, ON, Canada.|Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S 3E1, ON, Canada.|Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S 3E1, ON, Canada.|Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S 3E1, ON, Canada.|Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S 3E1, ON, Canada.|Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S 3E1, ON, Canada. pi@kimlab.org.', 'Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Amino Acid Sequence|Antibodies|Area Under Curve|Databases, Protein|Humans|Internet|Peptide Library|Protein Structure, Tertiary|Proteins|ROC Curve|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1001-1', '27039071', 'The identification of structured units in a protein sequence is an important first step for most biochemical studies. Importantly for this study, the identification of stable structured region is a crucial first step to generate novel synthetic antibodies. While many approaches to find domains or predict structured regions exist, important limitations remain, such as the optimization of domain boundaries and the lack of identification of non-domain structured units. Moreover, no integrated tool exists to find and optimize structural domains within protein sequences.'),
(97228, 2016, 'bmc', 'Mutanalyst, an online tool for assessing the mutational spectrum of epPCR libraries with poor sampling', 'Ferla, Matteo Paolo', 'Formerly Department of Biochemistry, University of Otago, Dunedin, New Zealand. matteo.ferla@gmail.com.', 'New Zealand', 2, 'Algorithms|DNA|Enzymes|Gene Library|Humans|Internet|Mutagenesis|Polymerase Chain Reaction|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-0996-7', '27044645', 'Assessing library diversity is an important control step in a directed evolution experiment. To do this, a limited amount of colonies from a test library are sequenced and tested. In the case of an error-prone PCR library, the spectrum of the identified mutations - the proportions of mutations of a specific nucleobase to another- is calculated enabling the user to make more informed predictions on library diversity and coverage. However, the calculations of the mutational spectrum are severely affected by the limited sample sizes.'),
(97229, 2016, 'bmc', 'SoftPanel: a website for grouping diseases and related disorders for generation of customized panels', 'Wang, Likun|Zhang, Cong|Watkins, Johnathan|Jin, Yan|McNutt, Michael|Yin, Yuxin', 'Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, 100191, China.|Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, 100191, China.|Institute for Mathematical and Molecular Biomedicine, Kings College London, Guys Campus, London, SE1 1UL, UK.|Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, 100191, China.|Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, 100191, China.|Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, 100191, China. yinyuxin@hsc.pku.edu.cn.', 'China|China|United Kingdom|China|China|China', 1, 'Area Under Curve|Databases, Factual|Disease|Exome|Humans|Internet|Phenotype|Precision Medicine|ROC Curve|Support Vector Machine|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-0998-5', '27044653', 'Targeted next-generation sequencing is playing an increasingly important role in biological research and clinical diagnosis by allowing researchers to sequence high priority genes at much higher depths and at a fraction of the cost of whole genome or exome sequencing. However, in designing the panel of genes to be sequenced, investigators need to consider the tradeoff between the better sensitivity of a broad panel and the higher specificity of a potentially more relevant panel. Although tools to prioritize candidate disease genes have been developed, the great majority of these require prior knowledge and a set of seed genes as input, which is only possible for diseases with a known genetic etiology.'),
(97230, 2016, 'bmc', 'The systems biology format converter', 'Rodriguez, Nicolas|Pettit, Jean-Baptiste|Dalle Pezze, Piero|Li, Lu|Henry, Arnaud|van Iersel, Martijn P|Jalowicki, Gael|Kutmon, Martina|Natarajan, Kedar N|Tolnay, David|Stefan, Melanie I|Evelo, Chris T|Le Novère, Nicolas', 'EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD, Cambridge, Hinxton, UK.|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD, Cambridge, Hinxton, UK.|The Babraham Institute, Babraham Campus, CB22 3AT, Cambridge, UK.|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD, Cambridge, Hinxton, UK.|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD, Cambridge, Hinxton, UK.|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD, Cambridge, Hinxton, UK.|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD, Cambridge, Hinxton, UK.|Department of Bioinformatics, BiGCaT, Maastricht University, Maastricht, 6229, The Netherlands.|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD, Cambridge, Hinxton, UK.|California Institute of Technology, Division of Biology and Biological Engineering, Pasadena, 91125, CA, USA.|California Institute of Technology, Division of Biology and Biological Engineering, Pasadena, 91125, CA, USA.|Department of Bioinformatics, BiGCaT, Maastricht University, Maastricht, 6229, The Netherlands.|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD, Cambridge, Hinxton, UK. lenov@babraham.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Netherlands|United Kingdom|United Kingdom|United Kingdom|Netherlands|United Kingdom', 13, 'Databases, Factual|Internet|Systems Biology|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1000-2', '27044654', 'Interoperability between formats is a recurring problem in systems biology research. Many tools have been developed to convert computational models from one format to another. However, they have been developed independently, resulting in redundancy of efforts and lack of synergy.'),
(97231, 2016, 'bmc', 'Structured feature selection using coordinate descent optimization', 'Ghalwash, Mohamed F|Cao, Xi Hang|Stojkovic, Ivan|Obradovic, Zoran', 'Center for Data Analytics and Biomedical Informatics, College of Science and Technology, Temple University, North 12th Street, Philadelphia, 19122, PA, USA. mohamed.ghalwash@temple.edu.|Center for Data Analytics and Biomedical Informatics, College of Science and Technology, Temple University, North 12th Street, Philadelphia, 19122, PA, USA.|Center for Data Analytics and Biomedical Informatics, College of Science and Technology, Temple University, North 12th Street, Philadelphia, 19122, PA, USA.|Center for Data Analytics and Biomedical Informatics, College of Science and Technology, Temple University, North 12th Street, Philadelphia, 19122, PA, USA.', 'United States|United States|United States|United States', 18, 'Algorithms|Corneal Neovascularization|Databases, Genetic|Gene Expression Regulation|Gene Ontology|Genetic Variation|HIV Infections|Hemoglobinuria|Humans|Melanoma|Microarray Analysis|Models, Theoretical|Multiple Myeloma|Neuroendocrine Tumors|Nevus|Stress, Physiological|Virus Diseases', 'Journal Article', '10.1186/s12859-016-0954-4', '27059502', 'Existing feature selection methods typically do not consider prior knowledge in the form of structural relationships among features. In this study, the features are structured based on prior knowledge into groups. The problem addressed in this article is how to select one representative feature from each group such that the selected features are jointly discriminating the classes. The problem is formulated as a binary constrained optimization and the combinatorial optimization is relaxed as a convex-concave problem, which is then transformed into a sequence of convex optimization problems so that the problem can be solved by any standard optimization algorithm. Moreover, a block coordinate gradient descent optimization algorithm is proposed for high dimensional feature selection, which in our experiments was four times faster than using a standard optimization algorithm.'),
(97232, 2016, 'bmc', 'CyTRANSFINDER: a Cytoscape 33 plugin for three-component (TF, gene, miRNA) signal transduction pathway construction', 'Politano, Gianfranco|Orso, Francesca|Raimo, Monica|Benso, Alfredo|Savino, Alessandro|Taverna, Daniela|Di Carlo, Stefano', 'Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy.|Molecular Biotechnology Center (MBC), Via Nizza, 52, Torino, 10126, Italy.|Molecular Biotechnology Center (MBC), Via Nizza, 52, Torino, 10126, Italy.|Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy.|Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy.|Molecular Biotechnology Center (MBC), Via Nizza, 52, Torino, 10126, Italy.|Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy. stefano.dicarlo@polito.it.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 7, 'Disease Progression|Gene Expression Regulation|Gene Regulatory Networks|Humans|Melanoma|MicroRNAs|Reproducibility of Results|Signal Transduction|Transcription Factors', 'Journal Article', '10.1186/s12859-016-0964-2', '27059647', 'Biological research increasingly relies on network models to study complex phenomena. Signal Transduction Pathways are molecular circuits that model how cells receive, process, and respond to information from the environment providing snapshots of the overall cell dynamics. Most of the attempts to reconstruct signal transduction pathways are limited to single regulator networks including only genes/proteins. However, networks involving a single type of regulator and neglecting transcriptional and post-transcriptional regulations mediated by transcription factors and microRNAs, respectively, may not fully reveal the complex regulatory mechanisms of a cell. We observed a lack of computational instruments supporting explorative analysis on this type of three-component signal transduction pathways.'),
(97233, 2016, 'bmc', 'CollapsABEL: an R library for detecting compound heterozygote alleles in genome-wide association studies', 'Zhong, Kaiyin|Karssen, Lennart C|Kayser, Manfred|Liu, Fan', 'Department of Genetic Identification, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands.|PolyOmica, Groningen, The Netherlands.|Department of Genetic Identification, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands.|Department of Genetic Identification, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands. liufan@big.ac.cn.', 'Netherlands|Netherlands|Netherlands|Netherlands', 9, 'Alleles|Computational Biology|Exome|Gene Library|Genetic Association Studies|Genetic Variation|Genotyping Techniques|Heterozygote|Humans|Linear Models|Logistic Models|Microarray Analysis|Middle Aged|Netherlands|Phenotype|Polymorphism, Single Nucleotide|Prospective Studies|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-016-1006-9', '27059780', 'Compound Heterozygosity (CH) in classical genetics is the presence of two different recessive mutations at a particular gene locus. A relaxed form of CH alleles may account for an essential proportion of the missing heritability, i.e. heritability of phenotypes so far not accounted for by single genetic variants. Methods to detect CH-like effects in genome-wide association studies (GWAS) may facilitate explaining the missing heritability, but to our knowledge no viable software tools for this purpose are currently available.'),
(97234, 2016, 'bmc', 'A computational method for designing diverse linear epitopes including citrullinated peptides with desired binding affinities to intravenous immunoglobulin', 'Patro, Rob|Norel, Raquel|Prill, Robert J|Saez-Rodriguez, Julio|Lorenz, Peter|Steinbeck, Felix|Ziems, Bjoern|Luštrek, Mitja|Barbarini, Nicola|Tiengo, Alessandra|Bellazzi, Riccardo|Thiesen, Hans-Jürgen|Stolovitzky, Gustavo|Kingsford, Carl', 'Department of Computer Science, Stony Brook, NY, USA.|IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.|IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, UK.|Institute of Immunology, University of Rostock, Rostock, Germany.|Institute of Immunology, University of Rostock, Rostock, Germany.|Gesellschaft für Individualisierte Medizin (IndyMed) mbH, Rostock, Germany.|Department of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenia.|Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.|Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.|Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.|Institute of Immunology, University of Rostock, Rostock, Germany.|IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.|Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. carlk@cs.cmu.edu.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|Germany|Germany|Germany|Slovenia|Italy|Italy|Italy|Germany|United Kingdom|United Kingdom', 1, 'Citrulline|Computational Biology|Cysteine|Epitopes|Humans|Immunoglobulins, Intravenous|Models, Molecular|Peptides, Cyclic|Reproducibility of Results|Support Vector Machine|Tryptophan', 'Journal Article', '10.1186/s12859-016-1008-7', '27059896', 'Understanding the interactions between antibodies and the linear epitopes that they recognize is an important task in the study of immunological diseases. We present a novel computational method for the design of linear epitopes of specified binding affinity to Intravenous Immunoglobulin (IVIg).'),
(97235, 2016, 'bmc', 'Unaccounted uncertainty from qPCR efficiency estimates entails uncontrolled false positive rates', 'Bilgrau, Anders E|Falgreen, Steffen|Petersen, Anders|Kjeldsen, Malene K|Bxc3xb8dker, Julie S|Johnsen, Hans E|Dybkxc3xa6r, Karen|Bxc3xb8gsted, Martin', 'Department of Haematology, Aalborg University Hospital, Sdr. Skovvej 15, Aalborg, 9000, Denmark. anders.ellern.bilgrau@gmail.com.|Department of Haematology, Aalborg University Hospital, Sdr. Skovvej 15, Aalborg, 9000, Denmark.|Department of Haematology, Aalborg University Hospital, Sdr. Skovvej 15, Aalborg, 9000, Denmark.|Department of Haematology, Aalborg University Hospital, Sdr. Skovvej 15, Aalborg, 9000, Denmark.|Department of Haematology, Aalborg University Hospital, Sdr. Skovvej 15, Aalborg, 9000, Denmark.|Department of Haematology, Aalborg University Hospital, Sdr. Skovvej 15, Aalborg, 9000, Denmark.|Department of Haematology, Aalborg University Hospital, Sdr. Skovvej 15, Aalborg, 9000, Denmark.|Department of Haematology, Aalborg University Hospital, Sdr. Skovvej 15, Aalborg, 9000, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark|Denmark|Denmark|Denmark', 1, 'Arabidopsis|Case-Control Studies|Cell Line, Tumor|Computer Simulation|Gene Expression Regulation, Neoplastic|Glutathione Transferase|Humans|Linear Models|Male|Models, Theoretical|Monte Carlo Method|Multiple Myeloma|Real-Time Polymerase Chain Reaction|Testis|Uncertainty', 'Journal Article', '10.1186/s12859-016-0997-6', '27067838', 'Accurate adjustment for the amplification efficiency (AE) is an important part of real-time quantitative polymerase chain reaction (qPCR) experiments. The most commonly used correction strategy is to estimate the AE by dilution experiments and use this as a plug-in when efficiency correcting the xcex94 xcex94 C q . Currently, it is recommended to determine the AE with high precision as this plug-in approach does not account for the AE uncertainty, implicitly assuming an infinitely precise AE estimate. Determining the AE with such precision, however, requires tedious laboratory work and vast amounts of biological material. Violation of the assumption leads to overly optimistic standard errors of the xcex94 xcex94 C q , confidence intervals, and p-values which ultimately increase the type I error rate beyond the expected significance level. As qPCR is often used for validation it should be a high priority to account for the uncertainty of the AE estimate and thereby properly bounding the type I error rate and achieve the desired significance level.'),
(97236, 2016, 'bmc', 'Implementation of a methodology for determining elastic properties of lipid assemblies from molecular dynamics simulations', 'Johner, Niklaus|Harries, Daniel|Khelashvili, George', 'Swiss Institute of Bioinformatics, Klingelbergstrasse 50/70, Basel, Switzerland. niklaus.johner@a3.epfl.ch.|Institute of Chemistry and the Fritz Haber Research Center, The Hebrew University, Jerusalem, 91904, Israel.|Department of Physiology and Biophysics, Weill Medical College of Cornell University, New York, NY, 10065, USA.', 'Switzerland|Israel|United States', 6, 'Algorithms|Lipid Bilayers|Membrane Proteins|Models, Theoretical|Molecular Dynamics Simulation|Software|Water', 'Journal Article', '10.1186/s12859-016-1003-z', '27071656', 'The importance of the material properties of membranes for diverse cellular processes is well established. Notably, the elastic properties of the membrane, which depend on its composition, can directly influence membrane reshaping and fusion processes as well as the organisation and function of membrane proteins. Determining these properties is therefore key for a mechanistic understanding of how the cell functions.'),
(97237, 2016, 'bmc', 'Predicting drug target interactions using meta-path-based semantic network analysis', 'Fu, Gang|Ding, Ying|Seal, Abhik|Chen, Bin|Sun, Yizhou|Bolton, Evan', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA. gang.fu@nih.gov.|School of Informatics & Computing, Indiana University, 107 S. Indiana Ave, Bloomington, IN, USA.|School of Informatics & Computing, Indiana University, 107 S. Indiana Ave, Bloomington, IN, USA.|Department of Medicine, Stanford University, 450 Serra Mall, Stanford, CA, USA.|College of Computer and Information Science, Northeastern University, 360 Huntington Avenue, Boston, MA, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA.', 'United States|India|India|United States|United States|United States', 37, 'Data Mining|Databases, Factual|Drug Design|Machine Learning|Models, Chemical|Pharmaceutical Preparations|Pharmacological Phenomena|Proteins', 'Journal Article', '10.1186/s12859-016-1005-x', '27071755', 'In the context of drug discovery, drug target interactions (DTIs) can be predicted based on observed topological features of a semantic network across the chemical and biological space. In a semantic network, the types of the nodes and links are different. In order to take into account the heterogeneity of the semantic network, meta-path-based topological patterns were investigated for link prediction.'),
(97238, 2016, 'bmc', 'Erratum to: Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature', 'Ravikumar, Komandur Elayavilli|Wagholikar, Kavishwar B|Li, Dingcheng|Kocher, Jean-Pierre|Liu, Hongfang', 'Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First St SW, Harvick 3rd, Rochester, MN, 55905, USA. Ravikumar@mayo.edu.|Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First St SW, Harvick 3rd, Rochester, MN, 55905, USA.|Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First St SW, Harvick 3rd, Rochester, MN, 55905, USA.|Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First St SW, Harvick 3rd, Rochester, MN, 55905, USA.|Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First St SW, Harvick 3rd, Rochester, MN, 55905, USA. Hongfang@mayo.edu.', 'United States|United States|United States|United States|United States', 0, '-', 'Published Erratum', '10.1186/s12859-016-0974-0', '27075759', '-'),
(97239, 2016, 'bmc', 'Sortal anaphora resolution to enhance relation extraction from biomedical literature', 'Kilicoglu, Halil|Rosemblat, Graciela|Fiszman, Marcelo|Rindflesch, Thomas C', 'Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, 8600 Rockville Pike, Bethesda, 20894, MD, USA. kilicogluh@mail.nih.gov.|Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, 8600 Rockville Pike, Bethesda, 20894, MD, USA.|Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, 8600 Rockville Pike, Bethesda, 20894, MD, USA.|Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, 8600 Rockville Pike, Bethesda, 20894, MD, USA.', 'United States|United States|United States|United States', 4, 'Biological Ontologies|Databases, Factual|Linguistics|Natural Language Processing|Semantics', 'Journal Article', '10.1186/s12859-016-1009-6', '27080229', 'Entity coreference is common in biomedical literature and it can affect text understanding systems that rely on accurate identification of named entities, such as relation extraction and automatic summarization. Coreference resolution is a foundational yet challenging natural language processing task which, if performed successfully, is likely to enhance such systems significantly. In this paper, we propose a semantically oriented, rule-based method to resolve sortal anaphora, a specific type of coreference that forms the majority of coreference instances in biomedical literature. The method addresses all entity types and relies on linguistic components of SemRep, a broad-coverage biomedical relation extraction system. It has been incorporated into SemRep, extending its core semantic interpretation capability from sentence level to discourse level.'),
(97240, 2016, 'bmc', 'UNDR ROVER - a fast and accurate variant caller for targeted DNA sequencing', 'Park, Daniel J|Li, Roger|Lau, Edmund|Georgeson, Peter|Nguyen-Dumont, Tú|Pope, Bernard J', 'Genetic Epidemiology Laboratory, School of Biomedical Sciences, Medical Building, The University of Melbourne, Melbourne, Victoria, 3010, Australia.|Department of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, 3010, Australia.|Victorian Life Sciences Computation Initiative, The University of Melbourne, Melbourne, Victoria, 3053, Australia.|Victorian Life Sciences Computation Initiative, The University of Melbourne, Melbourne, Victoria, 3053, Australia.|Genetic Epidemiology Laboratory, School of Biomedical Sciences, Medical Building, The University of Melbourne, Melbourne, Victoria, 3010, Australia.|Department of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, 3010, Australia. bjpope@unimelb.edu.au.', 'Australia|Australia|Australia|Australia|Australia|Australia', 3, 'Algorithms|Computational Biology|DNA Primers|Genetic Variation|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Models, Theoretical|Polymerase Chain Reaction|Reproducibility of Results|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-016-1014-9', '27083325', 'Previously, we described ROVER, a DNA variant caller which identifies genetic variants from PCR-targeted massively parallel sequencing (MPS) datasets generated by the Hi-Plex protocol. ROVER permits stringent filtering of sequencing chemistry-induced errors by requiring reported variants to appear in both reads of overlapping pairs above certain thresholds of occurrence. ROVER was developed in tandem with Hi-Plex and has been used successfully to screen for genetic mutations in the breast cancer predisposition gene PALB2. ROVER is applied to MPS data in BAM format and, therefore, relies on sequence reads being mapped to a reference genome. In this paper, we describe an improvement to ROVER, called UNDR ROVER (Unmapped primer-Directed ROVER), which accepts MPS data in FASTQ format, avoiding the need for a computationally expensive mapping stage. It does so by taking advantage of the location-specific nature of PCR-targeted MPS data.'),
(97241, 2016, 'bmc', 'BAGEL: a computational framework for identifying essential genes from pooled library screens', 'Hart, Traver|Moffat, Jason', 'Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. traver.hart@gmail.com.|Donnelly Centre, University of Toronto, Toronto, Canada.', 'United States|Canada', 55, 'Cell Line, Tumor|Computational Biology|Epithelial Cells|Gene Knockout Techniques|Gene Library|Genes, Essential|Genetic Fitness|Glioblastoma|HCT116 Cells|HeLa Cells|Humans|Machine Learning|Models, Genetic|RNA Interference|Sensitivity and Specificity', 'Journal Article', '10.1186/s12859-016-1015-8', '27083490', 'The adaptation of the CRISPR-Cas9 system to pooled library gene knockout screens in mammalian cells represents a major technological leap over RNA interference, the prior state of the art. New methods for analyzing the data and evaluating results are needed.'),
(97242, 2016, 'bmc', 'Analysis of significant factors for dengue fever incidence prediction', 'Siriyasatien, Padet|Phumee, Atchara|Ongruk, Phatsavee|Jampachaisri, Katechan|Kesorn, Kraisak', 'Department of Parasitology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.|Department of Parasitology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.|Department of Computer Science and Information Technology, Faculty of Science, Naresuan University, Phitsanulok, 65000, Thailand.|Department of Mathematics, Faculty of Science, Naresuan University, Phitsanulok, 65000, Thailand.|Department of Computer Science and Information Technology, Faculty of Science, Naresuan University, Phitsanulok, 65000, Thailand. kraisakk@nu.ac.th.', 'Thailand|Thailand|Thailand|Thailand|Thailand', 23, 'Aedes|Animals|Bayes Theorem|Dengue|Dengue Virus|Disease Outbreaks|Female|Humans|Incidence|Larva|Models, Theoretical|Multivariate Analysis|Seasons|Thailand', 'Journal Article', '10.1186/s12859-016-1034-5', '27083696', 'Many popular dengue forecasting techniques have been used by several researchers to extrapolate dengue incidence rates, including the K-H model, support vector machines (SVM), and artificial neural networks (ANN). The time series analysis methodology, particularly ARIMA and SARIMA, has been increasingly applied to the field of epidemiological research for dengue fever, dengue hemorrhagic fever, and other infectious diseases. The main drawback of these methods is that they do not consider other variables that are associated with the dependent variable. Additionally, new factors correlated to the disease are needed to enhance the prediction accuracy of the model when it is applied to areas of similar climates, where weather factors such as temperature, total rainfall, and humidity are not substantially different. Such drawbacks may consequently lower the predictive power for the outbreak.'),
(97243, 2016, 'bmc', 'Gene signature-based mapping of immunological systems and diseases', 'Liu, Hong|Liu, Jessica|Toups, Michelle|Soos, Timothy|Arendt, Christopher', 'Bio-Innovation, Sanofi Global Biotherapeutics, 38 Sidney Street, Cambridge, MA, 02139, USA. hong.liu@sanofi.com.|Bio-Innovation, Sanofi Global Biotherapeutics, 38 Sidney Street, Cambridge, MA, 02139, USA.|Bioinformatics Program, Northeastern University, 360 Huntington Avenue, Boston, MA, 02115, USA.|Bio-Innovation, Sanofi Global Biotherapeutics, 38 Sidney Street, Cambridge, MA, 02139, USA.|Bio-Innovation, Sanofi Global Biotherapeutics, 38 Sidney Street, Cambridge, MA, 02139, USA.', 'United States|United States|United States|United States|United States', 6, 'Animals|Chromosome Mapping|Gene Expression Profiling|Gene Targeting|Genomics|Humans|Immune System|Immune System Diseases|Mice|Transcriptome', 'Journal Article', '10.1186/s12859-016-1012-y', '27089880', 'The immune system is multifaceted, structured by diverse components that interconnect using multilayered dynamic cellular processes. Genomic technologies provide a means for investigating, at the molecular level, the adaptations of the immune system in host defense and its dysregulation in pathological conditions. A critical aspect of intersecting and investigating complex datasets is determining how to best integrate genomic data from diverse platforms and heterogeneous sample populations to capture immunological signatures in health and disease.'),
(97244, 2016, 'bmc', 'Evolutionary relationships between heme-binding ferredoxin xcexb1 + xcexb2 barrels', 'Acharya, Giriraj|Kaur, Gurmeet|Subramanian, Srikrishna', 'CSIR-Institute of Microbial Technology (IMTECH), Sector 39-A, Chandigarh, India.|CSIR-Institute of Microbial Technology (IMTECH), Sector 39-A, Chandigarh, India.|CSIR-Institute of Microbial Technology (IMTECH), Sector 39-A, Chandigarh, India. krishna@imtech.res.in.', 'India|India|India', 3, 'Amino Acid Sequence|Binding Sites|Carrier Proteins|Evolution, Molecular|Ferredoxins|Heme|Models, Molecular|Protein Multimerization|Protein Structure, Secondary', 'Journal Article', '10.1186/s12859-016-1033-6', '27089923', 'The xcexb1 + xcexb2 barrel superfamily of the ferredoxin-like fold consists of a functionally diverse group of evolutionarily related proteins. The barrel architecture of these proteins is formed by either homo-/hetero-dimerization or duplication and fusion of ferredoxin-like domains. Several members of this superfamily bind heme in order to carry out their functions.'),
(97245, 2016, 'bmc', 'gtrellis: an R/Bioconductor package for making genome-level Trellis graphics', 'Gu, Zuguang|Eils, Roland|Schlesner, Matthias', 'Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.|Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.|Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany. m.schlesner@dkfz.de.', 'Germany|Germany|Germany', 5, 'Computer Graphics|Databases, Genetic|Genome, Human|Genomics|Humans|Models, Theoretical|Sequence Alignment|Software', 'Journal Article', '10.1186/s12859-016-1051-4', '27089965', 'Trellis graphics are a visualization method that splits data by one or more categorical variables and displays subsets of the data in a grid of panels. Trellis graphics are broadly used in genomic data analysis to compare statistics over different categories in parallel and reveal multivariate relationships. However, current software packages to produce Trellis graphics have not been designed with genomic data in mind and lack some functionality that is required for effective visualization of genomic data.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97246, 2016, 'bmc', 'ChemiRs: a web application for microRNAs and chemicals', 'Su, Emily Chia-Yu|Chen, Yu-Sing|Tien, Yun-Cheng|Liu, Jeff|Ho, Bing-Ching|Yu, Sung-Liang|Singh, Sher', 'Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, 110, Taiwan.|Department of Life Science, College of Science, National Taiwan Normal University, Taipei, 116, Taiwan.|Department of Life Science, College of Science, National Taiwan Normal University, Taipei, 116, Taiwan.|Department of Civil Engineering, College of Engineering, National Taiwan University College of Engineering, Taipei, 106, Taiwan.|Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University, Taipei, 100, Taiwan.|Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University, Taipei, 100, Taiwan.|Department of Life Science, College of Science, National Taiwan Normal University, Taipei, 116, Taiwan. sher@ntnu.edu.tw.', '-|-|-|-|-|-|-', 10, 'Algorithms|Computational Biology|Databases, Genetic|Humans|Internet|Medical Subject Headings|MicroRNAs|PubMed|Software', 'Journal Article', '10.1186/s12859-016-1002-0', '27091357', 'MicroRNAs (miRNAs) are about 22 nucleotides, non-coding RNAs that affect various cellular functions, and play a regulatory role in different organisms including human. Until now, more than 2500 mature miRNAs in human have been discovered and registered, but still lack of information or algorithms to reveal the relations among miRNAs, environmental chemicals and human health. Chemicals in environment affect our health and daily life, and some of them can lead to diseases by inferring biological pathways.'),
(97247, 2016, 'bmc', 'MutSpec: a Galaxy toolbox for streamlined analyses of somatic mutation spectra in human and mouse cancer genomes', 'Ardin, Maude|Cahais, Vincent|Castells, Xavier|Bouaoun, Liacine|Byrnes, Graham|Herceg, Zdenko|Zavadil, Jiri|Olivier, Magali', 'Molecular Mechanisms and Biomarkers Group, International Agency for Research on Cancer, F69372, Lyon, France.|Epigenetic Group, International Agency for Research on Cancer, F69372, Lyon, France.|Molecular Mechanisms and Biomarkers Group, International Agency for Research on Cancer, F69372, Lyon, France.|Biostatistics Group, International Agency for Research on Cancer, F69372, Lyon, France.|Biostatistics Group, International Agency for Research on Cancer, F69372, Lyon, France.|Epigenetic Group, International Agency for Research on Cancer, F69372, Lyon, France.|Molecular Mechanisms and Biomarkers Group, International Agency for Research on Cancer, F69372, Lyon, France.|Molecular Mechanisms and Biomarkers Group, International Agency for Research on Cancer, F69372, Lyon, France. olivierm@iarc.fr.', 'France|France|France|France|France|France|France|France', 22, 'Animals|Computational Biology|Databases, Genetic|Exome|Genes, Neoplasm|Genetic Association Studies|Genome|Genome, Human|Humans|Mice|Mouth Neoplasms|Mutation|Neoplasms|Software', 'Journal Article', '10.1186/s12859-016-1011-z', '27091472', 'The nature of somatic mutations observed in human tumors at single gene or genome-wide levels can reveal information on past carcinogenic exposures and mutational processes contributing to tumor development. While large amounts of sequencing data are being generated, the associated analysis and interpretation of mutation patterns that may reveal clues about the natural history of cancer present complex and challenging tasks that require advanced bioinformatics skills. To make such analyses accessible to a wider community of researchers with no programming expertise, we have developed within the web-based user-friendly platform Galaxy a first-of-its-kind package called MutSpec.'),
(97248, 2016, 'bmc', 'Growthcurver: an R package for obtaining interpretable metrics from microbial growth curves', 'Sprouffske, Kathleen|Wagner, Andreas', 'Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland.|Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland. andreas.wagner@ieu.uzh.ch.', 'Switzerland|Switzerland', 83, 'Bacteria|Computational Biology|Databases, Factual|Logistic Models|Programming Languages|Software', 'Journal Article', '10.1186/s12859-016-1016-7', '27094401', 'Plate readers can measure the growth curves of many microbial strains in a high-throughput fashion. The hundreds of absorbance readings collected simultaneously for hundreds of samples create technical hurdles for data analysis.'),
(97249, 2016, 'bmc', 'Using expected sequence features to improve basecalling accuracy of amplicon pyrosequencing data', 'Rask, Thomas S|Petersen, Bent|Chen, Donald S|Day, Karen P|Pedersen, Anders Gorm', 'Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Building 208, Kongens Lyngby, DK-2800, Denmark. rask@cbs.dtu.dk.|Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Building 208, Kongens Lyngby, DK-2800, Denmark.|Division of Medical Parasitology, Department of Microbiology, New York University Langone Medical Center, 341 East 25th Street, New York, NY, 10010, USA.|Division of Medical Parasitology, Department of Microbiology, New York University Langone Medical Center, 341 East 25th Street, New York, NY, 10010, USA.|Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Building 208, Kongens Lyngby, DK-2800, Denmark.', 'Denmark|Denmark|United States|United States|Denmark', 0, 'Algorithms|DNA, Protozoan|Models, Molecular|Open Reading Frames|Plasmodium falciparum|Protozoan Proteins|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-016-1032-7', '27102804', 'Amplicon pyrosequencing targets a known genetic region and thus inherently produces reads highly anticipated to have certain features, such as conserved nucleotide sequence, and in the case of protein coding DNA, an open reading frame. Pyrosequencing errors, consisting mainly of nucleotide insertions and deletions, are on the other hand likely to disrupt open reading frames. Such an inverse relationship between errors and expectation based on prior knowledge can be used advantageously to guide the process known as basecalling, i.e. the inference of nucleotide sequence from raw sequencing data.'),
(97250, 2016, 'bmc', 'Empirical estimation of sequencing error rates using smoothing splines', 'Zhu, Xuan|Wang, Jian|Peng, Bo|Shete, Sanjay', 'Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.|Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.|Department of Bioinformatics & Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.|Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA. sshete@mdanderson.org.', 'United States|United States|United States|United States', 0, 'Bacteriophage phi X 174|Computer Simulation|DNA, Viral|Genomics|High-Throughput Nucleotide Sequencing|Humans|K562 Cells|Linear Models|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-016-1052-3', '27102907', 'Next-generation sequencing has been used by investigators to address a diverse range of biological problems through, for example, polymorphism and mutation discovery and microRNA profiling. However, compared to conventional sequencing, the error rates for next-generation sequencing are often higher, which impacts the downstream genomic analysis. Recently, Wang et al. (BMC Bioinformatics 13:185, 2012) proposed a shadow regression approach to estimate the error rates for next-generation sequencing data based on the assumption of a linear relationship between the number of reads sequenced and the number of reads containing errors (denoted as shadows). However, this linear read-shadow relationship may not be appropriate for all types of sequence data. Therefore, it is necessary to estimate the error rates in a more reliable way without assuming linearity. We proposed an empirical error rate estimation approach that employs cubic and robust smoothing splines to model the relationship between the number of reads sequenced and the number of shadows.'),
(97251, 2016, 'bmc', 'Stochastic epigenetic outliers can define field defects in cancer', 'Teschendorff, Andrew E|Jones, Allison|Widschwendter, Martin', 'CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai, China. a.teschendorff@ucl.ac.uk.|Department of Womens Cancer, University College London, 74 Huntley Street, London, WC1E 6AU, UK.|Department of Womens Cancer, University College London, 74 Huntley Street, London, WC1E 6AU, UK. m.widschwendter@ucl.ac.uk.', 'China|United Kingdom|United Kingdom', 0, 'Algorithms|Breast Neoplasms|Carcinogenesis|CpG Islands|DNA Methylation|Disease Progression|Epigenomics|Female|Humans|Sample Size|Uterine Cervical Neoplasms', 'Journal Article', '10.1186/s12859-016-1056-z', '27103033', 'There is growing evidence that DNA methylation alterations may contribute to carcinogenesis. Recent data also suggest that DNA methylation field defects in normal pre-neoplastic tissue represent infrequent stochastic outlier events. This presents a statistical challenge for standard feature selection algorithms, which assume frequent alterations in a disease phenotype. Although differential variability has emerged as a novel feature selection paradigm for the discovery of outliers, a growing concern is that these could result from technical confounders, in principle thus favouring algorithms which are robust to outliers.'),
(97252, 2016, 'bmc', 'CompNet: a GUI based tool for comparison of multiple biological interaction networks', 'Kuntal, Bhusan K|Dutta, Anirban|Mande, Sharmila S', 'Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Ltd., 54-B, Hadapsar Industrial Estate, Pune, 411 013, Maharashtra, India.|Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Ltd., 54-B, Hadapsar Industrial Estate, Pune, 411 013, Maharashtra, India.|Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Ltd., 54-B, Hadapsar Industrial Estate, Pune, 411 013, Maharashtra, India. sharmila.mande@tcs.com.', 'India|India|India', 14, 'Gene Expression Profiling|Humans|Mycobacterium tuberculosis|Protein Interaction Mapping|Protein Interaction Maps|Software', 'Journal Article', '10.1186/s12859-016-1013-x', '27112575', 'Network visualization and analysis tools aid in better understanding of complex biological systems. Furthermore, to understand the differences in behaviour of system(s) under various environmental conditions (e.g. stress, infection), comparing multiple networks becomes necessary. Such comparisons between multiple networks may help in asserting causation and in identifying key components of the studied biological system(s). Although many available network comparison methods exist, which employ techniques like network alignment and querying to compute pair-wise similarity between selected networks, most of them have limited features with respect to interactive visual comparison of multiple networks.'),
(97253, 2016, 'bmc', 'Detection and tracking of overlapping cell nuclei for large scale mitosis analyses', 'Li, Yingbo|Rose, France|di Pietro, Florencia|Morin, Xavier|Genovesio, Auguste', 'Scientific Center for Computational Biology, Institut de Biologie de lEcole Normale Superieure, CNRS-INSERM-ENS, PSL Research University, 46, rue dUlm, Paris, 75005, France.|Scientific Center for Computational Biology, Institut de Biologie de lEcole Normale Superieure, CNRS-INSERM-ENS, PSL Research University, 46, rue dUlm, Paris, 75005, France.|Division cellulaire et neurogenèse, Institut de Biologie de lEcole Normale Superieure, PSL Research University, 46, rue dUlm, Paris, 75005, France.|Division cellulaire et neurogenèse, Institut de Biologie de lEcole Normale Superieure, PSL Research University, 46, rue dUlm, Paris, 75005, France.|Scientific Center for Computational Biology, Institut de Biologie de lEcole Normale Superieure, CNRS-INSERM-ENS, PSL Research University, 46, rue dUlm, Paris, 75005, France. auguste.genovesio@ens.fr.', 'France|France|France|France|France', 0, 'Cell Nucleus|HeLa Cells|Humans|Image Processing, Computer-Assisted|Microscopy, Fluorescence|Microscopy, Video|Mitosis|Models, Statistical|Normal Distribution|Time-Lapse Imaging', 'Journal Article', '10.1186/s12859-016-1030-9', '27112769', 'Cell culture on printed micropatterns slides combined with automated fluorescent microscopy allows for extraction of tens of thousands of videos of small isolated growing cell clusters. The analysis of such large dataset in space and time is of great interest to the community in order to identify factors involved in cell growth, cell division or tissue formation by testing multiples conditions. However, cells growing on a micropattern tend to be tightly packed and to overlap with each other. Consequently, image analysis of those large dynamic datasets with no possible human intervention has proven impossible using state of the art automated cell detection methods.'),
(97254, 2016, 'bmc', 'Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding', 'Huang, Yu-An|You, Zhu-Hong|Chen, Xing|Chan, Keith|Luo, Xin', 'College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China.|School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China. zhuhongyou@cumt.edu.cn.|Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China. xingchen@amss.ac.cn.|Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China.|Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China.', 'China|China|China|China|China', 0, 'Bacterial Proteins|Computational Biology|Helicobacter pylori|Humans|Protein Interaction Mapping|Proteins|Proteomics|Saccharomyces cerevisiae Proteins|Sequence Analysis, Protein|Support Vector Machine', 'Evaluation Studies', '10.1186/s12859-016-1035-4', '27112932', 'Proteins are the important molecules which participate in virtually every aspect of cellular function within an organism in pairs. Although high-throughput technologies have generated considerable protein-protein interactions (PPIs) data for various species, the processes of experimental methods are both time-consuming and expensive. In addition, they are usually associated with high rates of both false positive and false negative results. Accordingly, a number of computational approaches have been developed to effectively and accurately predict protein interactions. However, most of these methods typically perform worse when other biological data sources (e.g., protein structure information, protein domains, or gene neighborhoods information) are not available. Therefore, it is very urgent to develop effective computational methods for prediction of PPIs solely using protein sequence information.'),
(97255, 2016, 'bmc', 'Erratum to: CompGO: an R package for comparing and visualizing Gene Ontology enrichment differences between DNA binding experiments', 'Waardenberg, Ashley J|Bassett, Samuel D|Bouveret, Romaric|Harvey, Richard P', 'Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia. awaardenberg@cmri.org.au.|Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia.|Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia.|Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia.', 'Australia|Australia|Australia|Australia', 0, '-', 'Published Erratum', '10.1186/s12859-016-1048-z', '27113060', '-'),
(97256, 2016, 'bmc', 'eSTGt: a programming and simulation environment for population dynamics', 'Spiro, Adam|Shapiro, Ehud', 'Department of Computer Science and Applied Mathematics and Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel.|Department of Computer Science and Applied Mathematics and Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel. ehud.shapiro@weizmann.ac.il.', 'Israel|Israel', 2, 'Biological Evolution|Computational Biology|Computer Simulation|Humans|Models, Theoretical|Population Dynamics|Software', 'Journal Article', '10.1186/s12859-016-1004-y', '27117841', 'We have previously presented a formal language for describing population dynamics based on environment-dependent Stochastic Tree Grammars (eSTG). The language captures in broad terms the effect of the changing environment while abstracting away details on interaction among individuals. An eSTG program consists of a set of stochastic tree grammar transition rules that are context-free. Transition rule probabilities and rates, however, can depend on global parameters such as population size, generation count and elapsed time. In addition, each individual may have an internal state, which can change during transitions.'),
(97257, 2016, 'bmc', 'Construction of dynamic probabilistic protein interaction networks for protein complex identification', 'Zhang, Yijia|Lin, Hongfei|Yang, Zhihao|Wang, Jian', 'College of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116023, China. zhyj@dlut.edu.cn.|College of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116023, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116023, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116023, China.', 'China|China|China|China', 13, 'Algorithms|Computational Biology|Protein Interaction Mapping|Protein Interaction Maps|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/s12859-016-1054-1', '27117946', 'Recently, high-throughput experimental techniques have generated a large amount of protein-protein interaction (PPI) data which can construct large complex PPI networks for numerous organisms. System biology attempts to understand cellular organization and function by analyzing these PPI networks. However, most studies still focus on static PPI networks which neglect the dynamic information of PPI.'),
(97258, 2016, 'bmc', 'ITD assembler: an algorithm for internal tandem duplication discovery from short-read sequencing data', 'Rustagi, Navin|Hampton, Oliver A|Li, Jie|Xi, Liu|Gibbs, Richard A|Plon, Sharon E|Kimmel, Marek|Wheeler, David A', 'Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA. Rustagi@bcm.edu.|Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.|Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.|Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.|Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.|Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.|Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.|Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Exome|Exons|Humans|Leukemia, Myeloid, Acute|Molecular Diagnostic Techniques|Mutation|Tandem Repeat Sequences|fms-Like Tyrosine Kinase 3', 'Journal Article', '10.1186/s12859-016-1031-8', '27121965', 'Detection of tandem duplication within coding exons, referred to as internal tandem duplication (ITD), remains challenging due to inefficiencies in alignment of ITD-containing reads to the reference genome. There is a critical need to develop efficient methods to recover these important mutational events.'),
(97259, 2016, 'bmc', 'ImiRP: a computational approach to microRNA target site mutation', 'Ryan, Bridget C|Werner, Torben S|Howard, Perry L|Chow, Robert L', 'Department of Biology, University of Victoria, Victoria, BC, V8W 3N5, Canada.|Department of Biology, University of Victoria, Victoria, BC, V8W 3N5, Canada.|Department of Biology, University of Victoria, Victoria, BC, V8W 3N5, Canada.|Department of Biology, University of Victoria, Victoria, BC, V8W 3N5, Canada. bobchow@uvic.ca.', 'Canada|Canada|Canada|Canada', 11, '3 Untranslated Regions|Base Pairing|Computational Biology|Gene Expression Regulation|MicroRNAs|Mutation|RNA, Messenger|Software', 'Journal Article', '10.1186/s12859-016-1057-y', '27122020', 'MicroRNAs (miRNAs) are small ~22 nucleotide non-coding RNAs that function as post-transcriptional regulators of messenger RNA (mRNA) through base-pairing to 6-8 nucleotide long target sites, usually located within the mRNA 3 untranslated region. A common approach to validate and probe microRNA-mRNA interactions is to mutate predicted target sites within the mRNA and determine whether it affects miRNA-mediated activity. The introduction of miRNA target site mutations, however, is potentially problematic as it may generate new, illegitimate sites target sites for other miRNAs, which may affect the experimental outcome. While it is possible to manually generate and check single miRNA target site mutations, this process can be time consuming, and becomes particularly onerous and error prone when multiple sites are to be mutated simultaneously. We have developed a modular Java-based system called ImiRP (Illegitimate miRNA Predictor) to solve this problem and to facilitate miRNA target site mutagenesis.'),
(97260, 2016, 'bmc', 'Addressing inaccuracies in BLOSUM computation improves homology search performance', 'Hess, Martin|Keul, Frank|Goesele, Michael|Hamacher, Kay', 'Graphics, Capture and Massively Parallel Computing, Department of Computer Science, Technische Universitxc3xa4t Darmstadt, Rundeturmstraxc3x9fe 12, Darmstadt, 64283, Germany.|Computational Biology and Simulation, Department of Biology, Technische Universitxc3xa4t Darmstadt, Schnittspahnstraxc3x9fe 2, Darmstadt, 64287, Germany. keul@bio.tu-darmstadt.de.|Graphics, Capture and Massively Parallel Computing, Department of Computer Science, Technische Universitxc3xa4t Darmstadt, Rundeturmstraxc3x9fe 12, Darmstadt, 64283, Germany.|Computational Biology and Simulation, Department of Biology, Technische Universitxc3xa4t Darmstadt, Schnittspahnstraxc3x9fe 2, Darmstadt, 64287, Germany.', 'Germany|Germany|Germany|Germany', 8, 'Algorithms|Databases, Protein|Sequence Alignment|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/s12859-016-1060-3', '27122148', 'BLOSUM matrices belong to the most commonly used substitution matrix series for protein homology search and sequence alignments since their publication in 1992. In 2008, Styczynski et al. discovered miscalculations in the clustering step of the matrix computation. Still, the RBLOSUM64 matrix based on the corrected BLOSUM code was reported to perform worse at a statistically significant level than the BLOSUM62. Here, we present a further correction of the (R)BLOSUM code and provide a thorough performance analysis of BLOSUM-, RBLOSUM- and the newly derived CorBLOSUM-type matrices. Thereby, we assess homology search performance of these matrix-types derived from three different BLOCKS databases on all versions of the ASTRAL20, ASTRAL40 and ASTRAL70 subsets resulting in 51 different benchmarks in total. Our analysis is focused on two of the most popular BLOSUM matrices - BLOSUM50 and BLOSUM62.'),
(97261, 2016, 'bmc', 'G-DOC Plus - an integrative bioinformatics platform for precision medicine', 'Bhuvaneshwar, Krithika|Belouali, Anas|Singh, Varun|Johnson, Robert M|Song, Lei|Alaoui, Adil|Harris, Michael A|Clarke, Robert|Weiner, Louis M|Gusev, Yuriy|Madhavan, Subha', 'Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA.|Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA.|Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA.|Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA.|Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA.|Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA.|Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA.|Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA.|Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA.|Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA.|Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA. sm696@georgetown.edu.', '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, Factual|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Precision Medicine|Transcriptome', 'Journal Article', '10.1186/s12859-016-1010-0', '27130330', 'G-DOC Plus is a data integration and bioinformatics platform that uses cloud computing and other advanced computational tools to handle a variety of biomedical BIG DATA including gene expression arrays, NGS and medical images so that they can be analyzed in the full context of other omics and clinical information.'),
(97262, 2016, 'bmc', 'IPED: a highly efficient denoising tool for Illumina MiSeq Paired-end 16S rRNA gene amplicon sequencing data', 'Mysara, Mohamed|Leys, Natalie|Raes, Jeroen|Monsieurs, Pieter', 'Unit of Microbiology, Belgian Nuclear Research Centre (SCK-CEN), Mol, Belgium.|Unit of Microbiology, Belgian Nuclear Research Centre (SCK-CEN), Mol, Belgium.|VIB Center for the Biology of Disease, VIB, Leuven, Belgium.|Unit of Microbiology, Belgian Nuclear Research Centre (SCK-CEN), Mol, Belgium. pieter.monsieurs@sckcen.be.', 'Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Animals|Genes, rRNA|High-Throughput Nucleotide Sequencing|Machine Learning|Metagenomics|Mice|RNA, Ribosomal, 16S', 'Journal Article', '10.1186/s12859-016-1061-2', '27130479', 'The development of high-throughput sequencing technologies has revolutionized the field of microbial ecology via the sequencing of phylogenetic marker genes (e.g. 16S rRNA gene amplicon sequencing). Denoising, the removal of sequencing errors, is an important step in preprocessing amplicon sequencing data. The increasing popularity of the Illumina MiSeq platform for these applications requires the development of appropriate denoising methods.'),
(97263, 2016, 'bmc', 'Automatic detection of diffusion modes within biological membranes using back-propagation neural network', 'Dosset, Patrice|Rassam, Patrice|Fernandez, Laurent|Espenel, Cedric|Rubinstein, Eric|Margeat, Emmanuel|Milhiet, Pierre-Emmanuel', 'Inserm, U1054, Montpellier, France.|Inserm, U1054, Montpellier, France.|Inserm, U1054, Montpellier, France.|Department of Cell and Developmental Biology, Weill Cornell Medical College of Cornell University, New York, NY, USA.|Inserm, U1004, Villejuif, France.|Inserm, U1054, Montpellier, France.|Inserm, U1054, Montpellier, France. pem@cbs.cnrs.fr.', 'France|France|France|United States|France|France|France', 24, 'Algorithms|Cell Membrane|Diffusion|Models, Biological|Motion|Neural Networks (Computer)', 'Journal Article', '10.1186/s12859-016-1064-z', '27141816', 'Single particle tracking (SPT) is nowadays one of the most popular technique to probe spatio-temporal dynamics of proteins diffusing within the plasma membrane. Indeed membrane components of eukaryotic cells are very dynamic molecules and can diffuse according to different motion modes. Trajectories are often reconstructed frame-by-frame and dynamic properties often evaluated using mean square displacement (MSD) analysis. However, to get statistically significant results in tracking experiments, analysis of a large number of trajectories is required and new methods facilitating this analysis are still needed.'),
(97264, 2016, 'bmc', 'Hierarchical classification strategy for Phenotype extraction from epidermal growth factor receptor endocytosis screening', 'Cao, Lu|Graauw, Marjo de|Yan, Kuan|Winkel, Leah|Verbeek, Fons J', 'Imaging and Bio-informatics group, LIACS, Leiden University, Niels Bohrweg 1, Leiden, 2333, CA, The Netherlands.|Division of Toxicology, LACDR, Leiden University, Einsteinweg 55, Leiden, 2333, CC, The Netherlands.|Imaging and Bio-informatics group, LIACS, Leiden University, Niels Bohrweg 1, Leiden, 2333, CA, The Netherlands.|Biomechanics Laboratory, Erasmus MC, Wytemaweg 80, Rotterdam, 3015, CN, The Netherlands.|Imaging and Bio-informatics group, LIACS, Leiden University, Niels Bohrweg 1, Leiden, 2333, CA, The Netherlands. f.j.verbeek@liacs.leidenuniv.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Breast Neoplasms|Endocytosis|ErbB Receptors|Female|High-Throughput Screening Assays|Humans|Pattern Recognition, Automated|Phenotype|Signal Transduction|Support Vector Machine|Tumor Cells, Cultured', 'Journal Article', '10.1186/s12859-016-1053-2', '27142862', 'Endocytosis is regarded as a mechanism of attenuating the epidermal growth factor receptor (EGFR) signaling and of receptor degradation. There is increasing evidence becoming available showing that breast cancer progression is associated with a defect in EGFR endocytosis. In order to find related Ribonucleic acid (RNA) regulators in this process, high-throughput imaging with fluorescent markers is used to visualize the complex EGFR endocytosis process. Subsequently a dedicated automatic image and data analysis system is developed and applied to extract the phenotype measurement and distinguish different developmental episodes from a huge amount of images acquired through high-throughput imaging. For the image analysis, a phenotype measurement quantifies the important image information into distinct features or measurements. Therefore, the manner in which prominent measurements are chosen to represent the dynamics of the EGFR process becomes a crucial step for the identification of the phenotype. In the subsequent data analysis, classification is used to categorize each observation by making use of all prominent measurements obtained from image analysis. Therefore, a better construction for a classification strategy will support to raise the performance level in our image and data analysis system.'),
(97265, 2016, 'bmc', 'TopHat-Recondition: a post-processor for TopHat unmapped reads', 'Brueffer, Christian|Saal, Lao H', 'Division of Oncology and Pathology, Department of Clinical Sciences, Lund University Cancer Center, Lund University, Medicon Village Building 404-B2, Lund, 223 81, Sweden.|Division of Oncology and Pathology, Department of Clinical Sciences, Lund University Cancer Center, Lund University, Medicon Village Building 404-B2, Lund, 223 81, Sweden. lao.saal@med.lu.se.', 'Sweden|Sweden', 5, 'Computational Biology|RNA|RNA Splicing|Sequence Alignment|Sequence Analysis, RNA|Software', 'Evaluation Studies', '10.1186/s12859-016-1058-x', '27142976', 'TopHat is a popular spliced junction mapper for RNA sequencing data, and writes files in the BAM format - the binary version of the Sequence Alignment/Map (SAM) format. BAM is the standard exchange format for aligned sequencing reads, thus correct format implementation is paramount for software interoperability and correct analysis. However, TopHat writes its unmapped reads in a way that is not compatible with other software that implements the SAM/BAM format.'),
(97266, 2016, 'bmc', 'QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics', 'OReilly, Paul G|Wen, Qing|Bankhead, Peter|Dunne, Philip D|McArt, Darragh G|McPherson, Suzanne|Hamilton, Peter W|Mills, Ken I|Zhang, Shu-Dong', 'Centre for Cancer Research and Cell Biology, Queens University Belfast, Belfast, BT9 7AE, UK.|Centre for Cancer Research and Cell Biology, Queens University Belfast, Belfast, BT9 7AE, UK.|Centre for Cancer Research and Cell Biology, Queens University Belfast, Belfast, BT9 7AE, UK.|Centre for Cancer Research and Cell Biology, Queens University Belfast, Belfast, BT9 7AE, UK.|Centre for Cancer Research and Cell Biology, Queens University Belfast, Belfast, BT9 7AE, UK.|Centre for Cancer Research and Cell Biology, Queens University Belfast, Belfast, BT9 7AE, UK.|Centre for Cancer Research and Cell Biology, Queens University Belfast, Belfast, BT9 7AE, UK.|Centre for Cancer Research and Cell Biology, Queens University Belfast, Belfast, BT9 7AE, UK. k.mills@qub.ac.uk.|Centre for Cancer Research and Cell Biology, Queens University Belfast, Belfast, BT9 7AE, UK. s.zhang@qub.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Chromosome Mapping|Drug Therapy|Gene Expression|Humans|Small Molecule Libraries|Software|United States|United States Food and Drug Administration|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1062-1', '27143038', 'Gene expression connectivity mapping has proven to be a powerful and flexible tool for research. Its application has been shown in a broad range of research topics, most commonly as a means of identifying potential small molecule compounds, which may be further investigated as candidates for repurposing to treat diseases. The public release of voluminous data from the Library of Integrated Cellular Signatures (LINCS) programme further enhanced the utilities and potentials of gene expression connectivity mapping in biomedicine.'),
(97267, 2016, 'bmc', 'readat: An R package for reading and working with SomaLogic ADAT files', 'Cotton, Richard J|Graumann, Johannes', 'Proteomics Core, Weill Cornell Medicine - Qatar, PO Box 24144, Doha, State of Qatar.|Proteomics Core, Weill Cornell Medicine - Qatar, PO Box 24144, Doha, State of Qatar. jog2030@qatar-med.cornell.edu.', 'Qatar|Qatar', 0, 'Adult|Aged|Chorionic Gonadotropin|Cohort Studies|Cross-Sectional Studies|Female|Follicle Stimulating Hormone|Humans|Internet|Male|Middle Aged|Prostate-Specific Antigen|Proteomics|Software', 'Evaluation Studies', '10.1186/s12859-016-1007-8', '27146037', 'SomaLogics SOMAscan™ assay platform allows the analysis of the relative abundance of over 1300 proteins directly from biological matrices such as blood plasma and serum. The data resulting from the assay is provided in a proprietary text-based format not easily imported into R.'),
(97268, 2016, 'bmc', 'A principal components method constrained by elementary flux modes: analysis of flux data sets', 'von Stosch, Moritz|Rodrigues de Azevedo, Cristiana|Luis, Mauro|Feyo de Azevedo, Sebastiao|Oliveira, Rui', 'REQUIMTE/DQ, Faculty of Science and Technology, University Nova de Lisboa, Campus de Caparica, 2829-516, Caparica, Portugal.|REQUIMTE/DQ, Faculty of Science and Technology, University Nova de Lisboa, Campus de Caparica, 2829-516, Caparica, Portugal.|REQUIMTE/DQ, Faculty of Science and Technology, University Nova de Lisboa, Campus de Caparica, 2829-516, Caparica, Portugal.|DEQ, Faculty of Engineering, University do Porto, Rua Dr. Roberto Frias s/n, 4200-465, Porto, Portugal.|REQUIMTE/DQ, Faculty of Science and Technology, University Nova de Lisboa, Campus de Caparica, 2829-516, Caparica, Portugal. rmo@fct.unl.pt.', 'Portugal|Portugal|Portugal|Portugal|Portugal', 2, 'Algorithms|Metabolic Networks and Pathways|Metabolomics|Models, Biological|Pichia|Principal Component Analysis|Saccharomyces cerevisiae', 'Evaluation Studies', '10.1186/s12859-016-1063-0', '27146133', 'Non-negative linear combinations of elementary flux modes (EMs) describe all feasible reaction flux distributions for a given metabolic network under the quasi steady state assumption. However, only a small subset of EMs contribute to the physiological state of a given cell.'),
(97269, 2016, 'bmc', 'DrugGenEx-Net: a novel computational platform for systems pharmacology and gene expression-based drug repurposing', 'Issa, Naiem T|Kruger, Jordan|Wathieu, Henri|Raja, Rajarajan|Byers, Stephen W|Dakshanamurthy, Sivanesan', 'Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC, 20057, USA.|Department of Biochemistry & Molecular Biology, Georgetown University, Washington DC, 20057, USA.|Georgetown University Medical Center, Washington DC, 20057, USA.|George Mason University, 4400 University Dr, Fairfax, VA, 22030, USA.|Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC, 20057, USA.|Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC, 20057, USA. sd233@georgetown.edu.', 'United States|United States|United States|United States|United States|United States', 9, 'Computational Biology|Databases as Topic|Disease|Drug Interactions|Drug Repositioning|Gene Expression Regulation|Humans|Protein Interaction Maps|Proteins|Reproducibility of Results|Software|Systems Biology', 'Journal Article', '10.1186/s12859-016-1065-y', '27151405', 'The targeting of disease-related proteins is important for drug discovery, and yet target-based discovery has not been fruitful. Contextualizing overall biological processes is critical to formulating successful drug-disease hypotheses. Network pharmacology helps to overcome target-based bottlenecks through systems biology analytics, such as protein-protein interaction (PPI) networks and pathway regulation.'),
(97270, 2016, 'bmc', 'DensityMap: a genome viewer for illustrating the densities of features', 'Guizard, Sébastien|Piégu, Benoît|Bigot, Yves', 'UMR INRA-CNRS 7247, PRC, Centre INRA Val de Loire, 37380, Nouzilly, France.|UMR INRA-CNRS 7247, PRC, Centre INRA Val de Loire, 37380, Nouzilly, France.|UMR INRA-CNRS 7247, PRC, Centre INRA Val de Loire, 37380, Nouzilly, France. yves.bigot@tours.inra.fr.', 'France|France|France', 0, 'Animals|Base Composition|Drosophila melanogaster|Exons|Genetic Linkage|Genome|Genomics|Long Interspersed Nucleotide Elements|RNA, Untranslated|Retroelements|Software', 'Journal Article', '10.1186/s12859-016-1055-0', '27153821', 'Several tools are available for visualizing genomic data. Some, such as Gbrowse and Jbrowse, are very efficient for small genomic regions, but they are not suitable for entire genomes. Others, like Phenogram and CViT, can be used to visualise whole genomes, but are not designed to display very dense genomic features (eg: interspersed repeats). We have therefore developed DensityMap, a lightweight Perl program that can display the densities of several features (genes, ncRNA, cpg, etc.) along chromosomes on the scale of the whole genome. A critical advantage of DensityMap is that it uses GFF annotation files directly to compute the densities of features without needing additional information from the user. The resulting picture is readily configurable, and the colour scales used can be customized for a best fit to the data plotted.'),
(97271, 2016, 'bmc', 'Parallel computation of genome-scale RNA secondary structure to detect structural constraints on human genome', 'Kawaguchi, Risa|Kiryu, Hisanori', 'Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan. kawaguchi-rs@cb.k.u-tokyo.ac.jp.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan.', 'Japan|Japan', 0, 'Animals|Area Under Curve|Base Sequence|Computational Biology|Computer Simulation|Gene Ontology|Genome, Human|Humans|Mice|Microfilament Proteins|Nucleic Acid Conformation|Propensity Score|RNA|RNA Precursors|RNA Splicing|RNA, Messenger|Reproducibility of Results|Software|Transcription, Genetic', 'Journal Article', '10.1186/s12859-016-1067-9', '27153986', 'RNA secondary structure around splice sites is known to assist normal splicing by promoting spliceosome recognition. However, analyzing the structural properties of entire intronic regions or pre-mRNA sequences has been difficult hitherto, owing to serious experimental and computational limitations, such as low read coverage and numerical problems.'),
(97272, 2016, 'bmc', 'Erratum to: Aro: a machine learning approach to identifying single molecules and estimating classification error in fluorescence microscopy images', 'Wu, Allison Chia-Yi|Rifkin, Scott A', 'Graduate Program in Bioinformatics and Systems Biology, University of California, San Diego, La Jolla, CA, USA.|Graduate Program in Bioinformatics and Systems Biology, University of California, San Diego, La Jolla, CA, USA. sarifkin@ucsd.edu.', 'United States|United States', 1, '-', 'Published Erratum', '10.1186/s12859-016-1049-y', '27161120', '-'),
(97273, 2016, 'bmc', 'SeqPurge: highly-sensitive adapter trimming for paired-end NGS data', 'Sturm, Marc|Schroeder, Christopher|Bauer, Peter', 'Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany. marc.sturm@med.uni-tuebingen.de.|Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany.|Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|High-Throughput Nucleotide Sequencing|Quality Control|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-016-1069-7', '27161244', 'Trimming of adapter sequences from short read data is a common preprocessing step during NGS data analysis. When performing paired-end sequencing, the overlap between forward and reverse read can be used to identify excess adapter sequences. This is exploited by several previously published adapter trimming tools. However, our evaluation on amplicon-based data shows that most of the current tools are not able to remove all adapter sequences and that adapter contamination may even lead to spurious variant calls.'),
(97274, 2016, 'bmc', 'A gene-signature progression approach to identifying candidate small-molecule cancer therapeutics with connectivity mapping', 'Wen, Qing|Kim, Chang-Sik|Hamilton, Peter W|Zhang, Shu-Dong', 'Centre for Cancer Research and Cell Biology (CCRCB), Queens University Belfast, Belfast, UK.|Centre for Cancer Research and Cell Biology (CCRCB), Queens University Belfast, Belfast, UK.|Centre for Cancer Research and Cell Biology (CCRCB), Queens University Belfast, Belfast, UK. P.Hamilton@qub.ac.uk.|Centre for Cancer Research and Cell Biology (CCRCB), Queens University Belfast, Belfast, UK. s.zhang@qub.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 6, 'Algorithms|Antineoplastic Agents|Breast Neoplasms|Carcinoma, Non-Small-Cell Lung|Computer Simulation|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Histone Deacetylase Inhibitors|Humans|Lung Neoplasms|Middle Aged|Small Molecule Libraries|Taiwan', 'Journal Article', '10.1186/s12859-016-1066-x', '27170106', 'Gene expression connectivity mapping has gained much popularity recently with a number of successful applications in biomedical research testifying its utility and promise. Previously methodological research in connectivity mapping mainly focused on two of the key components in the framework, namely, the reference gene expression profiles and the connectivity mapping algorithms. The other key component in this framework, the query gene signature, has been left to users to construct without much consensus on how this should be done, albeit it has been an issue most relevant to end users. As a key input to the connectivity mapping process, gene signature is crucially important in returning biologically meaningful and relevant results. This paper intends to formulate a standardized procedure for constructing high quality gene signatures from a users perspective.'),
(97275, 2016, 'bmc', 'Brain Radiation Information Data Exchange (BRIDE): integration of experimental data from low-dose ionising radiation research for pathway discovery', 'Karapiperis, Christos|Kempf, Stefan J|Quintens, Roel|Azimzadeh, Omid|Vidal, Victoria Linares|Pazzaglia, Simonetta|Bazyka, Dimitry|Mastroberardino, Pier G|Scouras, Zacharias G|Tapio, Soile|Benotmane, Mohammed Abderrafi|Ouzounis, Christos A', 'Department of Genetics, Development & Molecular Biology, School of Biology, Aristotle University of Thessalonica, 54124, Thessalonica, Greece.|Institute of Radiation Biology, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, 85764, Neuherberg, Germany.|Radiobiology Unit, Belgian Nuclear Research Centre (SCK•CEN), B-2400, Mol, Belgium.|Institute of Radiation Biology, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, 85764, Neuherberg, Germany.|School of Medicine, IISPV, Rovira i Virgili University, Sant Llorens 21, 43201, Reus, Spain.|Laboratory of Radiation Biology & Biomedicine, Agenzia Nazionale per le Nuove Tecnologie, lEnergia e lo Sviluppo Economico Sostenibile (ENEA) Centro Ricerche Casaccia, 00123, Rome, Italy.|National Research Center for Radiation Medicine of the National Academy of Medical Sciences of Ukraine, Melnykov str. 53, Kyiv, 04050, Ukraine.|Erasmus Medical Center, 3015GE, Rotterdam, The Netherlands.|Department of Genetics, Development & Molecular Biology, School of Biology, Aristotle University of Thessalonica, 54124, Thessalonica, Greece.|Institute of Radiation Biology, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, 85764, Neuherberg, Germany. soile.tapio@helmholtz-muenchen.de.|Radiobiology Unit, Belgian Nuclear Research Centre (SCK•CEN), B-2400, Mol, Belgium. abenotma@sckcen.be.|Department of Genetics, Development & Molecular Biology, School of Biology, Aristotle University of Thessalonica, 54124, Thessalonica, Greece. ouzounis@certh.gr.', 'Greece|Germany|Belgium|Germany|Spain|Italy|Ukraine|Netherlands|Greece|Germany|Belgium|Greece', 2, 'Brain|Dose-Response Relationship, Radiation|Humans|Radiation, Ionizing|Research|Software', 'Journal Article', '10.1186/s12859-016-1068-8', '27170263', 'The underlying molecular processes representing stress responses to low-dose ionising radiation (LDIR) in mammals are just beginning to be understood. In particular, LDIR effects on the brain and their possible association with neurodegenerative disease are currently being explored using omics technologies.'),
(97276, 2016, 'bmc', 'LimiTT: link miRNAs to targets', 'Bayer, Julia|Kuenne, Carsten|Preussner, Jens|Looso, Mario', 'Group of Bioinformatics, Max Planck Institute for Heart and Lung Research, Ludwigstrasse 43, D-61231, Bad Nauheim, Germany.|Group of Bioinformatics, Max Planck Institute for Heart and Lung Research, Ludwigstrasse 43, D-61231, Bad Nauheim, Germany.|Group of Bioinformatics, Max Planck Institute for Heart and Lung Research, Ludwigstrasse 43, D-61231, Bad Nauheim, Germany.|Group of Bioinformatics, Max Planck Institute for Heart and Lung Research, Ludwigstrasse 43, D-61231, Bad Nauheim, Germany. Mario.Looso@mpi-bn.mpg.de.', 'Germany|Germany|Germany|Germany', 0, 'Animals|Computer Simulation|Databases, Nucleic Acid|Gene Expression Regulation|MicroRNAs|Molecular Sequence Annotation|RNA, Messenger|Reproducibility of Results|Software', 'Journal Article', '10.1186/s12859-016-1070-1', '27170328', 'MicroRNAs (miRNAs) impact various biological processes within animals and plants. They complementarily bind target mRNAs, effecting a post-transcriptional negative regulation on mRNA level. The investigation of miRNA target interactions (MTIs) by high throughput screenings is challenging, as frequently used in silico target prediction tools are prone to emit false positives. This issue is aggravated for niche model organisms, where validated miRNAs and MTIs both have to be transferred from well described model organisms. Even though DBs exist that contain experimentally validated MTIs, they are limited in their search options and they utilize different miRNA and target identifiers.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97277, 2016, 'bmc', 'Joint analysis of histopathology image features and gene expression in breast cancer', 'Popovici, Vlad|Budinskxc3xa1, Eva|xc4x8cxc3xa1pkovxc3xa1, Lenka|Schwarz, Daniel|Duxc5xa1ek, Ladislav|Feit, Josef|Jaggi, Rolf', 'Institute of Biostatistics and Analyses, Faculty of Medicine, Masarykova Univerzita, Kamenice 5, Brno, 62500, Czech Republic. popovici@iba.muni.cz.|Institute of Biostatistics and Analyses, Faculty of Medicine, Masarykova Univerzita, Kamenice 5, Brno, 62500, Czech Republic.|Institute of Biostatistics and Analyses, Faculty of Medicine, Masarykova Univerzita, Kamenice 5, Brno, 62500, Czech Republic.|Institute of Biostatistics and Analyses, Faculty of Medicine, Masarykova Univerzita, Kamenice 5, Brno, 62500, Czech Republic.|Institute of Biostatistics and Analyses, Faculty of Medicine, Masarykova Univerzita, Kamenice 5, Brno, 62500, Czech Republic.|Institute of Biostatistics and Analyses, Faculty of Medicine, Masarykova Univerzita, Kamenice 5, Brno, 62500, Czech Republic.|Department of Clinical Research, Faculty of Medicine, University of Bern, Bern, Switzerland.', '-|-|-|-|-|-|Switzerland', 0, 'Breast Neoplasms|Cluster Analysis|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genomics|Humans|Image Processing, Computer-Assisted|Kaplan-Meier Estimate', 'Journal Article', '10.1186/s12859-016-1072-z', '27170365', 'Genomics and proteomics are nowadays the dominant techniques for novel biomarker discovery. However, histopathology images contain a wealth of information related to the tumor histology, morphology and tumor-host interactions that is not accessible through these techniques. Thus, integrating the histopathology images in the biomarker discovery workflow could potentially lead to the identification of new image-based biomarkers and the refinement or even replacement of the existing genomic and proteomic signatures. However, extracting meaningful and robust image features to be mined jointly with genomic (and clinical, etc.) data represents a real challenge due to the complexity of the images.'),
(97278, 2016, 'bmc', 'A novel procedure on next generation sequencing data analysis using text mining algorithm', 'Zhao, Weizhong|Chen, James J|Perkins, Roger|Wang, Yuping|Liu, Zhichao|Hong, Huixiao|Tong, Weida|Zou, Wen', 'Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA. wen.zou@fda.hhs.gov.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Biomarkers|Cluster Analysis|Data Mining|High-Throughput Nucleotide Sequencing|Models, Theoretical|Polymorphism, Single Nucleotide|Salmonella|Serotyping', 'Journal Article', '10.1186/s12859-016-1075-9', '27177941', 'Next-generation sequencing (NGS) technologies have provided researchers with vast possibilities in various biological and biomedical research areas. Efficient data mining strategies are in high demand for large scale comparative and evolutional studies to be performed on the large amounts of data derived from NGS projects. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining.'),
(97279, 2016, 'bmc', 'CINOEDV: a co-information based method for detecting and visualizing n-order epistatic interactions', 'Shang, Junliang|Sun, Yingxia|Liu, Jin-Xing|Xia, Junfeng|Zhang, Junying|Zheng, Chun-Hou', 'School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China. shangjunliang110@163.com.|School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China.|School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China.|Institute of Health Sciences, Anhui University, Hefei, Anhui, 230601, China.|School of Computer Science and Technology, Xidian University, Xian, 710071, China.|College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui, 230039, China.', 'China|China|China|China|China|China', 0, 'Algorithms|Computational Biology|Epistasis, Genetic|Genome-Wide Association Study|Humans|Macular Degeneration|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-016-1076-8', '27184783', 'Detecting and visualizing nonlinear interaction effects of single nucleotide polymorphisms (SNPs) or epistatic interactions are important topics in bioinformatics since they play an important role in unraveling the mystery of missing heritability. However, related studies are almost limited to pairwise epistatic interactions due to their methodological and computational challenges.'),
(97280, 2016, 'bmc', 'A comprehensive database of high-throughput sequencing-based RNA secondary structure probing data (Structure Surfer)', 'Berkowitz, Nathan D|Silverman, Ian M|Childress, Daniel M|Kazan, Hilal|Wang, Li-San|Gregory, Brian D', 'Department of Biology, University of Pennsylvania, 433 S. University Ave., Philadelphia, PA, 19104, USA.|Department of Biology, University of Pennsylvania, 433 S. University Ave., Philadelphia, PA, 19104, USA.|Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.|Department of Computer Engineering, Antalya International University, Antalya, Turkey.|Genomics and Computational Biology Graduate Group, Philadelphia, USA.|Department of Biology, University of Pennsylvania, 433 S. University Ave., Philadelphia, PA, 19104, USA. bdgregor@sas.upenn.edu.', 'United States|United States|United States|Turkey|United States|United States', 12, 'Animals|Databases, Factual|High-Throughput Nucleotide Sequencing|Humans|Mice|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1186/s12859-016-1071-0', '27188311', 'RNA molecules fold into complex three-dimensional shapes, guided by the pattern of hydrogen bonding between nucleotides. This pattern of base pairing, known as RNA secondary structure, is critical to their cellular function. Recently several diverse methods have been developed to assay RNA secondary structure on a transcriptome-wide scale using high-throughput sequencing. Each approach has its own strengths and caveats, however there is no widely available tool for visualizing and comparing the results from these varied methods.'),
(97281, 2016, 'bmc', 'RNA motif search with data-driven element ordering', 'Rampxc3xa1xc5xa1ek, Ladislav|Jimenez, Randi M|Luptxc3xa1k, Andrej|Vinaxc5x99, Tomxc3xa1xc5xa1|Brejovxc3xa1, Broxc5x88a', 'Department of Computer Science, University of Toronto, Toronto, M5R 3G4, ON, Canada.|Department of Pharmaceutical Sciences, Chemistry, and Molecular Biology and Biochemistry, University of California, Irvine, 2141 Natural Sciences 2, Irvine, 92697, CA, USA.|Department of Pharmaceutical Sciences, Chemistry, and Molecular Biology and Biochemistry, University of California, Irvine, 2141 Natural Sciences 2, Irvine, 92697, CA, USA. aluptak@uci.edu.|Faculty of Mathematics, Physics, and Informatics, Comenius University, Mlynskxc3xa1 dolina, Bratislava, 842 48, Slovakia.|Faculty of Mathematics, Physics, and Informatics, Comenius University, Mlynskxc3xa1 dolina, Bratislava, 842 48, Slovakia. brejova@dcs.fmph.uniba.sk.', 'Canada|United States|United States|Slovakia|Slovakia', 0, 'Algorithms|Entropy|Humans|Nucleotide Motifs|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-016-1074-x', '27188396', 'In this paper, we study the problem of RNA motif search in long genomic sequences. This approach uses a combination of sequence and structure constraints to uncover new distant homologs of known functional RNAs. The problem is NP-hard and is traditionally solved by backtracking algorithms.'),
(97282, 2016, 'bmc', 'ECL: an exhaustive search tool for the identification of cross-linked peptides using whole database', 'Yu, Fengchao|Li, Ning|Yu, Weichuan', 'Division of Biomedical Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.|Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong, China. boningli@ust.hk.|Division of Biomedical Engineering, The Hong Kong University of Science and Technology, Hong Kong, China. eeyu@ust.hk.', 'China|China|China', 0, 'Cross-Linking Reagents|Databases, Protein|Humans|Peptides|Search Engine', 'Journal Article', '10.1186/s12859-016-1073-y', '27206479', 'Chemical cross-linking combined with mass spectrometry (CX-MS) is a high-throughput approach to studying protein-protein interactions. The number of peptide-peptide combinations grows quadratically with respect to the number of proteins, resulting in a high computational complexity. Widely used methods including xQuest (Rinner et al., Nat Methods 5(4):315-8, 2008, Walzthoeni et al., Nat Methods 9(9):901-3, 2012), pLink (Yang et al., Nat Methods 9(9):904-6, 2012), ProteinProspector (Chu et al., Mol Cell Proteomics 9:25-31, 2010, Trnka et al., 13(2):420-34, 2014) and Kojak (Hoopmann et al., J Proteome Res 14(5):2190-198, 2015) avoid searching all peptide-peptide combinations by pre-selecting peptides with heuristic approaches. However, pre-selection procedures may cause missing findings. The most intuitive approach is searching all possible candidates. A tool that can exhaustively search a whole database without any heuristic pre-selection procedure is therefore desirable.'),
(97283, 2016, 'bmc', 'Efficient analysis of large datasets and sex bias with ADMIXTURE', 'Shringarpure, Suyash S|Bustamante, Carlos D|Lange, Kenneth|Alexander, David H', 'Department of Genetics, Stanford University, Stanford, California, USA. suyashs@stanford.edu.|Department of Genetics, Stanford University, Stanford, California, USA.|Department of Biomathematics, UCLA, Los Angeles, California, USA.|Pacific Biosciences, Menlo Park, California, USA.', 'United States|United States|United States|United States', 0, 'African Americans|Female|Gene Frequency|Genetics, Population|Genomics|HapMap Project|Humans|Male|Software|Southwestern United States', 'Journal Article', '10.1186/s12859-016-1082-x', '27216439', 'A number of large genomic datasets are being generated for studies of human ancestry and diseases. The ADMIXTURE program is commonly used to infer individual ancestry from genomic data.'),
(97284, 2016, 'bmc', 'Discovering causal interactions using Bayesian network scoring and information gain', 'Zeng, Zexian|Jiang, Xia|Neapolitan, Richard', 'Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. richard.neapolitan@northwestern.edu.', 'United States|United States|United States', 20, 'Bayes Theorem|Breast Neoplasms|Databases, Genetic|Female|Genome-Wide Association Study|Humans', 'Journal Article', '10.1186/s12859-016-1084-8', '27230078', 'The problem of learning causal influences from data has recently attracted much attention. Standard statistical methods can have difficulty learning discrete causes, which interacting to affect a target, because the assumptions in these methods often do not model discrete causal relationships well. An important task then is to learn such interactions from data. Motivated by the problem of learning epistatic interactions from datasets developed in genome-wide association studies (GWAS), researchers conceived new methods for learning discrete interactions. However, many of these methods do not differentiate a model representing a true interaction from a model representing non-interacting causes with strong individual affects. The recent algorithm MBS-IGain addresses this difficulty by using Bayesian network learning and information gain to discover interactions from high-dimensional datasets. However, MBS-IGain requires marginal effects to detect interactions containing more than two causes. If the dataset is not high-dimensional, we can avoid this shortcoming by doing an exhaustive search.'),
(97285, 2016, 'bmc', 'Identification of recurrent focal copy number variations and their putative targeted driver genes in ovarian cancer', 'Zhang, Liangcai|Yuan, Ying|Lu, Karen H|Zhang, Li', 'Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1410, Houston, TX, 77401, USA.|Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1410, Houston, TX, 77401, USA.|Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1410, Houston, TX, 77401, USA. lzhangli@mdanderson.org.', 'United States|United States|United States|United States', 7, 'DNA Copy Number Variations|Female|Genomics|Humans|Oncogenes|Ovarian Neoplasms', 'Journal Article', '10.1186/s12859-016-1085-7', '27230211', 'Genomic regions with recurrent DNA copy number variations (CNVs) are generally believed to encode oncogenes and tumor suppressor genes (TSGs) that drive cancer growth. However, it remains a challenge to delineate the key cancer driver genes from the regions encoding a large number of genes.'),
(97286, 2016, 'bmc', 'Crowdsourcing the nodulation gene network discovery environment', 'Li, Yupeng|Jackson, Scott A', 'Center for Applied Genetic Technologies, University of Georgia, 111 Riverbend Road, Athens, 30602, GA, USA.|Center for Applied Genetic Technologies, University of Georgia, 111 Riverbend Road, Athens, 30602, GA, USA. sjackson@uga.edu.', 'Georgia|Georgia', 1, 'Crowdsourcing|Gene Regulatory Networks|Humans|Plant Root Nodulation|Symbiosis', 'Journal Article', '10.1186/s12859-016-1089-3', '27230384', 'The Legumes (Fabaceae) are an economically and ecologically important group of plant species with the conspicuous capacity for symbiotic nitrogen fixation in root nodules, specialized plant organs containing symbiotic microbes. With the aim of understanding the underlying molecular mechanisms leading to nodulation, many efforts are underway to identify nodulation-related genes and determine how these genes interact with each other. In order to accurately and efficiently reconstruct nodulation gene network, a crowdsourcing platform, CrowdNodNet, was created.'),
(97287, 2016, 'bmc', 'Automatic learning of pre-miRNAs from different species', 'O N Lopes, Ivani de|Schliep, Alexander|de L F de Carvalho, André P', 'Empresa Brasileira de Pesquisa Agropecuária, Embrapa Soja, Caixa Postal 231, Londrina-PR, 86001-970, CEP, Brasil. ivani.negrao@embrapa.br.|Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, Piscataway, 08854, NJ, USA.|Instituto de Ciências Matemáticas e de Computação, Avenida Trabalhador são-carlense, 400 - Centro, São Carlos SP, Brasil.', 'United States|United States|United States', 4, 'Algorithms|Animals|Computational Biology|Humans|MicroRNAs|RNA Precursors|Software|Species Specificity', 'Journal Article', '10.1186/s12859-016-1036-3', '27233515', 'Discovery of microRNAs (miRNAs) relies on predictive models for characteristic features from miRNA precursors (pre-miRNAs). The short length of miRNA genes and the lack of pronounced sequence features complicate this task. To accommodate the peculiarities of plant and animal miRNAs systems, tools for both systems have evolved differently. However, these tools are biased towards the species for which they were primarily developed and, consequently, their predictive performance on data sets from other species of the same kingdom might be lower. While these biases are intrinsic to the species, their characterization can lead to computational approaches capable of diminishing their negative effect on the accuracy of pre-miRNAs predictive models. We investigate in this study how 45 predictive models induced for data sets from 45 species, distributed in eight subphyla/classes, perform when applied to a species different from the species used in its induction.'),
(97288, 2016, 'bmc', 'Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes', 'Zhang, Lina|Zhang, Chengjin|Gao, Rui|Yang, Runtao|Song, Qing', 'School of Control Science and Engineering, Shandong University, Jingshi Road No.17923, Jinan, 250061, China.|School of Control Science and Engineering, Shandong University, Jingshi Road No.17923, Jinan, 250061, China. cjzhang@sdu.edu.cn.|School of Control Science and Engineering, Shandong University, Jingshi Road No.17923, Jinan, 250061, China.|School of Control Science and Engineering, Shandong University, Jingshi Road No.17923, Jinan, 250061, China.|School of Electrical Engineering, University of Jinan, Nanxinzhuangxi Road No.336, Jinan, 250022, China.', 'China|China|China|China|China', 12, 'Amino Acid Sequence|Aptamers, Peptide|Humans|Models, Molecular|Proteins|SELEX Aptamer Technique', 'Journal Article', '10.1186/s12859-016-1087-5', '27245069', 'Aptamer-protein interacting pairs play a variety of physiological functions and therapeutic potentials in organisms. Rapidly and effectively predicting aptamer-protein interacting pairs is significant to design aptamers binding to certain interested proteins, which will give insight into understanding mechanisms of aptamer-protein interacting pairs and developing aptamer-based therapies.'),
(97289, 2016, 'bmc', 'Are orphan genes protein-coding, prediction artifacts, or non-coding RNAs?', 'Prabh, Neel|Rödelsperger, Christian', 'Department for Evolutionary Biology, Max-Planck-Institute for Developmental Biology, Spemannstrasse 35, 72076, Tübingen, Germany.|Department for Evolutionary Biology, Max-Planck-Institute for Developmental Biology, Spemannstrasse 35, 72076, Tübingen, Germany. christian.roedelsperger@tuebingen.mpg.de.', 'Germany|Germany', 19, 'Animals|Gene Expression|Humans|Multigene Family|Nematoda|Proteomics|RNA, Untranslated|Transcription, Genetic', 'Journal Article', '10.1186/s12859-016-1102-x', '27245157', 'Current genome sequencing projects reveal substantial numbers of taxonomically restricted, so called orphan genes that lack homology with genes from other evolutionary lineages. However, it is not clear to what extent orphan genes are real, genomic artifacts, or represent non-coding RNAs.'),
(97290, 2016, 'bmc', 'Automated prostate tissue referencing for cancer detection and diagnosis', 'Kwak, Jin Tae|Hewitt, Stephen M|Kajdacsy-Balla, Andrxc3xa9 Alexander|Sinha, Saurabh|Bhargava, Rohit', 'Department of Computer Science and Engineering, Sejong University, Seoul, 05006, Korea.|Tissue Array Research Program, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20850, USA.|Department of Pathology, University of Illinois at Chicago, Chicago, IL, 60612, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, 2122 Siebel Center, 201xc2xa0N. Goodwin Avenue, Urbana, IL, 61801, USA. sinhas@illinois.edu.|Beckman Institute for Advanced Science and Technology, Department of Bioengineering, Department of Mechanical Science and Engineering, Electrical and Computer Engineering, Chemical and Biomolecular Engineering and University of Illinois Cancer Center, University of Illinois at Urbana-Champaign, 4265 Beckman Institute 405xc2xa0N. Mathews Avenue, Urbana, IL, 61801, USA. rxb@illinois.edu.', 'United States|United States|United States|United States|United States', 15, 'Automation|Databases, Factual|Humans|Male|Neoplasm Grading|Prostate|Prostatic Neoplasms|Reproducibility of Results', 'Journal Article', '10.1186/s12859-016-1086-6', '27247129', 'The current practice of histopathology review is limited in speed and accuracy. The current diagnostic paradigm does not fully describe the complex and complicated patterns of cancer. To address these needs, we develop an automated and objective system that facilitates a comprehensive and easy information management and decision-making. We also develop a tissue similarity measure scheme to broaden our understanding of tissue characteristics.'),
(97291, 2016, 'bmc', 'SUSHI: an exquisite recipe for fully documented, reproducible and reusable NGS data analysis', 'Hatakeyama, Masaomi|Opitz, Lennart|Russo, Giancarlo|Qi, Weihong|Schlapbach, Ralph|Rehrauer, Hubert', 'Functional Genomics Center Zurich, ETH Zurich/University of Zurich, Winterthurerstrasse. 190, 8057, Zurich, Switzerland.|Functional Genomics Center Zurich, ETH Zurich/University of Zurich, Winterthurerstrasse. 190, 8057, Zurich, Switzerland.|Functional Genomics Center Zurich, ETH Zurich/University of Zurich, Winterthurerstrasse. 190, 8057, Zurich, Switzerland.|Functional Genomics Center Zurich, ETH Zurich/University of Zurich, Winterthurerstrasse. 190, 8057, Zurich, Switzerland.|Functional Genomics Center Zurich, ETH Zurich/University of Zurich, Winterthurerstrasse. 190, 8057, Zurich, Switzerland.|Functional Genomics Center Zurich, ETH Zurich/University of Zurich, Winterthurerstrasse. 190, 8057, Zurich, Switzerland. hubert.rehrauer@fgcz.ethz.ch.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 17, 'Computational Biology|High-Throughput Nucleotide Sequencing|Reproducibility of Results|Software|Statistics as Topic', 'Evaluation Studies', '10.1186/s12859-016-1104-8', '27255077', 'Next generation sequencing (NGS) produces massive datasets consisting of billions of reads and up to thousands of samples. Subsequent bioinformatic analysis is typically done with the help of open source tools, where each application performs a single step towards the final result. This situation leaves the bioinformaticians with the tasks to combine the tools, manage the data files and meta-information, document the analysis, and ensure reproducibility.'),
(97292, 2016, 'bmc', 'Bagging survival tree procedure for variable selection and prediction in the presence of nonsusceptible patients', 'Mbogning, Cyprien|Broët, Philippe', 'Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, INSERM, 14-16 Avenue Paul-Vaillant Couturier, Villejuif, 94807, France. cyprien.mbogning@inserm.fr.|Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, INSERM, 14-16 Avenue Paul-Vaillant Couturier, Villejuif, 94807, France.', 'France|France', 2, 'Breast Neoplasms|Computer Simulation|Disease Susceptibility|Female|Humans|Kaplan-Meier Estimate|Software', 'Journal Article', '10.1186/s12859-016-1090-x', '27266372', 'For clinical genomic studies with high-dimensional datasets, tree-based ensemble methods offer a powerful solution for variable selection and prediction taking into account the complex interrelationships between explanatory variables. One of the key component of the tree-building process is the splitting criterion. For survival data, the classical splitting criterion is the Logrank statistic. However, the presence of a fraction of nonsusceptible patients in the studied population advocates for considering a criterion tailored to this peculiar situation.'),
(97293, 2016, 'bmc', 'Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors', 'Sun, Meijian|Wang, Xia|Zou, Chuanxin|He, Zenghui|Liu, Wei|Li, Honglin', 'State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai, 200237, China.|State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai, 200237, China.|State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai, 200237, China.|State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai, 200237, China.|State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai, 200237, China.|State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai, 200237, China. hlli@ecust.edu.cn.', 'China|China|China|China|China|China', 12, 'Algorithms|Amino Acids|Binding Sites|Computational Biology|Databases, Protein|Models, Molecular|Nucleic Acid Conformation|RNA|RNA-Binding Proteins|ROC Curve|Reproducibility of Results|Static Electricity', 'Journal Article', '10.1186/s12859-016-1110-x', '27266516', 'RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers.'),
(97294, 2016, 'bmc', 'SCOUP: a probabilistic model based on the Ornstein-Uhlenbeck process to analyze single-cell expression data during differentiation', 'Matsumoto, Hirotaka|Kiryu, Hisanori', 'Bioinformatics Research Unit, Advanced Center for Computing and Communication, RIKEN, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan. hirotaka.matsumoto@riken.jp.|Department of Computational Biology and Medical Sciences, Faculty of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan.', 'Japan|Japan', 16, 'Algorithms|Area Under Curve|Cell Differentiation|Cell Line|Cell Lineage|Cluster Analysis|Gene Expression Regulation|Gene Ontology|Gene Regulatory Networks|Humans|Models, Statistical|Principal Component Analysis|Reproducibility of Results|Single-Cell Analysis|Stochastic Processes|Time Factors|Transcription Factors', 'Journal Article', '10.1186/s12859-016-1109-3', '27277014', 'Single-cell technologies make it possible to quantify the comprehensive states of individual cells, and have the power to shed light on cellular differentiation in particular. Although several methods have been developed to fully analyze the single-cell expression data, there is still room for improvement in the analysis of differentiation.'),
(97295, 2016, 'bmc', 'tarSVM: Improving the accuracy of variant calls derived from microfluidic PCR-based targeted next generation sequencing using a support vector machine', 'Gillies, Christopher E|Otto, Edgar A|Vega-Warner, Virginia|Robertson, Catherine C|Sanna-Cherchi, Simone|Gharavi, Ali|Crawford, Brendan|Bhimma, Rajendra|Winkler, Cheryl|Nephrotic Syndrome Study Network (NEPTUNE)|C-PROBE InvestigatorGroup of the Michigan Kidney Translational Core Center|Kang, Hyun Min|Sampson, Matthew G', 'Department of Pediatrics-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI, USA.|Department of Internal Medicine-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI, USA.|Department of Pediatrics-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI, USA.|Department of Pediatrics-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI, USA.|Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY, USA.|Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY, USA.|Department of Pediatrics-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI, USA.|Department of Paediatrics and Child Health, University of KwaZulu Natal, Durban, South Africa.|NCI, Frederick National Lab for Cancer Research, Molecular Genetics Epidemiology Section, Frederick, MD, USA.|NCI, Frederick National Lab for Cancer Research, Molecular Genetics Epidemiology Section, Frederick, MD, USA.|NCI, Frederick National Lab for Cancer Research, Molecular Genetics Epidemiology Section, Frederick, MD, USA.|Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.|Department of Pediatrics-Nephrology, University of Michigan School of Medicine, Ann Arbor, MI, USA. mgsamps@med.umich.edu.', 'United States|United States|United States|United States|United States|United States|United States|South Africa|United States|United States|United States|United States|United States', 0, 'Alleles|Data Accuracy|High-Throughput Nucleotide Sequencing|Humans|Microfluidics|Polymerase Chain Reaction|Sequence Analysis, DNA|Software|Support Vector Machine', 'Journal Article', '10.1186/s12859-016-1108-4', '27287006', 'Targeted sequencing of discrete gene sets is a cost effective strategy to screen subjects for monogenic forms of disease. One method to achieve this pairs microfluidic PCR with next generation sequencing. The PCR step of this pipeline creates challenges in accurate variant calling. This includes that most reads targeting a specific exon are duplicates that have been amplified from the PCR step. To reduce false positive variant calls from these experiments, previous studies have used threshold-based filtering of alternative allele depth ratio and manual inspection of the alignments. However even after manual inspection and filtering, many variants fail to be validated via Sanger sequencing. To improve the accuracy of variant calling from these experiments, we are challenged to design a variant filtering strategy that sufficiently models microfluidic PCR-specific issues.'),
(97296, 2016, 'bmc', 'Inferring differentially expressed pathways using kernel maximum mean discrepancy-based test', 'Vegas, Esteban|Oller, Josep M|Reverter, Ferran', 'Department of Statistics, University of Barcelona, Diagonal, 643, Barcelona, 08028, Spain. evegas@ub.edu.|Department of Statistics, University of Barcelona, Diagonal, 643, Barcelona, 08028, Spain.|Department of Statistics, University of Barcelona, Diagonal, 643, Barcelona, 08028, Spain.', 'Spain|Spain|Spain', 3, 'Algorithms|Animals|Computational Biology|Diet|Fatty Acids|Gene Expression|Genomics|Liver|Metabolomics|Mice|Mice, Knockout|Plant Oils|Sunflower Oil', 'Journal Article', '10.1186/s12859-016-1046-1', '27294256', 'Pathway expression is multivariate in nature. Thus, from a statistical perspective, to detect differentially expressed pathways between two conditions, methods for inferring differences between mean vectors need to be applied. Maximum mean discrepancy (MMD) is a statistical test to determine whether two samples are from the same distribution, its implementation being greatly simplified using the kernel method.'),
(97297, 2016, 'bmc', 'Analysis tools for the interplay between genome layout and regulation', 'Bouyioukos, Costas|Elati, Mohamed|Képès, François', 'institute of Systems and Synthetic Biology (iSSB), Genopole, CNRS, Université dÉvry Val dEssonne, Évry, France.|institute of Systems and Synthetic Biology (iSSB), Genopole, CNRS, Université dÉvry Val dEssonne, Évry, France.|institute of Systems and Synthetic Biology (iSSB), Genopole, CNRS, Université dÉvry Val dEssonne, Évry, France. francois.kepes@issb.genopole.fr.', 'France|France|France', 1, 'Binding Sites|Escherichia coli|Escherichia coli Proteins|Gene Regulatory Networks|Genome|Software|Transcription Factors', 'Journal Article', '10.1186/s12859-016-1047-0', '27294345', 'Genome layout and gene regulation appear to be interdependent. Understanding this interdependence is key to exploring the dynamic nature of chromosome conformation and to engineering functional genomes. Evidence for non-random genome layout, defined as the relative positioning of either co-functional or co-regulated genes, stems from two main approaches. Firstly, the analysis of contiguous genome segments across species, has highlighted the conservation of gene arrangement (synteny) along chromosomal regions. Secondly, the study of long-range interactions along a chromosome has emphasised regularities in the positioning of microbial genes that are co-regulated, co-expressed or evolutionarily correlated. While one-dimensional pattern analysis is a mature field, it is often powerless on biological datasets which tend to be incomplete, and partly incorrect. Moreover, there is a lack of comprehensive, user-friendly tools to systematically analyse, visualise, integrate and exploit regularities along genomes.'),
(97298, 2016, 'bmc', 'Separating common from distinctive variation', 'van der Kloet, Frans M|Sebastián-León, Patricia|Conesa, Ana|Smilde, Age K|Westerhuis, Johan A', 'Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098, XH, Amsterdam, The Netherlands.|Computational Genomics Program, Centro de Investigaciones Príncipe Felipe, Valencia, Spain.|Computational Genomics Program, Centro de Investigaciones Príncipe Felipe, Valencia, Spain.|Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098, XH, Amsterdam, The Netherlands.|Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098, XH, Amsterdam, The Netherlands. j.a.westerhuis@uva.nl.', 'Netherlands|Spain|Spain|Netherlands|Netherlands', 0, 'Algorithms|Brain Neoplasms|Glioblastoma|Humans|MicroRNAs|Principal Component Analysis|RNA, Messenger', 'Journal Article', '10.1186/s12859-016-1037-2', '27294690', 'Joint and individual variation explained (JIVE), distinct and common simultaneous component analysis (DISCO) and O2-PLS, a two-block (X-Y) latent variable regression method with an integral OSC filter can all be used for the integrated analysis of multiple data sets and decompose them in three terms: a low(er)-rank approximation capturing common variation across data sets, low(er)-rank approximations for structured variation distinctive for each data set, and residual noise. In this paper these three methods are compared with respect to their mathematical properties and their respective ways of defining common and distinctive variation.'),
(97299, 2016, 'bmc', 'A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactions', 'Lagani, Vincenzo|Karozou, Argyro D|Gomez-Cabrero, David|Silberberg, Gilad|Tsamardinos, Ioannis', 'Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Greece.|Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Greece.|Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, 171 77, Stockholm, Sweden.|Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, 171 77, Stockholm, Sweden.|Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Greece. tsamard@csd.uoc.gr.', 'Greece|Greece|Sweden|Sweden|Greece', 5, 'Algorithms|Area Under Curve|Escherichia coli|Gene Regulatory Networks|Humans|Leukocytes, Mononuclear|Meta-Analysis as Topic|ROC Curve|Saccharomyces cerevisiae', 'Journal Article', '10.1186/s12859-016-1038-1', '27294826', 'We address the problem of integratively analyzing multiple gene expression, microarray datasets in order to reconstruct gene-gene interaction networks. Integrating multiple datasets is generally believed to provide increased statistical power and to lead to a better characterization of the system under study. However, the presence of systematic variation across different studies makes network reverse-engineering tasks particularly challenging. We contrast two approaches that have been frequently used in the literature for addressing systematic biases: meta-analysis methods, which first calculate opportune statistics on single datasets and successively summarize them, and data-merging methods, which directly analyze the pooled data after removing eventual biases. This comparative evaluation is performed on both synthetic and real data, the latter consisting of two manually curated microarray compendia comprising several E. coli and Yeast studies, respectively. Furthermore, the reconstruction of the regulatory network of the transcription factor Ikaros in human Peripheral Blood Mononuclear Cells (PBMCs) is presented as a case-study.'),
(97300, 2016, 'bmc', 'Learning mixed graphical models with separate sparsity parameters and stability-based model selection', 'Sedgewick, Andrew J|Shi, Ivy|Donovan, Rory M|Benos, Panayiotis V', 'Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. ajs206@pitt.edu.|Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.', 'United States|United States|United States|United States', 22, 'Bayes Theorem|Humans|Lung Diseases|Models, Theoretical|Pulmonary Disease, Chronic Obstructive|Support Vector Machine|Transcriptome', 'Journal Article', '10.1186/s12859-016-1039-0', '27294886', 'Mixed graphical models (MGMs) are graphical models learned over a combination of continuous and discrete variables. Mixed variable types are common in biomedical datasets. MGMs consist of a parameterized joint probability density, which implies a network structure over these heterogeneous variables. The network structure reveals direct associations between the variables and the joint probability density allows one to ask arbitrary probabilistic questions on the data. This information can be used for feature selection, classification and other important tasks.'),
(97301, 2016, 'bmc', 'Inferring active regulatory networks from gene expression data using a combination of prior knowledge and enrichment analysis', 'Chouvardas, Panagiotis|Kollias, George|Nikolaou, Christoforos', 'Biomedical Sciences Research Center Alexander Fleming, Vari, 16672, Greece.|Division of Immunology, Biomedical Sciences Research Center Alexander Fleming, Vari, 16672, Greece.|Biomedical Sciences Research Center Alexander Fleming, Vari, 16672, Greece. cnikolaou@biology.uoc.gr.', 'Greece|Greece|Greece', 0, 'Animals|Gene Expression Profiling|Gene Regulatory Networks|Genome|Humans|Internet|Mice|MicroRNAs|Proteins|Transcriptome|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1040-7', '27295045', 'Under both physiological and pathological conditions gene expression programs are shaped through the interplay of regulatory proteins and their gene targets, interactions between which form intricate gene regulatory networks (GRN). While the assessment of genome-wide expression for the complete set of genes at a given condition has become rather straight-forward and is performed routinely, we are still far from being able to infer the topology of gene regulation simply by analyzing its descendant expression profile. In this work we are trying to overcome the existing limitations for the inference and study of such regulatory networks. We are combining our approach with state-of-the-art gene set enrichment analyses in order to create a tool, called Regulatory Network Enrichment Analysis (RNEA) that will prioritize regulatory and functional characteristics of a genome-wide expression experiment.'),
(97302, 2016, 'bmc', 'DrugQuest - a text mining workflow for drug association discovery', 'Papanikolaou, Nikolas|Pavlopoulos, Georgios A|Theodosiou, Theodosios|Vizirianakis, Ioannis S|Iliopoulos, Ioannis', 'Division of Basic Sciences, University of Crete, Medical School, Gouves, 71003, Heraklion, Crete, Greece.|Division of Basic Sciences, University of Crete, Medical School, Gouves, 71003, Heraklion, Crete, Greece.|Division of Basic Sciences, University of Crete, Medical School, Gouves, 71003, Heraklion, Crete, Greece.|School of Pharmacy, Laboratory of Pharmacology, Aristotle University of Thessaloniki, University Campus, 54124, Thessaloniki, Greece.|Division of Basic Sciences, University of Crete, Medical School, Gouves, 71003, Heraklion, Crete, Greece. iliopj@med.uoc.gr.', 'Greece|Greece|Greece|Greece|Greece', 14, 'Algorithms|Cluster Analysis|Databases, Factual|Drug Discovery|Humans|Internet|Pharmaceutical Preparations|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1041-6', '27295093', 'Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases.'),
(97303, 2016, 'bmc', 'Identifying peaks in *-seq data using shape information', 'Strino, Francesco|Lappe, Michael', 'Qiagen Aarhus, Silkeborgvej 2, Aarhus, 8000, DK, Denmark.|Qiagen Aarhus, Silkeborgvej 2, Aarhus, 8000, DK, Denmark. michael.lappe@qiagen.com.', 'Denmark|Denmark', 0, 'Algorithms|Area Under Curve|Chromatin Immunoprecipitation|Databases, Genetic|Genomics|Humans|Internet|ROC Curve|Sequence Analysis, DNA|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1042-5', '27295177', 'Peak calling is a fundamental step in the analysis of data generated by ChIP-seq or similar techniques to acquire epigenetics information. Current peak callers are often hard to parameterise and may therefore be difficult to use for non-bioinformaticians. In this paper, we present the ChIP-seq analysis tool available in CLC Genomics Workbench and CLC Genomics Server (version 7.5 and up), a user-friendly peak-caller designed to be not specific to a particular *-seq protocol.'),
(97304, 2016, 'bmc', 'Integration of multi-omics data for prediction of phenotypic traits using random forest', 'Acharjee, Animesh|Kloosterman, Bjorn|Visser, Richard G F|Maliepaard, Chris', 'Wageningen UR Plant Breeding, Wageningen University & Research Centre, PO Box 6700 AJ, Wageningen, The Netherlands.|Wageningen UR Plant Breeding, Wageningen University & Research Centre, PO Box 6700 AJ, Wageningen, The Netherlands.|Wageningen UR Plant Breeding, Wageningen University & Research Centre, PO Box 6700 AJ, Wageningen, The Netherlands.|Wageningen UR Plant Breeding, Wageningen University & Research Centre, PO Box 6700 AJ, Wageningen, The Netherlands. chris.maliepaard@wur.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands', 31, 'Chromatography, High Pressure Liquid|Chromosomes, Plant|Gas Chromatography-Mass Spectrometry|Gene Expression Regulation, Plant|Genomics|Mass Spectrometry|Metabolomics|Phenotype|Plant Proteins|Plant Tubers|Proteomics|Quantitative Trait Loci|Solanum tuberosum', 'Journal Article', '10.1186/s12859-016-1043-4', '27295212', 'In order to find genetic and metabolic pathways related to phenotypic traits of interest, we analyzed gene expression data, metabolite data obtained with GC-MS and LC-MS, proteomics data and a selected set of tuber quality phenotypic data from a diploid segregating mapping population of potato. In this study we present an approach to integrate thesexe2x80x89~xe2x80x89omics data sets for the purpose of predicting phenotypic traits. This gives us networks of relatively small sets of interrelatedxe2x80x89~xe2x80x89omics variables that can predict, with higher accuracy, a quality trait of interest.'),
(97305, 2016, 'bmc', 'Integrating multiple immunogenetic data sources for feature extraction and mining somatic hypermutation patterns: the case of towards analysis in chronic lymphocytic leukaemia', 'Kavakiotis, Ioannis|Xochelli, Aliki|Agathangelidis, Andreas|Tsoumakas, Grigorios|Maglaveras, Nicos|Stamatopoulos, Kostas|Hadzidimitriou, Anastasia|Vlahavas, Ioannis|Chouvarda, Ioanna', 'Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece. ikavak@csd.auth.gr.|Institute of Applied Biosciences, CERTH, Thessaloniki, Greece.|Division of Molecular Oncology and Department of Onco-Hematology, San Raffaele Scientific Institute, Milan, Italy.|Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.|Institute of Applied Biosciences, CERTH, Thessaloniki, Greece.|Institute of Applied Biosciences, CERTH, Thessaloniki, Greece.|Institute of Applied Biosciences, CERTH, Thessaloniki, Greece.|Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.|Institute of Applied Biosciences, CERTH, Thessaloniki, Greece.', 'Greece|Greece|Italy|Greece|Greece|Greece|Greece|Greece|Greece', 1, 'Adult|Databases, Genetic|Female|Germ-Line Mutation|Humans|Immunogenetics|Immunoglobulin Variable Region|Immunoglobulins|Leukemia, Lymphocytic, Chronic, B-Cell|Somatic Hypermutation, Immunoglobulin', 'Journal Article', '10.1186/s12859-016-1044-3', '27295298', 'Somatic Hypermutation (SHM) refers to the introduction of mutations within rearranged V(D)J genes, a process that increases the diversity of Immunoglobulins (IGs). The analysis of SHM has offered critical insight into the physiology and pathology of B cells, leading to strong prognostication markers for clinical outcome in chronic lymphocytic leukaemia (CLL), the most frequent adult B-cell malignancy. In this paper we present a methodology for integrating multiple immunogenetic and clinocobiological data sources in order to extract features and create high quality datasets for SHM analysis in IG receptors of CLL patients. This dataset is used as the basis for a higher level integration procedure, inspired form social choice theory. This is applied in the Towards Analysis, our attempt to investigate the potential ontogenetic transformation of genes belonging to specific stereotyped CLL subsets towards other genes or gene families, through SHM.'),
(97306, 2016, 'bmc', 'Network and biosignature analysis for the integration of transcriptomic and metabolomic data to characterize leaf senescence process in sunflower', 'Moschen, Sebastián|Higgins, Janet|Di Rienzo, Julio A|Heinz, Ruth A|Paniego, Norma|Fernandez, Paula', 'Instituto de Biotecnología, Centro de Investigaciones en Ciencias Agronómicas y Veterinarias, Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Buenos Aires, Argentina.|The Genome Analysis Centre, Norwich Research Park, Norwich, NR4 7UH, UK.|Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba, Córdoba, Argentina.|Instituto de Biotecnología, Centro de Investigaciones en Ciencias Agronómicas y Veterinarias, Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Buenos Aires, Argentina.|Instituto de Biotecnología, Centro de Investigaciones en Ciencias Agronómicas y Veterinarias, Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Buenos Aires, Argentina.|Instituto de Biotecnología, Centro de Investigaciones en Ciencias Agronómicas y Veterinarias, Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Buenos Aires, Argentina. fernandez.pc@inta.gob.ar.', 'Argentina|United Kingdom|Argentina|Argentina|Argentina|Argentina', 18, 'Gene Expression Regulation, Plant|Gene Regulatory Networks|Genomics|Helianthus|Metabolomics|Plant Leaves|Plant Proteins|Signal Transduction|Time Factors|Transcription Factors', 'Journal Article', '10.1186/s12859-016-1045-2', '27295368', 'In recent years, high throughput technologies have led to an increase of datasets from omics disciplines allowing the understanding of the complex regulatory networks associated with biological processes. Leaf senescence is a complex mechanism controlled by multiple genetic and environmental variables, which has a strong impact on crop yield. Transcription factors (TFs) are key proteins in the regulation of gene expression, regulating different signaling pathways, their function is crucial for triggering and/or regulating different aspects of the leaf senescence process. The study of TF interactions and their integration with metabolic profiles under different developmental conditions, especially for a non-model organism such as sunflower, will open new insights into the details of gene regulation of leaf senescence.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97307, 2016, 'bmc', 'Classification of polyhedral shapes from individual anisotropically resolved cryo-electron tomography reconstructions', 'Bag, Sukantadev|Prentice, Michael B|Liang, Mingzhi|Warren, Martin J|Roy Choudhury, Kingshuk', 'Statistics Department, University College Cork, Cork, Ireland.|Department of Microbiology, University College Cork, Cork, Ireland.|School of Biosciences, University of Kent, Canterbury, UK.|School of Biosciences, University of Kent, Canterbury, UK.|Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, 27705, USA. kingshuk@duke.edu.', 'Ireland|Ireland|United Kingdom|United Kingdom|United Kingdom', 1, 'Anisotropy|Bayes Theorem|Cryoelectron Microscopy|Electron Microscope Tomography|Escherichia coli|Image Processing, Computer-Assisted|Imaging, Three-Dimensional', 'Journal Article', '10.1186/s12859-016-1107-5', '27296169', 'Cryo-electron tomography (cryo-ET) enables 3D imaging of macromolecular structures. Reconstructed cryo-ET images have a missing wedge of data loss due to limitations in rotation of the mounting stage. Most current approaches for structure determination improve cryo-ET resolution either by some form of sub-tomogram averaging or template matching, respectively precluding detection of shapes that vary across objects or are a priori unknown. Various macromolecular structures possess polyhedral structure. We propose a classification method for polyhedral shapes from incomplete individual cryo-ET reconstructions, based on topological features of an extracted polyhedral graph (PG).'),
(97308, 2016, 'bmc', 'Evaluation of properties over phylogenetic trees using stochastic logics', 'Requeno, José Ignacio|Colom, José Manuel', 'Department of Computer Science and Systems Engineering (DIIS), Universidad de Zaragoza, C/ María de Luna 1, Zaragoza, 50018, Spain. nrequeno@unizar.es.|Department of Computer Science and Systems Engineering (DIIS), Universidad de Zaragoza, C/ María de Luna 1, Zaragoza, 50018, Spain.', 'Spain|Spain', 0, 'Algorithms|DNA|Likelihood Functions|Logic|Models, Genetic|Models, Statistical|Mutation|Phylogeny|Probability|Software|Stochastic Processes|Time Factors', 'Journal Article', '10.1186/s12859-016-1077-7', '27301397', 'Model checking has been recently introduced as an integrated framework for extracting information of the phylogenetic trees using temporal logics as a querying language, an extension of modal logics that imposes restrictions of a boolean formula along a path of events. The phylogenetic tree is considered a transition system modeling the evolution as a sequence of genomic mutations (we understand mutation as different ways that DNA can be changed), while this kind of logics are suitable for traversing it in a strict and exhaustive way. Given a biological property that we desire to inspect over the phylogeny, the verifier returns true if the specification is satisfied or a counterexample that falsifies it. However, this approach has been only considered over qualitative aspects of the phylogeny.'),
(97309, 2016, 'bmc', 'Erratum to: Implementation of a methodology for determining elastic properties of lipid assemblies from molecular dynamics simulations', 'Johner, Niklaus|Harries, Daniel|Khelashvili, George', 'Swiss Institute of Bioinformatics, Klingelbergstrasse 50/70, Basel, Switzerland. niklaus.johner@a3.epfl.ch.|Institute of Chemistry and the Fritz Haber Research Center, The Hebrew University, Jerusalem, 91904, Israel.|Department of Physiology and Biophysics, Weill Medical College of Cornell University, New York, NY, 10065, USA.', 'Switzerland|Israel|United States', 1, '-', 'Published Erratum', '10.1186/s12859-016-1091-9', '27301431', '-'),
(97310, 2016, 'bmc', 'Read mapping on de Bruijn graphs', 'Limasset, Antoine|Cazaux, Bastien|Rivals, Eric|Peterlongo, Pierre', 'IRISA Inria Rennes Bretagne Atlantique, GenScale team, Campus de Beaulieu, Rennes, 35042, France. antoine.limasset@irisa.fr.|L.I.R.M.M., UMR 5506, Université de Montpellier et CNRS, 860 rue de St Priest, Montpellier Cedex 5, F-34392, France.|L.I.R.M.M., UMR 5506, Université de Montpellier et CNRS, 860 rue de St Priest, Montpellier Cedex 5, F-34392, France.|IRISA Inria Rennes Bretagne Atlantique, GenScale team, Campus de Beaulieu, Rennes, 35042, France.', 'France|France|France|France', 0, 'Algorithms|Contig Mapping|Escherichia coli|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Evaluation Studies', '10.1186/s12859-016-1103-9', '27306641', 'Next Generation Sequencing (NGS) has dramatically enhanced our ability to sequence genomes, but not to assemble them. In practice, many published genome sequences remain in the state of a large set of contigs. Each contig describes the sequence found along some path of the assembly graph, however, the set of contigs does not record all the sequence information contained in that graph. Although many subsequent analyses can be performed with the set of contigs, one may ask whether mapping reads on the contigs is as informative as mapping them on the paths of the assembly graph. Currently, one lacks practical tools to perform mapping on such graphs.'),
(97311, 2016, 'bmc', 'Detection and sequence/structure mapping of biophysical constraints to protein variation in saturated mutational libraries and protein sequence alignments with a dedicated server', 'Abriata, Luciano A|Bovigny, Christophe|Dal Peraro, Matteo', 'Laboratory for Biomolecular Modeling, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, and Swiss Institute of Bioinformatics, AAB014 Station 19, Lausanne, 1015, Switzerland. luciano.abriata@epfl.ch.|Laboratory for Biomolecular Modeling, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, and Swiss Institute of Bioinformatics, AAB014 Station 19, Lausanne, 1015, Switzerland.|Laboratory for Biomolecular Modeling, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, and Swiss Institute of Bioinformatics, AAB014 Station 19, Lausanne, 1015, Switzerland.', 'Switzerland|Switzerland|Switzerland', 8, 'Amino Acid Sequence|Computational Biology|Evolution, Molecular|High-Throughput Nucleotide Sequencing|Humans|Mutation|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, DNA|Structure-Activity Relationship', 'Journal Article', '10.1186/s12859-016-1124-4', '27315797', 'Protein variability can now be studied by measuring high-resolution tolerance-to-substitution maps and fitness landscapes in saturated mutational libraries. But these rich and expensive datasets are typically interpreted coarsely, restricting detailed analyses to positions of extremely high or low variability or dubbed important beforehand based on existing knowledge about active sites, interaction surfaces, (de)stabilizing mutations, etc.'),
(97312, 2016, 'bmc', 'PGA: an R/Bioconductor package for identification of novel peptides using a customized database derived from RNA-Seq', 'Wen, Bo|Xu, Shaohang|Zhou, Ruo|Zhang, Bing|Wang, Xiaojing|Liu, Xin|Xu, Xun|Liu, Siqi', 'BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Shenzhen, Shenzhen, 518083, China.|Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA.|Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA.|BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Shenzhen, Shenzhen, 518083, China. siqiliu@genomics.cn.', 'China|China|China|United States|United States|China|China|China', 0, 'Databases, Protein|Humans|Peptides|Proteomics|Sequence Analysis, RNA|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1186/s12859-016-1133-3', '27316337', 'Peptide identification based upon mass spectrometry (MS) is generally achieved by comparison of the experimental mass spectra with the theoretically digested peptides derived from a reference protein database. Obviously, this strategy could not identify peptide and protein sequences that are absent from a reference database. A customized protein database on the basis of RNA-Seq data is thus proposed to assist with and improve the identification of novel peptides. Correspondingly, development of a comprehensive pipeline, which provides an end-to-end solution for novel peptide detection with the customized protein database, is necessary.'),
(97313, 2016, 'bmc', 'Gene expression prediction using low-rank matrix completion', 'Kapur, Arnav|Marwah, Kshitij|Alterovitz, Gil', 'Biomedical Cybernetics Laboratory, Harvard Medical School, Boston, 02115, MA, USA. arnavkapur@gmail.com.|Biomedical Cybernetics Laboratory, Harvard Medical School, Boston, 02115, MA, USA.|Biomedical Cybernetics Laboratory, Harvard Medical School, Boston, 02115, MA, USA.', 'United States|United States|United States', 16, 'Computer Simulation|Humans|Machine Learning|Models, Genetic|Transcriptome', 'Journal Article', '10.1186/s12859-016-1106-6', '27317252', 'An exponential growth of high-throughput biological information and data has occurred in the past decade, supported by technologies, such as microarrays and RNA-Seq. Most data generated using such methods are used to encode large amounts of rich information, and determine diagnostic and prognostic biomarkers. Although data storage costs have reduced, process of capturing data using aforementioned technologies is still expensive. Moreover, the time required for the assay, from sample preparation to raw value measurement is excessive (in the order of days). There is an opportunity to reduce both the cost and time for generating such expression datasets.'),
(97314, 2016, 'bmc', 'An integrative imputation method based on multi-omics datasets', 'Lin, Dongdong|Zhang, Jigang|Li, Jingyao|Xu, Chao|Deng, Hong-Wen|Wang, Yu-Ping', 'Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA.|Center for Bioinformatics and Genomics, Tulane University, New Orleans, LA, 70112, USA.|Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA.|Center for Bioinformatics and Genomics, Tulane University, New Orleans, LA, 70112, USA.|Center for Bioinformatics and Genomics, Tulane University, New Orleans, LA, 70112, USA.|Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA. wyp@tulane.edu.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Gene Regulatory Networks|Genomics|Glioma|Humans|MicroRNAs|RNA, Messenger|Sample Size', 'Journal Article', '10.1186/s12859-016-1122-6', '27329642', 'Integrative analysis of multi-omics data is becoming increasingly important to unravel functional mechanisms of complex diseases. However, the currently available multi-omics datasets inevitably suffer from missing values due to technical limitations and various constrains in experiments. These missing values severely hinder integrative analysis of multi-omics data. Current imputation methods mainly focus on using single omics data while ignoring biological interconnections and information imbedded in multi-omics data sets.'),
(97315, 2016, 'bmc', 'Generalized enrichment analysis improves the detection of adverse drug events from the biomedical literature', 'Winnenburg, Rainer|Shah, Nigam H', 'Stanford Center for Biomedical Informatics Research, 1265 Welch Road, MSOB, Stanford, CA, 94305, USA. rainer.winnenburg@stanford.edu.|Stanford Center for Biomedical Informatics Research, 1265 Welch Road, MSOB, Stanford, CA, 94305, USA.', 'United States|United States', 8, 'Animals|Computational Biology|Drug-Related Side Effects and Adverse Reactions|Humans|Hypoglycemic Agents|Information Storage and Retrieval|MEDLINE|Medical Subject Headings|Pioglitazone|Thiazolidinediones|Urinary Bladder Neoplasms', 'Journal Article', '10.1186/s12859-016-1080-z', '27333889', 'Identification of associations between marketed drugs and adverse events from the biomedical literature assists drug safety monitoring efforts. Assessing the significance of such literature-derived associations and determining the granularity at which they should be captured remains a challenge. Here, we assess how defining a selection of adverse event terms from MeSH, based on information content, can improve the detection of adverse events for drugs and drug classes.'),
(97316, 2016, 'bmc', 'multiDE: a dimension reduced model based statistical method for differential expression analysis using RNA-sequencing data with multiple treatment conditions', 'Kang, Guangliang|Du, Li|Zhang, Hong', 'Institute of Biostatistics, School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai, 200438, Peoples Republic of China.|Institute of Biostatistics, School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai, 200438, Peoples Republic of China.|Institute of Biostatistics, School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai, 200438, Peoples Republic of China. zhanghfd@fudan.edu.cn.', 'China|China|China', 0, 'Embryonic Stem Cells|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Linear Models|Psoriasis|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-016-1111-9', '27334001', 'The growing complexity of biological experiment design based on high-throughput RNA sequencing (RNA-seq) is calling for more accommodative statistical tools. We focus on differential expression (DE) analysis using RNA-seq data in the presence of multiple treatment conditions.'),
(97317, 2016, 'bmc', 'PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics', 'Fernandez-Ricaud, Luciano|Kourtchenko, Olga|Zackrisson, Martin|Warringer, Jonas|Blomberg, Anders', 'Department of Marine Sciences, Lundberg Laboratory, University of Gothenburg, Medicinaregatan 9c, 41390, Göteborg, Sweden.|Department of Marine Sciences, University of Gothenburg, P.O. Box 461, SE 405 30, Göteborg, Sweden.|Department of Cell and Molecular Biology, Lundberg Laboratory, University of Gothenburg, Medicinaregatan 9c, 41390, Göteborg, Sweden.|Department of Cell and Molecular Biology, Lundberg Laboratory, University of Gothenburg, Medicinaregatan 9c, 41390, Göteborg, Sweden.|Department of Marine Sciences, Lundberg Laboratory, University of Gothenburg, Medicinaregatan 9c, 41390, Göteborg, Sweden. anders.blomberg@marine.gu.se.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 16, 'Bacteria|Databases, Genetic|Phenotype|Software|Yeasts', 'Journal Article', '10.1186/s12859-016-1134-2', '27334112', 'Phenomics is a field in functional genomics that records variation in organismal phenotypes in the genetic, epigenetic or environmental context at a massive scale. For microbes, the key phenotype is the growth in population size because it contains information that is directly linked to fitness. Due to technical innovations and extensive automation our capacity to record complex and dynamic microbial growth data is rapidly outpacing our capacity to dissect and visualize this data and extract the fitness components it contains, hampering progress in all fields of microbiology.'),
(97318, 2016, 'bmc', 'An effective and robust method for tracking multiple fish in video image based on fish head detection', 'Qian, Zhi-Ming|Wang, Shuo Hong|Cheng, Xi En|Chen, Yan Qiu', 'School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China.|School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China.|School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China.|School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China. chenyq@fudan.edu.cn.', 'China|China|China|China', 15, 'Algorithms|Animals|Head|Humans|Image Processing, Computer-Assisted|Motion|Physiology|Video Recording|Zebrafish', 'Journal Article', '10.1186/s12859-016-1138-y', '27338122', 'Fish tracking is an important step for video based analysis of fish behavior. Due to severe body deformation and mutual occlusion of multiple swimming fish, accurate and robust fish tracking from video image sequence is a highly challenging problem. The current tracking methods based on motion information are not accurate and robust enough to track the waving body and handle occlusion. In order to better overcome these problems, we propose a multiple fish tracking method based on fish head detection.'),
(97319, 2016, 'bmc', 'Exploiting expression patterns across multiple tissues to map expression quantitative trait loci', 'Acharya, Chaitanya R|McCarthy, Janice M|Owzar, Kouros|Allen, Andrew S', 'Program in Computational Biology and Bioinformatics, Duke University, 101 Science Dr, Durham, 27708, USA.|Department of Biostatistics and Bioinformatics, Duke University, 2424 Erwin Rd, Durham, 27708, USA.|Department of Biostatistics and Bioinformatics, Duke University, 2424 Erwin Rd, Durham, 27708, USA.|Program in Computational Biology and Bioinformatics, Duke University, 101 Science Dr, Durham, 27708, USA. asallen@duke.edu.', 'United States|United States|United States|United States', 0, 'Brain|Gene Expression Profiling|Gene Expression Regulation|Genetic Variation|Genome-Wide Association Study|Humans|Organ Specificity|Quantitative Trait Loci|Regression Analysis|Regulatory Sequences, Nucleic Acid|Software', 'Journal Article', '10.1186/s12859-016-1123-5', '27341818', 'In order to better understand complex diseases, it is important to understand how genetic variation in the regulatory regions affects gene expression. Genetic variants found in these regulatory regions have been shown to activate transcription in a tissue-specific manner. Therefore, it is important to map the aforementioned expression quantitative trait loci (eQTL) using a statistically disciplined approach that jointly models all the tissues and makes use of all the information available to maximize the power of eQTL mapping. In this context, we are proposing a score test-based approach where we model tissue-specificity as a random effect and investigate an overall shift in the gene expression combined with tissue-specific effects due to genetic variants.'),
(97320, 2016, 'bmc', 'A model of the spatial tumour heterogeneity in colorectal adenocarcinoma tissue', 'Kovacheva, Violeta N|Snead, David|Rajpoot, Nasir M', 'Department of Systems Biology, University of Warwick, Coventry, CV4 7AL, UK. v.n.kovacheva@warwick.ac.uk.|Department of HistopathologyUniversity Hospitals Coventry and Warwickshire, Coventry, CV2 2DX, UK.|Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK.', 'United Kingdom|United Kingdom|United Kingdom', 7, 'Adenocarcinoma|Colon|Colonic Neoplasms|Computer Simulation|Eosine Yellowish-(YS)|Hematoxylin|Humans|Tumor Microenvironment', 'Journal Article', '10.1186/s12859-016-1126-2', '27342072', 'There have been great advancements in the field of digital pathology. The surge in development of analytical methods for such data makes it crucial to develop benchmark synthetic datasets for objectively validating and comparing these methods. In addition, developing a spatial model of the tumour microenvironment can aid our understanding of the underpinning laws of tumour heterogeneity.'),
(97321, 2016, 'bmc', 'EmpPrior: using outside empirical data to inform branch-length priors for Bayesian phylogenetics', 'Andersen, John J|Nelson, Bradley J|Brown, Jeremy M', 'Department of Biological Sciences and Museum of Natural Science, Louisiana State University, 202 Life Sciences Building, Baton Rouge, LA, 70803, USA.|Department of Biological Sciences and Museum of Natural Science, Louisiana State University, 202 Life Sciences Building, Baton Rouge, LA, 70803, USA.|Department of Biological Sciences and Museum of Natural Science, Louisiana State University, 202 Life Sciences Building, Baton Rouge, LA, 70803, USA. jembrown@lsu.edu.', 'United States|United States|United States', 0, 'Animals|Bayes Theorem|Echinodermata|Models, Genetic|Phylogeny|Research Design|Salamandridae|Software', 'Journal Article', '10.1186/s12859-016-1132-4', '27342194', 'Branch-length parameters are a central component of phylogenetic models and of intrinsic biological interest. Default branch-length priors in some Bayesian phylogenetic software can be unintentionally informative and lead to branch- and tree-length estimates that are unreasonable. Alternatively, priors may be uninformative, but lead to diffuse posterior estimates. Despite the widespread availability of relevant datasets from other groups, biologists rarely leverage outside information to specify branch-length priors that are specific to the analysis they are conducting.'),
(97322, 2016, 'bmc', 'Non-Markovian effects on protein sequence evolution due to site dependent substitution rates', 'Rizzato, Francesca|Rodriguez, Alex|Laio, Alessandro', 'International School for Advanced Studies (SISSA), Via Bonomea 265, Trieste, 34136, Italy.|International School for Advanced Studies (SISSA), Via Bonomea 265, Trieste, 34136, Italy.|International School for Advanced Studies (SISSA), Via Bonomea 265, Trieste, 34136, Italy. laio@sissa.it.', 'Italy|Italy|Italy', 0, 'Amino Acid Sequence|Codon|Evolution, Molecular|Markov Chains|Models, Genetic|Proteins', 'Journal Article', '10.1186/s12859-016-1135-1', '27342318', 'Many models of protein sequence evolution, in particular those based on Point Accepted Mutation (PAM) matrices, assume that its dynamics is Markovian. Nevertheless, it has been observed that evolution seems to proceed differently at different time scales, questioning this assumption. In 2011 Kosiol and Goldman proved that, if evolution is Markovian at the codon level, it can not be Markovian at the amino acid level. However, it remains unclear up to which point the Markov assumption is verified at the codon level.'),
(97323, 2016, 'bmc', 'Consistency of biological networks inferred from microarray and sequencing data', 'Vinciotti, Veronica|Wit, Ernst C|Jansen, Rick|de Geus, Eco J C N|Penninx, Brenda W J H|Boomsma, Dorret I|t Hoen, Peter A C', 'Department of Mathematics, Brunel University London, London, UK. veronica.vinciotti@brunel.ac.uk.|Johann Bernoulli Institute of Mathematics and Computer Science, University of Groningen, Groningen, The Netherlands.|VU University Medical Center, Amsterdam, The Netherlands.|VU University Medical Center, Amsterdam, The Netherlands.|VU University Medical Center, Amsterdam, The Netherlands.|VU University Medical Center, Amsterdam, The Netherlands.|Leiden University Medical Center, Leiden University, Leiden, The Netherlands.', 'United Kingdom|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 2, 'Anxiety|Depression|Female|Gene Regulatory Networks|High-Throughput Nucleotide Sequencing|Humans|Male|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sequence Analysis, RNA|Twin Studies as Topic', 'Journal Article', '10.1186/s12859-016-1136-0', '27342572', 'Sparse Gaussian graphical models are popular for inferring biological networks, such as gene regulatory networks. In this paper, we investigate the consistency of these models across different data platforms, such as microarray and next generation sequencing, on the basis of a rich dataset containing samples that are profiled under both techniques as well as a large set of independent samples.'),
(97324, 2016, 'bmc', 'TRaCE+: Ensemble inference of gene regulatory networks from transcriptional expression profiles of gene knock-out experiments', 'Ud-Dean, S M Minhaz|Heise, Sandra|Klamt, Steffen|Gunawan, Rudiyanto', 'Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland.|Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.|Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.|Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland. rudi.gunawan@chem.ethz.ch.', 'Switzerland|Germany|Germany|Switzerland', 0, 'Algorithms|Escherichia coli|Gene Knockout Techniques|Gene Regulatory Networks|Transcriptome', 'Journal Article', '10.1186/s12859-016-1137-z', '27342648', 'The inference of gene regulatory networks (GRNs) from transcriptional expression profiles is challenging, predominantly due to its underdetermined nature. One important consequence of underdetermination is the existence of many possible solutions to this inference. Our previously proposed ensemble inference algorithm TRaCE addressed this issue by inferring an ensemble of network directed graphs (digraphs) using differential gene expressions from gene knock-out (KO) experiments. However, TRaCE could not deal with the mode of the transcriptional regulations (activation or repression), an important feature of GRNs.'),
(97325, 2016, 'bmc', 'CerealsDB 30: expansion of resources and data integration', 'Wilkinson, Paul A|Winfield, Mark O|Barker, Gary L A|Tyrrell, Simon|Bian, Xingdong|Allen, Alexandra M|Burridge, Amanda|Coghill, Jane A|Waterfall, Christy|Caccamo, Mario|Davey, Robert P|Edwards, Keith J', 'School of Biological Sciences, University of Bristol, Bristol, BS8 1UG, UK. Paul.Wilkinson@bristol.ac.uk.|School of Biological Sciences, University of Bristol, Bristol, BS8 1UG, UK.|School of Biological Sciences, University of Bristol, Bristol, BS8 1UG, UK.|The Genome Analysis Centre (TGAC), Norwich Research Park, Norwich, UK.|The Genome Analysis Centre (TGAC), Norwich Research Park, Norwich, UK.|School of Biological Sciences, University of Bristol, Bristol, BS8 1UG, UK.|School of Biological Sciences, University of Bristol, Bristol, BS8 1UG, UK.|School of Biological Sciences, University of Bristol, Bristol, BS8 1UG, UK.|School of Biological Sciences, University of Bristol, Bristol, BS8 1UG, UK.|National Institute of Agricultural Botany (NIAB), Huntingdon Road, Cambridge, UK.|The Genome Analysis Centre (TGAC), Norwich Research Park, Norwich, UK.|School of Biological Sciences, University of Bristol, Bristol, BS8 1UG, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 22, 'Breeding|Crops, Agricultural|Database Management Systems|Genomics|Genotyping Techniques|Humans|Internet|Polymorphism, Single Nucleotide|Triticum|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1139-x', '27342803', 'The increase in human populations around the world has put pressure on resources, and as a consequence food security has become an important challenge for the 21st century. Wheat (Triticum aestivum) is one of the most important crops in human and livestock diets, and the development of wheat varieties that produce higher yields, combined with increased resistance to pests and resilience to changes in climate, has meant that wheat breeding has become an important focus of scientific research. In an attempt to facilitate these improvements in wheat, plant breeders have employed molecular tools to help them identify genes for important agronomic traits that can be bred into new varieties. Modern molecular techniques have ensured that the rapid and inexpensive characterisation of SNP markers and their validation with modern genotyping methods has produced a valuable resource that can be used in marker assisted selection. CerealsDB was created as a means of quickly disseminating this information to breeders and researchers around the globe.'),
(97326, 2016, 'bmc', 'Reference-free deconvolution of DNA methylation data and mediation by cell composition effects', 'Houseman, E Andres|Kile, Molly L|Christiani, David C|Ince, Tan A|Kelsey, Karl T|Marsit, Carmen J', 'School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA. andres.houseman@oregonstate.edu.|School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA.|Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA.|Department of Pathology, University of Miami, Miller School of Medicine, Miami, FL, USA.|Department of Epidemiology, Department of Pathology and Laboratory Medicine, Brown University, Providence, USA.|Department of Community and Family Medicine, Dartmouth Medical School, Hanover, NH, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|DNA Methylation|Epigenomics|Humans|Neoplasms', 'Journal Article', '10.1186/s12859-016-1140-4', '27358049', 'Recent interest in reference-free deconvolution of DNA methylation data has led to several supervised methods, but these methods do not easily permit the interpretation of underlying cell types.'),
(97327, 2016, 'bmc', 'A De-Novo Genome Analysis Pipeline (DeNoGAP) for large-scale comparative prokaryotic genomics studies', 'Thakur, Shalabh|Guttman, David S', 'Department of Cell & Systems Biology, University of Toronto, Toronto, ON, Canada.|Department of Cell & Systems Biology, University of Toronto, Toronto, ON, Canada. david.guttman@utoronto.ca.', 'Canada|Canada', 5, 'Algorithms|Amino Acid Sequence|Cluster Analysis|Computational Biology|Genome|Genomics|Markov Chains|Molecular Sequence Annotation|Prokaryotic Cells|Reproducibility of Results|Sequence Homology, Nucleic Acid|Software', 'Comparative Study', '10.1186/s12859-016-1142-2', '27363390', 'Comparative analysis of whole genome sequence data from closely related prokaryotic species or strains is becoming an increasingly important and accessible approach for addressing both fundamental and applied biological questions. While there are number of excellent tools developed for performing this task, most scale poorly when faced with hundreds of genome sequences, and many require extensive manual curation.'),
(97328, 2016, 'bmc', 'PhytoCRISP-Ex: a web-based and stand-alone application to find specific target sequences for CRISPR/CAS editing', 'Rastogi, Achal|Murik, Omer|Bowler, Chris|Tirichine, Leila', 'Institut de Biologie de lEcole Normale Supérieure (IBENS), Ecole Normale Supérieure, PSL Research University, CNRS UMR 8197, INSERM U1024, 46 rue dUlm, F-75005, Paris, France.|Institut de Biologie de lEcole Normale Supérieure (IBENS), Ecole Normale Supérieure, PSL Research University, CNRS UMR 8197, INSERM U1024, 46 rue dUlm, F-75005, Paris, France.|Institut de Biologie de lEcole Normale Supérieure (IBENS), Ecole Normale Supérieure, PSL Research University, CNRS UMR 8197, INSERM U1024, 46 rue dUlm, F-75005, Paris, France.|Institut de Biologie de lEcole Normale Supérieure (IBENS), Ecole Normale Supérieure, PSL Research University, CNRS UMR 8197, INSERM U1024, 46 rue dUlm, F-75005, Paris, France. leila.tirichine@ens.fr.', 'France|France|France|France', 21, 'CRISPR-Cas Systems|Genome, Human|Humans|Internet', 'Journal Article', '10.1186/s12859-016-1143-1', '27363443', 'With the emerging interest in phytoplankton research, the need to establish genetic tools for the functional characterization of genes is indispensable. The CRISPR/Cas9 system is now well recognized as an efficient and accurate reverse genetic tool for genome editing. Several computational tools have been published allowing researchers to find candidate target sequences for the engineering of the CRISPR vectors, while searching possible off-targets for the predicted candidates. These tools provide built-in genome databases of common model organisms that are used for CRISPR target prediction. Although their predictions are highly sensitive, the applicability to non-model genomes, most notably protists, makes their design inadequate. This motivated us to design a new CRISPR target finding tool, PhytoCRISP-Ex. Our software offers CRIPSR target predictions using an extended list of phytoplankton genomes and also delivers a user-friendly standalone application that can be used for any genome.'),
(97329, 2016, 'bmc', 'MethPed: an R package for the identification of pediatric brain tumor subtypes', 'Ahamed, Mohammad Tanvir|Danielsson, Anna|Nemes, Szilárd|Carén, Helena', 'Sahlgrenska Cancer Center, Department of Pathology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, PO Box 425, SE-405 30, Gothenburg, Sweden.|Sahlgrenska Cancer Center, Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, PO Box 425, SE-405 30, Gothenburg, Sweden.|Swedish Hip Arthroplasty Register, Centre of Registers Västra Götaland, Gothenburg, PO Box 425, SE-405 30, Gothenburg, Sweden.|Sahlgrenska Cancer Center, Department of Pathology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, PO Box 425, SE-405 30, Gothenburg, Sweden. helena.caren@gu.se.', 'Sweden|Sweden|Sweden|Sweden', 3, 'Algorithms|Brain Neoplasms|Child|DNA Methylation|Data Interpretation, Statistical|Humans', 'Journal Article', '10.1186/s12859-016-1144-0', '27370569', 'DNA methylation profiling of pediatric brain tumors offers a new way of diagnosing and subgrouping these tumors which improves current clinical diagnostics based on histopathology. We have therefore developed the MethPed classifier, which is a multiclass random forest algorithm, based on DNA methylation profiles from many subgroups of pediatric brain tumors.'),
(97330, 2016, 'bmc', 'Reusable, extensible, and modifiable R scripts and Kepler workflows for comprehensive single set ChIP-seq analysis', 'Cormier, Nathan|Kolisnik, Tyler|Bieda, Mark', 'Department of Biochemistry and Molecular Biology, University of Calgary Cumming School of Medicine, Rm HSC1151, 3330 Hospital Dr. NW, Calgary, AB, T2N4N1, Canada.|Department of Biochemistry and Molecular Biology, University of Calgary Cumming School of Medicine, Rm HSC1151, 3330 Hospital Dr. NW, Calgary, AB, T2N4N1, Canada.|Department of Biochemistry and Molecular Biology, University of Calgary Cumming School of Medicine, Rm HSC1151, 3330 Hospital Dr. NW, Calgary, AB, T2N4N1, Canada. mbieda@ucalgary.ca.', 'Canada|Canada|Canada', 0, 'Chromatin Immunoprecipitation|Computational Biology|High-Throughput Nucleotide Sequencing|Humans', 'Journal Article', '10.1186/s12859-016-1125-3', '27377783', 'There has been an enormous expansion of use of chromatin immunoprecipitation followed by sequencing (ChIP-seq) technologies. Analysis of large-scale ChIP-seq datasets involves a complex series of steps and production of several specialized graphical outputs. A number of systems have emphasized custom development of ChIP-seq pipelines. These systems are primarily based on custom programming of a single, complex pipeline or supply libraries of modules and do not produce the full range of outputs commonly produced for ChIP-seq datasets. It is desirable to have more comprehensive pipelines, in particular ones addressing common metadata tasks, such as pathway analysis, and pipelines producing standard complex graphical outputs. It is advantageous if these are highly modular systems, available as both turnkey pipelines and individual modules, that are easily comprehensible, modifiable and extensible to allow rapid alteration in response to new analysis developments in this growing area. Furthermore, it is advantageous if these pipelines allow data provenance tracking.'),
(97331, 2016, 'bmc', 'Profiling networks of distinct immune-cells in tumors', 'Clancy, Trevor|Hovig, Eivind', 'Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway. trevor.clancy@rr-research.no.|Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.', 'Norway|Norway', 9, 'Computational Biology|GTP Phosphohydrolases|Gene Expression Profiling|Gene Regulatory Networks|Genes, Neoplasm|Humans|Immune System|MAP Kinase Signaling System|Melanoma|Membrane Proteins|Mutation|Proto-Oncogene Proteins B-raf', 'Journal Article', '10.1186/s12859-016-1141-3', '27377892', 'It is now clearly evident that cancer outcome and response to therapy is guided by diverse immune-cell activity in tumors. Presently, a key challenge is to comprehensively identify networks of distinct immune-cell signatures present in complex tissue, at higher-resolution and at various stages of differentiation, activation or function. This is particularly so for closely related immune-cells with diminutive, yet critical, differences.'),
(97332, 2016, 'bmc', 'LEON-BIS: multiple alignment evaluation of sequence neighbours using a Bayesian inference system', 'Vanhoutreve, Renaud|Kress, Arnaud|Legrand, Baptiste|Gass, Hélène|Poch, Olivier|Thompson, Julie D', 'Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fédération de médecine translationnelle de Strasbourg, Strasbourg, France.|Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fédération de médecine translationnelle de Strasbourg, Strasbourg, France.|Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fédération de médecine translationnelle de Strasbourg, Strasbourg, France.|Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fédération de médecine translationnelle de Strasbourg, Strasbourg, France.|Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fédération de médecine translationnelle de Strasbourg, Strasbourg, France.|Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fédération de médecine translationnelle de Strasbourg, Strasbourg, France. thompson@unistra.fr.', 'France|France|France|France|France|France', 0, 'Amino Acid Sequence|Bayes Theorem|Computational Biology|Humans|Proteins|Sequence Alignment|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/s12859-016-1146-y', '27387560', 'A standard procedure in many areas of bioinformatics is to use a multiple sequence alignment (MSA) as the basis for various types of homology-based inference. Applications include 3D structure modelling, protein functional annotation, prediction of molecular interactions, etc. These applications, however sophisticated, are generally highly sensitive to the alignment used, and neglecting non-homologous or uncertain regions in the alignment can lead to significant bias in the subsequent inferences.'),
(97333, 2016, 'bmc', 'GCORE-sib: An efficient gene-gene interaction tool for genome-wide association studies based on discordant sib pairs', 'Sung, Pei-Yuan|Wang, Yi-Ting|Hsiung, Chao A|Chung, Ren-Hua', 'Institute of Statistics, National Tsing Hua University, Hsin-Chu, Taiwan.|Institute of Statistics, National Tsing Hua University, Hsin-Chu, Taiwan.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan. rchung@nhri.org.tw.', '-|-|-|-', 2, 'Epistasis, Genetic|Genome-Wide Association Study|Genomics|Humans|Polymorphism, Single Nucleotide|Siblings|Software|Statistics as Topic', 'Journal Article', '10.1186/s12859-016-1145-z', '27391654', 'A computationally efficient tool is required for a genome-wide gene-gene interaction analysis that tests an extremely large number of single-nucleotide polymorphism (SNP) interaction pairs in genome-wide association studies (GWAS). Current tools for GWAS interaction analysis are mainly developed for unrelated case-control samples. Relatively fewer tools for interaction analysis are available for complex disease studies with family-based design, and these tools tend to be computationally expensive.'),
(97334, 2016, 'bmc', 'Model-based clustering with certainty estimation: implication for clade assignment of influenza viruses', 'Zhang, Shunpu|Li, Zhong|Beland, Kevin|Lu, Guoqing', 'Department of Statistics, University of Central Florida, Orlando, FL, 32816, USA. shunpu.zhang@ucf.edu.|College of Science, Zhejiang Sci-Tech University, Hangzhou, 310018, China.|Department of Biology, University of Nebraska at Omaha, Omaha, NE, 68182, USA.|Department of Biology, University of Nebraska at Omaha, Omaha, NE, 68182, USA. glu3@unomaha.edu.', 'United States|China|United States|United States', 0, 'Animals|Base Sequence|Birds|Cluster Analysis|Hemagglutinins, Viral|Influenza A Virus, H5N1 Subtype|Influenza in Birds|Models, Theoretical|Phylogeny', 'Journal Article', '10.1186/s12859-016-1147-x', '27439701', 'Clustering is a common technique used by molecular biologists to group homologous sequences and study evolution. There remain issues such as how to cluster molecular sequences accurately and in particular how to evaluate the certainty of clustering results.'),
(97335, 2016, 'bmc', 'In-silico discovery of cancer-specific peptide-HLA complexes for targeted therapy', 'Dhanik, Ankur|Kirshner, Jessica R|MacDonald, Douglas|Thurston, Gavin|Lin, Hsin C|Murphy, Andrew J|Zhang, Wen', 'Regeneron Pharmaceuticals Inc, Old Saw Mill River Road, Tarrytown, NY, USA. ankur.dhanik@regeneron.com.|Regeneron Pharmaceuticals Inc, Old Saw Mill River Road, Tarrytown, NY, USA.|Regeneron Pharmaceuticals Inc, Old Saw Mill River Road, Tarrytown, NY, USA.|Regeneron Pharmaceuticals Inc, Old Saw Mill River Road, Tarrytown, NY, USA.|Regeneron Pharmaceuticals Inc, Old Saw Mill River Road, Tarrytown, NY, USA.|Regeneron Pharmaceuticals Inc, Old Saw Mill River Road, Tarrytown, NY, USA.|Regeneron Pharmaceuticals Inc, Old Saw Mill River Road, Tarrytown, NY, USA.', 'United States|United States|United States|United States|United States|United States|United States', 9, 'Antibodies|Computer Simulation|Histocompatibility Antigens Class I|Humans|Major Histocompatibility Complex|Molecular Targeted Therapy|Neoplasms|Peptides|T-Lymphocytes', 'Journal Article', '10.1186/s12859-016-1150-2', '27439771', 'Major Histocompatibility Complex (MHC) or Human Leukocyte Antigen (HLA) Class I molecules bind to peptide fragments of proteins degraded inside the cell and display them on the cell surface. We are interested in peptide-HLA complexes involving peptides that are derived from proteins specifically expressed in cancer cells. Such complexes have been shown to provide an effective means of precisely targeting cancer cells by engineered T-cells and antibodies, which would be an improvement over current chemotherapeutic agents that indiscriminately kill proliferating cells. An important concern with the targeting of peptide-HLA complexes is off-target toxicity that could occur due to the presence of complexes similar to the target complex in cells from essential, normal tissues.'),
(97336, 2016, 'bmc', 'Boosting the discriminatory power of sparse survival models via optimization of the concordance index and stability selection', 'Mayr, Andreas|Hofner, Benjamin|Schmid, Matthias', 'Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Waldstr. 6, Erlangen, 91054, Germany. andreas.mayr@fau.de.|Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Waldstr. 6, Erlangen, 91054, Germany.|Institut für Medizinische Biometrie, Informatik und Epidemiologie, Rheinische Friedrich-Wilhelms-Universität Bonn, Sigmund-Freud-Str. 25, Bonn, 53105, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Biomarkers|Breast Neoplasms|Discriminant Analysis|Female|Humans|Models, Theoretical|Neoplasm Metastasis|Prognosis|Proportional Hazards Models|Survival Analysis|Transcriptome', 'Journal Article', '10.1186/s12859-016-1149-8', '27444890', 'When constructing new biomarker or gene signature scores for time-to-event outcomes, the underlying aims are to develop a discrimination model that helps to predict whether patients have a poor or good prognosis and to identify the most influential variables for this task. In practice, this is often done fitting Cox models. Those are, however, not necessarily optimal with respect to the resulting discriminatory power and are based on restrictive assumptions. We present a combined approach to automatically select and fit sparse discrimination models for potentially high-dimensional survival data based on boosting a smooth version of the concordance index (C-index). Due to this objective function, the resulting prediction models are optimal with respect to their ability to discriminate between patients with longer and shorter survival times. The gradient boosting algorithm is combined with the stability selection approach to enhance and control its variable selection properties.'),
(97337, 2016, 'bmc', 'Integrating unified medical language system and association mining techniques into relevance feedback for biomedical literature search', 'Ji, Yanqing|Ying, Hao|Tran, John|Dews, Peter|Massanari, R Michael', 'Department of Electrical and Computer Engineering, Gonzaga University, Spokane, WA, USA. ji@gonzaga.edu.|Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI, USA.|Frontier Behavioral Health, Spokane, WA, USA.|Department of Medicine, St. Mary Mercy Hospital, Livonia, MI, USA.|Research for The Critical Junctures Institute, Bellingham, WA, USA.', 'United States|United States|United States|United States|United States', 4, 'Algorithms|Data Mining|Feedback|Genomics|Humans|Medicine in Literature|Search Engine|Software|Unified Medical Language System', 'Journal Article', '10.1186/s12859-016-1129-z', '27453982', 'Finding highly relevant articles from biomedical databases is challenging not only because it is often difficult to accurately express a users underlying intention through keywords but also because a keyword-based query normally returns a long list of hits with many citations being unwanted by the user. This paper proposes a novel biomedical literature search system, called BiomedSearch, which supports complex queries and relevance feedback.'),
(97338, 2016, 'bmc', 'A novel approach for multi-SNP GWAS and its application in Alzheimers disease', 'Bodily, Paul M|Fujimoto, M Stanley|Page, Justin T|Clement, Mark J|Ebbert, Mark T W|Ridge, Perry G|Alzheimer’s Disease Neuroimaging Initiative', 'Computer Science Department, Brigham Young University, Provo, 84602-6576, UT, USA. paulmbodily@gmail.com.|Computer Science Department, Brigham Young University, Provo, 84602-6576, UT, USA.|Department of Biology, Brigham Young University, Provo, 84602-6576, UT, USA.|Computer Science Department, Brigham Young University, Provo, 84602-6576, UT, USA.|Department of Biology, Brigham Young University, Provo, 84602-6576, UT, USA.|Department of Biology, Brigham Young University, Provo, 84602-6576, UT, USA.|Department of Biology, Brigham Young University, Provo, 84602-6576, UT, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Alzheimer Disease|Computational Biology|Epistasis, Genetic|Female|Genetic Predisposition to Disease|Genome-Wide Association Study|HLA-DRB5 Chains|Humans|Male|Models, Statistical|Polymorphism, Single Nucleotide|Receptors, AMPA', 'Journal Article', '10.1186/s12859-016-1093-7', '27453991', 'Genome-wide association studies (GWAS) have effectively identified genetic factors for many diseases. Many diseases, including Alzheimers disease (AD), have epistatic causes, requiring more sophisticated analyses to identify groups of variants which together affect phenotype.'),
(97339, 2016, 'bmc', 'RefSelect: a reference sequence selection algorithm for planted (l, d) motif search', 'Yu, Qiang|Huo, Hongwei|Zhao, Ruixing|Feng, Dazheng|Vitter, Jeffrey Scott|Huan, Jun', '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.|School of Computer Science and Technology, Xidian University, Xian, 710071, China.|School of Electronic Engineering, Xidian University, Xian, 710071, China.|Department of Computer and Information Science, The University of Mississippi, Oxford, MS, 38677-1848, USA.|Department of Electrical Engineering and Computer Science, the University of Kansas, Lawrence, KS, 66045, USA.', 'China|China|China|China|United States|United States', 3, 'Algorithms|Amino Acid Motifs|Computational Biology|Protein Domains|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/s12859-016-1130-6', '27454113', 'The planted (l, d) motif search (PMS) is an important yet challenging problem in computational biology. Pattern-driven PMS algorithms usually use k out of t input sequences as reference sequences to generate candidate motifs, and they can find all the (l, d) motifs in the input sequences. However, most of them simply take the first k sequences in the input as reference sequences without elaborate selection processes, and thus they may exhibit sharp fluctuations in running time, especially for large alphabets.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97340, 2016, 'bmc', 'A systematic framework to derive N-glycan biosynthesis process and the automated construction of glycosylation networks', 'Hou, Wenpin|Qiu, Yushan|Hashimoto, Nobuyuki|Ching, Wai-Ki|Aoki-Kinoshita, Kiyoko F', 'Department of Mathematics, The University of Hong Kong, Hong Kong, 999077, China. whou@connect.hku.hk.|Hematology Oncology Division, Northwestern University, Evanston, IL 60208, USA.|Faculty of Science and Engineering, Soka University, Tokyo, 192-8577, Japan.|Department of Mathematics, The University of Hong Kong, Hong Kong, 999077, China.|Faculty of Science and Engineering, Soka University, Tokyo, 192-8577, Japan.', 'China|United States|Japan|China|Japan', 3, 'Biosynthetic Pathways|Computer Simulation|Glycomics|Glycosylation|Humans|Hydrolases|Mass Spectrometry|Models, Biological|Polysaccharides|Transferases', 'Journal Article', '10.1186/s12859-016-1094-6', '27454116', 'Abnormalities in glycan biosynthesis have been conclusively related to various diseases, whereas the complexity of the glycosylation process has impeded the quantitative analysis of biochemical experimental data for the identification of glycoforms contributing to disease. To overcome this limitation, the automatic construction of glycosylation reaction networks in silico is a critical step.'),
(97341, 2016, 'bmc', 'Network topology measures for identifying disease-gene association in breast cancer', 'Ramadan, Emad|Alinsaif, Sadiq|Hassan, Md Rafiul', 'Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. eramadan@kfupm.edu.sa.|Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.|Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia', 0, 'Breast Neoplasms|Computational Biology|Female|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Genetic Predisposition to Disease|Humans|Machine Learning|Neoplasm Proteins|Protein Interaction Mapping|Protein Interaction Maps|Software', 'Journal Article', '10.1186/s12859-016-1095-5', '27454166', 'Massive biological datasets are generated in different locations all over the world. Analysis of these datasets is required in order to extract knowledge that might be helpful for biologists, physicians and pharmacists. Recently, analysis of biological networks has received a lot of attention, as an understanding of the network can reveal information about life at the cellular level. Biological networks can be generated that examine the interaction between proteins or the relationship amongst different genes at the expression level. Identifying information from biological networks is recognized as a significant challenge, due to the inherent complexity of the structures. Computational techniques are used to analyze such complex networks with varying success.'),
(97342, 2016, 'bmc', 'A method for exploring implicit concept relatedness in biomedical knowledge network', 'Bai, Tian|Gong, Leiguang|Wang, Ye|Wang, Yan|Kulikowski, Casimir A|Huang, Lan', 'College of Computer Science and Technology, Jilin Univesity, 2699 Qianjin St, Changchun, China.|College of Computer Science and Technology, Jilin Univesity, 2699 Qianjin St, Changchun, China.|College of Computer Science and Technology, Jilin Univesity, 2699 Qianjin St, Changchun, China.|College of Computer Science and Technology, Jilin Univesity, 2699 Qianjin St, Changchun, China.|Department of Computer Science, Rutgers, The State University of New Jersey, 2699 Qianjin St, Piscataway, NJ, USA.|College of Computer Science and Technology, Jilin Univesity, 2699 Qianjin St, Changchun, China. huanglan@jlu.edu.cn.', 'China|China|China|China|Jersey|China', 0, 'Biological Ontologies|Knowledge|Semantics', 'Journal Article', '10.1186/s12859-016-1131-5', '27454167', 'Biomedical information and knowledge, structural and non-structural, stored in different repositories can be semantically connected to form a hybrid knowledge network. How to compute relatedness between concepts and discover valuable but implicit information or knowledge from it effectively and efficiently is of paramount importance for precision medicine, and a major challenge facing the biomedical research community.'),
(97343, 2016, 'bmc', 'Protein complexes predictions within protein interaction networks using genetic algorithms', 'Ramadan, Emad|Naef, Ahmed|Ahmed, Moataz', 'Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. eramadan@kfupm.edu.sa.|Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.|Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia', 13, 'Algorithms|Cluster Analysis|Databases, Protein|Protein Interaction Mapping|Protein Interaction Maps', 'Journal Article', '10.1186/s12859-016-1096-4', '27454228', 'Protein-protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein-protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein-protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks.'),
(97344, 2016, 'bmc', 'Evaluating the necessity of PCR duplicate removal from next-generation sequencing data and a comparison of approaches', 'Ebbert, Mark T W|Wadsworth, Mark E|Staley, Lyndsay A|Hoyt, Kaitlyn L|Pickett, Brandon|Miller, Justin|Duce, John|Alzheimerxe2x80x99s Disease Neuroimaging Initiative|Kauwe, John S K|Ridge, Perry G', 'Department of Biology, Brigham Young University, Provo, UT, USA.|Department of Biology, Brigham Young University, Provo, UT, USA.|Department of Biology, Brigham Young University, Provo, UT, USA.|Department of Biology, Brigham Young University, Provo, UT, USA.|Department of Biology, Brigham Young University, Provo, UT, USA.|Department of Biology, Brigham Young University, Provo, UT, USA.|Department of Biology, Brigham Young University, Provo, UT, USA.|Department of Biology, Brigham Young University, Provo, UT, USA.|Department of Biology, Brigham Young University, Provo, UT, USA.|Department of Biology, Brigham Young University, Provo, UT, USA. perry.ridge@byu.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Data Accuracy|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Polymerase Chain Reaction|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-016-1097-3', '27454357', 'Analyzing next-generation sequencing data is difficult because datasets are large, second generation sequencing platforms have high error rates, and because each position in the target genome (exome, transcriptome, etc.) is sequenced multiple times. Given these challenges, numerous bioinformatic algorithms have been developed to analyze these data. These algorithms aim to find an appropriate balance between data loss, errors, analysis time, and memory footprint. Typical analysis pipelines require multiple steps. If one or more of these steps is unnecessary, it would significantly decrease compute time and data manipulation to remove the step. One step in many pipelines is PCR duplicate removal, where PCR duplicates arise from multiple PCR products from the same template molecule binding on the flowcell. These are often removed because there is concern they can lead to false positive variant calls. Picard (MarkDuplicates) and SAMTools (rmdup) are the two main softwares used for PCR duplicate removal.'),
(97345, 2016, 'bmc', 'Sensorimotor control: computing the immediate future from the delayed present', 'Sargolzaei, Arman|Abdelghani, Mohamed|Yen, Kang K|Sargolzaei, Saman', 'Department of Electrical and Computer Engineering, Florida International University, Miami, FL, 33174, USA.|Department of Mathematics and Statistics, University of Alberta, Edmonton, 30332, Canada. mnabdelghani@gmail.com.|Department of Electrical and Computer Engineering, Florida International University, Miami, FL, 33174, USA.|Research and Development Department, Rancs Group LLC, Miami, FL, 33172, USA.', 'United States|Canada|United States|United States', 4, 'Animals|Computer Simulation|Humans|Models, Biological|Primates|Reflex, Vestibulo-Ocular', 'Journal Article', '10.1186/s12859-016-1098-2', '27454449', 'The predictive nature of the primate sensorimotor systems, for example the smooth pursuit system and their ability to compensate for long delays have been proven by many physiological experiments. However, few theoretical models have tried to explain these facts comprehensively. Here, we propose a sensorimotor learning and control model that can be used to (1) predict the dynamics of variable time delays and current and future sensory states from delayed sensory information, (2) learn new sensorimotor realities, and (3) control a motor system in real time.'),
(97346, 2016, 'bmc', 'OMWare: a tool for efficient assembly of genome-wide physical maps', 'Sharp, Aaron R|Udall, Joshua A', 'College of Life Sciences, Brigham Young University, Provo, UT, 84602-2400, USA. sharp.aaron.r@gmail.com.|College of Life Sciences, Brigham Young University, Provo, UT, 84602-2400, USA.', 'United States|United States', 3, 'Data Accuracy|Genome, Plant|Gossypium|Physical Chromosome Mapping|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-016-1099-1', '27454532', 'Physical mapping of DNA with restriction enzymes allows for the characterization and assembly of much longer molecules than is feasible with sequencing. However, assemblies of physical map data are sensitive to input parameters, which describe noise inherent in the data collection process. One possible way to determine the parameter values that best describe a dataset is by trial and error.'),
(97347, 2016, 'bmc', 'Protein-protein interaction extraction with feature selection by evaluating contribution levels of groups consisting of related features', 'Thuy Phan, Thi Thanh|Ohkawa, Takenao', 'Department of Information Science, Graduate School of System Informatics, Kobe University, 1-1, Rokkodai, Nada, Kobe, 657-8501, Japan. thuy@iip.ist.i.kyoto-u.ac.jp.|Department of Information Science, Graduate School of System Informatics, Kobe University, 1-1, Rokkodai, Nada, Kobe, 657-8501, Japan.', 'Japan|Japan', 7, 'Algorithms|Data Mining|Humans|Language|Protein Interaction Mapping|Semantics', 'Journal Article', '10.1186/s12859-016-1100-z', '27454611', 'Protein-protein interaction (PPI) extraction from published scientific articles is one key issue in biological research due to its importance in grasping biological processes. Despite considerable advances of recent research in automatic PPI extraction from articles, demand remains to enhance the performance of the existing methods.'),
(97348, 2016, 'bmc', 'A method for predicting protein complex in dynamic PPI networks', 'Zhang, Yijia|Lin, Hongfei|Yang, Zhihao|Wang, Jian|Liu, Yiwei|Sang, Shengtian', 'College of Computer Science and Technology, Dalian University of Technology Dalian, Liaoning, China. zhyj@dlut.edu.cn.|College of Computer Science and Technology, Dalian University of Technology Dalian, Liaoning, China.|College of Computer Science and Technology, Dalian University of Technology Dalian, Liaoning, China.|College of Computer Science and Technology, Dalian University of Technology Dalian, Liaoning, China.|College of Computer Science and Technology, Dalian University of Technology Dalian, Liaoning, China.|College of Computer Science and Technology, Dalian University of Technology Dalian, Liaoning, China.', 'China|China|China|China|China|China', 0, 'Algorithms|Computational Biology|Protein Interaction Mapping|Protein Interaction Maps|Proteins|Transcriptome', 'Journal Article', '10.1186/s12859-016-1101-y', '27454775', 'Accurate determination of protein complexes has become a key task of system biology for revealing cellular organization and function. Up to now, the protein complex prediction methods are mostly focused on static protein protein interaction (PPI) networks. However, cellular systems are highly dynamic and responsive to cues from the environment. The shift from static PPI networks to dynamic PPI networks is essential to accurately predict protein complex.'),
(97349, 2016, 'bmc', 'Does encoding matter? A novel view on the quantitative genetic trait prediction problem', 'He, Dan|Parida, Laxmi', 'IBM T.J. Watson Research, Yorktown Heights, NY, USA. dhe@us.ibm.com.|IBM T.J. Watson Research, Yorktown Heights, NY, USA.', 'United States|United States', 7, 'Algorithms|Animals|Eukaryota|Genetic Markers|Genotype|Humans|Models, Genetic|Phenotype|Plants|Polymorphism, Single Nucleotide|Quantitative Trait Loci', 'Journal Article', '10.1186/s12859-016-1127-1', '27454886', 'Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models which require quantitative encodings for the genotypes. There are lots of work on the prediction algorithms, but none of the existing work investigated the effects of the encodings on the genetic trait prediction problem.'),
(97350, 2016, 'bmc', 'Parallel algorithms for large-scale biological sequence alignment on Xeon-Phi based clusters', 'Lan, Haidong|Chan, Yuandong|Xu, Kai|Schmidt, Bertil|Peng, Shaoliang|Liu, Weiguo', 'School of Computer Science and Technology, Shandong University, Shunhua Road 1500, Jinan, Shandong, China.|School of Computer Science and Technology, Shandong University, Shunhua Road 1500, Jinan, Shandong, China.|School of Computer Science and Technology, Shandong University, Shunhua Road 1500, Jinan, Shandong, China.|Johannes Gutenberg University, Mainz, Germany.|School of Computer Science, National University of Defense Technology, Changsha, Hunan, China.|School of Computer Science and Technology, Shandong University, Shunhua Road 1500, Jinan, Shandong, China. weiguo.liu@sdu.edu.cn.', 'China|China|China|Germany|China|China', 6, 'Algorithms|Amino Acid Sequence|Computational Biology|Databases, Nucleic Acid|Databases, Protein|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1186/s12859-016-1128-0', '27455061', 'Computing alignments between two or more sequences are common operations frequently performed in computational molecular biology. The continuing growth of biological sequence databases establishes the need for their efficient parallel implementation on modern accelerators.'),
(97351, 2016, 'bmc', 'Improving biomedical information retrieval by linear combinations of different query expansion techniques', 'Abdulla, Ahmed AbdoAziz Ahmed|Lin, Hongfei|Xu, Bo|Banbhrani, Santosh Kumar', 'School of Computer Science and Technology, Dalian University of Technology, No.2 Linggong Rd., Dalian, 116024, Peoples Republic of China.|School of Computer Science and Technology, Dalian University of Technology, No.2 Linggong Rd., Dalian, 116024, Peoples Republic of China. hflin@dlut.edu.cn.|School of Computer Science and Technology, Dalian University of Technology, No.2 Linggong Rd., Dalian, 116024, Peoples Republic of China.|School of Computer Science and Technology, Dalian University of Technology, No.2 Linggong Rd., Dalian, 116024, Peoples Republic of China.', 'China|China|China|China', 0, 'Algorithms|Information Systems|Semantics', 'Journal Article', '10.1186/s12859-016-1092-8', '27455377', 'Biomedical literature retrieval is becoming increasingly complex, and there is a fundamental need for advanced information retrieval systems. Information Retrieval (IR) programs scour unstructured materials such as text documents in large reserves of data that are usually stored on computers. IR is related to the representation, storage, and organization of information items, as well as to access. In IR one of the main problems is to determine which documents are relevant and which are not to the users needs. Under the current regime, users cannot precisely construct queries in an accurate way to retrieve particular pieces of data from large reserves of data. Basic information retrieval systems are producing low-quality search results. In our proposed system for this paper we present a new technique to refine Information Retrieval searches to better represent the users information need in order to enhance the performance of information retrieval by using different query expansion techniques and apply a linear combinations between them, where the combinations was linearly between two expansion results at one time. Query expansions expand the search query, for example, by finding synonyms and reweighting original terms. They provide significantly more focused, particularized search results than do basic search queries.'),
(97352, 2016, 'bmc', 'flowVS: channel-specific variance stabilization in flow cytometry', 'Azad, Ariful|Rajwa, Bartek|Pothen, Alex', 'Computational Research Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, 94720, CA, USA. azad@lbl.gov.|Bindley Bioscience Center, Purdue University, West Lafayette, 47907, IN, USA.|Department of Computer Science, Purdue University, West Lafayette, 47907, IN, USA.', 'United States|United States|United States', 5, 'Algorithms|Analysis of Variance|Antigens, CD|Flow Cytometry|Humans|Lymphocytes', 'Journal Article', '10.1186/s12859-016-1083-9', '27465477', 'Comparing phenotypes of heterogeneous cell populations from multiple biological conditions is at the heart of scientific discovery based on flow cytometry (FC). When the biological signal is measured by the average expression of a biomarker, standard statistical methods require that variance be approximately stabilized in populations to be compared. Since the mean and variance of a cell population are often correlated in fluorescence-based FC measurements, a preprocessing step is needed to stabilize the within-population variances.'),
(97353, 2016, 'bmc', 'IGDD: a database of intronless genes in dicots', 'Yan, Hanwei|Dai, Xiaogang|Feng, Kai|Ma, Qiuyue|Yin, Tongming', 'Key Laboratory of Forest Genetics & Biotechnology, Nanjing Forestry University, Nanjing, China.|Key Laboratory of Forest Genetics & Biotechnology, Nanjing Forestry University, Nanjing, China.|Key Laboratory of Forest Genetics & Biotechnology, Nanjing Forestry University, Nanjing, China.|Key Laboratory of Forest Genetics & Biotechnology, Nanjing Forestry University, Nanjing, China.|Key Laboratory of Forest Genetics & Biotechnology, Nanjing Forestry University, Nanjing, China. tmyin@njfu.com.cn.', 'China|China|China|China|China', 10, 'Arabidopsis|Carica|Databases, Genetic|Internet|Introns|Magnoliopsida|Populus|Salix|User-Computer Interface|Vitis', 'Journal Article', '10.1186/s12859-016-1148-9', '27465544', 'Intronless genes are a significant characteristic of prokaryotes. Systematic identification and annotation are primary and crucial steps for determining the functions of intronless genes and understanding their occurrence in eukaryotes.'),
(97354, 2016, 'bmc', 'Erratum to: A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactions', 'Lagani, Vincenzo|Karozou, Argyro D|Gomez-Cabrero, David|Silberberg, Gilad|Tsamardinos, Ioannis', 'Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Greece.|Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Greece.|Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, 171 77, Stockholm, Sweden.|Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, 171 77, Stockholm, Sweden.|Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Greece. tsamard@csd.uoc.gr.', 'Greece|Greece|Sweden|Sweden|Greece', 0, '-', 'Published Erratum', '10.1186/s12859-016-1153-z', '27465624', '-'),
(97355, 2016, 'bmc', 'Scalable metagenomics alignment research tool (SMART): a scalable, rapid, and complete search heuristic for the classification of metagenomic sequences from complex sequence populations', 'Lee, Aaron Y|Lee, Cecilia S|Van Gelder, Russell N', 'Department of Ophthalmology, University of Washington School of Medicine, Box 359608, 325 Ninth Avenue, Seattle, WA, 98104, USA. leeay@uw.edu.|Department of Ophthalmology, University of Washington School of Medicine, Box 359608, 325 Ninth Avenue, Seattle, WA, 98104, USA.|Department of Ophthalmology, University of Washington School of Medicine, Box 359608, 325 Ninth Avenue, Seattle, WA, 98104, USA.', 'United States|United States|United States', 11, 'Algorithms|Databases, Nucleic Acid|Heuristics|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-016-1159-6', '27465705', 'Next generation sequencing technology has enabled characterization of metagenomics through massively parallel genomic DNA sequencing. The complexity and diversity of environmental samples such as the human gut microflora, combined with the sustained exponential growth in sequencing capacity, has led to the challenge of identifying microbial organisms by DNA sequence. We sought to validate a Scalable Metagenomics Alignment Research Tool (SMART), a novel searching heuristic for shotgun metagenomics sequencing results.'),
(97356, 2016, 'bmc', 'Erratum to: NETTAB 2014: From high-throughput structural bioinformatics to integrative systems biology', 'Romano, Paolo|Cordero, Francesca', 'Bioinformatics, IRCCS AOU San Martino - IST, c/o Centro Biotecnologie Avanzate (CBA), Largo Rosanna Benzi 10, I-16132, Genoa, Italy. paolo.romano@hsanmartino.it.|Department of Computer Science, University of Torino, Torino, I-10149, Italy.', 'Italy|Italy', 0, '-', 'Published Erratum', '10.1186/s12859-016-1050-5', '27473054', '-'),
(97357, 2016, 'bmc', 'RevEcoR: an R package for the reverse ecology analysis of microbiomes', 'Cao, Yang|Wang, Yuanyuan|Zheng, Xiaofei|Li, Fei|Bo, Xiaochen', 'Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850, China.|Department of Basic Courses, Army Officer Academy, Hefei, 230031, China.|Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850, China.|Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850, China. pittacus@gmail.com.|Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850, China. boxc@bmi.ac.cn.', 'China|China|China|China|China', 8, 'Algorithms|Bacteria|Bacterial Physiological Phenomena|Ecology|Metabolic Networks and Pathways|Metagenomics|Microbial Interactions|Microbiota|Software', 'Journal Article', '10.1186/s12859-016-1088-4', '27473172', 'All species live in complex ecosystems. The structure and complexity of a microbial community reflects not only diversity and function, but also the environment in which it occurs. However, traditional ecological methods can only be applied on a small scale and for relatively well-understood biological systems. Recently, a graph-theory-based algorithm called the reverse ecology approach has been developed that can analyze the metabolic networks of all the species in a microbial community, and predict the metabolic interface between species and their environment.'),
(97358, 2016, 'bmc', 'DISMISS: detection of stranded methylation in MeDIP-Seq data', 'Niazi, Umar|Geyer, Kathrin K|Vickers, Martin J|Hoffmann, Karl F|Swain, Martin T', 'Institute of Biological, Environmental, and Rural Sciences (IBERS), Aberystwyth University, Penglais, Aberystwyth, Ceredigion, SY23 3FG, UK.|Institute of Biological, Environmental, and Rural Sciences (IBERS), Aberystwyth University, Penglais, Aberystwyth, Ceredigion, SY23 3FG, UK.|Institute of Biological, Environmental, and Rural Sciences (IBERS), Aberystwyth University, Penglais, Aberystwyth, Ceredigion, SY23 3FG, UK.|Institute of Biological, Environmental, and Rural Sciences (IBERS), Aberystwyth University, Penglais, Aberystwyth, Ceredigion, SY23 3FG, UK.|Institute of Biological, Environmental, and Rural Sciences (IBERS), Aberystwyth University, Penglais, Aberystwyth, Ceredigion, SY23 3FG, UK. mts11@aber.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, 'Animals|Base Sequence|Bees|DNA Methylation|Databases, Nucleic Acid|Genomics|Immunoprecipitation|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1186/s12859-016-1158-7', '27473283', 'DNA methylation is an important regulator of gene expression and chromatin structure. Methylated DNA immunoprecipitation sequencing (MeDIP-Seq) is commonly used to identify regions of DNA methylation in eukaryotic genomes. Within MeDIP-Seq libraries, methylated cytosines can be found in both double-stranded (symmetric) and single-stranded (asymmetric) genomic contexts. While symmetric CG methylation has been relatively well-studied, asymmetric methylation in any dinucleotide context has received less attention. Importantly, no currently available software for processing MeDIP-Seq reads is able to resolve these strand-specific DNA methylation signals. Here we introduce DISMISS, a new software package that detects strand-associated DNA methylation from existing MeDIP-Seq analyses.'),
(97359, 2016, 'bmc', 'TopoICSim: a new semantic similarity measure based on gene ontology', 'Ehsani, Rezvan|Drabløs, Finn', 'Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, P.O. Box 8905, NO-7491, Trondheim, Norway.|Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, P.O. Box 8905, NO-7491, Trondheim, Norway. finn.drablos@ntnu.no.', 'Norway|Norway', 11, 'Algorithms|Computational Biology|Gene Ontology|Humans|Molecular Sequence Annotation|Semantics|Vocabulary, Controlled', 'Evaluation Studies', '10.1186/s12859-016-1160-0', '27473391', 'The Gene Ontology (GO) is a dynamic, controlled vocabulary that describes the cellular function of genes and proteins according to tree major categories: biological process, molecular function and cellular component. It has become widely used in many bioinformatics applications for annotating genes and measuring their semantic similarity, rather than their sequence similarity. Generally speaking, semantic similarity measures involve the GO tree topology, information content of GO terms, or a combination of both.'),
(97360, 2016, 'bmc', 'Prediction of FAD binding sites in electron transport proteins according to efficient radial basis function networks and significant amino acid pairs', 'Le, Nguyen-Quoc-Khanh|Ou, Yu-Yen', 'Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan. khanhlee87@gmail.com.|Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan. yienou@gmail.com.', '-|-', 32, 'Algorithms|Amino Acid Sequence|Amino Acids|Area Under Curve|Binding Sites|Electron Transport Chain Complex Proteins|Flavin-Adenine Dinucleotide|Internet|Protein Binding|ROC Curve|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1163-x', '27475771', 'Cellular respiration is a catabolic pathway for producing adenosine triphosphate (ATP) and is the most efficient process through which cells harvest energy from consumed food. When cells undergo cellular respiration, they require a pathway to keep and transfer electrons (i.e., the electron transport chain). Due to oxidation-reduction reactions, the electron transport chain produces a transmembrane proton electrochemical gradient. In case protons flow back through this membrane, this mechanical energy is converted into chemical energy by ATP synthase. The convert process is involved in producing ATP which provides energy in a lot of cellular processes. In the electron transport chain process, flavin adenine dinucleotide (FAD) is one of the most vital molecules for carrying and transferring electrons. Therefore, predicting FAD binding sites in the electron transport chain is vital for helping biologists understand the electron transport chain process and energy production in cells.'),
(97361, 2016, 'bmc', 'Gene, Environment and Methylation (GEM): a tool suite to efficiently navigate large scale epigenome wide association studies and integrate genotype and interaction between genotype and environment', 'Pan, Hong|Holbrook, Joanna D|Karnani, Neerja|Kwoh, Chee Keong', 'Singapore Institute for Clinical Sciences (SICS), Agency for Science Technology and Research (A*STAR), Singapore, 117609, Singapore.|Singapore Institute for Clinical Sciences (SICS), Agency for Science Technology and Research (A*STAR), Singapore, 117609, Singapore.|Singapore Institute for Clinical Sciences (SICS), Agency for Science Technology and Research (A*STAR), Singapore, 117609, Singapore.|School of Computer Science and Engineering, Nanyang Technological University (NTU), Singapore, 639798, Singapore. ASCKKWOH@ntu.edu.sg.', 'Singapore|Singapore|Singapore|Singapore', 10, 'Algorithms|DNA Methylation|Epigenomics|Genetic Association Studies|Genomics|Genotype|Internet|Quantitative Trait Loci|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1161-z', '27480116', 'The interplay among genetic, environment and epigenetic variation is not fully understood. Advances in high-throughput genotyping methods, high-density DNA methylation detection and well-characterized sample collections, enable epigenetic association studies at the genomic and population levels (EWAS). The field has extended to interrogate the interaction of environmental and genetic (GxE) influences on epigenetic variation. Also, the detection of methylation quantitative trait loci (methQTLs) and their association with health status has enhanced our knowledge of epigenetic mechanisms in disease trajectory. However analysis of this type of data brings computational challenges and there are few practical solutions to enable large scale studies in standard computational environments.'),
(97362, 2016, 'bmc', 'Reduction, alignment and visualisation of large diverse sequence families', 'Taylor, William R', 'Francsis Crick Institute, 1 Midland Rd., London, NW1 1AT, UK. william.taylor@crick.co.uk.', 'United Kingdom', 3, 'Algorithms|Amino Acid Sequence|Cluster Analysis|Computational Biology|Internet|Molecular Sequence Data|Multigene Family|Peptides|Sequence Alignment|Software', 'Evaluation Studies', '10.1186/s12859-016-1059-9', '27484804', 'Current volumes of sequence data can lead to large numbers of hits identified on a search, typically in the range of 10s to 100s of thousands. It is often quite difficult to tell from these raw results whether the search has been a success or has picked-up sequences with little or no relationship to the query. The best approach to this problem is to cluster and align the resulting families, however, existing methods concentrate on fast clustering and either do not align the sequences or only perform a limited alignment.'),
(97363, 2016, 'bmc', 'Erratum to: A novel procedure on next generation sequencing data analysis using text mining algorithm', 'Zhao, Weizhong|Chen, James J|Perkins, Roger|Wang, Yuping|Liu, Zhichao|Hong, Huixiao|Tong, Weida|Zou, Wen', 'Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA. wen.zou@fda.hhs.gov.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Published Erratum', '10.1186/s12859-016-1156-9', '27489012', '-'),
(97364, 2016, 'bmc', 'Context-specific functional module based drug efficacy prediction', 'Hwang, Woochang|Choi, Jaejoon|Kwon, Mijin|Lee, Doheon', 'Department of Bio and Brain Engineering, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea.|Department of Bio and Brain Engineering, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea.|Department of Bio and Brain Engineering, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea.|Department of Bio and Brain Engineering, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea. dhlee@kaist.ac.kr.', '-|-|-|-', 4, 'Cell Line|Drug Evaluation, Preclinical|Drug Repositioning|Drug Therapy|Humans|Linear Models|Models, Biological|Precision Medicine', 'Journal Article', '10.1186/s12859-016-1078-6', '27490093', 'It is necessary to evaluate the efficacy of individual drugs on patients to realize personalized medicine. Testing drugs on patients in clinical trial is the only way to evaluate the efficacy of drugs. The approach is labour intensive and requires overwhelming costs and a number of experiments. Therefore, preclinical model system has been intensively investigated for predicting the efficacy of drugs. Current computational drug sensitivity prediction approaches use general biological network modules as their prediction features. Therefore, they miss indirect effectors or the effects from tissue-specific interactions.'),
(97365, 2016, 'bmc', 'Prediction of scaffold proteins based on protein interaction and domain architectures', 'Oh, Kimin|Yi, Gwan-Su', 'Department of Bio and Brain Engineering, KAIST, Daejeon, Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon, Korea. gsyi@kaist.ac.kr.', '-|-', 0, 'Drug Design|Gene Ontology|High-Throughput Screening Assays|Humans|Protein Binding|Protein Engineering|Protein Interaction Maps|Proteins|Proteomics|Signal Transduction', 'Journal Article', '10.1186/s12859-016-1079-5', '27490120', 'Scaffold proteins are known for being crucial regulators of various cellular functions by assembling multiple proteins involved in signaling and metabolic pathways. Identification of scaffold proteins and the study of their molecular mechanisms can open a new aspect of cellular systemic regulation and the results can be applied in the field of medicine and engineering. Despite being highlighted as the regulatory roles of dozens of scaffold proteins, there was only one known computational approach carried out so far to find scaffold proteins from interactomes. However, there were limitations in finding diverse types of scaffold proteins because their criteria were restricted to the classical scaffold proteins. In this paper, we will suggest a systematic approach to predict massive scaffold proteins from interactomes and to characterize the roles of scaffold proteins comprehensively.'),
(97366, 2016, 'bmc', 'Prediction of compound-target interactions of natural products using large-scale drug and protein information', 'Keum, Jongsoo|Yoo, Sunyong|Lee, Doheon|Nam, Hojung', 'School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju, Republic of Korea.|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 305-701, Republic of Korea.|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 305-701, Republic of Korea.|School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju, Republic of Korea. hjnam@gist.ac.kr.', '-|-|-|-', 8, 'Biological Products|Computer Simulation|Drug Discovery|Models, Chemical|Plants, Medicinal|Protein Binding|Support Vector Machine', 'Journal Article', '10.1186/s12859-016-1081-y', '27490208', 'Verifying the proteins that are targeted by compounds of natural herbs will be helpful to select natural herb-based drug candidates. However, this entails a great deal of effort to clarify the interaction throughout in vitro or in vivo experiments. In this light, in silico prediction of the interactions between compounds and target proteins can help ease the efforts.'),
(97367, 2016, 'bmc', 'Erratum to: Practical impacts of genomic data cleaning on biological discovery using surrogate variable analysis', 'Jaffe, Andrew E|Hyde, Thomas|Kleinman, Joel|Weinberger, Daniel R|Chenoweth, Joshua G|McKay, Ronald D|Leek, Jeffrey T|Colantuoni, Carlo', 'Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA.|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, 21205, USA.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. carlo.colantuoni@libd.org.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Published Erratum', '10.1186/s12859-016-1152-0', '27510709', '-'),
(97368, 2016, 'bmc', 'Erratum to: eSTGt: a programming and simulation environment for population dynamics', 'Spiro, Adam|Shapiro, Ehud', 'Department of Computer Science and Applied Mathematics and Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel.|Department of Computer Science and Applied Mathematics and Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel. ehud.shapiro@weizmann.ac.il.', 'Israel|Israel', 0, '-', 'Published Erratum', '10.1186/s12859-016-1155-x', '27510770', '-'),
(97369, 2016, 'bmc', 'BAMQL: a query language for extracting reads from BAM files', 'Masella, Andre P|Lalansingh, Christopher M|Sivasundaram, Pragash|Fraser, Michael|Bristow, Robert G|Boutros, Paul C', 'Ontario Institute for Cancer Research, Suite 510, 661 University Avenue, M5G 0A3, Toronto, Canada.|Ontario Institute for Cancer Research, Suite 510, 661 University Avenue, M5G 0A3, Toronto, Canada.|Ontario Institute for Cancer Research, Suite 510, 661 University Avenue, M5G 0A3, Toronto, Canada.|Ontario Cancer Institute, Princess Margaret Cancer Centre/University Health Network, Toronto, Canada.|Ontario Cancer Institute, Princess Margaret Cancer Centre/University Health Network, Toronto, Canada.|Ontario Institute for Cancer Research, Suite 510, 661 University Avenue, M5G 0A3, Toronto, Canada. Paul.Boutros@oicr.on.ca.', 'Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Base Sequence|Chromosomes|Internet|Mitochondria|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1162-y', '27516195', 'It is extremely common to need to select a subset of reads from a BAM file based on their specific properties. Typically, a user unpacks the BAM file to a text stream using SAMtools, parses and filters the lines using AWK, then repacks them using SAMtools. This process is tedious and error-prone. In particular, when working with many columns of data, mix-ups are common and the bit field containing the flags is unintuitive. There are several libraries for reading BAM files, such as Bio-SamTools for Perl and pysam for Python. Both allow access to the BAMs read information and can filter reads, but require substantial boilerplate code, this is high overhead for mostly ad hoc filtering.'),
(97370, 2016, 'bmc', 'A machine learning strategy for predicting localization of post-translational modification sites in protein-protein interacting regions', 'Saethang, Thammakorn|Payne, D Michael|Avihingsanon, Yingyos|Pisitkun, Trairak', 'Systems Biology Center, Research Affairs, Faculty of Medicine, Chulalongkorn University, 1873 Rama 4 Road, Pathumwan, Bangkok, 10330, Thailand.|Systems Biology Center, Research Affairs, Faculty of Medicine, Chulalongkorn University, 1873 Rama 4 Road, Pathumwan, Bangkok, 10330, Thailand.|Department of Medicine, Division of Nephrology, Faculty of Medicine, Chulalongkorn University, 1873 Rama 4 Road, Pathumwan, Bangkok, 10330, Thailand. fmedyah@md.chula.ac.th.|Systems Biology Center, Research Affairs, Faculty of Medicine, Chulalongkorn University, 1873 Rama 4 Road, Pathumwan, Bangkok, 10330, Thailand. pisitkut@nhlbi.nih.gov.', 'Thailand|Thailand|Thailand|Thailand', 3, 'Machine Learning|Protein Interaction Domains and Motifs|Protein Processing, Post-Translational|Proteins', 'Journal Article', '10.1186/s12859-016-1165-8', '27534850', 'One very important functional domain of proteins is the protein-protein interacting region (PPIR), which forms the binding interface between interacting polypeptide chains. Post-translational modifications (PTMs) that occur in the PPIR can either interfere with or facilitate the interaction between proteins. The ability to predict whether sites of protein modifications are inside or outside of PPIRs would be useful in further elucidating the regulatory mechanisms by which modifications of specific proteins regulate their cellular functions.'),
(97371, 2016, 'bmc', 'Replica exchange molecular dynamics simulations reveal the structural and molecular properties of levan-type fructo-oligosaccharides of various chain lengths', 'Kanjanatanin, Pongsakorn|Pichyangkura, Rath|Chunsrivirot, Surasak', 'Department of Biochemistry, Faculty of Science, Chulalongkorn University, 254 Phaya Thai road, Pathumwan, Bangkok, 10330, Thailand.|Department of Biochemistry, Faculty of Science, Chulalongkorn University, 254 Phaya Thai road, Pathumwan, Bangkok, 10330, Thailand.|Department of Biochemistry, Faculty of Science, Chulalongkorn University, 254 Phaya Thai road, Pathumwan, Bangkok, 10330, Thailand. surasak.ch@chula.ac.th.', 'Thailand|Thailand|Thailand', 0, 'Cell Line, Tumor|Fructans|Humans|Hydrogen Bonding|Molecular Conformation|Molecular Dynamics Simulation|Oligosaccharides', 'Journal Article', '10.1186/s12859-016-1182-7', '27534934', 'Levan and levan-type fructo-oligosaccharides (LFOs) have various potential applications in pharmaceutical and food industries due to their beneficial properties such as their low intrinsic viscosity and high water solubility. Previous studies showed that they exhibited prebiotic effects, anti-inflammatory and anti-tumor activities against Sarcoma-180 tumor cells of human. Despite their various potential applications, the structural and molecular properties of LFOs of various chain lengths are not well understood.'),
(97372, 2016, 'bmc', 'CloneCNA: detecting subclonal somatic copy number alterations in heterogeneous tumor samples from whole-exome sequencing data', 'Yu, Zhenhua|Li, Ao|Wang, Minghui', 'School of Information Science and Technology, University of Science and Technology of China, Hefei, AH230027, China.|School of Information Science and Technology, University of Science and Technology of China, Hefei, AH230027, China. aoli@ustc.edu.cn.|School of Information Science and Technology, University of Science and Technology of China, Hefei, AH230027, China.', 'China|China|China', 0, 'Algorithms|Aneuploidy|DNA Copy Number Variations|Exome|Gene Frequency|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-016-1174-7', '27538789', 'Copy number alteration is a main genetic structural variation that plays an important role in tumor initialization and progression. Accurate detection of copy number alterations is necessary for discovering cancer-causing genes. Whole-exome sequencing has become a widely used technology in the last decade for detecting various types of genomic aberrations in cancer genomes. However, there are several major issues encountered in these detection problems, including normal cell contamination, tumor aneuploidy, and intra-tumor heterogeneity. Especially, deciphering the intra-tumor heterogeneity is imperative for identifying clonal and subclonal copy number alterations.'),
(97373, 2016, 'bmc', 'Topology-aware illumination design for volume rendering', 'Zhou, Jianlong|Wang, Xiuying|Cui, Hui|Gong, Peng|Miao, Xianglin|Miao, Yalin|Xiao, Chun|Chen, Fang|Feng, Dagan', 'Xian Jiaotong University City College, 8715 Shangji Road, Xian, Shaanxi 710018, Peoples Republic of China.|The University of Sydney, 1 Cleveland Street, Darlington, 2008, NSW, Australia.|The University of Sydney, 1 Cleveland Street, Darlington, 2008, NSW, Australia.|The University of Sydney, 1 Cleveland Street, Darlington, 2008, NSW, Australia.|Xian Jiaotong University City College, 8715 Shangji Road, Xian, Shaanxi 710018, Peoples Republic of China. mxl@mail.xjtu.edu.cn.|Xian University of Technology, 5 Jinhua Nan Road, Xian, 710048, Shaanxi, Peoples Republic of China.|Xiangtan University, Xiangtan, 411105, Hunan, Peoples Republic of China.|DATA61, CSIRO, 13 Garden Street, Eveleigh, 2015, NSW, Australia.|The University of Sydney, 1 Cleveland Street, Darlington, 2008, NSW, Australia.', 'China|Australia|Australia|Australia|China|China|China|Australia|Australia', 0, 'Algorithms|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Signal-To-Noise Ratio', 'Journal Article', '10.1186/s12859-016-1177-4', '27538893', 'Direct volume rendering is one of flexible and effective approaches to inspect large volumetric data such as medical and biological images. In conventional volume rendering, it is often time consuming to set up a meaningful illumination environment. Moreover, conventional illumination approaches usually assign same values of variables of an illumination model to different structures manually and thus neglect the important illumination variations due to structure differences.'),
(97374, 2016, 'bmc', 'A scalable assembly-free variable selection algorithm for biomarker discovery from metagenomes', 'Gkanogiannis, Anestis|Gazut, Stéphane|Salanoubat, Marcel|Kanj, Sawsan|Brüls, Thomas', 'Commissariat à lEnergie Atomique et aux Energies Alternatives, Direction de la Recherche Fondamentale, Institut de Génomique, Genoscope, Evry, Essonne, 91057, France.|Commissariat à lEnergie Atomique et aux Energies Alternatives, Direction de la Recherche Technologique, CEA-Tech, LIST, Laboratoire dAnalyse de Données et Intelligence des Systèmes, 91191, Gif-sur-Yvette, France.|Commissariat à lEnergie Atomique et aux Energies Alternatives, Direction de la Recherche Fondamentale, Institut de Génomique, Genoscope, Evry, Essonne, 91057, France.|Commissariat à lEnergie Atomique et aux Energies Alternatives, Direction de la Recherche Technologique, CEA-Tech, LIST, Laboratoire dAnalyse de Données et Intelligence des Systèmes, 91191, Gif-sur-Yvette, France.|Commissariat à lEnergie Atomique et aux Energies Alternatives, Direction de la Recherche Fondamentale, Institut de Génomique, Genoscope, Evry, Essonne, 91057, France. bruls@genoscope.cns.fr.', 'France|France|France|France|France', 0, 'Algorithms|Biomarkers|Datasets as Topic|Humans|Metagenome|Metagenomics|Microbiota', 'Journal Article', '10.1186/s12859-016-1186-3', '27542753', 'Metagenomics holds great promises for deepening our knowledge of key bacterial driven processes, but metagenome assembly remains problematic, typically resulting in representation biases and discarding significant amounts of non-redundant sequence information. In order to alleviate constraints assembly can impose on downstream analyses, and/or to increase the fraction of raw reads assembled via targeted assemblies relying on pre-assembly binning steps, we developed a set of binning modules and evaluated their combination in a new assembly-free binning protocol.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97375, 2016, 'bmc', 'Improving feature selection performance using pairwise pre-evaluation', 'Li, Songlu|Oh, Sejong', 'Department of Nanobiomedical Science, Dankook University, Cheonan, 330-714, Korea.|Department of Nanobiomedical Science, Dankook University, Cheonan, 330-714, Korea. sejongoh@dankook.ac.kr.', '-|-', 8, 'Algorithms|Area Under Curve|Gene Expression|ROC Curve|Software', 'Journal Article', '10.1186/s12859-016-1178-3', '27544506', 'Biological data such as microarrays contain a huge number of features. Thus, it is necessary to select a small number of novel features to characterize the entire dataset. All combinations of the features subset must be evaluated to produce an ideal feature subset, but this is impossible using currently available computing power. Feature selection or feature subset selection provides a sub-optimal solution within a reasonable amount of time.'),
(97376, 2016, 'bmc', 'Additive methods for genomic signatures', 'Karamichalis, Rallis|Kari, Lila|Konstantinidis, Stavros|Kopecki, Steffen|Solis-Reyes, Stephen', 'Department of Computer Science, University of Western Ontario, London ON, N6A 5B7, Canada.|School of Computing Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada. lila.kari@uwaterloo.ca.|Department of Mathematics and Computing Science, Saint Marys University, Halifax NS, Canada.|Department of Computer Science, University of Western Ontario, London ON, N6A 5B7, Canada.|Department of Computer Science, University of Western Ontario, London ON, N6A 5B7, Canada.', 'Canada|Canada|Canada|Canada|Canada', 5, 'Animals|Bacteria|DNA, Bacterial|DNA, Mitochondrial|Genome, Bacterial|Genomics|High-Throughput Nucleotide Sequencing|Humans', 'Evaluation Studies', '10.1186/s12859-016-1157-8', '27549194', 'Studies exploring the potential of Chaos Game Representations (CGR) of genomic sequences to act as genomic signatures (to be species- and genome-specific) showed that CGR patterns of nuclear and organellar DNA sequences of the same organism can be very different. While the hypothesis that CGRs of mitochondrial DNA sequences can act as genomic signatures was validated for a snapshot of all sequenced mitochondrial genomes available in the NCBI GenBank sequence database, to our knowledge no such extensive analysis of CGRs of nuclear DNA sequences exists to date.'),
(97377, 2016, 'bmc', 'SHIVA - a web application for drug resistance and tropism testing in HIV', 'Riemenschneider, Mona|Hummel, Thomas|Heider, Dominik', 'Department of Bioinformatics, Straubing Center of Science, Petersgasse 18, Straubing, 94315, Germany.|Department of Bioinformatics, Straubing Center of Science, Petersgasse 18, Straubing, 94315, Germany.|Department of Bioinformatics, Straubing Center of Science, Petersgasse 18, Straubing, 94315, Germany. d.heider@wz-straubing.de.', 'Germany|Germany|Germany', 7, 'Anti-HIV Agents|Drug Resistance, Viral|HIV Infections|HIV-1|Humans|Internet|Receptors, CXCR4|Reverse Transcriptase Inhibitors|Software|Viral Tropism|Virus Replication', 'Journal Article', '10.1186/s12859-016-1179-2', '27549230', 'Drug resistance testing is mandatory in antiretroviral therapy in human immunodeficiency virus (HIV) infected patients for successful treatment. The emergence of resistances against antiretroviral agents remains the major obstacle in inhibition of viral replication and thus to control infection. Due to the high mutation rate the virus is able to adapt rapidly under drug pressure leading to the evolution of resistant variants and finally to therapy failure.'),
(97378, 2016, 'bmc', 'The tumor as an organ: comprehensive spatial and temporal modeling of the tumor and its microenvironment', 'Bloch, Naamah|Harel, David', 'Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 234 Herzl st, 7610001, Rehovot, Israel. Naamah.Bloch@weizmann.ac.il.|Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 234 Herzl st, 7610001, Rehovot, Israel.', 'Israel|Israel', 6, 'Computer Simulation|Disease Progression|Humans|Models, Biological|Neoplasms|Neovascularization, Pathologic|Oxygen|Tumor Microenvironment|Vascular Endothelial Growth Factor A', 'Journal Article', '10.1186/s12859-016-1168-5', '27553370', 'Research related to cancer is vast, and continues in earnest in many directions. Due to the complexity of cancer, a better understanding of tumor growth dynamics can be gleaned from a dynamic computational model. We present a comprehensive, fully executable, spatial and temporal 3D computational model of the development of a cancerous tumor together with its environment.'),
(97379, 2016, 'bmc', 'FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data', 'DeTomaso, David|Yosef, Nir', 'Department of Electrical Engineering and Computer Science and Center for Computational Biology, University of California, Berkeley, 97420, CA, USA.|Department of Electrical Engineering and Computer Science and Center for Computational Biology, University of California, Berkeley, 97420, CA, USA. niryosef@berkeley.edu.', 'United States|United States', 24, 'Algorithms|Biomarkers, Tumor|Computational Biology|Gene Expression Profiling|Glioblastoma|High-Throughput Nucleotide Sequencing|Humans|Models, Statistical|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1186/s12859-016-1176-5', '27553427', 'A key challenge in the emerging field of single-cell RNA-Seq is to characterize phenotypic diversity between cells and visualize this information in an informative manner. A common technique when dealing with high-dimensional data is to project the data to 2 or 3 dimensions for visualization. However, there are a variety of methods to achieve this result and once projected, it can be difficult to ascribe biological significance to the observed features. Additionally, when analyzing single-cell data, the relationship between cells can be obscured by technical confounders such as variable gene capture rates.'),
(97380, 2016, 'bmc', 'Interpreting transcriptional changes using causal graphs: new methods and their practical utility on public networks', 'Fakhry, Carl Tony|Choudhary, Parul|Gutteridge, Alex|Sidders, Ben|Chen, Ping|Ziemek, Daniel|Zarringhalam, Kourosh', 'Department of Computer Science, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, 02125, USA.|Computational Sciences, Pfizer Worldwide Research & Development, Cambridge, USA.|Computational Sciences, Pfizer Worldwide Research & Development, Cambridge, USA.|Computational Sciences, Pfizer Worldwide Research & Development, Cambridge, USA.|Department of Engineering, University of Massachusetts Boston, Boston, 100 Morrissey Boulevard02125, USA.|Computational Sciences, Pfizer Worldwide Research & Development, Berlin, USA.|Department of Mathematics, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, 02125, USA. kourosh.zarringhalam@umb.edu.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Cell Differentiation|Data Interpretation, Statistical|Gene Expression Regulation|Gene Regulatory Networks|Humans|Stem Cells|Transcription, Genetic', 'Journal Article', '10.1186/s12859-016-1181-8', '27553489', 'Inference of active regulatory cascades under specific molecular and environmental perturbations is a recurring task in transcriptional data analysis. Commercial tools based on large, manually curated networks of causal relationships offering such functionality have been used in thousands of articles in the biomedical literature. The adoption and extension of such methods in the academic community has been hampered by the lack of freely available, efficient algorithms and an accompanying demonstration of their applicability using current public networks.'),
(97381, 2016, 'bmc', 'Stepwise iterative maximum likelihood clustering approach', 'Sharma, Alok|Shigemizu, Daichi|Boroevich, Keith A|López, Yosvany|Kamatani, Yoichiro|Kubo, Michiaki|Tsunoda, Tatsuhiko', 'RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan. alok.fj@gmail.com.|RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.|RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.|RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.|RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.|RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.|RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan. tatsuhiko.tsunoda@riken.jp.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 8, 'Algorithms|Cluster Analysis|Gene Expression Profiling|Humans|Likelihood Functions|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-016-1184-5', '27553625', 'Biological/genetic data is a complex mix of various forms or topologies which makes it quite difficult to analyze. An abundance of such data in this modern era requires the development of sophisticated statistical methods to analyze it in a reasonable amount of time. In many biological/genetic analyses, such as genome-wide association study (GWAS) analysis or multi-omics data analysis, it is required to cluster the plethora of data into sub-categories to understand the subtypes of populations, cancers or any other diseases. Traditionally, the k-means clustering algorithm is a dominant clustering method. This is due to its simplicity and reasonable level of accuracy. Many other clustering methods, including support vector clustering, have been developed in the past, but do not perform well with the biological data, either due to computational reasons or failure to identify clusters.'),
(97382, 2016, 'bmc', 'Prediction of redox-sensitive cysteines using sequential distance and other sequence-based features', 'Sun, Ming-An|Zhang, Qing|Wang, Yejun|Ge, Wei|Guo, Dianjing', 'State Key Laboratory of Agrobiotechnology and School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, Peoples Republic of China.|State Key Laboratory of Agrobiotechnology and School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, Peoples Republic of China.|Department of Cell Biology and Genetics, School of Basic Medical Sciences, Shenzhen University Health Science Center, Nanhai Ave 3688, Shenzhen, 518060, Peoples Republic of China.|Centre of Reproduction, Development and Aging, Faculty of Health Sciences, University of Macau, Taipa, Macau, Peoples Republic of China.|State Key Laboratory of Agrobiotechnology and School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, Peoples Republic of China. djguo@cuhk.edu.hk.', 'China|China|China|China|China', 7, 'Cysteine|Oxidation-Reduction|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein|Support Vector Machine', 'Evaluation Studies', '10.1186/s12859-016-1185-4', '27553667', 'Reactive oxygen species can modify the structure and function of proteins and may also act as important signaling molecules in various cellular processes. Cysteine thiol groups of proteins are particularly susceptible to oxidation. Meanwhile, their reversible oxidation is of critical roles for redox regulation and signaling. Recently, several computational tools have been developed for predicting redox-sensitive cysteines, however, those methods either only focus on catalytic redox-sensitive cysteines in thiol oxidoreductases, or heavily depend on protein structural data, thus cannot be widely used.'),
(97383, 2016, 'bmc', 'An ensemble framework for identifying essential proteins', 'Zhang, Xue|Xiao, Wangxin|Acencio, Marcio Luis|Lemke, Ney|Wang, Xujing', 'Systems Biology Core, NHLBI, NIH, 9000 Rockville Pike, Bethesda, MD, 20892, USA.|Department of Computer Science, XiangNan University, Eastern Wangxian Park, Chenzhou, Hunan, 423000, China. xiaozhangdoctor@126.com.|Department of Physics and Biophysics, Institute of Biosciences of Botucatu, UNESP-São Paulo State University, CEP 18618-970, Botucatu, São Paulo, 510, Brazil.|Department of Physics and Biophysics, Institute of Biosciences of Botucatu, UNESP-São Paulo State University, CEP 18618-970, Botucatu, São Paulo, 510, Brazil.|Systems Biology Core, NHLBI, NIH, 9000 Rockville Pike, Bethesda, MD, 20892, USA. xujing.wang@nih.gov.', 'United States|China|Brazil|Brazil|United States', 7, 'Gene Expression|Genome, Fungal|Open Reading Frames|Protein Interaction Mapping|Protein Interaction Maps|Proteins|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/s12859-016-1166-7', '27557880', 'Many centrality measures have been proposed to mine and characterize the correlations between network topological properties and protein essentiality. However, most of them show limited prediction accuracy, and the number of common predicted essential proteins by different methods is very small.'),
(97384, 2016, 'bmc', 'Statistical inference for time course RNA-Seq data using a negative binomial mixed-effect model', 'Sun, Xiaoxiao|Dalpiaz, David|Wu, Di|S Liu, Jun|Zhong, Wenxuan|Ma, Ping', 'Department of Statistics, University of Georgia, 101 Cedar Street, Athens, 30602, USA.|Department of Statistics, University of Illinois at Urbana-Champaign, 725 South Wright Street, Champaign, 61820, USA.|Department of Statistics, Harvard University, One Oxford Street, Cambridge, 02138, USA.|Department of Statistics, Harvard University, One Oxford Street, Cambridge, 02138, USA.|Department of Statistics, University of Georgia, 101 Cedar Street, Athens, 30602, USA.|Department of Statistics, University of Georgia, 101 Cedar Street, Athens, 30602, USA. pingma@uga.edu.', 'Georgia|United States|United States|United States|Georgia|Georgia', 9, 'Animals|Drosophila|Likelihood Functions|Models, Statistical|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-016-1180-9', '27565575', 'Accurate identification of differentially expressed (DE) genes in time course RNA-Seq data is crucial for understanding the dynamics of transcriptional regulatory network. However, most of the available methods treat gene expressions at different time points as replicates and test the significance of the mean expression difference between treatments or conditions irrespective of time. They thus fail to identify many DE genes with different profiles across time. In this article, we propose a negative binomial mixed-effect model (NBMM) to identify DE genes in time course RNA-Seq data. In the NBMM, mean gene expression is characterized by a fixed effect, and time dependency is described by random effects. The NBMM is very flexible and can be fitted to both unreplicated and replicated time course RNA-Seq data via a penalized likelihood method. By comparing gene expression profiles over time, we further classify the DE genes into two subtypes to enhance the understanding of expression dynamics. A significance test for detecting DE genes is derived using a Kullback-Leibler distance ratio. Additionally, a significance test for gene sets is developed using a gene set score.'),
(97385, 2016, 'bmc', 'Identification of DNA-binding proteins using multi-features fusion and binary firefly optimization algorithm', 'Zhang, Jian|Gao, Bo|Chai, Haiting|Ma, Zhiqiang|Yang, Guifu', 'School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, Peoples Republic of China.|School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, Peoples Republic of China.|School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, Peoples Republic of China.|School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, Peoples Republic of China.|School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, Peoples Republic of China. guifuyang.nenu@gmail.com.', 'China|China|China|China|China', 20, 'Algorithms|Area Under Curve|DNA-Binding Proteins|Databases, Protein|Internet|Protein Structure, Secondary|ROC Curve|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1201-8', '27565741', 'DNA-binding proteins (DBPs) play fundamental roles in many biological processes. Therefore, the developing of effective computational tools for identifying DBPs is becoming highly desirable.'),
(97386, 2016, 'bmc', 'Integrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information', 'Hieke, Stefanie|Benner, Axel|Schlenl, Richard F|Schumacher, Martin|Bullinger, Lars|Binder, Harald', 'Institute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Stefan-Meier-Str. 26, Freiburg, 79104, Germany. hieke@imbi.uni-freiburg.de.|Division of Biostatistics, German Cancer Research Center, Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.|Department of Internal Medicine III, University Hospital of Ulm, Albert-Einstein-Allee 23, Ulm, 89081, Germany.|Institute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Stefan-Meier-Str. 26, Freiburg, 79104, Germany.|Department of Internal Medicine III, University Hospital of Ulm, Albert-Einstein-Allee 23, Ulm, 89081, Germany.|Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center Johannes Gutenberg University Mainz, Obere Zahlbacher Str. 69, Mainz, 55131, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 5, 'DNA Methylation|Gene Expression Profiling|Humans|Leukemia, Myeloid, Acute|Likelihood Functions|Models, Theoretical|Multivariate Analysis|Polymorphism, Single Nucleotide|Prognosis|Risk', 'Journal Article', '10.1186/s12859-016-1183-6', '27578050', 'High-throughput technology allows for genome-wide measurements at different molecular levels for the same patient, e.g. single nucleotide polymorphisms (SNPs) and gene expression. Correspondingly, it might be beneficial to also integrate complementary information from different molecular levels when building multivariable risk prediction models for a clinical endpoint, such as treatment response or survival. Unfortunately, such a high-dimensional modeling task will often be complicated by a limited overlap of molecular measurements at different levels between patients, i.e. measurements from all molecular levels are available only for a smaller proportion of patients.'),
(97387, 2016, 'bmc', 'Fold-specific sequence scoring improves protein sequence matching', 'Leelananda, Sumudu P|Kloczkowski, Andrzej|Jernigan, Robert L', 'Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, 112 Office and Lab Building, Ames, IA, 50011-3020, USA.|Present Address: Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Childrens Hospital, Columbus, OH, 43205, USA.|Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, 112 Office and Lab Building, Ames, IA, 50011-3020, USA. jernigan@iastate.edu.', 'United States|United States|United States', 1, 'Algorithms|Amino Acid Substitution|Protein Structure, Secondary|Sequence Analysis, Protein|Structural Homology, Protein', 'Journal Article', '10.1186/s12859-016-1198-z', '27578239', 'Sequence matching is extremely important for applications throughout biology, particularly for discovering information such as functional and evolutionary relationships, and also for discriminating between unimportant and disease mutants. At present the functions of a large fraction of genes are unknown, improvements in sequence matching will improve gene annotations. Universal amino acid substitution matrices such as Blosum62 are used to measure sequence similarities and to identify distant homologues, regardless of the structure class. However, such single matrices do not take into account important structural information evident within the different topologies of proteins and treats substitutions within all protein folds identically. Others have suggested that the use of structural information can lead to significant improvements in sequence matching but this has not yet been very effective. Here we develop novel substitution matrices that include not only general sequence information but also have a topology specific component that is unique for each CATH topology. This novel feature of using a combination of sequence and structure information for each protein topology significantly improves the sequence matching scores for the sequence pairs tested. We have used a novel multi-structure alignment method for each homology level of CATH in order to extract topological information.'),
(97388, 2016, 'bmc', 'Fusing literature and full network data improves disease similarity computation', 'Li, Ping|Nie, Yaling|Yu, Jingkai', 'State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 100190, China.|State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 100190, China.|State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 100190, China. jkyu@ipe.ac.cn.', 'China|China|China', 10, 'Algorithms|Data Mining|Disease|Genetic Association Studies|Humans|MEDLINE|Protein Interaction Maps', 'Journal Article', '10.1186/s12859-016-1205-4', '27578323', 'Identifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. A number of methods for computing disease similarity had been developed, however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. Most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. In this study, we propose a new method (MedNetSim) for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature.'),
(97389, 2016, 'bmc', 'A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs', 'Li, Dingfang|Luo, Longqiang|Zhang, Wen|Liu, Feng|Luo, Fei', 'School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China.|School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China.|State Key Lab of Software Engineering, Wuhan University, Wuhan, 430072, China. zhangwen@whu.edu.cn.|International School of Software, Wuhan University, Wuhan, 430072, China.|State Key Lab of Software Engineering, Wuhan University, Wuhan, 430072, China.', 'China|China|China|China|China', 0, 'Algorithms|Animals|Area Under Curve|DNA Transposable Elements|Drosophila|Humans|Mice|RNA, Small Interfering|ROC Curve|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-1206-3', '27578422', 'Predicting piwi-interacting RNA (piRNA) is an important topic in the small non-coding RNAs, which provides clues for understanding the generation mechanism of gamete. To the best of our knowledge, several machine learning approaches have been proposed for the piRNA prediction, but there is still room for improvements.'),
(97390, 2016, 'bmc', 'CryGetter: a tool to automate retrieval and analysis of Cry protein data', 'Buzatto, David|de Castro França, Suzelei|Zingaretti, Sônia Marli', 'Instituto Federal de Educação, Ciência e Tecnologia de São Paulo - IFSP, Câmpus São João da Boa Vista, Acesso Dr. João Batista Merlin, s/n, Jardim Itália, São João da Boa Vista, 13872-551, SP, Brazil. davidbuzatto@ifsp.edu.br.|Universidade de Ribeirão Preto - UNAERP, Av. Costábile Romano, 2201, Ribeirânia, Ribeirão Preto, 14096-000, SP, Brazil.|Universidade de Ribeirão Preto - UNAERP, Av. Costábile Romano, 2201, Ribeirânia, Ribeirão Preto, 14096-000, SP, Brazil.', 'Brazil|Brazil|Brazil', 1, 'Bacterial Proteins|Endotoxins|Hemolysin Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/s12859-016-1207-2', '27578522', 'For many years, the use of chemical agents to control crop pests has been degrading the environment, bringing problems to humans and all living things. An alternative to deal with the pests is the use of biopesticides, biological agents capable of controlling these harmful organisms. One kind of biopesticide is Bacillus thuringiensis, a Gram-positive bacterium that synthesizes a protein that, when ingested by the pests, kills them and does not harm other species.'),
(97391, 2016, 'bmc', 'Fine-grained parallelization of fitness functions in bioinformatics optimization problems: gene selection for cancer classification and biclustering of gene expression data', 'Gomez-Pulido, Juan A|Cerrada-Barrios, Jose L|Trinidad-Amado, Sebastian|Lanza-Gutierrez, Jose M|Fernandez-Diaz, Ramon A|Crawford, Broderick|Soto, Ricardo', 'Department of Technologies of Computers and Communications, University of Extremadura, Polytechnic School, Campus Universitario s/n, Caceres, 10003, Spain. jangomez@unex.es.|Department of Technologies of Computers and Communications, University of Extremadura, Polytechnic School, Campus Universitario s/n, Caceres, 10003, Spain.|Department of Technologies of Computers and Communications, University of Extremadura, Polytechnic School, Campus Universitario s/n, Caceres, 10003, Spain.|Department of Technologies of Computers and Communications, University of Extremadura, Polytechnic School, Campus Universitario s/n, Caceres, 10003, Spain.|Department of Computer and Aerospace Engineering, University of Leon, Computer Sciences School, Campus de Vegazana s/n, Leon, 24071, Spain.|Pontificia Universidad Católica de Valparaíso, Valparaíso, 2362807, Chile.|Pontificia Universidad Católica de Valparaíso, Valparaíso, 2362807, Chile.', 'Spain|Spain|Spain|Spain|Spain|Chile|Chile', 2, 'Algorithms|Computational Biology|Gene Expression Regulation, Neoplastic|Humans|Neoplasms|Software', 'Journal Article', '10.1186/s12859-016-1200-9', '27581798', 'Metaheuristics are widely used to solve large combinatorial optimization problems in bioinformatics because of the huge set of possible solutions. Two representative problems are gene selection for cancer classification and biclustering of gene expression data. In most cases, these metaheuristics, as well as other non-linear techniques, apply a fitness function to each possible solution with a size-limited population, and that step involves higher latencies than other parts of the algorithms, which is the reason why the execution time of the applications will mainly depend on the execution time of the fitness function. In addition, it is usual to find floating-point arithmetic formulations for the fitness functions. This way, a careful parallelization of these functions using the reconfigurable hardware technology will accelerate the computation, specially if they are applied in parallel to several solutions of the population.'),
(97392, 2016, 'bmc', 'Predicting existing targets for new drugs base on strategies for missing interactions', 'Shi, Jian-Yu|Li, Jia-Xin|Lu, Hui-Meng', 'School of Life Sciences, Northwestern Polytechnical University, Xian, 710072, China. jianyushi@nwpu.edu.cn.|School of Life Sciences, Northwestern Polytechnical University, Xian, 710072, China.|School of Life Sciences, Northwestern Polytechnical University, Xian, 710072, China.', 'China|China|China', 17, 'Area Under Curve|Drug Discovery|Drug Interactions|Humans|Models, Theoretical', 'Journal Article', '10.1186/s12859-016-1118-2', '27585458', 'There has been paid more and more attention to supervised classification models in the area of predicting drug-target interactions (DTIs). However, in terms of classification, unavoidable missing DTIs in data would cause three issues which have not yet been addressed appropriately by former approaches. Directly labeled as negatives (non-DTIs), missing DTIs increase the confusion of positives (DTIs) and negatives, aggravate the imbalance between few positives and many negatives, and are usually discriminated as highly-scored false positives, which influence the existing measures sharply.'),
(97393, 2016, 'bmc', 'GRASPx: efficient homolog-search of short peptide metagenome database through simultaneous alignment and assembly', 'Zhong, Cuncong|Yang, Youngik|Yooseph, Shibu', 'Informatics Department, J. Craig Venter Institute, La Jolla, CA, 92037, USA.|Informatics Department, J. Craig Venter Institute, La Jolla, CA, 92037, USA.|Informatics Department, J. Craig Venter Institute, La Jolla, CA, 92037, USA. syooseph@jcvi.org.', 'United States|United States|United States', 2, 'Algorithms|Amino Acid Sequence|Computer Simulation|Databases, Protein|Humans|Metagenome|Metagenomics|Peptides|Sequence Alignment|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/s12859-016-1119-1', '27585568', 'Metagenomics is a cultivation-independent approach that enables the study of the genomic composition of microbes present in an environment. Metagenomic samples are routinely sequenced using next-generation sequencing technologies that generate short nucleotide reads. Proteins identified from these reads are mostly of partial length. On the other hand, de novo assembly of a large metagenomic dataset is computationally demanding and the assembled contigs are often fragmented, resulting in the identification of protein sequences that are also of partial length and incomplete. Annotation of an incomplete protein sequence often proceeds by identifying its homologs in a database of reference sequences. Identifying the homologs of incomplete sequences is a challenge and can result in substandard annotation of proteins from metagenomic datasets. To address this problem, we recently developed a homology detection algorithm named GRASP (Guided Reference-based Assembly of Short Peptides) that identifies the homologs of a given reference protein sequence in a database of short peptide metagenomic sequences. GRASP was developed to implement a simultaneous alignment and assembly algorithm for annotation of short peptides identified on metagenomic reads. The program achieves significantly improved recall rate at the cost of computational efficiency. In this article, we adopted three techniques to speed up the original version of GRASP, including the pre-construction of extension links, local assembly of individual seeds, and the implementation of query-level parallelism.'),
(97394, 2016, 'bmc', 'A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis', 'Chrxc3xa9tien, Stxc3xa9phane|Guyeux, Christophe|Conesa, Bastien|Delage-Mouroux, Rxc3xa9gis|Jouvenot, Michxc3xa8le|Huetz, Philippe|Descxc3xb4tes, Franxc3xa7oise', 'National Physical Laboratory, Hampton Road, Teddington, Middlesex, UK. stephane.chretien@npl.co.uk.|FEMTO-ST Institute, UMR 6174 CNRS, DISC Computer Science Department, Universitxc3xa9 de Bourgogne Franche-Comtxc3xa9, 16, route de Gray, Besanxc3xa7on, 25000, France.|ISIFC, Universitxc3xa9 de Bourgogne Franche-Comtxc3xa9, 23, rue Alain Savary, Besanxc3xa7on, 25000, France.|EA 3922/IFR133, UFR Sciences et Techniques, Universitxc3xa9 de Bourgogne Franche-Comtxc3xa9, 16, route de Gray, Besanxc3xa7on, 25000, France.|EA 3922/IFR133, UFR Sciences et Techniques, Universitxc3xa9 de Bourgogne Franche-Comtxc3xa9, 16, route de Gray, Besanxc3xa7on, 25000, France.|ABC&T, 33, rue Charles Nodier, Besanxc3xa7on, 25000, France.|Service de Biochimie et Biologie Molxc3xa9culaire Sud, Pavillon 3D, Centre Hospitalier Lyon Sud, Pierre Bxc3xa9nite, Lyon, Cedex 69495, France.', 'United Kingdom|France|France|France|France|France|France', 2, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Models, Genetic|Normal Distribution|Principal Component Analysis|Urinary Bladder Neoplasms', 'Journal Article', '10.1186/s12859-016-1120-8', '27585655', 'Non-Negative Matrix factorization has become an essential tool for feature extraction in a wide spectrum of applications. In the present work, our objective is to extend the applicability of the method to the case of missing and/or corrupted data due to outliers.'),
(97395, 2016, 'bmc', 'PnpProbs: a better multiple sequence alignment tool by better handling of guide trees', 'Ye, Yongtao|Lam, Tak-Wah|Ting, Hing-Fung', 'HKU-BGI Bioinformatics Algorithms & Core Technology Research Lab, Computer Science Department, University of Hong Kong, Hong Kong, China.|HKU-BGI Bioinformatics Algorithms & Core Technology Research Lab, Computer Science Department, University of Hong Kong, Hong Kong, China.|HKU-BGI Bioinformatics Algorithms & Core Technology Research Lab, Computer Science Department, University of Hong Kong, Hong Kong, China. hfting@cs.hku.hk.', 'China|China|China', 0, 'Algorithms|Amino Acid Sequence|Computer Simulation|Databases, Protein|Phylogeny|Sequence Alignment|Software|Time Factors', 'Journal Article', '10.1186/s12859-016-1121-7', '27585754', 'This paper describes a new MSA tool called PnpProbs, which constructs better multiple sequence alignments by better handling of guide trees. It classifies sequences into two types: normally related and distantly related. For normally related sequences, it uses an adaptive approach to construct the guide tree needed for progressive alignment, it first estimates the inputs discrepancy by computing the standard deviation of their percent identities, and based on this estimate, it chooses the better method to construct the guide tree. For distantly related sequences, PnpProbs abandons the guide tree and uses instead some non-progressive alignment method to generate the alignment.'),
(97396, 2016, 'bmc', 'Risk-conscious correction of batch effects: maximising information extraction from high-throughput genomic datasets', 'Oytam, Yalchin|Sobhanmanesh, Fariborz|Duesing, Konsta|Bowden, Joshua C|Osmond-McLeod, Megan|Ross, Jason', 'CSIRO, Genomics and Cellular Sciences, Transformational Biology CP, North Ryde, NSW, Australia. yalchin.oytam@csiro.au.|CSIRO, Genomics and Cellular Sciences, Transformational Biology CP, North Ryde, NSW, Australia.|CSIRO, Genomics and Cellular Sciences, Transformational Biology CP, North Ryde, NSW, Australia.|CSIRO, IM&T, Science Applications, St Lucia, QLD, Australia.|CSIRO, Genomics and Cellular Sciences, Advanced Materials CP (Nanosafety), 11 Julius Avenue, North Ryde, NSW, 2113, Australia.|CSIRO, Genomics and Cellular Sciences, Transformational Biology CP, North Ryde, NSW, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia', 21, 'Genomics|Humans|Information Storage and Retrieval|Principal Component Analysis|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-016-1212-5', '27585881', 'Batch effects are a persistent and pervasive form of measurement noise which undermine the scientific utility of high-throughput genomic datasets. At their most benign, they reduce the power of statistical tests resulting in actual effects going unidentified. At their worst, they constitute confounds and render datasets useless. Attempting to remove batch effects will result in some of the biologically meaningful component of the measurement (i.e. signal) being lost. We present and benchmark a novel technique, called Harman. Harman maximises the removal of batch noise with the constraint that the risk of also losing biologically meaningful component of the measurement is kept to a fraction which is set by the user.'),
(97397, 2016, 'bmc', 'The parameter sensitivity of random forests', 'Huang, Barbara F F|Boutros, Paul C', 'Informatics and Bio-computing Program, Ontario Institute for Cancer Research, Toronto, Canada.|Informatics and Bio-computing Program, Ontario Institute for Cancer Research, Toronto, Canada. Paul.Boutros@oicr.on.ca.', 'Canada|Canada', 28, 'Algorithms|Computational Biology|Models, Theoretical|Reproducibility of Results', 'Journal Article', '10.1186/s12859-016-1228-x', '27586051', 'The Random Forest (RF) algorithm for supervised machine learning is an ensemble learning method widely used in science and many other fields. Its popularity has been increasing, but relatively few studies address the parameter selection process: a critical step in model fitting. Due to numerous assertions regarding the performance reliability of the default parameters, many RF models are fit using these values. However there has not yet been a thorough examination of the parameter-sensitivity of RFs in computational genomic studies. We address this gap here.'),
(97398, 2016, 'bmc', 'Clustering analysis of proteins from microbial genomes at multiple levels of resolution', 'Zaslavsky, Leonid|Ciufo, Stacy|Fedorov, Boris|Tatusova, Tatiana', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, 20894, MD, USA. zaslavsk@ncbi.nlm.nih.gov.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, 20894, MD, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, 20894, MD, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, 20894, MD, USA.', 'United States|United States|United States|United States', 4, 'Algorithms|Bacterial Proteins|Cluster Analysis|Genome, Microbial|Guanosine Triphosphate|Humans|Phylogeny|Statistics as Topic', 'Journal Article', '10.1186/s12859-016-1112-8', '27586436', 'Microbial genomes at the National Center for Biotechnology Information (NCBI) represent a large collection of more than 35,000 assemblies. There are several complexities associated with the data: a great variation in sampling density since human pathogens are densely sampled while other bacteria are less represented, different protein families occur in annotations with different frequencies, and the quality of genome annotation varies greatly. In order to extract useful information from these sophisticated data, the analysis needs to be performed at multiple levels of phylogenomic resolution and protein similarity, with an adequate sampling strategy.'),
(97399, 2016, 'bmc', 'Assessment of transfer methods for comparative genomics of regulatory networks in bacteria', 'Kxc4xb1lxc4xb1xc3xa7, Sefa|Erill, Ivan', 'Department of Biological Sciences, University of Maryland Baltimore County (UMBC), Baltimore, MD, 21250, USA.|Department of Biological Sciences, University of Maryland Baltimore County (UMBC), Baltimore, MD, 21250, USA. erill@umbc.edu.', 'United States|United States', 2, 'Area Under Curve|Bacteria|Binding Sites|Gene Regulatory Networks|Genomics|Phylogeny|ROC Curve|Regulon|Sequence Homology, Amino Acid', 'Comparative Study', '10.1186/s12859-016-1113-7', '27586594', 'Comparative genomics can leverage the vast amount of available genomic sequences to reconstruct and analyze transcriptional regulatory networks in Bacteria, but the efficacy of this approach hinges on the ability to transfer regulatory network information from reference species to the genomes under analysis. Several methods have been proposed to transfer regulatory information between bacterial species, but the paucity and distributed nature of experimental information on bacterial transcriptional networks have prevented their systematic evaluation.'),
(97400, 2016, 'bmc', 'Automated prediction of HIV drug resistance from genotype data', 'Shen, ChenHsiang|Yu, Xiaxia|Harrison, Robert W|Weber, Irene T', 'Department of Biology, Georgia State University, Atlanta, GA, 30303, USA.|Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA.|Department of Biology, Georgia State University, Atlanta, GA, 30303, USA.|Department of Biology, Georgia State University, Atlanta, GA, 30303, USA. iweber@gsu.edu.', 'Georgia|Georgia|Georgia|Georgia', 10, 'Algorithms|Anti-HIV Agents|Automation|Computational Biology|Databases, Genetic|Drug Resistance, Viral|Genotype|HIV Infections|HIV Protease|HIV Protease Inhibitors|HIV Reverse Transcriptase|HIV-1|Humans|Reverse Transcriptase Inhibitors', 'Journal Article', '10.1186/s12859-016-1114-6', '27586700', 'HIV/AIDS is a serious threat to public health. The emergence of drug resistance mutations diminishes the effectiveness of drug therapy for HIV/AIDS. Developing a computational prediction of drug resistance phenotype will enable efficient and timely selection of the best treatment regimens.'),
(97401, 2016, 'bmc', 'Predicting essential proteins based on subcellular localization, orthology and PPI networks', 'Li, Gaoshi|Li, Min|Wang, Jianxin|Wu, Jingli|Wu, Fang-Xiang|Pan, Yi', 'School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan, Peoples Republic of China.|School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan, Peoples Republic of China. limin@mail.csu.edu.cn.|School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan, Peoples Republic of China. jxwang@mail.csu.edu.cn.|Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, 541004, Guangxi, Peoples Republic of China.|School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan, Peoples Republic of China.|School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan, Peoples Republic of China.', 'China|China|China|China|China|China', 37, 'Algorithms|Databases, Protein|Protein Interaction Mapping|Protein Interaction Maps|Proteins|Sequence Homology, Amino Acid|Subcellular Fractions', 'Journal Article', '10.1186/s12859-016-1115-5', '27586883', 'Essential proteins play an indispensable role in the cellular survival and development. There have been a series of biological experimental methods for finding essential proteins, however they are time-consuming, expensive and inefficient. In order to overcome the shortcomings of biological experimental methods, many computational methods have been proposed to predict essential proteins. The computational methods can be roughly divided into two categories, the topology-based methods and the sequence-based ones. The former use the topological features of protein-protein interaction (PPI) networks while the latter use the sequence features of proteins to predict essential proteins. Nevertheless, it is still challenging to improve the prediction accuracy of the computational methods.'),
(97402, 2016, 'bmc', 'Modeling the functional state of the reverse transcriptase of hepatitis B virus and its application to probing drug-protein interaction', 'Xu, Xiaojun|Thai, Hong|Kitrinos, Kathryn M|Xia, Guoliang|Gaggar, Anuj|Paulson, Matthew|Ganova-Raeva, Lilia|Khudyakov, Yury|Lara, James', 'Division of Viral Hepatitis, National Center for HIV, Hepatitis, STD and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, 30333, USA.|Division of Viral Hepatitis, National Center for HIV, Hepatitis, STD and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, 30333, USA.|Gilead Sciences, Inc., Foster City, CA, USA.|Division of Viral Hepatitis, National Center for HIV, Hepatitis, STD and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, 30333, USA.|Gilead Sciences, Inc., Foster City, CA, USA.|Gilead Sciences, Inc., Foster City, CA, USA.|Division of Viral Hepatitis, National Center for HIV, Hepatitis, STD and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, 30333, USA.|Division of Viral Hepatitis, National Center for HIV, Hepatitis, STD and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, 30333, USA.|Division of Viral Hepatitis, National Center for HIV, Hepatitis, STD and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, 30333, USA. xzl5@cdc.gov.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 4, 'Antiviral Agents|Catalytic Domain|Drug Interactions|Drug Resistance, Viral|Hepatitis B virus|Humans|Ions|Magnesium|Models, Molecular|RNA-Directed DNA Polymerase|Reverse Transcriptase Inhibitors|Tenofovir|Thermodynamics|Viral Proteins', 'Journal Article', '10.1186/s12859-016-1116-4', '27587008', 'Herein, the predicted atomic structures of five representative sequence variants of the reverse transcriptase protein (RT) of hepatitis B virus (HBV), sampled from patients with rapid or slow response to tenofovir disoproxil fumarate (TDF) treatment, have been examined to identify structural variations between them in order to assess structural and functional properties of HBV-RT variants associated with the differential responses to TDF treatment.'),
(97403, 2016, 'bmc', 'FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information', 'Yaseen, Ashraf|Nijim, Mais|Williams, Brandon|Qian, Lei|Li, Min|Wang, Jianxin|Li, Yaohang', 'Department of Electrical Engineering & Computer Science, Texas A&M University-Kingsville, Kingsville, TX, 78363, USA. ashraf.yaseen@tamuk.edu.|Department of Electrical Engineering & Computer Science, Texas A&M University-Kingsville, Kingsville, TX, 78363, USA.|Department of Mathematics & Computer Science, Fisk University, Nashville, TN, 37208, USA.|Department of Mathematics & Computer Science, Fisk University, Nashville, TN, 37208, USA.|School of Information Science and Engineering, Central South University, Changsha, 410083, China.|School of Information Science and Engineering, Central South University, Changsha, 410083, China.|Department of Computer Science, Old Dominion University, Norfolk, VA, 23529, USA.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|China|China|United Kingdom', 5, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Neural Networks (Computer)|Pliability|Protein Structure, Secondary|Proteins|Software|Statistics as Topic', 'Journal Article', '10.1186/s12859-016-1117-3', '27587065', 'The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions.'),
(97404, 2016, 'bmc', 'Tilting the lasso by knowledge-based post-processing', 'Tharmaratnam, Kukatharmini|Sperrin, Matthew|Jaki, Thomas|Reppe, Sjur|Frigessi, Arnoldo', 'Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.|Institute of Population Health, The University of Manchester, Manchester, UK. matthew.sperrin@manchester.ac.uk.|Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.|Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway.|Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo, Norway.', 'United Kingdom|United Kingdom|United Kingdom|Norway|Norway', 0, 'Algorithms|Bone Density|Computer Simulation|Humans|Knowledge|Models, Theoretical|Reproducibility of Results', 'Journal Article', '10.1186/s12859-016-1210-7', '27590269', 'It is useful to incorporate biological knowledge on the role of genetic determinants in predicting an outcome. It is, however, not always feasible to fully elicit this information when the number of determinants is large. We present an approach to overcome this difficulty. First, using half of the available data, a shortlist of potentially interesting determinants are generated. Second, binary indications of biological importance are elicited for this much smaller number of determinants. Third, an analysis is carried out on this shortlist using the second half of the data.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97405, 2016, 'bmc', 'MMinte: an application for predicting metabolic interactions among the microbial species in a community', 'Mendes-Soares, Helena|Mundy, Michael|Soares, Luis Mendes|Chia, Nicholas', 'Microbiome Program, Center for Individualized Medicine, Mayo Clinic, 200 First St. SW, Rochester, 55905, MN, USA. soares.maria@mayo.edu.|Microbiome Program, Center for Individualized Medicine, Mayo Clinic, 200 First St. SW, Rochester, 55905, MN, USA.|Harvard Medical School, Boston, MA, USA.|Microbiome Program, Center for Individualized Medicine, Mayo Clinic, 200 First St. SW, Rochester, 55905, MN, USA. chia.nicholas@mayo.edu.', 'United States|United States|United States|United States', 0, 'Bacteria|Metabolic Networks and Pathways|Metabolism|Microbiota|Models, Biological|Software|Species Specificity', 'Journal Article', '10.1186/s12859-016-1230-3', '27590448', 'The explosive growth of microbiome research has yielded great quantities of data. These data provide us with many answers, but raise just as many questions. 16S rDNA-the backbone of microbiome analyses-allows us to assess xcexb1-diversity, xcexb2-diversity, and microbe-microbe associations, which characterize the overall properties of an ecosystem. However, we are still unable to use 16S rDNA data to directly assess the microbe-microbe and microbe-environment interactions that determine the broader ecology of that system. Thus, properties such as competition, cooperation, and nutrient conditions remain insufficiently analyzed. Here, we apply predictive community metabolic models of microbes identified with 16S rDNA data to probe the ecology of microbial communities.'),
(97406, 2016, 'bmc', 'Evaluating the accuracy of protein design using native secondary sub-structures', 'Movahedi, Marziyeh|Zare-Mirakabad, Fatemeh|Arab, Seyed Shahriar', 'Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran.|Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran. f.zare@aut.ac.ir.|Department of Biophysics, Faculty of Biological Sciences Tarbiat Modares University (TMU), Tehran, Iran.', '-|-|-', 2, 'Algorithms|Amino Acid Sequence|Chi-Square Distribution|Databases, Protein|Protein Folding|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1186/s12859-016-1199-y', '27597167', 'According to structure-dependent function of proteins, two main challenging problems called Protein Structure Prediction (PSP) and Inverse Protein Folding (IPF) are investigated. In spite of IPF essential applications, it has not been investigated as much as PSP problem. In fact, the ultimate goal of IPF problem or protein design is to create proteins with enhanced properties or even novel functions. One of the major computational challenges in protein design is its large sequence space, namely searching through all plausible sequences is impossible. Inasmuch as, protein secondary structure represents an appropriate primary scaffold of the protein conformation, undoubtedly studying the Protein Secondary Structure Inverse Folding (PSSIF) problem is a quantum leap forward in protein design, as it can reduce the search space. In this paper, a novel genetic algorithm which uses native secondary sub-structures is proposed to solve PSSIF problem. In essence, evolutionary information can lead the algorithm to design appropriate amino acid sequences respective to the target secondary structures. Furthermore, they can be folded to tertiary structures almost similar to their reference 3D structures.'),
(97407, 2016, 'bmc', 'NEAT: an efficient network enrichment analysis test', 'Signorelli, Mirko|Vinciotti, Veronica|Wit, Ernst C', 'Johann Bernoulli Institute, University of Groningen, Nijenborgh 9, Groningen, 9747 AG, Netherlands.|Department of Mathematics, Brunel University London, Uxbridge UB8 3PH, London, UK.|Johann Bernoulli Institute, University of Groningen, Nijenborgh 9, Groningen, 9747 AG, Netherlands. e.c.wit@rug.nl.', 'Netherlands|United Kingdom|Netherlands', 8, 'Computer Simulation|Gene Expression Profiling|Gene Ontology|Gene Regulatory Networks|Genes, Fungal|Saccharomyces cerevisiae|Software|Stress, Physiological', 'Journal Article', '10.1186/s12859-016-1203-6', '27597310', 'Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions.'),
(97408, 2016, 'bmc', 'LMAP: Lightweight Multigene Analyses in PAML', 'Maldonado, Emanuel|Almeida, Daniela|Escalona, Tibisay|Khan, Imran|Vasconcelos, Vitor|Antunes, Agostinho', 'CIIMAR/CIMAR - Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208, Matosinhos, Portugal.|CIIMAR/CIMAR - Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208, Matosinhos, Portugal.|CIIMAR/CIMAR - Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208, Matosinhos, Portugal.|CIIMAR/CIMAR - Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208, Matosinhos, Portugal.|CIIMAR/CIMAR - Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208, Matosinhos, Portugal.|CIIMAR/CIMAR - Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208, Matosinhos, Portugal. aantunes@ciimar.up.pt.', 'Portugal|Portugal|Portugal|Portugal|Portugal|Portugal', 6, 'Codon|Computational Biology|Internet|Phylogeny|Proteins|Sequence Alignment|Software', 'Evaluation Studies', '10.1186/s12859-016-1204-5', '27597435', 'Uncovering how phenotypic diversity arises and is maintained in nature has long been a major interest of evolutionary biologists. Recent advances in genome sequencing technologies have remarkably increased the efficiency to pinpoint genes involved in the adaptive evolution of phenotypes. Reliability of such findings is most often examined with statistical and computational methods using Maximum Likelihood codon-based models (i.e., site, branch, branch-site and clade models), such as those available in codeml from the Phylogenetic Analysis by Maximum Likelihood (PAML) package. While these models represent a well-defined workflow for documenting adaptive evolution, in practice they can be challenging for researchers having a vast amount of data, as multiple types of relevant codon-based datasets are generated, making the overall process hard and tedious to handle, error-prone and time-consuming.'),
(97409, 2016, 'bmc', 'BTR: training asynchronous Boolean models using single-cell expression data', 'Lim, Chee Yee|Wang, Huange|Woodhouse, Steven|Piterman, Nir|Wernisch, Lorenz|Fisher, Jasmin|Göttgens, Berthold', 'Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell Institute, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge, CB2 0XY, UK.|Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell Institute, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge, CB2 0XY, UK.|Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell Institute, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge, CB2 0XY, UK.|Department of Computer Science, University of Leicester, Leicester, UK.|Biostatistics Unit, Medical Research Council, Cambridge, UK.|Microsoft Research Cambridge, Cambridge, UK.|Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell Institute, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge, CB2 0XY, UK. bg200@cam.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 15, 'Algorithms|Animals|Bayes Theorem|Cells|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Humans|Models, Genetic|Single-Cell Analysis', 'Journal Article', '10.1186/s12859-016-1235-y', '27600248', 'Rapid technological innovation for the generation of single-cell genomics data presents new challenges and opportunities for bioinformatics analysis. One such area lies in the development of new ways to train gene regulatory networks. The use of single-cell expression profiling technique allows the profiling of the expression states of hundreds of cells, but these expression states are typically noisier due to the presence of technical artefacts such as drop-outs. While many algorithms exist to infer a gene regulatory network, very few of them are able to harness the extra expression states present in single-cell expression data without getting adversely affected by the substantial technical noise present.'),
(97410, 2016, 'bmc', 'A hybrid computational strategy to address WGS variant analysis in >5000 samples', 'Huang, Zhuoyi|Rustagi, Navin|Veeraraghavan, Narayanan|Carroll, Andrew|Gibbs, Richard|Boerwinkle, Eric|Venkata, Manjunath Gorentla|Yu, Fuli', 'Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.|Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.|Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.|DNAnexus, Mountain View, CA, USA.|Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.|Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.|Oak Ridge National Laboratory, Oak Ridge, TN, USA.|Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA. fyu@bcm.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States', 2, 'Databases, Genetic|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans', 'Journal Article', '10.1186/s12859-016-1211-6', '27612449', 'The decreasing costs of sequencing are driving the need for cost effective and real time variant calling of whole genome sequencing data. The scale of these projects are far beyond the capacity of typical computing resources available with most research labs. Other infrastructures like the cloud AWS environment and supercomputers also have limitations due to which large scale joint variant calling becomes infeasible, and infrastructure specific variant calling strategies either fail to scale up to large datasets or abandon joint calling strategies.'),
(97411, 2016, 'bmc', 'Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks', 'Zhang, Xiao-Fei|Ou-Yang, Le|Dai, Dao-Qing|Wu, Meng-Yun|Zhu, Yuan|Yan, Hong', 'School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Luoyu Road, Wuhan, 430079, China.|College of Information Engineering, Shenzhen University, Nanhai Ave 3688, Shenzhen, 518060, China.|Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, Guangzhou, 510275, China. stsddq@mail.sysu.edu.cn.|School of Statistics and Management, Shanghai University of Finance and Economics, Guoding Road, Shanghai, 200433, China.|School of Automation, China University of Geosciences, Lumo Road, Wuhan, 430074, China.|Department of Electronic and Engineering, City University of Hong Kong, Tat Chee Avenue, Hong Kong, China.', 'China|China|China|China|China|China', 0, 'Aging|Algorithms|Evolution, Molecular|Gene Ontology|Genes, Essential|Humans|Models, Theoretical|Neoplasms|Organ Specificity|Protein Interaction Maps|Protein Processing, Post-Translational|Viruses', 'Comparative Study', '10.1186/s12859-016-1233-0', '27612563', 'Several recent studies have used the Minimum Dominating Set (MDS) model to identify driver nodes, which provide the control of the underlying networks, in protein interaction networks. There may exist multiple MDS configurations in a given network, thus it is difficult to determine which one represents the real set of driver nodes. Because these previous studies only focus on static networks and ignore the contextual information on particular tissues, their findings could be insufficient or even be misleading.'),
(97412, 2016, 'bmc', 'A robust data scaling algorithm to improve classification accuracies in biomedical data', 'Cao, Xi Hang|Stojkovic, Ivan|Obradovic, Zoran', 'Center for Data Analytics and Biomedical Informatics, College of Science and Technology, Temple University, 1925 North 12th Street, Philadelphia, 19122, USA.|Center for Data Analytics and Biomedical Informatics, College of Science and Technology, Temple University, 1925 North 12th Street, Philadelphia, 19122, USA.|Center for Data Analytics and Biomedical Informatics, College of Science and Technology, Temple University, 1925 North 12th Street, Philadelphia, 19122, USA. zoran.obradovic@temple.edu.', 'United States|United States|United States', 20, 'Algorithms|Biomedical Research|Databases as Topic|Humans|Models, Theoretical|ROC Curve', 'Journal Article', '10.1186/s12859-016-1236-x', '27612635', 'Machine learning models have been adapted in biomedical research and practice for knowledge discovery and decision support. While mainstream biomedical informatics research focuses on developing more accurate models, the importance of data preprocessing draws less attention. We propose the Generalized Logistic (GL) algorithm that scales data uniformly to an appropriate interval by learning a generalized logistic function to fit the empirical cumulative distribution function of the data. The GL algorithm is simple yet effective, it is intrinsically robust to outliers, so it is particularly suitable for diagnostic/classification models in clinical/medical applications where the number of samples is usually small, it scales the data in a nonlinear fashion, which leads to potential improvement in accuracy.'),
(97413, 2016, 'bmc', 'Protein secondary structure prediction using a small training set (compact model) combined with a Complex-valued neural network approach', 'Rashid, Shamima|Saraswathi, Saras|Kloczkowski, Andrzej|Sundaram, Suresh|Kolinski, Andrzej', 'School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore, 639798, Singapore.|Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Childrens Hospital, 700 Childrens Drive, Columbus, USA.|Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Childrens Hospital, 700 Childrens Drive, Columbus, USA.|School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore, 639798, Singapore. ssundaram@ntu.edu.sg.|Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Pasteura 1, Warsaw, 02-093, Poland.', 'Singapore|United States|United States|Singapore|Poland', 18, 'Humans|Neural Networks (Computer)|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1186/s12859-016-1209-0', '27618812', 'Protein secondary structure prediction (SSP) has been an area of intense research interest. Despite advances in recent methods conducted on large datasets, the estimated upper limit accuracy is yet to be reached. Since the predictions of SSP methods are applied as input to higher-level structure prediction pipelines, even small errors may have large perturbations in final models. Previous works relied on cross validation as an estimate of classifier accuracy. However, training on large numbers of protein chains compromises the classifier ability to generalize to new sequences. This prompts a novel approach to training and an investigation into the possible structural factors that lead to poor predictions. Here, a small group of 55 proteins termed the compact model is selected from the CB513 dataset using a heuristics-based approach. In a prior work, all sequences were represented as probability matrices of residues adopting each of Helix, Sheet and Coil states, based on energy calculations using the C-Alpha, C-Beta, Side-chain (CABS) algorithm. The functional relationship between the conformational energies computed with CABS force-field and residue states is approximated using a classifier termed the Fully Complex-valued Relaxation Network (FCRN). The FCRN is trained with the compact model proteins.'),
(97414, 2016, 'bmc', 'Tissue enrichment analysis for C elegans genomics', 'Angeles-Albores, David|N Lee, Raymond Y|Chan, Juancarlos|Sternberg, Paul W', 'HHMI and California Institute of Technology, Division of Biology and Biological Engineering, 1200 E California Blvd, Pasadena, 91125, USA.|HHMI and California Institute of Technology, Division of Biology and Biological Engineering, 1200 E California Blvd, Pasadena, 91125, USA.|HHMI and California Institute of Technology, Division of Biology and Biological Engineering, 1200 E California Blvd, Pasadena, 91125, USA.|HHMI and California Institute of Technology, Division of Biology and Biological Engineering, 1200 E California Blvd, Pasadena, 91125, USA. pws@caltech.edu.', 'United States|United States|United States|United States', 27, 'Animals|Caenorhabditis elegans|Gene Expression Profiling|Gene Ontology|Genomics|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-016-1229-9', '27618863', 'Over the last ten years, there has been explosive development in methods for measuring gene expression. These methods can identify thousands of genes altered between conditions, but understanding these datasets and forming hypotheses based on them remains challenging. One way to analyze these datasets is to associate ontologies (hierarchical, descriptive vocabularies with controlled relations between terms) with genes and to look for enrichment of specific terms. Although Gene Ontology (GO) is available for Caenorhabditis elegans, it does not include anatomical information.'),
(97415, 2016, 'bmc', 'A haplotype-based normalization technique for the analysis and detection of allele specific expression', 'Hodgkinson, Alan|Grenier, Jean-Christophe|Gbeha, Elias|Awadalla, Philip', 'CHU Sainte Justine Research Centre, Department of Pediatrics, Faculty of Medicine, Universite de Montreal, 3175 Chemin de la Cote Sainte Catherine, Montreal, QC, Canada. alan.j.hodgkinson@gmail.com.|CHU Sainte Justine Research Centre, Department of Pediatrics, Faculty of Medicine, Universite de Montreal, 3175 Chemin de la Cote Sainte Catherine, Montreal, QC, Canada.|CHU Sainte Justine Research Centre, Department of Pediatrics, Faculty of Medicine, Universite de Montreal, 3175 Chemin de la Cote Sainte Catherine, Montreal, QC, Canada.|CHU Sainte Justine Research Centre, Department of Pediatrics, Faculty of Medicine, Universite de Montreal, 3175 Chemin de la Cote Sainte Catherine, Montreal, QC, Canada.', 'Canada|Canada|Canada|Canada', 0, 'Alleles|Gene Expression Profiling|Haplotypes|Humans|Polymorphism, Single Nucleotide|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-016-1238-8', '27618913', 'Allele specific expression (ASE) has become an important phenotype, being utilized for the detection of cis-regulatory variation, nonsense mediated decay and imprinting in the personal genome, and has been used to both identify disease loci and consider the penetrance of damaging alleles. The detection of ASE using high throughput technologies relies on aligning short-read sequencing data, a process that has inherent biases, and there is still a need to develop fast and accurate methods to detect ASE given the unprecedented growth of sequencing information in big data projects.'),
(97416, 2016, 'bmc', 'GeneSCF: a real-time based functional enrichment tool with support for multiple organisms', 'Subhash, Santhilal|Kanduri, Chandrasekhar', 'Department of Medical Genetics, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-40530, Sweden.|Department of Medical Genetics, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-40530, Sweden. kanduri.chandrasekhar@gu.se.', 'Sweden|Sweden', 0, 'Databases, Factual|Gene Ontology|Humans|Metabolomics|Sequence Analysis, DNA|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-016-1250-z', '27618934', 'High-throughput technologies such as ChIP-sequencing, RNA-sequencing, DNA sequencing and quantitative metabolomics generate a huge volume of data. Researchers often rely on functional enrichment tools to interpret the biological significance of the affected genes from these high-throughput studies. However, currently available functional enrichment tools need to be updated frequently to adapt to new entries from the functional database repositories. Hence there is a need for a simplified tool that can perform functional enrichment analysis by using updated information directly from the source databases such as KEGG, Reactome or Gene Ontology etc.'),
(97417, 2016, 'bmc', 'CellTree: an R/bioconductor package to infer the hierarchical structure of cell populations from single-cell RNA-seq data', 'duVerle, David A|Yotsukura, Sohiya|Nomura, Seitaro|Aburatani, Hiroyuki|Tsuda, Koji', 'Graduate School of Frontier Sciences at the University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Japan. dave@cb.k.u-tokyo.ac.jp.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Japan.|Genome Science Division, Laboratory of Systems Biology and Medicine, Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Tokyo, Japan.|Genome Science Division, Laboratory of Systems Biology and Medicine, Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Tokyo, Japan.|Graduate School of Frontier Sciences at the University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Japan. tsuda@k.u-tokyo.ac.jp.', 'Japan|Japan|Japan|Japan|Japan', 26, 'Cell Differentiation|Humans|RNA|Sequence Analysis, RNA|Stem Cells', 'Journal Article', '10.1186/s12859-016-1175-6', '27620863', 'Single-cell RNA sequencing is fast becoming one the standard method for gene expression measurement, providing unique insights into cellular processes. A number of methods, based on general dimensionality reduction techniques, have been suggested to help infer and visualise the underlying structure of cell populations from single-cell expression levels, yet their models generally lack proper biological grounding and struggle at identifying complex differentiation paths.'),
(97418, 2016, 'bmc', 'Protein complex detection based on partially shared multi-view clustering', 'Ou-Yang, Le|Zhang, Xiao-Fei|Dai, Dao-Qing|Wu, Meng-Yun|Zhu, Yuan|Liu, Zhiyong|Yan, Hong', 'College of Information Engineering, Shenzhen University, Nanhai Ave 3688, Shenzhen, 518060, China.|School of Mathematics and Statistics and Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, 430079, China.|Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xin Gang Road West, Guangzhou, 510275, China. stsddq@mail.sysu.edu.cn.|School of Statistics and Management, Shanghai University of Finance and Economics, Guoding Road, Shanghai, 200433, China.|School of Automation, China University of Geosciences, Wuhan, China.|Shenzhen Polytechnic, Shenzhen, 518055, China.|Department of Electronic and Engineering, City University of Hong Kong, Tat Chee Avenue, Hong Kong, China.', 'China|China|China|China|China|China|China', 0, 'Cluster Analysis|Information Storage and Retrieval|Protein Interaction Domains and Motifs|Protein Interaction Mapping', 'Journal Article', '10.1186/s12859-016-1164-9', '27623844', 'Protein complexes are the key molecular entities to perform many essential biological functions. In recent years, high-throughput experimental techniques have generated a large amount of protein interaction data. As a consequence, computational analysis of such data for protein complex detection has received increased attention in the literature. However, most existing works focus on predicting protein complexes from a single type of data, either physical interaction data or co-complex interaction data. These two types of data provide compatible and complementary information, so it is necessary to integrate them to discover the underlying structures and obtain better performance in complex detection.'),
(97419, 2016, 'bmc', 'NBLDA: negative binomial linear discriminant analysis for RNA-Seq data', 'Dong, Kai|Zhao, Hongyu|Tong, Tiejun|Wan, Xiang', 'Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong.|Department of Biostatistics, Yale University, New Haven, 06510, CT, USA.|Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong.|Department of Computer Science and Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong. xwan@comp.hkbu.edu.hk.', 'Hong Kong|United States|Hong Kong|Hong Kong', 11, 'Bayes Theorem|Binomial Distribution|Discriminant Analysis|Humans|RNA|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1186/s12859-016-1208-1', '27623864', 'RNA-sequencing (RNA-Seq) has become a powerful technology to characterize gene expression profiles because it is more accurate and comprehensive than microarrays. Although statistical methods that have been developed for microarray data can be applied to RNA-Seq data, they are not ideal due to the discrete nature of RNA-Seq data. The Poisson distribution and negative binomial distribution are commonly used to model count data. Recently, Witten (Annals Appl Stat 5:2493-2518, 2011) proposed a Poisson linear discriminant analysis for RNA-Seq data. The Poisson assumption may not be as appropriate as the negative binomial distribution when biological replicates are available and in the presence of overdispersion (i.e., when the variance is larger than or equal to the mean). However, it is more complicated to model negative binomial variables because they involve a dispersion parameter that needs to be estimated.'),
(97420, 2016, 'bmc', 'BmncRNAdb: a comprehensive database of non-coding RNAs in the silkworm, Bombyx mori', 'Zhou, Qiu-Zhong|Zhang, Bindan|Yu, Quan-You|Zhang, Ze', 'Laboratory of Evolutionary and Functional Genomics, School of Life Sciences, Chongqing University, Huxi Campus, No. 55 Daxuecheng South Rd., Shapingba, Chongqing, 401331, China.|School of Economics and Business Administration, Chongqing University, Campus A, No. 174 Shazheng Rd., Shapingba, Chongqing, 400044, China.|Laboratory of Evolutionary and Functional Genomics, School of Life Sciences, Chongqing University, Huxi Campus, No. 55 Daxuecheng South Rd., Shapingba, Chongqing, 401331, China.|Laboratory of Evolutionary and Functional Genomics, School of Life Sciences, Chongqing University, Huxi Campus, No. 55 Daxuecheng South Rd., Shapingba, Chongqing, 401331, China. zezhang@cqu.edu.cn.', 'China|China|China|China', 17, 'Animals|Bombyx|Databases, Nucleic Acid|Humans|MicroRNAs|RNA, Long Noncoding', 'Journal Article', '10.1186/s12859-016-1251-y', '27623959', 'Long non-coding RNAs (lncRNAs) may play critical roles in a wide range of developmental processes of higher organisms. Recently, lncRNAs have been widely identified across eukaryotes and many databases of lncRNAs have been developed for human, mouse, fruit fly, etc. However, there is rare information about them in the only completely domesticated insect, silkworm (Bombyx mori).'),
(97421, 2016, 'bmc', 'Reduction strategies for hierarchical multi-label classification in protein function prediction', 'Cerri, Ricardo|Barros, Rodrigo C|P L F de Carvalho, André C|Jin, Yaochu', 'Department of Computer Science, UFSCar Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, 13565-905, SP, Brazil. cerri@dc.ufscar.br.|Faculdade de Informática, Pontifícia Universidade Católica do Rio Grande do Sul, Av. Ipiranga, 6681, Porto Alegre, 90619-900, RS, Brazil.|Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, Campus de São Carlos 135, São Carlos, 13566-590, SP, Brazil.|Department of Computer Science, University of Surrey, GU2 7XH Guildford, Surrey, United Kingdom.', 'Brazil|Brazil|Brazil|United Kingdom', 0, 'Neural Networks (Computer)|Proteins', 'Journal Article', '10.1186/s12859-016-1232-1', '27627880', 'Hierarchical Multi-Label Classification is a classification task where the classes to be predicted are hierarchically organized. Each instance can be assigned to classes belonging to more than one path in the hierarchy. This scenario is typically found in protein function prediction, considering that each protein may perform many functions, which can be further specialized into sub-functions. We present a new hierarchical multi-label classification method based on multiple neural networks for the task of protein function prediction. A set of neural networks are incrementally training, each being responsible for the prediction of the classes belonging to a given level.'),
(97422, 2016, 'bmc', 'Interactive knowledge discovery and data mining on genomic expression data with numeric formal concept analysis', 'González-Calabozo, Jose M|Valverde-Albacete, Francisco J|Peláez-Moreno, Carmen', 'Department of Signal Theory and Communications, University Carlos III Madrid, Avda. Universidad, 30, Leganés (Madrid), Spain.|Department of Signal Theory and Communications, University Carlos III Madrid, Avda. Universidad, 30, Leganés (Madrid), Spain.|Department of Signal Theory and Communications, University Carlos III Madrid, Avda. Universidad, 30, Leganés (Madrid), Spain. carmen@tsc.uc3m.es.', 'Spain|Spain|Spain', 3, 'Algorithms|Cluster Analysis|Data Mining|Gene Expression Profiling|Gene Ontology|Genomics|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/s12859-016-1234-z', '27628041', 'Gene Expression Data (GED) analysis poses a great challenge to the scientific community that can be framed into the Knowledge Discovery in Databases (KDD) and Data Mining (DM) paradigm. Biclustering has emerged as the machine learning method of choice to solve this task, but its unsupervised nature makes result assessment problematic. This is often addressed by means of Gene Set Enrichment Analysis (GSEA).'),
(97423, 2016, 'bmc', 'Large-scale localization of touching somas from 3D images using density-peak clustering', 'Cheng, Shenghua|Quan, Tingwei|Liu, Xiaomao|Zeng, Shaoqun', 'School of Mathematics and Statistics, Huazhong University of Science and Technology, 1037 Luoyu Rd, Building of Science - 715, Wuhan, 430074, China.|Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, 430074, China.|School of Mathematics and Statistics, Huazhong University of Science and Technology, 1037 Luoyu Rd, Building of Science - 715, Wuhan, 430074, China. liuxiaomao2001@outlook.com.|Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, 430074, China.', 'China|China|China|China', 0, 'Algorithms|Animals|Cluster Analysis|Imaging, Three-Dimensional|Mice|Neurons|Signal-To-Noise Ratio', 'Journal Article', '10.1186/s12859-016-1252-x', '27628179', 'Soma localization is an important step in computational neuroscience to map neuronal circuits. However, locating somas from large-scale and complicated datasets is challenging. The challenges primarily originate from the dense distribution of somas, the diversity of soma sizes and the inhomogeneity of image contrast.'),
(97424, 2016, 'bmc', 'Development of a prediction system for tail-anchored proteins', 'Shigemitsu, Shunsuke|Cao, Wei|Terada, Tohru|Shimizu, Kentaro', 'Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan.|Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan.|Agricultural Bioinformatics Research Unit, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan.|Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan. shimizu@bi.a.u-tokyo.ac.jp.', 'Japan|Japan|Japan|Japan', 4, 'Humans|Markov Chains|Membrane Proteins|Protein Domains|Protein Sorting Signals|Sequence Analysis, Protein', 'Journal Article', '10.1186/s12859-016-1202-7', '27634135', 'Tail-anchored (TA) proteins is a collective term for transmembrane proteins with a C-terminal transmembrane domain (TMD) and without an N-terminal signal sequence. TA proteins account for approximately 3-5 % of all transmembrane proteins that mediate membrane fusion, regulation of apoptosis, and vesicular transport. The combined use of TMD and signal sequence prediction tools is typically required to predict TA proteins.'),
(97425, 2016, 'bmc', 'Pathogen metadata platform: software for accessing and analyzing pathogen strain information', 'Chang, Wenling E|Peterson, Matthew W|Garay, Christopher D|Korves, Tonia', 'Data Analytics Department, The MITRE Corporation, 2280 Historic Decatur Rd, San Diego, CA, 92106, USA.|Data Analytics Department, The MITRE Corporation, 202 Burlington Rd, Bedford, MA, 01730, USA.|Data Analytics Department, The MITRE Corporation, 202 Burlington Rd, Bedford, MA, 01730, USA.|Data Analytics Department, The MITRE Corporation, 202 Burlington Rd, Bedford, MA, 01730, USA. tkorves@mitre.org.', 'United States|United States|United States|United States', 0, 'Databases, Factual|Disease Outbreaks|Genome, Microbial|Genomics|Humans|Metadata|Software', 'Journal Article', '10.1186/s12859-016-1231-2', '27634291', 'Pathogen metadata includes information about where and when a pathogen was collected and the type of environment it came from. Along with genomic nucleotide sequence data, this metadata is growing rapidly and becoming a valuable resource not only for research but for biosurveillance and public health. However, current freely available tools for analyzing this data are geared towards bioinformaticians and/or do not provide summaries and visualizations needed to readily interpret results.'),
(97426, 2016, 'bmc', 'EnsCat: clustering of categorical data via ensembling', 'Clarke, Bertrand S|Amiri, Saeid|Clarke, Jennifer L', 'Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE, USA.|Department of Natural and Applied Sciences, University of Wisconsin Madison, Iowa City, IA, USA.|Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE, USA. jclarke3@unl.edu.', 'United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computational Biology|Genomics|Software', 'Journal Article', '10.1186/s12859-016-1245-9', '27634377', 'Clustering is a widely used collection of unsupervised learning techniques for identifying natural classes within a data set. It is often used in bioinformatics to infer population substructure. Genomic data are often categorical and high dimensional, e.g., long sequences of nucleotides. This makes inference challenging: The distance metric is often not well-defined on categorical data, running time for computations using high dimensional data can be considerable, and the Curse of Dimensionality often impedes the interpretation of the results. Up to the present, however, the literature and software addressing clustering for categorical data has not yet led to a standard approach.'),
(97427, 2016, 'bmc', 'Sierra platinum: a fast and robust peak-caller for replicated ChIP-seq experiments with visual quality-control and -steering', 'Müller, Lydia|Gerighausen, Daniel|Farman, Mariam|Zeckzer, Dirk', 'Bioinformatics Group, Department of Computer Science, University of Leipzig, Härtelstraße 16-18, Leipzig, 04107, Germany. lydia@bioinf.uni-leipzig.de.|Image and Signal Processing Group, Department of Computer Science, University of Leipzig, Augustusplatz 10, Leipzig, 04109, Germany.|Bioinformatics Group, Department of Computer Science, University of Leipzig, Härtelstraße 16-18, Leipzig, 04107, Germany.|Image and Signal Processing Group, Department of Computer Science, University of Leipzig, Augustusplatz 10, Leipzig, 04109, Germany.', 'Germany|Germany|Germany|Germany', 3, 'Chromatin Immunoprecipitation|Epigenomics|Histone Code|Quality Control|Sequence Analysis, DNA', 'Evaluation Studies', '10.1186/s12859-016-1248-6', '27634469', 'Histone modifications play an important role in gene regulation. Their genomic locations are of great interest. Usually, the location is measured by ChIP-seq and analyzed with a peak-caller. Replicated ChIP-seq experiments become more and more available. However, their analysis is based on single-experiment peak-calling or on tools like PePr which allows peak-calling of replicates but whose underlying model might not be suitable for the conditions under which the experiments are performed.'),
(97428, 2016, 'bmc', 'General continuous-time Markov model of sequence evolution via insertions/deletions: are alignment probabilities factorable?', 'Ezawa, Kiyoshi', 'Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, 820-8502, Japan. kezawa.ezawa3@gmail.com.', 'Japan', 0, 'Evolution, Molecular|Humans|INDEL Mutation|Markov Chains|Models, Genetic|Probability|Sequence Alignment', 'Journal Article', '10.1186/s12859-016-1105-7', '27638547', 'Insertions and deletions (indels) account for more nucleotide differences between two related DNA sequences than substitutions do, and thus it is imperative to develop a stochastic evolutionary model that enables us to reliably calculate the probability of the sequence evolution through indel processes. Recently, indel probabilistic models are mostly based on either hidden Markov models (HMMs) or transducer theories, both of which give the indel component of the probability of a given sequence alignment as a product of either probabilities of column-to-column transitions or block-wise contributions along the alignment. However, it is not a priori clear how these models are related with any genuine stochastic evolutionary model, which describes the stochastic evolution of an entire sequence along the time-axis. Moreover, currently none of these models can fully accommodate biologically realistic features, such as overlapping indels, power-law indel-length distributions, and indel rate variation across regions.'),
(97429, 2016, 'bmc', 'Structural alignment of protein descriptors - a combinatorial model', 'Antczak, Maciej|Kasprzak, Marta|Lukasiak, Piotr|Blazewicz, Jacek', 'Institute of Computing Science, Poznan University of Technology, Piotrowo 2, Poznan, 60-965, Poland. maciej.antczak@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, Poznan, 60-965, Poland.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, Poznan, 60-965, Poland.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, Poznan, 60-965, Poland.', 'Poland|Poland|Poland|Poland', 2, 'Algorithms|Amino Acid Sequence|Databases, Protein|Models, Molecular|Proteins|Sequence Alignment|Time Factors', 'Journal Article', '10.1186/s12859-016-1237-9', '27639380', 'Structural alignment of proteins is one of the most challenging problems in molecular biology. The tertiary structure of a protein strictly correlates with its function and computationally predicted structures are nowadays a main premise for understanding the latter. However, computationally derived 3D models often exhibit deviations from the native structure. A way to confirm a model is a comparison with other structures. The structural alignment of a pair of proteins can be defined with the use of a concept of protein descriptors. The protein descriptors are local substructures of protein molecules, which allow us to divide the original problem into a set of subproblems and, consequently, to propose a more efficient algorithmic solution. In the literature, one can find many applications of the descriptors concept that prove its usefulness for insight into protein 3D structures, but the proposed approaches are presented rather from the biological perspective than from the computational or algorithmic point of view. Efficient algorithms for identification and structural comparison of descriptors can become crucial components of methods for structural quality assessment as well as tertiary structure prediction.'),
(97430, 2016, 'bmc', 'nbCNV: a multi-constrained optimization model for discovering copy number variants in single-cell sequencing data', 'Zhang, Changsheng|Cai, Hongmin|Huang, Jingying|Song, Yan', 'School of Computer Science & Engineering, South China University of Technology, Guangzhou, 510006, China.|School of Computer Science & Engineering, South China University of Technology, Guangzhou, 510006, China. hmcai@scut.edu.cn.|School of Computer Science & Engineering, South China University of Technology, Guangzhou, 510006, China.|School of Computer Science & Engineering, South China University of Technology, Guangzhou, 510006, China.', 'China|China|China|China', 9, 'Binomial Distribution|Cluster Analysis|Computer Simulation|DNA Copy Number Variations|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Single-Cell Analysis|Software', 'Journal Article', '10.1186/s12859-016-1239-7', '27639558', 'Variations in DNA copy number have an important contribution to the development of several diseases, including autism, schizophrenia and cancer. Single-cell sequencing technology allows the dissection of genomic heterogeneity at the single-cell level, thereby providing important evolutionary information about cancer cells. In contrast to traditional bulk sequencing, single-cell sequencing requires the amplification of the whole genome of a single cell to accumulate enough samples for sequencing. However, the amplification process inevitably introduces amplification bias, resulting in an over-dispersing portion of the sequencing data. Recent study has manifested that the over-dispersed portion of the single-cell sequencing data could be well modelled by negative binomial distributions.'),
(97431, 2016, 'bmc', 'A method for identification of highly conserved elements and evolutionary analysis of superphylum Alveolata', 'Rubanov, Lev I|Seliverstov, Alexandr V|Zverkov, Oleg A|Lyubetsky, Vassily A', 'Institute for Information Transmission Problems (Kharkevich Institute), Russian Academy of Sciences, Bolshoi Karetnyi per. 19, Building 1, Moscow, 127051, Russia. rubanov@iitp.ru.|Institute for Information Transmission Problems (Kharkevich Institute), Russian Academy of Sciences, Bolshoi Karetnyi per. 19, Building 1, Moscow, 127051, Russia.|Institute for Information Transmission Problems (Kharkevich Institute), Russian Academy of Sciences, Bolshoi Karetnyi per. 19, Building 1, Moscow, 127051, Russia.|Institute for Information Transmission Problems (Kharkevich Institute), Russian Academy of Sciences, Bolshoi Karetnyi per. 19, Building 1, Moscow, 127051, Russia.', '-|-|-|-', 0, 'Algorithms|Alveolata|Base Sequence|Conserved Sequence|DNA|Evolution, Molecular|Genome|Phylogeny|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-016-1257-5', '27645252', 'Perfectly or highly conserved DNA elements were found in vertebrates, invertebrates, and plants by various methods. However, little is known about such elements in protists. The evolutionary distance between apicomplexans can be very high, in particular, due to the positive selection pressure on them. This complicates the identification of highly conserved elements in alveolates, which is overcome by the proposed algorithm.'),
(97432, 2016, 'bmc', 'systemPipeR: NGS workflow and report generation environment', 'H Backman, Tyler W|Girke, Thomas', 'Institute for Integrative Genome Biology, University of California, Riverside, 1207F Genomics Building, 3401 Watkins Drive, Riverside, 92521, CA, USA.|Institute for Integrative Genome Biology, University of California, Riverside, 1207F Genomics Building, 3401 Watkins Drive, Riverside, 92521, CA, USA. thomas.girke@ucr.edu.', 'United States|United States', 0, 'High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Sequence Analysis, RNA|Software|Workflow', 'Journal Article', '10.1186/s12859-016-1241-0', '27650223', 'Next-generation sequencing (NGS) has revolutionized how research is carried out in many areas of biology and medicine. However, the analysis of NGS data remains a major obstacle to the efficient utilization of the technology, as it requires complex multi-step processing of big data demanding considerable computational expertise from users. While substantial effort has been invested on the development of software dedicated to the individual analysis steps of NGS experiments, insufficient resources are currently available for integrating the individual software components within the widely used R/Bioconductor environment into automated workflows capable of running the analysis of most types of NGS applications from start-to-finish in a time-efficient and reproducible manner.'),
(97433, 2016, 'bmc', 'DBSecSys 20: a database of Burkholderia mallei and Burkholderia pseudomallei secretion systems', 'Memixc5xa1evixc4x87, Vesna|Kumar, Kamal|Zavaljevski, Nela|DeShazer, David|Wallqvist, Anders|Reifman, Jaques', 'Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.|Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.|Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.|Bacteriology Division, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD 21702, USA.|Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.|Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA. jaques.reifman.civ@mail.mil.', 'United States|United States|United States|United States|United States|United States', 2, 'Animals|Bacterial Proteins|Bacterial Secretion Systems|Burkholderia mallei|Burkholderia pseudomallei|Databases, Protein|Humans|Mice|Virulence Factors', 'Journal Article', '10.1186/s12859-016-1242-z', '27650316', 'Burkholderia mallei and B. pseudomallei are the causative agents of glanders and melioidosis, respectively, diseases with high morbidity and mortality rates. B. mallei and B. pseudomallei are closely related genetically, B. mallei evolved from an ancestral strain of B. pseudomallei by genome reduction and adaptation to an obligate intracellular lifestyle. Although these two bacteria cause different diseases, they share multiple virulence factors, including bacterial secretion systems, which represent key components of bacterial pathogenicity. Despite recent progress, the secretion system proteins for B. mallei and B. pseudomallei, their pathogenic mechanisms of action, and host factors are not well characterized.');
