INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96202, 2014, 'bmc', 'iGPSe: a visual analytic system for integrative genomic based cancer patient stratification', 'Ding, Hao|Wang, Chao|Huang, Kun|Machiraju, Raghu', 'Nationwide Childrens Hospital, 575 Childrens Crossroad, 43215 Columbus, OH, USA. ray.29@osu.edu.|Nationwide Childrens Hospital, 575 Childrens Crossroad, 43215 Columbus, OH, USA. ray.29@osu.edu.|Department of Computer Science and Engineering and Biomedical Informatics, The Ohio State University, 43210 Columbus, OH, USA. Kun.Huang@osumc.edu.|Department of Computer Science and Engineering and Biomedical Informatics, The Ohio State University, 43210 Columbus, OH, USA. Kun.Huang@osumc.edu.', 'United States|United States|United States|United States', 14, 'Breast Neoplasms|Gene Expression Regulation, Neoplastic|Genomics|Humans|MicroRNAs|Neoplasm Recurrence, Local|Prognosis|RNA, Messenger|Software|Survival Analysis', 'Journal Article', '10.1186/1471-2105-15-203', '25000928', 'Cancers are highly heterogeneous with different subtypes. These subtypes often possess different genetic variants, present different pathological phenotypes, and most importantly, show various clinical outcomes such as varied prognosis and response to treatment and likelihood for recurrence and metastasis. Recently, integrative genomics (or panomics) approaches are often adopted with the goal of combining multiple types of omics data to identify integrative biomarkers for stratification of patients into groups with different clinical outcomes.'),
(96203, 2014, 'bmc', 'inPHAP: interactive visualization of genotype and phased haplotype data', 'Jäger, Günter|Peltzer, Alexander|Nieselt, Kay', 'Integrative Transcriptomics, Center for Bioinformatics, University of Tübingen, Sand 14, 72076 Tübingen, Germany. guenter.jaeger@uni-tuebingen.de.|Integrative Transcriptomics, Center for Bioinformatics, University of Tübingen, Sand 14, 72076 Tübingen, Germany. guenter.jaeger@uni-tuebingen.de.|Integrative Transcriptomics, Center for Bioinformatics, University of Tübingen, Sand 14, 72076 Tübingen, Germany. guenter.jaeger@uni-tuebingen.de.', 'Germany|Germany|Germany', 0, 'Genome|Genotype|Haplotypes|Software', 'Journal Article', '10.1186/1471-2105-15-200', '25002076', 'To understand individual genomes it is necessary to look at the variations that lead to changes in phenotype and possibly to disease. However, genotype information alone is often not sufficient and additional knowledge regarding the phase of the variation is needed to make correct interpretations. Interactive visualizations, that allow the user to explore the data in various ways, can be of great assistance in the process of making well informed decisions. But, currently there is a lack for visualizations that are able to deal with phased haplotype data.'),
(96204, 2014, 'bmc', 'eXamine: exploring annotated modules in networks', 'Dinkla, Kasper|El-Kebir, Mohammed|Bucur, Cristina-Iulia|Siderius, Marco|Smit, Martine J|Westenberg, Michel A|Klau, Gunnar W', 'Integrative Transcriptomics, Center for Bioinformatics, University of Tübingen, Sand 14, 72076 Tübingen, Germany. guenter.jaeger@uni-tuebingen.de.|Integrative Transcriptomics, Center for Bioinformatics, University of Tübingen, Sand 14, 72076 Tübingen, Germany. guenter.jaeger@uni-tuebingen.de.|Integrative Transcriptomics, Center for Bioinformatics, University of Tübingen, Sand 14, 72076 Tübingen, Germany. guenter.jaeger@uni-tuebingen.de.|Integrative Transcriptomics, Center for Bioinformatics, University of Tübingen, Sand 14, 72076 Tübingen, Germany. guenter.jaeger@uni-tuebingen.de.|Integrative Transcriptomics, Center for Bioinformatics, University of Tübingen, Sand 14, 72076 Tübingen, Germany. guenter.jaeger@uni-tuebingen.de.|Eindhoven University of Technology, Den Dolech 2, 5600 MB, Eindhoven, The Netherlands. m.a.westenberg@tue.nl.|Eindhoven University of Technology, Den Dolech 2, 5600 MB, Eindhoven, The Netherlands. m.a.westenberg@tue.nl.', 'Germany|Germany|Germany|Germany|Germany|Netherlands|Netherlands', 0, 'Algorithms|Cluster Analysis|Models, Biological|Proteins|Software', 'Journal Article', '10.1186/1471-2105-15-201', '25002203', 'Biological networks have a growing importance for the interpretation of high-throughput omics data. Integrative network analysis makes use of statistical and combinatorial methods to extract smaller subnetwork modules, and performs enrichment analysis to annotate the modules with ontology terms or other available knowledge. This process results in an annotated module, which retains the original network structure and includes enrichment information as a set system. A major bottleneck is a lack of tools that allow exploring both network structure of extracted modules and its annotations.'),
(96205, 2014, 'bmc', 'MoTeX-II: structured MoTif eXtraction from large-scale datasets', 'Pissis, Solon P', 'Department of Informatics, Kings College London, The Strand, WC2R 2LS London, UK. solon.pissis@kcl.ac.uk.', 'United Kingdom', 15, 'Algorithms|Base Sequence|Computational Biology|Humans|Nucleotide Motifs|Transcription Factors', 'Journal Article', '10.1186/1471-2105-15-235', '25004797', 'Identifying repeated factors that occur in a string of letters or common factors that occur in a set of strings represents an important task in computer science and biology. Such patterns are called motifs, and the process of identifying them is called motif extraction. In biology, motif extraction constitutes a fundamental step in understanding regulation of gene expression. State-of-the-art tools for motif extraction have their own constraints. Most of these tools are only designed for single motif extraction, structured motifs additionally allow for distance intervals between their single motif components. Moreover, motif extraction from large-scale datasets-for instance, large-scale ChIP-Seq datasets-cannot be performed by current tools. Other constraints include high time and/or space complexity for identifying long motifs with higher error thresholds.'),
(96206, 2014, 'bmc', 'A test for comparing two groups of samples when analyzing multiple omics profiles', 'Chaturvedi, Nimisha|Goeman, Jelle J|Boer, Judith M|van Wieringen, Wessel N|de Menezes, Renée X', 'Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. n.chaturvedi@vumc.nl.|Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. n.chaturvedi@vumc.nl.|Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. n.chaturvedi@vumc.nl.|Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. n.chaturvedi@vumc.nl.|Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. n.chaturvedi@vumc.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 2, 'Breast Neoplasms|Computational Biology|Female|Gene Dosage|Gene Expression Profiling|Humans|Receptors, Estrogen', 'Comparative Study', '10.1186/1471-2105-15-236', '25004928', 'A number of statistical models has been proposed for studying the association between gene expression and copy number data in integrated analysis. The next step is to compare association patterns between different groups of samples.'),
(96207, 2014, 'bmc', 'Simulate_PCR for amplicon prediction and annotation from multiplex, degenerate primers and probes', 'Gardner, Shea N|Slezak, Tom', 'Computations/Global Security, Lawrence Livermore National Laboratory (LLNL), Livermore, CA 94550, USA. gardner26@llnl.gov.|Computations/Global Security, Lawrence Livermore National Laboratory (LLNL), Livermore, CA 94550, USA. gardner26@llnl.gov.', 'United States|United States', 11, 'Computational Biology|Coronaviridae|DNA Primers|DNA Probes|Drug Resistance, Microbial|HIV-1|Humans|Molecular Sequence Annotation|Polymerase Chain Reaction|Software', 'Journal Article', '10.1186/1471-2105-15-237', '25005023', 'Pairing up primers to amplify desired targets and avoid undesired cross reactions can be a combinatorial challenge. Effective prediction of specificity and inclusivity from multiplexed primers and TaqMan®/Luminex® probes is a critical step in PCR design.'),
(96208, 2014, 'bmc', 'Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal models', 'Martin, Florian|Sewer, Alain|Talikka, Marja|Xiang, Yang|Hoeng, Julia|Peitsch, Manuel C', 'Philip Morris International, R&D, Biological Systems Research, Quai Jeanrenaud 5, 2000 Neuchatel, Switzerland. florian.martin@pmi.com.|Philip Morris International, R&D, Biological Systems Research, Quai Jeanrenaud 5, 2000 Neuchatel, Switzerland. florian.martin@pmi.com.|Philip Morris International, R&D, Biological Systems Research, Quai Jeanrenaud 5, 2000 Neuchatel, Switzerland. florian.martin@pmi.com.|Philip Morris International, R&D, Biological Systems Research, Quai Jeanrenaud 5, 2000 Neuchatel, Switzerland. florian.martin@pmi.com.|Philip Morris International, R&D, Biological Systems Research, Quai Jeanrenaud 5, 2000 Neuchatel, Switzerland. florian.martin@pmi.com.|Philip Morris International, R&D, Biological Systems Research, Quai Jeanrenaud 5, 2000 Neuchatel, Switzerland. florian.martin@pmi.com.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 69, 'Animals|Anti-Inflammatory Agents|Colitis, Ulcerative|Dose-Response Relationship, Drug|Gene Expression Profiling|Humans|Models, Biological|Phenotype|Systems Biology|Toxicology', 'Journal Article', '10.1186/1471-2105-15-238', '25015298', 'High-throughput measurement technologies such as microarrays provide complex datasets reflecting mechanisms perturbed in an experiment, typically a treatment vs. control design. Analysis of these information rich data can be guided based on a priori knowledge, such as networks or set of related proteins or genes. Among those, cause-and-effect network models are becoming increasingly popular and more than eighty such models, describing processes involved in cell proliferation, cell fate, cell stress, and inflammation have already been published. A meaningful systems toxicology approach to study the response of a cell system, or organism, exposed to bio-active substances requires a quantitative measure of dose-response at network level, to go beyond the differential expression of single genes.'),
(96209, 2014, 'bmc', 'Structure-revealing data fusion', 'Acar, Evrim|Papalexakis, Evangelos E|Gürdeniz, Gözde|Rasmussen, Morten A|Lawaetz, Anders J|Nilsson, Mathias|Bro, Rasmus', 'Department of Food Science, Faculty of Science, University of Copenhagen, Frederiksberg C, Denmark. evrim@life.ku.dk.|Department of Food Science, Faculty of Science, University of Copenhagen, Frederiksberg C, Denmark. evrim@life.ku.dk.|Department of Food Science, Faculty of Science, University of Copenhagen, Frederiksberg C, Denmark. evrim@life.ku.dk.|Department of Food Science, Faculty of Science, University of Copenhagen, Frederiksberg C, Denmark. evrim@life.ku.dk.|Department of Food Science, Faculty of Science, University of Copenhagen, Frederiksberg C, Denmark. evrim@life.ku.dk.|Department of Food Science, Faculty of Science, University of Copenhagen, Frederiksberg C, Denmark. evrim@life.ku.dk.|Department of Food Science, Faculty of Science, University of Copenhagen, Frederiksberg C, Denmark. evrim@life.ku.dk.', 'Denmark|Denmark|Denmark|Denmark|Denmark|Denmark|Denmark', 47, 'Chemistry|Chromatography, Liquid|Magnetic Resonance Spectroscopy|Mass Spectrometry|Statistics as Topic', 'Journal Article', '10.1186/1471-2105-15-239', '25015427', 'Analysis of data from multiple sources has the potential to enhance knowledge discovery by capturing underlying structures, which are, otherwise, difficult to extract. Fusing data from multiple sources has already proved useful in many applications in social network analysis, signal processing and bioinformatics. However, data fusion is challenging since data from multiple sources are often (i) heterogeneous (i.e., in the form of higher-order tensors and matrices), (ii) incomplete, and (iii) have both shared and unshared components. In order to address these challenges, in this paper, we introduce a novel unsupervised data fusion model based on joint factorization of matrices and higher-order tensors.'),
(96210, 2014, 'bmc', 'MCA: Multiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data', 'Feigelman, Justin|Theis, Fabian J|Marr, Carsten', 'Department of Food Science, Faculty of Science, University of Copenhagen, Frederiksberg C, Denmark. evrim@life.ku.dk.|Department of Food Science, Faculty of Science, University of Copenhagen, Frederiksberg C, Denmark. evrim@life.ku.dk.|Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. carsten.marr@helmholtz-muenchen.de.', 'Denmark|Denmark|Germany', 6, 'Animals|Computational Biology|Computer Graphics|Embryonic Stem Cells|Flow Cytometry|Gene Expression Profiling|Gene Regulatory Networks|Mice|Phenotype|Single-Cell Analysis', 'Journal Article', '10.1186/1471-2105-15-240', '25015590', 'Biological data often originate from samples containing mixtures of subpopulations, corresponding e.g. to distinct cellular phenotypes. However, identification of distinct subpopulations may be difficult if biological measurements yield distributions that are not easily separable.'),
(96211, 2014, 'bmc', 'Dataset size and composition impact the reliability of performance benchmarks for peptide-MHC binding predictions', 'Kim, Yohan|Sidney, John|Buus, Søren|Sette, Alessandro|Nielsen, Morten|Peters, Bjoern', 'Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. carsten.marr@helmholtz-muenchen.de.|Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. carsten.marr@helmholtz-muenchen.de.|Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. carsten.marr@helmholtz-muenchen.de.|Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. carsten.marr@helmholtz-muenchen.de.|Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. carsten.marr@helmholtz-muenchen.de.|La Jolla Institute for Allergy & Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA. bpeters@liai.org.', 'Germany|Germany|Germany|Germany|Germany|United States', 40, 'Alleles|Animals|Benchmarking|Computational Biology|Epitopes|HLA Antigens|Humans|Mice|Oligopeptides|Protein Binding|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-15-241', '25017736', 'It is important to accurately determine the performance of peptide:MHC binding predictions, as this enables users to compare and choose between different prediction methods and provides estimates of the expected error rate. Two common approaches to determine prediction performance are cross-validation, in which all available data are iteratively split into training and testing data, and the use of blind sets generated separately from the data used to construct the predictive method. In the present study, we have compared cross-validated prediction performances generated on our last benchmark dataset from 2009 with prediction performances generated on data subsequently added to the Immune Epitope Database (IEDB) which served as a blind set.'),
(96212, 2014, 'bmc', 'Accurate genome relative abundance estimation for closely related species in a metagenomic sample', 'Sohn, Michael B|An, Lingling|Pookhao, Naruekamol|Li, Qike', 'La Jolla Institute for Allergy & Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA. bpeters@liai.org.|Interdisciplinary Program in Statistics, University of Arizona, Tucson AZ 85721, USA. anling@email.arizona.edu.|Interdisciplinary Program in Statistics, University of Arizona, Tucson AZ 85721, USA. anling@email.arizona.edu.|Interdisciplinary Program in Statistics, University of Arizona, Tucson AZ 85721, USA. anling@email.arizona.edu.', 'United States|United States|United States|United States', 12, 'Algorithms|Genome|Humans|Metagenomics|Microbiology|Mouth|Phylogeny|Sequence Alignment|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1186/1471-2105-15-242', '25027647', 'Metagenomics has a great potential to discover previously unattainable information about microbial communities. An important prerequisite for such discoveries is to accurately estimate the composition of microbial communities. Most of prevalent homology-based approaches utilize solely the results of an alignment tool such as BLAST, limiting their estimation accuracy to high ranks of the taxonomy tree.'),
(96213, 2014, 'bmc', 'In silico single strand melting curve: a new approach to identify nucleic acid polymorphisms in Totiviridae', 'Oliveira, Raffael A C|Almeida, Ricardo V M|Dantas, Márcia D A|Castro, Felipe N|Lima, João Paulo M S|Lanza, Daniel C F', 'Interdisciplinary Program in Statistics, University of Arizona, Tucson AZ 85721, USA. anling@email.arizona.edu.|Interdisciplinary Program in Statistics, University of Arizona, Tucson AZ 85721, USA. anling@email.arizona.edu.|Interdisciplinary Program in Statistics, University of Arizona, Tucson AZ 85721, USA. anling@email.arizona.edu.|Interdisciplinary Program in Statistics, University of Arizona, Tucson AZ 85721, USA. anling@email.arizona.edu.|Interdisciplinary Program in Statistics, University of Arizona, Tucson AZ 85721, USA. anling@email.arizona.edu.|Laboratório de Biologia Molecular Aplicada - LAPLIC, Departamento de Bioquímica, Centro de Biociências, Universidade Federal do Rio Grande do Norte, Natal, RN CEP: 59072-970, Brazil. danielclanza@gmail.com.', 'United States|United States|United States|United States|United States|Brazil', 4, 'Amino Acid Sequence|Capsid Proteins|Cluster Analysis|Computational Biology|Computer Simulation|DNA, Viral|Nucleic Acid Denaturation|Open Reading Frames|Phylogeny|Polymorphism, Genetic|RNA Replicase|RNA, Double-Stranded|RNA, Viral|Sequence Analysis, DNA|Software|Totiviridae|Transition Temperature', 'Journal Article', '10.1186/1471-2105-15-243', '25030031', 'The PCR technique and its variations have been increasingly used in the clinical laboratory and recent advances in this field generated new higher resolution techniques based on nucleic acid denaturation dynamics. The principle of these new molecular tools is based on the comparison of melting profiles, after denaturation of a DNA double strand. Until now, the secondary structure of single-stranded nucleic acids has not been exploited to develop identification systems based on PCR. To test the potential of single-strand RNA denaturation as a new alternative to detect specific nucleic acid variations, sequences from viruses of the Totiviridae family were compared using a new in silico melting curve approach. This family comprises double-stranded RNA virus, with a genome constituted by two ORFs, ORF1 and ORF2, which encodes the capsid/RNA binding proteins and an RNA-dependent RNA polymerase (RdRp), respectively.'),
(96214, 2014, 'bmc', 'DBSecSys: a database of Burkholderia mallei secretion systems', 'Memixc5xa1evixc4x87, Vesna|Kumar, Kamal|Cheng, Li|Zavaljevski, Nela|DeShazer, David|Wallqvist, Anders|Reifman, Jaques', 'Laboratório de Biologia Molecular Aplicada - LAPLIC, Departamento de Bioquímica, Centro de Biociências, Universidade Federal do Rio Grande do Norte, Natal, RN CEP: 59072-970, Brazil. danielclanza@gmail.com.|Laboratório de Biologia Molecular Aplicada - LAPLIC, Departamento de Bioquímica, Centro de Biociências, Universidade Federal do Rio Grande do Norte, Natal, RN CEP: 59072-970, Brazil. danielclanza@gmail.com.|Laboratório de Biologia Molecular Aplicada - LAPLIC, Departamento de Bioquímica, Centro de Biociências, Universidade Federal do Rio Grande do Norte, Natal, RN CEP: 59072-970, Brazil. danielclanza@gmail.com.|Laboratório de Biologia Molecular Aplicada - LAPLIC, Departamento de Bioquímica, Centro de Biociências, Universidade Federal do Rio Grande do Norte, Natal, RN CEP: 59072-970, Brazil. danielclanza@gmail.com.|Laboratório de Biologia Molecular Aplicada - LAPLIC, Departamento de Bioquímica, Centro de Biociências, Universidade Federal do Rio Grande do Norte, Natal, RN CEP: 59072-970, Brazil. danielclanza@gmail.com.|Laboratório de Biologia Molecular Aplicada - LAPLIC, Departamento de Bioquímica, Centro de Biociências, Universidade Federal do Rio Grande do Norte, Natal, RN CEP: 59072-970, Brazil. danielclanza@gmail.com.|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.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|United States', 8, 'Animals|Bacterial Proteins|Bacterial Secretion Systems|Burkholderia mallei|Databases, Protein|Host-Pathogen Interactions|Humans|Mice|Virulence Factors', 'Journal Article', '10.1186/1471-2105-15-244', '25030112', 'Bacterial pathogenicity represents a major public health concern worldwide. Secretion systems are a key component of bacterial pathogenicity, as they provide the means for bacterial proteins to penetrate host-cell membranes and insert themselves directly into the host cells cytosol. Burkholderia mallei is a Gram-negative bacterium that uses multiple secretion systems during its host infection life cycle. To date, the identities of secretion system proteins for B. mallei are not well known, and their pathogenic mechanisms of action and host factors are largely uncharacterized.'),
(96215, 2014, 'bmc', 'Computational reconstruction of proteome-wide protein interaction networks between HTLV retroviruses and Homo sapiens', 'Mei, Suyu|Zhu, Hao', 'Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China. meisygle@gmail.com.|Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China. meisygle@gmail.com.', 'China|China', 7, 'Cluster Analysis|Deltaretrovirus|Gene Ontology|Host-Pathogen Interactions|Humans|Protein Interaction Mapping|Proteomics', 'Journal Article', '10.1186/1471-2105-15-245', '25037487', 'Human T-cell leukemia viruses (HTLV) tend to induce some fatal human diseases like Adult T-cell Leukemia (ATL) by targeting human T lymphocytes. To indentify the protein-protein interactions (PPI) between HTLV viruses and Homo sapiens is one of the significant approaches to reveal the underlying mechanism of HTLV infection and host defence. At present, as biological experiments are labor-intensive and expensive, the identified part of the HTLV-human PPI networks is rather small. Although recent years have witnessed much progress in computational modeling for reconstructing pathogen-host PPI networks, data scarcity and data unavailability are two major challenges to be effectively addressed. To our knowledge, no computational method for proteome-wide HTLV-human PPI networks reconstruction has been reported.'),
(96216, 2014, 'bmc', 'Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations', 'Gualdrón Duarte, Jose L|Cantet, Rodolfo J C|Bates, Ronald O|Ernst, Catherine W|Raney, Nancy E|Steibel, Juan P', 'Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China. meisygle@gmail.com.|Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China. meisygle@gmail.com.|Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China. meisygle@gmail.com.|Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China. meisygle@gmail.com.|Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China. meisygle@gmail.com.|Department of Animal Science, Michigan State University, East Lansing, MI, USA. steibelj@msu.edu.', 'China|China|China|China|China|United States', 21, 'Animals|Breeding|Genetic Association Studies|Genetic Markers|Genetic Variation|Genomics|Models, Statistical|Swine|Time Factors', 'Journal Article', '10.1186/1471-2105-15-246', '25038782', 'Currently, association studies are analysed using statistical mixed models, with marker effects estimated by a linear transformation of genomic breeding values. The variances of marker effects are needed when performing the tests of association. However, approaches used to estimate the parameters rely on a prior variance or on a constant estimate of the additive variance. Alternatively, we propose a standardized test of association using the variance of each marker effect, which generally differ among each other. Random breeding values from a mixed model including fixed effects and a genomic covariance matrix are linearly transformed to estimate the marker effects.'),
(96217, 2014, 'bmc', 'Variant detection sensitivity and biases in whole genome and exome sequencing', 'Meynert, Alison M|Ansari, Morad|FitzPatrick, David R|Taylor, Martin S', 'MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, EH4 2XU Edinburgh, UK. alison.meynert@igmm.ed.ac.uk.|MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, EH4 2XU Edinburgh, UK. alison.meynert@igmm.ed.ac.uk.|MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, EH4 2XU Edinburgh, UK. alison.meynert@igmm.ed.ac.uk.|MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, EH4 2XU Edinburgh, UK. alison.meynert@igmm.ed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 113, 'Alleles|Exome|Genome, Human|Genomics|Humans|Open Reading Frames|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-15-247', '25038816', 'Less than two percent of the human genome is protein coding, yet that small fraction harbours the majority of known disease causing mutations. Despite rapidly falling whole genome sequencing (WGS) costs, much research and increasingly the clinical use of sequence data is likely to remain focused on the protein coding exome. We set out to quantify and understand how WGS compares with the targeted capture and sequencing of the exome (exome-seq), for the specific purpose of identifying single nucleotide polymorphisms (SNPs) in exome targeted regions.'),
(96218, 2014, 'bmc', 'Clinical phenotype-based gene prioritization: an initial study using semantic similarity and the human phenotype ontology', 'Masino, Aaron J|Dechene, Elizabeth T|Dulik, Matthew C|Wilkens, Alisha|Spinner, Nancy B|Krantz, Ian D|Pennington, Jeffrey W|Robinson, Peter N|White, Peter S', 'MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, EH4 2XU Edinburgh, UK. alison.meynert@igmm.ed.ac.uk.|MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, EH4 2XU Edinburgh, UK. alison.meynert@igmm.ed.ac.uk.|MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, EH4 2XU Edinburgh, UK. alison.meynert@igmm.ed.ac.uk.|MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, EH4 2XU Edinburgh, UK. alison.meynert@igmm.ed.ac.uk.|MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, EH4 2XU Edinburgh, UK. alison.meynert@igmm.ed.ac.uk.|MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, EH4 2XU Edinburgh, UK. alison.meynert@igmm.ed.ac.uk.|MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, EH4 2XU Edinburgh, UK. alison.meynert@igmm.ed.ac.uk.|MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, EH4 2XU Edinburgh, UK. alison.meynert@igmm.ed.ac.uk.|Department of Pediatrics, Cincinnati Childrens Hospital and Medical Center, Cincinnati, OH, USA. peter.white@cchmc.org.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 34, 'Algorithms|Biological Ontologies|Computational Biology|Data Mining|Databases, Genetic|Disease|Exome|Humans|Phenotype|Semantics|Software', 'Journal Article', '10.1186/1471-2105-15-248', '25047600', 'Exome sequencing is a promising method for diagnosing patients with a complex phenotype. However, variant interpretation relative to patient phenotype can be challenging in some scenarios, particularly clinical assessment of rare complex phenotypes. Each patients sequence reveals many possibly damaging variants that must be individually assessed to establish clear association with patient phenotype. To assist interpretation, we implemented an algorithm that ranks a given set of genes relative to patient phenotype. The algorithm orders genes by the semantic similarity computed between phenotypic descriptors associated with each gene and those describing the patient. Phenotypic descriptor terms are taken from the Human Phenotype Ontology (HPO) and semantic similarity is derived from each terms information content.'),
(96219, 2014, 'bmc', 'Lineage grammars: describing, simulating and analyzing population dynamics', 'Spiro, Adam|Cardelli, Luca|Shapiro, Ehud', 'Department of Pediatrics, Cincinnati Childrens Hospital and Medical Center, Cincinnati, OH, USA. peter.white@cchmc.org.|Department of Pediatrics, Cincinnati Childrens Hospital and Medical Center, Cincinnati, OH, USA. peter.white@cchmc.org.|Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel. ehud.shapiro@weizmann.ac.il.', 'United States|United States|Israel', 3, 'Biological Evolution|Computational Biology|Environment|Extinction, Biological|Models, Biological|Population Dynamics|Software', 'Journal Article', '10.1186/1471-2105-15-249', '25047682', 'Precise description of the dynamics of biological processes would enable the mathematical analysis and computational simulation of complex biological phenomena. Languages such as Chemical Reaction Networks and Process Algebras cater for the detailed description of interactions among individuals and for the simulation and analysis of ensuing behaviors of populations. However, often knowledge of such interactions is lacking or not available. Yet complete oblivion to the environment would make the description of any biological process vacuous. Here we present a language for describing population dynamics that abstracts away detailed interaction among individuals, yet captures in broad terms the effect of the changing environment, based on environment-dependent Stochastic Tree Grammars (eSTG). It is comprised of a set of stochastic tree grammar transition rules, which are context-free and as such abstract away specific interactions among individuals. Transition rule probabilities and rates, however, can depend on global parameters such as population size, generation count, and elapsed time.'),
(96220, 2014, 'bmc', 'Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data', 'Kiani, Narsis A|Kaderali, Lars', 'Technische Universität Dresden, Medical Faculty Carl Gustav Carus, Institute for Medical Informatics and Biometry, Fetscherstr, 74, 01307 Dresden, Germany. narsis.kiani@ki.se.|Technische Universität Dresden, Medical Faculty Carl Gustav Carus, Institute for Medical Informatics and Biometry, Fetscherstr, 74, 01307 Dresden, Germany. narsis.kiani@ki.se.', 'Germany|Germany', 10, 'Algorithms|Bayes Theorem|Markov Chains|Monte Carlo Method|Signal Transduction|Systems Biology|Time Factors', 'Journal Article', '10.1186/1471-2105-15-250', '25047753', 'Network inference deals with the reconstruction of molecular networks from experimental data. Given N molecular species, the challenge is to find the underlying network. Due to data limitations, this typically is an ill-posed problem, and requires the integration of prior biological knowledge or strong regularization. We here focus on the situation when time-resolved measurements of a systems response after systematic perturbations are available.'),
(96221, 2014, 'bmc', 'BactImAS: a platform for processing and analysis of bacterial time-lapse microscopy movies', 'Mekterovixc4x87, Igor|Mekterovixc4x87, Darko|Maglica, Zeljka', 'Technische Universität Dresden, Medical Faculty Carl Gustav Carus, Institute for Medical Informatics and Biometry, Fetscherstr, 74, 01307 Dresden, Germany. narsis.kiani@ki.se.|Technische Universität Dresden, Medical Faculty Carl Gustav Carus, Institute for Medical Informatics and Biometry, Fetscherstr, 74, 01307 Dresden, Germany. narsis.kiani@ki.se.|School of Life Sciences, Swiss Federal Institute of Technology in Lausanne (EPFL), 1015 Lausanne, Switzerland. zeljka.maglica@gmail.com.', 'Germany|Germany|Switzerland', 8, 'Algorithms|Bacterial Proteins|Cell Proliferation|Databases, Factual|Gene Expression Regulation, Bacterial|Image Processing, Computer-Assisted|Isocitrate Lyase|Microscopy|Mycobacterium smegmatis|Software', 'Journal Article', '10.1186/1471-2105-15-251', '25059528', 'The software available to date for analyzing image sequences from time-lapse microscopy works only for certain bacteria and under limited conditions. These programs, mostly MATLAB-based, fail for microbes with irregular shape, indistinct cell division sites, or that grow in closely packed microcolonies. Unfortunately, many organisms of interest have these characteristics, and analyzing their image sequences has been limited to time consuming manual processing.'),
(96222, 2014, 'bmc', 'Enhancing HMM-based protein profile-profile alignment with structural features and evolutionary coupling information', 'Deng, Xin|Cheng, Jianlin', 'School of Life Sciences, Swiss Federal Institute of Technology in Lausanne (EPFL), 1015 Lausanne, Switzerland. zeljka.maglica@gmail.com.|Computer Science Department, Informatics Institute, C, Bond Life Science Center, University of Missouri-Columbia, Columbia, MO 65211, USA. chengji@missouri.edu.', 'Switzerland|United States', 5, 'Amino Acid Sequence|Computational Biology|Evolution, Molecular|Markov Chains|Phylogeny|Proteins|Sequence Alignment|Solvents', 'Journal Article', '10.1186/1471-2105-15-252', '25062980', 'Protein sequence profile-profile alignment is an important approach to recognizing remote homologs and generating accurate pairwise alignments. It plays an important role in protein sequence database search, protein structure prediction, protein function prediction, and phylogenetic analysis.'),
(96223, 2014, 'bmc', 'Hamiltonian Monte Carlo methods for efficient parameter estimation in steady state dynamical systems', 'Kramer, Andrei|Calderhead, Ben|Radde, Nicole', 'Institute for Systems Theory and Automatic Control, Pfaffenwaldring 9, 70550 Stuttgart, Germany. andrei.kramer@ist.uni-stuttgart.de.|Institute for Systems Theory and Automatic Control, Pfaffenwaldring 9, 70550 Stuttgart, Germany. andrei.kramer@ist.uni-stuttgart.de.|Institute for Systems Theory and Automatic Control, Pfaffenwaldring 9, 70550 Stuttgart, Germany. andrei.kramer@ist.uni-stuttgart.de.', 'Germany|Germany|Germany', 14, 'Algorithms|Bayes Theorem|Humans|Insulin|MAP Kinase Signaling System|Markov Chains|Models, Biological|Monte Carlo Method|Phosphorylation|Receptor, Insulin|Systems Biology', 'Journal Article', '10.1186/1471-2105-15-253', '25066046', 'Parameter estimation for differential equation models of intracellular processes is a highly relevant bu challenging task. The available experimental data do not usually contain enough information to identify all parameters uniquely, resulting in ill-posed estimation problems with often highly correlated parameters. Sampling-based Bayesian statistical approaches are appropriate for tackling this problem. The samples are typically generated via Markov chain Monte Carlo, however such methods are computationally expensive and their convergence may be slow, especially if there are strong correlations between parameters. Monte Carlo methods based on Euclidean or Riemannian Hamiltonian dynamics have been shown to outperform other samplers by making proposal moves that take the local sensitivities of the systems states into account and accepting these moves with high probability. However, the high computational cost involved with calculating the Hamiltonian trajectories prevents their widespread use for all but the smallest differential equation models. The further development of efficient sampling algorithms is therefore an important step towards improving the statistical analysis of predictive models of intracellular processes.'),
(96224, 2014, 'bmc', 'ELM: enhanced lowest common ancestor based method for detecting a pathogenic virus from a large sequence dataset', 'Ueno, Keisuke|Ishii, Akihiro|Ito, Kimihito', 'Institute for Systems Theory and Automatic Control, Pfaffenwaldring 9, 70550 Stuttgart, Germany. andrei.kramer@ist.uni-stuttgart.de.|Institute for Systems Theory and Automatic Control, Pfaffenwaldring 9, 70550 Stuttgart, Germany. andrei.kramer@ist.uni-stuttgart.de.|Division of Bioinformatics, Research Center for Zoonosis Control, Hokkaido University, Sapporo, Hokkaido 001-0020, Japan. itok@czc.hokudai.ac.jp.', 'Germany|Germany|Japan', 5, 'Algorithms|Computational Biology|DNA Primers|Databases, Factual|Databases, Genetic|False Positive Reactions|Genome, Viral|High-Throughput Nucleotide Sequencing|Humans|Probability|Virus Diseases|Viruses', 'Journal Article', '10.1186/1471-2105-15-254', '25069839', 'Emerging viral diseases, most of which are caused by the transmission of viruses from animals to humans, pose a threat to public health. Discovering pathogenic viruses through surveillance is the key to preparedness for this potential threat. Next generation sequencing (NGS) helps us to identify viruses without the design of a specific PCR primer. The major task in NGS data analysis is taxonomic identification for vast numbers of sequences. However, taxonomic identification via a BLAST search against all the known sequences is a computational bottleneck.'),
(96225, 2014, 'bmc', 'Inferring the perturbed microRNA regulatory networks from gene expression data using a network propagation based method', 'Wang, Ting|Gu, Jin|Li, Yanda', 'Division of Bioinformatics, Research Center for Zoonosis Control, Hokkaido University, Sapporo, Hokkaido 001-0020, Japan. itok@czc.hokudai.ac.jp.|MOE Key Laboratory of Bioinformatics, TNLIST Bioinformatics Division & Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China. jgu@tsinghua.edu.cn.|MOE Key Laboratory of Bioinformatics, TNLIST Bioinformatics Division & Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China. jgu@tsinghua.edu.cn.', 'Japan|China|China', 0, 'Carcinogenesis|Cell Line, Tumor|Colorectal Neoplasms|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|MicroRNAs|Systems Biology', 'Journal Article', '10.1186/1471-2105-15-255', '25069957', 'MicroRNAs (miRNAs) are a class of endogenous small regulatory RNAs. Identifications of the dys-regulated or perturbed miRNAs and their key target genes are important for understanding the regulatory networks associated with the studied cellular processes. Several computational methods have been developed to infer the perturbed miRNA regulatory networks by integrating genome-wide gene expression data and sequence-based miRNA-target predictions. However, most of them only use the expression information of the miRNA direct targets, rarely considering the secondary effects of miRNA perturbation on the global gene regulatory networks.'),
(96226, 2014, 'bmc', 'A continuous optimization approach for inferring parameters in mathematical models of regulatory networks', 'Deng, Zhimin|Tian, Tianhai', 'MOE Key Laboratory of Bioinformatics, TNLIST Bioinformatics Division & Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China. jgu@tsinghua.edu.cn.|School of Mathematical Sciences, Monash University, Melbourne 3800, Victoria, Australia. tianhai.tian@monash.edu.', 'China|Australia', 0, 'Enzyme Activation|Extracellular Signal-Regulated MAP Kinases|G1 Phase|MAP Kinase Signaling System|Metabolic Networks and Pathways|Models, Biological|Reproducibility of Results|S Phase|Systems Biology', 'Journal Article', '10.1186/1471-2105-15-256', '25070047', 'The advances of systems biology have raised a large number of sophisticated mathematical models for describing the dynamic property of complex biological systems. One of the major steps in developing mathematical models is to estimate unknown parameters of the model based on experimentally measured quantities. However, experimental conditions limit the amount of data that is available for mathematical modelling. The number of unknown parameters in mathematical models may be larger than the number of observation data. The imbalance between the number of experimental data and number of unknown parameters makes reverse-engineering problems particularly challenging.'),
(96227, 2014, 'bmc', 'The BiSciCol Triplifier: bringing biodiversity data to the Semantic Web', 'Stucky, Brian J|Deck, John|Conlin, Tom|Ziemba, Lukasz|Cellinese, Nico|Guralnick, Robert', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States|United States|United States', 9, 'Biodiversity|Computational Biology|Internet|Semantics|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-15-257', '25073721', 'Recent years have brought great progress in efforts to digitize the worlds biodiversity data, but integrating data from many different providers, and across research domains, remains challenging. Semantic Web technologies have been widely recognized by biodiversity scientists for their potential to help solve this problem, yet these technologies have so far seen little use for biodiversity data. Such slow uptake has been due, in part, to the relative complexity of Semantic Web technologies along with a lack of domain-specific software tools to help non-experts publish their data to the Semantic Web.'),
(96228, 2014, 'bmc', 'Effects of error-correction of heterozygous next-generation sequencing data', 'Fujimoto, M|Bodily, Paul M|Okuda, Nozomu|Clement, Mark J|Snell, Quinn', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States|United States', 0, 'Diploidy|Genome|Genomics|Haplotypes|Heterozygote|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-15-S7-S3', '25077414', 'Error correction is an important step in increasing the quality of next-generation sequencing data for downstream analysis and use. Polymorphic datasets are a challenge for many bioinformatic software packages that are designed for or assume homozygosity of an input dataset. This assumption ignores the true genomic composition of many organisms that are diploid or polyploid. In this survey, two different error correction packages, Quake and ECHO, are examined to see how they perform on next-generation sequence data from heterozygous genomes.'),
(96229, 2014, 'bmc', 'Abstracts from the Ninth International Society for Computational Biology (ISCB) Student Council Symposium 2013, July 19, 2014, Berlin, Germany', '-', '-', '-', 0, 'Animals|Computational Biology|Humans', 'Congress', '10.1186/1471-2105-15-S7-S3', '25077567', '-'),
(96230, 2014, 'bmc', 'Identifying microRNA targets in different gene regions', 'Xu, Wenlong|San Lucas, Anthony|Wang, Zixing|Liu, Yin', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States', 45, 'Algorithms|Genomics|Humans|MicroRNAs|Protein Biosynthesis|Proteins|RNA, Messenger|Regulatory Sequences, Nucleic Acid', 'Journal Article', '10.1186/1471-2105-15-S7-S4', '25077573', 'Currently available microRNA (miRNA) target prediction algorithms require the presence of a conserved seed match to the 5 end of the miRNA and limit the target sites to the 3 untranslated regions of mRNAs. However, it has been noted that these requirements may be too stringent, leading to a substantial number of missing targets.'),
(96231, 2014, 'bmc', 'MDcons: Intermolecular contact maps as a tool to analyze the interface of protein complexes from molecular dynamics trajectories', 'Abdel-Azeim, Safwat|Chermak, Edrisse|Vangone, Anna|Oliva, Romina|Cavallo, Luigi', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States|United States', 0, 'Animals|Computational Biology|Databases, Protein|Humans|Molecular Conformation|Molecular Dynamics Simulation|Protein Binding|Proteins|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-15-S5-S1', '25077693', 'Molecular Dynamics (MD) simulations of protein complexes suffer from the lack of specific tools in the analysis step. Analyses of MD trajectories of protein complexes indeed generally rely on classical measures, such as the RMSD, RMSF and gyration radius, conceived and developed for single macromolecules. As a matter of fact, instead, researchers engaged in simulating the dynamics of a protein complex are mainly interested in characterizing the conservation/variation of its biological interface.'),
(96232, 2014, 'bmc', 'Inference of radio-responsive gene regulatory networks using the graphical lasso algorithm', 'Oh, Jung Hun|Deasy, Joseph O', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States', 13, 'Algorithms|Cell Line, Tumor|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Humans|Neoplasms|Protein Interaction Maps|Radiation Tolerance', 'Journal Article', '10.1186/1471-2105-15-S7-S5', '25077716', 'Inference of gene regulatory networks (GRNs) from gene microarray expression data is of great interest and remains a challenging task in systems biology. Despite many efforts to develop efficient computational methods, the successful modeling of GRNs thus far has been quite limited. To tackle this problem, we propose a novel framework to reconstruct radio-responsive GRNs based on the graphical lasso algorithm. In our attempt to study radiosensitivity, we reviewed the literature and analyzed two publicly available gene microarray datasets. The graphical lasso algorithm was applied to expression measurements for genes commonly found to be significant in these different analyses.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96233, 2014, 'bmc', 'Towards a HPC-oriented parallel implementation of a learning algorithm for bioinformatics applications', 'DAngelo, Gianni|Rampone, Salvatore', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States', 17, 'Algorithms|Animals|Computational Biology|Computing Methodologies|Databases, Nucleic Acid|Europe|Humans|Software', 'Journal Article', '10.1186/1471-2105-15-S5-S2', '25077818', 'The huge quantity of data produced in Biomedical research needs sophisticated algorithmic methodologies for its storage, analysis, and processing. High Performance Computing (HPC) appears as a magic bullet in this challenge. However, several hard to solve parallelization and load balancing problems arise in this context. Here we discuss the HPC-oriented implementation of a general purpose learning algorithm, originally conceived for DNA analysis and recently extended to treat uncertainty on data (U-BRAIN). The U-BRAIN algorithm is a learning algorithm that finds a Boolean formula in disjunctive normal form (DNF), of approximately minimum complexity, that is consistent with a set of data (instances) which may have missing bits. The conjunctive terms of the formula are computed in an iterative way by identifying, from the given data, a family of sets of conditions that must be satisfied by all the positive instances and violated by all the negative ones, such conditions allow the computation of a set of coefficients (relevances) for each attribute (literal), that form a probability distribution, allowing the selection of the term literals. The great versatility that characterizes it, makes U-BRAIN applicable in many of the fields in which there are data to be analyzed. However the memory and the execution time required by the running are of O(n(3)) and of O(n(5)) order, respectively, and so, the algorithm is unaffordable for huge data sets.'),
(96234, 2014, 'bmc', 'Mitochondrial genomic variation associated with higher mitochondrial copy number: the Cache County Study on Memory Health and Aging', 'Ridge, Perry G|Maxwell, Taylor J|Foutz, Spencer J|Bailey, Matthew H|Corcoran, Christopher D|Tschanz, JoAnn T|Norton, Maria C|Munger, Ronald G|OBrien, Elizabeth|Kerber, Richard A|Cawthon, Richard M|Kauwe, John S K', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Aged|Aging|Amino Acid Sequence|Animals|DNA Copy Number Variations|DNA, Mitochondrial|Electron Transport Complex IV|Female|Genetic Variation|Genome, Mitochondrial|Haplotypes|Humans|Longevity|Male|Mitochondria|Molecular Sequence Data|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-15-S7-S6', '25077862', 'The mitochondria are essential organelles and are the location of cellular respiration, which is responsible for the majority of ATP production. Each cell contains multiple mitochondria, and each mitochondrion contains multiple copies of its own circular genome. The ratio of mitochondrial genomes to nuclear genomes is referred to as mitochondrial copy number. Decreases in mitochondrial copy number are known to occur in many tissues as people age, and in certain diseases. The regulation of mitochondrial copy number by nuclear genes has been studied extensively. While mitochondrial variation has been associated with longevity and some of the diseases known to have reduced mitochondrial copy number, the role that the mitochondrial genome itself has in regulating mitochondrial copy number remains poorly understood.'),
(96235, 2014, 'bmc', 'timeClip: pathway analysis for time course data without replicates', 'Martini, Paolo|Sales, Gabriele|Calura, Enrica|Cagnin, Stefano|Chiogna, Monica|Romualdi, Chiara', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States|United States|United States', 8, 'Animals|Gene Expression Profiling|Humans|Mice|Muscles|Regeneration|Signal Transduction|TOR Serine-Threonine Kinases', 'Evaluation Studies', '10.1186/1471-2105-15-S5-S3', '25077979', 'Time-course gene expression experiments are useful tools for exploring biological processes. In this type of experiments, gene expression changes are monitored along time. Unfortunately, replication of time series is still costly and usually long time course do not have replicates. Many approaches have been proposed to deal with this data structure, but none of them in the field of pathway analysis. Pathway analyses have acquired great relevance for helping the interpretation of gene expression data. Several methods have been proposed to this aim: from the classical enrichment to the more complex topological analysis that gains power from the topology of the pathway. None of them were devised to identify temporal variations in time course data.'),
(96236, 2014, 'bmc', 'Multistable switches and their role in cellular differentiation networks', 'Ghaffarizadeh, Ahmadreza|Flann, Nicholas S|Podgorski, Gregory J', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States', 0, 'Animals|Cell Differentiation|Computer Simulation|Gene Regulatory Networks|Humans|Models, Biological|Myeloid Cells', 'Journal Article', '10.1186/1471-2105-15-S7-S7', '25078021', 'Cellular differentiation during development is controlled by gene regulatory networks (GRNs). This complex process is always subject to gene expression noise. There is evidence suggesting that commonly seen patterns in GRNs, referred to as biological multistable switches, play an important role in creating the structure of lineage trees by providing stability to cell types.'),
(96237, 2014, 'bmc', 'Use of Attribute Driven Incremental Discretization and Logic Learning Machine to build a prognostic classifier for neuroblastoma patients', 'Cangelosi, Davide|Muselli, Marco|Parodi, Stefano|Blengio, Fabiola|Becherini, Pamela|Versteeg, Rogier|Conte, Massimo|Varesio, Luigi', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Computational Biology|Gene Expression Profiling|Humans|Infant|Logic|Neuroblastoma|Prognosis|Software|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-15-S5-S4', '25078098', 'Cancer patients outcome is written, in part, in the gene expression profile of the tumor. We previously identified a 62-probe sets signature (NB-hypo) to identify tissue hypoxia in neuroblastoma tumors and showed that NB-hypo stratified neuroblastoma patients in good and poor outcome 1. It was important to develop a prognostic classifier to cluster patients into risk groups benefiting of defined therapeutic approaches. Novel classification and data discretization approaches can be instrumental for the generation of accurate predictors and robust tools for clinical decision support. We explored the application to gene expression data of Rulex, a novel software suite including the Attribute Driven Incremental Discretization technique for transforming continuous variables into simplified discrete ones and the Logic Learning Machine model for intelligible rule generation.'),
(96238, 2014, 'bmc', 'Population substructure in Cache County, Utah: the Cache County study', 'Sharp, Aaron R|Ridge, Perry G|Bailey, Matthew H|Boehme, Kevin L|Norton, Maria C|Tschanz, JoAnn T|Munger, Ronald G|Corcoran, Christopher D|Kauwe, John S K|Alzheimers Disease Neuroimaging Initiative', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 6, 'Alzheimer Disease|Ethnic Groups|European Continental Ancestry Group|Genetics, Population|HapMap Project|Homozygote|Humans|Utah', 'Journal Article', '10.1186/1471-2105-15-S7-S8', '25078123', 'Population stratification is a key concern for genetic association analyses. In addition, extreme homogeneity of ethnic origins of a population can make it difficult to interpret how genetic associations in that population may translate into other populations. Here we have evaluated the genetic substructure of samples from the Cache County study relative to the HapMap Reference populations and data from the Alzheimers Disease Neuroimaging Initiative (ADNI).'),
(96239, 2014, 'bmc', 'Knowledge Discovery and interactive Data Mining in Bioinformatics--State-of-the-Art, future challenges and research directions', 'Holzinger, Andreas|Dehmer, Matthias|Jurisica, Igor', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States', 0, 'Animals|Biomedical Research|Computational Biology|Data Mining|Humans|Knowledge', 'Journal Article', '10.1186/1471-2105-15-S6-I1', '25078282', '-'),
(96240, 2014, 'bmc', 'Proteomics, lipidomics, metabolomics: a mass spectrometry tutorial from a computer scientists point of view', 'Smith, Rob|Mathis, Andrew D|Ventura, Dan|Prince, John T', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States', 30, 'Algorithms|Mass Spectrometry|Mathematics|Metabolomics|Proteomics', 'Journal Article', '10.1186/1471-2105-15-S7-S9', '25078324', 'For decades, mass spectrometry data has been analyzed to investigate a wide array of research interests, including disease diagnostics, biological and chemical theory, genomics, and drug development. Progress towards solving any of these disparate problems depends upon overcoming the common challenge of interpreting the large data sets generated. Despite interim successes, many data interpretation problems in mass spectrometry are still challenging. Further, though these challenges are inherently interdisciplinary in nature, the significant domain-specific knowledge gap between disciplines makes interdisciplinary contributions difficult.'),
(96241, 2014, 'bmc', 'Biochemical systems identification by a random drift particle swarm optimization approach', 'Sun, Jun|Palade, Vasile|Cai, Yujie|Fang, Wei|Wu, Xiaojun', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Biochemistry|Computational Biology|Computer Simulation|Models, Theoretical|Nonlinear Dynamics', 'Evaluation Studies', '10.1186/1471-2105-15-S6-S1', '25078435', 'Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signalling pathways. The problem is stated as a data-driven nonlinear regression problem, which is converted into a nonlinear programming problem with many nonlinear differential and algebraic constraints. Due to the typical ill conditioning and multimodality nature of the problem, it is in general difficult for gradient-based local optimization methods to obtain satisfactory solutions. To surmount this limitation, many stochastic optimization methods have been employed to find the global solution of the problem.'),
(96242, 2014, 'bmc', 'Selection of entropy-measure parameters for knowledge discovery in heart rate variability data', 'Mayer, Christopher C|Bachler, Martin|Hxc3xb6rtenhuber, Matthias|Stocker, Christof|Holzinger, Andreas|Wassertheurer, Siegfried', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States|United States|United States', 49, 'Algorithms|Entropy|Heart Rate|Humans|Medical Informatics|Statistics, Nonparametric', 'Evaluation Studies', '10.1186/1471-2105-15-S6-S2', '25078574', 'Heart rate variability is the variation of the time interval between consecutive heartbeats. Entropy is a commonly used tool to describe the regularity of data sets. Entropy functions are defined using multiple parameters, the selection of which is controversial and depends on the intended purpose. This study describes the results of tests conducted to support parameter selection, towards the goal of enabling further biomarker discovery.'),
(96243, 2014, 'bmc', 'Data-driven discovery of seasonally linked diseases from an Electronic Health Records system', 'Melamed, Rachel D|Khiabanian, Hossein|Rabadan, Raul', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States', 0, 'Disease|Electronic Health Records|Epidemiologic Methods|Humans|Medical Informatics|Risk Factors|Seasons', 'Journal Article', '10.1186/1471-2105-15-S6-S3', '25078762', 'Patterns of disease incidence can identify new risk factors for the disease or provide insight into the etiology. For example, allergies and infectious diseases have been shown to follow periodic temporal patterns due to seasonal changes in environmental or infectious agents. Previous work searching for seasonal or other temporal patterns in disease diagnosis rates has been limited both in the scope of the diseases examined and in the ability to distinguish unexpected seasonal patterns. Electronic Health Records (EHR) compile extensive longitudinal clinical information, constituting a unique source for discovery of trends in occurrence of disease. However, the data suffer from inherent biases that preclude an identification of temporal trends.'),
(96244, 2014, 'bmc', 'Furby: fuzzy force-directed bicluster visualization', 'Streit, Marc|Gratzl, Samuel|Gillhofer, Michael|Mayr, Andreas|Mitterecker, Andreas|Hochreiter, Sepp', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computational Biology|Data Mining|Internet|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-15-S6-S4', '25078951', 'Cluster analysis is widely used to discover patterns in multi-dimensional data. Clustered heatmaps are the standard technique for visualizing one-way and two-way clustering results. In clustered heatmaps, rows and/or columns are reordered, resulting in a representation that shows the clusters as contiguous blocks. However, for biclustering results, where clusters can overlap, it is not possible to reorder the matrix in this way without duplicating rows and/or columns.'),
(96245, 2014, 'bmc', 'Analysis of biomedical data with multilevel glyphs', 'Müller, Heimo|Reihs, Robert|Zatloukal, Kurt|Holzinger, Andreas', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States', 22, 'Data Mining|Humans|Internet|Medical Informatics', 'Evaluation Studies', '10.1186/1471-2105-15-S6-S5', '25079119', 'This paper presents multilevel data glyphs optimized for the interactive knowledge discovery and visualization of large biomedical data sets. Data glyphs are three- dimensional objects defined by multiple levels of geometric descriptions (levels of detail) combined with a mapping of data attributes to graphical elements and methods, which specify their spatial position.'),
(96246, 2014, 'bmc', 'Functional and genetic analysis of the colon cancer network', 'Emmert-Streib, Frank|de Matos Simoes, Ricardo|Glazko, Galina|McDade, Simon|Haibe-Kains, Benjamin|Holzinger, Andreas|Dehmer, Matthias|Campbell, Frederick', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States', 22, 'Colonic Neoplasms|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Humans|Proteins', 'Journal Article', '10.1186/1471-2105-15-S6-S6', '25079297', 'Cancer is a complex disease that has proven to be difficult to understand on the single-gene level. For this reason a functional elucidation needs to take interactions among genes on a systems-level into account. In this study, we infer a colon cancer network from a large-scale gene expression data set by using the method BC3Net. We provide a structural and a functional analysis of this network and also connect its molecular interaction structure with the chromosomal locations of the genes enabling the definition of cis- and trans-interactions. Furthermore, we investigate the interaction of genes that can be found in close neighborhoods on the chromosomes to gain insight into regulatory mechanisms. To our knowledge this is the first study analyzing the genome-scale colon cancer network. '),
(96247, 2014, 'bmc', 'Knowledge discovery of drug data on the example of adverse reaction prediction', 'Yildirim, Pinar|Majnarixc4x87, Ljiljana|Ekmekci, Ozgur|Holzinger, Andreas', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States', 12, 'Adolescent|Algorithms|Anti-Bacterial Agents|Child|Croatia|Data Mining|Decision Making|Drug Therapy|Drug-Related Side Effects and Adverse Reactions|Female|Humans|Male|Medical Informatics', 'Journal Article', '10.1186/1471-2105-15-S6-S7', '25079450', 'Antibiotics are the widely prescribed drugs for children and most likely to be related with adverse reactions. Record on adverse reactions and allergies from antibiotics considerably affect the prescription choices. We consider this a biomedical decision-making problem and explore hidden knowledge in survey results on data extracted from a big data pool of health records of children, from the Health Center of Osijek, Eastern Croatia.'),
(96248, 2014, 'bmc', 'ADaM: augmenting existing approximate fast matching algorithms with efficient and exact range queries', 'Clement, Nathan L|Thompson, Lee P|Miranker, Daniel P', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States', 0, 'Algorithms|Animals|Artificial Intelligence|Base Sequence|Genome|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-15-S7-S1', '25079667', 'Drug discovery, disease detection, and personalized medicine are fast-growing areas of genomic research. With the advancement of next-generation sequencing techniques, researchers can obtain an abundance of data for many different biological assays in a short period of time. When this data is error-free, the result is a high-quality base-pair resolution picture of the genome. However, when the data is lossy the heuristic algorithms currently used when aligning next-generation sequences causes the corresponding accuracy to drop.'),
(96249, 2014, 'bmc', 'Reconstruction of gene co-expression network from microarray data using local expression patterns', 'Roy, Swarup|Bhattacharyya, Dhruba K|Kalita, Jugal K', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States', 0, 'Algorithms|Computer Simulation|Down-Regulation|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Humans|Models, Genetic|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-15-S7-S10', '25079873', 'Biological networks connect genes, gene products to one another. A network of co-regulated genes may form gene clusters that can encode proteins and take part in common biological processes. A gene co-expression network describes inter-relationships among genes. Existing techniques generally depend on proximity measures based on global similarity to draw the relationship between genes. It has been observed that expression profiles are sharing local similarity rather than global similarity. We propose an expression pattern based method called GeCON to extract Gene CO-expression Network from microarray data. Pair-wise supports are computed for each pair of genes based on changing tendencies and regulation patterns of the gene expression. Gene pairs showing negative or positive co-regulation under a given number of conditions are used to construct such gene co-expression network. We construct co-expression network with signed edges to reflect up- and down-regulation between pairs of genes. Most existing techniques do not emphasize computational efficiency. We exploit a fast correlogram matrix based technique for capturing the support of each gene pair to construct the network.'),
(96250, 2014, 'bmc', 'Structure prediction of polyglutamine disease proteins: comparison of methods', 'Wen, Jingran|Scoles, Daniel R|Facelli, Julio C', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States', 1, 'Amino Acid Sequence|Humans|Huntingtin Protein|Huntington Disease|Models, Molecular|Molecular Sequence Data|Nerve Tissue Proteins|Peptides|Protein Conformation|Software', 'Comparative Study', '10.1186/1471-2105-15-S7-S11', '25080018', 'The expansion of polyglutamine (poly-Q) repeats in several unrelated proteins is associated with at least ten neurodegenerative diseases. The length of the poly-Q regions plays an important role in the progression of the diseases. The number of glutamines (Q) is inversely related to the onset age of these polyglutamine diseases, and the expansion of poly-Q repeats has been associated with protein misfolding. However, very little is known about the structural changes induced by the expansion of the repeats. Computational methods can provide an alternative to determine the structure of these poly-Q proteins, but it is important to evaluate their performance before large scale prediction work is done.'),
(96251, 2014, 'bmc', 'Variant Tool Chest: an improved tool to analyze and manipulate variant call format (VCF) files', 'Ebbert, Mark T W|Wadsworth, Mark E|Boehme, Kevin L|Hoyt, Kaitlyn L|Sharp, Aaron R|OFallon, Brendan D|Kauwe, John S K|Ridge, Perry G', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Databases, Genetic|Genetic Variation|Genome, Human|Genotype|High-Throughput Nucleotide Sequencing|Humans|Software', 'Journal Article', '10.1186/1471-2105-15-S7-S12', '25080132', 'Since the advent of next-generation sequencing many previously untestable hypotheses have been realized. Next-generation sequencing has been used for a wide range of studies in diverse fields such as population and medical genetics, phylogenetics, microbiology, and others. However, this novel technology has created unanticipated challenges such as the large numbers of genetic variants. Each caucasian genome has more than four million single nucleotide variants, insertions and deletions, copy number variants, and structural variants. Several formats have been suggested for storing these variants, however, the variant call format (VCF) has become the community standard.'),
(96252, 2014, 'bmc', 'Yeast pheromone pathway modeling using Petri nets', 'Majumdar, Abhishek|Scott, Stephen D|Deogun, Jitender S|Harris, Steven', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States', 0, 'Computer Simulation|Environment|Models, Biological|Mutation|Pheromones|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction', 'Journal Article', '10.1186/1471-2105-15-S7-S13', '25080237', 'Our environment is composed of biological components of varying magnitude. The relationships between the different biological elements can be represented as a biological network. The process of mating in S. cerevisiae is initiated by secretion of pheromone by one of the cells. Our interest lies in one particular question: how does a cell dynamically adapt the pathway to continue mating under severe environmental changes or under mutation (which might result in the loss of functionality of some proteins known to participate in the pheromone pathway). Our work attempts to answer this question. To achieve this, we first propose a model to simulate the pheromone pathway using Petri nets. Petri nets are directed graphs that can be used for describing and modeling systems characterized as concurrent, asynchronous, distributed, parallel, non-deterministic, and/or stochastic. We then analyze our Petri net-based model of the pathway to investigate the following: 1) Given the model of the pheromone response pathway, under what conditions does the cell respond positively, i.e., mate? 2) What kinds of perturbations in the cell would result in changing a negative response to a positive one?'),
(96253, 2014, 'bmc', 'Analysis of interactions between the epigenome and structural mutability of the genome using Genboree Workbench tools', 'Coarfa, Cristian|Pichot, Christina|Jackson, Andrew|Tandon, Arpit|Amin, Viren|Raghuraman, Sriram|Paithankar, Sameer|Lee, Adrian V|McGuire, Sean E|Milosavljevic, Aleksandar', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|DNA|DNA Methylation|Epigenesis, Genetic|Epigenomics|Genome, Human|Genomic Instability|Germ Cells|Histones|Humans|Neoplasms|Software', 'Journal Article', '10.1186/1471-2105-15-S7-S2', '25080362', 'Interactions between the epigenome and structural genomic variation are potentially bi-directional. In one direction, structural variants may cause epigenomic changes in cis. In the other direction, specific local epigenomic states such as DNA hypomethylation associate with local genomic instability.'),
(96254, 2014, 'bmc', 'Computational prediction of hinge axes in proteins', 'Shamsuddin, Rittika|Doktorova, Milka|Jaswal, Sheila|Lee-St John, Audrey|McMenimen, Kathryn', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Models, Molecular|Molecular Conformation|Protein Conformation|Proteins', 'Evaluation Studies', '10.1186/1471-2105-15-S8-S2', '25080829', 'A proteins function is determined by the wide range of motions exhibited by its 3D structure. However, current experimental techniques are not able to reliably provide the level of detail required for elucidating the exact mechanisms of protein motion essential for effective drug screening and design. Computational tools are instrumental in the study of the underlying structure-function relationship. We focus on a special type of proteins called hinge proteins which exhibit a motion that can be interpreted as a rotation of one domain relative to another.'),
(96255, 2014, 'bmc', 'Template-based C8-SCORPION: a protein 8-state secondary structure prediction method using structural information and context-based features', 'Yaseen, Ashraf|Li, Yaohang', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States', 12, 'Algorithms|Artificial Intelligence|Computational Biology|Internet|Neural Networks (Computer)|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins', 'Evaluation Studies', '10.1186/1471-2105-15-S8-S3', '25080939', 'Secondary structures prediction of proteins is important to many protein structure modeling applications. Correct prediction of secondary structures can significantly reduce the degrees of freedom in protein tertiary structure modeling and therefore reduces the difficulty of obtaining high resolution 3D models.'),
(96256, 2014, 'bmc', 'Exploring representations of protein structure for automated remote homology detection and mapping of protein structure space', 'Molloy, Kevin|Van, M Jennifer|Barbara, Daniel|Shehu, Amarda', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Automation|Computational Biology|Natural Language Processing|Proteins', 'Evaluation Studies', '10.1186/1471-2105-15-S8-S4', '25080993', 'Due to rapid sequencing of genomes, there are now millions of deposited protein sequences with no known function. Fast sequence-based comparisons allow detecting close homologs for a protein of interest to transfer functional information from the homologs to the given protein. Sequence-based comparison cannot detect remote homologs, in which evolution has adjusted the sequence while largely preserving structure. Structure-based comparisons can detect remote homologs but most methods for doing so are too expensive to apply at a large scale over structural databases of proteins. Recently, fragment-based structural representations have been proposed that allow fast detection of remote homologs with reasonable accuracy. These representations have also been used to obtain linearly-reducible maps of protein structure space. It has been shown, as additionally supported from analysis in this paper that such maps preserve functional co-localization of the protein structure space.'),
(96257, 2014, 'bmc', 'Computational models of liver fibrosis progression for hepatitis C virus chronic infection', 'Lara, James|López-Labrador, F|González-Candelas, Fernando|Berenguer, Marina|Khudyakov, Yury E', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States|United States|United States', 7, 'Adult|Aged|Artificial Intelligence|Base Sequence|Bayes Theorem|Computational Biology|Computer Simulation|Disease Progression|Female|Genetic Markers|Hepacivirus|Humans|Liver Cirrhosis|Liver Transplantation|Male|Middle Aged|Viral Proteins', 'Journal Article', '10.1186/1471-2105-15-S8-S5', '25081062', 'Chronic infection with hepatitis C virus (HCV) is a risk factor for liver diseases such as fibrosis, cirrhosis and hepatocellular carcinoma. HCV genetic heterogeneity was hypothesized to be associated with severity of liver disease. However, no reliable viral markers predicting disease severity have been identified. Here, we report the utility of sequences from 3 HCV 1b genomic regions, Core, NS3 and NS5b, to identify viral genetic markers associated with fast and slow rate of fibrosis progression (RFP) among patients with and without liver transplantation (n = 42).'),
(96258, 2014, 'bmc', 'Multicore and GPU algorithms for Nussinov RNA folding', 'Li, Junjie|Ranka, Sanjay|Sahni, Sartaj', 'Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.|Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA. stuckyb@colorado.edu.', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Computers|Microcomputers|Nucleic Acid Conformation|RNA|RNA Folding', 'Journal Article', '10.1186/1471-2105-15-S8-S1', '25082539', 'One segment of a RNA sequence might be paired with another segment of the same RNA sequence due to the force of hydrogen bonds. This two-dimensional structure is called the RNA sequences secondary structure. Several algorithms have been proposed to predict an RNA sequences secondary structure. These algorithms are referred to as RNA folding algorithms.'),
(96259, 2014, 'bmc', 'GMOseek: a user friendly tool for optimized GMO testing', 'Morisset, Dany|Novak, Petra Kralj|Zupanixc4x8d, Darko|Gruden, Kristina|Lavraxc4x8d, Nada|xc5xbdel, Jana', 'Department of Biotechnology and Systems Biology, National Institute of Biology, Vexc4x8dna pot 111, SI-1000 Ljubljana, Slovenia. dany.morisset@nib.si.|Department of Biotechnology and Systems Biology, National Institute of Biology, Vexc4x8dna pot 111, SI-1000 Ljubljana, Slovenia. dany.morisset@nib.si.|Department of Biotechnology and Systems Biology, National Institute of Biology, Vexc4x8dna pot 111, SI-1000 Ljubljana, Slovenia. dany.morisset@nib.si.|Department of Biotechnology and Systems Biology, National Institute of Biology, Vexc4x8dna pot 111, SI-1000 Ljubljana, Slovenia. dany.morisset@nib.si.|Department of Biotechnology and Systems Biology, National Institute of Biology, Vexc4x8dna pot 111, SI-1000 Ljubljana, Slovenia. dany.morisset@nib.si.|Department of Biotechnology and Systems Biology, National Institute of Biology, Vexc4x8dna pot 111, SI-1000 Ljubljana, Slovenia. dany.morisset@nib.si.', 'Slovenia|Slovenia|Slovenia|Slovenia|Slovenia|Slovenia', 18, 'Computational Biology|Decision Making|Laboratories|Plants, Genetically Modified|Real-Time Polymerase Chain Reaction|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-15-258', '25084968', 'With the increasing pace of new Genetically Modified Organisms (GMOs) authorized or in pipeline for commercialization worldwide, the task of the laboratories in charge to test the compliance of food, feed or seed samples with their relevant regulations became difficult and costly. Many of them have already adopted the so called matrix approach to rationalize the resources and efforts used to increase their efficiency within a limited budget. Most of the time, the matrix approach is implemented using limited information and some proprietary (if any) computational tool to efficiently use the available data.'),
(96260, 2014, 'bmc', 'Helium: visualization of large scale plant pedigrees', 'Shaw, Paul D|Graham, Martin|Kennedy, Jessie|Milne, Iain|Marshall, David F', 'Information and Computational Sciences, The James Hutton Institute, Invergowrie, Dundee DD2 5DA, UK. paul.shaw@hutton.ac.uk.|Information and Computational Sciences, The James Hutton Institute, Invergowrie, Dundee DD2 5DA, UK. paul.shaw@hutton.ac.uk.|Information and Computational Sciences, The James Hutton Institute, Invergowrie, Dundee DD2 5DA, UK. paul.shaw@hutton.ac.uk.|Information and Computational Sciences, The James Hutton Institute, Invergowrie, Dundee DD2 5DA, UK. paul.shaw@hutton.ac.uk.|Information and Computational Sciences, The James Hutton Institute, Invergowrie, Dundee DD2 5DA, UK. paul.shaw@hutton.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 22, 'Alleles|Breeding|Computational Biology|Cooperative Behavior|Genotype|Hordeum|Phenotype|Phylogeny', 'Journal Article', '10.1186/1471-2105-15-259', '25085009', 'Plant breeders use an increasingly diverse range of data types to identify lines with desirable characteristics suitable to be taken forward in plant breeding programmes. There are a number of key morphological and physiological traits, such as disease resistance and yield that need to be maintained and improved upon if a commercial variety is to be successful. Computational tools that provide the ability to integrate and visualize this data with pedigree structure, will enable breeders to make better decisions on the lines that are used in crossings to meet both the demands for increased yield/production and adaptation to climate change.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96261, 2014, 'bmc', 'Gene set enrichment analysis for multiple continuous phenotypes', 'Wang, Xiaoming|Pyne, Saumyadipta|Dinu, Irina', 'School of Public Health, University of Alberta, Edmonton, AB T6G 1C9, Canada. xiaoming@ualberta.ca.|School of Public Health, University of Alberta, Edmonton, AB T6G 1C9, Canada. xiaoming@ualberta.ca.|School of Public Health, University of Alberta, Edmonton, AB T6G 1C9, Canada. xiaoming@ualberta.ca.', 'Canada|Canada|Canada', 0, 'Computational Biology|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Phenotype', 'Journal Article', '10.1186/1471-2105-15-260', '25086605', 'Gene set analysis (GSA) methods test the association of sets of genes with phenotypes in gene expression microarray studies. While GSA methods on a single binary or categorical phenotype abounds, little attention has been paid to the case of a continuous phenotype, and there is no method to accommodate correlated multiple continuous phenotypes.'),
(96262, 2014, 'bmc', 'Computational analyses of curcuminoid analogs against kinase domain of HER2', 'Yim-im, Wannarat|Sawatdichaikul, Orathai|Semsri, Suwanna|Horata, Natharinee|Mokmak, Wanwimon|Tongsima, Sissades|Suksamrarn, Apichart|Choowongkomon, Kiattawee', 'School of Public Health, University of Alberta, Edmonton, AB T6G 1C9, Canada. xiaoming@ualberta.ca.|Institute of Food Research and Product Development, Kasetsart University, 50 Ngam Wong Wan Rd, Chatuchak, Bangkok 10900, Thailand. ifrots@ku.ac.th.|Institute of Food Research and Product Development, Kasetsart University, 50 Ngam Wong Wan Rd, Chatuchak, Bangkok 10900, Thailand. ifrots@ku.ac.th.|Institute of Food Research and Product Development, Kasetsart University, 50 Ngam Wong Wan Rd, Chatuchak, Bangkok 10900, Thailand. ifrots@ku.ac.th.|Institute of Food Research and Product Development, Kasetsart University, 50 Ngam Wong Wan Rd, Chatuchak, Bangkok 10900, Thailand. ifrots@ku.ac.th.|Institute of Food Research and Product Development, Kasetsart University, 50 Ngam Wong Wan Rd, Chatuchak, Bangkok 10900, Thailand. ifrots@ku.ac.th.|Institute of Food Research and Product Development, Kasetsart University, 50 Ngam Wong Wan Rd, Chatuchak, Bangkok 10900, Thailand. ifrots@ku.ac.th.|Institute of Food Research and Product Development, Kasetsart University, 50 Ngam Wong Wan Rd, Chatuchak, Bangkok 10900, Thailand. ifrots@ku.ac.th.', 'Canada|Thailand|Thailand|Thailand|Thailand|Thailand|Thailand|Thailand', 15, 'Antineoplastic Agents|Curcumin|Drug Design|Female|Humans|Hydrophobic and Hydrophilic Interactions|Ligands|Molecular Docking Simulation|Molecular Dynamics Simulation|Protein Kinase Inhibitors|Protein Structure, Tertiary|Receptor, ErbB-2', 'Journal Article', '10.1186/1471-2105-15-261', '25089037', 'Human epidermal growth factor receptor 2 (HER2) has an important role in cancer aggressiveness and poor prognosis. HER2 has been used as a drug target for cancers. In particular, to effectively treat HER2-positive cancer, small molecule inhibitors were developed to target HER2 kinase. Knowing that curcumin has been used as food to inhibit cancer activity, this study evaluated the efficacy of natural curcumins and curcumin analogs as HER2 inhibitors using in vitro and in silico studies. The curcumin analogs considered in this study composed of 4 groups classified by their core structure, xcexb2-diketone, monoketone, pyrazole, and isoxazole.'),
(96263, 2014, 'bmc', 'Clinical PathoScope: rapid alignment and filtration for accurate pathogen identification in clinical samples using unassembled sequencing data', 'Byrd, Allyson L|Perez-Rogers, Joseph F|Manimaran, Solaiappan|Castro-Nallar, Eduardo|Toma, Ian|McCaffrey, Tim|Siegel, Marc|Benson, Gary|Crandall, Keith A|Johnson, William Evan', 'Institute of Food Research and Product Development, Kasetsart University, 50 Ngam Wong Wan Rd, Chatuchak, Bangkok 10900, Thailand. ifrots@ku.ac.th.|Institute of Food Research and Product Development, Kasetsart University, 50 Ngam Wong Wan Rd, Chatuchak, Bangkok 10900, Thailand. ifrots@ku.ac.th.|Institute of Food Research and Product Development, Kasetsart University, 50 Ngam Wong Wan Rd, Chatuchak, Bangkok 10900, Thailand. ifrots@ku.ac.th.|Institute of Food Research and Product Development, Kasetsart University, 50 Ngam Wong Wan Rd, Chatuchak, Bangkok 10900, Thailand. ifrots@ku.ac.th.|Institute of Food Research and Product Development, Kasetsart University, 50 Ngam Wong Wan Rd, Chatuchak, Bangkok 10900, Thailand. ifrots@ku.ac.th.|Institute of Food Research and Product Development, Kasetsart University, 50 Ngam Wong Wan Rd, Chatuchak, Bangkok 10900, Thailand. ifrots@ku.ac.th.|Institute of Food Research and Product Development, Kasetsart University, 50 Ngam Wong Wan Rd, Chatuchak, Bangkok 10900, Thailand. ifrots@ku.ac.th.|Institute of Food Research and Product Development, Kasetsart University, 50 Ngam Wong Wan Rd, Chatuchak, Bangkok 10900, Thailand. ifrots@ku.ac.th.|Department of Bioinformatics, Boston University, Boston, MA, USA. kcrandall@gwu.edu.|Department of Bioinformatics, Boston University, Boston, MA, USA. kcrandall@gwu.edu.', 'Thailand|Thailand|Thailand|Thailand|Thailand|Thailand|Thailand|Thailand|United States|United States', 32, 'Base Sequence|Computational Biology|Host-Pathogen Interactions|Humans|Microbiological Techniques|Phylogeny|Sequence Alignment|Sequence Analysis|Species Specificity|Time Factors', 'Journal Article', '10.1186/1471-2105-15-262', '25091138', 'The use of sequencing technologies to investigate the microbiome of a sample can positively impact patient healthcare by providing therapeutic targets for personalized disease treatment. However, these samples contain genomic sequences from various sources that complicate the identification of pathogens.'),
(96264, 2014, 'bmc', 'morFeus: a web-based program to detect remotely conserved orthologs using symmetrical best hits and orthology network scoring', 'Wagner, Ines|Volkmer, Michael|Sharan, Malvika|Villaveces, Jose M|Oswald, Felix|Surendranath, Vineeth|Habermann, Bianca H', 'Department of Bioinformatics, Boston University, Boston, MA, USA. kcrandall@gwu.edu.|Department of Bioinformatics, Boston University, Boston, MA, USA. kcrandall@gwu.edu.|Department of Bioinformatics, Boston University, Boston, MA, USA. kcrandall@gwu.edu.|Department of Bioinformatics, Boston University, Boston, MA, USA. kcrandall@gwu.edu.|Department of Bioinformatics, Boston University, Boston, MA, USA. kcrandall@gwu.edu.|Department of Bioinformatics, Boston University, Boston, MA, USA. kcrandall@gwu.edu.|Max Planck Institute of Biochemistry, Am Klopferspitz 18, Martinsried 82152, Germany. habermann@biochem.mpg.de.', 'United States|United States|United States|United States|United States|United States|Germany', 0, 'Amino Acid Sequence|Base Sequence|Computational Biology|Conserved Sequence|Data Interpretation, Statistical|Humans|Internet|Sequence Analysis|Sequence Homology|Software', 'Journal Article', '10.1186/1471-2105-15-263', '25096057', 'Searching the orthologs of a given protein or DNA sequence is one of the most important and most commonly used Bioinformatics methods in Biology. Programs like BLAST or the orthology search engine Inparanoid can be used to find orthologs when the similarity between two sequences is sufficiently high. They however fail when the level of conservation is low. The detection of remotely conserved proteins oftentimes involves sophisticated manual intervention that is difficult to automate.'),
(96265, 2014, 'bmc', 'Enhancing the detection of barcoded reads in high throughput DNA sequencing data by controlling the false discovery rate', 'Buschmann, Tilo|Zhang, Rong|Brash, Douglas E|Bystrykh, Leonid V', 'Max Planck Research Group: Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. tibu@cbs.mpg.de.|Max Planck Research Group: Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. tibu@cbs.mpg.de.|Max Planck Research Group: Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. tibu@cbs.mpg.de.|Max Planck Research Group: Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. tibu@cbs.mpg.de.', 'Germany|Germany|Germany|Germany', 8, 'Animals|DNA|DNA Contamination|DNA Primers|False Positive Reactions|Genome|High-Throughput Nucleotide Sequencing|Mice|Reference Standards|Sequence Analysis, DNA|Sodium-Potassium-Exchanging ATPase', 'Journal Article', '10.1186/1471-2105-15-264', '25099007', 'DNA barcodes are short unique sequences used to label DNA or RNA-derived samples in multiplexed deep sequencing experiments. During the demultiplexing step, barcodes must be detected and their position identified. In some cases (e.g., with PacBio SMRT), the position of the barcode and DNA context is not well defined. Many reads start inside the genomic insert so that adjacent primers might be missed. The matter is further complicated by coincidental similarities between barcode sequences and reference DNA. Therefore, a robust strategy is required in order to detect barcoded reads and avoid a large number of false positives or negatives.For mass inference problems such as this one, false discovery rate (FDR) methods are powerful and balanced solutions. Since existing FDR methods cannot be applied to this particular problem, we present an adapted FDR method that is suitable for the detection of barcoded reads as well as suggest possible improvements.'),
(96266, 2014, 'bmc', 'ReformAlign: improved multiple sequence alignments using a profile-based meta-alignment approach', 'Lyras, Dimitrios P|Metzler, Dirk', 'Faculty of Biology, Department II, Ludwig-Maximilians Universität München, Planegg-Martinsried 82152, Germany. lyras@biologie.uni-muenchen.de.|Faculty of Biology, Department II, Ludwig-Maximilians Universität München, Planegg-Martinsried 82152, Germany. lyras@biologie.uni-muenchen.de.', 'Germany|Germany', 8, 'Algorithms|Computational Biology|Quality Control|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-15-265', '25099134', 'Obtaining an accurate sequence alignment is fundamental for consistently analyzing biological data. Although this problem may be efficiently solved when only two sequences are considered, the exact inference of the optimal alignment easily gets computationally intractable for the multiple sequence alignment case. To cope with the high computational expenses, approximate heuristic methods have been proposed that address the problem indirectly by progressively aligning the sequences in pairs according to their relatedness. These methods however are not flexible to change the alignment of an already aligned group of sequences in the view of new data, resulting thus in compromises on the quality of the deriving alignment. In this paper we present ReformAlign, a novel meta-alignment approach that may significantly improve on the quality of the deriving alignments from popular aligners. We call ReformAlign a meta-aligner as it requires an initial alignment, for which a variety of alignment programs can be used. The main idea behind ReformAlign is quite straightforward: at first, an existing alignment is used to construct a standard profile which summarizes the initial alignment and then all sequences are individually re-aligned against the formed profile. From each sequence-profile comparison, the alignment of each sequence against the profile is recorded and the final alignment is indirectly inferred by merging all the individual sub-alignments into a unified set. The employment of ReformAlign may often result in alignments which are significantly more accurate than the starting alignments.'),
(96267, 2014, 'bmc', 'Natural language processing of radiology reports for the detection of thromboembolic diseases and clinically relevant incidental findings', 'Pham, Anne-Dominique|Névéol, Aurélie|Lavergne, Thomas|Yasunaga, Daisuke|Clément, Olivier|Meyer, Guy|Morello, Rémy|Burgun, Anita', 'Department of Biostatistics and Clinical Research, CHU de Caen, Caen F-14000, France. anndo.pham@gmail.com.|Department of Biostatistics and Clinical Research, CHU de Caen, Caen F-14000, France. anndo.pham@gmail.com.|Department of Biostatistics and Clinical Research, CHU de Caen, Caen F-14000, France. anndo.pham@gmail.com.|Department of Biostatistics and Clinical Research, CHU de Caen, Caen F-14000, France. anndo.pham@gmail.com.|Department of Biostatistics and Clinical Research, CHU de Caen, Caen F-14000, France. anndo.pham@gmail.com.|Department of Biostatistics and Clinical Research, CHU de Caen, Caen F-14000, France. anndo.pham@gmail.com.|Department of Biostatistics and Clinical Research, CHU de Caen, Caen F-14000, France. anndo.pham@gmail.com.|Department of Biostatistics and Clinical Research, CHU de Caen, Caen F-14000, France. anndo.pham@gmail.com.', 'France|France|France|France|France|France|France|France', 31, 'Algorithms|Computational Biology|Humans|Incidental Findings|Natural Language Processing|Pulmonary Embolism|Radiology|Research Report|Tomography, X-Ray Computed', 'Journal Article', '10.1186/1471-2105-15-266', '25099227', 'Natural Language Processing (NLP) has been shown effective to analyze the content of radiology reports and identify diagnosis or patient characteristics. We evaluate the combination of NLP and machine learning to detect thromboembolic disease diagnosis and incidental clinically relevant findings from angiography and venography reports written in French. We model thromboembolic diagnosis and incidental findings as a set of concepts, modalities and relations between concepts that can be used as features by a supervised machine learning algorithm. A corpus of 573 radiology reports was de-identified and manually annotated with the support of NLP tools by a physician for relevant concepts, modalities and relations. A machine learning classifier was trained on the dataset interpreted by a physician for diagnosis of deep-vein thrombosis, pulmonary embolism and clinically relevant incidental findings. Decision models accounted for the imbalanced nature of the data and exploited the structure of the reports.'),
(96268, 2014, 'bmc', 'A phenome-guided drug repositioning through a latent variable model', 'Bisgin, Halil|Liu, Zhichao|Fang, Hong|Kelly, Reagan|Xu, Xiaowei|Tong, Weida', 'Department of Biostatistics and Clinical Research, CHU de Caen, Caen F-14000, France. anndo.pham@gmail.com.|Department of Biostatistics and Clinical Research, CHU de Caen, Caen F-14000, France. anndo.pham@gmail.com.|Department of Biostatistics and Clinical Research, CHU de Caen, Caen F-14000, France. anndo.pham@gmail.com.|Department of Biostatistics and Clinical Research, CHU de Caen, Caen F-14000, France. anndo.pham@gmail.com.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. xwxu@ualr.edu.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. xwxu@ualr.edu.', 'France|France|France|France|United States|United States', 19, 'Computational Biology|Data Mining|Databases, Pharmaceutical|Drug Repositioning|Drug-Related Side Effects and Adverse Reactions|Models, Statistical|Phenotype', 'Journal Article', '10.1186/1471-2105-15-267', '25103881', 'The phenome represents a distinct set of information in the human population. It has been explored particularly in its relationship with the genome to identify correlations for diseases. The phenome has been also explored for drug repositioning with efforts focusing on the search space for the most similar candidate drugs. For a comprehensive analysis of the phenome, we assumed that all phenotypes (indications and side effects) were inter-connected with a probabilistic distribution and this characteristic may offer an opportunity to identify new therapeutic indications for a given drug. Correspondingly, we employed Latent Dirichlet Allocation (LDA), which introduces latent variables (topics) to govern the phenome distribution.'),
(96269, 2014, 'bmc', 'PhylDiag: identifying complex synteny blocks that include tandem duplications using phylogenetic gene trees', 'Lucas, Joseph M E X|Muffato, Matthieu|Roest Crollius, Hugues', 'Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. xwxu@ualr.edu.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. xwxu@ualr.edu.|Ecole Normale Supérieure, Institut de Biologie de lENS, IBENS, 46 rue dUlm, 75005 Paris, France. hrc@ens.fr.', 'United States|United States|France', 5, 'Animals|Gene Duplication|Genomics|Humans|Mice|Models, Genetic|Phylogeny|Software|Synteny', 'Journal Article', '10.1186/1471-2105-15-268', '25103980', 'Extant genomes share regions where genes have the same order and orientation, which are thought to arise from the conservation of an ancestral order of genes during evolution. Such regions of so-called conserved synteny, or synteny blocks, must be precisely identified and quantified, as a prerequisite to better understand the evolutionary history of genomes.'),
(96270, 2014, 'bmc', 'Study of cell differentiation by phylogenetic analysis using histone modification data', 'Nair, Nishanth Ulhas|Lin, Yu|Manasovska, Ana|Antic, Jelena|Grnarova, Paulina|Sahu, Avinash Das|Bucher, Philipp|Moret, Bernard M E', 'Ecole Normale Supérieure, Institut de Biologie de lENS, IBENS, 46 rue dUlm, 75005 Paris, France. hrc@ens.fr.|Ecole Normale Supérieure, Institut de Biologie de lENS, IBENS, 46 rue dUlm, 75005 Paris, France. hrc@ens.fr.|Ecole Normale Supérieure, Institut de Biologie de lENS, IBENS, 46 rue dUlm, 75005 Paris, France. hrc@ens.fr.|Ecole Normale Supérieure, Institut de Biologie de lENS, IBENS, 46 rue dUlm, 75005 Paris, France. hrc@ens.fr.|Ecole Normale Supérieure, Institut de Biologie de lENS, IBENS, 46 rue dUlm, 75005 Paris, France. hrc@ens.fr.|Ecole Normale Supérieure, Institut de Biologie de lENS, IBENS, 46 rue dUlm, 75005 Paris, France. hrc@ens.fr.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.', 'France|France|France|France|France|France|Switzerland|Switzerland', 7, 'Cell Differentiation|Chromatin Immunoprecipitation|Epigenomics|Histones|Humans|Phylogeny|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-15-269', '25104072', 'In cell differentiation, a cell of a less specialized type becomes one of a more specialized type, even though all cells have the same genome. Transcription factors and epigenetic marks like histone modifications can play a significant role in the differentiation process.'),
(96271, 2014, 'bmc', 'ICoVax 2013: the 3rd ISV Pre-conference Computational Vaccinology Workshop', 'De Groot, Anne S|De Groot, Phoebe|He, Yongqun', 'School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.', 'Switzerland|Switzerland|Switzerland', 0, 'Cardiovascular Diseases|China|Computational Biology|Databases, Nucleic Acid|Drugs, Chinese Herbal|Epitopes, T-Lymphocyte|Genome, Human|Humans|Receptors, Antigen, T-Cell|Vaccines, DNA|Viral Proteins|Viral Vaccines|Virus Diseases|Viruses', 'Introductory Journal Article', '10.1186/1471-2105-15-S4-I1', '25104130', 'Following last years computational vaccinology workshop in Shanghai, China, the third ISV Pre-conference Computational Vaccinology Workshop (ICoVax 2013) was held in Barcelona, Spain. ICoVax 2013 provided an international platform for the attendees to showcase their research and discuss problems and solutions in the development and application of computational vaccinology and vaccine informatics tools. The first of the three full-length papers presented at ICoVax discussed the discovery of viral camouflage through cross-conservation of T-cell epitopes using a tool called JanusMatrix. This important paper reports that viruses may camouflage their presence in the human body by incorporating sequences in their proteins that are highly cross-conserved at the T-cell receptor surface with human genome proteins, a discovery that has wide ranging implications for the development of vaccines against viruses that use the camouflage method. The other papers described a database for storing experimentally verified data on DNA vaccines and compared therapeutic targets of western drugs to Chinese herbal medicines for cardiovascular diseases. The short poster presentations covered various uses of informatics tools for processing the DNA and microRNA of pathogens to improve vaccine coverage, efficacy and development. A live (on-line) demonstration of the vaccine design toolkit, iVax, presented by Frances Terry of EpiVax, illustrated how computational vaccinology could be used in the design of next generation vaccines. '),
(96272, 2014, 'bmc', 'Integrated assessment of predicted MHC binding and cross-conservation with self reveals patterns of viral camouflage', 'He, Lu|De Groot, Anne S|Gutierrez, Andres H|Martin, William D|Moise, Lenny|Bailey-Kellogg, Chris', 'School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 23, 'Animals|Computational Biology|Cross Reactions|Databases, Factual|Epitopes, T-Lymphocyte|Histocompatibility Antigens Class II|Humans|Influenza A Virus, H1N1 Subtype|Influenza A Virus, H5N1 Subtype|Influenza, Human|Major Histocompatibility Complex|Models, Biological|Models, Molecular|Orthomyxoviridae Infections|Receptors, Antigen, T-Cell|T-Lymphocytes|Viral Proteins|Viral Vaccines|Virus Diseases|Viruses', 'Journal Article', '10.1186/1471-2105-15-S4-S1', '25104221', 'Immune recognition of foreign proteins by T cells hinges on the formation of a ternary complex sandwiching a constituent peptide of the protein between a major histocompatibility complex (MHC) molecule and a T cell receptor (TCR). Viruses have evolved means of camouflaging themselves, avoiding immune recognition by reducing the MHC and/or TCR binding of their constituent peptides. Computer-driven T cell epitope mapping tools have been used to evaluate the degree to which particular viruses have used this means of avoiding immune response, but most such analyses focus on MHC-facing agretopes. Here we set out a new means of evaluating the TCR faces of viral peptides in addition to their agretopes, integrating evaluations of both sides of the ternary complex in a single analysis.'),
(96273, 2014, 'bmc', 'DNAVaxDB: the first web-based DNA vaccine database and its data analysis', 'Racz, Rebecca|Li, Xinna|Patel, Mukti|Xiang, Zuoshuang|He, Yongqun', 'School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 9, 'Antigens, Bacterial|Bacteria|Bacterial Infections|Bacterial Vaccines|Cancer Vaccines|Databases, Nucleic Acid|Humans|Internet|Neoplasms|Proteins|Software|Vaccines, DNA|Viral Vaccines|Virus Diseases|Viruses', 'Journal Article', '10.1186/1471-2105-15-S4-S2', '25104313', 'Since the first DNA vaccine studies were done in the 1990s, thousands more studies have followed. Here we report the development and analysis of DNAVaxDB (http://www.violinet.org/dnavaxdb), the first publically available web-based DNA vaccine database that curates, stores, and analyzes experimentally verified DNA vaccines, DNA vaccine plasmid vectors, and protective antigens used in DNA vaccines. All data in DNAVaxDB are annotated from reliable resources, particularly peer-reviewed articles. Among over 140 DNA vaccine plasmids, some plasmids were more frequently used in one type of pathogen than others, for example, pCMVi-UB for G- bacterial DNA vaccines, and pCAGGS for viral DNA vaccines. Presently, over 400 DNA vaccines containing over 370 protective antigens from over 90 infectious and non-infectious diseases have been curated in DNAVaxDB. While extracellular and bacterial cell surface proteins and adhesin proteins were frequently used for DNA vaccine development, the majority of protective antigens used in Chlamydophila DNA vaccines are localized to the inner portion of the cell. The DNA vaccine priming, other vaccine boosting vaccination regimen has been widely used to induce protection against infection of different pathogens such as HIV. Parasitic and cancer DNA vaccines were also systematically analyzed. User-friendly web query and visualization interfaces are available in DNAVaxDB for interactive data search. To support data exchange, the information of DNA vaccines, plasmids, and protective antigens is stored in the Vaccine Ontology (VO). DNAVaxDB is targeted to become a timely and vital source of DNA vaccines and related data and facilitate advanced DNA vaccine research and development. '),
(96274, 2014, 'bmc', 'Target network differences between western drugs and Chinese herbal ingredients in treating cardiovascular disease', 'Fu, Peng|Yang, Linlin|Sun, Yi|Ye, Li|Cao, Zhiwei|Tang, Kailin', 'School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.|School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), EPFL IC IIF LCBB, INJ 211 (Batiment INJ), Station 14, CH-1015 Lausanne, Switzerland. philipp.bucher@epfl.ch.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 6, 'Apoptosis|Cardiovascular Agents|Cardiovascular Diseases|Databases, Pharmaceutical|Drugs, Chinese Herbal|Humans|Interleukins|Molecular Targeted Therapy|Proteins|Signal Transduction', 'Journal Article', '10.1186/1471-2105-15-S4-S3', '25104437', 'Western drugs have achieved great successes in CVDs treatment. However, they may lead to some side effects and drug resistance. On the other hand, more and more studies found that Traditional Chinese herbs have efficient therapeutic effects for CVDs, while their therapeutic mechanism is still not very clear. It may be a good view towards molecules, targets and network to decipher whether difference exists between anti-CVD western drugs and Chinese herbal ingredients.'),
(96275, 2014, 'bmc', 'Discovery of co-occurring driver pathways in cancer', 'Zhang, Junhua|Wu, Ling-Yun|Zhang, Xiang-Sun|Zhang, Shihua', 'National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China. zjh@amt.ac.cn.|National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China. zjh@amt.ac.cn.|National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China. zjh@amt.ac.cn.|National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China. zjh@amt.ac.cn.', 'China|China|China|China', 51, 'Algorithms|Carcinogenesis|Disease Progression|Humans|Mutation|Neoplasms|Signal Transduction|Software|Systems Biology', 'Journal Article', '10.1186/1471-2105-15-271', '25106096', 'It has been widely realized that pathways rather than individual genes govern the course of carcinogenesis. Therefore, discovering driver pathways is becoming an important step to understand the molecular mechanisms underlying cancer and design efficient treatments for cancer patients. Previous studies have focused mainly on observation of the alterations in cancer genomes at the individual gene or single pathway level. However, a great deal of evidence has indicated that multiple pathways often function cooperatively in carcinogenesis and other key biological processes.'),
(96276, 2014, 'bmc', 'GVCBLUP: a computer package for genomic prediction and variance component estimation of additive and dominance effects', 'Wang, Chunkao|Prakapenka, Dzianis|Wang, Shengwen|Pulugurta, Sujata|Runesha, Hakizumwami Birali|Da, Yang', 'National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China. zjh@amt.ac.cn.|National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China. zjh@amt.ac.cn.|National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China. zjh@amt.ac.cn.|National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China. zjh@amt.ac.cn.|National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China. zjh@amt.ac.cn.|Department of Animal Science, University of Minnesota, Saint Paul, MN 55108, USA. yda@umn.edu.', 'China|China|China|China|China|United States', 18, 'Algorithms|Animals|Breeding|Genetic Markers|Genomics|Genotype|Phenotype|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-15-270', '25107495', 'Dominance effect may play an important role in genetic variation of complex traits. Full featured and easy-to-use computing tools for genomic prediction and variance component estimation of additive and dominance effects using genome-wide single nucleotide polymorphism (SNP) markers are necessary to understand dominance contribution to a complex trait and to utilize dominance for selecting individuals with favorable genetic potential.'),
(96277, 2014, 'bmc', 'Cellular quantitative analysis of neuroblastoma tumor and splitting overlapping cells', 'Tafavogh, Siamak|Catchpoole, Daniel R|Kennedy, Paul J', 'Centre for Quantum Computation and Intelligent Systems (QCIS), Faculty of Engineering and IT, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007 Sydney, Australia. siamak.tafavogh@uts.edu.au.|Centre for Quantum Computation and Intelligent Systems (QCIS), Faculty of Engineering and IT, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007 Sydney, Australia. siamak.tafavogh@uts.edu.au.|Centre for Quantum Computation and Intelligent Systems (QCIS), Faculty of Engineering and IT, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007 Sydney, Australia. siamak.tafavogh@uts.edu.au.', 'Australia|Australia|Australia', 2, 'Algorithms|Cell Count|Humans|Lymphoma, Follicular|Neuroblastoma|Single-Cell Analysis', 'Journal Article', '10.1186/1471-2105-15-272', '25109603', 'Neuroblastoma Tumor (NT) is one of the most aggressive types of infant cancer. Essential to accurate diagnosis and prognosis is cellular quantitative analysis of the tumor. Counting enormous numbers of cells under an optical microscope is error-prone. There is therefore an urgent demand from pathologists for robust and automated cell counting systems. However, the main challenge in developing these systems is the inability of them to distinguish between overlapping cells and single cells, and to split the overlapping cells. We address this challenge in two stages by: 1) distinguishing overlapping cells from single cells using the morphological differences between them such as area, uniformity of diameters and cell concavity, and 2) splitting overlapping cells into single cells. We propose a novel approach by using the dominant concave regions of cells as markers to identify the overlap region. We then find the initial splitting points at the critical points of the concave regions by decomposing the concave regions into their components such as arcs, chords and edges, and the distance between the components is analyzed using the developed seed growing technique. Lastly, a shortest path determination approach is developed to determine the optimum splitting route between two candidate initial splitting points.'),
(96278, 2014, 'bmc', 'A new method for modeling coalescent processes with recombination', 'Wang, Ying|Zhou, Ying|Li, Linfeng|Chen, Xian|Liu, Yuting|Ma, Zhi-Ming|Xu, Shuhua', 'Centre for Quantum Computation and Intelligent Systems (QCIS), Faculty of Engineering and IT, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007 Sydney, Australia. siamak.tafavogh@uts.edu.au.|Centre for Quantum Computation and Intelligent Systems (QCIS), Faculty of Engineering and IT, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007 Sydney, Australia. siamak.tafavogh@uts.edu.au.|Centre for Quantum Computation and Intelligent Systems (QCIS), Faculty of Engineering and IT, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007 Sydney, Australia. siamak.tafavogh@uts.edu.au.|Centre for Quantum Computation and Intelligent Systems (QCIS), Faculty of Engineering and IT, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007 Sydney, Australia. siamak.tafavogh@uts.edu.au.|Centre for Quantum Computation and Intelligent Systems (QCIS), Faculty of Engineering and IT, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007 Sydney, Australia. siamak.tafavogh@uts.edu.au.|Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China. mazm@amt.ac.cn.|Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China. mazm@amt.ac.cn.', 'Australia|Australia|Australia|Australia|Australia|China|China', 9, 'Algorithms|Evolution, Molecular|Genetics, Population|Humans|Markov Chains|Models, Genetic|Recombination, Genetic', 'Journal Article', '10.1186/1471-2105-15-273', '25113665', 'Recombination plays an important role in the maintenance of genetic diversity in many types of organisms, especially diploid eukaryotes. Recombination can be studied and used to map diseases. However, recombination adds a great deal of complexity to the genetic information. This renders estimation of evolutionary parameters more difficult. After the coalescent process was formulated, models capable of describing recombination using graphs, such as ancestral recombination graphs (ARG) were also developed. There are two typical models based on which to simulate ARG: back-in-time model such as ms and spatial model including Wiuf&Heins, SMC, SMC, and MaCS.'),
(96279, 2014, 'bmc', 'A feature selection method for classification within functional genomics experiments based on the proportional overlapping score', 'Mahmoud, Osama|Harrison, Andrew|Perperoglou, Aris|Gul, Asma|Khan, Zardad|Metodiev, Metodi V|Lausen, Berthold', 'Department of Mathematical Sciences, University of Essex, Wivenhoe Park, CO4 3SQ Colchester, UK. ofamah@essex.ac.uk.|Department of Mathematical Sciences, University of Essex, Wivenhoe Park, CO4 3SQ Colchester, UK. ofamah@essex.ac.uk.|Department of Mathematical Sciences, University of Essex, Wivenhoe Park, CO4 3SQ Colchester, UK. ofamah@essex.ac.uk.|Department of Mathematical Sciences, University of Essex, Wivenhoe Park, CO4 3SQ Colchester, UK. ofamah@essex.ac.uk.|Department of Mathematical Sciences, University of Essex, Wivenhoe Park, CO4 3SQ Colchester, UK. ofamah@essex.ac.uk.|Department of Mathematical Sciences, University of Essex, Wivenhoe Park, CO4 3SQ Colchester, UK. ofamah@essex.ac.uk.|Department of Mathematical Sciences, University of Essex, Wivenhoe Park, CO4 3SQ Colchester, UK. ofamah@essex.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 23, 'Cluster Analysis|Gene Expression Profiling|Genomics|Humans|Oligonucleotide Array Sequence Analysis|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-15-274', '25113817', 'Microarray technology, as well as other functional genomics experiments, allow simultaneous measurements of thousands of genes within each sample. Both the prediction accuracy and interpretability of a classifier could be enhanced by performing the classification based only on selected discriminative genes. We propose a statistical method for selecting genes based on overlapping analysis of expression data across classes. This method results in a novel measure, called proportional overlapping score (POS), of a features relevance to a classification task.'),
(96280, 2014, 'bmc', 'miRPlant: an integrated tool for identification of plant miRNA from RNA sequencing data', 'An, Jiyuan|Lai, John|Sajjanhar, Atul|Lehman, Melanie L|Nelson, Colleen C', 'Australian Prostate Cancer Research Centre, Queensland, and Princess Alexandra Hospital, Woolloongabba, Brisbane, Australia. j.an@qut.edu.au.|Australian Prostate Cancer Research Centre, Queensland, and Princess Alexandra Hospital, Woolloongabba, Brisbane, Australia. j.an@qut.edu.au.|Australian Prostate Cancer Research Centre, Queensland, and Princess Alexandra Hospital, Woolloongabba, Brisbane, Australia. j.an@qut.edu.au.|Australian Prostate Cancer Research Centre, Queensland, and Princess Alexandra Hospital, Woolloongabba, Brisbane, Australia. j.an@qut.edu.au.|Australian Prostate Cancer Research Centre, Queensland, and Princess Alexandra Hospital, Woolloongabba, Brisbane, Australia. j.an@qut.edu.au.', 'Australia|Australia|Australia|Australia|Australia', 49, 'Base Sequence|Computational Biology|MicroRNAs|Plants|RNA, Plant|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-15-275', '25117656', 'Small RNA sequencing is commonly used to identify novel miRNAs and to determine their expression levels in plants. There are several miRNA identification tools for animals such as miRDeep, miRDeep2 and miRDeep*. miRDeep-P was developed to identify plant miRNA using miRDeeps probabilistic model of miRNA biogenesis, but it depends on several third party tools and lacks a user-friendly interface. The objective of our miRPlant program is to predict novel plant miRNA, while providing a user-friendly interface with improved accuracy of prediction.'),
(96281, 2014, 'bmc', 'Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks', 'Kane, Michael J|Price, Natalie|Scotch, Matthew|Rabinowitz, Peter', 'Yale Center for Analytical Sciences, Yale University, New Haven, CT, USA. michael.kane@yale.edu.|Yale Center for Analytical Sciences, Yale University, New Haven, CT, USA. michael.kane@yale.edu.|Yale Center for Analytical Sciences, Yale University, New Haven, CT, USA. michael.kane@yale.edu.|Yale Center for Analytical Sciences, Yale University, New Haven, CT, USA. michael.kane@yale.edu.', 'United States|United States|United States|United States', 99, 'Animals|Artificial Intelligence|Birds|Computational Biology|Disease Outbreaks|Egypt|Humans|Influenza A Virus, H5N1 Subtype|Influenza in Birds|Influenza, Human|Models, Statistical', 'Comparative Study', '10.1186/1471-2105-15-276', '25123979', 'Time series models can play an important role in disease prediction. Incidence data can be used to predict the future occurrence of disease events. Developments in modeling approaches provide an opportunity to compare different time series models for predictive power.'),
(96282, 2014, 'bmc', 'CRF-based models of protein surfaces improve protein-protein interaction site predictions', 'Dong, Zhijie|Wang, Keyu|Dang, Truong Khanh Linh|Gültas, Mehmet|Welter, Marlon|Wierschin, Torsten|Stanke, Mario|Waack, Stephan', 'Yale Center for Analytical Sciences, Yale University, New Haven, CT, USA. michael.kane@yale.edu.|Yale Center for Analytical Sciences, Yale University, New Haven, CT, USA. michael.kane@yale.edu.|Yale Center for Analytical Sciences, Yale University, New Haven, CT, USA. michael.kane@yale.edu.|Yale Center for Analytical Sciences, Yale University, New Haven, CT, USA. michael.kane@yale.edu.|Yale Center for Analytical Sciences, Yale University, New Haven, CT, USA. michael.kane@yale.edu.|Yale Center for Analytical Sciences, Yale University, New Haven, CT, USA. michael.kane@yale.edu.|Yale Center for Analytical Sciences, Yale University, New Haven, CT, USA. michael.kane@yale.edu.|Institute of Computer Science, University of Göttingen, Goldschmidtstr, 7, 37077 Göttingen, Germany. waack@informatik.uni-goettingen.de.', 'United States|United States|United States|United States|United States|United States|United States|Germany', 15, 'Algorithms|Binding Sites|Computational Biology|Models, Statistical|Protein Binding|Proteins|Software|Surface Properties', 'Journal Article', '10.1186/1471-2105-15-277', '25124108', 'The identification of protein-protein interaction sites is a computationally challenging task and important for understanding the biology of protein complexes. There is a rich literature in this field. A broad class of approaches assign to each candidate residue a real-valued score that measures how likely it is that the residue belongs to the interface. The prediction is obtained by thresholding this score.Some probabilistic models classify the residues on the basis of the posterior probabilities. In this paper, we introduce pairwise conditional random fields (pCRFs) in which edges are not restricted to the backbone as in the case of linear-chain CRFs utilized by Li et al. (2007). In fact, any 3D-neighborhood relation can be modeled. On grounds of a generalized Viterbi inference algorithm and a piecewise training process for pCRFs, we demonstrate how to utilize pCRFs to enhance a given residue-wise score-based protein-protein interface predictor on the surface of the protein under study. The features of the pCRF are solely based on the interface predictions scores of the predictor the performance of which shall be improved.'),
(96283, 2014, 'bmc', 'Equivalent input produces different output in the UniFrac significance test', 'Long, Jeffrey R|Pittet, Vanessa|Trost, Brett|Yan, Qingxiang|Vickers, David|Haakensen, Monique|Kusalik, Anthony', 'Department of Computer Science, University of Saskatchewan, 176 Thorvaldson Bldg, 110 Science Place, Saskatoon, Canada. jeff.long@usask.ca.|Department of Computer Science, University of Saskatchewan, 176 Thorvaldson Bldg, 110 Science Place, Saskatoon, Canada. jeff.long@usask.ca.|Department of Computer Science, University of Saskatchewan, 176 Thorvaldson Bldg, 110 Science Place, Saskatoon, Canada. jeff.long@usask.ca.|Department of Computer Science, University of Saskatchewan, 176 Thorvaldson Bldg, 110 Science Place, Saskatoon, Canada. jeff.long@usask.ca.|Department of Computer Science, University of Saskatchewan, 176 Thorvaldson Bldg, 110 Science Place, Saskatoon, Canada. jeff.long@usask.ca.|Department of Computer Science, University of Saskatchewan, 176 Thorvaldson Bldg, 110 Science Place, Saskatoon, Canada. jeff.long@usask.ca.|Department of Computer Science, University of Saskatchewan, 176 Thorvaldson Bldg, 110 Science Place, Saskatoon, Canada. jeff.long@usask.ca.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 4, 'Computational Biology|Geologic Sediments|Lakes|Metagenomics|Microbiology|Phylogeny', 'Letter', '10.1186/1471-2105-15-278', '25124232', 'UniFrac is a well-known tool for comparing microbial communities and assessing statistically significant differences between communities. In this paper we identify a discrepancy in the UniFrac methodology that causes semantically equivalent inputs to produce different outputs in tests of statistical significance.'),
(96284, 2014, 'bmc', 'SPARQLGraph: a web-based platform for graphically querying biological Semantic Web databases', 'Schweiger, Dominik|Trajanoski, Zlatko|Pabinger, Stephan', 'Department of Computer Science, University of Saskatchewan, 176 Thorvaldson Bldg, 110 Science Place, Saskatoon, Canada. jeff.long@usask.ca.|Department of Computer Science, University of Saskatchewan, 176 Thorvaldson Bldg, 110 Science Place, Saskatoon, Canada. jeff.long@usask.ca.|Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innsbruck, Austria. stephan.pabinger@gmail.com.', 'Canada|Canada|Austria', 0, 'Computer Graphics|Data Mining|Databases, Factual|Humans|Internet|Semantics|Software', 'Journal Article', '10.1186/1471-2105-15-279', '25127889', 'Semantic Web has established itself as a framework for using and sharing data across applications and database boundaries. Here, we present a web-based platform for querying biological Semantic Web databases in a graphical way.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96285, 2014, 'bmc', 'HiChIP: a high-throughput pipeline for integrative analysis of ChIP-Seq data', 'Yan, Huihuang|Evans, Jared|Kalmbach, Mike|Moore, Raymond|Middha, Sumit|Luban, Stanislav|Wang, Liguo|Bhagwate, Aditya|Li, Ying|Sun, Zhifu|Chen, Xianfeng|Kocher, Jean-Pierre A', 'Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innsbruck, Austria. stephan.pabinger@gmail.com.|Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innsbruck, Austria. stephan.pabinger@gmail.com.|Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innsbruck, Austria. stephan.pabinger@gmail.com.|Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innsbruck, Austria. stephan.pabinger@gmail.com.|Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innsbruck, Austria. stephan.pabinger@gmail.com.|Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innsbruck, Austria. stephan.pabinger@gmail.com.|Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innsbruck, Austria. stephan.pabinger@gmail.com.|Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innsbruck, Austria. stephan.pabinger@gmail.com.|Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innsbruck, Austria. stephan.pabinger@gmail.com.|Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innsbruck, Austria. stephan.pabinger@gmail.com.|Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innsbruck, Austria. stephan.pabinger@gmail.com.|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 1st St SW, Rochester, MN 55905, USA. Kocher.JeanPierre@mayo.edu.', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|United States', 21, 'Animals|Binding Sites|Chromatin Immunoprecipitation|Chromosome Mapping|Data Interpretation, Statistical|Genomics|High-Throughput Nucleotide Sequencing|Humans|Mice|Molecular Sequence Annotation|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-15-280', '25128017', 'Chromatin immunoprecipitation (ChIP) followed by next-generation sequencing (ChIP-Seq) has been widely used to identify genomic loci of transcription factor (TF) binding and histone modifications. ChIP-Seq data analysis involves multiple steps from read mapping and peak calling to data integration and interpretation. It remains challenging and time-consuming to process large amounts of ChIP-Seq data derived from different antibodies or experimental designs using the same approach. To address this challenge, there is a need for a comprehensive analysis pipeline with flexible settings to accelerate the utilization of this powerful technology in epigenetics research.'),
(96286, 2014, 'bmc', 'BESST--efficient scaffolding of large fragmented assemblies', 'Sahlin, Kristoffer|Vezzi, Francesco|Nystedt, Björn|Lundeberg, Joakim|Arvestad, Lars', 'Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, Solna, Sweden. ksahlin@kth.se.|Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, Solna, Sweden. ksahlin@kth.se.|Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, Solna, Sweden. ksahlin@kth.se.|Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, Solna, Sweden. ksahlin@kth.se.|Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, Solna, Sweden. ksahlin@kth.se.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Algorithms|Gene Library|Genomics|High-Throughput Nucleotide Sequencing|Humans|Reproducibility of Results|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-15-281', '25128196', 'The use of short reads from High Throughput Sequencing (HTS) techniques is now commonplace in de novo assembly. Yet, obtaining contiguous assemblies from short reads is challenging, thus making scaffolding an important step in the assembly pipeline. Different algorithms have been proposed but many of them use the number of read pairs supporting a linking of two contigs as an indicator of reliability. This reasoning is intuitive, but fails to account for variation in link count due to contig features.We have also noted that published scaffolders are only evaluated on small datasets using output from only one assembler. Two issues arise from this. Firstly, some of the available tools are not well suited for complex genomes. Secondly, these evaluations provide little support for inferring a softwares general performance.'),
(96287, 2014, 'bmc', 'Designing of peptides with desired half-life in intestine-like environment', 'Sharma, Arun|Singla, Deepak|Rashid, Mamoon|Raghava, Gajendra Pal Singh', 'Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, Solna, Sweden. ksahlin@kth.se.|Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, Solna, Sweden. ksahlin@kth.se.|Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, Solna, Sweden. ksahlin@kth.se.|Bioinformatics Centre, Institute of Microbial Technology, Sector 39A, Chandigarh, India. raghava@imtech.res.in.', 'Sweden|Sweden|Sweden|India', 23, 'Chemical Phenomena|Computational Biology|Databases, Protein|Drug Design|Half-Life|Internet|Intestinal Mucosa|Mutation|Peptides|Software', 'Journal Article', '10.1186/1471-2105-15-282', '25141912', 'In past, a number of peptides have been reported to possess highly diverse properties ranging from cell penetrating, tumor homing, anticancer, anti-hypertensive, antiviral to antimicrobials. Owing to their excellent specificity, low-toxicity, rich chemical diversity and availability from natural sources, FDA has successfully approved a number of peptide-based drugs and several are in various stages of drug development. Though peptides are proven good drug candidates, their usage is still hindered mainly because of their high susceptibility towards proteases degradation. We have developed an in silico method to predict the half-life of peptides in intestine-like environment and to design better peptides having optimized physicochemical properties and half-life.'),
(96288, 2014, 'bmc', 'Impact of variance components on reliability of absolute quantification using digital PCR', 'Jacobs, Bart K M|Goetghebeur, Els|Clement, Lieven', 'Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281, S9, 9000 Ghent, Belgium. BartKM.Jacobs@UGent.be.|Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281, S9, 9000 Ghent, Belgium. BartKM.Jacobs@UGent.be.|Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281, S9, 9000 Ghent, Belgium. BartKM.Jacobs@UGent.be.', 'Belgium|Belgium|Belgium', 0, 'Analysis of Variance|Gene Dosage|Polymerase Chain Reaction|Reproducibility of Results|Spectrometry, Fluorescence|Uncertainty', 'Journal Article', '10.1186/1471-2105-15-283', '25147026', 'Digital polymerase chain reaction (dPCR) is an increasingly popular technology for detecting and quantifying target nucleic acids. Its advertised strength is high precision absolute quantification without needing reference curves. The standard data analytic approach follows a seemingly straightforward theoretical framework but ignores sources of variation in the data generating process. These stem from both technical and biological factors, where we distinguish features that are 1) hard-wired in the equipment, 2) user-dependent and 3) provided by manufacturers but may be adapted by the user. The impact of the corresponding variance components on the accuracy and precision of target concentration estimators presented in the literature is studied through simulation.'),
(96289, 2014, 'bmc', 'MT-Toolbox: improved amplicon sequencing using molecule tags', 'Yourstone, Scott M|Lundberg, Derek S|Dangl, Jeffery L|Jones, Corbin D', 'Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill 27599, USA. scott.yourstone81@gmail.com.|Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill 27599, USA. scott.yourstone81@gmail.com.|Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill 27599, USA. scott.yourstone81@gmail.com.|Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill 27599, USA. scott.yourstone81@gmail.com.', 'United States|United States|United States|United States', 0, 'Computer Graphics|DNA|Metagenomics|Oligonucleotides|Polymerase Chain Reaction|Sequence Analysis, DNA|Software|Time Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-15-284', '25149069', 'Short oligonucleotides can be used as markers to tag and track DNA sequences. For example, barcoding techniques (i.e. Multiplex Identifiers or Indexing) use short oligonucleotides to distinguish between reads from different DNA samples pooled for high-throughput sequencing. A similar technique called molecule tagging uses the same principles but is applied to individual DNA template molecules. Each template molecule is tagged with a unique oligonucleotide prior to polymerase chain reaction. The resulting amplicon sequences can be traced back to their original templates by their oligonucleotide tag. Consensus building from sequences sharing the same tag enables inference of original template molecules thereby reducing effects of sequencing error and polymerase chain reaction bias. Several independent groups have developed similar protocols for molecule tagging, however, user-friendly software for build consensus sequences from molecule tagged reads is not readily available or is highly specific for a particular protocol.'),
(96290, 2014, 'bmc', 'A generalizable NLP framework for fast development of pattern-based biomedical relation extraction systems', 'Peng, Yifan|Torii, Manabu|Wu, Cathy H|Vijay-Shanker, K', 'Department of Computer and Information Sciences, University of Delaware, 18 Amstel Ave, Newark, DE 19716, USA. yfpeng@udel.edu.|Department of Computer and Information Sciences, University of Delaware, 18 Amstel Ave, Newark, DE 19716, USA. yfpeng@udel.edu.|Department of Computer and Information Sciences, University of Delaware, 18 Amstel Ave, Newark, DE 19716, USA. yfpeng@udel.edu.|Department of Computer and Information Sciences, University of Delaware, 18 Amstel Ave, Newark, DE 19716, USA. yfpeng@udel.edu.', 'United States|United States|United States|United States', 11, 'Biomedical Research|Data Mining|Language|Pattern Recognition, Automated|Publications|Time Factors', 'Journal Article', '10.1186/1471-2105-15-285', '25149151', 'Text mining is increasingly used in the biomedical domain because of its ability to automatically gather information from large amount of scientific articles. One important task in biomedical text mining is relation extraction, which aims to identify designated relations among biological entities reported in literature. A relation extraction system achieving high performance is expensive to develop because of the substantial time and effort required for its design and implementation. Here, we report a novel framework to facilitate the development of a pattern-based biomedical relation extraction system. It has several unique design features: (1) leveraging syntactic variations possible in a language and automatically generating extraction patterns in a systematic manner, (2) applying sentence simplification to improve the coverage of extraction patterns, and (3) identifying referential relations between a syntactic argument of a predicate and the actual target expected in the relation extraction task.'),
(96291, 2014, 'bmc', 'Identification of highly related references about gene-disease association', 'Liu, Rey-Long|Shih, Chia-Chun', 'Department of Medical Informatics, Tzu Chi University, Hualien, Taiwan. rlliutcu@mail.tcu.edu.tw.|Department of Medical Informatics, Tzu Chi University, Hualien, Taiwan. rlliutcu@mail.tcu.edu.tw.', '-|-', 0, 'Biomedical Research|Computational Biology|Data Mining|Disease|Humans|Publications|Search Engine', 'Journal Article', '10.1186/1471-2105-15-286', '25155502', 'Curation of gene-disease associations published in literature should be based on careful and frequent survey of the references that are highly related to specific gene-disease associations. Retrieval of the references is thus essential for timely and complete curation.'),
(96292, 2014, 'bmc', 'Content-based histopathology image retrieval using CometCloud', 'Qi, Xin|Wang, Daihou|Rodero, Ivan|Diaz-Montes, Javier|Gensure, Rebekah H|Xing, Fuyong|Zhong, Hua|Goodell, Lauri|Parashar, Manish|Foran, David J|Yang, Lin', 'Department of Pathology and Laboratory Medicine, Rutger Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ, USA. qixi@rutgers.edu.|Department of Pathology and Laboratory Medicine, Rutger Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ, USA. qixi@rutgers.edu.|Department of Pathology and Laboratory Medicine, Rutger Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ, USA. qixi@rutgers.edu.|Department of Pathology and Laboratory Medicine, Rutger Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ, USA. qixi@rutgers.edu.|Department of Pathology and Laboratory Medicine, Rutger Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ, USA. qixi@rutgers.edu.|Department of Pathology and Laboratory Medicine, Rutger Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ, USA. qixi@rutgers.edu.|Department of Pathology and Laboratory Medicine, Rutger Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ, USA. qixi@rutgers.edu.|Department of Pathology and Laboratory Medicine, Rutger Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ, USA. qixi@rutgers.edu.|Department of Pathology and Laboratory Medicine, Rutger Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ, USA. qixi@rutgers.edu.|Department of Pathology and Laboratory Medicine, Rutger Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ, USA. qixi@rutgers.edu.|Department of Pathology and Laboratory Medicine, Rutger Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ, USA. qixi@rutgers.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Diagnostic Imaging|Feedback|Information Storage and Retrieval|Pathology|Pattern Recognition, Automated|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-15-287', '25155691', 'The development of digital imaging technology is creating extraordinary levels of accuracy that provide support for improved reliability in different aspects of the image analysis, such as content-based image retrieval, image segmentation, and classification. This has dramatically increased the volume and rate at which data are generated. Together these facts make querying and sharing non-trivial and render centralized solutions unfeasible. Moreover, in many cases this data is often distributed and must be shared across multiple institutions requiring decentralized solutions. In this context, a new generation of data/information driven applications must be developed to take advantage of the national advanced cyber-infrastructure (ACI) which enable investigators to seamlessly and securely interact with information/data which is distributed across geographically disparate resources. This paper presents the development and evaluation of a novel content-based image retrieval (CBIR) framework. The methods were tested extensively using both peripheral blood smears and renal glomeruli specimens. The datasets and performance were evaluated by two pathologists to determine the concordance.'),
(96293, 2014, 'bmc', 'APTE: identification of indirect read-out A-DNA promoter elements in genomes', 'Whitley, David C|Runfola, Valeria|Cary, Peter|Nazlamova, Liliya|Guille, Matt|Scarlett, Garry', 'Department of Pathology and Laboratory Medicine, Rutger Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ, USA. qixi@rutgers.edu.|Department of Pathology and Laboratory Medicine, Rutger Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ, USA. qixi@rutgers.edu.|Department of Pathology and Laboratory Medicine, Rutger Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ, USA. qixi@rutgers.edu.|Department of Pathology and Laboratory Medicine, Rutger Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ, USA. qixi@rutgers.edu.|Department of Pathology and Laboratory Medicine, Rutger Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ, USA. qixi@rutgers.edu.|Biophysics Laboratories, School of Biological Sciences, Institute of Biomedical and Biomolecular Science, University of Portsmouth, King Henry Building, King Henry I Street, Portsmouth PO1 2DY, UK. garry.scarlett@port.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Base Sequence|DNA, A-Form|GATA2 Transcription Factor|Gene Expression Regulation|Genome|Genomics|Nuclear Factor 90 Proteins|Promoter Regions, Genetic|Software|Xenopus|Xenopus Proteins', 'Journal Article', '10.1186/1471-2105-15-288', '25158845', 'Transcriptional regulation is normally based on the recognition by a transcription factor of a defined base sequence in a process of direct read-out. However, the nucleic acid secondary and tertiary structure can also act as a recognition site for the transcription factor in a process known as indirect read-out, although this is much less understood. We have previously identified such a transcriptional control mechanism in early Xenopus development where the interaction of the transcription factor ilf3 and the gata2 promoter requires the presence of both an unusual A-form DNA structure and a CCAAT sequence. Rapid identification of such promoters elsewhere in the Xenopus and other genomes would provide insight into a less studied area of gene regulation, although currently there are few tools to analyse genomes in such ways.'),
(96294, 2014, 'bmc', 'Quality versus accuracy: result of a reanalysis of protein-binding microarrays from the DREAM5 challenge by using BayesPI2 including dinucleotide interdependence', 'Wang, Junbai', 'Pathology Department, Oslo University Hospital - Norwegian Radium Hospital, Montebello, Oslo, 0310, Norway. junbai.wang@rr-research.no.', 'Norway', 6, 'Algorithms|Animals|Bayes Theorem|Binding Sites|Computational Biology|DNA|Mice|Nucleotide Motifs|Oligonucleotides|Principal Component Analysis|Protein Array Analysis|Protein Binding|Transcription Factors', 'Journal Article', '10.1186/1471-2105-15-289', '25158938', 'Computational modeling transcription factor (TF) sequence specificity is an important research topic in regulatory genomics. A systematic comparison of 26 algorithms to learn TF-DNA binding specificity in in vitro protein-binding microarray (PBM) data was published recently, but the quality of those examined PBMs was not evaluated completely.'),
(96295, 2014, 'bmc', 'MendeLIMS: a web-based laboratory information management system for clinical genome sequencing', 'Grimes, Susan M|Ji, Hanlee P', 'Pathology Department, Oslo University Hospital - Norwegian Radium Hospital, Montebello, Oslo, 0310, Norway. junbai.wang@rr-research.no.|Stanford Genome Technology Center, Stanford University, Palo Alto, CA 94304, USA. genomics_ji@stanford.edu.', 'Norway|United States', 0, 'Clinical Laboratory Techniques|Databases, Genetic|Gene Library|Genetic Testing|Genomics|Health Information Management|High-Throughput Nucleotide Sequencing|Humans|Internet|Medical Informatics|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-15-290', '25159034', 'Large clinical genomics studies using next generation DNA sequencing require the ability to select and track samples from a large population of patients through many experimental steps. With the number of clinical genome sequencing studies increasing, it is critical to maintain adequate laboratory information management systems to manage the thousands of patient samples that are subject to this type of genetic analysis.'),
(96296, 2014, 'bmc', 'Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study', 'Li, Hongjian|Leung, Kwong-Sak|Wong, Man-Hon|Ballester, Pedro J', 'Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China. jackyleehongjian@gmail.com.|Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China. jackyleehongjian@gmail.com.|Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China. jackyleehongjian@gmail.com.|Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China. jackyleehongjian@gmail.com.', 'China|China|China|China', 0, 'Artificial Intelligence|Computational Biology|Ligands|Linear Models|Protein Binding|Proteins', 'Journal Article', '10.1186/1471-2105-15-291', '25159129', 'State-of-the-art protein-ligand docking methods are generally limited by the traditionally low accuracy of their scoring functions, which are used to predict binding affinity and thus vital for discriminating between active and inactive compounds. Despite intensive research over the years, classical scoring functions have reached a plateau in their predictive performance. These assume a predetermined additive functional form for some sophisticated numerical features, and use standard multivariate linear regression (MLR) on experimental data to derive the coefficients.'),
(96297, 2014, 'bmc', 'Native structure-based modeling and simulation of biomolecular systems per mouse click', 'Lutz, Benjamin|Sinner, Claude|Bozic, Stefan|Kondov, Ivan|Schug, Alexander', 'Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China. jackyleehongjian@gmail.com.|Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China. jackyleehongjian@gmail.com.|Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China. jackyleehongjian@gmail.com.|Steinbuch Centre for Computing, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany. ivan.kondov@kit.edu.|Steinbuch Centre for Computing, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany. ivan.kondov@kit.edu.', 'China|China|China|Germany|Germany', 2, 'Computer Graphics|Molecular Dynamics Simulation|Protein Folding|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-15-292', '25176255', 'Molecular dynamics (MD) simulations provide valuable insight into biomolecular systems at the atomic level. Notwithstanding the ever-increasing power of high performance computers current MD simulations face several challenges: the fastest atomic movements require time steps of a few femtoseconds which are small compared to biomolecular relevant timescales of milliseconds or even seconds for large conformational motions. At the same time, scalability to a large number of cores is limited mostly due to long-range interactions. An appealing alternative to atomic-level simulations is coarse-graining the resolution of the system or reducing the complexity of the Hamiltonian to improve sampling while decreasing computational costs. Native structure-based models, also called Gxc5x8d-type models, are based on energy landscape theory and the principle of minimal frustration. They have been tremendously successful in explaining fundamental questions of, e.g., protein folding, RNA folding or protein function. At the same time, they are computationally sufficiently inexpensive to run complex simulations on smaller computing systems or even commodity hardware. Still, their setup and evaluation is quite complex even though sophisticated software packages support their realization.'),
(96298, 2014, 'bmc', 'jvenn: an interactive Venn diagram viewer', 'Bardou, Philippe|Mariette, Jérôme|Escudié, Frédéric|Djemiel, Christophe|Klopp, Christophe', 'Steinbuch Centre for Computing, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany. ivan.kondov@kit.edu.|Plate-forme bio-informatique Genotoul/MIA-T, INRA, Borde Rouge, 31326 Castanet-Tolosan, France. Jerome.Mariette@toulouse.inra.fr.|Plate-forme bio-informatique Genotoul/MIA-T, INRA, Borde Rouge, 31326 Castanet-Tolosan, France. Jerome.Mariette@toulouse.inra.fr.|Plate-forme bio-informatique Genotoul/MIA-T, INRA, Borde Rouge, 31326 Castanet-Tolosan, France. Jerome.Mariette@toulouse.inra.fr.|Plate-forme bio-informatique Genotoul/MIA-T, INRA, Borde Rouge, 31326 Castanet-Tolosan, France. Jerome.Mariette@toulouse.inra.fr.', 'Germany|France|France|France|France', 0, 'Computational Biology|Computer Graphics|Internet|Software', 'Journal Article', '10.1186/1471-2105-15-293', '25176396', 'Venn diagrams are commonly used to display list comparison. In biology, they are widely used to show the differences between gene lists originating from different differential analyses, for instance. They thus allow the comparison between different experimental conditions or between different methods. However, when the number of input lists exceeds four, the diagram becomes difficult to read. Alternative layouts and dynamic display features can improve its use and its readability.'),
(96299, 2014, 'bmc', 'AliGROOVE--visualization of heterogeneous sequence divergence within multiple sequence alignments and detection of inflated branch support', 'Kück, Patrick|Meid, Sandra A|Groß, Christian|Wägele, Johann W|Misof, Bernhard', 'Zoologisches Forschungsmuseum A, Koenig, Adenauerallee 160-163, 53113 Bonn, Germany. patrick_kueck@web.de.|Zoologisches Forschungsmuseum A, Koenig, Adenauerallee 160-163, 53113 Bonn, Germany. patrick_kueck@web.de.|Zoologisches Forschungsmuseum A, Koenig, Adenauerallee 160-163, 53113 Bonn, Germany. patrick_kueck@web.de.|Zoologisches Forschungsmuseum A, Koenig, Adenauerallee 160-163, 53113 Bonn, Germany. patrick_kueck@web.de.|Zoologisches Forschungsmuseum A, Koenig, Adenauerallee 160-163, 53113 Bonn, Germany. patrick_kueck@web.de.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Animals|Arthropods|Computational Biology|Computer Graphics|DNA, Mitochondrial|Genetic Variation|Monte Carlo Method|Phylogeny|Reproducibility of Results|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-15-294', '25176556', 'Masking of multiple sequence alignment blocks has become a powerful method to enhance the tree-likeness of the underlying data. However, existing masking approaches are insensitive to heterogeneous sequence divergence which can mislead tree reconstructions. We present AliGROOVE, a new method based on a sliding window and a Monte Carlo resampling approach, that visualizes heterogeneous sequence divergence or alignment ambiguity related to single taxa or subsets of taxa within a multiple sequence alignment and tags suspicious branches on a given tree.'),
(96300, 2014, 'bmc', 'Fractal-based analysis of optical coherence tomography data to quantify retinal tissue damage', 'Somfai, Gxc3xa1bor Mxc3xa1rk|Txc3xa1trai, Erika|Laurik, Lenke|Varga, Boglxc3xa1rka E|xc3x96lvedy, Vera|Smiddy, William E|Tchitnga, Robert|Somogyi, Anikxc3xb3|DeBuc, Delia Cabrera', 'Zoologisches Forschungsmuseum A, Koenig, Adenauerallee 160-163, 53113 Bonn, Germany. patrick_kueck@web.de.|Zoologisches Forschungsmuseum A, Koenig, Adenauerallee 160-163, 53113 Bonn, Germany. patrick_kueck@web.de.|Zoologisches Forschungsmuseum A, Koenig, Adenauerallee 160-163, 53113 Bonn, Germany. patrick_kueck@web.de.|Zoologisches Forschungsmuseum A, Koenig, Adenauerallee 160-163, 53113 Bonn, Germany. patrick_kueck@web.de.|Zoologisches Forschungsmuseum A, Koenig, Adenauerallee 160-163, 53113 Bonn, Germany. patrick_kueck@web.de.|Zoologisches Forschungsmuseum A, Koenig, Adenauerallee 160-163, 53113 Bonn, Germany. patrick_kueck@web.de.|Zoologisches Forschungsmuseum A, Koenig, Adenauerallee 160-163, 53113 Bonn, Germany. patrick_kueck@web.de.|Zoologisches Forschungsmuseum A, Koenig, Adenauerallee 160-163, 53113 Bonn, Germany. patrick_kueck@web.de.|Miller School of Medicine, Bascom Palmer Eye Institute, University of Miami, Miami, Florida 33136, USA. dcabrera2@med.miami.edu.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|United States', 10, 'Adult|Algorithms|Case-Control Studies|Diabetic Retinopathy|Early Diagnosis|Female|Fractals|Humans|Image Processing, Computer-Assisted|Male|ROC Curve|Retina|Tomography, Optical Coherence|Wavelet Analysis', 'Journal Article', '10.1186/1471-2105-15-295', '25178846', 'The sensitivity of Optical Coherence Tomography (OCT) images to identify retinal tissue morphology characterized by early neural loss from normal healthy eyes is tested by calculating structural information and fractal dimension. OCT data from 74 healthy eyes and 43 eyes with type 1 diabetes mellitus with mild diabetic retinopathy (MDR) on biomicroscopy was analyzed using a custom-built algorithm (OCTRIMA) to measure locally the intraretinal layer thickness. A power spectrum method was used to calculate the fractal dimension in intraretinal regions of interest identified in the images. ANOVA followed by Newman-Keuls post-hoc analyses were used to test for differences between pathological and normal groups. A modified p value of <0.001 was considered statistically significant. Receiver operating characteristic (ROC) curves were constructed to describe the ability of each parameter to discriminate between eyes of pathological patients and normal healthy eyes.'),
(96301, 2014, 'bmc', 'A fault-tolerant method for HLA typing with PacBio data', 'Chang, Chia-Jung|Chen, Pei-Lung|Yang, Wei-Shiung|Chao, Kun-Mao', 'Miller School of Medicine, Bascom Palmer Eye Institute, University of Miami, Miami, Florida 33136, USA. dcabrera2@med.miami.edu.|Miller School of Medicine, Bascom Palmer Eye Institute, University of Miami, Miami, Florida 33136, USA. dcabrera2@med.miami.edu.|Miller School of Medicine, Bascom Palmer Eye Institute, University of Miami, Miami, Florida 33136, USA. dcabrera2@med.miami.edu.|Department of Computer Science and Information Engineering, National Taiwan University, No,1, Sec,4, Roosevelt Road, Taipei 10617, Taiwan. kmchao@csie.ntu.edu.tw.', 'United States|United States|United States|United States', 0, 'Alleles|Bayes Theorem|Computational Biology|Exons|Genotyping Techniques|HLA Antigens|High-Throughput Nucleotide Sequencing|Humans|Polymorphism, Genetic|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-15-296', '25183223', 'Human leukocyte antigen (HLA) genes are critical genes involved in important biomedical aspects, including organ transplantation, autoimmune diseases and infectious diseases. The gene family contains the most polymorphic genes in humans and the difference between two alleles is only a single base pair substitution in many cases. The next generation sequencing (NGS) technologies could be used for high throughput HLA typing but in silico methods are still needed to correctly assign the alleles of a sample. Computer scientists have developed such methods for various NGS platforms, such as Illumina, Roche 454 and Ion Torrent, based on the characteristics of the reads they generate. However, the method for PacBio reads was less addressed, probably owing to its high error rates. The PacBio system has the longest read length among available NGS platforms, and therefore is the only platform capable of having exon 2 and exon 3 of HLA genes on the same read to unequivocally solve the ambiguity problem caused by the phasing issue.'),
(96302, 2014, 'bmc', 'Enhancing protein-vitamin binding residues prediction by multiple heterogeneous subspace SVMs ensemble', 'Yu, Dong-Jun|Hu, Jun|Yan, Hui|Yang, Xi-Bei|Yang, Jing-Yu|Shen, Hong-Bin', 'School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, China. njyudj@njust.edu.cn.|School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, China. njyudj@njust.edu.cn.|School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, China. njyudj@njust.edu.cn.|School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, China. njyudj@njust.edu.cn.|School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, China. njyudj@njust.edu.cn.|School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, China. njyudj@njust.edu.cn.', 'China|China|China|China|China|China', 21, 'Binding Sites|Models, Biological|Position-Specific Scoring Matrices|Protein Binding|Protein Structure, Secondary|Proteins|Software|Support Vector Machine|Vitamins', 'Journal Article', '10.1186/1471-2105-15-297', '25189131', 'Vitamins are typical ligands that play critical roles in various metabolic processes. The accurate identification of the vitamin-binding residues solely based on a protein sequence is of significant importance for the functional annotation of proteins, especially in the post-genomic era, when large volumes of protein sequences are accumulating quickly without being functionally annotated.'),
(96303, 2014, 'bmc', 'nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification', 'Song, Li|Li, Dapeng|Zeng, Xiangxiang|Wu, Yunfeng|Guo, Li|Zou, Quan', 'School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, China. njyudj@njust.edu.cn.|School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, China. njyudj@njust.edu.cn.|School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, China. njyudj@njust.edu.cn.|School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, China. njyudj@njust.edu.cn.|School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China. gl8008@163.com.|School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China. gl8008@163.com.', 'China|China|China|China|China|China', 139, 'Algorithms|Artificial Intelligence|Computational Biology|DNA-Binding Proteins|Databases, Protein', 'Journal Article', '10.1186/1471-2105-15-298', '25196432', 'DNA-binding proteins are vital for the study of cellular processes. In recent genome engineering studies, the identification of proteins with certain functions has become increasingly important and needs to be performed rapidly and efficiently. In previous years, several approaches have been developed to improve the identification of DNA-binding proteins. However, the currently available resources are insufficient to accurately identify these proteins. Because of this, the previous research has been limited by the relatively unbalanced accuracy rate and the low identification success of the current methods.'),
(96304, 2014, 'bmc', 'Towards accurate characterization of clonal heterogeneity based on structural variation', 'Fan, Xian|Zhou, Wanding|Chong, Zechen|Nakhleh, Luay|Chen, Ken', 'School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China. gl8008@163.com.|School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China. gl8008@163.com.|School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China. gl8008@163.com.|School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China. gl8008@163.com.|Department of Bioinformatics and Computational Biology, The University of Texas M,D, Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA. kchen3@mdanderson.org.', 'China|China|China|China|United States', 0, 'Alleles|Chromosome Duplication|Chromosome Inversion|Clone Cells|DNA|Genomics|High-Throughput Nucleotide Sequencing|Humans|Likelihood Functions|Neoplasms|Sequence Alignment|Sequence Analysis, DNA|Sequence Deletion|Software', 'Journal Article', '10.1186/1471-2105-15-299', '25201439', 'Recent advances in deep digital sequencing have unveiled an unprecedented degree of clonal heterogeneity within a single tumor DNA sample. Resolving such heterogeneity depends on accurate estimation of fractions of alleles that harbor somatic mutations. Unlike substitutions or small indels, structural variants such as deletions, duplications, inversions and translocations involve segments of DNAs and are potentially more accurate for allele fraction estimations. However, no systematic method exists that can support such analysis.'),
(96305, 2014, 'bmc', 'Recognizing flu-like symptoms from videos', 'Thi, Tuan Hue|Wang, Li|Ye, Ning|Zhang, Jian|Maurer-Stroh, Sebastian|Cheng, Li', 'Department of Bioinformatics and Computational Biology, The University of Texas M,D, Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA. kchen3@mdanderson.org.|Department of Bioinformatics and Computational Biology, The University of Texas M,D, Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA. kchen3@mdanderson.org.|Department of Bioinformatics and Computational Biology, The University of Texas M,D, Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA. kchen3@mdanderson.org.|Department of Bioinformatics and Computational Biology, The University of Texas M,D, Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA. kchen3@mdanderson.org.|Department of Bioinformatics and Computational Biology, The University of Texas M,D, Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA. kchen3@mdanderson.org.|Bioinformatics Institute, A*STAR, Singapore, Singapore. chengli@bii.a-star.edu.sg.', 'United States|United States|United States|United States|United States|Singapore', 0, 'Adult|Algorithms|Behavior|Disease Outbreaks|Epidemiological Monitoring|Female|Humans|Influenza, Human|Male|Medical Informatics|Middle Aged|Video Recording|Young Adult', 'Journal Article', '10.1186/1471-2105-15-300', '25217118', 'Vision-based surveillance and monitoring is a potential alternative for early detection of respiratory disease outbreaks in urban areas complementing molecular diagnostics and hospital and doctor visit-based alert systems. Visible actions representing typical flu-like symptoms include sneeze and cough that are associated with changing patterns of hand to head distances, among others. The technical difficulties lie in the high complexity and large variation of those actions as well as numerous similar background actions such as scratching head, cell phone use, eating, drinking and so on.'),
(96306, 2014, 'bmc', 'Efficient deformation algorithm for plasmid DNA simulations', 'Raposo, Adriano N|Gomes, Abel J P', 'Bioinformatics Institute, A*STAR, Singapore, Singapore. chengli@bii.a-star.edu.sg.|Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal, Av, Marquês Dávila e Bolama, 6200-001 Covilhã, Portugal. agomes@di.ubi.pt.', 'Singapore|Portugal', 0, 'Algorithms|DNA|Models, Molecular|Monte Carlo Method|Movement|Nucleic Acid Conformation|Plasmids', 'Journal Article', '10.1186/1471-2105-15-301', '25225011', 'Plasmid DNA molecules are closed circular molecules that are widely used in life sciences, particularly in gene therapy research. Monte Carlo methods have been used for several years to simulate the conformational behavior of DNA molecules. In each iteration these simulation methods randomly generate a new trial conformation, which is either accepted or rejected according to a criterion based on energy calculations and stochastic rules. These simulation trials are generated using a method based on crankshaft motion that, apart from some slight improvements, has remained the same for many years.'),
(96307, 2014, 'bmc', 'SAGE: String-overlap Assembly of GEnomes', 'Ilie, Lucian|Haider, Bahlul|Molnar, Michael|Solis-Oba, Roberto', 'Department of Computer Science, University of Western Ontario, N6A 5B7 London, Ontario, Canada. ilie@csd.uwo.ca.|Department of Computer Science, University of Western Ontario, N6A 5B7 London, Ontario, Canada. ilie@csd.uwo.ca.|Department of Computer Science, University of Western Ontario, N6A 5B7 London, Ontario, Canada. ilie@csd.uwo.ca.|Department of Computer Science, University of Western Ontario, N6A 5B7 London, Ontario, Canada. ilie@csd.uwo.ca.', 'Canada|Canada|Canada|Canada', 0, 'Algorithms|Computer Graphics|Genome Size|Genomics|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-15-302', '25225118', 'De novo genome assembly of next-generation sequencing data is one of the most important current problems in bioinformatics, essential in many biological applications. In spite of significant amount of work in this area, better solutions are still very much needed.'),
(96308, 2014, 'bmc', 'ECMIS: computational approach for the identification of hotspots at protein-protein interfaces', 'Shingate, Prashant|Manoharan, Malini|Sukhwal, Anshul|Sowdhamini, Ramanathan', 'Department of Computer Science, University of Western Ontario, N6A 5B7 London, Ontario, Canada. ilie@csd.uwo.ca.|Department of Computer Science, University of Western Ontario, N6A 5B7 London, Ontario, Canada. ilie@csd.uwo.ca.|Department of Computer Science, University of Western Ontario, N6A 5B7 London, Ontario, Canada. ilie@csd.uwo.ca.|National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore 560065, India. mini@ncbs.res.in.', 'Canada|Canada|Canada|India', 12, 'Algorithms|Cluster Analysis|Computational Biology|Hydrogen Bonding|Hydrophobic and Hydrophilic Interactions|Protein Conformation|Protein Interaction Mapping|Proteins|Software|Thermodynamics', 'Journal Article', '10.1186/1471-2105-15-303', '25228146', 'Various methods have been developed to computationally predict hotspot residues at novel protein-protein interfaces. However, there are various challenges in obtaining accurate prediction. We have developed a novel method which uses different aspects of protein structure and sequence space at residue level to highlight interface residues crucial for the protein-protein complex formation.'),
(96309, 2014, 'bmc', 'Predicting disease associations via biological network analysis', 'Sun, Kai|Gonçalves, Joana P|Larminie, Chris|Przulj, Nataša', 'National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore 560065, India. mini@ncbs.res.in.|National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore 560065, India. mini@ncbs.res.in.|National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore 560065, India. mini@ncbs.res.in.|Department of Computing, Imperial College London, London, SW7 2AZ, UK. natasha@imperial.ac.uk.', 'India|India|India|United Kingdom', 0, 'Disease|Gene Ontology|Genome-Wide Association Study|Genomics|Humans|Molecular Sequence Annotation|PubMed', 'Journal Article', '10.1186/1471-2105-15-304', '25228247', 'Understanding the relationship between diseases based on the underlying biological mechanisms is one of the greatest challenges in modern biology and medicine. Exploring disease-disease associations by using system-level biological data is expected to improve our current knowledge of disease relationships, which may lead to further improvements in disease diagnosis, prognosis and treatment.'),
(96310, 2014, 'bmc', 'Personalized Oncology Suite: integrating next-generation sequencing data and whole-slide bioimages', 'Dander, Andreas|Baldauf, Matthias|Sperk, Michael|Pabinger, Stephan|Hiltpolt, Benjamin|Trajanoski, Zlatko', 'Department of Computing, Imperial College London, London, SW7 2AZ, UK. natasha@imperial.ac.uk.|Department of Computing, Imperial College London, London, SW7 2AZ, UK. natasha@imperial.ac.uk.|Department of Computing, Imperial College London, London, SW7 2AZ, UK. natasha@imperial.ac.uk.|Department of Computing, Imperial College London, London, SW7 2AZ, UK. natasha@imperial.ac.uk.|Department of Computing, Imperial College London, London, SW7 2AZ, UK. natasha@imperial.ac.uk.|Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innrain 80-82, 6020 Innsbruck, Austria. zlatko.trajanoski@i-med.ac.at.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Austria', 7, 'Databases, Factual|Genomics|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Internet|Molecular Imaging|Neoplasms|Polymorphism, Single Nucleotide|Precision Medicine|Software', 'Journal Article', '10.1186/1471-2105-15-306', '25230706', 'Cancer immunotherapy has recently entered a remarkable renaissance phase with the approval of several agents for treatment. Cancer treatment platforms have demonstrated profound tumor regressions including complete cure in patients with metastatic cancer. Moreover, technological advances in next-generation sequencing (NGS) as well as the development of devices for scanning whole-slide bioimages from tissue sections and image analysis software for quantitation of tumor-infiltrating lymphocytes (TILs) allow, for the first time, the development of personalized cancer immunotherapies that target patient specific mutations. However, there is currently no bioinformatics solution that supports the integration of these heterogeneous datasets.'),
(96311, 2014, 'bmc', 'Computational survey of peptides derived from disulphide-bonded protein loops that may serve as mediators of protein-protein interactions', 'Duffy, Fergal J|Devocelle, Marc|Croucher, David R|Shields, Denis C', 'Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innrain 80-82, 6020 Innsbruck, Austria. zlatko.trajanoski@i-med.ac.at.|Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innrain 80-82, 6020 Innsbruck, Austria. zlatko.trajanoski@i-med.ac.at.|Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innrain 80-82, 6020 Innsbruck, Austria. zlatko.trajanoski@i-med.ac.at.|School of Medicine and Medical Science, University College Dublin, Belfield, Dublin 4, Ireland. denis.shields@ucd.ie.', 'Austria|Austria|Austria|Ireland', 2, 'Computational Biology|Conserved Sequence|Disulfides|Extracellular Space|Humans|Peptides, Cyclic|Protein Binding|Proteins', 'Journal Article', '10.1186/1471-2105-15-305', '25231912', 'Bioactive cyclic peptides derived from natural sources are well studied, particularly those derived from non-ribosomal synthetases in fungi or bacteria. Ribosomally synthesised bioactive disulphide-bonded loops represent a large, naturally enriched library of potential bioactive compounds, worthy of systematic investigation.'),
(96312, 2014, 'bmc', 'ROTAS: a rotamer-dependent, atomic statistical potential for assessment and prediction of protein structures', 'Park, Jungkap|Saitou, Kazuhiro', 'School of Medicine and Medical Science, University College Dublin, Belfield, Dublin 4, Ireland. denis.shields@ucd.ie.|Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA. kazu@umich.edu.', 'Ireland|United States', 0, 'Computational Biology|Isomerism|Models, Theoretical|Protein Folding|Proteins|Thermodynamics', 'Journal Article', '10.1186/1471-2105-15-307', '25236673', 'Multibody potentials accounting for cooperative effects of molecular interactions have shown better accuracy than typical pairwise potentials. The main challenge in the development of such potentials is to find relevant structural features that characterize the tightly folded proteins. Also, the side-chains of residues adopt several specific, staggered conformations, known as rotamers within protein structures. Different molecular conformations result in different dipole moments and induce charge reorientations. However, until now modeling of the rotameric state of residues had not been incorporated into the development of multibody potentials for modeling non-bonded interactions in protein structures.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96313, 2014, 'bmc', 'Distinguishing between driver and passenger mutations in individual cancer genomes by network enrichment analysis', 'Merid, Simon Kebede|Goranskaya, Daria|Alexeyenko, Andrey', 'Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA. kazu@umich.edu.|Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA. kazu@umich.edu.|Department of Microbiology, Tumour and Cell biology, Bioinformatics Infrastructure for Life Sciences, Science for Life Laboratory, Karolinska Institutet, 17177 Stockholm, Sweden. andrej.alekseenko@scilifelab.se.', 'United States|United States|Sweden', 37, 'Algorithms|Female|Genomics|Glioblastoma|Humans|Mutation|Ovarian Neoplasms|ROC Curve|Software', 'Journal Article', '10.1186/1471-2105-15-308', '25236784', 'In somatic cancer genomes, delineating genuine driver mutations against a background of multiple passenger events is a challenging task. The difficulty of determining function from sequence data and the low frequency of mutations are increasingly hindering the search for novel, less common cancer drivers. The accumulation of extensive amounts of data on somatic point and copy number alterations necessitates the development of systematic methods for driver mutation analysis.'),
(96314, 2014, 'bmc', 'Predicting virus mutations through statistical relational learning', 'Cilia, Elisa|Teso, Stefano|Ammendola, Sergio|Lenaerts, Tom|Passerini, Andrea', 'Department of Microbiology, Tumour and Cell biology, Bioinformatics Infrastructure for Life Sciences, Science for Life Laboratory, Karolinska Institutet, 17177 Stockholm, Sweden. andrej.alekseenko@scilifelab.se.|Department of Microbiology, Tumour and Cell biology, Bioinformatics Infrastructure for Life Sciences, Science for Life Laboratory, Karolinska Institutet, 17177 Stockholm, Sweden. andrej.alekseenko@scilifelab.se.|Department of Microbiology, Tumour and Cell biology, Bioinformatics Infrastructure for Life Sciences, Science for Life Laboratory, Karolinska Institutet, 17177 Stockholm, Sweden. andrej.alekseenko@scilifelab.se.|Department of Microbiology, Tumour and Cell biology, Bioinformatics Infrastructure for Life Sciences, Science for Life Laboratory, Karolinska Institutet, 17177 Stockholm, Sweden. andrej.alekseenko@scilifelab.se.|Department of Computer Science and Information Engineering, University of Trento, via Sommarive 5, I-38123 (Povo) Trento, Italy. passerini@disi.unitn.it.', 'Sweden|Sweden|Sweden|Sweden|Italy', 4, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Drug Resistance, Viral|HIV|HIV Infections|HIV Reverse Transcriptase|Humans|Models, Biological|Models, Genetic|Models, Statistical|Molecular Sequence Data|Mutation|Nucleosides|Reverse Transcriptase Inhibitors', 'Journal Article', '10.1186/1471-2105-15-309', '25238967', 'Viruses are typically characterized by high mutation rates, which allow them to quickly develop drug-resistant mutations. Mining relevant rules from mutation data can be extremely useful to understand the virus adaptation mechanism and to design drugs that effectively counter potentially resistant mutants.'),
(96315, 2014, 'bmc', 'PLEK: a tool for predicting long non-coding RNAs and messenger RNAs based on an improved k-mer scheme', 'Li, Aimin|Zhang, Junying|Zhou, Zhongyin', 'Department of Computer Science and Information Engineering, University of Trento, via Sommarive 5, I-38123 (Povo) Trento, Italy. passerini@disi.unitn.it.|School of Computer Science and Technology, Xidian University, Xian, PR China. jyzhang@mail.xidian.edu.cn.|School of Computer Science and Technology, Xidian University, Xian, PR China. jyzhang@mail.xidian.edu.cn.', 'Italy|China|China', 174, 'Algorithms|Animals|Computational Biology|Databases, Genetic|Gene Expression Profiling|Humans|Mice|Molecular Sequence Annotation|Proteins|RNA, Long Noncoding|RNA, Messenger|Sequence Analysis, RNA|Software|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-15-311', '25239089', 'High-throughput transcriptome sequencing (RNA-seq) technology promises to discover novel protein-coding and non-coding transcripts, particularly the identification of long non-coding RNAs (lncRNAs) from de novo sequencing data. This requires tools that are not restricted by prior gene annotations, genomic sequences and high-quality sequencing.'),
(96316, 2014, 'bmc', 'Evaluation of microarray-based DNA methylation measurement using technical replicates: the Atherosclerosis Risk In Communities (ARIC) Study', 'Bose, Maitreyee|Wu, Chong|Pankow, James S|Demerath, Ellen W|Bressler, Jan|Fornage, Myriam|Grove, Megan L|Mosley, Thomas H|Hicks, Chindo|North, Kari|Kao, Wen Hong|Zhang, Yu|Boerwinkle, Eric|Guan, Weihua', 'School of Computer Science and Technology, Xidian University, Xian, PR China. jyzhang@mail.xidian.edu.cn.|School of Computer Science and Technology, Xidian University, Xian, PR China. jyzhang@mail.xidian.edu.cn.|School of Computer Science and Technology, Xidian University, Xian, PR China. jyzhang@mail.xidian.edu.cn.|School of Computer Science and Technology, Xidian University, Xian, PR China. jyzhang@mail.xidian.edu.cn.|School of Computer Science and Technology, Xidian University, Xian, PR China. jyzhang@mail.xidian.edu.cn.|School of Computer Science and Technology, Xidian University, Xian, PR China. jyzhang@mail.xidian.edu.cn.|School of Computer Science and Technology, Xidian University, Xian, PR China. jyzhang@mail.xidian.edu.cn.|School of Computer Science and Technology, Xidian University, Xian, PR China. jyzhang@mail.xidian.edu.cn.|School of Computer Science and Technology, Xidian University, Xian, PR China. jyzhang@mail.xidian.edu.cn.|School of Computer Science and Technology, Xidian University, Xian, PR China. jyzhang@mail.xidian.edu.cn.|School of Computer Science and Technology, Xidian University, Xian, PR China. jyzhang@mail.xidian.edu.cn.|School of Computer Science and Technology, Xidian University, Xian, PR China. jyzhang@mail.xidian.edu.cn.|School of Computer Science and Technology, Xidian University, Xian, PR China. jyzhang@mail.xidian.edu.cn.|Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA. wguan@umn.edu.', 'China|China|China|China|China|China|China|China|China|China|China|China|China|United States', 16, 'Atherosclerosis|Cluster Analysis|CpG Islands|DNA Methylation|Epigenomics|Female|Genetic Predisposition to Disease|Humans|Male|Middle Aged|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Residence Characteristics', 'Evaluation Studies', '10.1186/1471-2105-15-312', '25239148', 'DNA methylation is a widely studied epigenetic phenomenon, alterations in methylation patterns influence human phenotypes and risk of disease. As part of the Atherosclerosis Risk in Communities (ARIC) study, the Illumina Infinium HumanMethylation450 (HM450) BeadChip was used to measure DNA methylation in peripheral blood obtained from ~3000 African American study participants. Over 480,000 cytosine-guanine (CpG) dinucleotide sites were surveyed on the HM450 BeadChip. To evaluate the impact of technical variation, 265 technical replicates from 130 participants were included in the study.'),
(96317, 2014, 'bmc', 'Novel image markers for non-small cell lung cancer classification and survival prediction', 'Wang, Hongyuan|Xing, Fuyong|Su, Hai|Stromberg, Arnold|Yang, Lin', 'Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA. wguan@umn.edu.|Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA. wguan@umn.edu.|Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA. wguan@umn.edu.|Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA. wguan@umn.edu.|J, Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, 1275 Center Drive, 32611 Gainesville, FL, USA. linyang711@gmail.com.', 'United States|United States|United States|United States|United States', 35, 'Adenocarcinoma|Adenocarcinoma of Lung|Algorithms|Carcinoma, Non-Small-Cell Lung|Carcinoma, Squamous Cell|Diagnosis, Computer-Assisted|Female|Humans|Likelihood Functions|Lung Neoplasms|Male|Models, Statistical|Survival Analysis', 'Journal Article', '10.1186/1471-2105-15-310', '25240495', 'Non-small cell lung cancer (NSCLC), the most common type of lung cancer, is one of serious diseases causing death for both men and women. Computer-aided diagnosis and survival prediction of NSCLC, is of great importance in providing assistance to diagnosis and personalize therapy planning for lung cancer patients.'),
(96318, 2014, 'bmc', 'Prediction of nucleosome rotational positioning in yeast and human genomes based on sequence-dependent DNA anisotropy', 'Cui, Feng|Chen, Linlin|LoVerso, Peter R|Zhurkin, Victor B', 'Thomas H, Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA. fxcsbi@rit.edu.|Thomas H, Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA. fxcsbi@rit.edu.|Thomas H, Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA. fxcsbi@rit.edu.|Thomas H, Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA. fxcsbi@rit.edu.', 'United States|United States|United States|United States', 9, 'Anisotropy|Base Sequence|Chromatin Assembly and Disassembly|DNA, Fungal|Genome, Human|Humans|Models, Genetic|Nucleosomes|Rotation|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-15-313', '25244936', 'An organisms DNA sequence is one of the key factors guiding the positioning of nucleosomes within a cells nucleus. Sequence-dependent bending anisotropy dictates how DNA is wrapped around a histone octamer. One of the best established sequence patterns consistent with this anisotropy is the periodic occurrence of AT-containing dinucleotides (WW) and GC-containing dinucleotides (SS) in the nucleosomal locations where DNA is bent in the minor and major grooves, respectively. Although this simple pattern has been observed in nucleosomes across eukaryotic genomes, its use for prediction of nucleosome positioning was not systematically tested.'),
(96319, 2014, 'bmc', 'A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects', 'Dundar, Murat|Akova, Ferit|Yerebakan, Halid Z|Rajwa, Bartek', 'Computer Science Department, IUPUI, 723 W, Michigan St,, 46037 Indianapolis IN, US. dundar@cs.iupui.edu.|Computer Science Department, IUPUI, 723 W, Michigan St,, 46037 Indianapolis IN, US. dundar@cs.iupui.edu.|Computer Science Department, IUPUI, 723 W, Michigan St,, 46037 Indianapolis IN, US. dundar@cs.iupui.edu.|Computer Science Department, IUPUI, 723 W, Michigan St,, 46037 Indianapolis IN, US. dundar@cs.iupui.edu.', 'India|India|India|India', 20, 'Algorithms|Area Under Curve|Artificial Intelligence|Bayes Theorem|Cluster Analysis|Computational Biology|Flow Cytometry|Leukemia, Myeloid, Acute|Phenotype|ROC Curve|Statistics, Nonparametric|Stochastic Processes', 'Journal Article', '10.1186/1471-2105-15-314', '25248977', 'Flow cytometry (FC)-based computer-aided diagnostics is an emerging technique utilizing modern multiparametric cytometry systems.The major difficulty in using machine-learning approaches for classification of FC data arises from limited access to a wide variety of anomalous samples for training. In consequence, any learning with an abundance of normal cases and a limited set of specific anomalous cases is biased towards the types of anomalies represented in the training set. Such models do not accurately identify anomalies, whether previously known or unknown, that may exist in future samples tested. Although one-class classifiers trained using only normal cases would avoid such a bias, robust sample characterization is critical for a generalizable model. Owing to sample heterogeneity and instrumental variability, arbitrary characterization of samples usually introduces feature noise that may lead to poor predictive performance. Herein, we present a non-parametric Bayesian algorithm called ASPIRE (anomalous sample phenotype identification with random effects) that identifies phenotypic differences across a batch of samples in the presence of random effects. Our approach involves simultaneous clustering of cellular measurements in individual samples and matching of discovered clusters across all samples in order to recover global clusters using probabilistic sampling techniques in a systematic way.'),
(96320, 2014, 'bmc', 'PRISE2: software for designing sequence-selective PCR primers and probes', 'Huang, Yu-Ting|Yang, Jiue-in|Chrobak, Marek|Borneman, James', 'Computer Science Department, IUPUI, 723 W, Michigan St,, 46037 Indianapolis IN, US. dundar@cs.iupui.edu.|Computer Science Department, IUPUI, 723 W, Michigan St,, 46037 Indianapolis IN, US. dundar@cs.iupui.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.', 'India|India|United States|United States', 9, 'Conserved Sequence|DNA Primers|Oligonucleotide Probes|Polymerase Chain Reaction|Software', 'Journal Article', '10.1186/1471-2105-15-317', '25252611', 'PRISE2 is a new software tool for designing sequence-selective PCR primers and probes. To achieve high level of selectivity, PRISE2 allows the user to specify a collection of target sequences that the primers are supposed to amplify, as well as non-target sequences that should not be amplified. The program emphasizes primer selectivity on the 3 end, which is crucial for selective amplification of conserved sequences such as rRNA genes. In PRISE2, users can specify desired properties of primers, including length, GC content, and others. They can interactively manipulate the list of candidate primers, to choose primer pairs that are best suited for their needs. A similar process is used to add probes to selected primer pairs. More advanced features include, for example, the capability to define a custom mismatch penalty function. PRISE2 is equipped with a graphical, user-friendly interface, and it runs on Windows, Macintosh or Linux machines.'),
(96321, 2014, 'bmc', 'Piecing the puzzle together: a revisit to transcript reconstruction problem in RNA-seq', 'Huang, Yan|Hu, Yin|Liu, Jinze', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.', 'United States|United States|United States', 0, 'Algorithms|Alternative Splicing|Exons|Gene Expression Profiling|Humans|RNA, Messenger|Sequence Alignment|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1186/1471-2105-15-S9-S3', '25252653', 'The advancement of RNA sequencing (RNA-seq) has provided an unprecedented opportunity to assess both the diversity and quantity of transcript isoforms in an mRNA transcriptome. In this paper, we revisit the computational problem of transcript reconstruction and quantification. Unlike existing methods which focus on how to explain the exons and splice variants detected by the reads with a set of isoforms, we aim at reconstructing transcripts by piecing the reads into individual effective transcript copies. Simultaneously, the quantity of each isoform is explicitly measured by the number of assembled effective copies, instead of estimated solely based on the collective read count. We have developed a novel method named Astroid that solves the problem of effective copy reconstruction on the basis of a flow network. The RNA-seq reads are represented as vertices in the flow network and are connected by weighted edges that evaluate the likelihood of two reads originating from the same effective copy. A maximum likelihood set of transcript copies is then reconstructed by solving a minimum-cost flow problem on the flow network. Simulation studies on the human transcriptome have demonstrated the superior sensitivity and specificity of Astroid in transcript reconstruction as well as improved accuracy in transcript quantification over several existing approaches. The application of Astroid on two real RNA-seq datasets has further demonstrated its accuracy through high correlation between the estimated isoform abundance and the qRT-PCR validations. '),
(96322, 2014, 'bmc', 'Assembly-free genome comparison based on next-generation sequencing reads and variable length patterns', 'Comin, Matteo|Schimd, Michele', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.', 'United States|United States', 14, 'Algorithms|Animals|Archaea|Bacteria|Drosophila|Genome|Genomics|High-Throughput Nucleotide Sequencing|Models, Statistical|Pattern Recognition, Automated|Phylogeny|Sequence Analysis, DNA', 'Comparative Study', '10.1186/1471-2105-15-S9-S1', '25252700', 'With the advent of Next-Generation Sequencing technologies (NGS), a large amount of short read data has been generated. If a reference genome is not available, the assembly of a template sequence is usually challenging because of repeats and the short length of reads. When NGS reads cannot be mapped onto a reference genome alignment-based methods are not applicable. However it is still possible to study the evolutionary relationship of unassembled genomes based on NGS data.'),
(96323, 2014, 'bmc', 'Near-optimal assembly for shotgun sequencing with noisy reads', 'Lam, Ka-Kit|Khalak, Asif|Tse, David', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.', 'United States|United States|United States', 19, 'Algorithms|Base Sequence|Computer Simulation|Escherichia coli|Genome|Genomics|Models, Genetic|Probability|Repetitive Sequences, Nucleic Acid|Sequence Analysis, DNA|Signal-To-Noise Ratio', 'Journal Article', '10.1186/1471-2105-15-S9-S4', '25252708', 'Recent work identified the fundamental limits on the information requirements in terms of read length and coverage depth required for successful de novo genome reconstruction from shotgun sequencing data, based on the idealistic assumption of no errors in the reads (noiseless reads). In this work, we show that even when there is noise in the reads, one can successfully reconstruct with information requirements close to the noiseless fundamental limit. A new assembly algorithm, X-phased Multibridging, is designed based on a probabilistic model of the genome. It is shown through analysis to perform well on the model, and through simulations to perform well on real genomes. '),
(96324, 2014, 'bmc', 'CASPER: context-aware scheme for paired-end reads from high-throughput amplicon sequencing', 'Kwon, Sunyoung|Lee, Byunghan|Yoon, Sungroh', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.', 'United States|United States|United States', 0, 'Algorithms|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-15-S9-S10', '25252785', 'Merging the forward and reverse reads from paired-end sequencing is a critical task that can significantly improve the performance of downstream tasks, such as genome assembly and mapping, by providing them with virtually elongated reads. However, due to the inherent limitations of most paired-end sequencers, the chance of observing erroneous bases grows rapidly as the end of a read is approached, which becomes a critical hurdle for accurately merging paired-end reads. Although there exist several sophisticated approaches to this problem, their performance in terms of quality of merging often remains unsatisfactory. To address this issue, here we present a context-aware scheme for paired-end reads (CASPER): a computational method to rapidly and robustly merge overlapping paired-end reads. Being particularly well suited to amplicon sequencing applications, CASPER is thoroughly tested with both simulated and real high-throughput amplicon sequencing data. According to our experimental results, CASPER significantly outperforms existing state-of-the art paired-end merging tools in terms of accuracy and robustness. CASPER also exploits the parallelism in the task of paired-end merging and effectively speeds up by multithreading. CASPER is freely available for academic use at http://best.snu.ac.kr/casper. '),
(96325, 2014, 'bmc', 'On the complexity of Minimum Path Cover with Subpath Constraints for multi-assembly', 'Rizzi, Romeo|Tomescu, Alexandru I|Mäkinen, Veli', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.', 'United States|United States|United States', 0, 'Algorithms|High-Throughput Nucleotide Sequencing|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1186/1471-2105-15-S9-S5', '25252805', 'Multi-assembly problems have gathered much attention in the last years, as Next-Generation Sequencing technologies have started being applied to mixed settings, such as reads from the transcriptome (RNA-Seq), or from viral quasi-species. One classical model that has resurfaced in many multi-assembly methods (e.g. in Cufflinks, ShoRAH, BRANCH, CLASS) is the Minimum Path Cover (MPC) Problem, which asks for the minimum number of directed paths that cover all the nodes of a directed acyclic graph. The MPC Problem is highly popular because the acyclicity of the graph ensures its polynomial-time solvability.'),
(96326, 2014, 'bmc', 'PuFFIN--a parameter-free method to build nucleosome maps from paired-end reads', 'Polishko, Anton|Bunnik, Evelien M|Le Roch, Karine G|Lonardi, Stefano', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.', 'United States|United States|United States|United States', 7, 'Algorithms|Genomics|Nucleosomes|Plasmodium falciparum|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-15-S9-S11', '25252810', 'We introduce a novel method, called PuFFIN, that takes advantage of paired-end short reads to build genome-wide nucleosome maps with larger numbers of detected nucleosomes and higher accuracy than existing tools. In contrast to other approaches that require users to optimize several parameters according to their data (e.g., the maximum allowed nucleosome overlap or legal ranges for the fragment sizes) our algorithm can accurately determine a genome-wide set of non-overlapping nucleosomes without any user-defined parameter. This feature makes PuFFIN significantly easier to use and prevents users from choosing the wrong parameters and obtain sub-optimal nucleosome maps.'),
(96327, 2014, 'bmc', 'BADGE: a novel Bayesian model for accurate abundance quantification and differential analysis of RNA-Seq data', 'Gu, Jinghua|Wang, Xiao|Halakivi-Clarke, Leena|Clarke, Robert|Xuan, Jianhua', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.', 'United States|United States|United States|United States|United States', 6, 'Bayes Theorem|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Genetic|RNA, Messenger|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-15-S9-S6', '25252852', 'Recent advances in RNA sequencing (RNA-Seq) technology have offered unprecedented scope and resolution for transcriptome analysis. However, precise quantification of mRNA abundance and identification of differentially expressed genes are complicated due to biological and technical variations in RNA-Seq data.'),
(96328, 2014, 'bmc', 'ARYANA: Aligning Reads by Yet Another Approach', 'Gholami, Milad|Arbabi, Aryan|Sharifi-Zarchi, Ali|Chitsaz, Hamidreza|Sadeghi, Mehdi', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-15-S9-S12', '25252881', 'Although there are many different algorithms and software tools for aligning sequencing reads, fast gapped sequence search is far from solved. Strong interest in fast alignment is best reflected in the $10(6) prize for the Innocentive competition on aligning a collection of reads to a given database of reference genomes. In addition, de novo assembly of next-generation sequencing long reads requires fast overlap-layout-concensus algorithms which depend on fast and accurate alignment.'),
(96329, 2014, 'bmc', 'Fast lossless compression via cascading Bloom filters', 'Rozov, Roye|Shamir, Ron|Halperin, Eran', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.', 'United States|United States|United States', 16, 'Algorithms|Data Compression|Genome|High-Throughput Nucleotide Sequencing|Software', 'Journal Article', '10.1186/1471-2105-15-S9-S7', '25252952', 'Data from large Next Generation Sequencing (NGS) experiments present challenges both in terms of costs associated with storage and in time required for file transfer. It is sometimes possible to store only a summary relevant to particular applications, but generally it is desirable to keep all information needed to revisit experimental results in the future. Thus, the need for efficient lossless compression methods for NGS reads arises. It has been shown that NGS-specific compression schemes can improve results over generic compression methods, such as the Lempel-Ziv algorithm, Burrows-Wheeler transform, or Arithmetic Coding. When a reference genome is available, effective compression can be achieved by first aligning the reads to the reference genome, and then encoding each read using the alignment position combined with the differences in the read relative to the reference. These reference-based methods have been shown to compress better than reference-free schemes, but the alignment step they require demands several hours of CPU time on a typical dataset, whereas reference-free methods can usually compress in minutes.'),
(96330, 2014, 'bmc', 'Ensemble analysis of adaptive compressed genome sequencing strategies', 'Taghavi, Zeinab', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.', 'United States', 0, 'Algorithms|Bacteria|Data Compression|Genome, Bacterial|High-Throughput Nucleotide Sequencing|Metagenomics|Probability|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-15-S9-S13', '25252999', 'Acquiring genomes at single-cell resolution has many applications such as in the study of microbiota. However, deep sequencing and assembly of all of millions of cells in a sample is prohibitively costly. A property that can come to rescue is that deep sequencing of every cell should not be necessary to capture all distinct genomes, as the majority of cells are biological replicates. Biologically important samples are often sparse in that sense. In this paper, we propose an adaptive compressed method, also known as distilled sensing, to capture all distinct genomes in a sparse microbial community with reduced sequencing effort. As opposed to group testing in which the number of distinct events is often constant and sparsity is equivalent to rarity of an event, sparsity in our case means scarcity of distinct events in comparison to the data size. Previously, we introduced the problem and proposed a distilled sensing solution based on the breadth first search strategy. We simulated the whole process which constrained our ability to study the behavior of the algorithm for the entire ensemble due to its computational intensity.'),
(96331, 2014, 'bmc', 'Gene finding in metatranscriptomic sequences', 'Ismail, Wazim Mohammed|Ye, Yuzhen|Tang, Haixu', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.', 'United States|United States|United States', 0, 'Bacteria|Genes, Bacterial|Genome, Bacterial|Markov Chains|Metagenome|Metagenomics|Models, Genetic|Operon|RNA, Antisense|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1186/1471-2105-15-S9-S8', '25253067', 'Metatranscriptomic sequencing is a highly sensitive bioassay of functional activity in a microbial community, providing complementary information to the metagenomic sequencing of the community. The acquisition of the metatranscriptomic sequences will enable us to refine the annotations of the metagenomes, and to study the gene activities and their regulation in complex microbial communities and their dynamics.'),
(96332, 2014, 'bmc', 'A better sequence-read simulator program for metagenomics', 'Johnson, Stephen|Trost, Brett|Long, Jeffrey R|Pittet, Vanessa|Kusalik, Anthony', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.', 'United States|United States|United States|United States|United States', 0, 'Artificial Intelligence|Computer Simulation|Genome|Metagenomics|Models, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-15-S9-S14', '25253095', 'There are many programs available for generating simulated whole-genome shotgun sequence reads. The data generated by many of these programs follow predefined models, which limits their use to the authors original intentions. For example, many models assume that read lengths follow a uniform or normal distribution. Other programs generate models from actual sequencing data, but are limited to reads from single-genome studies. To our knowledge, there are no programs that allow a user to generate simulated data following non-parametric read-length distributions and quality profiles based on empirically-derived information from metagenomics sequencing data.'),
(96333, 2014, 'bmc', 'ILP-based maximum likelihood genome scaffolding', 'Lindsay, James|Salooti, Hamed|Mxc4x83ndoiu, Ion|Zelikovsky, Alex', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.', 'United States|United States|United States|United States', 6, 'Algorithms|Animals|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Likelihood Functions|Metagenomics|Probability|Programming, Linear|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-15-S9-S9', '25253180', 'Interest in de novo genome assembly has been renewed in the past decade due to rapid advances in high-throughput sequencing (HTS) technologies which generate relatively short reads resulting in highly fragmented assemblies consisting of contigs. Additional long-range linkage information is typically used to orient, order, and link contigs into larger structures referred to as scaffolds. Due to library preparation artifacts and erroneous mapping of reads originating from repeats, scaffolding remains a challenging problem. In this paper, we provide a scalable scaffolding algorithm (SILP2) employing a maximum likelihood model capturing read mapping uncertainty and/or non-uniformity of contig coverage which is solved using integer linear programming. A Non-Serial Dynamic Programming (NSDP) paradigm is applied to render our algorithm useful in the processing of larger mammalian genomes. To compare scaffolding tools, we employ novel quantitative metrics in addition to the extant metrics in the field. We have also expanded the set of experiments to include scaffolding of low-complexity metagenomic samples.'),
(96334, 2014, 'bmc', 'ProbeAlign: incorporating high-throughput sequencing-based structure probing information into ncRNA homology search', 'Ge, Ping|Zhong, Cuncong|Zhang, Shaojie', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.', 'United States|United States|United States', 0, 'Algorithms|Base Sequence|Genome|Genomics|High-Throughput Nucleotide Sequencing|Molecular Sequence Data|Nucleic Acid Conformation|RNA, Untranslated|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-15-S9-S15', '25253206', 'Recent advances in RNA structure probing technologies, including the ones based on high-throughput sequencing, have improved the accuracy of thermodynamic folding with quantitative nucleotide-resolution structural information.'),
(96335, 2014, 'bmc', 'Detecting epigenetic motifs in low coverage and metagenomics settings', 'Beckmann, Noam D|Karri, Sashank|Fang, Gang|Bashir, Ali', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.', 'United States|United States|United States|United States', 4, 'Algorithms|Bacteria|Base Sequence|Computer Simulation|Epigenesis, Genetic|Genome, Bacterial|Metagenomics|Models, Genetic|Nucleotide Motifs', 'Journal Article', '10.1186/1471-2105-15-S9-S16', '25253358', 'It has recently become possible to rapidly and accurately detect epigenetic signatures in bacterial genomes using third generation sequencing data. Monitoring the speed at which a single polymerase inserts a base in the read strand enables one to infer whether a modification is present at that specific site on the template strand. These sites can be challenging to detect in the absence of high coverage and reliable reference genomes.'),
(96336, 2014, 'bmc', 'SWAP-Assembler: scalable and efficient genome assembly towards thousands of cores', 'Meng, Jintao|Wang, Bingqiang|Wei, Yanjie|Feng, Shengzhong|Balaji, Pavan', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA. marek@cs.ucr.edu.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-15-S9-S2', '25253533', 'There is a widening gap between the throughput of massive parallel sequencing machines and the ability to analyze these sequencing data. Traditional assembly methods requiring long execution time and large amount of memory on a single workstation limit their use on these massive data.'),
(96337, 2014, 'bmc', 'A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF, OMIM and PubMed records', 'Jiang, Li|Edwards, Stefan M|Thomsen, Bo|Workman, Christopher T|Guldbrandtsen, Bernt|Sørensen, Peter', 'Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark. jiang@cbs.dtu.dk.|Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark. jiang@cbs.dtu.dk.|Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark. jiang@cbs.dtu.dk.|Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark. jiang@cbs.dtu.dk.|Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark. jiang@cbs.dtu.dk.|Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark. jiang@cbs.dtu.dk.', 'Denmark|Denmark|Denmark|Denmark|Denmark|Denmark', 13, 'Algorithms|Data Mining|Databases, Genetic|Disease|Gene Regulatory Networks|Genome-Wide Association Study|Genomics|Humans|Likelihood Functions|Models, Statistical|Phenotype|Protein Interaction Maps|Proteins|PubMed|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-15-315', '25253562', 'Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic profile of genes with respect to their connection to disease phenotypes. The importance of protein-protein interaction networks in the genetic heterogeneity of common diseases or complex traits is becoming increasingly recognized. Thus, the development of a network-based approach combined with phenotypic profiling would be useful for disease gene prioritization.'),
(96338, 2014, 'bmc', 'MOSBIE: a tool for comparison and analysis of rule-based biochemical models', 'Wenskovitch, John E|Harris, Leonard A|Tapia, Jose-Juan|Faeder, James R|Marai, G Elisabeta', 'Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark. jiang@cbs.dtu.dk.|Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark. jiang@cbs.dtu.dk.|Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark. jiang@cbs.dtu.dk.|Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark. jiang@cbs.dtu.dk.|Electronic Visualization Lab, Department of Computer Science, University of Illinois at Chicago, 60607 Chicago, USA. g.elisabeta.marai@gmail.com.', 'Denmark|Denmark|Denmark|Denmark|United States', 13, 'Models, Biological|Signal Transduction|Software|Systems Biology', 'Comparative Study', '10.1186/1471-2105-15-316', '25253680', 'Mechanistic models that describe the dynamical behaviors of biochemical systems are common in computational systems biology, especially in the realm of cellular signaling. The development of families of such models, either by a single research group or by different groups working within the same area, presents significant challenges that range from identifying structural similarities and differences between models to understanding how these differences affect system dynamics.'),
(96339, 2014, 'bmc', 'Metabolic network prediction through pairwise rational kernels', 'Roche-Lima, Abiel|Domaratzki, Michael|Fristensky, Brian', 'Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada. aroche@cs.umanitoba.ca.|Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada. aroche@cs.umanitoba.ca.|Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada. aroche@cs.umanitoba.ca.', 'Canada|Canada|Canada', 0, 'Algorithms|Computational Biology|Metabolic Networks and Pathways|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Software|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-15-318', '25260372', 'Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations.'),
(96340, 2014, 'bmc', 'Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes', 'Pierce, Brian G|Vreven, Thom|Weng, Zhiping', 'Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA 01605, USA. pierce@umd.edu.|Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA 01605, USA. pierce@umd.edu.|Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA 01605, USA. pierce@umd.edu.', 'United States|United States|United States', 0, 'Algorithms|Animals|Antigens, CD1|Antigens, CD1d|Galactosylceramides|Humans|Molecular Docking Simulation|Receptors, Antigen, T-Cell, alpha-beta|Receptors, Antigen, T-Cell, gamma-delta|Vitamins', 'Journal Article', '10.1186/1471-2105-15-319', '25260513', 'T cell receptors (TCRs) can recognize diverse lipid and metabolite antigens presented by MHC-like molecules CD1 and MR1, and the molecular basis of many of these interactions has not been determined. Here we applied our protein docking algorithm TCRFlexDock, previously developed to perform docking of TCRs to peptide-MHC (pMHC) molecules, to predict the binding of xcexb1xcexb2 and xcexb3xcexb4 TCRs to CD1 and MR1, starting with the structures of the unbound molecules.'),
(96341, 2014, 'bmc', 'DiscML: an R package for estimating evolutionary rates of discrete characters using maximum likelihood', 'Kim, Tane|Hao, Weilong', 'Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA 01605, USA. pierce@umd.edu.|Department of Biological Sciences, Wayne State University, 48202 Detroit, USA. haow@wayne.edu.', 'United States|United States', 0, 'Computational Biology|Evolution, Molecular|Likelihood Functions|Models, Biological|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-15-320', '25260628', 'The study of discrete characters is crucial for the understanding of evolutionary processes. Even though great advances have been made in the analysis of nucleotide sequences, computer programs for non-DNA discrete characters are often dedicated to specific analyses and lack flexibility. Discrete characters often have different transition rate matrices, variable rates among sites and sometimes contain unobservable states. To obtain the ability to accurately estimate a variety of discrete characters, programs with sophisticated methodologies and flexible settings are desired.'),
(96342, 2014, 'bmc', 'An improved alignment-free model for DNA sequence similarity metric', 'Bao, Junpeng|Yuan, Ruiyu|Bao, Zhe', 'Department of Computer Science and Technology Xian Jiaotong University, West Xianning Road, 710049 Xian, P,R, China. baojp@mail.xjtu.edu.cn.|Department of Computer Science and Technology Xian Jiaotong University, West Xianning Road, 710049 Xian, P,R, China. baojp@mail.xjtu.edu.cn.|Department of Computer Science and Technology Xian Jiaotong University, West Xianning Road, 710049 Xian, P,R, China. baojp@mail.xjtu.edu.cn.', 'China|China|China', 0, 'Algorithms|Base Sequence|Cluster Analysis|Computational Biology|DNA|Entropy|Models, Statistical|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-15-321', '25261973', 'DNA Clustering is an important technology to automatically find the inherent relationships on a large scale of DNA sequences. But the DNA clustering quality can still be improved greatly. The DNA sequences similarity metric is one of the key points of clustering. The alignment-free methodology is a very popular way to calculate DNA sequence similarity. It normally converts a sequence into a feature space based on words probability distribution rather than directly matches strings. Existing alignment-free models, e.g. k-tuple, merely employ word frequency information and ignore many types of useful information contained in the DNA sequence, such as classifications of nucleotide bases, position and the like. It is believed that the better data mining results can be achieved with compounded information. Therefore, we present a new alignment-free model that employs compounded information to improve the DNA clustering quality.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96343, 2014, 'bmc', 'Polyphony: superposition independent methods for ensemble-based drug discovery', 'Pitt, William R|Montalvxc3xa3o, Rinaldo W|Blundell, Tom L', 'Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK. will.pitt@ucb.com.|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK. will.pitt@ucb.com.|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK. will.pitt@ucb.com.', 'United Kingdom|United Kingdom|United Kingdom', 8, 'Algorithms|Allosteric Site|Computational Biology|Crystallography, X-Ray|Cyclin-Dependent Kinase 2|Drug Design|Drug Discovery|Ligands|Mitogen-Activated Protein Kinase 14|Molecular Dynamics Simulation|Phosphorylation|Protein Conformation', 'Journal Article', '10.1186/1471-2105-15-324', '25265915', 'Structure-based drug design is an iterative process, following cycles of structural biology, computer-aided design, synthetic chemistry and bioassay. In favorable circumstances, this process can lead to the structures of hundreds of protein-ligand crystal structures. In addition, molecular dynamics simulations are increasingly being used to further explore the conformational landscape of these complexes. Currently, methods capable of the analysis of ensembles of crystal structures and MD trajectories are limited and usually rely upon least squares superposition of coordinates.'),
(96344, 2014, 'bmc', 'MAE-FMD: multi-agent evolutionary method for functional module detection in protein-protein interaction networks', 'Ji, Jun Zhong|Jiao, Lang|Yang, Cui Cui|Lv, Jia Wei|Zhang, Ai Dong', 'College of Computer Science, Beijing University of Technology, Chaoyang District, Beijing, China. jjz01@bjut.edu.cn.|College of Computer Science, Beijing University of Technology, Chaoyang District, Beijing, China. jjz01@bjut.edu.cn.|College of Computer Science, Beijing University of Technology, Chaoyang District, Beijing, China. jjz01@bjut.edu.cn.|College of Computer Science, Beijing University of Technology, Chaoyang District, Beijing, China. jjz01@bjut.edu.cn.|College of Computer Science, Beijing University of Technology, Chaoyang District, Beijing, China. jjz01@bjut.edu.cn.', 'China|China|China|China|China', 0, 'Algorithms|Computational Biology|Evolution, Molecular|Fungal Proteins|Humans|Protein Interaction Mapping|Yeasts', 'Journal Article', '10.1186/1471-2105-15-325', '25265982', 'Studies of functional modules in a Protein-Protein Interaction (PPI) network contribute greatly to the understanding of biological mechanisms. With the development of computing science, computational approaches have played an important role in detecting functional modules.'),
(96345, 2014, 'bmc', 'Comprehensive analysis of forty yeast microarray datasets reveals a novel subset of genes (APha-RiB) consistently negatively associated with ribosome biogenesis', 'Abu-Jamous, Basel|Fa, Rui|Roberts, David J|Nandi, Asoke K', 'College of Computer Science, Beijing University of Technology, Chaoyang District, Beijing, China. jjz01@bjut.edu.cn.|College of Computer Science, Beijing University of Technology, Chaoyang District, Beijing, China. jjz01@bjut.edu.cn.|College of Computer Science, Beijing University of Technology, Chaoyang District, Beijing, China. jjz01@bjut.edu.cn.|Department of Electronic and Computer Engineering, Brunel University, Uxbridge, Middlesex UB8 3PH, UK. asoke.nandi@brunel.ac.uk.', 'China|China|China|United Kingdom', 0, 'Cluster Analysis|Gene Expression Profiling|Gene Expression Regulation, Fungal|Gene Regulatory Networks|Genes, Fungal|Oligonucleotide Array Sequence Analysis|Ribosomes|Saccharomycetales', 'Journal Article', '10.1186/1471-2105-15-322', '25267386', 'The scale and complexity of genomic data lend themselves to analysis using sophisticated mathematical techniques to yield information that can generate new hypotheses and so guide further experimental investigations. An ensemble clustering method has the ability to perform consensus clustering over the same set of genes from different microarray datasets by combining results from different clustering methods into a single consensus result.'),
(96346, 2014, 'bmc', 'DupChecker: a bioconductor package for checking high-throughput genomic data redundancy in meta-analysis', 'Sheng, Quanhu|Shyr, Yu|Chen, Xi', 'Department of Electronic and Computer Engineering, Brunel University, Uxbridge, Middlesex UB8 3PH, UK. asoke.nandi@brunel.ac.uk.|Department of Electronic and Computer Engineering, Brunel University, Uxbridge, Middlesex UB8 3PH, UK. asoke.nandi@brunel.ac.uk.|Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232, USA. xi.steven.chen@gmail.com.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Cluster Analysis|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Genomics|High-Throughput Nucleotide Sequencing|Meta-Analysis as Topic|Software', 'Journal Article', '10.1186/1471-2105-15-323', '25267467', 'Meta-analysis has become a popular approach for high-throughput genomic data analysis because it often can significantly increase power to detect biological signals or patterns in datasets. However, when using public-available databases for meta-analysis, duplication of samples is an often encountered problem, especially for gene expression data. Not removing duplicates could lead false positive finding, misleading clustering pattern or model over-fitting issue, etc in the subsequent data analysis.'),
(96347, 2014, 'bmc', 'Prediction of uridine modifications in tRNA sequences', 'Panwar, Bharat|Raghava, Gajendra P S', 'Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232, USA. xi.steven.chen@gmail.com.|Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India. raghava@imtech.res.in.', 'United States|India', 0, 'Base Sequence|Models, Genetic|RNA Processing, Post-Transcriptional|RNA, Transfer|Support Vector Machine|Uridine', 'Journal Article', '10.1186/1471-2105-15-326', '25272949', 'In past number of methods have been developed for predicting post-translational modifications in proteins. In contrast, limited attempt has been made to understand post-transcriptional modifications. Recently it has been shown that tRNA modifications play direct role in the genome structure and codon usage. This study is an attempt to understand kingdom-wise tRNA modifications particularly uridine modifications (UMs), as majority of modifications are uridine-derived.'),
(96348, 2014, 'bmc', 'tigaR: integrative significance analysis of temporal differential gene expression induced by genomic abnormalities', 'Miok, Viktorian|Wilting, Saskia M|van de Wiel, Mark A|Jaspers, Annelieke|van Noort, Paula I|Brakenhoff, Ruud H|Snijders, Peter J F|Steenbergen, Renske D M|van Wieringen, Wessel N', 'Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India. raghava@imtech.res.in.|Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India. raghava@imtech.res.in.|Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India. raghava@imtech.res.in.|Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India. raghava@imtech.res.in.|Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India. raghava@imtech.res.in.|Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India. raghava@imtech.res.in.|Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India. raghava@imtech.res.in.|Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India. raghava@imtech.res.in.|Department of Epidemiology and Biostatistics, VU University Medical Center, P,O, Box 7057, 1007 MB, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl.', 'India|India|India|India|India|India|India|India|Netherlands', 0, 'Bayes Theorem|Cell Line|Computer Simulation|DNA|DNA, Complementary|Gene Dosage|Gene Expression Regulation|Genome|Genomics|Host-Pathogen Interactions|Human papillomavirus 16|Human papillomavirus 18|Humans|Keratinocytes|Models, Genetic|Papillomavirus Infections', 'Journal Article', '10.1186/1471-2105-15-327', '25278371', 'To determine which changes in the host cell genome are crucial for cervical carcinogenesis, a longitudinal in vitro model system of HPV-transformed keratinocytes was profiled in a genome-wide manner. Four cell lines affected with either HPV16 or HPV18 were assayed at 8 sequential time points for gene expression (mRNA) and gene copy number (DNA) using high-resolution microarrays. Available methods for temporal differential expression analysis are not designed for integrative genomic studies.'),
(96349, 2014, 'bmc', 'Visualization and correction of automated segmentation, tracking and lineaging from 5-D stem cell image sequences', 'Wait, Eric|Winter, Mark|Bjornsson, Chris|Kokovay, Erzsebet|Wang, Yue|Goderie, Susan|Temple, Sally|Cohen, Andrew R', 'Department of Epidemiology and Biostatistics, VU University Medical Center, P,O, Box 7057, 1007 MB, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl.|Department of Epidemiology and Biostatistics, VU University Medical Center, P,O, Box 7057, 1007 MB, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl.|Department of Epidemiology and Biostatistics, VU University Medical Center, P,O, Box 7057, 1007 MB, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl.|Department of Epidemiology and Biostatistics, VU University Medical Center, P,O, Box 7057, 1007 MB, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl.|Department of Epidemiology and Biostatistics, VU University Medical Center, P,O, Box 7057, 1007 MB, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl.|Department of Epidemiology and Biostatistics, VU University Medical Center, P,O, Box 7057, 1007 MB, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl.|Department of Epidemiology and Biostatistics, VU University Medical Center, P,O, Box 7057, 1007 MB, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl.|Drexel University, 19104 Philadelphia, USA. acohen@coe.drexel.edu.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|United States', 29, 'Algorithms|Automation|Cell Lineage|Computer Graphics|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Microscopy, Confocal|Microscopy, Fluorescence|Neural Stem Cells|Software', 'Journal Article', '10.1186/1471-2105-15-328', '25281197', 'Neural stem cells are motile and proliferative cells that undergo mitosis, dividing to produce daughter cells and ultimately generating differentiated neurons and glia. Understanding the mechanisms controlling neural stem cell proliferation and differentiation will play a key role in the emerging fields of regenerative medicine and cancer therapeutics. Stem cell studies in vitro from 2-D image data are well established. Visualizing and analyzing large three dimensional images of intact tissue is a challenging task. It becomes more difficult as the dimensionality of the image data increases to include time and additional fluorescence channels. There is a pressing need for 5-D image analysis and visualization tools to study cellular dynamics in the intact niche and to quantify the role that environmental factors play in determining cell fate.'),
(96350, 2014, 'bmc', 'Systematic identification of transcriptional and post-transcriptional regulations in human respiratory epithelial cells during influenza A virus infection', 'Liu, Zhi-Ping|Wu, Hulin|Zhu, Jian|Miao, Hongyu', 'Drexel University, 19104 Philadelphia, USA. acohen@coe.drexel.edu.|Drexel University, 19104 Philadelphia, USA. acohen@coe.drexel.edu.|Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA. jian_zhu@urmc.rochester.edu.|Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA. jian_zhu@urmc.rochester.edu.', 'United States|United States|United States|United States', 22, 'Algorithms|Bayes Theorem|Computational Biology|Databases, Genetic|Epithelial Cells|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Humans|Influenza A Virus, H1N1 Subtype|MicroRNAs|RNA, Messenger|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-15-336', '25281301', 'Respiratory epithelial cells are the primary target of influenza virus infection in human. However, the molecular mechanisms of airway epithelial cell responses to viral infection are not fully understood. Revealing genome-wide transcriptional and post-transcriptional regulatory relationships can further advance our understanding of this problem, which motivates the development of novel and more efficient computational methods to simultaneously infer the transcriptional and post-transcriptional regulatory networks.'),
(96351, 2014, 'bmc', 'An algorithm of discovering signatures from DNA databases on a computer cluster', 'Lee, Hsiao Ping|Sheu, Tzu-Fang', 'Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA. jian_zhu@urmc.rochester.edu.|Department of Computer Science and Communication Engineering, Providence University, 200, Sec, 7, Taiwan Boulevard, 43301 Shalu Dist,, Taichung, Taiwan. fang@pu.edu.tw.', 'United States|United States', 0, 'Algorithms|Cluster Analysis|Databases, Nucleic Acid|Genomics|High-Throughput Nucleotide Sequencing|Humans', 'Journal Article', '10.1186/1471-2105-15-339', '25282047', 'Signatures are short sequences that are unique and not similar to any other sequence in a database that can be used as the basis to identify different species. Even though several signature discovery algorithms have been proposed in the past, these algorithms require the entirety of databases to be loaded in the memory, thus restricting the amount of data that they can process. It makes those algorithms unable to process databases with large amounts of data. Also, those algorithms use sequential models and have slower discovery speeds, meaning that the efficiency can be improved.'),
(96352, 2014, 'bmc', 'CLAP: a web-server for automatic classification of proteins with special reference to multi-domain proteins', 'Gnanavel, Mutharasu|Mehrotra, Prachi|Rakshambikai, Ramaswamy|Martin, Juliette|Srinivasan, Narayanaswamy|Bhaskara, Ramachandra M', 'Department of Computer Science and Communication Engineering, Providence University, 200, Sec, 7, Taiwan Boulevard, 43301 Shalu Dist,, Taichung, Taiwan. fang@pu.edu.tw.|Department of Computer Science and Communication Engineering, Providence University, 200, Sec, 7, Taiwan Boulevard, 43301 Shalu Dist,, Taichung, Taiwan. fang@pu.edu.tw.|Department of Computer Science and Communication Engineering, Providence University, 200, Sec, 7, Taiwan Boulevard, 43301 Shalu Dist,, Taichung, Taiwan. fang@pu.edu.tw.|Department of Computer Science and Communication Engineering, Providence University, 200, Sec, 7, Taiwan Boulevard, 43301 Shalu Dist,, Taichung, Taiwan. fang@pu.edu.tw.|Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India. ns@mbu.iisc.ernet.in.|Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India. ns@mbu.iisc.ernet.in.', '-|-|-|-|India|India', 7, 'Algorithms|Amino Acid Sequence|Automation|Cluster Analysis|Computational Biology|Humans|Internet|Protein Structure, Tertiary|Proteins|Software', 'Journal Article', '10.1186/1471-2105-15-343', '25282152', 'The function of a protein can be deciphered with higher accuracy from its structure than from its amino acid sequence. Due to the huge gap in the available protein sequence and structural space, tools that can generate functionally homogeneous clusters using only the sequence information, hold great importance. For this, traditional alignment-based tools work well in most cases and clustering is performed on the basis of sequence similarity. But, in the case of multi-domain proteins, the alignment quality might be poor due to varied lengths of the proteins, domain shuffling or circular permutations. Multi-domain proteins are ubiquitous in nature, hence alignment-free tools, which overcome the shortcomings of alignment-based protein comparison methods, are required. Further, existing tools classify proteins using only domain-level information and hence miss out on the information encoded in the tethered regions or accessory domains. Our method, on the other hand, takes into account the full-length sequence of a protein, consolidating the complete sequence information to understand a given protein better.'),
(96353, 2014, 'bmc', 'Detecting temporal protein complexes from dynamic protein-protein interaction networks', 'Ou-Yang, Le|Dai, Dao-Qing|Li, Xiao-Li|Wu, Min|Zhang, Xiao-Fei|Yang, Peng', 'Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India. ns@mbu.iisc.ernet.in.|Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China. stsddq@mail.sysu.edu.cn.|Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China. stsddq@mail.sysu.edu.cn.|Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China. stsddq@mail.sysu.edu.cn.|Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China. stsddq@mail.sysu.edu.cn.|Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China. stsddq@mail.sysu.edu.cn.', 'India|China|China|China|China|China', 42, 'Algorithms|Computational Biology|Gene Expression Profiling|Protein Interaction Mapping|Proteins|Time Factors', 'Journal Article', '10.1186/1471-2105-15-335', '25282536', 'Proteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization.'),
(96354, 2014, 'bmc', 'Systematic exploration of guide-tree topology effects for small protein alignments', 'Sievers, Fabian|Hughes, Graham M|Higgins, Desmond G', 'University College Dublin, Conway Institute, Dublin, Ireland. fabian.sievers@ucd.ie.|University College Dublin, Conway Institute, Dublin, Ireland. fabian.sievers@ucd.ie.|University College Dublin, Conway Institute, Dublin, Ireland. fabian.sievers@ucd.ie.', 'Ireland|Ireland|Ireland', 0, 'Cluster Analysis|Phylogeny|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-15-338', '25282640', 'Guide-trees are used as part of an essential heuristic to enable the calculation of multiple sequence alignments. They have been the focus of much method development but there has been little effort at determining systematically, which guide-trees, if any, give the best alignments. Some guide-tree construction schemes are based on pair-wise distances amongst unaligned sequences. Others try to emulate an underlying evolutionary tree and involve various iteration methods.'),
(96355, 2014, 'bmc', 'trieFinder: an efficient program for annotating Digital Gene Expression (DGE) tags', 'Renaud, Gabriel|LaFave, Matthew C|Liang, Jin|Wolfsberg, Tyra G|Burgess, Shawn M', 'University College Dublin, Conway Institute, Dublin, Ireland. fabian.sievers@ucd.ie.|University College Dublin, Conway Institute, Dublin, Ireland. fabian.sievers@ucd.ie.|University College Dublin, Conway Institute, Dublin, Ireland. fabian.sievers@ucd.ie.|University College Dublin, Conway Institute, Dublin, Ireland. fabian.sievers@ucd.ie.|Translational and Functional Genomics Branch, Division of Intramural Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892-8004, USA. burgess@mail.nih.gov.', 'Ireland|Ireland|Ireland|Ireland|United States', 1, 'Algorithms|DNA, Complementary|Databases, Genetic|Gene Expression Profiling|Genomics|Molecular Sequence Annotation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-15-329', '25311246', 'Quantification of a transcriptional profile is a useful way to evaluate the activity of a cell at a given point in time. Although RNA-Seq has revolutionized transcriptional profiling, the costs of RNA-Seq are still significantly higher than microarrays, and often the depth of data delivered from RNA-Seq is in excess of what is needed for simple transcript quantification. Digital Gene Expression (DGE) is a cost-effective, sequence-based approach for simple transcript quantification: by sequencing one read per molecule of RNA, this technique can be used to efficiently count transcripts while obviating the need for transcript-length normalization and reducing the total numbers of reads necessary for accurate quantification. Here, we present trieFinder, a program specifically designed to rapidly map, parse, and annotate DGE tags of various lengths against cDNA and/or genomic sequence databases.'),
(96356, 2014, 'bmc', 'Interactively illustrating polymerization using three-level model fusion', 'Kolesar, Ivan|Parulek, Julius|Viola, Ivan|Bruckner, Stefan|Stavrum, Anne-Kristin|Hauser, Helwig', 'Department of Informatics, University of Bergen, N-5020 Bergen, Norway. Ivan.Kolesar@UiB.no.|Department of Informatics, University of Bergen, N-5020 Bergen, Norway. Ivan.Kolesar@UiB.no.|Department of Informatics, University of Bergen, N-5020 Bergen, Norway. Ivan.Kolesar@UiB.no.|Department of Informatics, University of Bergen, N-5020 Bergen, Norway. Ivan.Kolesar@UiB.no.|Department of Informatics, University of Bergen, N-5020 Bergen, Norway. Ivan.Kolesar@UiB.no.|Department of Informatics, University of Bergen, N-5020 Bergen, Norway. Ivan.Kolesar@UiB.no.', 'Norway|Norway|Norway|Norway|Norway|Norway', 7, 'Models, Biological|Physiological Phenomena|Polymerization', 'Journal Article', '10.1186/1471-2105-15-345', '25315282', 'Research in cell biology is steadily contributing new knowledge about many aspects of physiological processes, both with respect to the involved molecular structures as well as their related function. Illustrations of the spatio-temporal development of such processes are not only used in biomedical education, but also can serve scientists as an additional platform for in-silico experiments.'),
(96357, 2014, 'bmc', 'Detection of internal exon deletion with exon Del', 'Guo, Yan|Zhao, Shilin|Lehmann, Brian D|Sheng, Quanhu|Shaver, Timothy M|Stricker, Thomas P|Pietenpol, Jennifer A|Shyr, Yu', 'Vanderbilt Ingram Cancer Center, Center for Quantitative Sciences, 2220 Pierce Ave, 549 Preston Research Building, Nashville, TN 37232, USA. yan.guo@vanderbilt.edu.|Vanderbilt Ingram Cancer Center, Center for Quantitative Sciences, 2220 Pierce Ave, 549 Preston Research Building, Nashville, TN 37232, USA. yan.guo@vanderbilt.edu.|Vanderbilt Ingram Cancer Center, Center for Quantitative Sciences, 2220 Pierce Ave, 549 Preston Research Building, Nashville, TN 37232, USA. yan.guo@vanderbilt.edu.|Vanderbilt Ingram Cancer Center, Center for Quantitative Sciences, 2220 Pierce Ave, 549 Preston Research Building, Nashville, TN 37232, USA. yan.guo@vanderbilt.edu.|Vanderbilt Ingram Cancer Center, Center for Quantitative Sciences, 2220 Pierce Ave, 549 Preston Research Building, Nashville, TN 37232, USA. yan.guo@vanderbilt.edu.|Vanderbilt Ingram Cancer Center, Center for Quantitative Sciences, 2220 Pierce Ave, 549 Preston Research Building, Nashville, TN 37232, USA. yan.guo@vanderbilt.edu.|Vanderbilt Ingram Cancer Center, Center for Quantitative Sciences, 2220 Pierce Ave, 549 Preston Research Building, Nashville, TN 37232, USA. yan.guo@vanderbilt.edu.|Vanderbilt Ingram Cancer Center, Center for Quantitative Sciences, 2220 Pierce Ave, 549 Preston Research Building, Nashville, TN 37232, USA. yan.guo@vanderbilt.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States', 6, 'DNA Copy Number Variations|Exome|Exons|Genome, Human|Genomics|Homozygote|Humans|Sequence Analysis, DNA|Sequence Deletion', 'Journal Article', '10.1186/1471-2105-15-332', '25322818', 'Exome sequencing allows researchers to study the human genome in unprecedented detail. Among the many types of variants detectable through exome sequencing, one of the most over looked types of mutation is internal deletion of exons. Internal exon deletions are the absence of consecutive exons in a gene. Such deletions have potentially significant biological meaning, and they are often too short to be considered copy number variation. Therefore, to the need for efficient detection of such deletions using exome sequencing data exists.'),
(96358, 2014, 'bmc', 'Bison: bisulfite alignment on nodes of a cluster', 'Ryan, Devon Patrick|Ehninger, Dan', 'German Center for Neurodegenerative Diseases (DZNE), Ludwig-Erhard-Allee 2, Bonn 53175, Germany. devon.ryan@dzne.de.|German Center for Neurodegenerative Diseases (DZNE), Ludwig-Erhard-Allee 2, Bonn 53175, Germany. devon.ryan@dzne.de.', 'Germany|Germany', 6, 'Cluster Analysis|DNA Methylation|Sequence Alignment|Sequence Analysis, DNA|Software|Sulfites|Time Factors', 'Journal Article', '10.1186/1471-2105-15-337', '25326660', 'DNA methylation changes are associated with a wide array of biological processes. Bisulfite conversion of DNA followed by high-throughput sequencing is increasingly being used to assess genome-wide methylation at single-base resolution. The relative slowness of most commonly used aligners for processing such data introduces an unnecessarily long delay between receipt of raw data and statistical analysis. While this process can be sped-up by using computer clusters, current tools are not designed with them in mind and end-users must create such implementations themselves.'),
(96359, 2014, 'bmc', 'SummonChimera infers integrated viral genomes with nucleotide precision from NGS data', 'Katz, Joshua P|Pipas, James M', 'Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, 15260, USA. joshpk105@gmail.com.|Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, 15260, USA. pipas@pitt.edu.', 'United States|United States', 8, 'Bacteriophages|Carcinoma, Hepatocellular|Genome, Viral|Genomics|Hepatitis B virus|High-Throughput Nucleotide Sequencing|Humans|Liver Neoplasms|Nucleotides|Salmonella|Software|Virus Integration', 'Journal Article', '10.1186/s12859-014-0348-4', '25331652', 'Viral integration into a host genome is defined by two chimeric junctions that join viral and host DNA. Recently, computational tools have been developed that utilize NGS data to detect chimeric junctions. These methods identify individual viral-host junctions but do not associate chimeric pairs as an integration event. Without knowing the chimeric boundaries of an integration, its genetic content cannot be determined.'),
(96360, 2014, 'bmc', 'A comparative study of cell classifiers for image-based high-throughput screening', 'Abbas, Syed Saiden|Dijkstra, Tjeerd M H|Heskes, Tom', 'Institute for Computing and Information Sciences, Radboud University, Nijmegen, Netherlands. saiden@science.ru.nl.|Institute for Computing and Information Sciences, Radboud University, Nijmegen, Netherlands. saiden@science.ru.nl.|Institute for Computing and Information Sciences, Radboud University, Nijmegen, Netherlands. saiden@science.ru.nl.', 'Netherlands|Netherlands|Netherlands', 11, 'Computational Biology|Discriminant Analysis|HT29 Cells|HeLa Cells|High-Throughput Screening Assays|Humans|Linear Models|Molecular Imaging|Support Vector Machine', 'Comparative Study', '10.1186/1471-2105-15-342', '25336059', 'Millions of cells are present in thousands of images created in high-throughput screening (HTS). Biologists could classify each of these cells into a phenotype by visual inspection. But in the presence of millions of cells this visual classification task becomes infeasible. Biologists train classification models on a few thousand visually classified example cells and iteratively improve the training data by visual inspection of the important misclassified phenotypes. Classification methods differ in performance and performance evaluation time. We present a comparative study of computational performance of gentle boosting, joint boosting CellProfiler Analyst (CPA), support vector machines (linear and radial basis function) and linear discriminant analysis (LDA) on two data sets of HT29 and HeLa cancer cells.'),
(96361, 2014, 'bmc', 'Improving the accuracy of expression data analysis in time course experiments using resampling', 'Walter, Wencke|Striberny, Bernd|Gaquerel, Emmanuel|Baldwin, Ian T|Kim, Sang-Gyu|Heiland, Ines', 'Department of Molecular Ecology, Max Planck Institute for Chemical Ecology, Hans-Knöll-Straße 8, D-07745, Jena, Germany. wwalter@cnic.es.|Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Naturfagbygget, Dramsvegen 201, 9037, Tromsø, Norway. bernd.ketelsen@uit.no.|Department of Molecular Ecology, Max Planck Institute for Chemical Ecology, Hans-Knöll-Straße 8, D-07745, Jena, Germany. egaquerel@ice.mpg.de.|Department of Molecular Ecology, Max Planck Institute for Chemical Ecology, Hans-Knöll-Straße 8, D-07745, Jena, Germany. baldwin@ice.mpg.de.|Department of Molecular Ecology, Max Planck Institute for Chemical Ecology, Hans-Knöll-Straße 8, D-07745, Jena, Germany. skim@ice.mpg.de.|Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Naturfagbygget, Dramsvegen 201, 9037, Tromsø, Norway. ines.heiland@uit.no.', 'Germany|Norway|Germany|Germany|Germany|Norway', 4, 'Algorithms|Circadian Clocks|Gene Expression Profiling|Kinetics|Reproducibility of Results', 'Journal Article', '10.1186/s12859-014-0352-8', '25344112', 'As time series experiments in higher eukaryotes usually obtain data from different individuals collected at the different time points, a time series sample itself is not equivalent to a true biological replicate but is, rather, a combination of several biological replicates. The analysis of expression data derived from a time series sample is therefore often performed with a low number of replicates due to budget limitations or limitations in sample availability. In addition, most algorithms developed to identify specific patterns in time series dataset do not consider biological variation in samples collected at the same conditions.'),
(96362, 2014, 'bmc', 'Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies', 'Moni, Mohammad Ali|Lixc3xb2, Pietro', 'Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK. Mohammad.Moni@cl.cam.ac.uk.|Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK. Mohammad.Moni@cl.cam.ac.uk.', 'United Kingdom|United Kingdom', 40, 'Chronic Disease|Comorbidity|HIV Infections|HIV-1|Humans|Protein Interaction Maps|Risk Factors|SARS Virus|Severe Acute Respiratory Syndrome', 'Journal Article', '10.1186/1471-2105-15-333', '25344230', 'Infections are often associated to comorbidity that increases the risk of medical conditions which can lead to further morbidity and mortality. SARS is a threat which is similar to MERS virus, but the comorbidity is the key aspect to underline their different impacts. One UK doctor says Id rather have HIV than diabetes as life expectancy among diabetes patients is lower than that of HIV. However, HIV has a comorbidity impact on the diabetes.'),
(96363, 2014, 'bmc', 'FastMG: a simple, fast, and accurate maximum likelihood procedure to estimate amino acid replacement rate matrices from large data sets', 'Dang, Cuong Cao|Le, Vinh Sy|Gascuel, Olivier|Hazes, Bart|Le, Quang Si', 'Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK. Mohammad.Moni@cl.cam.ac.uk.|Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK. Mohammad.Moni@cl.cam.ac.uk.|Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK. Mohammad.Moni@cl.cam.ac.uk.|Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK. Mohammad.Moni@cl.cam.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, 'Algorithms|Amino Acid Substitution|Animals|Evolution, Molecular|Likelihood Functions|Phylogeny|Probability|Proteins', 'Journal Article', '10.1186/1471-2105-15-341', '25344302', 'Amino acid replacement rate matrices are a crucial component of many protein analysis systems such as sequence similarity search, sequence alignment, and phylogenetic inference. Ideally, the rate matrix reflects the mutational behavior of the actual data under study, however, estimating amino acid replacement rate matrices requires large protein alignments and is computationally expensive and complex. As a compromise, sub-optimal pre-calculated generic matrices are typically used for protein-based phylogeny. Sequence availability has now grown to a point where problem-specific rate matrices can often be calculated if the computational cost can be controlled.'),
(96364, 2014, 'bmc', 'SeqAssist: a novel toolkit for preliminary analysis of next-generation sequencing data', 'Peng, Yan|Maxwell, Andrew S|Barker, Natalie D|Laird, Jennifer G|Kennedy, Alan J|Wang, Nan|Zhang, Chaoyang|Gong, Ping', 'The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Software', 'Journal Article', '10.1186/1471-2105-15-S11-S10', '25349885', 'While next-generation sequencing (NGS) technologies are rapidly advancing, an area that lags behind is the development of efficient and user-friendly tools for preliminary analysis of massive NGS data. As an effort to fill this gap to keep up with the fast pace of technological advancement and to accelerate data-to-results turnaround, we developed a novel software package named SeqAssist (Sequencing Assistant or SA).'),
(96365, 2014, 'bmc', 'Competitive molecular docking approach for predicting estrogen receptor subtype xcexb1 agonists and antagonists', 'Ng, Hui Wen|Zhang, Wenqian|Shu, Mao|Luo, Heng|Ge, Weigong|Perkins, Roger|Tong, Weida|Hong, Huixiao', 'The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 35, 'Computer Simulation|Endocrine Disruptors|Estrogen Receptor Antagonists|Estrogen Receptor alpha|Estrogens|Ligands|Molecular Docking Simulation', 'Journal Article', '10.1186/1471-2105-15-S11-S4', '25349983', 'Endocrine disrupting chemicals (EDCs) are exogenous compounds that interfere with the endocrine system of vertebrates, often through direct or indirect interactions with nuclear receptor proteins. Estrogen receptors (ERs) are particularly important protein targets and many EDCs are ER binders, capable of altering normal homeostatic transcription and signaling pathways. An estrogenic xenobiotic can bind ER as either an agonist or antagonist to increase or inhibit transcription, respectively. The receptor conformations in the complexes of ER bound with agonists and antagonists are different and dependent on interactions with co-regulator proteins that vary across tissue type. Assessment of chemical endocrine disruption potential depends not only on binding affinity to ERs, but also on changes that may alter the receptor conformation and its ability to subsequently bind DNA response elements and initiate transcription. Using both agonist and antagonist conformations of the ERxcexb1, we developed an in silico approach that can be used to differentiate agonist versus antagonist status of potential binders.'),
(96366, 2014, 'bmc', 'Topic modeling for cluster analysis of large biological and medical datasets', 'Zhao, Weizhong|Zou, Wen|Chen, James J', 'The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 25, 'Algorithms|Breast Neoplasms|Cluster Analysis|Data Mining|Electrophoresis, Gel, Pulsed-Field|Female|Humans|Lung Neoplasms|Models, Statistical|Salmonella|Survival Analysis', 'Journal Article', '10.1186/1471-2105-15-S11-S11', '25350106', 'The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. 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. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets.'),
(96367, 2014, 'bmc', 'Quality evaluation of extracted ion chromatograms and chromatographic peaks in liquid chromatography/mass spectrometry-based metabolomics data', 'Zhang, Wenchao|Zhao, Patrick X', 'The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.', 'United Kingdom|United Kingdom', 14, 'Chromatography, Liquid|Metabolomics|Signal-To-Noise Ratio|Software|Spectrometry, Mass, Electrospray Ionization', 'Evaluation Studies', '10.1186/1471-2105-15-S11-S5', '25350128', 'Extracted ion chromatogram (EIC) extraction and chromatographic peak detection are two important processing procedures in liquid chromatography/mass spectrometry (LC/MS)-based metabolomics data analysis. Most commonly, the LC/MS technique employs electrospray ionization as the ionization method. The EICs from LC/MS data are often noisy and contain high background signals. Furthermore, the chromatographic peak quality varies with respect to its location in the chromatogram and most peaks have zigzag shapes. Therefore, there is a critical need to develop effective metrics for quality evaluation of EICs and chromatographic peaks in LC/MS based metabolomics data analysis.'),
(96368, 2014, 'bmc', 'Haystack, a web-based tool for metabolomics research', 'Grace, Stephen C|Embry, Stephen|Luo, Heng', 'The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 11, 'Chromatography, Liquid|Cluster Analysis|Internet|Mass Spectrometry|Metabolomics|Software', 'Journal Article', '10.1186/1471-2105-15-S11-S12', '25350247', 'Liquid chromatography coupled to mass spectrometry (LCMS) has become a widely used technique in metabolomics research for differential profiling, the broad screening of biomolecular constituents across multiple samples to diagnose phenotypic differences and elucidate relevant features. However, a significant limitation in LCMS-based metabolomics is the high-throughput data processing required for robust statistical analysis and data modeling for large numbers of samples with hundreds of unique chemical species.'),
(96369, 2014, 'bmc', 'Whole genome sequencing of 35 individuals provides insights into the genetic architecture of Korean population', 'Zhang, Wenqian|Meehan, Joe|Su, Zhenqiang|Ng, Hui Wen|Shu, Mao|Luo, Heng|Ge, Weigong|Perkins, Roger|Tong, Weida|Hong, Huixiao', 'The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 21, 'Asian Continental Ancestry Group|Disease|Gene Ontology|Genetic Association Studies|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Korea|Mutation|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-15-S11-S6', '25350283', 'Due to a significant decline in the costs associated with next-generation sequencing, it has become possible to decipher the genetic architecture of a population by sequencing a large number of individuals to a deep coverage. The Korean Personal Genomes Project (KPGP) recently sequenced 35 Korean genomes at high coverage using the Illumina Hiseq platform and made the deep sequencing data publicly available, providing the scientific community opportunities to decipher the genetic architecture of the Korean population.'),
(96370, 2014, 'bmc', 'Predicting genome-scale Arabidopsis-Pseudomonas syringae interactome using domain and interolog-based approaches', 'Sahu, Sitanshu S|Weirick, Tyler|Kaundal, Rakesh', 'The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 18, 'Arabidopsis|Arabidopsis Proteins|Bacterial Proteins|Genome, Bacterial|Genome, Plant|Host-Pathogen Interactions|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Protein Interaction Maps|Pseudomonas syringae', 'Journal Article', '10.1186/1471-2105-15-S11-S13', '25350354', 'Every year pathogenic organisms cause billions of dollars worth damage to crops and livestock. In agriculture, study of plant-microbe interactions is demanding a special attention to develop management strategies for the destructive pathogen induced diseases that cause huge crop losses every year worldwide. Pseudomonas syringae is a major bacterial leaf pathogen that causes diseases in a wide range of plant species. Among its various strains, pathovar tomato strain DC3000 (PstDC3000) is asserted to infect the plant host Arabidopsis thaliana and thus, has been accepted as a model system for experimental characterization of the molecular dynamics of plant-pathogen interactions. Protein-protein interactions (PPIs) play a critical role in initiating pathogenesis and maintaining infection. Understanding the PPI network between a host and pathogen is a critical step for studying the molecular basis of pathogenesis. The experimental study of PPIs at a large scale is very scarce and also the high throughput experimental results show high false positive rate. Hence, there is a need for developing efficient computational models to predict the interaction between host and pathogen in a genome scale, and find novel candidate effectors and/or their targets.'),
(96371, 2014, 'bmc', 'Trends in the production of scientific data analysis resources', 'Hennessey, Jason|Georgescu, Constantin|Wren, Jonathan D', 'The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 4, 'Authorship|Computational Biology|Databases, Factual|Internet|MEDLINE|Periodicals as Topic|Research|Software', 'Journal Article', '10.1186/1471-2105-15-S11-S7', '25350391', 'As the amount of scientific data grows, peer-reviewed Scientific Data Analysis Resources (SDARs) such as published software programs, databases and web servers have had a strong impact on the productivity of scientific research. SDARs are typically linked to using an Internet URL, which have been shown to decay in a time-dependent fashion. What is less clear is whether or not SDAR-producing group size or prior experience in SDAR production correlates with SDAR persistence or whether certain institutions or regions account for a disproportionate number of peer-reviewed resources.'),
(96372, 2014, 'bmc', 'Improving protein fold recognition by random forest', 'Jo, Taeho|Cheng, Jianlin', 'The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.', 'United Kingdom|United Kingdom', 0, 'Algorithms|Artificial Intelligence|Decision Trees|Protein Folding|Protein Structure, Tertiary|Software', 'Journal Article', '10.1186/1471-2105-15-S11-S14', '25350499', 'Recognizing the correct structural fold among known template protein structures for a target protein (i.e. fold recognition) is essential for template-based protein structure modeling. Since the fold recognition problem can be defined as a binary classification problem of predicting whether or not the unknown fold of a target protein is similar to an already known template protein structure in a library, machine learning methods have been effectively applied to tackle this problem. In our work, we developed RF-Fold that uses random forest - one of the most powerful and scalable machine learning classification methods - to recognize protein folds.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96373, 2014, 'bmc', 'Divergence of protein-coding capacity and regulation in the Bacillus cereus sensu lato group', 'Toby, Inimary T|Widmer, Jonah|Dyer, David W', 'The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Bacillus cereus|Bacterial Proteins|Cluster Analysis|Evolution, Molecular|Genome, Bacterial|Phenotype|Phylogeny|Regulon', 'Journal Article', '10.1186/1471-2105-15-S11-S8', '25350501', 'The Bacillus cereus sensu lato group contains ubiquitous facultative anaerobic soil-borne Gram-positive spore-forming bacilli. Molecular phylogeny and comparative genome sequencing have suggested that these organisms should be classified as a single species. While clonal in nature, there do not appear to be species-specific clonal lineages, excepting B. anthracis, in spite of the wide array of phenotypes displayed by these organisms.'),
(96374, 2014, 'bmc', 'LacSubPred: predicting subtypes of Laccases, an important lignin metabolism-related enzyme class, using in silico approaches', 'Weirick, Tyler|Sahu, Sitanshu S|Mahalingam, Ramamurthy|Kaundal, Rakesh', 'The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 6, 'Artificial Intelligence|Bacteria|Computer Simulation|Fungi|Laccase|Phylogeny|Plants|Protein Structure, Tertiary|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-15-S11-S15', '25350584', 'Laccases (E.C. 1.10.3.2) are multi-copper oxidases that have gained importance in many industries such as biofuels, pulp production, textile dye bleaching, bioremediation, and food production. Their usefulness stems from the ability to act on a diverse range of phenolic compounds such as o-/p-quinols, aminophenols, polyphenols, polyamines, aryl diamines, and aromatic thiols. Despite acting on a wide range of compounds as a family, individual Laccases often exhibit distinctive and varied substrate ranges. This is likely due to Laccases involvement in many metabolic roles across diverse taxa. Classification systems for multi-copper oxidases have been developed using multiple sequence alignments, however, these systems seem to largely follow species taxonomy rather than substrate ranges, enzyme properties, or specific function. It has been suggested that the roles and substrates of various Laccases are related to their optimal pH. This is consistent with the observation that fungal Laccases usually prefer acidic conditions, whereas plant and bacterial Laccases prefer basic conditions. Based on these observations, we hypothesize that a descriptor-based unsupervised learning system could generate homology independent classification system for better describing the functional properties of Laccases.'),
(96375, 2014, 'bmc', 'Revealing the inherent heterogeneity of human malignancies by variant consensus strategies coupled with cancer clonal analysis', 'Peterson, Erich A|Chavan, Shweta S|Bauer, Michael A|Heuck, Christoph J|Johann, Donald J', 'The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Clonal Evolution|Clone Cells|Computational Biology|Humans|Mutation|Neoplasms|Software', 'Journal Article', '10.1186/1471-2105-15-S11-S9', '25350589', 'Tumors are heterogeneous in composition. They are composed of cancer cells proper, along with stromal elements that collectively form a microenvironment, all of which are necessary to nurture the malignant process. In addition, many of the stromal cells are modified to support the unique needs of the malignant state. Tumors are composed of a variety of clones or subpopulations of cancer cells, which may differ in karyotype, growth rate, expression of cell surface markers, sensitivity to therapeutics, etc. New tools and methods to provide an improved understanding of tumor clonal architecture are needed to guide therapy. '),
(96376, 2014, 'bmc', 'XLPM: efficient algorithm for the analysis of protein-protein contacts using chemical cross-linking mass spectrometry', 'Jaiswal, Mihir|Crabtree, Nathaniel|Bauer, Michael A|Hall, Roger|Raney, Kevin D|Zybailov, Boris L', 'The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 7, 'Algorithms|Cross-Linking Reagents|DNA-Binding Proteins|Humans|Mass Spectrometry|Peptides|Protein Interaction Mapping|Protein Multimerization|Software|Succinimides', 'Journal Article', '10.1186/1471-2105-15-S11-S16', '25350700', 'Chemical cross-linking is used for protein-protein contacts mapping and for structural analysis. One of the difficulties in cross-linking studies is the analysis of mass-spectrometry data and the assignment of the site of cross-link incorporation. The difficulties are due to higher charges of fragment ions, and to the overall low-abundance of cross-link species in the background of linear peptides. Cross-linkers non-specific at one end, such as photo-inducible diazirines, may complicate the analysis further. In this report, we design and validate a novel cross-linked peptide mapping algorithm (XLPM) and compare it to StavroX, which is currently one of the best algorithms in this class.'),
(96377, 2014, 'bmc', 'Exploiting dependencies of pairwise comparison outcomes to predict patterns of gene response', 'Vo, Nam S|Phan, Vinhthuy', 'The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.', 'United Kingdom|United Kingdom', 0, 'Animals|Cluster Analysis|Gene Expression Profiling|Gene Regulatory Networks|Rats, Sprague-Dawley|Sample Size|Transcription Factors', 'Journal Article', '10.1186/1471-2105-15-S11-S2', '25350806', 'The analysis of gene expression has played an important role in medical and bioinformatics research. Although it is known that a large number of samples is needed to determine the patterns of gene expression accurately, practical designs of gene expression studies occasionally have insufficient numbers of samples, making it difficult to ascertain true response patterns of variantly expressed genes.'),
(96378, 2014, 'bmc', 'Proceedings of the 2014 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference', 'Wren, Jonathan D|Dozmorov, Mikhail G|Burian, Dennis|Perkins, Andy|Zhang, Chaoyang|Hoyt, Peter|Kaundal, Rakesh', 'The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Gene Expression Profiling|Gene Regulatory Networks|Genomics|Proteomics|Systems Biology', 'Introductory Journal Article', '10.1186/1471-2105-15-S11-I1', '25350879', '-'),
(96379, 2014, 'bmc', 'Leveraging the new with the old: providing a framework for the integration of historic microarray studies with next generation sequencing', 'Bauer, Michael A|Chavan, Shweta S|Peterson, Erich A|Heuck, Christoph J|Johann, Donald J', 'The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, 'Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Proto-Oncogene Proteins|Proto-Oncogene Proteins p21(ras)|Sequence Analysis, DNA|Sequence Analysis, RNA|Software|ras Proteins', 'Journal Article', '10.1186/1471-2105-15-S11-S3', '25350881', 'Next Generation Sequencing (NGS) methods are rapidly providing remarkable advances in our ability to study the molecular profiles of human cancers. However, the scientific discovery offered by NGS also includes challenges concerning the interpretation of large and non-trivial experimental results. This task is potentially further complicated when a multitude of molecular profiling modalities are available, with the goal of a more integrative and comprehensive analysis of the cancer biology. '),
(96380, 2014, 'bmc', 'SLDR: a computational technique to identify novel genetic regulatory relationships', 'Yue, Zongliang|Wan, Ping|Huang, Hui|Xie, Zhan|Chen, Jake Y', 'The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.|The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK. quang@well.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Gene Expression Profiling|Gene Regulatory Networks|Genomics|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-15-S11-S1', '25350940', 'We developed a new computational technique called Step-Level Differential Response (SLDR) to identify genetic regulatory relationships. Our technique takes advantages of functional genomics data for the same species under different perturbation conditions, therefore complementary to current popular computational techniques. It can particularly identify rare activation/inhibition relationship events that can be difficult to find in experimental results. In SLDR, we model each candidate target gene as being controlled by N binary-state regulators that lead to xe2x89xa42N observable states (step-levels) for the target. We applied SLDR to the study of the GEO microarray data set GSE25644, which consists of 158 different mutant S. cerevisiae gene expressional profiles. For each target gene t, we first clustered ordered samples into various clusters, each approximating an observable step-level of t to screen out the de-centric target. Then, we ordered each gene x as a candidate regulator and aligned t to x for the purpose of examining the step-level correlations between low expression set of x (Ro) and high expression set of x (Rh) from the regulator x to t, by finding max f(t, x): |Ro-Rh| over all candidate xc3x97 in the genome for each t. We therefore obtained activation and inhibitions events from different combinations of Ro and Rh. Furthermore, we developed criteria for filtering out less-confident regulators, estimated the number of regulators for each target t, and evaluated identified top-ranking regulator-target relationship. Our results can be cross-validated with the Yeast Fitness database. SLDR is also computationally efficient with o(Nxc2xb2) complexity. In summary, we believe SLDR can be applied to the mining of functional genomics big data for future network biology and network medicine applications.'),
(96381, 2014, 'bmc', 'SketchBio: a scientists 3D interface for molecular modeling and animation', 'Waldon, Shawn M|Thompson, Peter M|Hahn, Patrick J|Taylor, Russell M', 'University of North Carolina at Chapel Hill, 27599 Chapel Hill, NC, USA. swaldon@cs.unc.edu.|University of North Carolina at Chapel Hill, 27599 Chapel Hill, NC, USA. swaldon@cs.unc.edu.|University of North Carolina at Chapel Hill, 27599 Chapel Hill, NC, USA. swaldon@cs.unc.edu.|University of North Carolina at Chapel Hill, 27599 Chapel Hill, NC, USA. swaldon@cs.unc.edu.', 'United States|United States|United States|United States', 9, 'Computer Simulation|Humans|Models, Molecular|Molecular Conformation|Software', 'Journal Article', '10.1186/1471-2105-15-334', '25359079', 'Because of the difficulties involved in learning and using 3D modeling and rendering software, many scientists hire programmers or animators to create models and animations. This both slows the discovery process and provides opportunities for miscommunication. Working with multiple collaborators, a tool was developed (based on a set of design goals) to enable them to directly construct models and animations.'),
(96382, 2014, 'bmc', 'CIG-P: Circular Interaction Graph for Proteomics', 'Hobbs, Christopher K|Leung, Michelle|Tsang, Herbert H|Ebhardt, H Alexander', 'University of North Carolina at Chapel Hill, 27599 Chapel Hill, NC, USA. swaldon@cs.unc.edu.|University of North Carolina at Chapel Hill, 27599 Chapel Hill, NC, USA. swaldon@cs.unc.edu.|Applied Research Lab, Faculty of Natural and Applied Sciences, Trinity Western University Canada, Langley, BC, Canada. herbert.tsang@twu.ca.|Applied Research Lab, Faculty of Natural and Applied Sciences, Trinity Western University Canada, Langley, BC, Canada. herbert.tsang@twu.ca.', 'United States|United States|Canada|Canada', 0, 'Chromatography, Affinity|Computer Graphics|Humans|Mass Spectrometry|Protein Interaction Mapping|Protein Kinases|Proteomics', 'Journal Article', '10.1186/1471-2105-15-344', '25359149', 'A typical affinity purification coupled to mass spectrometry (AP-MS) experiment includes the purification of a target protein (bait) using an antibody and subsequent mass spectrometry analysis of all proteins co-purifying with the bait (aka prey proteins). Like any other systems biology approach, AP-MS experiments generate a lot of data and visualization has been challenging, especially when integrating AP-MS experiments with orthogonal datasets.'),
(96383, 2014, 'bmc', 'A p-Median approach for predicting drug response in tumour cells', 'Fersini, Elisabetta|Messina, Enza|Archetti, Francesco', 'Applied Research Lab, Faculty of Natural and Applied Sciences, Trinity Western University Canada, Langley, BC, Canada. herbert.tsang@twu.ca.|Applied Research Lab, Faculty of Natural and Applied Sciences, Trinity Western University Canada, Langley, BC, Canada. herbert.tsang@twu.ca.|Applied Research Lab, Faculty of Natural and Applied Sciences, Trinity Western University Canada, Langley, BC, Canada. herbert.tsang@twu.ca.', 'Canada|Canada|Canada', 8, 'Antineoplastic Agents|Cell Line, Tumor|Cluster Analysis|Computer Simulation|Gene Expression Profiling|Humans|Models, Biological|Models, Statistical|Neoplasms', 'Journal Article', '10.1186/s12859-014-0353-7', '25359173', 'The complexity of biological data related to the genetic origins of tumour cells, originates significant challenges to glean valuable knowledge that can be used to predict therapeutic responses. In order to discover a link between gene expression profiles and drug responses, a computational framework based on Consensus p-Median clustering is proposed. The main goal is to simultaneously predict (in silico) anticancer responses by extracting common patterns among tumour cell lines, selecting genes that could potentially explain the therapy outcome and finally learning a probabilistic model able to predict the therapeutic responses.'),
(96384, 2014, 'bmc', 'A shortcut for multiple testing on the directed acyclic graph of gene ontology', 'Saunders, Garrett|Stevens, John R|Isom, S Clay', 'Utah State University, Department of Mathematics & Statistics, Logan, Utah, USA. saundersg@byui.edu.|Utah State University, Department of Mathematics & Statistics, Logan, Utah, USA. john.r.stevens@usu.edu.|Utah State University, Department of Animal, Dairy, and Veterinary Sciences, Logan, Utah, USA. clay.isom@usu.edu.', 'United States|United States|United States', 0, 'Gene Expression Profiling|Gene Ontology|High-Throughput Nucleotide Sequencing|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/s12859-014-0349-3', '25366961', 'Gene set testing has become an important analysis technique in high throughput microarray and next generation sequencing studies for uncovering patterns of differential expression of various biological processes. Often, the large number of gene sets that are tested simultaneously require some sort of multiplicity correction to account for the multiplicity effect. This work provides a substantial computational improvement to an existing familywise error rate controlling multiplicity approach (the Focus Level method) for gene set testing in high throughput microarray and next generation sequencing studies using Gene Ontology graphs, which we call the Short Focus Level.'),
(96385, 2014, 'bmc', 'A MATLAB tool for pathway enrichment using a topology-based pathway regulation score', 'Ibrahim, Maysson|Jassim, Sabah|Cawthorne, Michael Anthony|Langlands, Kenneth', 'Department of Applied Computing, the University of Buckingham, Buckingham, MK18 1EG, UK. maysson.ibrahim@buckingham.co.uk.|Department of Applied Computing, the University of Buckingham, Buckingham, MK18 1EG, UK. sabah.jassim@buckingham.ac.uk.|The Buckingham Institute for Translational Medicine, the University of Buckingham, Buckingham, MK18 1EG, UK. mike.cawthorne@buckingham.ac.uk.|The Buckingham Institute for Translational Medicine, the University of Buckingham, Buckingham, MK18 1EG, UK. kenneth.langlands@buckingham.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 8, 'Gene Expression Profiling|Genomics|Metabolic Networks and Pathways|Oligonucleotide Array Sequence Analysis|Signal Transduction|Software', 'Journal Article', '10.1186/s12859-014-0358-2', '25367050', 'Handling the vast amount of gene expression data generated by genome-wide transcriptional profiling techniques is a challenging task, demanding an informed combination of pre-processing, filtering and analysis methods if meaningful biological conclusions are to be drawn. For example, a range of traditional statistical and computational pathway analysis approaches have been used to identify over-represented processes in microarray data derived from various disease states. However, most of these approaches tend not to exploit the full spectrum of gene expression data, or the various relationships and dependencies. Previously, we described a pathway enrichment analysis tool created in MATLAB that yields a Pathway Regulation Score (PRS) by considering signalling pathway topology, and the overrepresentation and magnitude of differentially-expressed genes (J Comput Biol 19:563-573, 2012). Herein, we extended this approach to include metabolic pathways, and described the use of a graphical user interface (GUI).'),
(96386, 2014, 'bmc', 'Missing value imputation in high-dimensional phenomic data: imputable or not, and how?', 'Liao, Serena G|Lin, Yan|Kang, Dongwan D|Chandra, Divay|Bon, Jessica|Kaminski, Naftali|Sciurba, Frank C|Tseng, George C', 'The Buckingham Institute for Translational Medicine, the University of Buckingham, Buckingham, MK18 1EG, UK. kenneth.langlands@buckingham.ac.uk.|The Buckingham Institute for Translational Medicine, the University of Buckingham, Buckingham, MK18 1EG, UK. kenneth.langlands@buckingham.ac.uk.|The Buckingham Institute for Translational Medicine, the University of Buckingham, Buckingham, MK18 1EG, UK. kenneth.langlands@buckingham.ac.uk.|The Buckingham Institute for Translational Medicine, the University of Buckingham, Buckingham, MK18 1EG, UK. kenneth.langlands@buckingham.ac.uk.|The Buckingham Institute for Translational Medicine, the University of Buckingham, Buckingham, MK18 1EG, UK. kenneth.langlands@buckingham.ac.uk.|The Buckingham Institute for Translational Medicine, the University of Buckingham, Buckingham, MK18 1EG, UK. kenneth.langlands@buckingham.ac.uk.|The Buckingham Institute for Translational Medicine, the University of Buckingham, Buckingham, MK18 1EG, UK. kenneth.langlands@buckingham.ac.uk.|The Buckingham Institute for Translational Medicine, the University of Buckingham, Buckingham, MK18 1EG, UK. kenneth.langlands@buckingham.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 39, 'Algorithms|Cluster Analysis|Computational Biology|Computer Simulation|Datasets as Topic|Epidemiologic Methods|Humans|Research Design|Software', 'Journal Article', '10.1186/s12859-014-0346-6', '25371041', 'In modern biomedical research of complex diseases, a large number of demographic and clinical variables, herein called phenomic data, are often collected and missing values (MVs) are inevitable in the data collection process. Since many downstream statistical and bioinformatics methods require complete data matrix, imputation is a common and practical solution. In high-throughput experiments such as microarray experiments, continuous intensities are measured and many mature missing value imputation methods have been developed and widely applied. Numerous methods for missing data imputation of microarray data have been developed. Large phenomic data, however, contain continuous, nominal, binary and ordinal data types, which void application of most methods. Though several methods have been developed in the past few years, not a single complete guideline is proposed with respect to phenomic missing data imputation.'),
(96387, 2014, 'bmc', 'Optimising parallel R correlation matrix calculations on gene expression data using MapReduce', 'Wang, Shicai|Pandis, Ioannis|Johnson, David|Emam, Ibrahim|Guitton, Florian|Oehmichen, Axel|Guo, Yike', 'Data Science Institute, Imperial College London, London, UK. s.wang11@imperial.ac.uk.|Data Science Institute, Imperial College London, London, UK. i.pandis@imperial.ac.uk.|Data Science Institute, Imperial College London, London, UK. david.johnson@imperial.ac.uk.|Data Science Institute, Imperial College London, London, UK. i.emam@imperial.ac.uk.|Data Science Institute, Imperial College London, London, UK. f.guitton@imperial.ac.uk.|Data Science Institute, Imperial College London, London, UK. axelfrancois.oehmichen11@imperial.ac.uk.|Data Science Institute, Imperial College London, London, UK. y.guo@imperial.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 18, 'Algorithms|Cluster Analysis|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Software', 'Evaluation Studies', '10.1186/s12859-014-0351-9', '25371114', 'High-throughput molecular profiling data has been used to improve clinical decision making by stratifying subjects based on their molecular profiles. Unsupervised clustering algorithms can be used for stratification purposes. However, the current speed of the clustering algorithms cannot meet the requirement of large-scale molecular data due to poor performance of the correlation matrix calculation. With high-throughput sequencing technologies promising to produce even larger datasets per subject, we expect the performance of the state-of-the-art statistical algorithms to be further impacted unless efforts towards optimisation are carried out. MapReduce is a widely used high performance parallel framework that can solve the problem.'),
(96388, 2014, 'bmc', 'MLGO: phylogeny reconstruction and ancestral inference from gene-order data', 'Hu, Fei|Lin, Yu|Tang, Jijun', 'Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, 300072, China. hufeiyc@gmail.com.|Department of Computer Science and Engineering, University of California, San Diego, 92093 La Jolla, CA, USA. yul280@ucsd.edu.|Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, 300072, China. jtang@ces.sc.edu.', 'China|United States|China', 48, 'Animals|Gene Order|Genome|Genomics|Likelihood Functions|Phylogeny|Software', 'Journal Article', '10.1186/s12859-014-0354-6', '25376663', 'The rapid accumulation of whole-genome data has renewed interest in the study of using gene-order data for phylogenetic analyses and ancestral reconstruction. Current software and web servers typically do not support duplication and loss events along with rearrangements.'),
(96389, 2014, 'bmc', 'Impact of T-RFLP data analysis choices on assessments of microbial community structure and dynamics', 'Fredriksson, Nils Johan|Hermansson, Malte|Wilén, Britt-Marie', 'Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. johan.fredriksson@gu.se.|Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden. malte.hermansson@cmb.gu.se.|Department of Civil and Environmental Engineering, Water Environment Technology, Chalmers University of Technology, Gothenburg, Sweden. britt-marie.wilen@chalmers.se.', 'Sweden|Sweden|Sweden', 7, 'Bacteria|DNA Fingerprinting|DNA, Bacterial|Data Interpretation, Statistical|Genes, Bacterial|Polymerase Chain Reaction|Polymorphism, Restriction Fragment Length|Reproducibility of Results', 'Journal Article', '10.1186/s12859-014-0360-8', '25381552', 'Terminal restriction fragment length polymorphism (T-RFLP) analysis is a common DNA-fingerprinting technique used for comparisons of complex microbial communities. Although the technique is well established there is no consensus on how to treat T-RFLP data to achieve the highest possible accuracy and reproducibility. This study focused on two critical steps in the T-RFLP data treatment: the alignment of the terminal restriction fragments (T-RFs), which enables comparisons of samples, and the normalization of T-RF profiles, which adjusts for differences in signal strength, total fluorescence, between samples.'),
(96390, 2014, 'bmc', 'Tools for T-RFLP data analysis using Excel', 'Fredriksson, Nils Johan|Hermansson, Malte|Wilén, Britt-Marie', 'Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. johan.fredriksson@gu.se.|Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. johan.fredriksson@gu.se.|Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. johan.fredriksson@gu.se.', 'Sweden|Sweden|Sweden', 8, 'Bacteria|Polymorphism, Restriction Fragment Length|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-014-0361-7', '25388093', 'Terminal restriction fragment length polymorphism (T-RFLP) analysis is a DNA-fingerprinting method that can be used for comparisons of the microbial community composition in a large number of samples. There is no consensus on how T-RFLP data should be treated and analyzed before comparisons between samples are made, and several different approaches have been proposed in the literature. The analysis of T-RFLP data can be cumbersome and time-consuming, and for large datasets manual data analysis is not feasible. The currently available tools for automated T-RFLP analysis, although valuable, offer little flexibility, and few, if any, options regarding what methods to use. To enable comparisons and combinations of different data treatment methods an analysis template and an extensive collection of macros for T-RFLP data analysis using Microsoft Excel were developed.'),
(96391, 2014, 'bmc', 'A meta-learning approach for B-cell conformational epitope prediction', 'Hu, Yuh-Jyh|Lin, Shun-Chien|Lin, Yu-Lung|Lin, Kuan-Hui|You, Shun-Ning', 'Department of Computer Science, National Chiao Tung University, 1001 University Rd,, Hsinchu, Taiwan. yhu@cs.nctu.edu.tw.|Department of Computer Science, National Chiao Tung University, 1001 University Rd,, Hsinchu, Taiwan. yhu@cs.nctu.edu.tw.|Department of Computer Science, National Chiao Tung University, 1001 University Rd,, Hsinchu, Taiwan. yhu@cs.nctu.edu.tw.|Department of Computer Science, National Chiao Tung University, 1001 University Rd,, Hsinchu, Taiwan. yhu@cs.nctu.edu.tw.|Department of Computer Science, National Chiao Tung University, 1001 University Rd,, Hsinchu, Taiwan. yhu@cs.nctu.edu.tw.', '-|-|-|-|-', 0, 'Algorithms|Artificial Intelligence|B-Lymphocytes|Computer Simulation|Epitopes, B-Lymphocyte|Models, Biological|Molecular Conformation', 'Journal Article', '10.1186/s12859-014-0378-y', '25403375', 'One of the major challenges in the field of vaccine design is identifying B-cell epitopes in continuously evolving viruses. Various tools have been developed to predict linear or conformational epitopes, each relying on different physicochemical properties and adopting distinct search strategies. We propose a meta-learning approach for epitope prediction based on stacked and cascade generalizations. Through meta learning, we expect a meta learner to be able integrate multiple prediction models, and outperform the single best-performing model. The objective of this study is twofold: (1) to analyze the complementary predictive strengths in different prediction tools, and (2) to introduce a generic computational model to exploit the synergy among various prediction tools. Our primary goal is not to develop any particular classifier for B-cell epitope prediction, but to advocate the feasibility of meta learning to epitope prediction. With the flexibility of meta learning, the researcher can construct various meta classification hierarchies that are applicable to epitope prediction in different protein domains.'),
(96392, 2014, 'bmc', 'Anatomy of enzyme channels', 'Pravda, Lukxc3xa1xc5xa1|Berka, Karel|Svobodovxc3xa1 Vaxc5x99ekovxc3xa1, Radka|Sehnal, David|Banxc3xa1xc5xa1, Pavel|Laskowski, Roman A|Koxc4x8da, Jaroslav|Otyepka, Michal', 'National Centre for Biomolecular Research, Faculty of Science and CEITEC, Central European Institute of Technology, Masaryk University Brno, Kamenice 5, Brno-Bohunice, 625 00, Czech Republic. xpravda@ncbr.muni.cz.|Department of Physical Chemistry, Regional Centre of Advanced Technologies and Materials, Faculty of Science, Palackxc3xbd University Olomouc, txc5x99. 17. listopadu 12, Olomouc, 771 46, Czech Republic. karel.berka@upol.cz.|National Centre for Biomolecular Research, Faculty of Science and CEITEC, Central European Institute of Technology, Masaryk University Brno, Kamenice 5, Brno-Bohunice, 625 00, Czech Republic. radka.svobodova@ceitec.muni.cz.|National Centre for Biomolecular Research, Faculty of Science and CEITEC, Central European Institute of Technology, Masaryk University Brno, Kamenice 5, Brno-Bohunice, 625 00, Czech Republic. david.sehnal@mail.muni.cz.|Department of Physical Chemistry, Regional Centre of Advanced Technologies and Materials, Faculty of Science, Palackxc3xbd University Olomouc, txc5x99. 17. listopadu 12, Olomouc, 771 46, Czech Republic. pavel.banas@upol.cz.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. roman@ebi.ac.uk.|National Centre for Biomolecular Research, Faculty of Science and CEITEC, Central European Institute of Technology, Masaryk University Brno, Kamenice 5, Brno-Bohunice, 625 00, Czech Republic. jaroslav.koca@ceitec.muni.cz.|Department of Physical Chemistry, Regional Centre of Advanced Technologies and Materials, Faculty of Science, Palackxc3xbd University Olomouc, txc5x99. 17. listopadu 12, Olomouc, 771 46, Czech Republic. michal.otyepka@upol.cz.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Amino Acids|Catalytic Domain|Enzymes|Humans|Ion Channels|Models, Molecular|Protein Conformation', 'Journal Article', '10.1186/s12859-014-0379-x', '25403510', 'Enzyme active sites can be connected to the exterior environment by one or more channels passing through the protein. Despite our current knowledge of enzyme structure and function, surprisingly little is known about how often channels are present or about any structural features such channels may have in common.'),
(96393, 2014, 'bmc', 'Using phylogenetically-informed annotation (PIA) to search for light-interacting genes in transcriptomes from non-model organisms', 'Speiser, Daniel I|Pankey, M Sabrina|Zaharoff, Alexander K|Battelle, Barbara A|Bracken-Grissom, Heather D|Breinholt, Jesse W|Bybee, Seth M|Cronin, Thomas W|Garm, Anders|Lindgren, Annie R|Patel, Nipam H|Porter, Megan L|Protas, Meredith E|Rivera, Ajna S|Serb, Jeanne M|Zigler, Kirk S|Crandall, Keith A|Oakley, Todd H', 'Department of Ecology, Evolution, and Marine Biology, University of California Santa Barbara, Santa Barbara, CA, USA. speiser@mailbox.sc.edu.|Department of Ecology, Evolution, and Marine Biology, University of California Santa Barbara, Santa Barbara, CA, USA. abrinas@gmail.com.|Department of Ecology, Evolution, and Marine Biology, University of California Santa Barbara, Santa Barbara, CA, USA. sashazaharoff@gmail.com.|The Whitney Laboratory for Marine Bioscience, University of Florida, St. Augustine, FL, USA. Battelle@whitney.ufl.edu.|Department of Biological Sciences, Florida International University-Biscayne Bay Campus, North Miami, FL, USA. hbracken@fiu.edu.|Florida Museum of Natural History, University of Florida, Gainesville, FL, USA. jessebreinholt@gmail.com.|Department of Biology, Brigham Young University, Provo, UT, USA. seth.bybee@gmail.com.|Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, USA. cronin@umbc.edu.|Department of Biology, Marine Biological Section, University of Copenhagen, Copenhagen, Denmark. algarm@bio.ku.dk.|Department of Biology, Portland State University, Portland, OR, USA. arl3@pdx.edu.|Department of Molecular and Cell Biology & Department of Integrative Biology, University of California, Berkeley, CA, USA. nipam@uclink.berkeley.edu.|Department of Biology, University of South Dakota, Vermillion, SD, USA. Megan.Porter@usd.edu.|Department of Natural Sciences and Mathematics, Dominican University of California, San Rafael, CA, USA. meredith.protas@dominican.edu.|Department of Biology, University of the Pacific, Stockton, CA, USA. arivera@pacific.edu.|Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA. serb@iastate.edu.|Department of Biology, Sewanee: The University of the South, Sewanee, TN, USA. kzigler@sewanee.edu.|Computational Biology Institute, George Washington University, Ashburn, VA, USA. kcrandall@gwu.edu.|Department of Ecology, Evolution, and Marine Biology, University of California Santa Barbara, Santa Barbara, CA, USA. todd.oakley@lifesci.ucsb.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|Denmark|United States|United States|United States|Dominica|United States|United States|United States|United States|United States', 38, 'Algorithms|Animals|Eye Proteins|Genome|High-Throughput Nucleotide Sequencing|Light|Likelihood Functions|Molecular Sequence Annotation|Phylogeny|Sequence Analysis, Protein|Transcriptome|Vision, Ocular', 'Journal Article', '10.1186/s12859-014-0350-x', '25407802', 'Tools for high throughput sequencing and de novo assembly make the analysis of transcriptomes (i.e. the suite of genes expressed in a tissue) feasible for almost any organism. Yet a challenge for biologists is that it can be difficult to assign identities to gene sequences, especially from non-model organisms. Phylogenetic analyses are one useful method for assigning identities to these sequences, but such methods tend to be time-consuming because of the need to re-calculate trees for every gene of interest and each time a new data set is analyzed. In response, we employed existing tools for phylogenetic analysis to produce a computationally efficient, tree-based approach for annotating transcriptomes or new genomes that we term Phylogenetically-Informed Annotation (PIA), which places uncharacterized genes into pre-calculated phylogenies of gene families.'),
(96394, 2014, 'bmc', 'NeatFreq: reference-free data reduction and coverage normalization for De Novo sequence assembly', 'McCorrison, Jamison M|Venepally, Pratap|Singh, Indresh|Fouts, Derrick E|Lasken, Roger S|Methé, Barbara A', 'Informatics Core Services, The J. Craig Venter Institute (JCVI), 9704 Medical Center Drive, Rockville, MD, 20850, USA. jmccorri@jcvi.org.|Informatics Core Services, The J. Craig Venter Institute (JCVI), 9704 Medical Center Drive, Rockville, MD, 20850, USA. pratap@jcvi.org.|Informatics Core Services, The J. Craig Venter Institute (JCVI), 9704 Medical Center Drive, Rockville, MD, 20850, USA. isingh@jcvi.org.|Department of Genomic Medicine, The J. Craig Venter Institute (JCVI), 9704 Medical Center Drive, Rockville, MD, 20850, USA. dfouts@jcvi.org.|Department of Microbial & Environmental Genomics, The J. Craig Venter Institute (JCVI), 9704 Medical Center Drive, Rockville, MD20850, USA. rlasken@jcvi.org.|Department of Genomic Medicine, The J. Craig Venter Institute (JCVI), 9704 Medical Center Drive, Rockville, MD, 20850, USA. bmethe@jcvi.org.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Genomics|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-014-0357-3', '25407910', 'Deep shotgun sequencing on next generation sequencing (NGS) platforms has contributed significant amounts of data to enrich our understanding of genomes, transcriptomes, amplified single-cell genomes, and metagenomes. However, deep coverage variations in short-read data sets and high sequencing error rates of modern sequencers present new computational challenges in data interpretation, including mapping and de novo assembly. New lab techniques such as multiple displacement amplification (MDA) of single cells and sequence independent single primer amplification (SISPA) allow for sequencing of organisms that cannot be cultured, but generate highly variable coverage due to amplification biases.'),
(96395, 2014, 'bmc', 'Vindel: a simple pipeline for checking indel redundancy', 'Li, Zhiyi|Wu, Xiaowei|He, Bin|Zhang, Liqing', 'Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA. zli04@vt.edu.|Department of Statistics, Virginia Tech, Blacksburg, VA, 24061, USA. xwwu@vt.edu.|Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA. binhe@vt.edu.|Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA. lqzhang@vt.edu.', 'United States|United States|United States|United States', 5, 'Algorithms|Genome|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-014-0359-1', '25407965', 'With the advance of next generation sequencing (NGS) technologies, a large number of insertion and deletion (indel) variants have been identified in human populations. Despite much research into variant calling, it has been found that a non-negligible proportion of the identified indel variants might be false positives due to sequencing errors, artifacts caused by ambiguous alignments, and annotation errors.'),
(96396, 2014, 'bmc', 'Rapid evaluation and quality control of next generation sequencing data with FaQCs', 'Lo, Chien-Chi|Chain, Patrick S G', 'Bioenergy and Biome Sciences Group, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. chienchi@lanl.gov.|Bioenergy and Biome Sciences Group, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. pchain@lanl.gov.', 'United States|United States', 65, 'Algorithms|Computational Biology|Genes, Bacterial|Genomics|High-Throughput Nucleotide Sequencing|Polymorphism, Single Nucleotide|Quality Control|Research Design|Software', 'Journal Article', '10.1186/s12859-014-0366-2', '25408143', 'Next generation sequencing (NGS) technologies that parallelize the sequencing process and produce thousands to millions, or even hundreds of millions of sequences in a single sequencing run, have revolutionized genomic and genetic research. Because of the vagaries of any platforms sequencing chemistry, the experimental processing, machine failure, and so on, the quality of sequencing reads is never perfect, and often declines as the read is extended. These errors invariably affect downstream analysis/application and should therefore be identified early on to mitigate any unforeseen effects.'),
(96397, 2014, 'bmc', 'TE-Tracker: systematic identification of transposition events through whole-genome resequencing', 'Gilly, Arthur|Etcheverry, Mathilde|Madoui, Mohammed-Amin|Guy, Julie|Quadrana, Leandro|Alberti, Adriana|Martin, Antoine|Heitkam, Tony|Engelen, Stefan|Labadie, Karine|Le Pen, Jeremie|Wincker, Patrick|Colot, Vincent|Aury, Jean-Marc', 'Commissariat a lEnergie Atomique (CEA), Institut de Genomique (IG), Genoscope, 2 rue Gaston Crémieux, BP5706, 91057, Evry, France. ag15@sanger.ac.uk.|Institut de Biologie de lEcole Normale Supérieure, F-75230, Paris, Cedex 05, France. etcheverry.mathilde@biologie.ens.fr.|Commissariat a lEnergie Atomique (CEA), Institut de Genomique (IG), Genoscope, 2 rue Gaston Crémieux, BP5706, 91057, Evry, France. amadoui@genoscope.cns.fr.|Commissariat a lEnergie Atomique (CEA), Institut de Genomique (IG), Genoscope, 2 rue Gaston Crémieux, BP5706, 91057, Evry, France. jguy@genoscope.cns.fr.|Institut de Biologie de lEcole Normale Supérieure, F-75230, Paris, Cedex 05, France. quadrana@biologie.ens.fr.|Commissariat a lEnergie Atomique (CEA), Institut de Genomique (IG), Genoscope, 2 rue Gaston Crémieux, BP5706, 91057, Evry, France. aalberti@genoscope.cns.fr.|Institut de Biologie de lEcole Normale Supérieure, F-75230, Paris, Cedex 05, France. Antoine.martin@supagro.inra.fr.|Institut de Biologie de lEcole Normale Supérieure, F-75230, Paris, Cedex 05, France. tony.heitkam@tu-dresden.de.|Commissariat a lEnergie Atomique (CEA), Institut de Genomique (IG), Genoscope, 2 rue Gaston Crémieux, BP5706, 91057, Evry, France. sengelen@genoscope.cns.fr.|Commissariat a lEnergie Atomique (CEA), Institut de Genomique (IG), Genoscope, 2 rue Gaston Crémieux, BP5706, 91057, Evry, France. klabadie@genoscope.cns.fr.|Institut de Biologie de lEcole Normale Supérieure, F-75230, Paris, Cedex 05, France. jl572@cam.ac.uk.|Commissariat a lEnergie Atomique (CEA), Institut de Genomique (IG), Genoscope, 2 rue Gaston Crémieux, BP5706, 91057, Evry, France. pwincker@genoscope.cns.fr.|Institut de Biologie de lEcole Normale Supérieure, F-75230, Paris, Cedex 05, France. colot@biologie.ens.fr.|Commissariat a lEnergie Atomique (CEA), Institut de Genomique (IG), Genoscope, 2 rue Gaston Crémieux, BP5706, 91057, Evry, France. jmaury@genoscope.cns.fr.', 'France|France|France|France|France|France|France|France|France|France|France|France|France|France', 0, 'Arabidopsis|DNA Methylation|DNA Transposable Elements|Genes, Plant|Genome, Plant|High-Throughput Nucleotide Sequencing|Humans|Software', 'Journal Article', '10.1186/s12859-014-0377-z', '25408240', 'Transposable elements (TEs) are DNA sequences that are able to move from their location in the genome by cutting or copying themselves to another locus. As such, they are increasingly recognized as impacting all aspects of genome function. With the dramatic reduction in cost of DNA sequencing, it is now possible to resequence whole genomes in order to systematically characterize novel TE mobilization in a particular individual. However, this task is made difficult by the inherently repetitive nature of TE sequences, which in some eukaryotes compose over half of the genome sequence. Currently, only a few software tools dedicated to the detection of TE mobilization using next-generation-sequencing are described in the literature. They often target specific TEs for which annotation is available, and are only able to identify families of closely related TEs, rather than individual elements.'),
(96398, 2014, 'bmc', 'Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVM', 'Li, Liqi|Yu, Sanjiu|Xiao, Weidong|Li, Yongsheng|Huang, Lan|Zheng, Xiaoqi|Zhou, Shiwen|Yang, Hua', 'Commissariat a lEnergie Atomique (CEA), Institut de Genomique (IG), Genoscope, 2 rue Gaston Crémieux, BP5706, 91057, Evry, France. jmaury@genoscope.cns.fr.|Commissariat a lEnergie Atomique (CEA), Institut de Genomique (IG), Genoscope, 2 rue Gaston Crémieux, BP5706, 91057, Evry, France. jmaury@genoscope.cns.fr.|Commissariat a lEnergie Atomique (CEA), Institut de Genomique (IG), Genoscope, 2 rue Gaston Crémieux, BP5706, 91057, Evry, France. jmaury@genoscope.cns.fr.|Commissariat a lEnergie Atomique (CEA), Institut de Genomique (IG), Genoscope, 2 rue Gaston Crémieux, BP5706, 91057, Evry, France. jmaury@genoscope.cns.fr.|Commissariat a lEnergie Atomique (CEA), Institut de Genomique (IG), Genoscope, 2 rue Gaston Crémieux, BP5706, 91057, Evry, France. jmaury@genoscope.cns.fr.|Department of General Surgery, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China. xqzheng@shnu.edu.cn.|Department of General Surgery, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China. xqzheng@shnu.edu.cn.|Department of General Surgery, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China. xqzheng@shnu.edu.cn.', 'France|France|France|France|France|China|China|China', 21, 'DNA|Nucleic Acids|Recombination, Genetic|Software|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-15-340', '25409550', 'Identification of the recombination hot/cold spots is critical for understanding the mechanism of recombination as well as the genome evolution process. However, experimental identification of recombination spots is both time-consuming and costly. Developing an accurate and automated method for reliably and quickly identifying recombination spots is thus urgently needed.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96399, 2014, 'bmc', 'A ratiometric-based measure of gene co-expression', 'Abelin, Anna C T|Marinov, Georgi K|Williams, Brian A|McCue, Kenneth|Wold, Barbara J', 'Department of General Surgery, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China. xqzheng@shnu.edu.cn.|Department of General Surgery, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China. xqzheng@shnu.edu.cn.|Department of General Surgery, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China. xqzheng@shnu.edu.cn.|Department of General Surgery, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China. xqzheng@shnu.edu.cn.|Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Blvd, Pasadena, CA 91125, USA. woldb@caltech.edu.', 'China|China|China|China|United States', 0, 'B-Lymphocytes|Cell Line, Transformed|Gene Expression Profiling|Genetic Association Studies|Human Genome Project|Humans|Sequence Analysis, RNA|Single-Cell Analysis', 'Journal Article', '10.1186/1471-2105-15-331', '25411051', 'Gene co-expression analysis has previously been based on measures that include correlation coefficients and mutual information, as well as newcomers such as MIC. These measures depend primarily on the degree of association between the RNA levels of two genes and to a lesser extent on their variability. They focus on the similarity of expression value trajectories that change in like manner across samples. However there are relationships of biological interest for which these classical measures are expected to be insensitive. These include genes whose expression levels are ratiometrically stable and genes whose variance is tightly constrained. Large-scale studies of relatively homogeneous samples, including single cell RNA-seq, are experimental settings in which such relationships might be especially pertinent.'),
(96400, 2014, 'bmc', 'A framework for generalized subspace pattern mining in high-dimensional datasets', 'Curry, Edward W J', 'Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Blvd, Pasadena, CA 91125, USA. woldb@caltech.edu.', 'United States', 1, 'Algorithms|Cluster Analysis|DNA Methylation|Female|Gene Expression Profiling|Genome-Wide Association Study|Humans|Ovarian Neoplasms|Software', 'Journal Article', '10.1186/s12859-014-0355-5', '25413436', 'A generalized notion of biclustering involves the identification of patterns across subspaces within a data matrix. This approach is particularly well-suited to analysis of heterogeneous molecular biology datasets, such as those collected from populations of cancer patients. Different definitions of biclusters will offer different opportunities to discover information from datasets, making it pertinent to tailor the desired patterns to the intended application. This paper introduces GABi, a customizable framework for subspace pattern mining suited to large heterogeneous datasets. Most existing biclustering algorithms discover biclusters of only a few distinct structures. However, by enabling definition of arbitrary bicluster models, the GABi framework enables the application of biclustering to tasks for which no existing algorithm could be used.'),
(96401, 2014, 'bmc', 'A fast algorithm for determining bounds and accurate approximate p-values of the rank product statistic for replicate experiments', 'Heskes, Tom|Eisinga, Rob|Breitling, Rainer', 'Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands. t.heskes@science.ru.nl.|Department of Social Science Research Methods, Radboud University Nijmegen, Nijmegen, The Netherlands. r.eisinga@maw.ru.nl.|Manchester Institute of Biotechnology, Faculty of Life Sciences, University of Manchester, Manchester, UK. rainer.breitling@manchester.ac.uk.', 'Netherlands|Netherlands|United Kingdom', 0, 'Aging|Algorithms|Biomarkers|Databases, Genetic|Gene Expression Profiling|Humans|Probability', 'Journal Article', '10.1186/s12859-014-0367-1', '25413493', 'The rank product method is a powerful statistical technique for identifying differentially expressed molecules in replicated experiments. A critical issue in molecule selection is accurate calculation of the p-value of the rank product statistic to adequately address multiple testing. Both exact calculation and permutation and gamma approximations have been proposed to determine molecule-level significance. These current approaches have serious drawbacks as they are either computationally burdensome or provide inaccurate estimates in the tail of the p-value distribution.'),
(96402, 2014, 'bmc', 'Bayesian neural networks for detecting epistasis in genetic association studies', 'Beam, Andrew L|Motsinger-Reif, Alison|Doyle, Jon', 'Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA. Andrew_Beam@hms.harvard.edu.|Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA. alison_motsinger@ncsu.edu.|Department of Computer Science, North Carolina State University, Raleigh, NC, USA. Jon_Doyle@ncsu.edu.', 'United States|United States|United States', 18, 'Bayes Theorem|Case-Control Studies|Computational Biology|Epistasis, Genetic|Genetic Association Studies|Humans|Models, Genetic|Mycobacterium|Neural Networks (Computer)|Polymorphism, Single Nucleotide|Tuberculosis', 'Comparative Study', '10.1186/s12859-014-0368-0', '25413600', 'Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions.'),
(96403, 2014, 'bmc', 'Computational approach for calculating the probability of eukaryotic translation initiation from ribo-seq data that takes into account leaky scanning', 'Michel, Audrey M|Andreev, Dmitry E|Baranov, Pavel V', 'Department of Computer Science, North Carolina State University, Raleigh, NC, USA. Jon_Doyle@ncsu.edu.|Department of Computer Science, North Carolina State University, Raleigh, NC, USA. Jon_Doyle@ncsu.edu.|Department of Computer Science, North Carolina State University, Raleigh, NC, USA. Jon_Doyle@ncsu.edu.', 'United States|United States|United States', 0, 'Animals|Cells, Cultured|Codon, Initiator|Computational Biology|Genetic Techniques|Peptide Chain Initiation, Translational|Probability|Protein Biosynthesis|RNA, Messenger|Ribosomes', 'Journal Article', '10.1186/s12859-014-0380-4', '25413677', 'Ribosome profiling (ribo-seq) provides experimental data on the density of elongating or initiating ribosomes at the whole transcriptome level that can be potentially used for estimating absolute levels of translation initiation at individual Translation Initiation Sites (TISs). These absolute levels depend on the mutual organisation of TISs within individual mRNAs. For example, according to the leaky scanning model of translation initiation in eukaryotes, a strong TIS downstream of another strong TIS is unlikely to be productive, since only a few scanning ribosomes would be able to reach the downstream TIS. In order to understand the dependence of translation initiation efficiency on the surrounding nucleotide context, it is important to estimate the strength of TISs independently of their mutual organisation, i.e. to estimate with what probability a ribosome would initiate at a particular TIS.'),
(96404, 2014, 'bmc', 'ANGSD: Analysis of Next Generation Sequencing Data', 'Korneliussen, Thorfinn Sand|Albrechtsen, Anders|Nielsen, Rasmus', 'Centre for GeoGenetics, Natural History Museum of Denmark, Copenhagen, Denmark. thorfinn@binf.ku.dk.|Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaloes Vej 5, Copenhagen, DK-2200, Denmark. albrecht@binf.ku.dk.|Centre for GeoGenetics, Natural History Museum of Denmark, Copenhagen, Denmark. rasmus_nielsen@berkeley.edu.', 'Denmark|Denmark|Denmark', 0, 'Gene Frequency|Genetics, Population|Genotype|High-Throughput Nucleotide Sequencing|Likelihood Functions|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/s12859-014-0356-4', '25420514', 'High-throughput DNA sequencing technologies are generating vast amounts of data. Fast, flexible and memory efficient implementations are needed in order to facilitate analyses of thousands of samples simultaneously.'),
(96405, 2014, 'bmc', 'A statistical approach for 5 splice site prediction using short sequence motifs and without encoding sequence data', 'Meher, Prabina Kumar|Sahu, Tanmaya Kumar|Rao, Atmakuri Ramakrishna|Wahi, Sant Dass', 'Division of Statistical Genetics, Indian Agricultural Statistics Research Institute, New Delhi, 110012, India. meherprabin@yahoo.com.|Centre for Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, New Delhi, 110012, India. tanmayabioinfo@gmail.com.|Centre for Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, New Delhi, 110012, India. rao.cshl.work@gmail.com.|Division of Statistical Genetics, Indian Agricultural Statistics Research Institute, New Delhi, 110012, India. sdwahi@iasri.res.in.', 'India|India|India|India', 13, 'Algorithms|Artificial Intelligence|Base Sequence|Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Models, Statistical|Molecular Sequence Data|Nucleotide Motifs|RNA Splice Sites|RNA Splicing|ROC Curve|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-014-0362-6', '25420551', 'Most of the approaches for splice site prediction are based on machine learning techniques. Though, these approaches provide high prediction accuracy, the window lengths used are longer in size. Hence, these approaches may not be suitable to predict the novel splice variants using the short sequence reads generated from next generation sequencing technologies. Further, machine learning techniques require numerically encoded data and produce different accuracy with different encoding procedures. Therefore, splice site prediction with short sequence motifs and without encoding sequence data became a motivation for the present study.'),
(96406, 2014, 'bmc', 'PhenUMA: a tool for integrating the biomedical relationships among genes and diseases', 'Rodríguez-López, Rocío|Reyes-Palomares, Armando|Sánchez-Jiménez, Francisca|Medina, Miguel Ángel', 'Departamento de Biología Molecular y Bioquímica, Universidad de Málaga, Andalucía Tech, Facultad de Ciencias, and IBIMA (Biomedical Research Institute of Málaga), Málaga, Spain. rorodriguez@uma.es.|Departamento de Biología Molecular y Bioquímica, Universidad de Málaga, Andalucía Tech, Facultad de Ciencias, and IBIMA (Biomedical Research Institute of Málaga), Málaga, Spain. armando@uma.es.|Departamento de Biología Molecular y Bioquímica, Universidad de Málaga, Andalucía Tech, Facultad de Ciencias, and IBIMA (Biomedical Research Institute of Málaga), Málaga, Spain. kika@uma.es.|Departamento de Biología Molecular y Bioquímica, Universidad de Málaga, Andalucía Tech, Facultad de Ciencias, and IBIMA (Biomedical Research Institute of Málaga), Málaga, Spain. medina@uma.es.', 'Spain|Spain|Spain|Spain', 5, 'Amino Acid Metabolism, Inborn Errors|Databases, Factual|Developmental Disabilities|Disease|Gene Regulatory Networks|Genes|Genomics|Humans|Internet|Models, Biological|Phenotype|Software|Succinate-Semialdehyde Dehydrogenase|Systems Integration', 'Journal Article', '10.1186/s12859-014-0375-1', '25420641', 'Several types of genetic interactions in humans can be directly or indirectly associated with the causal effects of mutations. These interactions are usually based on their co-associations to biological processes, coexistence in cellular locations, coexpression in cell lines, physical interactions and so on. In addition, pathological processes can present similar phenotypes that have mutations either in the same genomic location or in different genomic regions. Therefore, integrative resources for all of these complex interactions can help us prioritize the relationships between genes and diseases that are most deserving to be studied by researchers and physicians.'),
(96407, 2014, 'bmc', 'A simple peak detection and label-free quantitation algorithm for chromatography-mass spectrometry', 'Aoshima, Ken|Takahashi, Kentaro|Ikawa, Masayuki|Kimura, Takayuki|Fukuda, Mitsuru|Tanaka, Satoshi|Parry, Howell E|Fujita, Yuichiro|Yoshizawa, Akiyasu C|Utsunomiya, Shin-Ichi|Kajihara, Shigeki|Tanaka, Koichi|Oda, Yoshiya', 'Eisai Co., Ltd., Tsukuba, Ibaraki, 300-2635, Japan. k3-aoshima@hhc.eisai.co.jp.|Eisai Co., Ltd., Tsukuba, Ibaraki, 300-2635, Japan. k20-takahashi@hhc.eisai.co.jp.|iBio Tech Co., Ltd., Tsukuba, Ibaraki, 305-0031, Japan. m_ikawa@ibio-tech.com.|Eisai Co., Ltd., Tsukuba, Ibaraki, 300-2635, Japan. t4-kimura@hhc.eisai.co.jp.|iBio Tech Co., Ltd., Tsukuba, Ibaraki, 305-0031, Japan. m_fukuda@ibio-tech.com.|Shimadzu Corporation, Kyoto, 604-8511, Japan. satstnka@shimadzu.co.jp.|Shimadzu Corporation, Kyoto, 604-8511, Japan. howell.parry@thermofisher.com.|Shimadzu Corporation, Kyoto, 604-8511, Japan. yfujita@shimadzu.co.jp.|Shimadzu Corporation, Kyoto, 604-8511, Japan. acyshzw@shimadzu.co.jp.|Shimadzu Corporation, Kyoto, 604-8511, Japan. utu@shimadzu.co.jp.|Shimadzu Corporation, Kyoto, 604-8511, Japan. kajihara@shimadzu.co.jp.|Shimadzu Corporation, Kyoto, 604-8511, Japan. koichi-t@shimadzu.co.jp.|Eisai Co., Ltd., Tsukuba, Ibaraki, 300-2635, Japan. y-oda@hhc.eisai.co.jp.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Animals|Cattle|Chromatography, Liquid|Databases, Protein|HeLa Cells|Humans|Mass Spectrometry|Peptide Fragments|Proteins|Proteome|Proteomics|Serum Albumin, Bovine|Software', 'Journal Article', '10.1186/s12859-014-0376-0', '25420746', 'Label-free quantitation of mass spectrometric data is one of the simplest and least expensive methods for differential expression profiling of proteins and metabolites. The need for high accuracy and performance computational label-free quantitation methods is still high in the biomarker and drug discovery research field. However, recent most advanced types of LC-MS generate huge amounts of analytical data with high scan speed, high accuracy and resolution, which is often impossible to interpret manually. Moreover, there are still issues to be improved for recent label-free methods, such as how to reduce false positive/negatives of the candidate peaks, how to expand scalability and how to enhance and automate data processing. AB3D (A simple label-free quantitation algorithm for Biomarker Discovery in Diagnostics and Drug discovery using LC-MS) has addressed these issues and has the capability to perform label-free quantitation using MS1 for proteomics study.'),
(96408, 2014, 'bmc', 'Correspondence regarding Zhong et al, BMC Bioinformatics 2013 Mar 7,14:89', 'Kuhn, Alexandre', 'Microfluidics Systems Biology Lab, Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Proteos Building, Room #03-04, 61 Biopolis Drive, Singapore 138673, Singapore. alexandre.m.kuhn@gmail.com.', 'Singapore', 0, 'Algorithms|Gene Expression Profiling', 'Letter', '10.1186/s12859-014-0347-5', '25431099', 'Computational expression deconvolution aims to estimate the contribution of individual cell populations to expression profiles measured in samples of heterogeneous composition. Zhong et al. recently proposed Digital Sorting Algorithm (BMC Bioinformatics 2013 Mar 7,14:89) and showed that they could accurately estimate population-specific expression levels and expression differences between two populations. They compared DSA with Population-Specific Expression Analysis (PSEA), a previous deconvolution method that we developed to detect expression changes occurring within the same population between two conditions (e.g. disease versus non-disease). However, Zhong et al. compared PSEA-derived specific expression levels across different cell populations. Specific expression levels obtained with PSEA cannot be directly compared across different populations as they are on a relative scale. They are accurate as we demonstrate by deconvolving the same dataset used by Zhong et al. and, importantly, allow for comparison of population-specific expression across conditions. '),
(96409, 2014, 'bmc', 'PyTMs: a useful PyMOL plugin for modeling common post-translational modifications', 'Warnecke, Andreas|Sandalova, Tatyana|Achour, Adnane|Harris, Robert A', 'Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Applied Immunology & Immunotherapy, L8:04, Karolinska Hospital, SE-171 76, Stockholm, Sweden. andreas.warnecke@ki.se.|Department of Medicine Solna, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden. Tatyana.Sandalova@ki.se.|Department of Medicine Solna, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden. Adnane.Achour@ki.se.|Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Applied Immunology & Immunotherapy, L8:04, Karolinska Hospital, SE-171 76, Stockholm, Sweden. robert.harris@ki.se.', 'Sweden|Sweden|Sweden|Sweden', 0, 'Animals|Cattle|Ether-A-Go-Go Potassium Channels|Humans|Peptide Fragments|Protein Conformation|Protein Processing, Post-Translational|Proteins|Serum Albumin, Bovine|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-014-0370-6', '25431162', 'Post-translational modifications (PTMs) constitute a major aspect of protein biology, particularly signaling events. Conversely, several different pathophysiological PTMs are hallmarks of oxidative imbalance or inflammatory states and are strongly associated with pathogenesis of autoimmune diseases or cancers. Accordingly, it is of interest to assess both the biological and structural effects of modification. For the latter, computer-based modeling offers an attractive option. We thus identified the need for easily applicable modeling options for PTMs.'),
(96410, 2014, 'bmc', 'An efficient algorithm to explore liquid association on a genome-wide scale', 'Gunderson, Tina|Ho, Yen-Yi', 'Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St. S.E., MMC 303, Minneapolis, 55455, MN, USA. gunderson.tina@gmail.com.|Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St. S.E., MMC 303, Minneapolis, 55455, MN, USA. yho@umn.edu.', 'United States|United States', 0, 'Algorithms|Cell Cycle|Cell Cycle Proteins|Data Interpretation, Statistical|Gene Expression|Gene Expression Profiling|Genome, Fungal|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/s12859-014-0371-5', '25431229', 'The growing wealth of public available gene expression data has made the systemic studies of how genes interact in a cell become more feasible. Liquid association (LA) describes the extent to which coexpression of two genes may vary based on the expression level of a third gene (the controller gene). However, genome-wide application has been difficult and resource-intensive. We propose a new screening algorithm for more efficient processing of LA estimation on a genome-wide scale and apply its use to a Saccharomyces cerevisiae data set.'),
(96411, 2014, 'bmc', 'CAR: contig assembly of prokaryotic draft genomes using rearrangements', 'Lu, Chin Lung|Chen, Kun-Tze|Huang, Shih-Yuan|Chiu, Hsien-Tai', 'Department of Computer Science, National Tsing Hua University, Hsinchu, 300, Taiwan. cllu@cs.nthu.edu.tw.|Department of Computer Science, National Tsing Hua University, Hsinchu, 300, Taiwan. holystu@gmail.com.|Department of Computer Science, National Tsing Hua University, Hsinchu, 300, Taiwan. happyday5296@hotmail.com.|Department of Chemistry, National Cheng Kung University, Tainan City, 701, Taiwan. hchiu@mail.ncku.edu.tw.', '-|-|-|-', 0, 'Algorithms|Chromosome Mapping|Computational Biology|Contig Mapping|Gene Rearrangement|Genome|Prokaryotic Cells|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-014-0381-3', '25431302', 'Next generation sequencing technology has allowed efficient production of draft genomes for many organisms of interest. However, most draft genomes are just collections of independent contigs, whose relative positions and orientations along the genome being sequenced are unknown. Although several tools have been developed to order and orient the contigs of draft genomes, more accurate tools are still needed.'),
(96412, 2014, 'bmc', 'Meta-eQTL: a tool set for flexible eQTL meta-analysis', 'Di Narzo, Antonio Fabio|Cheng, Haoxiang|Lu, Jianwei|Hao, Ke', 'Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. antonio.dinarzo@mssm.edu.|College of Electronics and Information Engineering, CIMS Research Center, Tongji University, Shanghai, China. hxchengtj@gmail.com.|School of Software Engineering and The Advanced Institute of Translational Medicine, Tongji University, Shanghai, China. jwlu33@hotmail.com.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. ke.hao@mssm.edu.', 'United States|China|China|United States', 0, 'Chromosomes, Human, X|Diabetes Mellitus|Dual-Specificity Phosphatases|Female|Genome-Wide Association Study|Humans|Liver|Male|Meta-Analysis as Topic|Mitogen-Activated Protein Kinase Phosphatases|Models, Theoretical|Quantitative Trait Loci|Software', 'Journal Article', '10.1186/s12859-014-0392-0', '25431350', 'Increasing number of eQTL (Expression Quantitative Trait Loci) datasets facilitate genetics and systems biology research. Meta-analysis tools are in need to jointly analyze datasets of same or similar issue types to improve statistical power especially in trans-eQTL mapping. Meta-analysis framework is also necessary for ChrX eQTL discovery.'),
(96413, 2014, 'bmc', 'Comparison of classification methods that combine clinical data and high-dimensional mass spectrometry data', 'Truntzer, Caroline|Mostacci, Elise|Jeannin, Aline|Petit, Jean-Michel|Ducoroy, Patrick|Cardot, Hervé', 'Proteomic Platform CLIPP, Centre Hospitalier Universitaire, Dijon, 21000, France. caroline.truntzer@clipproteomic.fr.|Proteomic Platform CLIPP, Centre Hospitalier Universitaire, Dijon, 21000, France. elise.mostacci@clipproteomic.fr.|Proteomic Platform CLIPP, Centre Hospitalier Universitaire, Dijon, 21000, France. aline.jeannin@clipproteomic.fr.|Service Endocrinologie, Centre Hospitalier Universitaire, Dijon, 21000, France. jean-michel.petit@chu-dijon.fr.|Proteomic Platform CLIPP, Centre Hospitalier Universitaire, Dijon, 21000, France. patrick.ducoroy@clipproteomic.fr.|Institut de Mathématiques de Bourgogne, UMR CNRS 5584, Dijon, 21000, France. herve.cardot@clipproteomic.fr.', 'France|France|France|France|France|France', 3, 'Algorithms|Biomarkers|Classification|Fatty Liver|Humans|Proteomics|Sample Size|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1186/s12859-014-0385-z', '25432156', 'The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. Technologies like mass spectrometry are commonly being used in proteomic research. Mass spectrometry signals show the proteomic profiles of the individuals under study at a given time. These profiles correspond to the recording of a large number of proteins, much larger than the number of individuals. These variables come in addition to or to complete classical clinical variables. The objective of this study is to evaluate and compare the predictive ability of new and existing models combining mass spectrometry data and classical clinical variables. This study was conducted in the context of binary prediction.'),
(96414, 2014, 'bmc', 'ContextD: an algorithm to identify contextual properties of medical terms in a Dutch clinical corpus', 'Afzal, Zubair|Pons, Ewoud|Kang, Ning|Sturkenboom, Miriam C J M|Schuemie, Martijn J|Kors, Jan A', 'Department of Medical Informatics, Erasmus Medical Center, P.O. Box 2040, Rotterdam, CA, 3000, Netherlands. m.afzal@erasmusmc.nl.|Department of Medical Informatics, Erasmus Medical Center, P.O. Box 2040, Rotterdam, CA, 3000, Netherlands. e.pons@erasmusmc.nl.|Department of Medical Informatics, Erasmus Medical Center, P.O. Box 2040, Rotterdam, CA, 3000, Netherlands. n.kang@erasmusmc.nl.|Department of Medical Informatics, Erasmus Medical Center, P.O. Box 2040, Rotterdam, CA, 3000, Netherlands. m.sturkenboom@erasmusmc.nl.|Janssen Research and Development LLC, Titusville, NJ, USA. mschuemi@its.jnj.com.|Department of Medical Informatics, Erasmus Medical Center, P.O. Box 2040, Rotterdam, CA, 3000, Netherlands. j.kors@erasmusmc.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands|United States|Netherlands', 16, 'Algorithms|Databases, Factual|Humans|Medical Informatics|Medical Records Systems, Computerized|Natural Language Processing|Netherlands|Unified Medical Language System', 'Journal Article', '10.1186/s12859-014-0373-3', '25432799', 'In order to extract meaningful information from electronic medical records, such as signs and symptoms, diagnoses, and treatments, it is important to take into account the contextual properties of the identified information: negation, temporality, and experiencer. Most work on automatic identification of these contextual properties has been done on English clinical text. This study presents ContextD, an adaptation of the English ConText algorithm to the Dutch language, and a Dutch clinical corpus. We created a Dutch clinical corpus containing four types of anonymized clinical documents: entries from general practitioners, specialists letters, radiology reports, and discharge letters. Using a Dutch list of medical terms extracted from the Unified Medical Language System, we identified medical terms in the corpus with exact matching. The identified terms were annotated for negation, temporality, and experiencer properties. To adapt the ConText algorithm, we translated English trigger terms to Dutch and added several general and document specific enhancements, such as negation rules for general practitioners entries and a regular expression based temporality module.'),
(96415, 2014, 'bmc', 'CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks', 'Gillani, Zeeshan|Akash, Muhammad Sajid Hamid|Rahaman, M D Matiur|Chen, Ming', 'Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China. zeeshan_gillani100@hotmail.com.|Institute of Pharmacology, Toxicology and Biochemical Pharmaceutics, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China. sajidakash@gcuf.edu.pk.|Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China. matiur.stat@gmail.com.|Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China. mchen@zju.edu.cn.', 'China|China|China|China', 26, 'Algorithms|Computational Biology|Escherichia coli Proteins|Gene Expression Profiling|Gene Regulatory Networks|Humans|Metabolic Networks and Pathways|Saccharomyces cerevisiae Proteins|Signal Transduction|Software|Support Vector Machine|Systems Biology', 'Journal Article', '10.1186/s12859-014-0395-x', '25433465', 'Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size.'),
(96416, 2014, 'bmc', 'Computational prediction of protein interactions related to the invasion of erythrocytes by malarial parasites', 'Liu, Xuewu|Huang, Yuxiao|Liang, Jiao|Zhang, Shuai|Li, Yinghui|Wang, Jun|Shen, Yan|Xu, Zhikai|Zhao, Ya', 'Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. liu_xuewu@hotmail.com.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. celina_xiao@163.com.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. liangjiao_18@163.com.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. MACNK@163.com.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. yinghlzb@fmmu.edu.cn.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. 977753561@qq.com.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. 37762810@qq.com.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhikaixu@fmmu.edu.cn.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.', 'China|China|China|China|China|China|China|China|China', 13, 'Algorithms|Animals|Arabidopsis|Caenorhabditis elegans|Computational Biology|Drosophila melanogaster|Erythrocytes|Gene Ontology|Host-Pathogen Interactions|Humans|Malaria, Falciparum|Membrane Proteins|Mice|Microarray Analysis|Plasmodium falciparum|Protein Interaction Domains and Motifs|Protozoan Proteins|Saccharomyces cerevisiae|Schizosaccharomyces', 'Journal Article', '10.1186/s12859-014-0393-z', '25433733', 'The invasion of red blood cells (RBCs) by malarial parasites is an essential step in the life cycle of Plasmodium falciparum. Human-parasite surface protein interactions play a critical role in this process. Although several interactions between human and parasite proteins have been discovered, the mechanism related to invasion remains poorly understood because numerous human-parasite protein interactions have not yet been identified. High-throughput screening experiments are not feasible for malarial parasites due to difficulty in expressing the parasite proteins. Here, we performed computational prediction of the PPIs involved in malaria parasite invasion to elucidate the mechanism by which invasion occurs.'),
(96417, 2014, 'bmc', 'Guest editors introduction to the Proceedings of the 9th International Symposium on Biomedical Research and Applications', 'Gibas, Cynthia J|Cai, Zhipeng|Eulenstein, Oliver', 'Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.', 'China|China|China', 0, 'Biomedical Research|Computational Biology|Humans', 'Editorial', '10.1186/1471-2105-15-S13-S1', '25434555', '-'),
(96418, 2014, 'bmc', 'RiboFSM: frequent subgraph mining for the discovery of RNA structures and interactions', 'Gawronski, Alex R|Turcotte, Marcel', 'Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.', 'China|China', 3, 'Algorithms|Base Sequence|Computer Graphics|Meiotic Prophase I|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|RNA|RNA Editing|Trypanosoma brucei brucei', 'Journal Article', '10.1186/1471-2105-15-S13-S2', '25434643', 'Frequent subgraph mining is a useful method for extracting meaningful patterns from a set of graphs or a single large graph. Here, the graph represents all possible RNA structures and interactions. Patterns that are significantly more frequent in this graph over a random graph are extracted. We hypothesize that these patterns are most likely to represent biological mechanisms. The graph representation used is a directed dual graph, extended to handle intermolecular interactions. The graph is sampled for subgraphs, which are labeled using a canonical labeling method and counted. The resulting patterns are compared to those created from a randomized dataset and scored. The algorithm was applied to the mitochondrial genome of the kinetoplastid species Trypanosoma brucei, which has a unique RNA editing mechanism. The most significant patterns contain two stem-loops, indicative of gRNA, and represent interactions of these structures with target mRNA. '),
(96419, 2014, 'bmc', 'Refining discordant gene trees', 'Górecki, Pawel|Eulenstein, Oliver', 'Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.', 'China|China', 3, 'Algorithms|Biological Evolution|Gene Duplication|Models, Genetic|Phylogeny', 'Journal Article', '10.1186/1471-2105-15-S13-S3', '25434729', 'Evolutionary studies are complicated by discordance between gene trees and the species tree in which they evolved. Dealing with discordant trees often relies on comparison costs between gene and species trees, including the well-established Robinson-Foulds, gene duplication, and deep coalescence costs. While these costs have provided credible results for binary rooted gene trees, corresponding cost definitions for non-binary unrooted gene trees, which are frequently occurring in practice, are challenged by biological realism.'),
(96420, 2014, 'bmc', 'Feature selection and classifier performance on diverse bio- logical datasets', 'Hemphill, Edward|Lindsay, James|Lee, Chih|Mxc4x83ndoiu, Ion I|Nelson, Craig E', 'Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.', 'China|China|China|China|China', 0, 'Algorithms|Biomarkers, Tumor|Computational Biology|DNA Copy Number Variations|Data Mining|Databases, Factual|Humans|MicroRNAs|Models, Biological|Neoplasms|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Proteins|RNA, Messenger|Tumor Cells, Cultured', 'Journal Article', '10.1186/1471-2105-15-S13-S4', '25434802', 'There is an ever-expanding range of technologies that generate very large numbers of biomarkers for research and clinical applications. Choosing the most informative biomarkers from a high-dimensional data set, combined with identifying the most reliable and accurate classification algorithms to use with that biomarker set, can be a daunting task. Existing surveys of feature selection and classification algorithms typically focus on a single data type, such as gene expression microarrays, and rarely explore the models performance across multiple biological data types.'),
(96421, 2014, 'bmc', 'The sensitivity and significance analysis of parameters in the model of pH regulation on lactic acid production by Lactobacillus bulgaricus', 'Liu, Ke|Zeng, Xiangmiao|Qiao, Lei|Li, Xisheng|Yang, Yubo|Dai, Cuihong|Hou, Aiju|Xu, Dechang', 'Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.', 'China|China|China|China|China|China|China|China', 5, 'Algorithms|Cells, Cultured|Gene Expression Regulation|Hydrogen-Ion Concentration|Lac Operon|Lactic Acid|Lactobacillus|Membrane Transport Proteins|Promoter Regions, Genetic|Sensitivity and Specificity|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-15-S13-S5', '25434877', 'The excessive production of lactic acid by L. bulgaricus during yogurt storage is a phenomenon we are always tried to prevent. The methods used in industry either control the post-acidification inefficiently or kill the probiotics in yogurt. Genetic methods of changing the activity of one enzyme related to lactic acid metabolism make the bacteria short of energy to growth, although they are efficient ways in controlling lactic acid production.'),
(96422, 2014, 'bmc', 'Network-based modular latent structure analysis', 'Yu, Tianwei|Bai, Yun', 'Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.|Department of Pathogenic Biology, The Fourth Military Medical University, Xian, 710032, P. R. China. zhaoya@fmmu.edu.cn.', 'China|China', 2, 'Algorithms|Cell Cycle|Computer Simulation|Gene Expression Profiling|Gene Regulatory Networks|Humans|Models, Biological|Models, Statistical', 'Journal Article', '10.1186/1471-2105-15-S13-S6', '25435002', 'High-throughput expression data, such as gene expression and metabolomics data, exhibit modular structures. Groups of features in each module follow a latent factor model, while between modules, the latent factors are quasi-independent. Recovering the latent factors can shed light on the hidden regulation patterns of the expression. The difficulty in detecting such modules and recovering the latent factors lies in the high dimensionality of the data, and the lack of knowledge in module membership.'),
(96423, 2014, 'bmc', 'VTBuilder: a tool for the assembly of multi isoform transcriptomes', 'Archer, John|Whiteley, Gareth|Casewell, Nicholas R|Harrison, Robert A|Wagstaff, Simon C', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. john.archer.jpa@gmail.com.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. gareth.whiteley@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. nicholas.casewell@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. robert.harrison@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Computational Biology|Databases, Factual|High-Throughput Nucleotide Sequencing|Molecular Sequence Annotation|Protein Isoforms|Software|Transcriptome|Viper Venoms|Viperidae', 'Journal Article', '10.1186/s12859-014-0389-8', '25465054', 'Within many research areas, such as transcriptomics, the millions of short DNA fragments (reads) produced by current sequencing platforms need to be assembled into transcript sequences before they can be utilized. Despite recent advances in assembly software, creating such transcripts from read data harboring isoform variation remains challenging. This is because current approaches fail to identify all variants present or they create chimeric transcripts within which relationships between co-evolving sites and other evolutionary factors are disrupted. We present VTBuilder, a tool for constructing non-chimeric transcripts from read data that has been sequenced from sources containing isoform complexity.'),
(96424, 2014, 'bmc', 'NETTAB 2013: Semantic, social, and mobile applications for bioinformatics and biomedical laboratories', 'Romano, Paolo|Cannata, Nicola', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom', 0, 'Algorithms|Computational Biology|Data Mining|Humans|Mobile Applications|Semantics|Social Media|Software', 'Introductory Journal Article', '10.1186/1471-2105-15-S14-S1', '25471662', 'The thirteenth NETTAB workshop, NETTAB 2013, was devoted to semantic, social, and mobile applications for bioinformatics and biomedical laboratories. Topics included issues, methods, algorithms, and technologies for the design and development of tools and platforms able to provide semantic, social, and mobile applications supporting bioinformatics and the activities carried out in a biomedical laboratory. About 30 scientific contributions were presentedat NETTAB 2013, including keynote and tutorial talks, oral communications, and posters. 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.'),
(96425, 2014, 'bmc', 'Calculating semantic relatedness for biomedical use in a knowledge-poor environment', 'Rybinski, Maciej|Aldana-Montes, José', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom', 4, 'Biological Science Disciplines|Data Mining|Databases, Genetic|Disease|Humans|Knowledge Bases|Semantics', 'Journal Article', '10.1186/1471-2105-15-S14-S2', '25471751', 'Computing semantic relatedness between textual labels representing biological and medical concepts is a crucial task in many automated knowledge extraction and processing applications relevant to the biomedical domain, specifically due to the huge amount of new findings being published each year. Most methods benefit from making use of highly specific resources, thus reducing their usability in many real world scenarios that differ from the original assumptions. In this paper we present a simple resource-efficient method for calculating semantic relatedness in a knowledge-poor environment. The method obtains results comparable to state-of-the-art methods, while being more generic and flexible. The solution being presented here was designed to use only a relatively generic and small document corpus and its statistics, without referring to a previously defined knowledge base, thus it does not assume a closed problem.'),
(96426, 2014, 'bmc', 'SMITH: a LIMS for handling next-generation sequencing workflows', 'Venco, Francesco|Vaskin, Yuriy|Ceol, Arnaud|Muller, Heiko', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 8, 'Algorithms|Automation|Genomics|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software|Workflow', 'Journal Article', '10.1186/1471-2105-15-S14-S3', '25471934', 'Life-science laboratories make increasing use of Next Generation Sequencing (NGS) for studying bio-macromolecules and their interactions. Array-based methods for measuring gene expression or protein-DNA interactions are being replaced by RNA-Seq and ChIP-Seq. Sequencing is generally performed by specialized facilities that have to keep track of sequencing requests, trace samples, ensure quality and make data available according to predefined privileges. An integrated tool helps to troubleshoot problems, to maintain a high quality standard, to reduce time and costs. Commercial and non-commercial tools called LIMS (Laboratory Information Management Systems) are available for this purpose. However, they often come at prohibitive cost and/or lack the flexibility and scalability needed to adjust seamlessly to the frequently changing protocols employed. In order to manage the flow of sequencing data produced at the Genomic Unit of the Italian Institute of Technology (IIT), we developed SMITH (Sequencing Machine Information Tracking and Handling).'),
(96427, 2014, 'bmc', 'linkedISA: semantic representation of ISA-Tab experimental metadata', 'González-Beltrán, Alejandra|Maguire, Eamonn|Sansone, Susanna-Assunta|Rocca-Serra, Philippe', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 27, 'Biological Science Disciplines|Data Curation|Datasets as Topic|Internet|Research Design|Semantics|Software', 'Journal Article', '10.1186/1471-2105-15-S14-S4', '25472428', 'Reporting and sharing experimental metadata- such as the experimental design, characteristics of the samples, and procedures applied, along with the analysis results, in a standardised manner ensures that datasets are comprehensible and, in principle, reproducible, comparable and reusable. Furthermore, sharing datasets in formats designed for consumption by humans and machines will also maximize their use. The Investigation/Study/Assay (ISA) open source metadata tracking framework facilitates standards-compliant collection, curation, visualization, storage and sharing of datasets, leveraging on other platforms to enable analysis and publication. The ISA software suite includes several components used in increasingly diverse set of life science and biomedical domains, it is underpinned by a general-purpose format, ISA-Tab, and conversions exist into formats required by public repositories. While ISA-Tab works well mainly as a human readable format, we have also implemented a linked data approach to semantically define the ISA-Tab syntax.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96428, 2014, 'bmc', 'EXACT2: the semantics of biomedical protocols', 'Soldatova, Larisa N|Nadis, Daniel|King, Ross D|Basu, Piyali S|Haddi, Emma|Baumlé, Véronique|Saunders, Nigel J|Marwan, Wolfgang|Rudkin, Brian B', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 10, 'Biological Ontologies|Data Mining|Electronic Data Processing|Language|Reproducibility of Results|Semantics|Software', 'Journal Article', '10.1186/1471-2105-15-S14-S5', '25472549', 'The reliability and reproducibility of experimental procedures is a cornerstone of scientific practice. There is a pressing technological need for the better representation of biomedical protocols to enable other agents (human or machine) to better reproduce results. A framework that ensures that all information required for the replication of experimental protocols is essential to achieve reproducibility. To construct EXACT2 we manually inspected hundreds of published and commercial biomedical protocols from several areas of biomedicine. After establishing a clear pattern for extracting the required information we utilized text-mining tools to translate the protocols into a machine amenable format. We have verified the utility of EXACT2 through the successful processing of previously unseen (not used for the construction of EXACT2)protocols.'),
(96429, 2014, 'bmc', 'OntoGene web services for biomedical text mining', 'Rinaldi, Fabio|Clematide, Simon|Marques, Hernani|Ellendorff, Tilia|Romacker, Martin|Rodriguez-Esteban, Raul', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Data Mining|Drug Discovery|Literature Based Discovery|Software', 'Journal Article', '10.1186/1471-2105-15-S14-S6', '25472638', 'Text mining services are rapidly becoming a crucial component of various knowledge management pipelines, for example in the process of database curation, or for exploration and enrichment of biomedical data within the pharmaceutical industry. Traditional architectures, based on monolithic applications, do not offer sufficient flexibility for a wide range of use case scenarios, and therefore open architectures, as provided by web services, are attracting increased interest. We present an approach towards providing advanced text mining capabilities through web services, using a recently proposed standard for textual data interchange (BioC). The web services leverage a state-of-the-art platform for text mining (OntoGene) which has been tested in several community-organized evaluation challenges,with top ranked results in several of them.'),
(96430, 2014, 'bmc', 'Community-driven development for computational biology at Sprints, Hackathons and Codefests', 'Möller, Steffen|Afgan, Enis|Banck, Michael|Bonnal, Raoul J P|Booth, Timothy|Chilton, John|Cock, Peter J A|Gumbel, Markus|Harris, Nomi|Holland, Richard|Kalaš, Matúš|Kaján, László|Kibukawa, Eri|Powel, David R|Prins, Pjotr|Quinn, Jacqueline|Sallou, Olivier|Strozzi, Francesco|Seemann, Torsten|Sloggett, Clare|Soiland-Reyes, Stian|Spooner, William|Steinbiss, Sascha|Tille, Andreas|Travis, Anthony J|Guimera, Roman|Katayama, Toshiaki|Chapman, Brad A', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.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|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|', 22, 'Communication|Computational Biology|Cooperative Behavior|Internet|Software', 'Journal Article', '10.1186/1471-2105-15-S14-S7', '25472764', 'Computational biology comprises a wide range of technologies and approaches. Multiple technologies can be combined to create more powerful workflows if the individuals contributing the data or providing tools for its interpretation can find mutual understanding and consensus. Much conversation and joint investigation are required in order to identify and implement the best approaches. Traditionally, scientific conferences feature talks presenting novel technologies or insights, followed up by informal discussions during coffee breaks. In multi-institution collaborations, in order to reach agreement on implementation details or to transfer deeper insights in a technology and practical skills, a representative of one group typically visits the other. However, this does not scale well when the number of technologies or research groups is large. Conferences have responded to this issue by introducing Birds-of-a-Feather (BoF) sessions, which offer an opportunity for individuals with common interests to intensify their interaction. However, parallel BoF sessions often make it hard for participants to join multiple BoFs and find common ground between the different technologies, and BoFs are generally too short to allow time for participants to program together.'),
(96431, 2014, 'bmc', 'Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density', 'Wu, Qianqian|Smith-Miles, Kate|Tian, Tianhai', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 11, 'Algorithms|Bayes Theorem|Models, Chemical|Models, Statistical|Stochastic Processes|Systems Biology', 'Journal Article', '10.1186/1471-2105-15-S12-S3', '25473744', 'Mathematical modeling is an important tool in systems biology to study the dynamic property of complex biological systems. However, one of the major challenges in systems biology is how to infer unknown parameters in mathematical models based on the experimental data sets, in particular, when the data are sparse and the regulatory network is stochastic.'),
(96432, 2014, 'bmc', 'Molecular cancer classification using a meta-sample-based regularized robust coding method', 'Wang, Shu-Lin|Sun, Liuchao|Fang, Jianwen', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 1, 'Algorithms|Classification|Cluster Analysis|Gene Expression Profiling|Humans|Neoplasms', 'Journal Article', '10.1186/1471-2105-15-S15-S2', '25473795', 'Previous studies have demonstrated that machine learning based molecular cancer classification using gene expression profiling (GEP) data is promising for the clinic diagnosis and treatment of cancer. Novel classification methods with high efficiency and prediction accuracy are still needed to deal with high dimensionality and small sample size of typical GEP data. Recently the sparse representation (SR) method has been successfully applied to the cancer classification. Nevertheless, its efficiency needs to be improved when analyzing large-scale GEP data.'),
(96433, 2014, 'bmc', 'Identification of single-stranded and double-stranded DNA binding proteins based on protein structure', 'Wang, Wei|Liu, Juan|Zhou, Xionghui', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 10, 'DNA|DNA, Single-Stranded|DNA-Binding Proteins|Protein Binding|Protein Structure, Tertiary|Support Vector Machine', 'Evaluation Studies', '10.1186/1471-2105-15-S12-S4', '25474071', 'Protein-DNA interactions are essential for many biological processes. However, the structural mechanisms underlying these interactions are not fully understood. DNA binding proteins can be classified into double-stranded DNA binding proteins (DSBs) and single-stranded DNA binding proteins (SSBs), and they take part in different biological functions. DSBs usually act as transcriptional factors to regulate the genes expressions, while SSBs usually play roles in DNA replication, recombination, and repair, etc. Understanding the binding specificity of a DNA binding protein is helpful for the research of protein functions.'),
(96434, 2014, 'bmc', 'Gene differential coexpression analysis based on biweight correlation and maximum clique', 'Zheng, Chun-Hou|Yuan, Lin|Sha, Wen|Sun, Zhan-Li', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Data Interpretation, Statistical|Diabetes Mellitus, Type 2|Gene Expression Profiling|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis|Rats', 'Journal Article', '10.1186/1471-2105-15-S15-S3', '25474074', 'Differential coexpression analysis usually requires the definition of distance or similarity between measured datasets. Until now, the most common choice is Pearson correlation coefficient. However, Pearson correlation coefficient is sensitive to outliers. Biweight midcorrelation is considered to be a good alternative to Pearson correlation since it is more robust to outliers. In this paper, we introduce to use Biweight Midcorrelation to measure similarity between gene expression profiles, and provide a new approach for gene differential coexpression analysis. Firstly, we calculate the biweight midcorrelation coefficients between all gene pairs. Then, we filter out non-informative correlation pairs using the half-thresholding strategy and calculate the differential coexpression value of gene, The experimental results on simulated data show that the new approach performed better than three previously published differential coexpression analysis (DCEA) methods. Moreover, we use the maximum clique analysis to gene subset included genes identified by our approach and previously reported T2D-related genes, many additional discoveries can be found through our method. '),
(96435, 2014, 'bmc', 'LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone', 'Chen, Peng|Huang, Jianhua Z|Gao, Xin', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 11, 'Amino Acids|Artificial Intelligence|Binding Sites|Ligands|Protein Conformation|Proteins|Sequence Analysis, Protein', 'Evaluation Studies', '10.1186/1471-2105-15-S15-S4', '25474163', 'Protein-ligand binding is important for some proteins to perform their functions. Protein-ligand binding sites are the residues of proteins that physically bind to ligands. Despite of the recent advances in computational prediction for protein-ligand binding sites, the state-of-the-art methods search for similar, known structures of the query and predict the binding sites based on the solved structures. However, such structural information is not commonly available.'),
(96436, 2014, 'bmc', 'Improved packing of protein side chains with parallel ant colonies', 'Quan, Lijun|Lü, Qiang|Li, Haiou|Xia, Xiaoyan|Wu, Hongjie', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 6, 'Algorithms|Amino Acids|Computational Biology|Models, Molecular|Protein Conformation|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-15-S12-S5', '25474164', 'The accurate packing of protein side chains is important for many computational biology problems, such as ab initio protein structure prediction, homology modelling, and protein design and ligand docking applications. Many of existing solutions are modelled as a computational optimisation problem. As well as the design of search algorithms, most solutions suffer from an inaccurate energy function for judging whether a prediction is good or bad. Even if the search has found the lowest energy, there is no certainty of obtaining the protein structures with correct side chains.'),
(96437, 2014, 'bmc', 'DBBP: database of binding pairs in protein-nucleic acid interactions', 'Park, Byungkyu|Kim, Hyungchan|Han, Kyungsook', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 4, 'Amino Acids|Binding Sites|DNA|DNA-Binding Proteins|Databases, Genetic|Hydrogen Bonding|Protein Binding|RNA|RNA-Binding Proteins', 'Journal Article', '10.1186/1471-2105-15-S15-S5', '25474259', 'Interaction of proteins with other molecules plays an important role in many biological activities. As many structures of protein-DNA complexes and protein-RNA complexes have been determined in the past years, several databases have been constructed to provide structure data of the complexes. However, the information on the binding sites between proteins and nucleic acids is not readily available from the structure data since the data consists mostly of the three-dimensional coordinates of the atoms in the complexes.'),
(96438, 2014, 'bmc', 'BIMMER: a novel algorithm for detecting differential DNA methylation regions from MBDCap-seq data', 'Mao, Zijing|Ma, Chifeng|Huang, Tim H-M|Chen, Yidong|Huang, Yufei', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Algorithms|Breast Neoplasms|DNA Methylation|Female|Genome, Human|Humans|Markov Chains|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-15-S12-S6', '25474268', 'DNA methylation is a common epigenetic marker that regulates gene expression. A robust and cost-effective way for measuring whole genome methylation is Methyl-CpG binding domain-based capture followed by sequencing (MBDCap-seq). In this study, we proposed BIMMER, a Hidden Markov Model (HMM) for differential Methylation Regions (DMRs) identification, where HMMs were proposed to model the methylation status in normal and cancer samples in the first layer and another HMM was introduced to model the relationship between differential methylation and methylation statuses in normal and cancer samples. To carry out the prediction for BIMMER, an Expectation-Maximization algorithm was derived. BIMMER was validated on the simulated data and applied to real MBDCap-seq data of normal and cancer samples. BIMMER revealed that 8.83% of the breast cancer genome are differentially methylated and the majority are hypo-methylated in breast cancer. '),
(96439, 2014, 'bmc', 'Adjusting protein graphs based on graph entropy', 'Peng, Sheng-Lung|Tsay, Yu-Wei', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom', 0, 'Entropy|Models, Molecular|Protein Conformation|Proteins', 'Journal Article', '10.1186/1471-2105-15-S15-S6', '25474347', 'Measuring protein structural similarity attempts to establish a relationship of equivalence between polymer structures based on their conformations. In several recent studies, researchers have explored protein-graph remodeling, instead of looking a minimum superimposition for pairwise proteins. When graphs are used to represent structured objects, the problem of measuring object similarity become one of computing the similarity between graphs. Graph theory provides an alternative perspective as well as efficiency. Once a protein graph has been created, its structural stability must be verified. Therefore, a criterion is needed to determine if a protein graph can be used for structural comparison. In this paper, we propose a measurement for protein graph remodeling based on graph entropy. We extend the concept of graph entropy to determine whether a graph is suitable for representing a protein. The experimental results suggest that when applied, graph entropy helps a conformational on protein graph modeling. Furthermore, it indirectly contributes to protein structural comparison if a protein graph is solid. '),
(96440, 2014, 'bmc', 'An efficient protein complex mining algorithm based on Multistage Kernel Extension', 'Shen, Xianjun|Zhao, Yanli|Li, Yanan|He, Tingting|Yang, Jincai|Hu, Xiaohua', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Cluster Analysis|Multiprotein Complexes|Protein Interaction Mapping|Protein Interaction Maps', 'Journal Article', '10.1186/1471-2105-15-S12-S7', '25474367', 'In recent years, many protein complex mining algorithms, such as classical clique percolation (CPM) method and markov clustering (MCL) algorithm, have developed for protein-protein interaction network. However, most of the available algorithms primarily concentrate on mining dense protein subgraphs as protein complexes, failing to take into account the inherent organizational structure within protein complexes. Thus, there is a critical need to study the possibility of mining protein complexes using the topological information hidden in edges. Moreover, the recent massive experimental analyses reveal that protein complexes have their own intrinsic organization.'),
(96441, 2014, 'bmc', 'EasyCluster2: an improved tool for clustering and assembling long transcriptome reads', 'Bevilacqua, Vitoantonio|Pietroleonardo, Nicola|Giannino, Ely|Stroppa, Fabio|Simone, Domenico|Pesole, Graziano|Picardi, Ernesto', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Alternative Splicing|Cluster Analysis|Exons|Expressed Sequence Tags|Gene Expression Profiling|Genomics|High-Throughput Nucleotide Sequencing|Humans|Software', 'Journal Article', '10.1186/1471-2105-15-S15-S7', '25474441', 'Expressed sequences (e.g. ESTs) are a strong source of evidence to improve gene structures and predict reliable alternative splicing events. When a genome assembly is available, ESTs are suitable to generate gene-oriented clusters through the well-established EasyCluster software. Nowadays, EST-like sequences can be massively produced using Next Generation Sequencing (NGS) technologies. In order to handle genome-scale transcriptome data, we present here EasyCluster2, a reimplementation of EasyCluster able to speed up the creation of gene-oriented clusters and facilitate downstream analyses as the assembly of full-length transcripts and the detection of splicing isoforms.'),
(96442, 2014, 'bmc', 'An improved independent component analysis model for 3D chromatogram separation and its solution by multi-areas genetic algorithm', 'Cui, Lizhi|Poon, Josiah|Poon, Simon K|Chen, Hao|Gao, Junbin|Kwan, Paul|Fan, Kei|Ling, Zhihao', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 3, 'Algorithms|Chromatography, High Pressure Liquid|Computer Simulation', 'Journal Article', '10.1186/1471-2105-15-S12-S8', '25474487', 'The 3D chromatogram generated by High Performance Liquid Chromatography-Diode Array Detector (HPLC-DAD) has been researched widely in the field of herbal medicine, grape wine, agriculture, petroleum and so on. Currently, most of the methods used for separating a 3D chromatogram need to know the compounds number in advance, which could be impossible especially when the compounds are complex or white noise exist. New method which extracts compounds from 3D chromatogram directly is needed.'),
(96443, 2014, 'bmc', 'Reverse engineering gene regulatory networks: coupling an optimization algorithm with a parameter identification technique', 'Hsiao, Yu-Ting|Lee, Wei-Po', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom', 0, 'Algorithms|Animals|Central Nervous System|Computational Biology|Escherichia coli|Gene Expression Profiling|Gene Regulatory Networks|Glycosylation|Rats|SOS Response (Genetics)|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-15-S15-S8', '25474560', 'To infer gene regulatory networks from time series gene profiles, two important tasks that are related to biological systems must be undertaken. One task is to determine a valid network structure that has topological properties that can influence the network dynamics profoundly. The other task is to optimize the network parameters to minimize the accumulated discrepancy between the gene expression data and the values produced by the inferred network model. Though the above two tasks must be conducted simultaneously, most existing work addresses only one of the tasks.'),
(96444, 2014, 'bmc', 'Enhancing the accuracy of knowledge discovery: a supervised learning method', 'Cheng, Liangxi|Lin, Hongfei|Zhou, Feng|Yang, Zhihao|Wang, Jian', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Data Interpretation, Statistical|Data Mining|MEDLINE|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-15-S12-S9', '25474584', 'The amount of biomedical literature available is growing at an explosive speed, but a large amount of useful information remains undiscovered in it. Researchers can make informed biomedical hypotheses through mining this literature. Unfortunately, popular mining methods based on co-occurrence produce too many target concepts, leading to the declining relevance ranking of the potential target concepts.'),
(96445, 2014, 'bmc', 'G-Bean: an ontology-graph based web tool for biomedical literature retrieval', 'Wang, James Z|Zhang, Yuanyuan|Dong, Liang|Li, Lin|Srimani, Pradip K|Yu, Philip S', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Biological Ontologies|Information Storage and Retrieval|Internet|MEDLINE|PubMed|Software', 'Journal Article', '10.1186/1471-2105-15-S12-S1', '25474588', 'Currently, most people use NCBIs PubMed to search the MEDLINE database, an important bibliographical information source for life science and biomedical information. However, PubMed has some drawbacks that make it difficult to find relevant publications pertaining to users individual intentions, especially for non-expert users. To ameliorate the disadvantages of PubMed, we developed G-Bean, a graph based biomedical search engine, to search biomedical articles in MEDLINE database more efficiently.'),
(96446, 2014, 'bmc', 'Learning to rank-based gene summary extraction', 'Shang, Yue|Hao, Huihui|Wu, Jiajin|Lin, Hongfei', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 6, 'Algorithms|Artificial Intelligence|Databases, Genetic|Gene Ontology|Genes|Humans|Information Storage and Retrieval|MEDLINE|Molecular Sequence Annotation|Software', 'Journal Article', '10.1186/1471-2105-15-S12-S10', '25474678', 'In recent years, the biomedical literature has been growing rapidly. These articles provide a large amount of information about proteins, genes and their interactions. Reading such a huge amount of literature is a tedious task for researchers to gain knowledge about a gene. As a result, it is significant for biomedical researchers to have a quick understanding of the query concept by integrating its relevant resources.'),
(96447, 2014, 'bmc', 'Prediction of protein-protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set', 'You, Zhu-Hong|Zhu, Lin|Zheng, Chun-Hou|Yu, Hong-Jie|Deng, Su-Ping|Ji, Zhen', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 67, 'Protein Interaction Mapping|Saccharomyces cerevisiae Proteins|Sequence Analysis, Protein|Support Vector Machine', 'Evaluation Studies', '10.1186/1471-2105-15-S15-S9', '25474679', 'Identifying protein-protein interactions (PPIs) is essential for elucidating protein functions and understanding the molecular mechanisms inside the cell. However, the experimental methods for detecting PPIs are both time-consuming and expensive. Therefore, computational prediction of protein interactions are becoming increasingly popular, which can provide an inexpensive way of predicting the most likely set of interactions at the entire proteome scale, and can be used to complement experimental approaches. Although much progress has already been achieved in this direction, the problem is still far from being solved and new approaches are still required to overcome the limitations of the current prediction models.'),
(96448, 2014, 'bmc', 'Protein structure prediction with local adjust tabu search algorithm', 'Lin, Xiaoli|Zhang, Xiaolong|Zhou, Fengli', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Amino Acids|Computational Biology|Hydrophobic and Hydrophilic Interactions|Models, Molecular|Protein Conformation|Protein Folding|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-15-S15-S1', '25474708', 'Protein folding structure prediction is one of the most challenging problems in the bioinformatics domain. Because of the complexity of the realistic protein structure, the simplified structure model and the computational method should be adopted in the research. The AB off-lattice model is one of the simplification models, which only considers two classes of amino acids, hydrophobic (A) residues and hydrophilic (B) residues.'),
(96449, 2014, 'bmc', 'Discovering co-occurring patterns and their biological significance in protein families', 'Lee, En-Shiun|Fung, Sanderz|Sze-To, Ho-Yin|Wong, Andrew K C', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Algorithms|Amino Acids|Cluster Analysis|Cytochromes c|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Ubiquitin', 'Journal Article', '10.1186/1471-2105-15-S12-S2', '25474736', 'The large influx of biological sequences poses the importance of identifying and correlating conserved regions in homologous sequences to acquire valuable biological knowledge. These conserved regions contain statistically significant residue associations as sequence patterns. Thus, patterns from two conserved regions co-occurring frequently on the same sequences are inferred to have joint functionality. A method for finding conserved regions in protein families with frequent co-occurrence patterns is proposed. The biological significance of the discovered clusters of conserved regions with co-occurrences patterns can be validated by their three-dimensional closeness of amino acids and the biological functionality found in those regions as supported by published work.'),
(96450, 2014, 'bmc', 'Compression of next-generation sequencing quality scores using memetic algorithm', 'Zhou, Jiarui|Ji, Zhen|Zhu, Zexuan|He, Shan', 'Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.|Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA2, UK. simon.wagstaff@lstmed.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Data Compression|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-15-S15-S10', '25474747', 'The exponential growth of next-generation sequencing (NGS) derived DNA data poses great challenges to data storage and transmission. Although many compression algorithms have been proposed for DNA reads in NGS data, few methods are designed specifically to handle the quality scores.'),
(96451, 2014, 'bmc', 'Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework', 'Yang, Lingjian|Ainali, Chrysanthi|Tsoka, Sophia|Papageorgiou, Lazaros G', 'Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, London, WC1E 7JE, UK. lingjian.yang.10@ucl.ac.uk.|Department of Informatics, School of Natural and Mathematical Sciences, Kings College London, London, WC2R 2LS, UK. chrysanthi.ainali@kcl.ac.uk.|Department of Informatics, School of Natural and Mathematical Sciences, Kings College London, London, WC2R 2LS, UK. Sophia.tsoka@kcl.ac.uk.|Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, London, WC1E 7JE, UK. l.papageorgiou@ucl.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Artificial Intelligence|Breast Neoplasms|Databases, Genetic|Disease|Female|Gene Expression Profiling|Humans|Lung Neoplasms|Male|Mathematical Computing|Models, Theoretical|Oligonucleotide Array Sequence Analysis|Prostatic Neoplasms|Psoriasis|Signal Transduction', 'Journal Article', '10.1186/s12859-014-0390-2', '25475756', 'Applying machine learning methods on microarray gene expression profiles for disease classification problems is a popular method to derive biomarkers, i.e. sets of genes that can predict disease state or outcome. Traditional approaches where expression of genes were treated independently suffer from low prediction accuracy and difficulty of biological interpretation. Current research efforts focus on integrating information on protein interactions through biochemical pathway datasets with expression profiles to propose pathway-based classifiers that can enhance disease diagnosis and prognosis. As most of the pathway activity inference methods in literature are either unsupervised or applied on two-class datasets, there is good scope to address such limitations by proposing novel methodologies.'),
(96452, 2014, 'bmc', 'Comparative evaluation of gene set analysis approaches for RNA-Seq data', 'Rahmatallah, Yasir|Emmert-Streib, Frank|Glazko, Galina', 'Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA. yrahmatallah@uams.edu.|Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queens University Belfast, 97 Lisburn Road, Belfast, BT9 7BL, UK. v@bio-complexity.com.|Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA. gvglazko@uams.edu.', 'United Kingdom|United Kingdom|United Kingdom', 14, 'Computer Simulation|Female|Gene Expression Profiling|Genes, X-Linked|High-Throughput Nucleotide Sequencing|Humans|Lymphocytes|Male|RNA|Sequence Analysis, RNA|Signal Transduction|Software', 'Comparative Study', '10.1186/s12859-014-0397-8', '25475910', 'Over the last few years transcriptome sequencing (RNA-Seq) has almost completely taken over microarrays for high-throughput studies of gene expression. Currently, the most popular use of RNA-Seq is to identify genes which are differentially expressed between two or more conditions. Despite the importance of Gene Set Analysis (GSA) in the interpretation of the results from RNA-Seq experiments, the limitations of GSA methods developed for microarrays in the context of RNA-Seq data are not well understood.'),
(96453, 2014, 'bmc', 'Finding gene regulatory network candidates using the gene expression knowledge base', 'Venkatesan, Aravind|Tripathi, Sushil|Sanz de Galdeano, Alejandro|Blondé, Ward|Lægreid, Astrid|Mironov, Vladimir|Kuiper, Martin', 'Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491, Trondheim, Norway. aravind.venkatesan@ntnu.no.|Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489, Trondheim, Norway. sushil.tripathi@ntnu.no.|Escuela Nacional de Sanidad, Instituto de Salud Carlos III, 28029, Madrid, Spain. asdgvarela@gmail.com.|Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491, Trondheim, Norway. w.blonde@vumc.nl.|Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489, Trondheim, Norway. astrid.lagreid@ntnu.no.|Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491, Trondheim, Norway. vladimir.mironov@ntnu.no.|Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491, Trondheim, Norway. martin.kuiper@ntnu.no.', 'Norway|Norway|Spain|Norway|Norway|Norway|Norway', 4, 'Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Genomics|Humans|Knowledge Bases|Models, Biological|Protein Interaction Maps|Semantics|Signal Transduction', 'Journal Article', '10.1186/s12859-014-0386-y', '25490885', 'Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of omics data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis.');
