INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95504, 2013, 'bmc', 'Protein function annotation with Structurally Aligned Local Sites of Activity (SALSAs)', 'Wang, Zhouxi|Yin, Pengcheng|Lee, Joslynn S|Parasuram, Ramya|Somarowthu, Srinivas|Ondrechen, Mary Jo', 'Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, USA.|Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, USA.|Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, USA.|Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, USA.|Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, USA.|Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, USA.', 'United States|United States|United States|United States|United States|United States', 10, 'Anabaena|Binding Sites|Catalytic Domain|Computational Biology|Enoyl-CoA Hydratase|Glycoside Hydrolases|Hydrolases|Molecular Sequence Annotation|Orotidine-5-Phosphate Decarboxylase|Proteins|Structural Homology, Protein', 'Journal Article', '10.1186/1471-2105-14-S3-S13', '23514271', 'The prediction of biochemical function from the 3D structure of a protein has proved to be much more difficult than was originally foreseen. A reliable method to test the likelihood of putative annotations and to predict function from structure would add tremendous value to structural genomics data. We report on a new method, Structurally Aligned Local Sites of Activity (SALSA), for the prediction of biochemical function based on a local structural match at the predicted catalytic or binding site.'),
(95505, 2013, 'bmc', 'Bioinformatics analysis of the epitope regions for norovirus capsid protein', 'Chen, Liping|Wu, Di|Ji, Lei|Wu, Xiaofang|Xu, Deshun|Cao, Zhiwei|Han, Jiankang', 'Huzhou Center For Didsease Control and Preventron, Zhejiang 311000, China.|Huzhou Center For Didsease Control and Preventron, Zhejiang 311000, China.|Huzhou Center For Didsease Control and Preventron, Zhejiang 311000, China.|Huzhou Center For Didsease Control and Preventron, Zhejiang 311000, China.|Huzhou Center For Didsease Control and Preventron, Zhejiang 311000, China.|Huzhou Center For Didsease Control and Preventron, Zhejiang 311000, China.|Huzhou Center For Didsease Control and Preventron, Zhejiang 311000, China.', 'China|China|China|China|China|China|China', 25, 'Amino Acid Sequence|Antibodies, Viral|Caliciviridae Infections|Capsid Proteins|Computational Biology|Epitopes|Humans|Molecular Sequence Data|Norovirus|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-14-S4-S5', '23514273', 'Norovirus is the major cause of nonbacterial epidemic gastroenteritis, being highly prevalent in both developing and developed countries. Despite of the available monoclonal antibodies (MAbs) for different sub-genogroups, a comprehensive epitope analysis based on various bioinformatics technology is highly desired for future potential antibody development in clinical diagonosis and treatment.'),
(95506, 2013, 'bmc', 'Protein function prediction using text-based features extracted from the biomedical literature: the CAFA challenge', 'Wong, Andrew|Shatkay, Hagit', 'Computational Biology and Machine Learning Lab, School of Computing, Queens University, Kingston, ON, K7L 3N6, Canada.|Computational Biology and Machine Learning Lab, School of Computing, Queens University, Kingston, ON, K7L 3N6, Canada.', 'Canada|Canada', 34, 'Cluster Analysis|Computational Biology|Data Mining|Databases, Protein|Molecular Sequence Annotation|Proteins|Sequence Analysis, Protein|Software|Vocabulary, Controlled', 'Evaluation Studies', '10.1186/1471-2105-14-S3-S14', '23514326', 'Advances in sequencing technology over the past decade have resulted in an abundance of sequenced proteins whose function is yet unknown. As such, computational systems that can automatically predict and annotate protein function are in demand. Most computational systems use features derived from protein sequence or protein structure to predict function. In an earlier work, we demonstrated the utility of biomedical literature as a source of text features for predicting protein subcellular location. We have also shown that the combination of text-based and sequence-based prediction improves the performance of location predictors. Following up on this work, for the Critical Assessment of Function Annotations (CAFA) Challenge, we developed a text-based system that aims to predict molecular function and biological process (using Gene Ontology terms) for unannotated proteins. In this paper, we present the preliminary work and evaluation that we performed for our system, as part of the CAFA challenge.'),
(95507, 2013, 'bmc', 'In-depth performance evaluation of PFP and ESG sequence-based function prediction methods in CAFA 2011 experiment', 'Chitale, Meghana|Khan, Ishita K|Kihara, Daisuke', 'Department of Computer Science, Purdue University, 305 N, University Street, West Lafayette, Indiana 47907, USA.|Department of Computer Science, Purdue University, 305 N, University Street, West Lafayette, Indiana 47907, USA.|Department of Computer Science, Purdue University, 305 N, University Street, West Lafayette, Indiana 47907, USA.', 'India|India|India', 4, 'Algorithms|Computational Biology|Databases, Protein|Proteins|Sequence Analysis, Protein', 'Evaluation Studies', '10.1186/1471-2105-14-S3-S2', '23514353', 'Many Automatic Function Prediction (AFP) methods were developed to cope with an increasing growth of the number of gene sequences that are available from high throughput sequencing experiments. To support the development of AFP methods, it is essential to have community wide experiments for evaluating performance of existing AFP methods. Critical Assessment of Function Annotation (CAFA) is one such community experiment. The meeting of CAFA was held as a Special Interest Group (SIG) meeting at the Intelligent Systems in Molecular Biology (ISMB) conference in 2011. Here, we perform a detailed analysis of two sequence-based function prediction methods, PFP and ESG, which were developed in our lab, using the predictions submitted to CAFA.'),
(95508, 2013, 'bmc', 'Three-level prediction of protein function by combining profile-sequence search, profile-profile search, and domain co-occurrence networks', 'Wang, Zheng|Cao, Renzhi|Cheng, Jianlin', 'Department of Computer Science, University of Missouri, Columbia, Missouri 65211, USA.|Department of Computer Science, University of Missouri, Columbia, Missouri 65211, USA.|Department of Computer Science, University of Missouri, Columbia, Missouri 65211, USA.', 'United States|United States|United States', 15, 'Algorithms|Databases, Protein|Genomics|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-14-S3-S3', '23514381', 'Predicting protein function from sequence is useful for biochemical experiment design, mutagenesis analysis, protein engineering, protein design, biological pathway analysis, drug design, disease diagnosis, and genome annotation as a vast number of protein sequences with unknown function are routinely being generated by DNA, RNA and protein sequencing in the genomic era. However, despite significant progresses in the last several years, the accuracy of protein function prediction still needs to be improved in order to be used effectively in practice, particularly when little or no homology exists between a target protein and proteins with annotated function. Here, we developed a method that integrated profile-sequence alignment, profile-profile alignment, and Domain Co-Occurrence Networks (DCN) to predict protein function at different levels of complexity, ranging from obvious homology, to remote homology, to no homology. We tested the method blindingly in the 2011 Critical Assessment of Function Annotation (CAFA). Our experiments demonstrated that our three-level prediction method effectively increased the recall of function prediction while maintaining a reasonable precision. Particularly, our method can predict function terms defined by the Gene Ontology more accurately than three standard baseline methods in most situations, handle multi-domain proteins naturally, and make ab initio function prediction when no homology exists. These results show that our approach can combine complementary strengths of most widely used BLAST-based function prediction methods, rarely used in function prediction but more sensitive profile-profile comparison-based homology detection methods, and non-homology-based domain co-occurrence networks, to effectively extend the power of function prediction from high homology, to low homology, to no homology (ab initio cases).'),
(95509, 2013, 'bmc', 'How to inherit statistically validated annotation within BAR+ protein clusters', 'Piovesan, Damiano|Martelli, Pier Luigi|Fariselli, Piero|Profiti, Giuseppe|Zauli, Andrea|Rossi, Ivan|Casadio, Rita', 'Bologna Biocomputing Group, University of Bologna, Italy.|Bologna Biocomputing Group, University of Bologna, Italy.|Bologna Biocomputing Group, University of Bologna, Italy.|Bologna Biocomputing Group, University of Bologna, Italy.|Bologna Biocomputing Group, University of Bologna, Italy.|Bologna Biocomputing Group, University of Bologna, Italy.|Bologna Biocomputing Group, University of Bologna, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 7, 'Cluster Analysis|Data Interpretation, Statistical|Databases, Protein|Genomics|Molecular Sequence Annotation|Proteins|Sequence Analysis, Protein|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-S3-S4', '23514411', 'In the genomic era a key issue is protein annotation, namely how to endow protein sequences, upon translation from the corresponding genes, with structural and functional features. Routinely this operation is electronically done by deriving and integrating information from previous knowledge. The reference database for protein sequences is UniProtKB divided into two sections, UniProtKB/TrEMBL which is automatically annotated and not reviewed and UniProtKB/Swiss-Prot which is manually annotated and reviewed. The annotation process is essentially based on sequence similarity search. The question therefore arises as to which extent annotation based on transfer by inheritance is valuable and specifically if it is possible to statistically validate inherited features when little homology exists among the target sequence and its template(s).'),
(95510, 2013, 'bmc', 'Protein function prediction using domain families', 'Rentzsch, Robert|Orengo, Christine A', 'Robert Koch Institut, Research Group Bioinformatics Ng4, Nordufer 20, 13353 Berlin, Germany. rentzschr@rki.de|Robert Koch Institut, Research Group Bioinformatics Ng4, Nordufer 20, 13353 Berlin, Germany. rentzschr@rki.de', 'Germany|Germany', 42, 'Cluster Analysis|Databases, Protein|Molecular Sequence Annotation|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-S3-S5', '23514456', 'Here we assessed the use of domain families for predicting the functions of whole proteins. These functional families (FunFams) were derived using a protocol that combines sequence clustering with supervised cluster evaluation, relying on available high-quality Gene Ontology (GO) annotation data in the latter step. In essence, the protocol groups domain sequences belonging to the same superfamily into families based on the GO annotations of their parent proteins. An initial test based on enzyme sequences confirmed that the FunFams resemble enzyme (domain) families much better than do families produced by sequence clustering alone. For the CAFA 2011 experiment, we further associated the FunFams with GO terms probabilistically. All target proteins were first submitted to domain superfamily assignment, followed by FunFam assignment and, eventually, function assignment. The latter included an integration step for multi-domain target proteins. The CAFA results put our domain-based approach among the top ten of 31 competing groups and 56 prediction methods, confirming that it outperforms simple pairwise whole-protein sequence comparisons.'),
(95511, 2013, 'bmc', 'Function prediction from networks of local evolutionary similarity in protein structure', 'Erdin, Serkan|Venner, Eric|Lisewski, Andreas Martin|Lichtarge, Olivier', 'Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA.', 'United States|United States|United States|United States', 9, 'Algorithms|Computational Biology|Databases, Protein|Enzymes|Evolution, Molecular|Genomics|Molecular Sequence Annotation|Protein Conformation|Proteins', 'Journal Article', '10.1186/1471-2105-14-S3-S6', '23514548', 'Annotating protein function with both high accuracy and sensitivity remains a major challenge in structural genomics. One proven computational strategy has been to group a few key functional amino acids into templates and search for these templates in other protein structures, so as to transfer function when a match is found. To this end, we previously developed Evolutionary Trace Annotation (ETA) and showed that diffusing known annotations over a network of template matches on a structural genomic scale improved predictions of function. In order to further increase sensitivity, we now let each protein contribute multiple templates rather than just one, and also let the template size vary.'),
(95512, 2013, 'bmc', 'Homology-based inference sets the bar high for protein function prediction', 'Hamp, Tobias|Kassner, Rebecca|Seemayer, Stefan|Vicedo, Esmeralda|Schaefer, Christian|Achten, Dominik|Auer, Florian|Boehm, Ariane|Braun, Tatjana|Hecht, Maximilian|Heron, Mark|Hönigschmid, Peter|Hopf, Thomas A|Kaufmann, Stefanie|Kiening, Michael|Krompass, Denis|Landerer, Cedric|Mahlich, Yannick|Roos, Manfred|Rost, Burkhard', 'TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.|TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germ', 25, 'Algorithms|Proteins|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-14-S3-S7', '23514582', 'Any method that de novo predicts protein function should do better than random. More challenging, it also ought to outperform simple homology-based inference.'),
(95513, 2013, 'bmc', 'MS-kNN: protein function prediction by integrating multiple data sources', 'Lan, Liang|Djuric, Nemanja|Guo, Yuhong|Vucetic, Slobodan', 'Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA.|Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA.|Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA.|Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA.', 'United States|United States|United States|United States', 50, 'Algorithms|Genomics|Humans|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|Transcriptome|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-S3-S8', '23514608', 'Protein function determination is a key challenge in the post-genomic era. Experimental determination of protein functions is accurate, but time-consuming and resource-intensive. A cost-effective alternative is to use the known information about sequence, structure, and functional properties of genes and proteins to predict functions using statistical methods. In this paper, we describe the Multi-Source k-Nearest Neighbor (MS-kNN) algorithm for function prediction, which finds k-nearest neighbors of a query protein based on different types of similarity measures and predicts its function by weighted averaging of its neighbors functions. Specifically, we used 3 data sources to calculate the similarity scores: sequence similarity, protein-protein interactions, and gene expressions.'),
(95514, 2013, 'bmc', 'Mobilomics in Saccharomyces cerevisiae strains', 'Menconi, Giulia|Battaglia, Giovanni|Grossi, Roberto|Pisanti, Nadia|Marangoni, Roberto', 'Istituto Nazionale di Alta Matematica, Città Universitaria, Roma, Italia.|Istituto Nazionale di Alta Matematica, Città Universitaria, Roma, Italia.|Istituto Nazionale di Alta Matematica, Città Universitaria, Roma, Italia.|Istituto Nazionale di Alta Matematica, Città Universitaria, Roma, Italia.|Istituto Nazionale di Alta Matematica, Città Universitaria, Roma, Italia.', '-|-|-|-|-', 3, 'Genome, Fungal|Genomics|Retroelements|Saccharomyces cerevisiae|Software|Terminal Repeat Sequences', 'Journal Article', '10.1186/1471-2105-14-102', '23514613', 'Mobile Genetic Elements (MGEs) are selfish DNA integrated in the genomes. Their detection is mainly based on consensus-like searches by scanning the investigated genome against the sequence of an already identified MGE. Mobilomics aims at discovering all the MGEs in a genome and understanding their dynamic behavior: The data for this kind of investigation can be provided by comparative genomics of closely related organisms. The amount of data thus involved requires a strong computational effort, which should be alleviated.'),
(95515, 2013, 'bmc', 'A domain-centric solution to functional genomics via dcGO Predictor', 'Fang, Hai|Gough, Julian', 'Department of Computer Science, University of Bristol, The Merchant Venturers Building, Bristol BS8 1UB, UK. hfang@cs.bris.ac.uk|Department of Computer Science, University of Bristol, The Merchant Venturers Building, Bristol BS8 1UB, UK. hfang@cs.bris.ac.uk', 'United Kingdom|United Kingdom', 16, 'Genes|Genome|Genomics|Molecular Sequence Annotation|Phenotype|Protein Structure, Tertiary|Proteins|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-S3-S9', '23514627', 'Computational/manual annotations of protein functions are one of the first routes to making sense of a newly sequenced genome. Protein domain predictions form an essential part of this annotation process. This is due to the natural modularity of proteins with domains as structural, evolutionary and functional units. Sometimes two, three, or more adjacent domains (called supra-domains) are the operational unit responsible for a function, e.g. via a binding site at the interface. These supra-domains have contributed to functional diversification in higher organisms. Traditionally functional ontologies have been applied to individual proteins, rather than families of related domains and supra-domains. We expect, however, to some extent functional signals can be carried by protein domains and supra-domains, and consequently used in function prediction and functional genomics.'),
(95516, 2013, 'bmc', 'Application of text-mining for updating protein post-translational modification annotation in UniProtKB', 'Veuthey, Anne-Lise|Bridge, Alan|Gobeill, Julien|Ruch, Patrick|McEntyre, Johanna R|Bougueleret, Lydie|Xenarios, Ioannis', 'Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, 1 Michel Servet, 1211 Geneva 4, Switzerland. anne-lise.veuthey@isb-sib.ch|Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, 1 Michel Servet, 1211 Geneva 4, Switzerland. anne-lise.veuthey@isb-sib.ch|Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, 1 Michel Servet, 1211 Geneva 4, Switzerland. anne-lise.veuthey@isb-sib.ch|Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, 1 Michel Servet, 1211 Geneva 4, Switzerland. anne-lise.veuthey@isb-sib.ch|Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, 1 Michel Servet, 1211 Geneva 4, Switzerland. anne-lise.veuthey@isb-sib.ch|Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, 1 Michel Servet, 1211 Geneva 4, Switzerland. anne-lise.veuthey@isb-sib.ch|Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, 1 Michel Servet, 1211 Geneva 4, Switzerland. anne-lise.veuthey@isb-sib.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 12, 'Data Mining|Databases, Protein|Humans|Knowledge Bases|Molecular Sequence Annotation|Protein Processing, Post-Translational|Proteomics', 'Journal Article', '10.1186/1471-2105-14-104', '23517090', 'The annotation of protein post-translational modifications (PTMs) is an important task of UniProtKB curators and, with continuing improvements in experimental methodology, an ever greater number of articles are being published on this topic. To help curators cope with this growing body of information we have developed a system which extracts information from the scientific literature for the most frequently annotated PTMs in UniProtKB.'),
(95517, 2013, 'bmc', 'SDM-Assist software to design site-directed mutagenesis primers introducing silent restriction sites', 'Karnik, Abhijit|Karnik, Rucha|Grefen, Christopher', 'Department of Computer Science, University of Bristol, Bristol, UK.|Department of Computer Science, University of Bristol, Bristol, UK.|Department of Computer Science, University of Bristol, Bristol, UK.', 'United Kingdom|United Kingdom|United Kingdom', 22, 'Algorithms|Arabidopsis Proteins|DNA Primers|DNA Restriction Enzymes|Escherichia coli|Mutagenesis, Site-Directed|Polymerase Chain Reaction|Potassium Channels|Software', 'Journal Article', '10.1186/1471-2105-14-105', '23522286', 'Over the past decades site-directed mutagenesis (SDM) has become an indispensable tool for biological structure-function studies. In principle, SDM uses modified primer pairs in a PCR reaction to introduce a mutation in a cDNA insert. DpnI digestion of the reaction mixture is used to eliminate template copies before amplification in E. coli, however, this process is inefficient resulting in un-mutated clones which can only be distinguished from mutant clones by sequencing.'),
(95518, 2013, 'bmc', 'SMOTE for high-dimensional class-imbalanced data', 'Blagus, Rok|Lusa, Lara', 'Institute for Biostatistics and Medical Informatics, University of Ljubljana, Ljubljana, Slovenia.|Institute for Biostatistics and Medical Informatics, University of Ljubljana, Ljubljana, Slovenia.', 'Slovenia|Slovenia', 178, 'Algorithms|Classification|Computer Simulation|Gene Expression Profiling|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-14-106', '23522326', 'Classification using class-imbalanced data is biased in favor of the majority class. The bias is even larger for high-dimensional data, where the number of variables greatly exceeds the number of samples. The problem can be attenuated by undersampling or oversampling, which produce class-balanced data. Generally undersampling is helpful, while random oversampling is not. Synthetic Minority Oversampling TEchnique (SMOTE) is a very popular oversampling method that was proposed to improve random oversampling but its behavior on high-dimensional data has not been thoroughly investigated. In this paper we investigate the properties of SMOTE from a theoretical and empirical point of view, using simulated and real high-dimensional data.'),
(95519, 2013, 'bmc', 'Non-negative matrix factorization by maximizing correntropy for cancer clustering', 'Wang, Jim Jing-Yan|Wang, Xiaolei|Gao, Xin', 'Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.|Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.|Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia', 81, 'Algorithms|Cluster Analysis|Gene Expression Profiling|Humans|Neoplasms', 'Journal Article', '10.1186/1471-2105-14-107', '23522344', 'Non-negative matrix factorization (NMF) has been shown to be a powerful tool for clustering gene expression data, which are widely used to classify cancers. NMF aims to find two non-negative matrices whose product closely approximates the original matrix. Traditional NMF methods minimize either the l2 norm or the Kullback-Leibler distance between the product of the two matrices and the original matrix. Correntropy was recently shown to be an effective similarity measurement due to its stability to outliers or noise.'),
(95520, 2013, 'bmc', 'LASAGNA: a novel algorithm for transcription factor binding site alignment', 'Lee, Chih|Huang, Chun-Hsi', 'Department of Computer Science and Engineering, University of Connecticut, Fairfield Road, Storrs, CT 06269, USA.|Department of Computer Science and Engineering, University of Connecticut, Fairfield Road, Storrs, CT 06269, USA.', 'United States|United States', 14, 'Algorithms|Animals|Binding Sites|Chromatin Immunoprecipitation|DNA|Genome|Humans|Mice|Nucleotide Motifs|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-14-108', '23522376', 'Scientists routinely scan DNA sequences for transcription factor (TF) binding sites (TFBSs). Most of the available tools rely on position-specific scoring matrices (PSSMs) constructed from aligned binding sites. Because of the resolutions of assays used to obtain TFBSs, databases such as TRANSFAC, ORegAnno and PAZAR store unaligned variable-length DNA segments containing binding sites of a TF. These DNA segments need to be aligned to build a PSSM. While the TRANSFAC database provides scoring matrices for TFs, nearly 78% of the TFs in the public release do not have matrices available. As work on TFBS alignment algorithms has been limited, it is highly desirable to have an alignment algorithm tailored to TFBSs.'),
(95521, 2013, 'bmc', 'TPMS: a set of utilities for querying collections of gene trees', 'Bigot, Thomas|Daubin, Vincent|Lassalle, Florent|Perrière, Guy', 'Laboratoire de Biométrie et Biologie Évolutive, UMR CNRS 5558, Université Claude Bernard - Lyon 1, 43 bd, du 11 Novembre 1918, 69622 Villeurbanne Cedex, France.|Laboratoire de Biométrie et Biologie Évolutive, UMR CNRS 5558, Université Claude Bernard - Lyon 1, 43 bd, du 11 Novembre 1918, 69622 Villeurbanne Cedex, France.|Laboratoire de Biométrie et Biologie Évolutive, UMR CNRS 5558, Université Claude Bernard - Lyon 1, 43 bd, du 11 Novembre 1918, 69622 Villeurbanne Cedex, France.|Laboratoire de Biométrie et Biologie Évolutive, UMR CNRS 5558, Université Claude Bernard - Lyon 1, 43 bd, du 11 Novembre 1918, 69622 Villeurbanne Cedex, France.', 'France|France|France|France', 4, 'Algorithms|Animals|Mammals|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-14-109', '23530580', 'The information in large collections of phylogenetic trees is useful for many comparative genomic studies. Therefore, there is a need for flexible tools that allow exploration of such collections in order to retrieve relevant data as quickly as possible.'),
(95522, 2013, 'bmc', 'Differential expression analysis for paired RNA-Seq data', 'Chung, Lisa M|Ferguson, John P|Zheng, Wei|Qian, Feng|Bruno, Vincent|Montgomery, Ruth R|Zhao, Hongyu', 'Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA. lisa.chung@yale.edu|Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA. lisa.chung@yale.edu|Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA. lisa.chung@yale.edu|Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA. lisa.chung@yale.edu|Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA. lisa.chung@yale.edu|Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA. lisa.chung@yale.edu|Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA. lisa.chung@yale.edu', 'United States|United States|United States|United States|United States|United States|United States', 14, 'Bayes Theorem|Gene Expression Profiling|Humans|Poisson Distribution|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-14-110', '23530607', 'RNA-Seq technology measures the transcript abundance by generating sequence reads and counting their frequencies across different biological conditions. To identify differentially expressed genes between two conditions, it is important to consider the experimental design as well as the distributional property of the data. In many RNA-Seq studies, the expression data are obtained as multiple pairs, e.g., pre- vs. post-treatment samples from the same individual. We seek to incorporate paired structure into analysis.'),
(95523, 2013, 'bmc', 'A benchmark server using high resolution protein structure data, and benchmark results for membrane helix predictions', 'Rath, Emma M|Tessier, Dominique|Campbell, Alexander A|Lee, Hong Ching|Werner, Tim|Salam, Noeris K|Lee, Lawrence K|Church, W Bret', 'Group in Biomolecular Structure and Informatics, Faculty of Pharmacy, The University of Sydney, Darlinghurst, Sydney NSW 2006, Australia.|Group in Biomolecular Structure and Informatics, Faculty of Pharmacy, The University of Sydney, Darlinghurst, Sydney NSW 2006, Australia.|Group in Biomolecular Structure and Informatics, Faculty of Pharmacy, The University of Sydney, Darlinghurst, Sydney NSW 2006, Australia.|Group in Biomolecular Structure and Informatics, Faculty of Pharmacy, The University of Sydney, Darlinghurst, Sydney NSW 2006, Australia.|Group in Biomolecular Structure and Informatics, Faculty of Pharmacy, The University of Sydney, Darlinghurst, Sydney NSW 2006, Australia.|Group in Biomolecular Structure and Informatics, Faculty of Pharmacy, The University of Sydney, Darlinghurst, Sydney NSW 2006, Australia.|Group in Biomolecular Structure and Informatics, Faculty of Pharmacy, The University of Sydney, Darlinghurst, Sydney NSW 2006, Australia.|Group in Biomolecular Structure and Informatics, Faculty of Pharmacy, The University of Sydney, Darlinghurst, Sydney NSW 2006, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 10, 'Benchmarking|Databases, Protein|Membrane Proteins|Protein Structure, Secondary|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-14-111', '23530628', 'Helical membrane proteins are vital for the interaction of cells with their environment. Predicting the location of membrane helices in protein amino acid sequences provides substantial understanding of their structure and function and identifies membrane proteins in sequenced genomes. Currently there is no comprehensive benchmark tool for evaluating prediction methods, and there is no publication comparing all available prediction tools. Current benchmark literature is outdated, as recently determined membrane protein structures are not included. Current literature is also limited to global assessments, as specialised benchmarks for predicting specific classes of membrane proteins were not previously carried out.'),
(95524, 2013, 'bmc', 'A systematic comparison of the MetaCyc and KEGG pathway databases', 'Altman, Tomer|Travers, Michael|Kothari, Anamika|Caspi, Ron|Karp, Peter D', 'Bioinformatics Research Group, SRI International, Menlo Park, USA.|Bioinformatics Research Group, SRI International, Menlo Park, USA.|Bioinformatics Research Group, SRI International, Menlo Park, USA.|Bioinformatics Research Group, SRI International, Menlo Park, USA.|Bioinformatics Research Group, SRI International, Menlo Park, USA.', 'United States|United States|United States|United States|United States', 73, 'Databases, Factual|Enzymes|Genome|Metabolic Networks and Pathways', 'Comparative Study', '10.1186/1471-2105-14-112', '23530693', 'The MetaCyc and KEGG projects have developed large metabolic pathway databases that are used for a variety of applications including genome analysis and metabolic engineering. We present a comparison of the compound, reaction, and pathway content of MetaCyc version 16.0 and a KEGG version downloaded on Feb-27-2012 to increase understanding of their relative sizes, their degree of overlap, and their scope. To assess their overlap, we must know the correspondences between compounds, reactions, and pathways in MetaCyc, and those in KEGG. We devoted significant effort to computational and manual matching of these entities, and we evaluated the accuracy of the correspondences.'),
(95525, 2013, 'bmc', 'Using cited references to improve the retrieval of related biomedical documents', 'Ortuño, Francisco M|Rojas, Ignacio|Andrade-Navarro, Miguel A|Fontaine, Jean-Fred', 'Computer Architecture and Computer Technology Department, University of Granada, Granada, Spain.|Computer Architecture and Computer Technology Department, University of Granada, Granada, Spain.|Computer Architecture and Computer Technology Department, University of Granada, Granada, Spain.|Computer Architecture and Computer Technology Department, University of Granada, Granada, Spain.', 'Spain|Spain|Spain|Spain', 11, 'Algorithms|Data Mining|Medical Subject Headings|PubMed', 'Journal Article', '10.1186/1471-2105-14-113', '23537461', 'A popular query from scientists reading a biomedical abstract is to search for topic-related documents in bibliographic databases. Such a query is challenging because the amount of information attached to a single abstract is little, whereas classification-based retrieval algorithms are optimally trained with large sets of relevant documents. As a solution to this problem, we propose a query expansion method that extends the information related to a manuscript using its cited references.'),
(95526, 2013, 'bmc', 'Computing minimal nutrient sets from metabolic networks via linear constraint solving', 'Eker, Steven|Krummenacker, Markus|Shearer, Alexander G|Tiwari, Ashish|Keseler, Ingrid M|Talcott, Carolyn|Karp, Peter D', 'Computer Science Laboratory, SRI International, Menlo Park, CA 94025, USA.|Computer Science Laboratory, SRI International, Menlo Park, CA 94025, USA.|Computer Science Laboratory, SRI International, Menlo Park, CA 94025, USA.|Computer Science Laboratory, SRI International, Menlo Park, CA 94025, USA.|Computer Science Laboratory, SRI International, Menlo Park, CA 94025, USA.|Computer Science Laboratory, SRI International, Menlo Park, CA 94025, USA.|Computer Science Laboratory, SRI International, Menlo Park, CA 94025, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Culture Media|Escherichia coli K12|Escherichia coli Proteins|Genomics|Membrane Transport Proteins|Metabolic Networks and Pathways|Models, Biological', 'Journal Article', '10.1186/1471-2105-14-114', '23537498', 'As more complete genome sequences become available, bioinformatics challenges arise in how to exploit genome sequences to make phenotypic predictions. One type of phenotypic prediction is to determine sets of compounds that will support the growth of a bacterium from the metabolic network inferred from the genome sequence of that organism.'),
(95527, 2013, 'bmc', 'PlanktoVision--an automated analysis system for the identification of phytoplankton', 'Schulze, Katja|Tillich, Ulrich M|Dandekar, Thomas|Frohme, Marcus', 'Biotechnology and Functional Genomics, Technical University of Applied Sciences, Bahnhofstraße, Wildau 15745, Germany. katja.schulze@stud-mail.uni-wuerzburg.de|Biotechnology and Functional Genomics, Technical University of Applied Sciences, Bahnhofstraße, Wildau 15745, Germany. katja.schulze@stud-mail.uni-wuerzburg.de|Biotechnology and Functional Genomics, Technical University of Applied Sciences, Bahnhofstraße, Wildau 15745, Germany. katja.schulze@stud-mail.uni-wuerzburg.de|Biotechnology and Functional Genomics, Technical University of Applied Sciences, Bahnhofstraße, Wildau 15745, Germany. katja.schulze@stud-mail.uni-wuerzburg.de', 'Germany|Germany|Germany|Germany', 23, 'Image Processing, Computer-Assisted|Microscopy|Neural Networks (Computer)|Phytoplankton|Software', 'Journal Article', '10.1186/1471-2105-14-115', '23537512', 'Phytoplankton communities are often used as a marker for the determination of fresh water quality. The routine analysis, however, is very time consuming and expensive as it is carried out manually by trained personnel. The goal of this work is to develop a system for an automated analysis.'),
(95528, 2013, 'bmc', 'PASTA: splice junction identification from RNA-sequencing data', 'Tang, Shaojun|Riva, Alberto', 'Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida, Gainesville, FL, USA.|Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida, Gainesville, FL, USA.', 'United States|United States', 0, 'Algorithms|Alternative Splicing|Animals|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Logistic Models|Mice|RNA Splice Sites|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-14-116', '23557086', 'Next generation transcriptome sequencing (RNA-Seq) is emerging as a powerful experimental tool for the study of alternative splicing and its regulation, but requires ad-hoc analysis methods and tools. PASTA (Patterned Alignments for Splicing and Transcriptome Analysis) is a splice junction detection algorithm specifically designed for RNA-Seq data, relying on a highly accurate alignment strategy and on a combination of heuristic and statistical methods to identify exon-intron junctions with high accuracy.'),
(95529, 2013, 'bmc', 'CUDASW++ 30: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions', 'Liu, Yongchao|Wirawan, Adrianto|Schmidt, Bertil', 'Institut für Informatik, Johannes Gutenberg Universität Mainz, Mainz, Germany. liuy@uni-mainz.de|Institut für Informatik, Johannes Gutenberg Universität Mainz, Mainz, Germany. liuy@uni-mainz.de|Institut für Informatik, Johannes Gutenberg Universität Mainz, Mainz, Germany. liuy@uni-mainz.de', 'Germany|Germany|Germany', 121, 'Algorithms|Databases, Protein|Sequence Alignment|Sequence Analysis, Protein|Software', 'Evaluation Studies', '10.1186/1471-2105-14-117', '23557111', 'The maximal sensitivity for local alignments makes the Smith-Waterman algorithm a popular choice for protein sequence database search based on pairwise alignment. However, the algorithm is compute-intensive due to a quadratic time complexity. Corresponding runtimes are further compounded by the rapid growth of sequence databases.'),
(95530, 2013, 'bmc', 'Effects of using coding potential, sequence conservation and mRNA structure conservation for predicting pyrrolysine containing genes', 'Theil Have, Christian|Zambach, Sine|Christiansen, Henning', 'Research group PLIS: Programming, Logic and Intelligent Systems, Department of Communication, Business and Information Technologies, Roskilde University, P.O. Box 260, Roskilde, DK-4000, Denmark. cth@ruc.dk|Research group PLIS: Programming, Logic and Intelligent Systems, Department of Communication, Business and Information Technologies, Roskilde University, P.O. Box 260, Roskilde, DK-4000, Denmark. cth@ruc.dk|Research group PLIS: Programming, Logic and Intelligent Systems, Department of Communication, Business and Information Technologies, Roskilde University, P.O. Box 260, Roskilde, DK-4000, Denmark. cth@ruc.dk', 'Denmark|Denmark|Denmark', 2, 'Base Sequence|Cluster Analysis|Codon, Terminator|Conserved Sequence|Evolution, Molecular|Genome, Archaeal|Genome, Bacterial|Genomics|Lysine|Methyltransferases|Open Reading Frames|Protein Biosynthesis|RNA, Messenger|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-14-118', '23557142', 'Pyrrolysine (the 22nd amino acid) is in certain organisms and under certain circumstances encoded by the amber stop codon, UAG. The circumstances driving pyrrolysine translation are not well understood. The involvement of a predicted mRNA structure in the region downstream UAG has been suggested, but the structure does not seem to be present in all pyrrolysine incorporating genes.'),
(95531, 2013, 'bmc', 'An AUC-based permutation variable importance measure for random forests', 'Janitza, Silke|Strobl, Carolin|Boulesteix, Anne-Laure', 'Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr. 15, D-81377, Munich, Germany. janitza@ibe.med.uni-muenchen.de|Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr. 15, D-81377, Munich, Germany. janitza@ibe.med.uni-muenchen.de|Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr. 15, D-81377, Munich, Germany. janitza@ibe.med.uni-muenchen.de', 'Germany|Germany|Germany', 0, 'Algorithms|Area Under Curve|RNA Editing|Sample Size', 'Journal Article', '10.1186/1471-2105-14-119', '23560875', 'The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, i.e. data where response class sizes differ considerably. Suggestions were made to obtain better classification performance based either on sampling procedures or on cost sensitivity analyses. However to our knowledge the performance of the VIMs has not yet been examined in the case of unbalanced response classes. In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance.'),
(95532, 2013, 'bmc', 'Clustering evolving proteins into homologous families', 'Chan, Cheong Xin|Mahbob, Maisarah|Ragan, Mark A', 'Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.', 'Australia|Australia|Australia', 4, 'Algorithms|Bacterial Proteins|Base Composition|Cluster Analysis|DNA, Bacterial|Evolution, Molecular|Genome, Bacterial|Markov Chains|Proteins|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-14-120', '23566217', 'Clustering sequences into groups of putative homologs (families) is a critical first step in many areas of comparative biology and bioinformatics. The performance of clustering approaches in delineating biologically meaningful families depends strongly on characteristics of the data, including content bias and degree of divergence. New, highly scalable methods have recently been introduced to cluster the very large datasets being generated by next-generation sequencing technologies. However, there has been little systematic investigation of how characteristics of the data impact the performance of these approaches.'),
(95533, 2013, 'bmc', 'BioCode: two biologically compatible Algorithms for embedding data in non-coding and coding regions of DNA', 'Haughton, David|Balado, Félix', 'School of Computer Science and Informatics, University College Dublin, Belfield, Co, Dublin, Ireland. david.haughton@ucdconnect.ie|School of Computer Science and Informatics, University College Dublin, Belfield, Co, Dublin, Ireland. david.haughton@ucdconnect.ie', 'Ireland|Ireland', 20, 'Algorithms|Codon|DNA|DNA, Intergenic|Open Reading Frames|Proteins', 'Journal Article', '10.1186/1471-2105-14-121', '23570444', 'In recent times, the application of deoxyribonucleic acid (DNA) has diversified with the emergence of fields such as DNA computing and DNA data embedding. DNA data embedding, also known as DNA watermarking or DNA steganography, aims to develop robust algorithms for encoding non-genetic information in DNA. Inherently DNA is a digital medium whereby the nucleotide bases act as digital symbols, a fact which underpins all bioinformatics techniques, and which also makes trivial information encoding using DNA straightforward. However, the situation is more complex in methods which aim at embedding information in the genomes of living organisms. DNA is susceptible to mutations, which act as a noisy channel from the point of view of information encoded using DNA. This means that the DNA data embedding field is closely related to digital communications. Moreover it is a particularly unique digital communications area, because important biological constraints must be observed by all methods. Many DNA data embedding algorithms have been presented to date, all of which operate in one of two regions: non-coding DNA (ncDNA) or protein-coding DNA (pcDNA).'),
(95534, 2012, 'bmc', 'Proceedings from the Great Lakes Bioinformatics Conference, May 2-4, 2011, Athens, Ohio', '-', '-', '-', 0, 'Animals|Computational Biology|Humans', 'Congress', '10.1186/1471-2105-14-121', '23573529', '-'),
(95535, 2013, 'bmc', 'Computational identification of biologically functional non-hairpin GC-helices in human Argonaute mRNA', 'Dornseifer, Simon|Sczakiel, Georg', 'Institut für Molekulare Medizin, Center for Structural and Cell Biology in Medicine (CSCM), Universität zu Lübeck, Ratzeburger Allee 160, Lübeck D-23538, Germany.|Institut für Molekulare Medizin, Center for Structural and Cell Biology in Medicine (CSCM), Universität zu Lübeck, Ratzeburger Allee 160, Lübeck D-23538, Germany.', 'Germany|Germany', 2, '5 Untranslated Regions|Algorithms|Argonaute Proteins|Base Pairing|GC Rich Sequence|Humans|RNA Folding|RNA, Messenger|Software', 'Journal Article', '10.1186/1471-2105-14-122', '23574946', 'Perfectly formed duplex elements in RNA occur within folding units, often as a part of hairpin motifs which can be reliably predicted by various RNA folding algorithms. Double helices with consecutive Watson-Crick base-pairing may also be formed between distant RNA segments thereby facilitating long-range interactions of long-chain RNA that may be biologically functional. Here we addressed the potential formation of RNA duplex motifs by long-range RNA-RNA interactions of distantly located matching sequence elements of a single long-chain RNA.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95536, 2013, 'bmc', 'An efficient post-hoc integration method improving peak alignment of metabolomics data from GCxGC/TOF-MS', 'Jeong, Jaesik|Zhang, Xiang|Shi, Xue|Kim, Seongho|Shen, Changyu', 'Department of Biostatistics, 410 West 10th st., Indianapolis, IN 46202, USA.|Department of Biostatistics, 410 West 10th st., Indianapolis, IN 46202, USA.|Department of Biostatistics, 410 West 10th st., Indianapolis, IN 46202, USA.|Department of Biostatistics, 410 West 10th st., Indianapolis, IN 46202, USA.|Department of Biostatistics, 410 West 10th st., Indianapolis, IN 46202, USA.', 'India|India|India|India|India', 10, 'Algorithms|Animals|Biomarkers|Gas Chromatography-Mass Spectrometry|Metabolomics|Mice', 'Journal Article', '10.1186/1471-2105-14-123', '23575005', 'Since peak alignment in metabolomics has a huge effect on the subsequent statistical analysis, it is considered a key preprocessing step and many peak alignment methods have been developed. However, existing peak alignment methods do not produce satisfactory results. Indeed, the lack of accuracy results from the fact that peak alignment is done separately from another preprocessing step such as identification. Therefore, a post-hoc approach, which integrates both identification and alignment results, is in urgent need for the purpose of increasing the accuracy of peak alignment.'),
(95537, 2013, 'bmc', 'Random forest methodology for model-based recursive partitioning: the mobForest package for R', 'Garge, Nikhil R|Bobashev, Georgiy|Eggleston, Barry', 'Health Sciences Division, Social, Statistical and Environmental Sciences, Research Triangle Institute, 3040 Cornwallis Road, Cox 342, Research Triangle Park, NC 27709, USA. ngarge@rti.org|Health Sciences Division, Social, Statistical and Environmental Sciences, Research Triangle Institute, 3040 Cornwallis Road, Cox 342, Research Triangle Park, NC 27709, USA. ngarge@rti.org|Health Sciences Division, Social, Statistical and Environmental Sciences, Research Triangle Institute, 3040 Cornwallis Road, Cox 342, Research Triangle Park, NC 27709, USA. ngarge@rti.org', 'United States|United States|United States', 0, 'Algorithms|Models, Statistical|Software|Statistics, Nonparametric', 'Journal Article', '10.1186/1471-2105-14-125', '23577585', 'Recursive partitioning is a non-parametric modeling technique, widely used in regression and classification problems. Model-based recursive partitioning is used to identify groups of observations with similar values of parameters of the model of interest. The mob() function in the party package in R implements model-based recursive partitioning method. This method produces predictions based on single tree models. Predictions obtained through single tree models are very sensitive to small changes to the learning sample. We extend the model-based recursive partition method to produce predictions based on multiple tree models constructed on random samples achieved either through bootstrapping (random sampling with replacement) or subsampling (random sampling without replacement) on learning data.'),
(95538, 2013, 'bmc', 'The impact of quantile and rank normalization procedures on the testing power of gene differential expression analysis', 'Qiu, Xing|Wu, Hulin|Hu, Rui', 'Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, New York 14642, USA.|Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, New York 14642, USA.|Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, New York 14642, USA.', 'United States|United States|United States', 36, 'Data Interpretation, Statistical|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Sample Size', 'Journal Article', '10.1186/1471-2105-14-124', '23578321', 'Quantile and rank normalizations are two widely used pre-processing techniques designed to remove technological noise presented in genomic data. Subsequent statistical analysis such as gene differential expression analysis is usually based on normalized expressions. In this study, we find that these normalization procedures can have a profound impact on differential expression analysis, especially in terms of testing power.'),
(95539, 2013, 'bmc', 'Clever generation of rich SPARQL queries from annotated relational schema: application to Semantic Web Service creation for biological databases', 'Wollbrett, Julien|Larmande, Pierre|de Lamotte, Frédéric|Ruiz, Manuel', 'CIRAD, UMR AGAP, Montpellier F-34398, France. julien.wollbrett@cirad.fr|CIRAD, UMR AGAP, Montpellier F-34398, France. julien.wollbrett@cirad.fr|CIRAD, UMR AGAP, Montpellier F-34398, France. julien.wollbrett@cirad.fr|CIRAD, UMR AGAP, Montpellier F-34398, France. julien.wollbrett@cirad.fr', 'France|France|France|France', 4, 'Algorithms|Databases, Genetic|Genome, Plant|Internet|Oryza|Semantics|Software|Systems Integration|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-126', '23586394', 'In recent years, a large amount of -omics data have been produced. However, these data are stored in many different species-specific databases that are managed by different institutes and laboratories. Biologists often need to find and assemble data from disparate sources to perform certain analyses. Searching for these data and assembling them is a time-consuming task. The Semantic Web helps to facilitate interoperability across databases. A common approach involves the development of wrapper systems that map a relational database schema onto existing domain ontologies. However, few attempts have been made to automate the creation of such wrappers.'),
(95540, 2013, 'bmc', 'VaccImm: simulating peptide vaccination in cancer therapy', 'von Eichborn, Joachim|Woelke, Anna Lena|Castiglione, Filippo|Preissner, Robert', 'Charité-Universitätsmedizin Berlin, Institute for Physiology, Berlin, Germany.|Charité-Universitätsmedizin Berlin, Institute for Physiology, Berlin, Germany.|Charité-Universitätsmedizin Berlin, Institute for Physiology, Berlin, Germany.|Charité-Universitätsmedizin Berlin, Institute for Physiology, Berlin, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Amino Acid Sequence|Cancer Vaccines|Computer Simulation|Epitopes|Genotype|Humans|Major Histocompatibility Complex|Neoplasms|Peptides|Software|Tumor Burden|Vaccines, Subunit', 'Journal Article', '10.1186/1471-2105-14-127', '23586423', 'Despite progress in conventional cancer therapies, cancer is still one of the leading causes of death in industrial nations. Therefore, an urgent need of progress in fighting cancer remains. A promising alternative to conventional methods is immune therapy. This relies on the fact that low-immunogenic tumours can be eradicated if an immune response against them is induced. Peptide vaccination is carried out by injecting tumour peptides into a patient to trigger a specific immune response against the tumour in its entirety. However, peptide vaccination is a highly complicated treatment and currently many factors like the optimal number of epitopes are not known precisely. Therefore, it is necessary to evaluate how certain parameters influence the therapy.'),
(95541, 2013, 'bmc', 'Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool', 'Chen, Edward Y|Tan, Christopher M|Kou, Yan|Duan, Qiaonan|Wang, Zichen|Meirelles, Gabriela Vaz|Clark, Neil R|Maayan, Avi', 'Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L, Levy Place, Box 1215, New York, NY 10029, USA.|Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L, Levy Place, Box 1215, New York, NY 10029, USA.|Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L, Levy Place, Box 1215, New York, NY 10029, USA.|Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L, Levy Place, Box 1215, New York, NY 10029, USA.|Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L, Levy Place, Box 1215, New York, NY 10029, USA.|Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L, Levy Place, Box 1215, New York, NY 10029, USA.|Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L, Levy Place, Box 1215, New York, NY 10029, USA.|Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L, Levy Place, Box 1215, New York, NY 10029, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 1406, 'Animals|Cell Line, Tumor|Gene Expression Regulation, Neoplastic|Gene Library|Histones|Humans|Internet|Mice|Polycomb-Group Proteins|Proteins|Software|Transcriptome|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-14-128', '23586463', 'System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement.'),
(95542, 2013, 'bmc', 'Base calling for high-throughput short-read sequencing: dynamic programming solutions', 'Das, Shreepriya|Vikalo, Haris', 'Electrical and Computer Engineering Department, The University of Texas at Austin, Austin, Texas 78712, USA. shreedas@utexas.edu|Electrical and Computer Engineering Department, The University of Texas at Austin, Austin, Texas 78712, USA. shreedas@utexas.edu', 'United States|United States', 8, 'Algorithms|High-Throughput Nucleotide Sequencing|Models, Statistical|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-14-129', '23586484', 'Next-generation DNA sequencing platforms are capable of generating millions of reads in a matter of days at rapidly reducing costs. Despite its proliferation and technological improvements, the performance of next-generation sequencing remains adversely affected by the imperfections in the underlying biochemical and signal acquisition procedures. To this end, various techniques, including statistical methods, are used to improve read lengths and accuracy of these systems. Development of high performing base calling algorithms that are computationally efficient and scalable is an ongoing challenge.'),
(95543, 2013, 'bmc', 'Predicting substrates of the human breast cancer resistance protein using a support vector machine method', 'Hazai, Eszter|Hazai, Istvan|Ragueneau-Majlessi, Isabelle|Chung, Sophie P|Bikadi, Zsolt|Mao, Qingcheng', 'Virtua Drug Ltd., Csalogany Street 4, Budapest H-1015, Hungary.|Virtua Drug Ltd., Csalogany Street 4, Budapest H-1015, Hungary.|Virtua Drug Ltd., Csalogany Street 4, Budapest H-1015, Hungary.|Virtua Drug Ltd., Csalogany Street 4, Budapest H-1015, Hungary.|Virtua Drug Ltd., Csalogany Street 4, Budapest H-1015, Hungary.|Virtua Drug Ltd., Csalogany Street 4, Budapest H-1015, Hungary.', 'Hungary|Hungary|Hungary|Hungary|Hungary|Hungary', 0, 'ATP Binding Cassette Transporter, Subfamily G, Member 2|ATP-Binding Cassette Transporters|Breast Neoplasms|Drug Evaluation|Drug Resistance, Neoplasm|Humans|Models, Biological|Neoplasm Proteins|Substrate Specificity|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-14-130', '23586520', 'Human breast cancer resistance protein (BCRP) is an ATP-binding cassette (ABC) efflux transporter that confers multidrug resistance in cancers and also plays an important role in the absorption, distribution and elimination of drugs. Prediction as to if drugs or new molecular entities are BCRP substrates should afford a cost-effective means that can help evaluate the pharmacokinetic properties, efficacy, and safety of these drugs or drug candidates. At present, limited studies have been done to develop in silico prediction models for BCRP substrates. In this study, we developed support vector machine (SVM) models to predict wild-type BCRP substrates based on a total of 263 known BCRP substrates and non-substrates collected from literature. The final SVM model was integrated to a free web server.'),
(95544, 2013, 'bmc', 'Eliciting candidate anatomical routes for protein interactions: a scenario from endocrine physiology', 'Grenon, Pierre|de Bono, Bernard', 'EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK. pgrenon@ebi.ac.uk|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK. pgrenon@ebi.ac.uk', 'United Kingdom|United Kingdom', 0, 'Algorithms|Atrial Natriuretic Factor|Models, Biological|Protein Interaction Mapping|Protein Transport|Receptors, Atrial Natriuretic Factor', 'Journal Article', '10.1186/1471-2105-14-131', '23590598', 'In this paper, we use: i) formalised anatomical knowledge of connectivity between body structures and ii) a formal theory of physiological transport between fluid compartments in order to define and make explicit the routes followed by proteins to a site of interaction. The underlying processes are the objects of mathematical models of physiology and, therefore, the motivation for the approach can be understood as using knowledge representation and reasoning methods to propose concrete candidate routes corresponding to correlations between variables in mathematical models of physiology. In so doing, the approach projects physiology models onto a representation of the anatomical and physiological reality which underpins them.'),
(95545, 2013, 'bmc', 'Robust methods for population stratification in genome wide association studies', 'Liu, Li|Zhang, Donghui|Liu, Hong|Arendt, Christopher', 'Department of Biostatistics and Programming, Mail Stop 55C-305A, 55 Corporate Drive, Sanofi, Bridgewater, NJ 08807, USA. li.liu@sanofi.com|Department of Biostatistics and Programming, Mail Stop 55C-305A, 55 Corporate Drive, Sanofi, Bridgewater, NJ 08807, USA. li.liu@sanofi.com|Department of Biostatistics and Programming, Mail Stop 55C-305A, 55 Corporate Drive, Sanofi, Bridgewater, NJ 08807, USA. li.liu@sanofi.com|Department of Biostatistics and Programming, Mail Stop 55C-305A, 55 Corporate Drive, Sanofi, Bridgewater, NJ 08807, USA. li.liu@sanofi.com', 'United States|United States|United States|United States', 22, 'Arthritis, Rheumatoid|Cluster Analysis|Gene Frequency|Genome-Wide Association Study|Humans|Logistic Models|Population|Principal Component Analysis', 'Journal Article', '10.1186/1471-2105-14-132', '23601181', 'Genome-wide association studies can provide novel insights into diseases of interest, as well as to the responsiveness of an individual to specific treatments. In such studies, it is very important to correct for population stratification, which refers to allele frequency differences between cases and controls due to systematic ancestry differences. Population stratification can cause spurious associations if not adjusted properly. The principal component analysis (PCA) method has been relied upon as a highly useful methodology to adjust for population stratification in these types of large-scale studies. Recently, the linear mixed model (LMM) has also been proposed to account for family structure or cryptic relatedness. However, neither of these approaches may be optimal in properly correcting for sample structures in the presence of subject outliers.'),
(95546, 2013, 'bmc', 'How cyanobacteria pose new problems to old methods: challenges in microarray time series analysis', 'Lehmann, Robert|Machné, Rainer|Georg, Jens|Benary, Manuela|Axmann, Ilka|Steuer, Ralf', 'Institute for Theoretical Biology, Humboldt University Berlin, Invalidenstraße 43, D-10115 Berlin, Germany. r.lehmann@biologie.hu-berlin.de|Institute for Theoretical Biology, Humboldt University Berlin, Invalidenstraße 43, D-10115 Berlin, Germany. r.lehmann@biologie.hu-berlin.de|Institute for Theoretical Biology, Humboldt University Berlin, Invalidenstraße 43, D-10115 Berlin, Germany. r.lehmann@biologie.hu-berlin.de|Institute for Theoretical Biology, Humboldt University Berlin, Invalidenstraße 43, D-10115 Berlin, Germany. r.lehmann@biologie.hu-berlin.de|Institute for Theoretical Biology, Humboldt University Berlin, Invalidenstraße 43, D-10115 Berlin, Germany. r.lehmann@biologie.hu-berlin.de|Institute for Theoretical Biology, Humboldt University Berlin, Invalidenstraße 43, D-10115 Berlin, Germany. r.lehmann@biologie.hu-berlin.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Circadian Rhythm|Cluster Analysis|Cyanobacteria|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-14-133', '23601192', 'The transcriptomes of several cyanobacterial strains have been shown to exhibit diurnal oscillation patterns, reflecting the diurnal phototrophic lifestyle of the organisms. The analysis of such genome-wide transcriptional oscillations is often facilitated by the use of clustering algorithms in conjunction with a number of pre-processing steps. Biological interpretation is usually focussed on the time and phase of expression of the resulting groups of genes. However, the use of microarray technology in such studies requires the normalization of pre-processing data, with unclear impact on the qualitative and quantitative features of the derived information on the number of oscillating transcripts and their respective phases.'),
(95547, 2013, 'bmc', 'An efficient algorithm to perform multiple testing in epistasis screening', 'Van Lishout, François|Mahachie John, Jestinah M|Gusareva, Elena S|Urrea, Victor|Cleynen, Isabelle|Théâtre, Emilie|Charloteaux, Benoît|Calle, Malu Luz|Wehenkel, Louis|Van Steen, Kristel', 'Systems and Modeling Unit, Montefiore Institute, University of Liège, 4000 Liège, Belgium. F.VanLishout@ulg.ac.be|Systems and Modeling Unit, Montefiore Institute, University of Liège, 4000 Liège, Belgium. F.VanLishout@ulg.ac.be|Systems and Modeling Unit, Montefiore Institute, University of Liège, 4000 Liège, Belgium. F.VanLishout@ulg.ac.be|Systems and Modeling Unit, Montefiore Institute, University of Liège, 4000 Liège, Belgium. F.VanLishout@ulg.ac.be|Systems and Modeling Unit, Montefiore Institute, University of Liège, 4000 Liège, Belgium. F.VanLishout@ulg.ac.be|Systems and Modeling Unit, Montefiore Institute, University of Liège, 4000 Liège, Belgium. F.VanLishout@ulg.ac.be|Systems and Modeling Unit, Montefiore Institute, University of Liège, 4000 Liège, Belgium. F.VanLishout@ulg.ac.be|Systems and Modeling Unit, Montefiore Institute, University of Liège, 4000 Liège, Belgium. F.VanLishout@ulg.ac.be|Systems and Modeling Unit, Montefiore Institute, University of Liège, 4000 Liège, Belgium. F.VanLishout@ulg.ac.be|Systems and Modeling Unit, Montefiore Institute, University of Liège, 4000 Liège, Belgium. F.VanLishout@ulg.ac.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Crohn Disease|Epistasis, Genetic|Genetic Association Studies|Humans|Models, Genetic|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-14-138', '23617239', 'Research in epistasis or gene-gene interaction detection for human complex traits has grown over the last few years. It has been marked by promising methodological developments, improved translation efforts of statistical epistasis to biological epistasis and attempts to integrate different omics information sources into the epistasis screening to enhance power. The quest for gene-gene interactions poses severe multiple-testing problems. In this context, the maxT algorithm is one technique to control the false-positive rate. However, the memory needed by this algorithm rises linearly with the amount of hypothesis tests. Gene-gene interaction studies will require a memory proportional to the squared number of SNPs. A genome-wide epistasis search would therefore require terabytes of memory. Hence, cache problems are likely to occur, increasing the computation time. In this work we present a new version of maxT, requiring an amount of memory independent from the number of genetic effects to be investigated. This algorithm was implemented in C++ in our epistasis screening software MBMDR-3.0.3. We evaluate the new implementation in terms of memory efficiency and speed using simulated data. The software is illustrated on real-life data for Crohns disease.'),
(95548, 2013, 'bmc', 'Ensemble-based prediction of RNA secondary structures', 'Aghaeepour, Nima|Hoos, Holger H', 'Department of Computer Science, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada. hoos@cs.ubc.ca|Department of Computer Science, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada. hoos@cs.ubc.ca', 'Canada|Canada', 8, 'Algorithms|Nucleic Acid Conformation|RNA|Software', 'Journal Article', '10.1186/1471-2105-14-139', '23617269', 'Accurate structure prediction methods play an important role for the understanding of RNA function. Energy-based, pseudoknot-free secondary structure prediction is one of the most widely used and versatile approaches, and improved methods for this task have received much attention over the past five years. Despite the impressive progress that as been achieved in this area, existing evaluations of the prediction accuracy achieved by various algorithms do not provide a comprehensive, statistically sound assessment. Furthermore, while there is increasing evidence that no prediction algorithm consistently outperforms all others, no work has been done to exploit the complementary strengths of multiple approaches.'),
(95549, 2013, 'bmc', 'D-Light on promoters: a client-server system for the analysis and visualization of cis-regulatory elements', 'Laimer, Josef|Zuzan, Clemens J|Ehrenberger, Tobias|Freudenberger, Monika|Gschwandtner, Simone|Lebherz, Carina|Lackner, Peter', 'Department of Molecular Biology, University of Salzburg, Hellbrunnerstr, 34, 5020 Salzburg, Austria.|Department of Molecular Biology, University of Salzburg, Hellbrunnerstr, 34, 5020 Salzburg, Austria.|Department of Molecular Biology, University of Salzburg, Hellbrunnerstr, 34, 5020 Salzburg, Austria.|Department of Molecular Biology, University of Salzburg, Hellbrunnerstr, 34, 5020 Salzburg, Austria.|Department of Molecular Biology, University of Salzburg, Hellbrunnerstr, 34, 5020 Salzburg, Austria.|Department of Molecular Biology, University of Salzburg, Hellbrunnerstr, 34, 5020 Salzburg, Austria.|Department of Molecular Biology, University of Salzburg, Hellbrunnerstr, 34, 5020 Salzburg, Austria.', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria', 0, 'Binding Sites|Computer Graphics|Genome|Promoter Regions, Genetic|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-14-140', '23617301', 'The binding of transcription factors to DNA plays an essential role in the regulation of gene expression. Numerous experiments elucidated binding sequences which subsequently have been used to derive statistical models for predicting potential transcription factor binding sites (TFBS). The rapidly increasing number of genome sequence data requires sophisticated computational approaches to manage and query experimental and predicted TFBS data in the context of other epigenetic factors and across different organisms.'),
(95550, 2013, 'bmc', 'Bacterial cell identification in differential interference contrast microscopy images', 'Obara, Boguslaw|Roberts, Mark A J|Armitage, Judith P|Grau, Vicente', 'School of Engineering and Computing Sciences, University of Durham, Durham, UK. boguslaw.obara@durham.ac.uk|School of Engineering and Computing Sciences, University of Durham, Durham, UK. boguslaw.obara@durham.ac.uk|School of Engineering and Computing Sciences, University of Durham, Durham, UK. boguslaw.obara@durham.ac.uk|School of Engineering and Computing Sciences, University of Durham, Durham, UK. boguslaw.obara@durham.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Bacteria|Image Processing, Computer-Assisted|Microscopy, Fluorescence|Microscopy, Interference|Rhodobacter sphaeroides', 'Journal Article', '10.1186/1471-2105-14-134', '23617824', 'Microscopy image segmentation lays the foundation for shape analysis, motion tracking, and classification of biological objects. Despite its importance, automated segmentation remains challenging for several widely used non-fluorescence, interference-based microscopy imaging modalities. For example in differential interference contrast microscopy which plays an important role in modern bacterial cell biology. Therefore, new revolutions in the field require the development of tools, technologies and work-flows to extract and exploit information from interference-based imaging data so as to achieve new fundamental biological insights and understanding.'),
(95551, 2013, 'bmc', 'Empirical Bayesian analysis of paired high-throughput sequencing data with a beta-binomial distribution', 'Hardcastle, Thomas J|Kelly, Krystyna A', 'Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA, UK. tjh48@cam.ac.uk|Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA, UK. tjh48@cam.ac.uk', 'United Kingdom|United Kingdom', 0, 'Bayes Theorem|Binomial Distribution|Carcinoma, Squamous Cell|Gene Expression Profiling|Genomics|Head and Neck Neoplasms|High-Throughput Nucleotide Sequencing|Humans|Linear Models|Squamous Cell Carcinoma of Head and Neck', 'Journal Article', '10.1186/1471-2105-14-135', '23617841', 'Pairing of samples arises naturally in many genomic experiments, for example, gene expression in tumour and normal tissue from the same patients. Methods for analysing high-throughput sequencing data from such experiments are required to identify differential expression, both within paired samples and between pairs under different experimental conditions.'),
(95552, 2013, 'bmc', 'Compression-based distance (CBD): a simple, rapid, and accurate method for microbiota composition comparison', 'Yang, Fang|Chia, Nicholas|White, Bryan A|Schook, Lawrence B', 'Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.|Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.|Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.|Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.', 'United States|United States|United States|United States', 3, 'Algorithms|Animals|Data Compression|Gastrointestinal Tract|Genes, rRNA|Humans|Intestinal Mucosa|Metagenome|Mice|RNA, Ribosomal, 16S|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-14-136', '23617892', 'Perturbations in intestinal microbiota composition have been associated with a variety of gastrointestinal tract-related diseases. The alleviation of symptoms has been achieved using treatments that alter the gastrointestinal tract microbiota toward that of healthy individuals. Identifying differences in microbiota composition through the use of 16S rRNA gene hypervariable tag sequencing has profound health implications. Current computational methods for comparing microbial communities are usually based on multiple alignments and phylogenetic inference, making them time consuming and requiring exceptional expertise and computational resources. As sequencing data rapidly grows in size, simpler analysis methods are needed to meet the growing computational burdens of microbiota comparisons. Thus, we have developed a simple, rapid, and accurate method, independent of multiple alignments and phylogenetic inference, to support microbiota comparisons.'),
(95553, 2013, 'bmc', 'A flood-based information flow analysis and network minimization method for gene regulatory networks', 'Pavlogiannis, Andreas|Mozhayskiy, Vadim|Tagkopoulos, Ilias', 'Department of Computer Science, University of California Davis, One Shields Avenue, Davis, CA 95616, USA.|Department of Computer Science, University of California Davis, One Shields Avenue, Davis, CA 95616, USA.|Department of Computer Science, University of California Davis, One Shields Avenue, Davis, CA 95616, USA.', 'United States|United States|United States', 0, 'Algorithms|Escherichia coli|Gene Regulatory Networks', 'Journal Article', '10.1186/1471-2105-14-137', '23617932', 'Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context.'),
(95554, 2013, 'bmc', 'An automated graphics tool for comparative genomics: the Coulson plot generator', 'Field, Helen I|Coulson, Richard M R|Field, Mark C', 'LGC Genomics Ltd, Pindar Road, Hoddesdon, Hertfordshire EN11 0WZ, UK. imogen.field@lgcgenomics.com|LGC Genomics Ltd, Pindar Road, Hoddesdon, Hertfordshire EN11 0WZ, UK. imogen.field@lgcgenomics.com|LGC Genomics Ltd, Pindar Road, Hoddesdon, Hertfordshire EN11 0WZ, UK. imogen.field@lgcgenomics.com', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Computer Graphics|Genomics|Protein Subunits|Software', 'Journal Article', '10.1186/1471-2105-14-141', '23621955', 'Comparative analysis is an essential component to biology. When applied to genomics for example, analysis may require comparisons between the predicted presence and absence of genes in a group of genomes under consideration. Frequently, genes can be grouped into small categories based on functional criteria, for example membership of a multimeric complex, participation in a metabolic or signaling pathway or shared sequence features and/or paralogy. These patterns of retention and loss are highly informative for the prediction of function, and hence possible biological context, and can provide great insights into the evolutionary history of cellular functions. However, representation of such information in a standard spreadsheet is a poor visual means from which to extract patterns within a dataset.'),
(95555, 2013, 'bmc', 'Incorporating phylogenetic-based covarying mutations into RNAalifold for RNA consensus structure prediction', 'Ge, Ping|Zhang, Shaojie', 'Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816-2362, USA.|Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816-2362, USA.', 'United States|United States', 5, 'Algorithms|Base Pairing|Mutation|Nucleic Acid Conformation|Phylogeny|RNA|RNA Folding|Sequence Alignment|Sequence Analysis, RNA|Thermodynamics', 'Evaluation Studies', '10.1186/1471-2105-14-142', '23621982', 'RNAalifold, a popular computational method for RNA consensus structure prediction, incorporates covarying mutations into a thermodynamic model to fold the aligned RNA sequences. When quantifying covariance, it evaluates conserved signals of two aligned columns with base-pairing rules. This scoring scheme performs better than some other approaches, such as mutual information. However it ignores the phylogenetic history of the aligned sequences, which is an important criterion to evaluate the level of sequence covariance.'),
(95556, 2013, 'bmc', 'Investigating the concordance of Gene Ontology terms reveals the intra- and inter-platform reproducibility of enrichment analysis', 'Zhang, Lifang|Zhang, Juan|Yang, Gang|Wu, Di|Jiang, Lina|Wen, Zhining|Li, Menglong', 'College of Chemistry, Sichuan University, Chengdu, 610064, Peoples Republic of China.|College of Chemistry, Sichuan University, Chengdu, 610064, Peoples Republic of China.|College of Chemistry, Sichuan University, Chengdu, 610064, Peoples Republic of China.|College of Chemistry, Sichuan University, Chengdu, 610064, Peoples Republic of China.|College of Chemistry, Sichuan University, Chengdu, 610064, Peoples Republic of China.|College of Chemistry, Sichuan University, Chengdu, 610064, Peoples Republic of China.|College of Chemistry, Sichuan University, Chengdu, 610064, Peoples Republic of China.', 'China|China|China|China|China|China|China', 0, 'Gene Expression Profiling|Genes|Oligonucleotide Array Sequence Analysis|Quality Control|Reproducibility of Results|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-143', '23627640', 'Reliability and Reproducibility of differentially expressed genes (DEGs) are essential for the biological interpretation of microarray data. The microarray quality control (MAQC) project launched by US Food and Drug Administration (FDA) elucidated that the lists of DEGs generated by intra- and inter-platform comparisons can reach a high level of concordance, which mainly depended on the statistical criteria used for ranking and selecting DEGs. Generally, it will produce reproducible lists of DEGs when combining fold change ranking with a non-stringent p-value cutoff. For further interpretation of the gene expression data, statistical methods of gene enrichment analysis provide powerful tools for associating the DEGs with prior biological knowledge, e.g. Gene Ontology (GO) terms and pathways, and are widely used in genome-wide research. Although the DEG lists generated from the same compared conditions proved to be reliable, the reproducible enrichment results are still crucial to the discovery of the underlying molecular mechanism differentiating the two conditions. Therefore, it is important to know whether the enrichment results are still reproducible, when using the lists of DEGs generated by different statistic criteria from inter-laboratory and cross-platform comparisons. In our study, we used the MAQC data sets for systematically accessing the intra- and inter-platform concordance of GO terms enriched by Gene Set Enrichment Analysis (GSEA) and LRpath.'),
(95557, 2013, 'bmc', 'Steiner tree methods for optimal sub-network identification: an empirical study', 'Sadeghi, Afshin|Fröhlich, Holger', 'Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms Universitat Bonn, Dahlmannstr 2, 53113 Bonn, Germany. sadeghi.afshin@yahoo.com|Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms Universitat Bonn, Dahlmannstr 2, 53113 Bonn, Germany. sadeghi.afshin@yahoo.com', 'Germany|Germany', 18, 'Algorithms|Alopecia|Breast Neoplasms|Female|Gene Expression Profiling|Gene Regulatory Networks|Histone Deacetylases|Humans|Male|Prognosis|Protein Interaction Mapping|Receptors, Androgen|Repressor Proteins|Systems Biology', 'Journal Article', '10.1186/1471-2105-14-144', '23627667', 'Analysis and interpretation of biological networks is one of the primary goals of systems biology. In this context identification of sub-networks connecting sets of seed proteins or seed genes plays a crucial role. Given that no natural node and edge weighting scheme is available retrieval of a minimum size sub-graph leads to the classical Steiner tree problem, which is known to be NP-complete. Many approximate solutions have been published and theoretically analyzed in the computer science literature, but far less is known about their practical performance in the bioinformatics field.'),
(95558, 2013, 'bmc', 'Characterizing the state of the art in the computational assignment of gene function: lessons from the first critical assessment of functional annotation (CAFA)', 'Gillis, Jesse|Pavlidis, Paul', 'Stanley Institute for Cognitive Genomic, Cold Spring Harbor Laboratory, 196 Genome Research Center, 500 Sunnyside Boulevard Woodbury, NY 11797, USA.|Stanley Institute for Cognitive Genomic, Cold Spring Harbor Laboratory, 196 Genome Research Center, 500 Sunnyside Boulevard Woodbury, NY 11797, USA.', 'United States|United States', 0, 'Algorithms|Computational Biology|Databases, Protein|Genes|Molecular Sequence Annotation|Proteins|Vocabulary, Controlled', 'Evaluation Studies', '10.1186/1471-2105-14-144', '23630983', 'The assignment of gene function remains a difficult but important task in computational biology. The establishment of the first Critical Assessment of Functional Annotation (CAFA) was aimed at increasing progress in the field. We present an independent analysis of the results of CAFA, aimed at identifying challenges in assessment and at understanding trends in prediction performance. We found that well-accepted methods based on sequence similarity (i.e., BLAST) have a dominant effect. Many of the most informative predictions turned out to be either recovering existing knowledge about sequence similarity or were post-dictions already documented in the literature. These results indicate that deep challenges remain in even defining the task of function assignment, with a particular difficulty posed by the problem of defining function in a way that is not dependent on either flawed gold standards or the input data itself. In particular, we suggest that using the Gene Ontology (or other similar systematizations of function) as a gold standard is unlikely to be the way forward.'),
(95559, 2013, 'bmc', 'Quality control, analysis and secure sharing of Luminex® immunoassay data using the open source LabKey Server platform', 'Eckels, Josh|Nathe, Cory|Nelson, Elizabeth K|Shoemaker, Sara G|Nostrand, Elizabeth Van|Yates, Nicole L|Ashley, Vicki C|Harris, Linda J|Bollenbeck, Mark|Fong, Youyi|Tomaras, Georgia D|Piehler, Britt', 'LabKey Software, Seattle, WA, USA.|LabKey Software, Seattle, WA, USA.|LabKey Software, Seattle, WA, USA.|LabKey Software, Seattle, WA, USA.|LabKey Software, Seattle, WA, USA.|LabKey Software, Seattle, WA, USA.|LabKey Software, Seattle, WA, USA.|LabKey Software, Seattle, WA, USA.|LabKey Software, Seattle, WA, USA.|LabKey Software, Seattle, WA, USA.|LabKey Software, Seattle, WA, USA.|LabKey Software, Seattle, WA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 7, 'Algorithms|Computer Security|Cooperative Behavior|HIV-1|Humans|Immunoassay|Quality Control|Software', 'Journal Article', '10.1186/1471-2105-14-145', '23631706', 'Immunoassays that employ multiplexed bead arrays produce high information content per sample. Such assays are now frequently used to evaluate humoral responses in clinical trials. Integrated software is needed for the analysis, quality control, and secure sharing of the high volume of data produced by such multiplexed assays. Software that facilitates data exchange and provides flexibility to perform customized analyses (including multiple curve fits and visualizations of assay performance over time) could increase scientists capacity to use these immunoassays to evaluate human clinical trials.'),
(95560, 2013, 'bmc', 'Combining MEDLINE and publisher data to create parallel corpora for the automatic translation of biomedical text', 'Yepes, Antonio Jimeno|Prieur-Gaston, Elise|Névéol, Aurélie', 'Lister Hill National Center for Biomedical Communications, US National Library of Medicine, Bethesda, USA. antonio.jimeno@gmail.com|Lister Hill National Center for Biomedical Communications, US National Library of Medicine, Bethesda, USA. antonio.jimeno@gmail.com|Lister Hill National Center for Biomedical Communications, US National Library of Medicine, Bethesda, USA. antonio.jimeno@gmail.com', 'United States|United States|United States', 13, 'Linguistics|MEDLINE|Models, Statistical|Publishing|Translating', 'Journal Article', '10.1186/1471-2105-14-146', '23631733', 'Most of the institutional and research information in the biomedical domain is available in the form of English text. Even in countries where English is an official language, such as the United States, language can be a barrier for accessing biomedical information for non-native speakers. Recent progress in machine translation suggests that this technique could help make English texts accessible to speakers of other languages. However, the lack of adequate specialized corpora needed to train statistical models currently limits the quality of automatic translations in the biomedical domain.'),
(95561, 2013, 'bmc', 'Quantifying variances in comparative RNA secondary structure prediction', 'Anderson, James W J|Novák, Ádám|Sükösd, Zsuzsanna|Golden, Michael|Arunapuram, Preeti|Edvardsson, Ingolfur|Hein, Jotun', 'Department of Statistics, South Parks Road, Oxford, UK. anderson@stats.ox.ac.uk|Department of Statistics, South Parks Road, Oxford, UK. anderson@stats.ox.ac.uk|Department of Statistics, South Parks Road, Oxford, UK. anderson@stats.ox.ac.uk|Department of Statistics, South Parks Road, Oxford, UK. anderson@stats.ox.ac.uk|Department of Statistics, South Parks Road, Oxford, UK. anderson@stats.ox.ac.uk|Department of Statistics, South Parks Road, Oxford, UK. anderson@stats.ox.ac.uk|Department of Statistics, South Parks Road, Oxford, UK. anderson@stats.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 3, 'Algorithms|Base Pairing|Evolution, Molecular|Nucleic Acid Conformation|Probability|RNA|Reproducibility of Results|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-14-149', '23634662', 'With the advancement of next-generation sequencing and transcriptomics technologies, regulatory effects involving RNA, in particular RNA structural changes are being detected. These results often rely on RNA secondary structure predictions. However, current approaches to RNA secondary structure modelling produce predictions with a high variance in predictive accuracy, and we have little quantifiable knowledge about the reasons for these variances.'),
(95562, 2013, 'bmc', 'CNV-TV: a robust method to discover copy number variation from short sequencing reads', 'Duan, Junbo|Zhang, Ji-Gang|Deng, Hong-Wen|Wang, Yu-Ping', 'Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA.|Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA.|Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA.|Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA.', 'United States|United States|United States|United States', 26, 'DNA Copy Number Variations|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Least-Squares Analysis|Sensitivity and Specificity|Sequence Analysis, DNA', 'Evaluation Studies', '10.1186/1471-2105-14-150', '23634703', 'Copy number variation (CNV) is an important structural variation (SV) in human genome. Various studies have shown that CNVs are associated with complex diseases. Traditional CNV detection methods such as fluorescence in situ hybridization (FISH) and array comparative genomic hybridization (aCGH) suffer from low resolution. The next generation sequencing (NGS) technique promises a higher resolution detection of CNVs and several methods were recently proposed for realizing such a promise. However, the performances of these methods are not robust under some conditions, e.g., some of them may fail to detect CNVs of short sizes. There has been a strong demand for reliable detection of CNVs from high resolution NGS data.'),
(95563, 2013, 'bmc', 'A metadata-aware application for remote scoring and exchange of tissue microarray images', 'Morris, Lorna|Tsui, Andrew|Crichton, Charles|Harris, Steve|Maccallum, Peter H|Howat, William J|Davies, Jim|Brenton, James D|Caldas, Carlos', 'Department of Oncology, University of Cambridge and Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, CB2 0RE, UK.|Department of Oncology, University of Cambridge and Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, CB2 0RE, UK.|Department of Oncology, University of Cambridge and Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, CB2 0RE, UK.|Department of Oncology, University of Cambridge and Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, CB2 0RE, UK.|Department of Oncology, University of Cambridge and Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, CB2 0RE, UK.|Department of Oncology, University of Cambridge and Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, CB2 0RE, UK.|Department of Oncology, University of Cambridge and Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, CB2 0RE, UK.|Department of Oncology, University of Cambridge and Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, CB2 0RE, UK.|Department of Oncology, University of Cambridge and Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, CB2 0RE, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Breast Neoplasms|Female|Humans|Image Processing, Computer-Assisted|Internet|Software|Tissue Array Analysis', 'Journal Article', '10.1186/1471-2105-14-147', '23635078', 'The use of tissue microarrays (TMA) and advances in digital scanning microscopy has enabled the collection of thousands of tissue images. There is a need for software tools to annotate, query and share this data amongst researchers in different physical locations.'),
(95564, 2013, 'bmc', 'Software for selecting the most informative sets of genomic loci for multi-target microbial typing', 'OSullivan, Matthew V N|Sintchenko, Vitali|Gilbert, Gwendolyn L', 'Centre for Infectious Diseases and Microbiology and Sydney Institute for Emerging Infections and Biosecurity, University of Sydney, Westmead Hospital, Hawkesbury Road, Westmead, NSW 2145, Australia. matthew.osullivan@swahs.health.nsw.gov.au|Centre for Infectious Diseases and Microbiology and Sydney Institute for Emerging Infections and Biosecurity, University of Sydney, Westmead Hospital, Hawkesbury Road, Westmead, NSW 2145, Australia. matthew.osullivan@swahs.health.nsw.gov.au|Centre for Infectious Diseases and Microbiology and Sydney Institute for Emerging Infections and Biosecurity, University of Sydney, Westmead Hospital, Hawkesbury Road, Westmead, NSW 2145, Australia. matthew.osullivan@swahs.health.nsw.gov.au', 'Australia|Australia|Australia', 4, 'Bacterial Typing Techniques|Cryptococcus|Genomics|Methicillin-Resistant Staphylococcus aureus|Multilocus Sequence Typing|Software|Streptococcus pneumoniae', 'Journal Article', '10.1186/1471-2105-14-148', '23635100', 'High-throughput sequencing can identify numerous potential genomic targets for microbial strain typing, but identification of the most informative combinations requires the use of computational screening tools. This paper describes novel software-- Automated Selection of Typing Target Subsets (AuSeTTS)--that allows intelligent selection of optimal targets for pathogen strain typing. The objective of this software is to maximise both discriminatory power, using Simpsons index of diversity (D), and concordance with existing typing methods, using the adjusted Wallace coefficient (AW). The program interrogates molecular typing results for panels of isolates, based on large target sets, and iteratively examines each target, one-by-one, to determine the most informative subset.'),
(95565, 2013, 'bmc', 'Analysis of multiple phenotypes in genome-wide genetic mapping studies', 'Suo, Chen|Toulopoulou, Timothea|Bramon, Elvira|Walshe, Muriel|Picchioni, Marco|Murray, Robin|Ott, Jurg', 'Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China. chen.suo@ki.se|Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China. chen.suo@ki.se|Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China. chen.suo@ki.se|Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China. chen.suo@ki.se|Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China. chen.suo@ki.se|Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China. chen.suo@ki.se|Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China. chen.suo@ki.se', 'China|China|China|China|China|China|China', 17, 'Chromosome Mapping|Genome, Human|Genome-Wide Association Study|Humans|Phenotype|Polymorphism, Single Nucleotide|Principal Component Analysis|Schizophrenia', 'Evaluation Studies', '10.1186/1471-2105-14-151', '23639181', 'Complex traits may be defined by a range of different criteria. It would result in a loss of information to perform analyses simply on the basis of a final clinical dichotomized affected / unaffected variable.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95566, 2013, 'bmc', 'Comparison study on statistical features of predicted secondary structures for protein structural class prediction: From content to position', 'Dai, Qi|Li, Yan|Liu, Xiaoqing|Yao, Yuhua|Cao, Yunjie|He, Pingan', 'College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, 310018, China. daiailiu2004@yahoo.com.cn|College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, 310018, China. daiailiu2004@yahoo.com.cn|College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, 310018, China. daiailiu2004@yahoo.com.cn|College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, 310018, China. daiailiu2004@yahoo.com.cn|College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, 310018, China. daiailiu2004@yahoo.com.cn|College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, 310018, China. daiailiu2004@yahoo.com.cn', 'China|China|China|China|China|China', 29, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Protein Folding|Protein Structure, Secondary|Proteins|Sequence Homology, Amino Acid|Support Vector Machine', 'Comparative Study', '10.1186/1471-2105-14-152', '23641706', 'Many content-based statistical features of secondary structural elements (CBF-PSSEs) have been proposed and achieved promising results in protein structural class prediction, but until now position distribution of the successive occurrences of an element in predicted secondary structure sequences hasnt been used. It is necessary to extract some appropriate position-based features of the secondary structural elements for prediction task.'),
(95567, 2013, 'bmc', 'Iterative rank-order normalization of gene expression microarray data', 'Welsh, Eric A|Eschrich, Steven A|Berglund, Anders E|Fenstermacher, David A', 'H Lee Moffitt Cancer Center and Research Institute, University of South Florida, Tampa, FL 33612, USA. Eric.Welsh@moffitt.org|H Lee Moffitt Cancer Center and Research Institute, University of South Florida, Tampa, FL 33612, USA. Eric.Welsh@moffitt.org|H Lee Moffitt Cancer Center and Research Institute, University of South Florida, Tampa, FL 33612, USA. Eric.Welsh@moffitt.org|H Lee Moffitt Cancer Center and Research Institute, University of South Florida, Tampa, FL 33612, USA. Eric.Welsh@moffitt.org', 'United States|United States|United States|United States', 0, 'Algorithms|Artifacts|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-14-153', '23647742', 'Many gene expression normalization algorithms exist for Affymetrix GeneChip microarrays. The most popular of these is RMA, primarily due to the precision and low noise produced during the process. A significant strength of this and similar approaches is the use of the entire set of arrays during both normalization and model-based estimation of signal. However, this leads to differing estimates of expression based on the starting set of arrays, and estimates can change when a single, additional chip is added to the set. Additionally, outlier chips can impact the signals of other arrays, and can themselves be skewed by the majority of the population.'),
(95568, 2013, 'bmc', 'Reconstituting protein interaction networks using parameter-dependent domain-domain interactions', 'Memixc5xa1evixc4x87, Vesna|Wallqvist, Anders|Reifman, Jaques', 'Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.|Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.|Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.', 'United States|United States|United States', 14, 'Databases, Protein|Molecular Sequence Annotation|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/1471-2105-14-154', '23651452', 'We can describe protein-protein interactions (PPIs) as sets of distinct domain-domain interactions (DDIs) that mediate the physical interactions between proteins. Experimental data confirm that DDIs are more consistent than their corresponding PPIs, lending support to the notion that analyses of DDIs may improve our understanding of PPIs and lead to further insights into cellular function, disease, and evolution. However, currently available experimental DDI data cover only a small fraction of all existing PPIs and, in the absence of structural data, determining which particular DDI mediates any given PPI is a challenge.'),
(95569, 2013, 'bmc', 'Greedy feature selection for glycan chromatography data with the generalized Dirichlet distribution', 'Galligan, Marie C|Saldova, Radka|Campbell, Matthew P|Rudd, Pauline M|Murphy, Thomas B', 'School of Mathematical Sciences, University College Dublin, Belfield, Dublin 4, Ireland. mgalliga@tcd.ie|School of Mathematical Sciences, University College Dublin, Belfield, Dublin 4, Ireland. mgalliga@tcd.ie|School of Mathematical Sciences, University College Dublin, Belfield, Dublin 4, Ireland. mgalliga@tcd.ie|School of Mathematical Sciences, University College Dublin, Belfield, Dublin 4, Ireland. mgalliga@tcd.ie|School of Mathematical Sciences, University College Dublin, Belfield, Dublin 4, Ireland. mgalliga@tcd.ie', 'Ireland|Ireland|Ireland|Ireland|Ireland', 2, 'Algorithms|Bayes Theorem|Biomarkers, Tumor|Chromatography, High Pressure Liquid|Female|Glycosylation|Humans|Lung Neoplasms|Male|Neoplasm Staging|Polysaccharides|Prostatic Hyperplasia|Prostatic Neoplasms|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-14-155', '23651459', 'Glycoproteins are involved in a diverse range of biochemical and biological processes. Changes in protein glycosylation are believed to occur in many diseases, particularly during cancer initiation and progression. The identification of biomarkers for human disease states is becoming increasingly important, as early detection is key to improving survival and recovery rates. To this end, the serum glycome has been proposed as a potential source of biomarkers for different types of cancers.High-throughput hydrophilic interaction liquid chromatography (HILIC) technology for glycan analysis allows for the detailed quantification of the glycan content in human serum. However, the experimental data from this analysis is compositional by nature. Compositional data are subject to a constant-sum constraint, which restricts the sample space to a simplex. Statistical analysis of glycan chromatography datasets should account for their unusual mathematical properties.As the volume of glycan HILIC data being produced increases, there is a considerable need for a framework to support appropriate statistical analysis. Proposed here is a methodology for feature selection in compositional data. The principal objective is to provide a template for the analysis of glycan chromatography data that may be used to identify potential glycan biomarkers.'),
(95570, 2013, 'bmc', 'Real-time interactive data mining for chemical imaging information: application to automated histopathology', 'Mayerich, David|Walsh, Michael|Schulmerich, Matthew|Bhargava, Rohit', 'Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.', 'United States|United States|United States|United States', 11, 'Biopsy|Breast Neoplasms|Data Mining|Female|Humans|Image Interpretation, Computer-Assisted|Pathology|Spectrum Analysis|Spectrum Analysis, Raman', 'Journal Article', '10.1186/1471-2105-14-156', '23651487', 'Vibrational spectroscopic imaging is now used in several fields to acquire molecular information from microscopically heterogeneous systems. Recent advances have led to promising applications in tissue analysis for cancer research, where chemical information can be used to identify cell types and disease. However, recorded spectra are affected by the morphology of the tissue sample, making identification of chemical structures difficult.'),
(95571, 2013, 'bmc', 'Copy number variation genotyping using family information', 'Chu, Jen-Hwa|Rogers, Angela|Ionita-Laza, Iuliana|Darvishi, Katayoon|Mills, Ryan E|Lee, Charles|Raby, Benjamin A', 'Channing Division of Network Medicine, Brigham and Womens Hospital, ,MA, USA. stjhc@channing.harvard.edu|Channing Division of Network Medicine, Brigham and Womens Hospital, ,MA, USA. stjhc@channing.harvard.edu|Channing Division of Network Medicine, Brigham and Womens Hospital, ,MA, USA. stjhc@channing.harvard.edu|Channing Division of Network Medicine, Brigham and Womens Hospital, ,MA, USA. stjhc@channing.harvard.edu|Channing Division of Network Medicine, Brigham and Womens Hospital, ,MA, USA. stjhc@channing.harvard.edu|Channing Division of Network Medicine, Brigham and Womens Hospital, ,MA, USA. stjhc@channing.harvard.edu|Channing Division of Network Medicine, Brigham and Womens Hospital, ,MA, USA. stjhc@channing.harvard.edu', 'United States|United States|United States|United States|United States|United States|United States', 5, 'Asthma|DNA Copy Number Variations|Family|Genome-Wide Association Study|Genotyping Techniques|Humans|Polymerase Chain Reaction', 'Journal Article', '10.1186/1471-2105-14-157', '23656838', 'In recent years there has been a growing interest in the role of copy number variations (CNV) in genetic diseases. Though there has been rapid development of technologies and statistical methods devoted to detection in CNVs from array data, the inherent challenges in data quality associated with most hybridization techniques remains a challenging problem in CNV association studies.'),
(95572, 2013, 'bmc', 'Phylotastic! Making tree-of-life knowledge accessible, reusable and convenient', 'Stoltzfus, Arlin|Lapp, Hilmar|Matasci, Naim|Deus, Helena|Sidlauskas, Brian|Zmasek, Christian M|Vaidya, Gaurav|Pontelli, Enrico|Cranston, Karen|Vos, Rutger|Webb, Campbell O|Harmon, Luke J|Pirrung, Megan|OMeara, Brian|Pennell, Matthew W|Mirarab, Siavash|Rosenberg, Michael S|Balhoff, James P|Bik, Holly M|Heath, Tracy A|Midford, Peter E|Brown, Joseph W|McTavish, Emily Jane|Sukumaran, Jeet|Westneat, Mark|Alfaro, Michael E|Steele, Aaron|Jordan, Greg', 'Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu|Institute for Bioscience and Biotechnology Research, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. arlin@umd.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|Uni', 22, 'Internet|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-14-158', '23668630', 'Scientists rarely reuse expert knowledge of phylogeny, in spite of years of effort to assemble a great Tree of Life (ToL). A notable exception involves the use of Phylomatic, which provides tools to generate custom phylogenies from a large, pre-computed, expert phylogeny of plant taxa. This suggests great potential for a more generalized system that, starting with a query consisting of a list of any known species, would rectify non-standard names, identify expert phylogenies containing the implicated taxa, prune away unneeded parts, and supply branch lengths and annotations, resulting in a custom phylogeny suited to the users needs. Such a system could become a sustainable community resource if implemented as a distributed system of loosely coupled parts that interact through clearly defined interfaces.'),
(95573, 2013, 'bmc', 'MIMO: an efficient tool for molecular interaction maps overlap', 'Di Lena, Pietro|Wu, Gang|Martelli, Pier Luigi|Casadio, Rita|Nardini, Christine', 'CAS Key Laboratory For Computational Biology Chinese Academy of Sciences-Max Plank Institute Partner Institute for Computational Biology, Yue Yang Road 320, Shanghai 200031, PRC. dilena@cs.unibo.it|CAS Key Laboratory For Computational Biology Chinese Academy of Sciences-Max Plank Institute Partner Institute for Computational Biology, Yue Yang Road 320, Shanghai 200031, PRC. dilena@cs.unibo.it|CAS Key Laboratory For Computational Biology Chinese Academy of Sciences-Max Plank Institute Partner Institute for Computational Biology, Yue Yang Road 320, Shanghai 200031, PRC. dilena@cs.unibo.it|CAS Key Laboratory For Computational Biology Chinese Academy of Sciences-Max Plank Institute Partner Institute for Computational Biology, Yue Yang Road 320, Shanghai 200031, PRC. dilena@cs.unibo.it|CAS Key Laboratory For Computational Biology Chinese Academy of Sciences-Max Plank Institute Partner Institute for Computational Biology, Yue Yang Road 320, Shanghai 200031, PRC. dilena@cs.unibo.it', '-|-|-|-|-', 10, 'Algorithms|Amino Acids|Citric Acid Cycle|Computer Graphics|Databases, Factual|Electron Transport|Humans|Metabolic Networks and Pathways|Mitosis|Signal Transduction|Software|Systems Biology|Wnt Signaling Pathway', 'Journal Article', '10.1186/1471-2105-14-159', '23672344', 'Molecular pathways represent an ensemble of interactions occurring among molecules within the cell and between cells. The identification of similarities between molecular pathways across organisms and functions has a critical role in understanding complex biological processes. For the inference of such novel information, the comparison of molecular pathways requires to account for imperfect matches (flexibility) and to efficiently handle complex network topologies. To date, these characteristics are only partially available in tools designed to compare molecular interaction maps.'),
(95574, 2013, 'bmc', 'Disk-based k-mer counting on a PC', 'Deorowicz, Sebastian|Debudaj-Grabysz, Agnieszka|Grabowski, Szymon', 'Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland. sebastian.deorowicz@polsl.pl|Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland. sebastian.deorowicz@polsl.pl|Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland. sebastian.deorowicz@polsl.pl', 'Poland|Poland|Poland', 35, 'Algorithms|Animals|Caenorhabditis elegans|Genome, Human|Genomics|Humans|Microcomputers|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-14-160', '23679007', 'The k-mer counting problem, which is to build the histogram of occurrences of every k-symbol long substring in a given text, is important for many bioinformatics applications. They include developing de Bruijn graph genome assemblers, fast multiple sequence alignment and repeat detection.'),
(95575, 2013, 'bmc', 'Efficient algorithms for biological stems search', 'Mi, Tian|Rajasekaran, Sanguthevar', 'Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA. rajasek@engr.uconn.edu|Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA. rajasek@engr.uconn.edu', 'United States|United States', 2, 'Algorithms|Amino Acid Motifs|DNA|Nucleotide Motifs|RNA|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-14-161', '23679045', 'Motifs are significant patterns in DNA, RNA, and protein sequences, which play an important role in biological processes and functions, like identification of open reading frames, RNA transcription, protein binding, etc. Several versions of the motif search problem have been studied in the literature. One such version is called the Planted Motif Search (PMS)or (l, d)-motif Search. PMS is known to be NP complete. The time complexities of most of the planted motif search algorithms depend exponentially on the alphabet size. Recently a new version of the motif search problem has been introduced by Kuksa and Pavlovic. We call this version as the Motif Stems Search (MSS) problem. A motif stem is an l-mer (for some relevant value of l)with some wildcard characters and hence corresponds to a set of l-mers (without wildcards), some of which are (l, d)-motifs. Kuksa and Pavlovic have presented an efficient algorithm to find motif stems for inputs from large alphabets. Ideally, the number of stems output should be as small as possible since the stems form a superset of the motifs.'),
(95576, 2013, 'bmc', 'Calcium (Ca2+) waves data calibration and analysis using image processing techniques', 'Milovic, Carlos|Oses, Carolina|Villalón, Manuel|Uribe, Sergio|Lizama, Carlos|Prieto, Claudia|Andia, Marcelo E|Irarrazaval, Pablo|Tejos, Cristian', 'Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile. cmilovic@puc.cl|Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile. cmilovic@puc.cl|Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile. cmilovic@puc.cl|Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile. cmilovic@puc.cl|Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile. cmilovic@puc.cl|Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile. cmilovic@puc.cl|Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile. cmilovic@puc.cl|Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile. cmilovic@puc.cl|Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile. cmilovic@puc.cl', 'Chile|Chile|Chile|Chile|Chile|Chile|Chile|Chile|Chile', 0, 'Animals|Calcium|Calcium Signaling|Calibration|Cells, Cultured|Image Processing, Computer-Assisted|Microscopy, Fluorescence|Rats|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-14-162', '23679062', 'Calcium (Ca2+) propagates within tissues serving as an important information carrier. In particular, cilia beat frequency in oviduct cells is partially regulated by Ca2+ changes. Thus, measuring the calcium density and characterizing the traveling wave plays a key role in understanding biological phenomena. However, current methods to measure propagation velocities and other wave characteristics involve several manual or time-consuming procedures. This limits the amount of information that can be extracted, and the statistical quality of the analysis.'),
(95577, 2013, 'bmc', 'Protein complex detection using interaction reliability assessment and weighted clustering coefficient', 'Zaki, Nazar|Efimov, Dmitry|Berengueres, Jose', 'Intelligent Systems, College of Information Technology, UAEU, Al Ain, UAE. nzaki@uaeu.ac.ae|Intelligent Systems, College of Information Technology, UAEU, Al Ain, UAE. nzaki@uaeu.ac.ae|Intelligent Systems, College of Information Technology, UAEU, Al Ain, UAE. nzaki@uaeu.ac.ae', '-|-|-', 64, 'Algorithms|Cluster Analysis|Multiprotein Complexes|Protein Interaction Mapping|Reproducibility of Results|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/1471-2105-14-163', '23688127', 'Predicting protein complexes from protein-protein interaction data is becoming a fundamental problem in computational biology. The identification and characterization of protein complexes implicated are crucial to the understanding of the molecular events under normal and abnormal physiological conditions. On the other hand, large datasets of experimentally detected protein-protein interactions were determined using High-throughput experimental techniques. However, experimental data is usually liable to contain a large number of spurious interactions. Therefore, it is essential to validate these interactions before exploiting them to predict protein complexes.'),
(95578, 2013, 'bmc', 'Learning smoothing models of copy number profiles using breakpoint annotations', 'Hocking, Toby Dylan|Schleiermacher, Gudrun|Janoueix-Lerosey, Isabelle|Boeva, Valentina|Cappo, Julie|Delattre, Olivier|Bach, Francis|Vert, Jean-Philippe', 'INRIA Sierra Project-Team, Paris F-75013, France. toby.hocking@inria.fr|INRIA Sierra Project-Team, Paris F-75013, France. toby.hocking@inria.fr|INRIA Sierra Project-Team, Paris F-75013, France. toby.hocking@inria.fr|INRIA Sierra Project-Team, Paris F-75013, France. toby.hocking@inria.fr|INRIA Sierra Project-Team, Paris F-75013, France. toby.hocking@inria.fr|INRIA Sierra Project-Team, Paris F-75013, France. toby.hocking@inria.fr|INRIA Sierra Project-Team, Paris F-75013, France. toby.hocking@inria.fr|INRIA Sierra Project-Team, Paris F-75013, France. toby.hocking@inria.fr', 'France|France|France|France|France|France|France|France', 0, 'Algorithms|Chromosome Breakpoints|Chromosome Mapping|Gene Dosage|Gene Expression Profiling|Humans|Models, Genetic', 'Journal Article', '10.1186/1471-2105-14-164', '23697330', 'Many models have been proposed to detect copy number alterations in chromosomal copy number profiles, but it is usually not obvious to decide which is most effective for a given data set. Furthermore, most methods have a smoothing parameter that determines the number of breakpoints and must be chosen using various heuristics.'),
(95579, 2013, 'bmc', 'Separate-channel analysis of two-channel microarrays: recovering inter-spot information', 'Smyth, Gordon K|Altman, Naomi S', 'Walter and Eliza Hall Institute of Medical Research, Melbourne, Vic 3052, Australia.|Walter and Eliza Hall Institute of Medical Research, Melbourne, Vic 3052, Australia.', 'Australia|Australia', 20, 'Apolipoprotein A-I|Bayes Theorem|Eschscholzia|Gene Expression Profiling|Humans|Jurkat Cells|Linear Models|MCF-7 Cells|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-14-165', '23705896', 'Two-channel (or two-color) microarrays are cost-effective platforms for comparative analysis of gene expression. They are traditionally analysed in terms of the log-ratios (M-values) of the two channel intensities at each spot, but this analysis does not use all the information available in the separate channel observations. Mixed models have been proposed to analyse intensities from the two channels as separate observations, but such models can be complex to use and the gain in efficiency over the log-ratio analysis is difficult to quantify. Mixed models yield test statistics for the null distributions can be specified only approximately, and some approaches do not borrow strength between genes.'),
(95580, 2013, 'bmc', 'GWAS on your notebook: fast semi-parallel linear and logistic regression for genome-wide association studies', 'Sikorska, Karolina|Lesaffre, Emmanuel|Groenen, Patrick F J|Eilers, Paul H C', 'Department of Biostatistics, Erasmus MC, Rotterdam, The Netherlands. k.sikorska@erasmusmc.nl|Department of Biostatistics, Erasmus MC, Rotterdam, The Netherlands. k.sikorska@erasmusmc.nl|Department of Biostatistics, Erasmus MC, Rotterdam, The Netherlands. k.sikorska@erasmusmc.nl|Department of Biostatistics, Erasmus MC, Rotterdam, The Netherlands. k.sikorska@erasmusmc.nl', 'Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Computational Biology|Genome-Wide Association Study|Genotype|Humans|Linear Models|Logistic Models|Polymorphism, Single Nucleotide|Software|Time Factors', 'Journal Article', '10.1186/1471-2105-14-166', '23711206', 'Genome-wide association studies have become very popular in identifying genetic contributions to phenotypes. Millions of SNPs are being tested for their association with diseases and traits using linear or logistic regression models. This conceptually simple strategy encounters the following computational issues: a large number of tests and very large genotype files (many Gigabytes) which cannot be directly loaded into the software memory. One of the solutions applied on a grand scale is cluster computing involving large-scale resources. We show how to speed up the computations using matrix operations in pure R code.'),
(95581, 2013, 'bmc', 'Identifying cancer mutation targets across thousands of samples: MuteProc, a high throughput mutation analysis pipeline', 'Hadj Khodabakhshi, Alireza|Fejes, Anthony P|Birol, Inanc|Jones, Steven J M', 'Genome Sciences Centre, BC Cancer Agency, Suite 100 - 570 West 7th Ave, Vancouver, British Columbia, V5Z 4S6, Canada. alirezak@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Suite 100 - 570 West 7th Ave, Vancouver, British Columbia, V5Z 4S6, Canada. alirezak@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Suite 100 - 570 West 7th Ave, Vancouver, British Columbia, V5Z 4S6, Canada. alirezak@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Suite 100 - 570 West 7th Ave, Vancouver, British Columbia, V5Z 4S6, Canada. alirezak@bcgsc.ca', 'Canada|Canada|Canada|Canada', 1, 'DNA Mutational Analysis|Genes, Neoplasm|High-Throughput Nucleotide Sequencing|Humans|Molecular Sequence Annotation|Mutation|Neoplasms|Software', 'Journal Article', '10.1186/1471-2105-14-167', '23714400', 'In the past decade, bioinformatics tools have matured enough to reliably perform sophisticated primary data analysis on Next Generation Sequencing (NGS) data, such as mapping, assemblies and variant calling, however, there is still a dire need for improvements in the higher level analysis such as NGS data organization, analysis of mutation patterns and Genome Wide Association Studies (GWAS).'),
(95582, 2013, 'bmc', 'MAPs: a database of modular antibody parts for predicting tertiary structures and designing affinity matured antibodies', 'Pantazes, Robert J|Maranas, Costas D', 'Genome Sciences Centre, BC Cancer Agency, Suite 100 - 570 West 7th Ave, Vancouver, British Columbia, V5Z 4S6, Canada. alirezak@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Suite 100 - 570 West 7th Ave, Vancouver, British Columbia, V5Z 4S6, Canada. alirezak@bcgsc.ca', 'Canada|Canada', 0, 'Amino Acid Sequence|Animals|Antibodies, Neutralizing|Antibodies, Viral|Antibody Affinity|Databases, Protein|HIV Antibodies|Humans|Mice|Models, Molecular|Orthomyxoviridae|Protein Engineering|Protein Structure, Tertiary', 'Journal Article', '10.1186/1471-2105-14-168', '23718826', 'The de novo design of a novel protein with a particular function remains a formidable challenge with only isolated and hard-to-repeat successes to date. Due to their many structurally conserved features, antibodies are a family of proteins amenable to predictable rational design. Design algorithms must consider the structural diversity of possible naturally occurring antibodies. The human immune system samples this design space (2 1012) by randomly combining variable, diversity, and joining genes in a process known as V-(D)-J recombination.'),
(95583, 2013, 'bmc', 'Accounting for immunoprecipitation efficiencies in the statistical analysis of ChIP-seq data', 'Bao, Yanchun|Vinciotti, Veronica|Wit, Ernst|t Hoen, Peter A C', 'School of Information Systems, Computing and Mathematics, Brunel University, London, UK.|School of Information Systems, Computing and Mathematics, Brunel University, London, UK.|School of Information Systems, Computing and Mathematics, Brunel University, London, UK.|School of Information Systems, Computing and Mathematics, Brunel University, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 16, 'Binding Sites|Chromatin Immunoprecipitation|DNA-Binding Proteins|High-Throughput Nucleotide Sequencing|Models, Statistical|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-14-169', '23721376', 'ImmunoPrecipitation (IP) efficiencies may vary largely between different antibodies and between repeated experiments with the same antibody. These differences have a large impact on the quality of ChIP-seq data: a more efficient experiment will necessarily lead to a higher signal to background ratio, and therefore to an apparent larger number of enriched regions, compared to a less efficient experiment. In this paper, we show how IP efficiencies can be explicitly accounted for in the joint statistical modelling of ChIP-seq data.'),
(95584, 2013, 'bmc', 'Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods', 'Chang, Siow-Wee|Abdul-Kareem, Sameem|Merican, Amir Feisal|Zain, Rosnah Binti', 'Bioinformatics and Computational Biology, Institute of Biological Science, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia. changsiowwee@yahoo.com|Bioinformatics and Computational Biology, Institute of Biological Science, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia. changsiowwee@yahoo.com|Bioinformatics and Computational Biology, Institute of Biological Science, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia. changsiowwee@yahoo.com|Bioinformatics and Computational Biology, Institute of Biological Science, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia. changsiowwee@yahoo.com', 'Malaysia|Malaysia|Malaysia|Malaysia', 0, 'Adult|Aged|Artificial Intelligence|Biomarkers, Tumor|Female|Genome, Human|Humans|Logistic Models|Male|Middle Aged|Models, Biological|Mouth Neoplasms|Neural Networks (Computer)|Prognosis|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-14-170', '23725313', 'Machine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers.'),
(95585, 2013, 'bmc', 'GeneRIF indexing: sentence selection based on machine learning', 'Jimeno-Yepes, Antonio J|Sticco, J Caitlin|Mork, James G|Aronson, Alan R', 'National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. antonio.jimeno@gmail.com|National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. antonio.jimeno@gmail.com|National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. antonio.jimeno@gmail.com|National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. antonio.jimeno@gmail.com', 'United States|United States|United States|United States', 19, 'Abstracting and Indexing as Topic|Algorithms|Artificial Intelligence|Bayes Theorem|Databases, Genetic|Genes|Humans|MEDLINE', 'Journal Article', '10.1186/1471-2105-14-171', '23725347', 'A Gene Reference Into Function (GeneRIF) describes novel functionality of genes. GeneRIFs are available from the National Center for Biotechnology Information (NCBI) Gene database. GeneRIF indexing is performed manually, and the intention of our work is to provide methods to support creating the GeneRIF entries. The creation of GeneRIF entries involves the identification of the genes mentioned in MEDLINE®, citations and the sentences describing a novel function.'),
(95586, 2013, 'bmc', 'A semi-automated genome annotation comparison and integration scheme', 'Liu, Zhe|Ma, Hongwu|Goryanin, Igor', 'Computational Systems Biology and Bioinformatics, School of Informatics, University of Edinburgh, Informatics Forum, 10 Crichton Street, Edinburgh EH8 9AB, UK.|Computational Systems Biology and Bioinformatics, School of Informatics, University of Edinburgh, Informatics Forum, 10 Crichton Street, Edinburgh EH8 9AB, UK.|Computational Systems Biology and Bioinformatics, School of Informatics, University of Edinburgh, Informatics Forum, 10 Crichton Street, Edinburgh EH8 9AB, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Arcobacter|Chromosome Mapping|Escherichia coli|Genes, Bacterial|Genome, Bacterial|Genomics|Molecular Sequence Annotation', 'Journal Article', '10.1186/1471-2105-14-172', '23725374', 'Different genome annotation services have been developed in recent years and widely used. However, the functional annotation results from different services are often not the same and a scheme to obtain consensus functional annotations by integrating different results is in demand.'),
(95587, 2013, 'bmc', 'Region-based progressive localization of cell nuclei in microscopic images with data adaptive modeling', 'Song, Yang|Cai, Weidong|Huang, Heng|Wang, Yue|Feng, David Dagan|Chen, Mei', 'Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, NSW 2006, Australia. yson1723@uni.sydney.edu.au|Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, NSW 2006, Australia. yson1723@uni.sydney.edu.au|Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, NSW 2006, Australia. yson1723@uni.sydney.edu.au|Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, NSW 2006, Australia. yson1723@uni.sydney.edu.au|Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, NSW 2006, Australia. yson1723@uni.sydney.edu.au|Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, NSW 2006, Australia. yson1723@uni.sydney.edu.au', 'Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Cell Nucleus|Image Processing, Computer-Assisted|Microscopy, Fluorescence|Models, Theoretical', 'Journal Article', '10.1186/1471-2105-14-173', '23725412', 'Segmenting cell nuclei in microscopic images has become one of the most important routines in modern biological applications. With the vast amount of data, automatic localization, i.e. detection and segmentation, of cell nuclei is highly desirable compared to time-consuming manual processes. However, automated segmentation is challenging due to large intensity inhomogeneities in the cell nuclei and the background.'),
(95588, 2013, 'bmc', 'PMTED: a plant microRNA target expression database', 'Sun, Xiuli|Dong, Boquan|Yin, Lingjie|Zhang, Rongzhi|Du, Wei|Liu, Dongfeng|Shi, Nan|Li, Aili|Liang, Yanchun|Mao, Long', 'Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.|Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.|Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.|Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.|Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.|Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.|Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.|Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.|Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.|Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.', 'China|China|China|China|China|China|China|China|China|China', 32, 'Arabidopsis|Databases, Nucleic Acid|Gene Expression Profiling|Gene Expression Regulation, Plant|MicroRNAs|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-14-174', '23725466', 'MicroRNAs (miRNAs) are identified in nearly all plants where they play important roles in development and stress responses by target mRNA cleavage or translation repression. MiRNAs exert their functions by sequence complementation with target genes and hence their targets can be predicted using bioinformatics algorithms. In the past two decades, microarray technology has been employed to study genes involved in important biological processes such as biotic response, abiotic response, and specific tissues and developmental stages, many of which are miRNA targets. Despite their value in assisting research work for plant biologists, miRNA target genes are difficult to access without pre-processing and assistance of necessary analytical and visualization tools because they are embedded in a large body of microarray data that are scattered around in public databases.'),
(95589, 2013, 'bmc', 'Wide coverage biomedical event extraction using multiple partially overlapping corpora', 'Miwa, Makoto|Pyysalo, Sampo|Ohta, Tomoko|Ananiadou, Sophia', 'The National Centre for Text Mining and School of Computer Science, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. makoto.miwa@manchester.ac.uk|The National Centre for Text Mining and School of Computer Science, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. makoto.miwa@manchester.ac.uk|The National Centre for Text Mining and School of Computer Science, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. makoto.miwa@manchester.ac.uk|The National Centre for Text Mining and School of Computer Science, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. makoto.miwa@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Data Mining|Humans|Models, Theoretical|Semantics', 'Evaluation Studies', '10.1186/1471-2105-14-175', '23731785', 'Biomedical events are key to understanding physiological processes and disease, and wide coverage extraction is required for comprehensive automatic analysis of statements describing biomedical systems in the literature. In turn, the training and evaluation of extraction methods requires manually annotated corpora. However, as manual annotation is time-consuming and expensive, any single event-annotated corpus can only cover a limited number of semantic types. Although combined use of several such corpora could potentially allow an extraction system to achieve broad semantic coverage, there has been little research into learning from multiple corpora with partially overlapping semantic annotation scopes.'),
(95590, 2013, 'bmc', 'Routine performance and errors of 454 HLA exon sequencing in diagnostics', 'Niklas, Norbert|Pröll, Johannes|Danzer, Martin|Stabentheiner, Stephanie|Hofer, Katja|Gabriel, Christian', 'Red Cross Transfusion Service for Upper Austria, Krankenhausstraße 7, 4017 Linz, Austria. norbert.niklas@o.roteskreuz.at|Red Cross Transfusion Service for Upper Austria, Krankenhausstraße 7, 4017 Linz, Austria. norbert.niklas@o.roteskreuz.at|Red Cross Transfusion Service for Upper Austria, Krankenhausstraße 7, 4017 Linz, Austria. norbert.niklas@o.roteskreuz.at|Red Cross Transfusion Service for Upper Austria, Krankenhausstraße 7, 4017 Linz, Austria. norbert.niklas@o.roteskreuz.at|Red Cross Transfusion Service for Upper Austria, Krankenhausstraße 7, 4017 Linz, Austria. norbert.niklas@o.roteskreuz.at|Red Cross Transfusion Service for Upper Austria, Krankenhausstraße 7, 4017 Linz, Austria. norbert.niklas@o.roteskreuz.at', 'Austria|Austria|Austria|Austria|Austria|Austria', 20, 'Exons|HLA Antigens|High-Throughput Nucleotide Sequencing|Histocompatibility Testing|Humans|Sequence Analysis, DNA', 'Evaluation Studies', '10.1186/1471-2105-14-176', '23731822', 'Next-generation sequencing (NGS) has changed genomics significantly. More and more applications strive for sequencing with different platforms. Now, in 2012, after a decade of development and evolution, NGS has been accepted for a variety of research fields. Determination of sequencing errors is essential in order to follow next-generation sequencing beyond research use only. This study describes the overall 454 system performance of using multiple GS Junior runs with an in-house established and validated diagnostic assay for human leukocyte antigen (HLA) exon sequencing. Based on this data, we extracted, evaluated and characterized errors and variants of 60 HLA loci per run with respect to their adjacencies.'),
(95591, 2013, 'bmc', 'Evaluating genome architecture of a complex region via generalized bipartite matching', 'Lo, Christine|Kim, Sangwoo|Zakov, Shay|Bafna, Vineet', 'Department of Computer Science and Engineering, University of California, San Diego, CA, USA. cylo@eng.ucsd.edu.|Department of Computer Science and Engineering, University of California, San Diego, CA, USA. cylo@eng.ucsd.edu.|Department of Computer Science and Engineering, University of California, San Diego, CA, USA. cylo@eng.ucsd.edu.|Department of Computer Science and Engineering, University of California, San Diego, CA, USA. cylo@eng.ucsd.edu.', 'United States|United States|United States|United States', 2, 'Algorithms|Chromosome Mapping|Diploidy|Genome, Human|Genomics|Haploidy|Haplotypes|High-Throughput Nucleotide Sequencing|Humans|Receptors, KIR|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-14-S5-S13', '23734567', 'With the remarkable development in inexpensive sequencing technologies and supporting computational tools, we have the promise of medicine being personalized by knowledge of the individual genome. Current technologies provide high throughput, but short reads. Reconstruction of the donor genome is based either on de novo assembly of the (short) reads, or on mapping donor reads to a standard reference. While such techniques demonstrate high success rates for inferring simple genomic segments, they are confounded by segments with complex duplication patterns, including regions of direct medical relevance, like the HLA and the KIR regions.In this work, we address this problem with a method for assessing the quality of a predicted genome sequence for complex regions of the genome. This method combines two natural types of evidence: sequence similarity of the mapped reads to the predicted donor genome, and distribution of reads across the predicted genome. We define a new scoring function for read-to-genome matchings, which penalizes for sequence dissimilarities and deviations from expected read location distribution, and present an efficient algorithm for finding matchings that minimize the penalty. The algorithm is based on a formal problem, first defined in this paper, called Coverage Sensitive many-to-many min-cost bipartite Matching (CSM). This new problem variant generalizes the standard (one-to-one) weighted bipartite matching problem, and can be solved using network flows. The resulting Java-based tool, called SAGE (Scoring function for Assembled GEnomes), is freely available upon request. We demonstrate over simulated data that SAGE can be used to infer correct haplotypes of the highly repetitive KIR region on the Human chromosome 19.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95592, 2013, 'bmc', 'Investigating mathematical models of immuno-interactions with early-stage cancer under an agent-based modelling perspective', 'Figueredo, Grazziela P|Siebers, Peer-Olaf|Aickelin, Uwe', 'Intelligent Modelling and Analysis Research Group, School of Computer Science, The University of Nottingham, UK. grazziela.figueredo@nottingham.ac.uk|Intelligent Modelling and Analysis Research Group, School of Computer Science, The University of Nottingham, UK. grazziela.figueredo@nottingham.ac.uk|Intelligent Modelling and Analysis Research Group, School of Computer Science, The University of Nottingham, UK. grazziela.figueredo@nottingham.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Computer Simulation|Humans|Interleukin-2|Models, Biological|Neoplasms|Transforming Growth Factor beta', 'Journal Article', '10.1186/1471-2105-14-S6-S6', '23734575', 'Many advances in research regarding immuno-interactions with cancer were developed with the help of ordinary differential equation (ODE) models. These models, however, are not effectively capable of representing problems involving individual localisation, memory and emerging properties, which are common characteristics of cells and molecules of the immune system. Agent-based modelling and simulation is an alternative paradigm to ODE models that overcomes these limitations. In this paper we investigate the potential contribution of agent-based modelling and simulation when compared to ODE modelling and simulation. We seek answers to the following questions: Is it possible to obtain an equivalent agent-based model from the ODE formulation? Do the outcomes differ? Are there any benefits of using one method compared to the other? To answer these questions, we have considered three case studies using established mathematical models of immune interactions with early-stage cancer. These case studies were re-conceptualised under an agent-based perspective and the simulation results were then compared with those from the ODE models. Our results show that it is possible to obtain equivalent agent-based models (i.e. implementing the same mechanisms), the simulation output of both types of models however might differ depending on the attributes of the system to be modelled. In some cases, additional insight from using agent-based modelling was obtained. Overall, we can confirm that agent-based modelling is a useful addition to the tool set of immunologists, as it has extra features that allow for simulations with characteristics that are closer to the biological phenomena.'),
(95593, 2013, 'bmc', 'On the computational modeling of the innate immune system', 'Pigozzo, Alexandre Bittencourt|Macedo, Gilson Costa|Santos, Rodrigo Weber dos|Lobosco, Marcelo', 'Universidade Federal de Juiz de Fora, Campus Universitário, Bairro São Pedro, Rua José Lourenço Kelmer s/n, Juiz de Fora, MG, Brazil. alexbprr@gmail.com|Universidade Federal de Juiz de Fora, Campus Universitário, Bairro São Pedro, Rua José Lourenço Kelmer s/n, Juiz de Fora, MG, Brazil. alexbprr@gmail.com|Universidade Federal de Juiz de Fora, Campus Universitário, Bairro São Pedro, Rua José Lourenço Kelmer s/n, Juiz de Fora, MG, Brazil. alexbprr@gmail.com|Universidade Federal de Juiz de Fora, Campus Universitário, Bairro São Pedro, Rua José Lourenço Kelmer s/n, Juiz de Fora, MG, Brazil. alexbprr@gmail.com', 'Brazil|Brazil|Brazil|Brazil', 24, 'Computer Simulation|Gram-Negative Bacteria|Humans|Immune System|Immunity, Innate|Inflammation|Lipopolysaccharides|Models, Biological', 'Journal Article', '10.1186/1471-2105-14-S6-S7', '23734602', 'In recent years, there has been an increasing interest in the mathematical and computational modeling of the human immune system (HIS). Computational models of HIS dynamics may contribute to a better understanding of the relationship between complex phenomena and immune response, in addition, computational models will support the development of new drugs and therapies for different diseases. However, modeling the HIS is an extremely difficult task that demands a huge amount of work to be performed by multidisciplinary teams. In this study, our objective is to model the spatio-temporal dynamics of representative cells and molecules of the HIS during an immune response after the injection of lipopolysaccharide (LPS) into a section of tissue. LPS constitutes the cellular wall of Gram-negative bacteria, and it is a highly immunogenic molecule, which means that it has a remarkable capacity to elicit strong immune responses. We present a descriptive, mechanistic and deterministic model that is based on partial differential equations (PDE). Therefore, this model enables the understanding of how the different complex phenomena interact with structures and elements during an immune response. In addition, the models parameters reflect physiological features of the system, which makes the model appropriate for general use.'),
(95594, 2013, 'bmc', 'CLASS: constrained transcript assembly of RNA-seq reads', 'Song, Li|Florea, Liliana', 'McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MA 21205, USA.|McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MA 21205, USA.', 'United States|United States', 23, 'Algorithms|Alternative Splicing|Exons|Gene Expression Profiling|Humans|RNA Isoforms|Sequence Analysis, RNA|Software', 'Evaluation Studies', '10.1186/1471-2105-14-S5-S14', '23734605', 'RNA-seq has revolutionized our ability to survey the cellular transcriptome in great detail. However, while several approaches have been developed, the problem of assembling the short reads into full-length transcripts remains challenging.'),
(95595, 2013, 'bmc', 'A novel min-cost flow method for estimating transcript expression with RNA-Seq', 'Tomescu, Alexandru I|Kuosmanen, Anna|Rizzi, Romeo|Mäkinen, Veli', 'Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Finland. tomescu@cs.helsinki.fi.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Finland. tomescu@cs.helsinki.fi.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Finland. tomescu@cs.helsinki.fi.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Finland. tomescu@cs.helsinki.fi.', 'Finland|Finland|Finland|Finland', 35, 'Algorithms|Alternative Splicing|Gene Expression Profiling|Humans|Models, Statistical|RNA Isoforms|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-14-S5-S15', '23734627', 'Through transcription and alternative splicing, a gene can be transcribed into different RNA sequences (isoforms), depending on the individual, on the tissue the cell is in, or in response to some stimuli. Recent RNA-Seq technology allows for new high-throughput ways for isoform identification and quantification based on short reads, and various methods have been put forward for this non-trivial problem.'),
(95596, 2013, 'bmc', 'How oligoclonal are germinal centers? A new method for estimating clonal diversity from immunohistological sections', 'Faro, Jose|Or-Guil, Michal', 'Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain. jfaro@uvigo.es|Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain. jfaro@uvigo.es', 'Spain|Spain', 6, 'Animals|Antibody Affinity|B-Lymphocytes|Germinal Center|Immunologic Memory|Models, Statistical|Rats|Spleen', 'Journal Article', '10.1186/1471-2105-14-S6-S8', '23734629', 'The germinal center (GC) reaction leads to antibody affinity maturation and generation of memory B cells, but its underlying mechanisms are poorly understood. To assemble this puzzle, several key pieces of information are needed, one in particular being the number of participating B cell clones. Since this clonal diversity cannot be observed directly, earlier studies resorted to interpreting two types of available experimental data: Immunohistology of GCs containing two phenotypically distinct B-cell populations, and antibody gene sequences of small B-cell samples from GCs. Based on a simple model, investigators concluded that a typical GC was seeded by 2-8 B cells, endorsing the current notion that GCs are oligoclonal from the onset.'),
(95597, 2013, 'bmc', 'Gene set enrichment analysis of RNA-Seq data: integrating differential expression and splicing', 'Wang, Xi|Cairns, Murray J', 'School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, New South Wales, Australia.|School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, New South Wales, Australia.', 'Australia|Australia', 32, 'Alternative Splicing|Gene Expression Profiling|Humans|Male|Prostatic Neoplasms|Schizophrenia|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-14-S5-S16', '23734663', 'RNA-Seq has become a key technology in transcriptome studies because it can quantify overall expression levels and the degree of alternative splicing for each gene simultaneously. To interpret high-throughout transcriptome profiling data, functional enrichment analysis is critical. However, existing functional analysis methods can only account for differential expression, leaving differential splicing out altogether.'),
(95598, 2013, 'bmc', 'In silico investigation into dendritic cell regulation of CD8Treg mediated killing of Th1 cells in murine experimental autoimmune encephalomyelitis', 'Williams, Richard A|Greaves, Richard|Read, Mark|Timmis, Jon|Andrews, Paul S|Kumar, Vipin', 'Department of Computer Science, University of York, York, UK. raw507@york.ac.uk|Department of Computer Science, University of York, York, UK. raw507@york.ac.uk|Department of Computer Science, University of York, York, UK. raw507@york.ac.uk|Department of Computer Science, University of York, York, UK. raw507@york.ac.uk|Department of Computer Science, University of York, York, UK. raw507@york.ac.uk|Department of Computer Science, University of York, York, UK. raw507@york.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 11, 'Animals|Antigen-Presenting Cells|Autoimmune Diseases|CD8-Positive T-Lymphocytes|Computer Simulation|Dendritic Cells|Encephalomyelitis, Autoimmune, Experimental|Histocompatibility Antigens Class I|Humans|Mice|T-Lymphocytes, Cytotoxic|T-Lymphocytes, Regulatory|Th1 Cells', 'Journal Article', '10.1186/1471-2105-14-S6-S9', '23734666', 'Experimental autoimmune encephalomyelitis has been used extensively as an animal model of T cell mediated autoimmunity. A down-regulatory pathway through which encephalitogenic CD4Th1 cells are killed by CD8 regulatory T cells (Treg) has recently been proposed. With the CD8Treg cells being primed by dendritic cells, regulation of recovery may be occuring around these antigen presenting cells. CD4Treg cells provide critical help within this process, by licensing dendritic cells to prime CD8Treg cells, however the spatial and temporal aspects of this help in the CTL response is currently unclear.'),
(95599, 2013, 'bmc', 'De novo inference of stratification and local admixture in sequencing studies', 'Zhang, Yu', 'Department of Statistics, The Pennsylvania State University 326 Thomas Building, University Park, PA 16802, USA. yzz2@psu.edu.', 'United States', 0, 'Algorithms|Bayes Theorem|Genetics, Population|Genome, Human|Genomics|Genotype|Haplotypes|High-Throughput Nucleotide Sequencing|Humans|Markov Chains|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-14-S5-S17', '23734678', 'Analysis of population structures and genome local ancestry has become increasingly important in population and disease genetics. With the advance of next generation sequencing technologies, complete genetic variants in individuals genomes are quickly generated, providing unprecedented opportunities for learning population evolution histories and identifying local genetic signatures at the SNP resolution. The successes of those studies critically rely on accurate and powerful computational tools that can fully utilize the sequencing information. Although many algorithms have been developed for population structure inference and admixture mapping, many of them only work for independent SNPs in genotype or haplotype format, and require a large panel of reference individuals. In this paper, we propose a novel probabilistic method for detecting population structure and local admixture. The method takes input of sequencing data, genotype data and haplotype data. The method characterizes the dependence of genetic variants via haplotype segmentation, such that all variants detected in a sequencing study can be fully utilized for inference. The method further utilizes a infinite-state Bayesian Markov model to perform de novo stratification and admixture inference. Using simulated datasets from HapMapII and 1000Genomes, we show that our method performs superior than several existing algorithms, particularly when limited or no reference individuals are available. Our method is applicable to not only human studies but also studies of other species of interests, for which little reference information is available.Software Availability: http://stat.psu.edu/~yuzhang/software/dbm.tar.'),
(95600, 2013, 'bmc', 'metaBEETL: high-throughput analysis of heterogeneous microbial populations from shotgun DNA sequences', 'Ander, Christina|Schulz-Trieglaff, Ole B|Stoye, Jens|Cox, Anthony J', 'Computational Biology Group, Illumina Cambridge Ltd,, Chesterford Research Park, Little Chesterford, Essex, United Kingdom.|Computational Biology Group, Illumina Cambridge Ltd,, Chesterford Research Park, Little Chesterford, Essex, United Kingdom.|Computational Biology Group, Illumina Cambridge Ltd,, Chesterford Research Park, Little Chesterford, Essex, United Kingdom.|Computational Biology Group, Illumina Cambridge Ltd,, Chesterford Research Park, Little Chesterford, Essex, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 11, 'Algorithms|Environmental Microbiology|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Microbiota|Phylogeny|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-14-S5-S2', '23734710', 'Environmental shotgun sequencing (ESS) has potential to give greater insight into microbial communities than targeted sequencing of 16S regions, but requires much higher sequence coverage. The advent of next-generation sequencing has made it feasible for the Human Microbiome Project and other initiatives to generate ESS data on a large scale, but computationally efficient methods for analysing such data sets are needed.Here we present metaBEETL, a fast taxonomic classifier for environmental shotgun sequences. It uses a Burrows-Wheeler Transform (BWT) index of the sequencing reads and an indexed database of microbial reference sequences. Unlike other BWT-based tools, our method has no upper limit on the number or the total size of the reference sequences in its database. By capturing sequence relationships between strains, our reference index also allows us to classify reads which are not unique to an individual strain but are nevertheless specific to some higher phylogenetic order.Tested on datasets with known taxonomic composition, metaBEETL gave results that are competitive with existing similarity-based tools: due to normalization steps which other classifiers lack, the taxonomic profile computed by metaBEETL closely matched the true environmental profile. At the same time, its moderate running time and low memory footprint allow metaBEETL to scale well to large data sets.Code to construct the BWT indexed database and for the taxonomic classification is part of the BEETL library, available as a github repository at git@github.com:BEETL/BEETL.git.'),
(95601, 2013, 'bmc', 'Joint genotype inference with germline and somatic mutations', 'Bareke, Eric|Saillour, Virginie|Spinella, Jean-Franxc3xa7ois|Vidal, Ramon|Healy, Jasmine|Sinnett, Daniel|Csxc5xb1rxc3xb6s, Miklxc3xb3s', 'Department of Computer Science and Operations Research, University of Montrxc3xa9al, QC, Canada.|Department of Computer Science and Operations Research, University of Montrxc3xa9al, QC, Canada.|Department of Computer Science and Operations Research, University of Montrxc3xa9al, QC, Canada.|Department of Computer Science and Operations Research, University of Montrxc3xa9al, QC, Canada.|Department of Computer Science and Operations Research, University of Montrxc3xa9al, QC, Canada.|Department of Computer Science and Operations Research, University of Montrxc3xa9al, QC, Canada.|Department of Computer Science and Operations Research, University of Montrxc3xa9al, QC, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Algorithms|Child|DNA Mutational Analysis|Exome|Genome, Human|Genotype|Genotyping Techniques|Germ-Line Mutation|Humans|Mutation|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Software', 'Journal Article', '10.1186/1471-2105-14-S5-S3', '23734724', 'The joint sequencing of related genomes has become an important means to discover rare variants. Normal-tumor genome pairs are routinely sequenced together to find somatic mutations and their associations with different cancers. Parental and sibling genomes reveal de novo germline mutations and inheritance patterns related to Mendelian diseases.Acute lymphoblastic leukemia (ALL) is the most common paediatric cancer and the leading cause of cancer-related death among children. With the aim of uncovering the full spectrum of germline and somatic genetic alterations in childhood ALL genomes, we conducted whole-exome re-sequencing on a unique cohort of over 120 exomes of childhood ALL quartets, each comprising a patients tumor and matched-normal material, and DNA from both parents. We developed a general probabilistic model for such quartet sequencing reads mapped to the reference human genome. The model is used to infer joint genotypes at homologous loci across a normal-tumor genome pair and two parental genomes.We describe the algorithms and data structures for genotype inference, model parameter training. We implemented the methods in an open-source software package (QUADGT) that uses the standard file formats of the 1000 Genomes Project. Our methods utility is illustrated on quartets from the ALL cohort.'),
(95602, 2013, 'bmc', 'Discovering and mapping chromatin states using a tree hidden Markov model', 'Biesinger, Jacob|Wang, Yuanfeng|Xie, Xiaohui', 'Department of Computer Science, University of California-Irvine, CA, USA.|Department of Computer Science, University of California-Irvine, CA, USA.|Department of Computer Science, University of California-Irvine, CA, USA.', 'United States|United States|United States', 20, 'Bayes Theorem|Cell Differentiation|Chromatin|Chromatin Assembly and Disassembly|Chromatin Immunoprecipitation|Disease Progression|Epigenesis, Genetic|Genome, Human|Histones|Humans|Markov Chains|Models, Statistical|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-14-S5-S4', '23734743', 'New biological techniques and technological advances in high-throughput sequencing are paving the way for systematic, comprehensive annotation of many genomes, allowing differences between cell types or between disease/normal tissues to be determined with unprecedented breadth. Epigenetic modifications have been shown to exhibit rich diversity between cell types, correlate tightly with cell-type specific gene expression, and changes in epigenetic modifications have been implicated in several diseases. Previous attempts to understand chromatin state have focused on identifying combinations of epigenetic modification, but in cases of multiple cell types, have not considered the lineage of the cells in question.We present a Bayesian network that uses epigenetic modifications to simultaneously model 1) chromatin mark combinations that give rise to different chromatin states and 2) propensities for transitions between chromatin states through differentiation or disease progression. We apply our model to a recent dataset of histone modifications, covering nine human cell types with nine epigenetic modifications measured for each. Since exact inference in this model is intractable for all the scale of the datasets, we develop several variational approximations and explore their accuracy. Our method exhibits several desirable features including improved accuracy of inferring chromatin states, improved handling of missing data, and linear scaling with dataset size. The source code for our model is available at http:// http://github.com/uci-cbcl/tree-hmm.'),
(95603, 2013, 'bmc', 'Prioritization of candidate disease genes by topological similarity between disease and protein diffusion profiles', 'Zhu, Jie|Qin, Yufang|Liu, Taigang|Wang, Jun|Zheng, Xiaoqi', 'Department of Mathematics, Shanghai Normal University, Shanghai, China.|Department of Mathematics, Shanghai Normal University, Shanghai, China.|Department of Mathematics, Shanghai Normal University, Shanghai, China.|Department of Mathematics, Shanghai Normal University, Shanghai, China.|Department of Mathematics, Shanghai Normal University, Shanghai, China.', 'China|China|China|China|China', 0, 'Algorithms|Alzheimer Disease|Disease|Genes|Genetic Association Studies|Humans|Male|Phenotype|Prostatic Neoplasms|Protein Interaction Mapping', 'Journal Article', '10.1186/1471-2105-14-S5-S5', '23734762', 'Identification of gene-phenotype relationships is a fundamental challenge in human health clinic. Based on the observation that genes causing the same or similar phenotypes tend to correlate with each other in the protein-protein interaction network, a lot of network-based approaches were proposed based on different underlying models. A recent comparative study showed that diffusion-based methods achieve the state-of-the-art predictive performance.'),
(95604, 2013, 'bmc', 'Bellerophon: a hybrid method for detecting interchromosomal rearrangements at base pair resolution using next-generation sequencing data', 'Hayes, Matthew|Li, Jing', 'Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Ave,, Cleveland, OH, USA.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Ave,, Cleveland, OH, USA.', 'United States|United States', 9, 'Algorithms|Base Pairing|Chromosome Breakpoints|High-Throughput Nucleotide Sequencing|Humans|Male|Prostatic Neoplasms|Sequence Alignment|Sequence Analysis, DNA|Software|Translocation, Genetic', 'Journal Article', '10.1186/1471-2105-14-S5-S6', '23734783', 'Somatically-acquired translocations may serve as important markers for assessing the cause and nature of diseases like cancer. Algorithms to locate translocations may use next-generation sequencing (NGS) platform data. However, paired-end strategies do not accurately predict precise translocation breakpoints, and split-read methods may lose sensitivity if a translocation boundary is not captured by many sequenced reads. To address these challenges, we have developed Bellerophon, a method that uses discordant read pairs to identify potential translocations, and subsequently uses soft-clipped reads to predict the location of the precise breakpoints. Furthermore, for each chimeric breakpoint, our method attempts to classify it as a participant in an unbalanced translocation, balanced translocation, or interchromosomal insertion.'),
(95605, 2013, 'bmc', 'De Bruijn Superwalk with Multiplicities Problem is NP-hard', 'Kapun, Evgeny|Tsarev, Fedor', 'St. Petersburg National Research University of Information Technologies, Mechanics and Optics Genome Assembly Algorithms Laboratory, Kronverksky pr. 49, St. Petersburg, Russia.|St. Petersburg National Research University of Information Technologies, Mechanics and Optics Genome Assembly Algorithms Laboratory, Kronverksky pr. 49, St. Petersburg, Russia.', '-|-', 8, 'Algorithms|Genomics', 'Journal Article', '10.1186/1471-2105-14-S5-S7', '23734822', 'De Bruijn Superwalk with Multiplicities Problem is the problem of finding a walk in the de Bruijn graph containing several walks as subwalks and passing through each edge the exactly predefined number of times (equal to the multiplicity of this edge). This problem has been stated in the talk by Paul Medvedev and Michael Brudno on the first RECOMB Satellite Conference on Open Problems in Algorithmic Biology in August 2012. In this paper we show that this problem is NP-hard. Combined with results of previous works it means that all known models for genome assembly are NP-hard.'),
(95606, 2013, 'bmc', 'A random-permutations-based approach to fast read alignment', 'Lederman, Roy', 'Applied Mathematics Program, Yale University, 51 Prospect St., New Haven, CT 06511, USA. roy.lederman@yale.edu', 'United States', 0, 'Algorithms|Humans|INDEL Mutation|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-14-S5-S8', '23734846', 'Read alignment is a computational bottleneck in some sequencing projects. Most of the existing software packages for read alignment are based on two algorithmic approaches: prefix-trees and hash-tables. We propose a new approach to read alignment using random permutations of strings.'),
(95607, 2013, 'bmc', 'Assembling contigs in draft genomes using reversals and block-interchanges', 'Li, Chi-Long|Chen, Kun-Tze|Lu, Chin Lung', 'Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan. cllu@cs.nthu.edu.tw|Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan. cllu@cs.nthu.edu.tw|Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan. cllu@cs.nthu.edu.tw', '-|-|-', 9, 'Algorithms|Contig Mapping|Genome|Genomics|Phylogeny', 'Journal Article', '10.1186/1471-2105-14-S5-S9', '23734866', 'The techniques of next generation sequencing allow an increasing number of draft genomes to be produced rapidly in a decreasing cost. However, these draft genomes usually are just partially sequenced as collections of unassembled contigs, which cannot be used directly by currently existing algorithms for studying their genome rearrangements and phylogeny reconstruction. In this work, we study the one-sided block (or contig) ordering problem with weighted reversal and block-interchange distance. Given a partially assembled genome xcfx80 and a completely assembled genome xcfx83, the problem is to find an optimal ordering to assemble (i.e., order and orient) the contigs of xcfx80 such that the rearrangement distance measured by reversals and block-interchanges (also called generalized transpositions) with the weight ratio 1:2 between the assembled contigs of xcfx80 and xcfx83 is minimized. In addition to genome rearrangements and phylogeny reconstruction, the one-sided block ordering problem particularly has a useful application in genome resequencing, because its algorithms can be used to assemble the contigs of a draft genome xcfx80 based on a reference genome xcfx83. By using permutation groups, we design an efficient algorithm to solve this one-sided block ordering problem in Oxcexb4n time, where n is the number of genes or markers and xcexb4 is the number of used reversals and block-interchanges. We also show that the assembly of the partially assembled genome can be done in On time and its weighted rearrangement distance from the completely assembled genome can be calculated in advance in On time. Finally, we have implemented our algorithm into a program and used some simulated datasets to compare its accuracy performance to a currently existing similar tool, called SIS that was implemented by a heuristic algorithm that considers only reversals, on assembling the contigs in draft genomes based on their reference genomes. Our experimental results have shown that the accuracy performance of our program is better than that of SIS, when the number of reversals and transpositions involved in the rearrangement events between the complete genomes of xcfx80 and xcfx83 is increased. In particular, if there are more transpositions involved in the rearrangement events, then the gap of accuracy performance between our program and SIS is increasing.'),
(95608, 2013, 'bmc', 'Reconstruction of regulatory networks through temporal enrichment profiling and its application to H1N1 influenza viral infection', 'Zaslavsky, Elena|Nudelman, German|Marquez, Susanna|Hershberg, Uri|Hartmann, Boris M|Thakar, Juilee|Sealfon, Stuart C|Kleinstein, Steven H', 'Center for Translational Systems Biology and Department of Neurology, Mount Sinai School of Medicine, New York, NY 10029, USA. elena.zaslavsky@mssm.edu.|Center for Translational Systems Biology and Department of Neurology, Mount Sinai School of Medicine, New York, NY 10029, USA. elena.zaslavsky@mssm.edu.|Center for Translational Systems Biology and Department of Neurology, Mount Sinai School of Medicine, New York, NY 10029, USA. elena.zaslavsky@mssm.edu.|Center for Translational Systems Biology and Department of Neurology, Mount Sinai School of Medicine, New York, NY 10029, USA. elena.zaslavsky@mssm.edu.|Center for Translational Systems Biology and Department of Neurology, Mount Sinai School of Medicine, New York, NY 10029, USA. elena.zaslavsky@mssm.edu.|Center for Translational Systems Biology and Department of Neurology, Mount Sinai School of Medicine, New York, NY 10029, USA. elena.zaslavsky@mssm.edu.|Center for Translational Systems Biology and Department of Neurology, Mount Sinai School of Medicine, New York, NY 10029, USA. elena.zaslavsky@mssm.edu.|Center for Translational Systems Biology and Department of Neurology, Mount Sinai School of Medicine, New York, NY 10029, USA. elena.zaslavsky@mssm.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States', 9, 'Computational Biology|Dendritic Cells|Gene Expression Regulation, Viral|Gene Regulatory Networks|Humans|Influenza A Virus, H1N1 Subtype|Influenza, Human|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-14-S6-S1', '23734902', 'H1N1 influenza viruses were responsible for the 1918 pandemic that caused millions of deaths worldwide and the 2009 pandemic that caused approximately twenty thousand deaths. The cellular response to such virus infections involves extensive genetic reprogramming resulting in an antiviral state that is critical to infection control. Identifying the underlying transcriptional network driving these changes, and how this program is altered by virally-encoded immune antagonists, is a fundamental challenge in systems immunology.'),
(95609, 2013, 'bmc', 'Analytical results on the Beauchemin model of lymphocyte migration', 'Textor, Johannes|Sinn, Mathieu|de Boer, Rob J', 'Theoretical Biology & Bioinformatics, Utrecht University, Utrecht, The Netherlands. johannes.textor@gmx.de|Theoretical Biology & Bioinformatics, Utrecht University, Utrecht, The Netherlands. johannes.textor@gmx.de|Theoretical Biology & Bioinformatics, Utrecht University, Utrecht, The Netherlands. johannes.textor@gmx.de', 'Netherlands|Netherlands|Netherlands', 3, 'Animals|Cell Movement|Chemotaxis|Lymphocytes|Models, Biological', 'Journal Article', '10.1186/1471-2105-14-S6-S10', '23734948', 'The Beauchemin model is a simple particle-based description of stochastic lymphocyte migration in tissue, which has been successfully applied to studying immunological questions. In addition to being easy to implement, the model is also to a large extent mathematically tractable. This article provides a comprehensive overview of both existing and new analytical results on the Beauchemin model within a common mathematical framework. Specifically, we derive the motility coefficient, the mean square displacement, and the confinement ratio, and discuss four different methods for simulating biased migration of pre-defined speed. The results provide new insight into published studies and a reference point for future research based on this simple and popular lymphocyte migration model.'),
(95610, 2013, 'bmc', 'Multi-level model for the investigation of oncoantigen-driven vaccination effect', 'Cordero, Francesca|Beccuti, Marco|Fornari, Chiara|Lanzardo, Stefania|Conti, Laura|Cavallo, Federica|Balbo, Gianfranco|Calogero, Raffaele', 'Computer Science Department, University of Turin, Corso Svizzera 185, Torino, Italy. fcordero@di.unito.it|Computer Science Department, University of Turin, Corso Svizzera 185, Torino, Italy. fcordero@di.unito.it|Computer Science Department, University of Turin, Corso Svizzera 185, Torino, Italy. fcordero@di.unito.it|Computer Science Department, University of Turin, Corso Svizzera 185, Torino, Italy. fcordero@di.unito.it|Computer Science Department, University of Turin, Corso Svizzera 185, Torino, Italy. fcordero@di.unito.it|Computer Science Department, University of Turin, Corso Svizzera 185, Torino, Italy. fcordero@di.unito.it|Computer Science Department, University of Turin, Corso Svizzera 185, Torino, Italy. fcordero@di.unito.it|Computer Science Department, University of Turin, Corso Svizzera 185, Torino, Italy. fcordero@di.unito.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 10, 'Animals|Breast Neoplasms|Cancer Vaccines|Humans|Mice|Models, Biological|Neoplasms|Neoplastic Stem Cells|Receptor, ErbB-2', 'Journal Article', '10.1186/1471-2105-14-S6-S11', '23734974', 'Cancer stem cell theory suggests that cancers are derived by a population of cells named Cancer Stem Cells (CSCs) that are involved in the growth and in the progression of tumors, and lead to a hierarchical structure characterized by differentiated cell population. This cell heterogeneity affects the choice of cancer therapies, since many current cancer treatments have limited or no impact at all on CSC population, while they reveal a positive effect on the differentiated cell populations.'),
(95611, 2013, 'bmc', 'Simulating the decentralized processes of the human immune system in a virtual anatomy model', 'Sarpe, Vladimir|Jacob, Christian', 'Department of Computer Science, Faculty of Science, University of Calgary, Alberta, Canada.|Department of Computer Science, Faculty of Science, University of Calgary, Alberta, Canada.', 'Canada|Canada', 8, 'Adaptive Immunity|Computer Simulation|Education, Medical|Female|Humans|Imaging, Three-Dimensional|Immune System|Influenza A virus|Influenza, Human|Male|Models, Anatomic|Software', 'Journal Article', '10.1186/1471-2105-14-S6-S2', '23734994', 'Many physiological processes within the human body can be perceived and modeled as large systems of interacting particles or swarming agents. The complex processes of the human immune system prove to be challenging to capture and illustrate without proper reference to the spatial distribution of immune-related organs and systems. Our work focuses on physical aspects of immune system processes, which we implement through swarms of agents. This is our first prototype for integrating different immune processes into one comprehensive virtual physiology simulation.'),
(95612, 2013, 'bmc', 'Meta-analysis of variables affecting mouse protection efficacy of whole organism Brucella vaccines and vaccine candidates', 'Todd, Thomas E|Tibi, Omar|Lin, Yu|Sayers, Samantha|Bronner, Denise N|Xiang, Zuoshuang|He, Yongqun', 'Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA.|Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA.|Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA.|Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA.|Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA.|Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA.|Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Analysis of Variance|Animals|Brucella|Brucella Vaccine|Brucellosis|Computational Biology|Female|Humans|Mice|Mice, Inbred BALB C|Models, Animal|Vaccines, Attenuated', 'Journal Article', '10.1186/1471-2105-14-S6-S3', '23735014', 'Vaccine protection investigation includes three processes: vaccination, pathogen challenge, and vaccine protection efficacy assessment. Many variables can affect the results of vaccine protection. Brucella, a genus of facultative intracellular bacteria, is the etiologic agent of brucellosis in humans and multiple animal species. Extensive research has been conducted in developing effective live attenuated Brucella vaccines. We hypothesized that some variables play a more important role than others in determining vaccine protective efficacy. Using Brucella vaccines and vaccine candidates as study models, this hypothesis was tested by meta-analysis of Brucella vaccine studies reported in the literature.'),
(95613, 2013, 'bmc', 'AllerTOP--a server for in silico prediction of allergens', 'Dimitrov, Ivan|Flower, Darren R|Doytchinova, Irini', 'Faculty of Pharmacy, Medical University of Sofia, 2 Dunav st,, Sofia, Bulgaria.|Faculty of Pharmacy, Medical University of Sofia, 2 Dunav st,, Sofia, Bulgaria.|Faculty of Pharmacy, Medical University of Sofia, 2 Dunav st,, Sofia, Bulgaria.', 'Bulgaria|Bulgaria|Bulgaria', 60, 'Algorithms|Allergens|Amino Acid Sequence|Artificial Intelligence|Bayes Theorem|Computational Biology|Computer Simulation|Databases, Protein|Food Hypersensitivity|Humans|Hypersensitivity|Proteins|Toxins, Biological', 'Journal Article', '10.1186/1471-2105-14-S6-S4', '23735058', 'Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences.'),
(95614, 2013, 'bmc', 'Discovering motifs that induce sequencing errors', 'Allhoff, Manuel|Schönhuth, Alexander|Martin, Marcel|Costa, Ivan G|Rahmann, Sven|Marschall, Tobias', 'Life Sciences Group, Centrum Wiskunde & Informatica, Amsterdam, Netherlands. T.Marschall@cwi.nl.|Life Sciences Group, Centrum Wiskunde & Informatica, Amsterdam, Netherlands. T.Marschall@cwi.nl.|Life Sciences Group, Centrum Wiskunde & Informatica, Amsterdam, Netherlands. T.Marschall@cwi.nl.|Life Sciences Group, Centrum Wiskunde & Informatica, Amsterdam, Netherlands. T.Marschall@cwi.nl.|Life Sciences Group, Centrum Wiskunde & Informatica, Amsterdam, Netherlands. T.Marschall@cwi.nl.|Life Sciences Group, Centrum Wiskunde & Informatica, Amsterdam, Netherlands. T.Marschall@cwi.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Algorithms|DNA|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Nucleotide Motifs|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-14-S5-S1', '23735080', 'Elevated sequencing error rates are the most predominant obstacle in single-nucleotide polymorphism (SNP) detection, which is a major goal in the bulk of current studies using next-generation sequencing (NGS). Beyond routinely handled generic sources of errors, certain base calling errors relate to specific sequence patterns. Statistically principled ways to associate sequence patterns with base calling errors have not been previously described. Extant approaches either incur decisive losses in power, due to relating errors with individual genomic positions rather than motifs, or do not properly distinguish between motif-induced and sequence-unspecific sources of errors.'),
(95615, 2013, 'bmc', 'Software tool for 3D extraction of germinal centers', 'Olivieri, David N|Escalona, Merly|Faro, Jose', 'School of Computer Engineering, University of Vigo, Ourense, Spain. olivieri@ei.uvigo.es|School of Computer Engineering, University of Vigo, Ourense, Spain. olivieri@ei.uvigo.es|School of Computer Engineering, University of Vigo, Ourense, Spain. olivieri@ei.uvigo.es', 'Spain|Spain|Spain', 1, 'Algorithms|Animals|CD4-Positive T-Lymphocytes|Germinal Center|Imaging, Three-Dimensional|Mice|Microscopy, Confocal|Models, Biological|Software|Spleen', 'Journal Article', '10.1186/1471-2105-14-S6-S5', '23735122', 'Germinal Centers (GC) are short-lived micro-anatomical structures, within lymphoid organs, where affinity maturation is initiated. Theoretical modeling of the dynamics of the GC reaction including follicular CD4+ T helper and the recently described follicular regulatory CD4+ T cell populations, predicts that the intensity and life span of such reactions is driven by both types of T cells, yet controlled primarily by follicular regulatory CD4+ T cells. In order to calibrate GC models, it is necessary to properly analyze the kinetics of GC sizes. Presently, the estimation of spleen GC volumes relies upon confocal microscopy images from 20-30 slices spanning a depth of ~ 20 - 50 xcexbcm, whose GC areas are analyzed, slice-by-slice, for subsequent 3D reconstruction and quantification. The quantity of data to be analyzed from such images taken for kinetics experiments is usually prohibitively large to extract semi-manually with existing software. As a result, the entire procedure is highly time-consuming, and inaccurate, thereby motivating the need for a new software tool that can automatically identify and calculate the 3D spot volumes from GC multidimensional images.'),
(95616, 2013, 'bmc', 'An optimized algorithm for detecting and annotating regional differential methylation', 'Li, Sheng|Garrett-Bakelman, Francine E|Akalin, Altuna|Zumbo, Paul|Levine, Ross|To, Bik L|Lewis, Ian D|Brown, Anna L|DAndrea, Richard J|Melnick, Ari|Mason, Christopher E', 'Department of Physiology and Biophysics,Weill Cornell Medical College, 1305 York Ave., New York, NY 10065, USA.|Department of Physiology and Biophysics,Weill Cornell Medical College, 1305 York Ave., New York, NY 10065, USA.|Department of Physiology and Biophysics,Weill Cornell Medical College, 1305 York Ave., New York, NY 10065, USA.|Department of Physiology and Biophysics,Weill Cornell Medical College, 1305 York Ave., New York, NY 10065, USA.|Department of Physiology and Biophysics,Weill Cornell Medical College, 1305 York Ave., New York, NY 10065, USA.|Department of Physiology and Biophysics,Weill Cornell Medical College, 1305 York Ave., New York, NY 10065, USA.|Department of Physiology and Biophysics,Weill Cornell Medical College, 1305 York Ave., New York, NY 10065, USA.|Department of Physiology and Biophysics,Weill Cornell Medical College, 1305 York Ave., New York, NY 10065, USA.|Department of Physiology and Biophysics,Weill Cornell Medical College, 1305 York Ave., New York, NY 10065, USA.|Department of Physiology and Biophysics,Weill Cornell Medical College, 1305 York Ave., New York, NY 10065, USA.|Department of Physiology and Biophysics,Weill Cornell Medical College, 1305 York Ave., New York, NY 10065, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 57, 'Algorithms|CpG Islands|DNA Methylation|Epigenomics|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Leukemia|Molecular Sequence Annotation|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-14-S5-S10', '23735126', 'DNA methylation profiling reveals important differentially methylated regions (DMRs) of the genome that are altered during development or that are perturbed by disease. To date, few programs exist for regional analysis of enriched or whole-genome bisulfate conversion sequencing data, even though such data are increasingly common. Here, we describe an open-source, optimized method for determining empirically based DMRs (eDMR) from high-throughput sequence data that is applicable to enriched whole-genome methylation profiling datasets, as well as other globally enriched epigenetic modification data.'),
(95617, 2013, 'bmc', 'A mixture model for expression deconvolution from RNA-seq in heterogeneous tissues', 'Li, Yi|Xie, Xiaohui', 'Department of Computer Science, University of California-Irvine, CA, USA.|Department of Computer Science, University of California-Irvine, CA, USA.', 'United States|United States', 0, 'Algorithms|Cell Line|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Models, Statistical|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-14-S5-S11', '23735186', 'RNA-seq, a next-generation sequencing based method for transcriptome analysis, is rapidly emerging as the method of choice for comprehensive transcript abundance estimation. The accuracy of RNA-seq can be highly impacted by the purity of samples. A prominent, outstanding problem in RNA-seq is how to estimate transcript abundances in heterogeneous tissues, where a sample is composed of more than one cell type and the inhomogeneity can substantially confound the transcript abundance estimation of each individual cell type. Although experimental methods have been proposed to dissect multiple distinct cell types, computationally deconvoluting heterogeneous tissues provides an attractive alternative, since it keeps the tissue sample as well as the subsequent molecular content yield intact.'),
(95618, 2013, 'bmc', 'Gene prediction in metagenomic fragments based on the SVM algorithm', 'Liu, Yongchu|Guo, Jiangtao|Hu, Gangqing|Zhu, Huaiqiu', 'State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China.|State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China.|State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China.|State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China.', 'China|China|China|China', 43, 'Artificial Intelligence|Gastrointestinal Tract|Genes, Archaeal|Genes, Bacterial|Humans|Metagenomics|Microbiota|Open Reading Frames|Phylogeny|Sequence Analysis, DNA|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-14-S5-S12', '23735199', 'Metagenomic sequencing is becoming a powerful technology for exploring micro-ogranisms from various environments, such as human body, without isolation and cultivation. Accurately identifying genes from metagenomic fragments is one of the most fundamental issues.'),
(95619, 2013, 'bmc', 'The admixture maximum likelihood test to test for association between rare variants and disease phenotypes', 'Tyrer, Jonathan P|Guo, Qi|Easton, Douglas F|Pharoah, Paul D P', 'Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK. jpt34@medschl.cam.ac.uk|Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK. jpt34@medschl.cam.ac.uk|Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK. jpt34@medschl.cam.ac.uk|Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK. jpt34@medschl.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 9, 'Disease|Disease Susceptibility|Genetic Association Studies|Genetic Variation|Genotype|Humans|Likelihood Functions|Phenotype', 'Journal Article', '10.1186/1471-2105-14-177', '23738568', 'The development of genotyping arrays containing hundreds of thousands of rare variants across the genome and advances in high-throughput sequencing technologies have made feasible empirical genetic association studies to search for rare disease susceptibility alleles. As single variant testing is underpowered to detect associations, the development of statistical methods to combine analysis across variants - so-called burden tests - is an area of active research interest. We previously developed a method, the admixture maximum likelihood test, to test multiple, common variants for association with a trait of interest. We have extended this method, called the rare admixture maximum likelihood test (RAML), for the analysis of rare variants. In this paper we compare the performance of RAML with six other burden tests designed to test for association of rare variants.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95620, 2013, 'bmc', 'Exploring the transcription factor activity in high-throughput gene expression data using RLQ analysis', 'Baty, Florent|Rüdiger, Jochen|Miglino, Nicola|Kern, Lukas|Borger, Peter|Brutsche, Martin', 'Division of Pulmonary Medicine, Cantonal Hospital St, Gallen, Rorschacherstrasse 95, CH-9007 St, Gallen, Switzerland. florent.baty@kssg.ch|Division of Pulmonary Medicine, Cantonal Hospital St, Gallen, Rorschacherstrasse 95, CH-9007 St, Gallen, Switzerland. florent.baty@kssg.ch|Division of Pulmonary Medicine, Cantonal Hospital St, Gallen, Rorschacherstrasse 95, CH-9007 St, Gallen, Switzerland. florent.baty@kssg.ch|Division of Pulmonary Medicine, Cantonal Hospital St, Gallen, Rorschacherstrasse 95, CH-9007 St, Gallen, Switzerland. florent.baty@kssg.ch|Division of Pulmonary Medicine, Cantonal Hospital St, Gallen, Rorschacherstrasse 95, CH-9007 St, Gallen, Switzerland. florent.baty@kssg.ch|Division of Pulmonary Medicine, Cantonal Hospital St, Gallen, Rorschacherstrasse 95, CH-9007 St, Gallen, Switzerland. florent.baty@kssg.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 6, 'Binding Sites|Cell Proliferation|Fibroblasts|Gene Expression Profiling|Humans|Lung|Molecular Sequence Annotation|Mometasone Furoate|Multivariate Analysis|Oligonucleotide Array Sequence Analysis|Pregnadienediols|Transcription Factors', 'Journal Article', '10.1186/1471-2105-14-178', '23742070', 'Interpretation of gene expression microarray data in the light of external information on both columns and rows (experimental variables and gene annotations) facilitates the extraction of pertinent information hidden in these complex data. Biologists classically interpret genes of interest after retrieving functional information from a subset of genes of interest. Transcription factors play an important role in orchestrating the regulation of gene expression. Their activity can be deduced by examining the presence of putative transcription factors binding sites in the gene promoter regions.'),
(95621, 2013, 'bmc', 'Modelling and visualizing fine-scale linkage disequilibrium structure', 'Edwards, David', 'Department of Molecular Biology and Genetics, Centre for Quantitative Genetics and Genomics, Blichers Allé 20, Tjele 8830, Denmark. David.Edwards@agrsci.dk', 'Denmark', 3, 'Algorithms|Alleles|Animals|Computer Graphics|Genetic Variation|Genotyping Techniques|Haplotypes|Linkage Disequilibrium|Models, Statistical|Polymorphism, Single Nucleotide|Swine', 'Journal Article', '10.1186/1471-2105-14-179', '23742095', 'Detailed study of genetic variation at the population level in humans and other species is now possible due to the availability of large sets of single nucleotide polymorphism data. Alleles at two or more loci are said to be in linkage disequilibrium (LD) when they are correlated or statistically dependent. Current efforts to understand the genetic basis of complex phenotypes are based on the existence of such associations, making study of the extent and distribution of linkage disequilibrium central to this endeavour. The objective of this paper is to develop methods to study fine-scale patterns of allelic association using probabilistic graphical models.'),
(95622, 2013, 'bmc', 'Computational framework to support integration of biomolecular and clinical data within a translational approach', 'Miyoshi, Newton Shydeo Brandão|Pinheiro, Daniel Guariz|Silva, Wilson Araújo|Felipe, Joaquim Cezar', 'Department of Computing and Mathematics, Faculty of Philosophy, Sciences and Languages of Ribeirão Preto, University of São Paulo, São Paulo, Brazil.|Department of Computing and Mathematics, Faculty of Philosophy, Sciences and Languages of Ribeirão Preto, University of São Paulo, São Paulo, Brazil.|Department of Computing and Mathematics, Faculty of Philosophy, Sciences and Languages of Ribeirão Preto, University of São Paulo, São Paulo, Brazil.|Department of Computing and Mathematics, Faculty of Philosophy, Sciences and Languages of Ribeirão Preto, University of São Paulo, São Paulo, Brazil.', 'Brazil|Brazil|Brazil|Brazil', 12, 'Biomedical Research|Carcinoma|Computational Biology|Databases, Factual|Genome, Human|Genomics|Head and Neck Neoplasms|Humans|Software|Translational Medical Research', 'Journal Article', '10.1186/1471-2105-14-180', '23742129', 'The use of the knowledge produced by sciences to promote human health is the main goal of translational medicine. To make it feasible we need computational methods to handle the large amount of information that arises from bench to bedside and to deal with its heterogeneity. A computational challenge that must be faced is to promote the integration of clinical, socio-demographic and biological data. In this effort, ontologies play an essential role as a powerful artifact for knowledge representation. Chado is a modular ontology-oriented database model that gained popularity due to its robustness and flexibility as a generic platform to store biological data, however it lacks supporting representation of clinical and socio-demographic information.'),
(95623, 2013, 'bmc', 'Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing', 'Xu, Rong|Wang, Quanqiu', 'Medical Informatics Division, Case Western Reserve, Cleveland, OH, USA. rxx@case.edu|Medical Informatics Division, Case Western Reserve, Cleveland, OH, USA. rxx@case.edu', 'United States|United States', 60, 'Algorithms|Artificial Intelligence|Drug Discovery|Drug Repositioning|Drug Therapy|Humans|Knowledge Bases|MEDLINE|Pharmaceutical Preparations|United States', 'Journal Article', '10.1186/1471-2105-14-181', '23742147', 'A large-scale, highly accurate, machine-understandable drug-disease treatment relationship knowledge base is important for computational approaches to drug repurposing. The large body of published biomedical research articles and clinical case reports available on MEDLINE is a rich source of FDA-approved drug-disease indication as well as drug-repurposing knowledge that is crucial for applying FDA-approved drugs for new diseases. However, much of this information is buried in free text and not captured in any existing databases. The goal of this study is to extract a large number of accurate drug-disease treatment pairs from published literature.'),
(95624, 2013, 'bmc', 'Clustering cliques for graph-based summarization of the biomedical research literature', 'Zhang, Han|Fiszman, Marcelo|Shin, Dongwook|Wilkowski, Bartlomiej|Rindflesch, Thomas C', 'Department of Medical Informatics, China Medical University, Shenyang, Liaoning 110001, China. zhanghan@mail.cmu.edu.cn|Department of Medical Informatics, China Medical University, Shenyang, Liaoning 110001, China. zhanghan@mail.cmu.edu.cn|Department of Medical Informatics, China Medical University, Shenyang, Liaoning 110001, China. zhanghan@mail.cmu.edu.cn|Department of Medical Informatics, China Medical University, Shenyang, Liaoning 110001, China. zhanghan@mail.cmu.edu.cn|Department of Medical Informatics, China Medical University, Shenyang, Liaoning 110001, China. zhanghan@mail.cmu.edu.cn', 'China|China|China|China|China', 30, 'Algorithms|Biomedical Research|Cluster Analysis|Data Mining|Medical Subject Headings|PubMed|Semantics', 'Journal Article', '10.1186/1471-2105-14-182', '23742159', 'Graph-based notions are increasingly used in biomedical data mining and knowledge discovery tasks. In this paper, we present a clique-clustering method to automatically summarize graphs of semantic predications produced from PubMed citations (titles and abstracts).'),
(95625, 2013, 'bmc', 'xcex94xcex94PT: a comprehensive toolbox for the analysis of protein motion', 'Rodgers, Thomas L|Burnell, David|Townsend, Phil D|Pohl, Ehmke|Cann, Martin J|Wilson, Mark R|McLeish, Tom C B', 'Biophysical Sciences Institute, Durham University, Durham, UK. thomas.rodgers@durham.ac.uk|Biophysical Sciences Institute, Durham University, Durham, UK. thomas.rodgers@durham.ac.uk|Biophysical Sciences Institute, Durham University, Durham, UK. thomas.rodgers@durham.ac.uk|Biophysical Sciences Institute, Durham University, Durham, UK. thomas.rodgers@durham.ac.uk|Biophysical Sciences Institute, Durham University, Durham, UK. thomas.rodgers@durham.ac.uk|Biophysical Sciences Institute, Durham University, Durham, UK. thomas.rodgers@durham.ac.uk|Biophysical Sciences Institute, Durham University, Durham, UK. thomas.rodgers@durham.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 12, 'Entropy|Molecular Dynamics Simulation|Motion|Proteins|Software', 'Journal Article', '10.1186/1471-2105-14-183', '23758746', 'Normal Mode Analysis is one of the most successful techniques for studying motions in proteins and macromolecules. It can provide information on the mechanism of protein functions, used to aid crystallography and NMR data reconstruction, and calculate protein free energies.'),
(95626, 2013, 'bmc', 'Benchmarking short sequence mapping tools', 'Hatem, Ayat|Bozdaxc4x9f, Doruk|Toland, Amanda E|xc3x87atalyxc3xbcrek, xc3x9cmit V', 'Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA.|Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA.|Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA.|Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA.', 'United States|United States|United States|United States', 115, 'Algorithms|Benchmarking|Genome|High-Throughput Nucleotide Sequencing|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-14-184', '23758764', 'The development of next-generation sequencing instruments has led to the generation of millions of short sequences in a single run. The process of aligning these reads to a reference genome is time consuming and demands the development of fast and accurate alignment tools. However, the current proposed tools make different compromises between the accuracy and the speed of mapping. Moreover, many important aspects are overlooked while comparing the performance of a newly developed tool to the state of the art. Therefore, there is a need for an objective evaluation method that covers all the aspects. In this work, we introduce a benchmarking suite to extensively analyze sequencing tools with respect to various aspects and provide an objective comparison.'),
(95627, 2013, 'bmc', 'bioWeb3D: an online webGL 3D data visualisation tool', 'Pettit, Jean-Baptiste|Marioni, John C', 'EMBL-EBI, European Molecular Biology Laboratory - European Bioinformatics Institute, CB10 1SD, Cambridge, UK. jbpettit@ebi.ac.uk|EMBL-EBI, European Molecular Biology Laboratory - European Bioinformatics Institute, CB10 1SD, Cambridge, UK. jbpettit@ebi.ac.uk', 'United Kingdom|United Kingdom', 12, 'Computer Graphics|Humans|Imaging, Three-Dimensional|Internet|Software', 'Journal Article', '10.1186/1471-2105-14-185', '23758781', 'Data visualization is critical for interpreting biological data. However, in practice it can prove to be a bottleneck for non trained researchers, this is especially true for three dimensional (3D) data representation. Whilst existing software can provide all necessary functionalities to represent and manipulate biological 3D datasets, very few are easily accessible (browser based), cross platform and accessible to non-expert users.'),
(95628, 2013, 'bmc', 'MITE Digger, an efficient and accurate algorithm for genome wide discovery of miniature inverted repeat transposable elements', 'Yang, Guojun', 'Department of Biology, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada. gage.yang@utoronto.ca', 'Canada', 23, 'Algorithms|DNA Transposable Elements|Genome-Wide Association Study|Genomics|Inverted Repeat Sequences|Oryza|Software', 'Journal Article', '10.1186/1471-2105-14-186', '23758809', 'Miniature inverted repeat transposable elements (MITEs) are abundant non-autonomous elements, playing important roles in shaping gene and genome evolution. Their characteristic structural features are suitable for automated identification by computational approaches, however, de novo MITE discovery at genomic levels is still resource expensive. Efficient and accurate computational tools are desirable. Existing algorithms process every member of a MITE family, therefore a major portion of the computing task is redundant.'),
(95629, 2013, 'bmc', 'QualComp: a new lossy compressor for quality scores based on rate distortion theory', 'Ochoa, Idoia|Asnani, Himanshu|Bharadia, Dinesh|Chowdhury, Mainak|Weissman, Tsachy|Yona, Golan', 'Department of Electrical Engineering, Stanford University, Stanford, CA, USA. iochoa@stanford.edu|Department of Electrical Engineering, Stanford University, Stanford, CA, USA. iochoa@stanford.edu|Department of Electrical Engineering, Stanford University, Stanford, CA, USA. iochoa@stanford.edu|Department of Electrical Engineering, Stanford University, Stanford, CA, USA. iochoa@stanford.edu|Department of Electrical Engineering, Stanford University, Stanford, CA, USA. iochoa@stanford.edu|Department of Electrical Engineering, Stanford University, Stanford, CA, USA. iochoa@stanford.edu', 'United States|United States|United States|United States|United States|United States', 36, 'Algorithms|Animals|Data Compression|Genome|Genomics|High-Throughput Nucleotide Sequencing|Mice|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-14-187', '23758828', 'Next Generation Sequencing technologies have revolutionized many fields in biology by reducing the time and cost required for sequencing. As a result, large amounts of sequencing data are being generated. A typical sequencing data file may occupy tens or even hundreds of gigabytes of disk space, prohibitively large for many users. This data consists of both the nucleotide sequences and per-base quality scores that indicate the level of confidence in the readout of these sequences. Quality scores account for about half of the required disk space in the commonly used FASTQ format (before compression), and therefore the compression of the quality scores can significantly reduce storage requirements and speed up analysis and transmission of sequencing data.'),
(95630, 2013, 'bmc', 'ChIPXpress: using publicly available gene expression data to improve ChIP-seq and ChIP-chip target gene ranking', 'Wu, George|Ji, Hongkai', 'Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA.|Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA.', 'United States|United States', 12, 'Algorithms|Animals|Binding Sites|Chromatin Immunoprecipitation|Gene Expression Profiling|Genome|Humans|Mice|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-14-188', '23758851', 'ChIPx (i.e., ChIP-seq and ChIP-chip) is increasingly used to map genome-wide transcription factor (TF) binding sites. A single ChIPx experiment can identify thousands of TF bound genes, but typically only a fraction of these genes are functional targets that respond transcriptionally to perturbations of TF expression. To identify promising functional target genes for follow-up studies, researchers usually collect gene expression data from TF perturbation experiments to determine which of the TF targets respond transcriptionally to binding. Unfortunately, approximately 40% of ChIPx studies do not have accompanying gene expression data from TF perturbation experiments. For these studies, genes are often prioritized solely based on the binding strengths of ChIPx signals in order to choose follow-up candidates. ChIPXpress is a novel method that improves upon this ChIPx-only ranking approach by integrating ChIPx data with large amounts of Publicly available gene Expression Data (PED).'),
(95631, 2013, 'bmc', 'Comparing somatic mutation-callers: beyond Venn diagrams', 'Kim, Su Yeon|Speed, Terence P', 'Department of Statistics, University of California at Berkeley, 367 Evans Hall, Berkeley, CA 94720, USA. skim@stat.berkeley.edu|Department of Statistics, University of California at Berkeley, 367 Evans Hall, Berkeley, CA 94720, USA. skim@stat.berkeley.edu', 'United States|United States', 42, 'Artifacts|DNA Mutational Analysis|Exome|Genome|High-Throughput Nucleotide Sequencing|Humans|Mutation|Neoplasms|Sequence Analysis, RNA|Software', 'Comparative Study', '10.1186/1471-2105-14-189', '23758877', 'Somatic mutation-calling based on DNA from matched tumor-normal patient samples is one of the key tasks carried by many cancer genome projects. One such large-scale project is The Cancer Genome Atlas (TCGA), which is now routinely compiling catalogs of somatic mutations from hundreds of paired tumor-normal DNA exome-sequence data. Nonetheless, mutation calling is still very challenging. TCGA benchmark studies revealed that even relatively recent mutation callers from major centers showed substantial discrepancies. Evaluation of the mutation callers or understanding the sources of discrepancies is not straightforward, since for most tumor studies, validation data based on independent whole-exome DNA sequencing is not available, only partial validation data for a selected (ascertained) subset of sites.'),
(95632, 2013, 'bmc', 'Utilizing protein structure to identify non-random somatic mutations', 'Ryslik, Gregory A|Cheng, Yuwei|Cheung, Kei-Hoi|Modis, Yorgo|Zhao, Hongyu', 'Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA. gregory.ryslik@yale.edu|Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA. gregory.ryslik@yale.edu|Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA. gregory.ryslik@yale.edu|Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA. gregory.ryslik@yale.edu|Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA. gregory.ryslik@yale.edu', 'United States|United States|United States|United States|United States', 29, 'Algorithms|Cluster Analysis|Humans|Mutation|Neoplasm Proteins|Protein Structure, Tertiary', 'Journal Article', '10.1186/1471-2105-14-190', '23758891', 'Human cancer is caused by the accumulation of somatic mutations in tumor suppressors and oncogenes within the genome. In the case of oncogenes, recent theory suggests that there are only a few key driver mutations responsible for tumorigenesis. As there have been significant pharmacological successes in developing drugs that treat cancers that carry these driver mutations, several methods that rely on mutational clustering have been developed to identify them. However, these methods consider proteins as a single strand without taking their spatial structures into account. We propose an extension to current methodology that incorporates protein tertiary structure in order to increase our power when identifying mutation clustering.'),
(95633, 2013, 'bmc', 'KNODWAT: a scientific framework application for testing knowledge discovery methods for the biomedical domain', 'Holzinger, Andreas|Zupan, Mario', 'Research Unit Human-Computer Interaction (HCI4MED), Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, Graz 8036, Austria. a.holzinger@hci4all.at|Research Unit Human-Computer Interaction (HCI4MED), Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, Graz 8036, Austria. a.holzinger@hci4all.at', 'Austria|Austria', 14, 'Algorithms|Data Mining|Databases, Factual|Software', 'Journal Article', '10.1186/1471-2105-14-191', '23763826', 'Professionals in the biomedical domain are confronted with an increasing mass of data. Developing methods to assist professional end users in the field of Knowledge Discovery to identify, extract, visualize and understand useful information from these huge amounts of data is a huge challenge. However, there are so many diverse methods and methodologies available, that for biomedical researchers who are inexperienced in the use of even relatively popular knowledge discovery methods, it can be very difficult to select the most appropriate method for their particular research problem.'),
(95634, 2013, 'bmc', 'Boosting forward-time population genetic simulators through genotype compression', 'Ruths, Troy|Nakhleh, Luay', 'Department of Computer Science, Rice University, Houston, USA. troy.ruths@rice.edu|Department of Computer Science, Rice University, Houston, USA. troy.ruths@rice.edu', 'United States|United States', 3, 'Algorithms|Computer Simulation|Data Compression|Evolution, Molecular|Genetics, Population|Genome|Genotype|Mutation|Recombination, Genetic', 'Journal Article', '10.1186/1471-2105-14-192', '23763838', 'Forward-time population genetic simulations play a central role in deriving and testing evolutionary hypotheses. Such simulations may be data-intensive, depending on the settings to the various parameters controlling them. In particular, for certain settings, the data footprint may quickly exceed the memory of a single compute node.'),
(95635, 2013, 'bmc', 'FusionQ: a novel approach for gene fusion detection and quantification from paired-end RNA-Seq', 'Liu, Chenglin|Ma, Jinwen|Chang, ChungChe Jeff|Zhou, Xiaobo', 'Department of Diagnostic Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.|Department of Diagnostic Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.|Department of Diagnostic Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.|Department of Diagnostic Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.', 'United States|United States|United States|United States', 20, 'Algorithms|Breast Neoplasms|Cell Line, Tumor|Female|Gene Fusion|Humans|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1186/1471-2105-14-193', '23768108', 'Gene fusions, which result from abnormal chromosome rearrangements, are a pathogenic factor in cancer development. The emerging RNA-Seq technology enables us to detect gene fusions and profile their features.'),
(95636, 2013, 'bmc', 'bioNerDS: exploring bioinformatics database and software use through literature mining', 'Duck, Geraint|Nenadic, Goran|Brass, Andy|Robertson, David L|Stevens, Robert', 'School of Computer Science, The University of Manchester, Manchester, UK.|School of Computer Science, The University of Manchester, Manchester, UK.|School of Computer Science, The University of Manchester, Manchester, UK.|School of Computer Science, The University of Manchester, Manchester, UK.|School of Computer Science, The University of Manchester, Manchester, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 17, 'Computational Biology|Data Mining|Databases, Factual|Genomics|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-194', '23768135', 'Biology-focused databases and software define bioinformatics and their use is central to computational biology. In such a complex and dynamic field, it is of interest to understand what resources are available, which are used, how much they are used, and for what they are used. While scholarly literature surveys can provide some insights, large-scale computer-based approaches to identify mentions of bioinformatics databases and software from primary literature would automate systematic cataloguing, facilitate the monitoring of usage, and provide the foundations for the recovery of computational methods for analysing biological data, with the long-term aim of identifying best/common practice in different areas of biology.'),
(95637, 2013, 'bmc', 'Quantifying single nucleotide variant detection sensitivity in exome sequencing', 'Meynert, Alison M|Bicknell, Louise S|Hurles, Matthew E|Jackson, Andrew P|Taylor, Martin S', 'MRC Human Genetics Unit, MRC Institute for Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, UK. alison.meynert@igmm.ed.ac.uk|MRC Human Genetics Unit, MRC Institute for Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, UK. alison.meynert@igmm.ed.ac.uk|MRC Human Genetics Unit, MRC Institute for Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, UK. alison.meynert@igmm.ed.ac.uk|MRC Human Genetics Unit, MRC Institute for Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, UK. alison.meynert@igmm.ed.ac.uk|MRC Human Genetics Unit, MRC Institute for Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, UK. alison.meynert@igmm.ed.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Alleles|Exome|Exons|Genetic Variation|Genome, Human|Genomics|Genotype|Genotyping Techniques|Heterozygote|High-Throughput Nucleotide Sequencing|Homozygote|Humans', 'Journal Article', '10.1186/1471-2105-14-195', '23773188', 'The targeted capture and sequencing of genomic regions has rapidly demonstrated its utility in genetic studies. Inherent in this technology is considerable heterogeneity of target coverage and this is expected to systematically impact our sensitivity to detect genuine polymorphisms. To fully interpret the polymorphisms identified in a genetic study it is often essential to both detect polymorphisms and to understand where and with what probability real polymorphisms may have been missed.'),
(95638, 2013, 'bmc', 'HASP server: a database and structural visualization platform for comparative models of influenza A hemagglutinin proteins', 'Ambroggio, Xavier I|Dommer, Jennifer|Gopalan, Vivek|Dunham, Eleca J|Taubenberger, Jeffery K|Hurt, Darrell E', 'Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.|Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.|Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.|Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.|Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.|Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Databases, Protein|Hemagglutinin Glycoproteins, Influenza Virus|Protein Conformation|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-14-197', '23777206', 'Influenza A viruses possess RNA genomes that mutate frequently in response to immune pressures. The mutations in the hemagglutinin genes are particularly significant, as the hemagglutinin proteins mediate attachment and fusion to host cells, thereby influencing viral pathogenicity and species specificity. Large-scale influenza A genome sequencing efforts have been ongoing to understand past epidemics and pandemics and anticipate future outbreaks. Sequencing efforts thus far have generated nearly 9,000 distinct hemagglutinin amino acid sequences.'),
(95639, 2013, 'bmc', 'Sparse logistic regression with a L1/2 penalty for gene selection in cancer classification', 'Liang, Yong|Liu, Cheng|Luan, Xin-Ze|Leung, Kwong-Sak|Chan, Tak-Ming|Xu, Zong-Ben|Zhang, Hai', 'Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, China. yliang@must.edu.mo|Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, China. yliang@must.edu.mo|Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, China. yliang@must.edu.mo|Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, China. yliang@must.edu.mo|Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, China. yliang@must.edu.mo|Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, China. yliang@must.edu.mo|Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, China. yliang@must.edu.mo', 'China|China|China|China|China|China|China', 0, 'Algorithms|Gene Expression Regulation|Genetic Markers|Humans|Logistic Models|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-14-198', '23777239', 'Microarray technology is widely used in cancer diagnosis. Successfully identifying gene biomarkers will significantly help to classify different cancer types and improve the prediction accuracy. The regularization approach is one of the effective methods for gene selection in microarray data, which generally contain a large number of genes and have a small number of samples. In recent years, various approaches have been developed for gene selection of microarray data. Generally, they are divided into three categories: filter, wrapper and embedded methods. Regularization methods are an important embedded technique and perform both continuous shrinkage and automatic gene selection simultaneously. Recently, there is growing interest in applying the regularization techniques in gene selection. The popular regularization technique is Lasso (L1), and many L1 type regularization terms have been proposed in the recent years. Theoretically, the Lq type regularization with the lower value of q would lead to better solutions with more sparsity. Moreover, the L1/2 regularization can be taken as a representative of Lq (0 <q < 1) regularizations and has been demonstrated many attractive properties.'),
(95640, 2013, 'bmc', 'Incorporating time-delays in S-System model for reverse engineering genetic networks', 'Chowdhury, Ahsan Raja|Chetty, Madhu|Vinh, Nguyen Xuan', 'Gippsland School of Information Technology, Monash University, Churchill, Victoria-3842, Australia. ahsan.chowdhury@monash.edu.|Gippsland School of Information Technology, Monash University, Churchill, Victoria-3842, Australia. ahsan.chowdhury@monash.edu.|Gippsland School of Information Technology, Monash University, Churchill, Victoria-3842, Australia. ahsan.chowdhury@monash.edu.', 'Australia|Australia|Australia', 32, 'Algorithms|Escherichia coli|Gene Regulatory Networks|Genetic Engineering|Models, Biological|Models, Genetic|SOS Response (Genetics)|Time Factors|Transcription Factors', 'Journal Article', '10.1186/1471-2105-14-196', '23777625', 'In any gene regulatory network (GRN), the complex interactions occurring amongst transcription factors and target genes can be either instantaneousxe2x80x89or time-delayed. However, many existing modeling approaches currently applied for inferring GRNs are unable to represent both these interactions simultaneously. As a result, all these approaches cannot detect important interactions of the other type. S-System model, a differential equation based approach which has been increasingly applied for modeling GRNs, also suffers from this limitation. In fact, all S-System based existing modeling approaches have been designed to capture onlyxe2x80x89instantaneous interactions, and are unable to infer time-delayed interactions.'),
(95641, 2013, 'bmc', 'SeqSIMLA: a sequence and phenotype simulation tool for complex disease studies', 'Chung, Ren-Hua|Shih, Chung-Chin', 'Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli, Taiwan. rchung@nhri.org.tw|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli, Taiwan. rchung@nhri.org.tw', '-|-', 0, 'Computer Simulation|Data Interpretation, Statistical|Disease|Genetic Association Studies|High-Throughput Nucleotide Sequencing|Humans|Models, Genetic|Phenotype|Quantitative Trait Loci|Software', 'Journal Article', '10.1186/1471-2105-14-199', '23782512', 'Association studies based on next-generation sequencing (NGS) technology have become popular, and statistical association tests for NGS data have been developed rapidly. A flexible tool for simulating sequence data in either unrelated case-control or family samples with different disease and quantitative trait models would be useful for evaluating the statistical power for planning a study design and for comparing power among statistical methods based on NGS data.'),
(95642, 2013, 'bmc', 'Using ancestral information to detect and localize quantitative trait loci in genome-wide association studies', 'Thompson, Katherine L|Kubatko, Laura S', 'Department of Statistics, The Ohio State University, Columbus, OH 43210, USA. thompson.1897@osu.edu|Department of Statistics, The Ohio State University, Columbus, OH 43210, USA. thompson.1897@osu.edu', 'United States|United States', 14, 'Animals|Cholesterol, HDL|Genome-Wide Association Study|Mice|Phenotype|Polymorphism, Single Nucleotide|Quantitative Trait Loci', 'Journal Article', '10.1186/1471-2105-14-200', '23786262', 'In mammalian genetics, many quantitative traits, such as blood pressure, are thought to be influenced by specific genes, but are also affected by environmental factors, making the associated genes difficult to identify and locate from genetic data alone. In particular, the application of classical statistical methods to single nucleotide polymorphism (SNP) data collected in genome-wide association studies has been especially challenging. We propose a coalescent approach to search for SNPs associated with quantitative traits in genome-wide association study (GWAS) data by taking into account the evolutionary history among SNPs.'),
(95643, 2013, 'bmc', 'Bioinformatic pipelines in Python with Leaf', 'Napolitano, Francesco|Mariani-Costantini, Renato|Tagliaferri, Roberto', 'Department of Computer Science (DI),University of Salerno, Fisciano (SA) 84084, Italy. fnapolitano@unisa.it|Department of Computer Science (DI),University of Salerno, Fisciano (SA) 84084, Italy. fnapolitano@unisa.it|Department of Computer Science (DI),University of Salerno, Fisciano (SA) 84084, Italy. fnapolitano@unisa.it', 'Italy|Italy|Italy', 4, 'Computational Biology|DNA Copy Number Variations|Software', 'Journal Article', '10.1186/1471-2105-14-201', '23786315', 'An incremental, loosely planned development approach is often used in bioinformatic studies when dealing with custom data analysis in a rapidly changing environment. Unfortunately, the lack of a rigorous software structuring can undermine the maintainability, communicability and replicability of the process. To ameliorate this problem we propose the Leaf system, the aim of which is to seamlessly introduce the pipeline formality on top of a dynamical development process with minimum overhead for the programmer, thus providing a simple layer of software structuring.'),
(95644, 2013, 'bmc', 'MetaPathways: a modular pipeline for constructing pathway/genome databases from environmental sequence information', 'Konwar, Kishori M|Hanson, Niels W|Pagé, Antoine P|Hallam, Steven J', 'Department of Microbiology & Immunology, University of British Columbia, Vancouver, BC V6T1Z3, Canada. shallam@mail.ubc.ca|Department of Microbiology & Immunology, University of British Columbia, Vancouver, BC V6T1Z3, Canada. shallam@mail.ubc.ca|Department of Microbiology & Immunology, University of British Columbia, Vancouver, BC V6T1Z3, Canada. shallam@mail.ubc.ca|Department of Microbiology & Immunology, University of British Columbia, Vancouver, BC V6T1Z3, Canada. shallam@mail.ubc.ca', 'Canada|Canada|Canada|Canada', 0, 'Algorithms|Animals|Databases, Genetic|Databases, Nucleic Acid|Ecosystem|Environment|Forecasting|Genomics|Humans|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-14-202', '23800136', 'A central challenge to understanding the ecological and biogeochemical roles of microorganisms in natural and human engineered ecosystems is the reconstruction of metabolic interaction networks from environmental sequence information. The dominant paradigm in metabolic reconstruction is to assign functional annotations using BLAST. Functional annotations are then projected onto symbolic representations of metabolism in the form of KEGG pathways or SEED subsystems.'),
(95645, 2013, 'bmc', 'Prediction of gene-phenotype associations in humans, mice, and plants using phenologs', 'Woods, John O|Singh-Blom, Ulf Martin|Laurent, Jon M|McGary, Kriston L|Marcotte, Edward M', 'Center for Systems & Synthetic Biology, Institute for Cellular & Molecular Biology, The University of Texas at Austin, Austin, TX 78712, USA.|Center for Systems & Synthetic Biology, Institute for Cellular & Molecular Biology, The University of Texas at Austin, Austin, TX 78712, USA.|Center for Systems & Synthetic Biology, Institute for Cellular & Molecular Biology, The University of Texas at Austin, Austin, TX 78712, USA.|Center for Systems & Synthetic Biology, Institute for Cellular & Molecular Biology, The University of Texas at Austin, Austin, TX 78712, USA.|Center for Systems & Synthetic Biology, Institute for Cellular & Molecular Biology, The University of Texas at Austin, Austin, TX 78712, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Caenorhabditis elegans|Chickens|Cluster Analysis|Databases, Genetic|Escherichia coli|Humans|Male|Mice|Phenotype|Plants|Zebrafish', 'Journal Article', '10.1186/1471-2105-14-203', '23800157', 'Phenotypes and diseases may be related to seemingly dissimilar phenotypes in other species by means of the orthology of underlying genes. Such orthologous phenotypes, or phenologs, are examples of deep homology, and may be used to predict additional candidate disease genes.'),
(95646, 2013, 'bmc', 'Deconvolution of gene expression from cell populations across the C elegans lineage', 'Burdick, Joshua T|Murray, John Isaac', 'Genomics and Computational Biology Group, University of Pennsylvania, 440 Clinical Research Building, 415 Curie Boulevard, Philadelphia, PA 19104, USA.|Genomics and Computational Biology Group, University of Pennsylvania, 440 Clinical Research Building, 415 Curie Boulevard, Philadelphia, PA 19104, USA.', 'United States|United States', 2, 'Animals|Caenorhabditis elegans|Caenorhabditis elegans Proteins|Cell Lineage|Gene Expression Profiling|Gene Expression Regulation, Developmental', 'Journal Article', '10.1186/1471-2105-14-204', '23800200', 'Knowledge of when and in which cells each gene is expressed across multicellular organisms is critical in understanding both gene function and regulation of cell type diversity. However, methods for measuring expression typically involve a trade-off between imaging-based methods, which give the precise location of a limited number of genes, and higher throughput methods such as RNA-seq, which include all genes, but are more limited in their resolution to apply to many tissues. We propose an intermediate method, which estimates expression in individual cells, based on high-throughput measurements of expression from multiple overlapping groups of cells. This approach has particular benefits in organisms such as C. elegans where invariant developmental patterns make it possible to define these overlapping populations of cells at single-cell resolution.'),
(95647, 2013, 'bmc', 'Automatic workflow for the classification of local DNA conformations', 'xc4x8cech, Petr|Kukal, Jaromxc3xadr|xc4x8cernxc3xbd, Jixc5x99xc3xad|Schneider, Bohdan|Svozil, Daniel', 'Laboratory of Informatics and Chemistry, ICT Prague, Technickxc3xa1 5, Prague 6, 166 28, Czech Republic.|Laboratory of Informatics and Chemistry, ICT Prague, Technickxc3xa1 5, Prague 6, 166 28, Czech Republic.|Laboratory of Informatics and Chemistry, ICT Prague, Technickxc3xa1 5, Prague 6, 166 28, Czech Republic.|Laboratory of Informatics and Chemistry, ICT Prague, Technickxc3xa1 5, Prague 6, 166 28, Czech Republic.|Laboratory of Informatics and Chemistry, ICT Prague, Technickxc3xa1 5, Prague 6, 166 28, Czech Republic.', '-|-|-|-|-', 0, 'Algorithms|Classification|Cluster Analysis|Crystallography, X-Ray|DNA|G-Quadruplexes|Nuclear Magnetic Resonance, Biomolecular|Nucleic Acid Conformation|Workflow', 'Journal Article', '10.1186/1471-2105-14-205', '23800225', 'A growing number of crystal and NMR structures reveals a considerable structural polymorphism of DNA architecture going well beyond the usual image of a double helical molecule. DNA is highly variable with dinucleotide steps exhibiting a substantial flexibility in a sequence-dependent manner. An analysis of the conformational space of the DNA backbone and the enhancement of our understanding of the conformational dependencies in DNA are therefore important for full comprehension of DNA structural polymorphism.'),
(95648, 2013, 'bmc', 'Infection status outcome, machine learning method and virus type interact to affect the optimised prediction of hepatitis virus immunoassay results from routine pathology laboratory assays in unbalanced data', 'Richardson, Alice M|Lidbury, Brett A', 'Faculty of Education, Science, Technology & Mathematics, University of Canberra ACT 2601, Canberra, Australia. alice.richardson@canberra.edu.au|Faculty of Education, Science, Technology & Mathematics, University of Canberra ACT 2601, Canberra, Australia. alice.richardson@canberra.edu.au', 'Australia|Australia', 9, 'Artificial Intelligence|Decision Support Techniques|Decision Trees|Hepacivirus|Hepatitis B|Hepatitis B virus|Hepatitis C|Humans|Immunoassay|Immunologic Tests', 'Journal Article', '10.1186/1471-2105-14-206', '23800244', 'Advanced data mining techniques such as decision trees have been successfully used to predict a variety of outcomes in complex medical environments. Furthermore, previous research has shown that combining the results of a set of individually trained trees into an ensemble-based classifier can improve overall classification accuracy. This paper investigates the effect of data pre-processing, the use of ensembles constructed by bagging, and a simple majority vote to combine classification predictions from routine pathology laboratory data, particularly to overcome a large imbalance of negative Hepatitis B virus (HBV) and Hepatitis C virus (HCV) cases versus HBV or HCV immunoassay positive cases. These methods were illustrated using a never before analysed data set from ACT Pathology (Canberra, Australia) relating to HBV and HCV patients.'),
(95649, 2013, 'bmc', 'Integrative relational machine-learning for understanding drug side-effect profiles', 'Bresso, Emmanuel|Grisoni, Renaud|Marchetti, Gino|Karaboga, Arnaud Sinan|Souchet, Michel|Devignes, Marie-Dominique|Smaïl-Tabbone, Malika', 'Faculty of Education, Science, Technology & Mathematics, University of Canberra ACT 2601, Canberra, Australia. alice.richardson@canberra.edu.au|Faculty of Education, Science, Technology & Mathematics, University of Canberra ACT 2601, Canberra, Australia. alice.richardson@canberra.edu.au|Faculty of Education, Science, Technology & Mathematics, University of Canberra ACT 2601, Canberra, Australia. alice.richardson@canberra.edu.au|Faculty of Education, Science, Technology & Mathematics, University of Canberra ACT 2601, Canberra, Australia. alice.richardson@canberra.edu.au|Faculty of Education, Science, Technology & Mathematics, University of Canberra ACT 2601, Canberra, Australia. alice.richardson@canberra.edu.au|Faculty of Education, Science, Technology & Mathematics, University of Canberra ACT 2601, Canberra, Australia. alice.richardson@canberra.edu.au|Faculty of Education, Science, Technology & Mathematics, University of Canberra ACT 2601, Canberra, Australia. alice.richardson@canberra.edu.au', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 27, 'Artificial Intelligence|Computational Biology|Databases, Pharmaceutical|Decision Trees|Drug-Related Side Effects and Adverse Reactions|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-14-207', '23802887', 'Drug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence.'),
(95650, 2013, 'bmc', 'MeSH indexing based on automatically generated summaries', 'Jimeno-Yepes, Antonio J|Plaza, Laura|Mork, James G|Aronson, Alan R|Díaz, Alberto', 'National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. antonio.jimeno@gmail.com|National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. antonio.jimeno@gmail.com|National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. antonio.jimeno@gmail.com|National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. antonio.jimeno@gmail.com|National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. antonio.jimeno@gmail.com', 'United States|United States|United States|United States|United States', 0, 'Abstracting and Indexing as Topic|Algorithms|Artificial Intelligence|MEDLINE|Medical Subject Headings', 'Journal Article', '10.1186/1471-2105-14-208', '23802936', 'MEDLINE citations are manually indexed at the U.S. National Library of Medicine (NLM) using as reference the Medical Subject Headings (MeSH) controlled vocabulary. For this task, the human indexers read the full text of the article. Due to the growth of MEDLINE, the NLM Indexing Initiative explores indexing methodologies that can support the task of the indexers. Medical Text Indexer (MTI) is a tool developed by the NLM Indexing Initiative to provide MeSH indexing recommendations to indexers. Currently, the input to MTI is MEDLINE citations, title and abstract only. Previous work has shown that using full text as input to MTI increases recall, but decreases precision sharply. We propose using summaries generated automatically from the full text for the input to MTI to use in the task of suggesting MeSH headings to indexers. Summaries distill the most salient information from the full text, which might increase the coverage of automatic indexing approaches based on MEDLINE. We hypothesize that if the results were good enough, manual indexers could possibly use automatic summaries instead of the full texts, along with the recommendations of MTI, to speed up the process while maintaining high quality of indexing results.'),
(95651, 2013, 'bmc', 'GenPhyloData: realistic simulation of gene family evolution', 'Sjöstrand, Joel|Arvestad, Lars|Lagergren, Jens|Sennblad, Bengt', 'Department of Numerical Analysis and Computer Science, Stockholm University, Stockholm, Sweden.|Department of Numerical Analysis and Computer Science, Stockholm University, Stockholm, Sweden.|Department of Numerical Analysis and Computer Science, Stockholm University, Stockholm, Sweden.|Department of Numerical Analysis and Computer Science, Stockholm University, Stockholm, Sweden.', 'Sweden|Sweden|Sweden|Sweden', 17, 'Biological Clocks|Computer Simulation|Evolution, Molecular|Gene Duplication|Gene Transfer Techniques|Humans|Models, Biological|Multigene Family|Phylogeny|Species Specificity', 'Journal Article', '10.1186/1471-2105-14-209', '23803001', 'PrIME-GenPhyloData is a suite of tools for creating realistic simulated phylogenetic trees, in particular for families of homologous genes. It supports generation of trees based on a birth-death process and--perhaps more interestingly--also supports generation of gene family trees guided by a known (synthetic or biological) species tree while accounting for events such as gene duplication, gene loss, and lateral gene transfer (LGT). The suite also supports a wide range of branch rate models enabling relaxation of the molecular clock.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95652, 2013, 'bmc', 'Gene set analysis using variance component tests', 'Huang, Yen-Tsung|Lin, Xihong', 'Department of Epidemiology, Brown University, 121 South Main Street, Providence, RI 02912, USA.|Department of Epidemiology, Brown University, 121 South Main Street, Providence, RI 02912, USA.', 'United States|United States', 0, 'Diabetes Mellitus, Type 2|Gene Expression Profiling|Humans|Linear Models|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-14-210', '23806107', 'Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses.'),
(95653, 2013, 'bmc', 'Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions', 'Jaiswal, Varun|Chanumolu, Sree Krishna|Gupta, Ankit|Chauhan, Rajinder S|Rout, Chittaranjan', 'Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh 173234, India.|Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh 173234, India.|Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh 173234, India.|Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh 173234, India.|Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh 173234, India.', 'India|India|India|India|India', 31, 'Adhesins, Bacterial|Antigens, Bacterial|Bacterial Proteins|Bacterial Vaccines|Epitopes|Escherichia coli|Host-Pathogen Interactions|Protein Structure, Tertiary|Proteome|Software|Streptococcus pneumoniae|Vaccines, Subunit|Virulence Factors', 'Journal Article', '10.1186/1471-2105-14-211', '23815072', 'Subunit vaccines based on recombinant proteins have been effective in preventing infectious diseases and are expected to meet the demands of future vaccine development. Computational approach, especially reverse vaccinology (RV) method has enormous potential for identification of protein vaccine candidates (PVCs) from a proteome. The existing protective antigen prediction software and web servers have low prediction accuracy leading to limited applications for vaccine development. Besides machine learning techniques, those software and web servers have considered only proteins adhesin-likeliness as criterion for identification of PVCs. Several non-adhesin functional classes of proteins involved in host-pathogen interactions and pathogenesis are known to provide protection against bacterial infections. Therefore, knowledge of bacterial pathogenesis has potential to identify PVCs.'),
(95654, 2013, 'bmc', 'Robust PCA based method for discovering differentially expressed genes', 'Liu, Jin-Xing|Wang, Yu-Tian|Zheng, Chun-Hou|Sha, Wen|Mi, Jian-Xun|Xu, Yong', 'Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China.|Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China.|Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China.|Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China.|Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China.|Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China.', 'China|China|China|China|China|China', 0, 'Colonic Neoplasms|Computer Simulation|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Principal Component Analysis', 'Journal Article', '10.1186/1471-2105-14-S8-S3', '23815087', 'How to identify a set of genes that are relevant to a key biological process is an important issue in current molecular biology. In this paper, we propose a novel method to discover differentially expressed genes based on robust principal component analysis (RPCA). In our method, we treat the differentially and non-differentially expressed genes as perturbation signals S and low-rank matrix A, respectively. Perturbation signals S can be recovered from the gene expression data by using RPCA. To discover the differentially expressed genes associated with special biological progresses or functions, the scheme is given as follows. Firstly, the matrix D of expression data is decomposed into two adding matrices A and S by using RPCA. Secondly, the differentially expressed genes are identified based on matrix S. Finally, the differentially expressed genes are evaluated by the tools based on Gene Ontology. A larger number of experiments on hypothetical and real gene expression data are also provided and the experimental results show that our method is efficient and effective. '),
(95655, 2013, 'bmc', 'Linear combination test for gene set analysis of a continuous phenotype', 'Dinu, Irina|Wang, Xiaoming|Kelemen, Linda E|Vatanpour, Shabnam|Pyne, Saumyadipta', 'School of Public Health, University of Alberta, Edmonton, Alberta T6G 1C9, Canada. idinu@ualberta.ca|School of Public Health, University of Alberta, Edmonton, Alberta T6G 1C9, Canada. idinu@ualberta.ca|School of Public Health, University of Alberta, Edmonton, Alberta T6G 1C9, Canada. idinu@ualberta.ca|School of Public Health, University of Alberta, Edmonton, Alberta T6G 1C9, Canada. idinu@ualberta.ca|School of Public Health, University of Alberta, Edmonton, Alberta T6G 1C9, Canada. idinu@ualberta.ca', 'Canada|Canada|Canada|Canada|Canada', 0, 'Gene Expression Profiling|Humans|Leptin|Oligonucleotide Array Sequence Analysis|Phenotype', 'Journal Article', '10.1186/1471-2105-14-212', '23815123', 'Gene set analysis (GSA) methods test the association of sets of genes with a phenotype in gene expression microarray studies. Many GSA methods have been proposed, especially methods for use with a binary phenotype. Equally, if not more importantly however, is the ability to test the enrichment of a gene signature or pathway against the continuous phenotypes which are routinely and commonly observed in, for example, clinicopathological measurements. It is not always easy or meaningful to dichotomize continuous phenotypes into two classes, and attempting to do this may lead to the inaccurate classification of samples, which would affect the downstream enrichment analysis. In the present study, we have build on recent efforts to incorporate correlation structure within gene sets and pathways into the GSA test statistic. To address the issue of continuous phenotypes directly without the need for artificial discrete classification and thus increase the power of the test while ensuring computational efficiency and rigor, new GSA methods that can incorporate a covariance matrix estimator for a continuous phenotype may present an effective approach.'),
(95656, 2013, 'bmc', 'Protein localization prediction using random walks on graphs', 'Xu, Xiaohua|Lu, Lin|He, Ping|Chen, Ling', 'Department of Computer Science, Yangzhou University, Yangzhou 225009, China. arterx@gmail.com|Department of Computer Science, Yangzhou University, Yangzhou 225009, China. arterx@gmail.com|Department of Computer Science, Yangzhou University, Yangzhou 225009, China. arterx@gmail.com|Department of Computer Science, Yangzhou University, Yangzhou 225009, China. arterx@gmail.com', 'China|China|China|China', 0, 'Bacterial Proteins|Computational Biology|Fungal Proteins|Gram-Negative Bacteria|Protein Transport|Support Vector Machine|Yeasts', 'Journal Article', '10.1186/1471-2105-14-S8-S4', '23815126', 'Understanding the localization of proteins in cells is vital to characterizing their functions and possible interactions. As a result, identifying the (sub)cellular compartment within which a protein is located becomes an important problem in protein classification. This classification issue thus involves predicting labels in a dataset with a limited number of labeled data points available. By utilizing a graph representation of protein data, random walk techniques have performed well in sequence classification and functional prediction, however, this method has not yet been applied to protein localization. Accordingly, we propose a novel classifier in the site prediction of proteins based on random walks on a graph.'),
(95657, 2013, 'bmc', 'Bioinformatics in Italy: BITS 2012, the ninth annual meeting of the Italian Society of Bioinformatics', 'Gissi, Carmela|Romano, Paolo|Ferro, Alfredo|Giugno, Rosalba|Pulvirenti, Alfredo|Facchiano, Angelo|Helmer-Citterich, Manuela', 'Dipartimento di Bioscienze, Università degli Studi di Milano, Milano, I-20133, Italy.|Dipartimento di Bioscienze, Università degli Studi di Milano, Milano, I-20133, Italy.|Dipartimento di Bioscienze, Università degli Studi di Milano, Milano, I-20133, Italy.|Dipartimento di Bioscienze, Università degli Studi di Milano, Milano, I-20133, Italy.|Dipartimento di Bioscienze, Università degli Studi di Milano, Milano, I-20133, Italy.|Dipartimento di Bioscienze, Università degli Studi di Milano, Milano, I-20133, Italy.|Dipartimento di Bioscienze, Università degli Studi di Milano, Milano, I-20133, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 1, 'Computational Biology|Genomics|Humans|Peer Review', 'Introductory Journal Article', '10.1186/1471-2105-14-S7-S1', '23815154', 'The BITS2012 meeting, held in Catania on May 2-4, 2012, brought together almost 100 Italian researchers working in the field of Bioinformatics, as well as students in the same or related disciplines. About 90 original research works were presented either as oral communication or as posters, representing a landscape of Italian current research in bioinformatics. This preface provides a brief overview of the meeting and introduces the manuscripts that were accepted for publication in this supplement, after a strict and careful peer-review by an International board of referees.'),
(95658, 2013, 'bmc', 'Scalable high-throughput identification of genetic targets by network filtering', 'Bevilacqua, Vitoantonio|Pannarale, Paolo', 'Department of Electrical and Electronics, Polytechnic of Bari, Via E. Orabona, 4, 70125 Bari, Italy. bevilacqua@poliba.it|Department of Electrical and Electronics, Polytechnic of Bari, Via E. Orabona, 4, 70125 Bari, Italy. bevilacqua@poliba.it', 'Italy|Italy', 0, 'Algorithms|Gene Expression|Gene Expression Profiling|Molecular Targeted Therapy|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1186/1471-2105-14-S8-S5', '23815162', 'Discovering the molecular targets of compounds or the cause of physiological conditions, among the multitude of known genes, is one of the major challenges of bioinformatics. One of the most common approaches to this problem is finding sets of differentially expressed, and more recently differentially co-expressed, genes. Other approaches require libraries of genetic mutants or require to perform a large number of assays. Another elegant approach is the filtering of mRNA expression profiles using reverse-engineered gene network models of the target cell. This approach has the advantage of not needing control samples, libraries or numerous assays. Nevertheless, the impementations of this strategy proposed so far are computationally demanding. Moreover the user has to arbitrarily choose a threshold on the number of potentially relevant genes from the algorithm output.'),
(95659, 2013, 'bmc', 'NGS-Trex: Next Generation Sequencing Transcriptome profile explorer', 'Boria, Ilenia|Boatti, Lara|Pesole, Graziano|Mignone, Flavio', 'Dipartimento di Chimica, Università degli Studi di Milano, Milano, Italy.|Dipartimento di Chimica, Università degli Studi di Milano, Milano, Italy.|Dipartimento di Chimica, Università degli Studi di Milano, Milano, Italy.|Dipartimento di Chimica, Università degli Studi di Milano, Milano, Italy.', 'Italy|Italy|Italy|Italy', 0, 'Computational Biology|Data Mining|High-Throughput Nucleotide Sequencing|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1186/1471-2105-14-S7-S10', '23815181', 'Next-Generation Sequencing (NGS) technology has exceptionally increased the ability to sequence DNA in a massively parallel and cost-effective manner. Nevertheless, NGS data analysis requires bioinformatics skills and computational resources well beyond the possibilities of many wet biology laboratories. Moreover, most of projects only require few sequencing cycles and standard tools or workflows to carry out suitable analyses for the identification and annotation of genes, transcripts and splice variants found in the biological samples under investigation. These projects can take benefits from the availability of easy to use systems to automatically analyse sequences and to mine data without the preventive need of strong bioinformatics background and hardware infrastructure.'),
(95660, 2013, 'bmc', 'Distinct position-specific sequence features of hexa-peptides that form amyloid-fibrils: application to discriminate between amyloid fibril and amorphous xcexb2-aggregate forming peptide sequences', 'Thangakani, A Mary|Kumar, Sandeep|Velmurugan, D|Gromiha, M Michael', 'Department of Crystallography and Biophysics, University of Madras, Chennai 600025, India.|Department of Crystallography and Biophysics, University of Madras, Chennai 600025, India.|Department of Crystallography and Biophysics, University of Madras, Chennai 600025, India.|Department of Crystallography and Biophysics, University of Madras, Chennai 600025, India.', 'India|India|India|India', 10, 'Amino Acid Sequence|Amyloid|Artificial Intelligence|Energy Metabolism|Humans|Hydrophobic and Hydrophilic Interactions|Peptides', 'Journal Article', '10.1186/1471-2105-14-S8-S6', '23815227', 'Comparison of short peptides which form amyloid-fibrils with their homologues that may form amorphous xcexb2-aggregates but not fibrils, can aid development of novel amyloid-containing nanomaterials with well defined morphologies and characteristics. The knowledge gained from the comparative analysis could also be applied towards identifying potential aggregation prone regions in proteins, which are important for biotechnology applications or have been implicated in neurodegenerative diseases. In this work we have systematically analyzed a set of 139 amyloid-fibril hexa-peptides along with a highly homologous set of 168 hexa-peptides that do not form amyloid fibrils for their position-wise as well as overall amino acid compositions and averages of 49 selected amino acid properties.'),
(95661, 2013, 'bmc', 'WEP: a high-performance analysis pipeline for whole-exome data', 'DAntonio, Mattia|DOnorio De Meo, Paolo|Paoletti, Daniele|Elmi, Berardino|Pallocca, Matteo|Sanna, Nico|Picardi, Ernesto|Pesole, Graziano|Castrignanò, Tiziana', 'Dipartimento di Bioscienze, Biotecnologie e Scienze Farmacologiche, Università degli Studi di Bari, Bari, Italy.|Dipartimento di Bioscienze, Biotecnologie e Scienze Farmacologiche, Università degli Studi di Bari, Bari, Italy.|Dipartimento di Bioscienze, Biotecnologie e Scienze Farmacologiche, Università degli Studi di Bari, Bari, Italy.|Dipartimento di Bioscienze, Biotecnologie e Scienze Farmacologiche, Università degli Studi di Bari, Bari, Italy.|Dipartimento di Bioscienze, Biotecnologie e Scienze Farmacologiche, Università degli Studi di Bari, Bari, Italy.|Dipartimento di Bioscienze, Biotecnologie e Scienze Farmacologiche, Università degli Studi di Bari, Bari, Italy.|Dipartimento di Bioscienze, Biotecnologie e Scienze Farmacologiche, Università degli Studi di Bari, Bari, Italy.|Dipartimento di Bioscienze, Biotecnologie e Scienze Farmacologiche, Università degli Studi di Bari, Bari, Italy.|Dipartimento di Bioscienze, Biotecnologie e Scienze Farmacologiche, Università degli Studi di Bari, Bari, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Exome|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Internet|Polymorphism, Single Nucleotide|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-14-S7-S11', '23815231', 'The advent of massively parallel sequencing technologies (Next Generation Sequencing, NGS) profoundly modified the landscape of human genetics.In particular, Whole Exome Sequencing (WES) is the NGS branch that focuses on the exonic regions of the eukaryotic genomes, exomes are ideal to help us understanding high-penetrance allelic variation and its relationship to phenotype. A complete WES analysis involves several steps which need to be suitably designed and arranged into an efficient pipeline.Managing a NGS analysis pipeline and its huge amount of produced data requires non trivial IT skills and computational power.'),
(95662, 2013, 'bmc', 'Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients', 'Cangelosi, Davide|Blengio, Fabiola|Versteeg, Rogier|Eggert, Angelika|Garaventa, Alberto|Gambini, Claudio|Conte, Massimo|Eva, Alessandra|Muselli, Marco|Varesio, Luigi', 'Laboratory of Molecular Biology, Gaslini Institute, Largo Gaslini 5, 16147 Genoa, Italy.|Laboratory of Molecular Biology, Gaslini Institute, Largo Gaslini 5, 16147 Genoa, Italy.|Laboratory of Molecular Biology, Gaslini Institute, Largo Gaslini 5, 16147 Genoa, Italy.|Laboratory of Molecular Biology, Gaslini Institute, Largo Gaslini 5, 16147 Genoa, Italy.|Laboratory of Molecular Biology, Gaslini Institute, Largo Gaslini 5, 16147 Genoa, Italy.|Laboratory of Molecular Biology, Gaslini Institute, Largo Gaslini 5, 16147 Genoa, Italy.|Laboratory of Molecular Biology, Gaslini Institute, Largo Gaslini 5, 16147 Genoa, Italy.|Laboratory of Molecular Biology, Gaslini Institute, Largo Gaslini 5, 16147 Genoa, Italy.|Laboratory of Molecular Biology, Gaslini Institute, Largo Gaslini 5, 16147 Genoa, Italy.|Laboratory of Molecular Biology, Gaslini Institute, Largo Gaslini 5, 16147 Genoa, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 15, 'Algorithms|Artificial Intelligence|Child|Humans|Logic|Middle Aged|Neoplasm Staging|Neuroblastoma|Prognosis|Proto-Oncogene Proteins c-myc|Risk Factors', 'Journal Article', '10.1186/1471-2105-14-S7-S12', '23815266', 'Neuroblastoma is the most common pediatric solid tumor. About fifty percent of high risk patients die despite treatment making the exploration of new and more effective strategies for improving stratification mandatory. Hypoxia is a condition of low oxygen tension occurring in poorly vascularized areas of the tumor associated with poor prognosis. We had previously defined a robust gene expression signature measuring the hypoxic component of neuroblastoma tumors (NB-hypo) which is a molecular risk factor. We wanted to develop a prognostic classifier of neuroblastoma patients outcome blending existing knowledge on clinical and molecular risk factors with the prognostic NB-hypo signature. Furthermore, we were interested in classifiers outputting explicit rules that could be easily translated into the clinical setting.'),
(95663, 2013, 'bmc', 'Acceleration of sequence clustering using longest common subsequence filtering', 'Namiki, Youhei|Ishida, Takashi|Akiyama, Yutaka', 'Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Ookayama, Meguro, Tokyo 152-8552, Japan.|Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Ookayama, Meguro, Tokyo 152-8552, Japan.|Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Ookayama, Meguro, Tokyo 152-8552, Japan.', 'Japan|Japan|Japan', 0, 'Algorithms|Cluster Analysis|Data Mining|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-14-S8-S7', '23815271', 'Huge numbers of genomes can now be sequenced rapidly with recent improvements in sequencing throughput. However, data analysis methods have not kept up, making it difficult to process the vast amounts of available sequence data. This increased processing time is especially critical in DNA sequence clustering because of the intrinsic difficulty in parallelization. Thus, there is a strong demand for a faster clustering algorithm.'),
(95664, 2013, 'bmc', 'A subgraph isomorphism algorithm and its application to biochemical data', 'Bonnici, Vincenzo|Giugno, Rosalba|Pulvirenti, Alfredo|Shasha, Dennis|Ferro, Alfredo', 'Dept. Computer Science - University of Verona, Verona, 37134, Italy.|Dept. Computer Science - University of Verona, Verona, 37134, Italy.|Dept. Computer Science - University of Verona, Verona, 37134, Italy.|Dept. Computer Science - University of Verona, Verona, 37134, Italy.|Dept. Computer Science - University of Verona, Verona, 37134, Italy.', 'Italy|Italy|Italy|Italy|Italy', 75, 'Algorithms|Artificial Intelligence|Protein Interaction Maps|Proteins|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-14-S7-S13', '23815292', 'Graphs can represent biological networks at the molecular, protein, or species level. An important query is to find all matches of a pattern graph to a target graph. Accomplishing this is inherently difficult (NP-complete) and the efficiency of heuristic algorithms for the problem may depend upon the input graphs. The common aim of existing algorithms is to eliminate unsuccessful mappings as early as and as inexpensively as possible.'),
(95665, 2013, 'bmc', 'Biomimicry of quorum sensing using bacterial lifecycle model', 'Niu, Ben|Wang, Hong|Duan, Qiqi|Li, Li', 'College of Management, Shenzhen University, Shenzhen 518060, China. drniuben@gmail.com|College of Management, Shenzhen University, Shenzhen 518060, China. drniuben@gmail.com|College of Management, Shenzhen University, Shenzhen 518060, China. drniuben@gmail.com|College of Management, Shenzhen University, Shenzhen 518060, China. drniuben@gmail.com', 'China|China|China|China', 9, 'Algorithms|Bacteria|Biological Evolution|Computer Simulation|Models, Biological|Models, Genetic|Quorum Sensing', 'Journal Article', '10.1186/1471-2105-14-S8-S8', '23815296', 'Recent microbiologic studies have shown that quorum sensing mechanisms, which serve as one of the fundamental requirements for bacterial survival, exist widely in bacterial intra- and inter-species cell-cell communication. Many simulation models, inspired by the social behavior of natural organisms, are presented to provide new approaches for solving realistic optimization problems. Most of these simulation models follow population-based modelling approaches, where all the individuals are updated according to the same rules. Therefore, it is difficult to maintain the diversity of the population.'),
(95666, 2013, 'bmc', 'Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features', 'Wang, Bing|Zhang, Jun|Chen, Peng|Ji, Zhiwei|Deng, Shuping|Li, Chi', 'The Advanced Research Institute of Intelligent Sensing Network, Tongji University, shanghai, 201804, China. wangbing@ustc.edu|The Advanced Research Institute of Intelligent Sensing Network, Tongji University, shanghai, 201804, China. wangbing@ustc.edu|The Advanced Research Institute of Intelligent Sensing Network, Tongji University, shanghai, 201804, China. wangbing@ustc.edu|The Advanced Research Institute of Intelligent Sensing Network, Tongji University, shanghai, 201804, China. wangbing@ustc.edu|The Advanced Research Institute of Intelligent Sensing Network, Tongji University, shanghai, 201804, China. wangbing@ustc.edu|The Advanced Research Institute of Intelligent Sensing Network, Tongji University, shanghai, 201804, China. wangbing@ustc.edu', 'China|China|China|China|China|China', 0, 'Algorithms|Least-Squares Analysis|Mass Spectrometry|Peptides|Proteins|Proteomics|Regression Analysis|Sequence Analysis, Protein|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-14-S8-S9', '23815343', 'Ion mobility-mass spectrometry (IMMS), an analytical technique which combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS), can rapidly separates ions on a millisecond time-scale. IMMS becomes a powerful tool to analyzing complex mixtures, especially for the analysis of peptides in proteomics. The high-throughput nature of this technique provides a challenge for the identification of peptides in complex biological samples. As an important parameter, peptide drift time can be used for enhancing downstream data analysis in IMMS-based proteomics.'),
(95667, 2013, 'bmc', 'Examples of sequence conservation analyses capture a subset of mouse long non-coding RNAs sharing homology with fish conserved genomic elements', 'Basu, Swaraj|Müller, Ferenc|Sanges, Remo', 'Laboratory of Animal Physiology and Evolution, Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy.|Laboratory of Animal Physiology and Evolution, Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy.|Laboratory of Animal Physiology and Evolution, Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy.', 'Italy|Italy|Italy', 13, 'Animals|Base Sequence|Conserved Sequence|Mice|RNA, Long Noncoding|Sequence Alignment|Zebrafish', 'Journal Article', '10.1186/1471-2105-14-S7-S14', '23815359', 'Long non-coding RNAs (lncRNA) are a major class of non-coding RNAs. They are involved in diverse intra-cellular mechanisms like molecular scaffolding, splicing and DNA methylation. Through these mechanisms they are reported to play a role in cellular differentiation and development. They show an enriched expression in the brain where they are implicated in maintaining cellular identity, homeostasis, stress responses and plasticity. Low sequence conservation and lack of functional annotations make it difficult to identify homologs of mammalian lncRNAs in other vertebrates. A computational evaluation of the lncRNAs through systematic conservation analyses of both sequences as well as their genomic architecture is required.'),
(95668, 2013, 'bmc', 'State of art fusion-finder algorithms are suitable to detect transcription-induced chimeras in normal tissues?', 'Carrara, Matteo|Beccuti, Marco|Cavallo, Federica|Donatelli, Susanna|Lazzarato, Fulvio|Cordero, Francesca|Calogero, Raffaele A', 'University of Torino, Bioinformatics & Genomics unit, Molecular Biotechnology Center, Via Nizza 52, 10126 Torino, Italy.|University of Torino, Bioinformatics & Genomics unit, Molecular Biotechnology Center, Via Nizza 52, 10126 Torino, Italy.|University of Torino, Bioinformatics & Genomics unit, Molecular Biotechnology Center, Via Nizza 52, 10126 Torino, Italy.|University of Torino, Bioinformatics & Genomics unit, Molecular Biotechnology Center, Via Nizza 52, 10126 Torino, Italy.|University of Torino, Bioinformatics & Genomics unit, Molecular Biotechnology Center, Via Nizza 52, 10126 Torino, Italy.|University of Torino, Bioinformatics & Genomics unit, Molecular Biotechnology Center, Via Nizza 52, 10126 Torino, Italy.|University of Torino, Bioinformatics & Genomics unit, Molecular Biotechnology Center, Via Nizza 52, 10126 Torino, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Animals|Gene Fusion|Humans|Sequence Analysis, RNA|Software|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-14-S7-S2', '23815381', 'RNA-seq has the potential to discover genes created by chromosomal rearrangements. Fusion genes, also known as chimeras, are formed by the breakage and re-joining of two different chromosomes. It is known that chimeras have been implicated in the development of cancer. Few publications in the past showed the presence of fusion events also in normal tissue, but with very limited overlaps between their results. More recently, two fusion genes in normal tissues were detected using both RNA-seq and protein data.Due to heterogeneous results in identifying chimeras in normal tissue, we decided to evaluate the efficacy of state of the art fusion finders in detecting chimeras in RNA-seq data from normal tissues.'),
(95669, 2013, 'bmc', 'Analysis and consensus of currently available intrinsic protein disorder annotation sources in the MobiDB database', 'Di Domenico, Tomás|Walsh, Ian|Tosatto, Silvio C E', 'Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.|Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.|Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.', 'Italy|Italy|Italy', 0, 'Animals|Databases, Protein|Eukaryota|Humans|Proteins|Proteome', 'Journal Article', '10.1186/1471-2105-14-S7-S3', '23815411', 'Intrinsic protein disorder is becoming an increasingly important topic in protein science. During the last few years, intrinsically disordered proteins (IDPs) have been shown to play a role in many important biological processes, e.g. protein signalling and regulation. This has sparked a need to better understand and characterize different types of IDPs, their functions and roles. Our recently published database, MobiDB, provides a centralized resource for accessing and analysing intrinsic protein disorder annotations.'),
(95670, 2013, 'bmc', 'Alignment-free analysis of barcode sequences by means of compression-based methods', 'La Rosa, Massimo|Fiannaca, Antonino|Rizzo, Riccardo|Urso, Alfonso', 'ICAR-CNR, National Research Council of Italy, Viale delle Scienze Ed. 11, 90128, Palermo, Italy. larosa@pa.icar.cnr.it|ICAR-CNR, National Research Council of Italy, Viale delle Scienze Ed. 11, 90128, Palermo, Italy. larosa@pa.icar.cnr.it|ICAR-CNR, National Research Council of Italy, Viale delle Scienze Ed. 11, 90128, Palermo, Italy. larosa@pa.icar.cnr.it|ICAR-CNR, National Research Council of Italy, Viale delle Scienze Ed. 11, 90128, Palermo, Italy. larosa@pa.icar.cnr.it', 'Italy|Italy|Italy|Italy', 16, 'Animals|Computer Simulation|DNA Barcoding, Taxonomic|Electron Transport Complex IV|Genes, Mitochondrial|Humans|Phylogeny', 'Journal Article', '10.1186/1471-2105-14-S7-S4', '23815444', 'The key idea of DNA barcode initiative is to identify, for each group of species belonging to different kingdoms of life, a short DNA sequence that can act as a true taxon barcode. DNA barcode represents a valuable type of information that can be integrated with ecological, genetic, and morphological data in order to obtain a more consistent taxonomy. Recent studies have shown that, for the animal kingdom, the mitochondrial gene cytochrome c oxidase I (COI), about 650 bp long, can be used as a barcode sequence for identification and taxonomic purposes of animals. In the present work we aims at introducing the use of an alignment-free approach in order to make taxonomic analysis of barcode sequences. Our approach is based on the use of two compression-based versions of non-computable Universal Similarity Metric (USM) class of distances. Our purpose is to justify the employ of USM also for the analysis of short DNA barcode sequences, showing how USM is able to correctly extract taxonomic information among those kind of sequences.'),
(95671, 2013, 'bmc', 'VIRGO: visualization of A-to-I RNA editing sites in genomic sequences', 'Distefano, Rosario|Nigita, Giovanni|Macca, Valentina|Laganà, Alessandro|Giugno, Rosalba|Pulvirenti, Alfredo|Ferro, Alfredo', 'Department of Mathematics and Computer Science - University of Catania, Catania, Italy.|Department of Mathematics and Computer Science - University of Catania, Catania, Italy.|Department of Mathematics and Computer Science - University of Catania, Catania, Italy.|Department of Mathematics and Computer Science - University of Catania, Catania, Italy.|Department of Mathematics and Computer Science - University of Catania, Catania, Italy.|Department of Mathematics and Computer Science - University of Catania, Catania, Italy.|Department of Mathematics and Computer Science - University of Catania, Catania, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 8, 'Base Sequence|Databases, Genetic|Expressed Sequence Tags|Genomics|Molecular Sequence Data|RNA Editing|Software', 'Journal Article', '10.1186/1471-2105-14-S7-S5', '23815474', 'RNA Editing is a type of post-transcriptional modification that takes place in the eukaryotes. It alters the sequence of primary RNA transcripts by deleting, inserting or modifying residues. Several forms of RNA editing have been discovered including A-to-I, C-to-U, U-to-C and G-to-A. In recent years, the application of global approaches to the study of A-to-I editing, including high throughput sequencing, has led to important advances. However, in spite of enormous efforts, the real biological mechanism underlying this phenomenon remains unknown.'),
(95672, 2013, 'bmc', 'GAM-NGS: genomic assemblies merger for next generation sequencing', 'Vicedomini, Riccardo|Vezzi, Francesco|Scalabrin, Simone|Arvestad, Lars|Policriti, Alberto', 'Department of Mathematics and Computer Science, University of Udine, 33100 Udine, Italy. rvicedomini@appliedgenomics.org|Department of Mathematics and Computer Science, University of Udine, 33100 Udine, Italy. rvicedomini@appliedgenomics.org|Department of Mathematics and Computer Science, University of Udine, 33100 Udine, Italy. rvicedomini@appliedgenomics.org|Department of Mathematics and Computer Science, University of Udine, 33100 Udine, Italy. rvicedomini@appliedgenomics.org|Department of Mathematics and Computer Science, University of Udine, 33100 Udine, Italy. rvicedomini@appliedgenomics.org', 'Italy|Italy|Italy|Italy|Italy', 43, 'Algorithms|Chromosomes|Genome, Bacterial|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Rhodobacter sphaeroides|Sequence Analysis, DNA|Software|Staphylococcus aureus', 'Journal Article', '10.1186/1471-2105-14-S7-S6', '23815503', 'In recent years more than 20 assemblers have been proposed to tackle the hard task of assembling NGS data. A common heuristic when assembling a genome is to use several assemblers and then select the best assembly according to some criteria. However, recent results clearly show that some assemblers lead to better statistics than others on specific regions but are outperformed on other regions or on different evaluation measures. To limit these problems we developed GAM-NGS (Genomic Assemblies Merger for Next Generation Sequencing), whose primary goal is to merge two or more assemblies in order to enhance contiguity and correctness of both. GAM-NGS does not rely on global alignment: regions of the two assemblies representing the same genomic locus (called blocks) are identified through reads alignments and stored in a weighted graph. The merging phase is carried out with the help of this weighted graph that allows an optimal resolution of local problematic regions.'),
(95673, 2013, 'bmc', 'Quasi-cellular systems: stochastic simulation analysis at nanoscale range', 'Calviello, Lorenzo|Stano, Pasquale|Mavelli, Fabio|Luisi, Pier Luigi|Marangoni, Roberto', 'Dipartimento di Informatica, Università di Pisa, L.go B. Pontecorvo 3, 56127 Pisa, Italy.|Dipartimento di Informatica, Università di Pisa, L.go B. Pontecorvo 3, 56127 Pisa, Italy.|Dipartimento di Informatica, Università di Pisa, L.go B. Pontecorvo 3, 56127 Pisa, Italy.|Dipartimento di Informatica, Università di Pisa, L.go B. Pontecorvo 3, 56127 Pisa, Italy.|Dipartimento di Informatica, Università di Pisa, L.go B. Pontecorvo 3, 56127 Pisa, Italy.', 'Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Cell-Free System|Computer Simulation|Green Fluorescent Proteins|Kinetics|Liposomes|Protein Biosynthesis|Software|Stochastic Processes|Synthetic Biology|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-14-S7-S7', '23815522', 'The wet-lab synthesis of the simplest forms of life (minimal cells) is a challenging aspect in modern synthetic biology. Quasi-cellular systems able to produce proteins directly from DNA can be obtained by encapsulating the cell-free transcription/translation system PURESYSTEM(PS) in liposomes. It is possible to detect the intra-vesicle protein production using DNA encoding for GFP and monitoring the fluorescence emission over time. The entrapment of solutes in small-volume liposomes is a fundamental open problem. Stochastic simulation is a valuable tool in the study of biochemical reaction at nanoscale range. QDC (Quick Direct-Method Controlled), a stochastic simulation software based on the well-known Gillespies SSA algorithm, was used. A suitable model formally describing the PS reactions network was developed, to predict, from inner species concentrations (very difficult to measure in small-volumes), the resulting fluorescence signal (experimentally observable).'),
(95674, 2013, 'bmc', 'A novel biclustering algorithm for the discovery of meaningful biological correlations between microRNAs and their target genes', 'Pio, Gianvito|Ceci, Michelangelo|DElia, Domenica|Loglisci, Corrado|Malerba, Donato', 'Department of Computer Science, University of Bari Aldo Moro, Via Orabona 4, 70125, Bari, Italy. gianvito.pio@uniba.it|Department of Computer Science, University of Bari Aldo Moro, Via Orabona 4, 70125, Bari, Italy. gianvito.pio@uniba.it|Department of Computer Science, University of Bari Aldo Moro, Via Orabona 4, 70125, Bari, Italy. gianvito.pio@uniba.it|Department of Computer Science, University of Bari Aldo Moro, Via Orabona 4, 70125, Bari, Italy. gianvito.pio@uniba.it|Department of Computer Science, University of Bari Aldo Moro, Via Orabona 4, 70125, Bari, Italy. gianvito.pio@uniba.it', 'Italy|Italy|Italy|Italy|Italy', 34, 'Algorithms|Animals|Gene Expression Regulation|Humans|MicroRNAs|RNA, Messenger', 'Journal Article', '10.1186/1471-2105-14-S7-S8', '23815553', 'microRNAs (miRNAs) are a class of small non-coding RNAs which have been recognized as ubiquitous post-transcriptional regulators. The analysis of interactions between different miRNAs and their target genes is necessary for the understanding of miRNAs role in the control of cell life and death. In this paper we propose a novel data mining algorithm, called HOCCLUS2, specifically designed to bicluster miRNAs and target messenger RNAs (mRNAs) on the basis of their experimentally-verified and/or predicted interactions. Indeed, existing biclustering approaches, typically used to analyze gene expression data, fail when applied to miRNA:mRNA interactions since they usually do not extract possibly overlapping biclusters (miRNAs and their target genes may have multiple roles), extract a huge amount of biclusters (difficult to browse and rank on the basis of their importance) and work on similarities of feature values (do not limit the analysis to reliable interactions).'),
(95675, 2013, 'bmc', 'Identification and analysis of conserved pockets on protein surfaces', 'Cammisa, Marco|Correra, Antonella|Andreotti, Giuseppina|Cubellis, Maria Vittoria', 'Department of Biology, University Federico II, Via Cinthia, 80126, Naples, Italy.|Department of Biology, University Federico II, Via Cinthia, 80126, Naples, Italy.|Department of Biology, University Federico II, Via Cinthia, 80126, Naples, Italy.|Department of Biology, University Federico II, Via Cinthia, 80126, Naples, Italy.', 'Italy|Italy|Italy|Italy', 17, 'Algorithms|Amino Acid Sequence|Binding Sites|Catalytic Domain|Computer Simulation|Conserved Sequence|Humans|Ligands|Models, Molecular|Protein Conformation|Proteins', 'Journal Article', '10.1186/1471-2105-14-S7-S9', '23815589', 'The interaction between proteins and ligands occurs at pockets that are often lined by conserved amino acids. These pockets can represent the targets for low molecular weight drugs. In order to make the research for new medicines as productive as possible, it is necessary to exploit in silico techniques, high throughput and fragment-based screenings that require the identification of druggable pockets on the surface of proteins, which may or may not correspond to active sites.'),
(95676, 2013, 'bmc', 'Integrating peptides sequence and energy of contact residues information improves prediction of peptide and HLA-I binding with unknown alleles', 'Luo, Fei|Gao, Yangyang|Zhu, Yongqiong|Liu, Juan', 'School of Computer, Wuhan University, Wuhan, Hubei, China.|School of Computer, Wuhan University, Wuhan, Hubei, China.|School of Computer, Wuhan University, Wuhan, Hubei, China.|School of Computer, Wuhan University, Wuhan, Hubei, China.', 'China|China|China|China', 0, 'Algorithms|Alleles|Area Under Curve|Energy Metabolism|HLA Antigens|Histocompatibility Antigens Class I|Humans|Neural Networks (Computer)|Oligopeptides', 'Journal Article', '10.1186/1471-2105-14-S8-S1', '23815611', 'The HLA (human leukocyte antigen) class I is a kind of molecule encoded by a large family of genes and is characteristic of high polymorphism. Now the number of the registered HLA-I molecules has exceeded 3000. Slight differences in the amino acid sequences of HLAs would make them bind to different sets of peptides. In the past decades, although many methods have been proposed to predict the binding between peptides and HLA-I molecules and achieved good performance, most experimental data used by them is limited to the HLAs with a small number of alleles. Thus they are inclined to obtain high prediction accuracy only for data with similar alleles. Because the peptides and HLAs together determine the binding, its necessary to consider their contribution meanwhile.'),
(95677, 2013, 'bmc', 'Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis', 'You, Zhu-Hong|Lei, Ying-Ke|Zhu, Lin|Xia, Junfeng|Wang, Bing', 'College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China. zhuhongyou@gmail.com|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China. zhuhongyou@gmail.com|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China. zhuhongyou@gmail.com|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China. zhuhongyou@gmail.com|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China. zhuhongyou@gmail.com', 'China|China|China|China|China', 165, 'Amino Acid Sequence|Artificial Intelligence|Helicobacter pylori|Principal Component Analysis|Protein Interaction Mapping|Protein Interaction Maps|Saccharomyces cerevisiae|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-14-S8-S10', '23815620', 'Protein-protein interactions (PPIs) play crucial roles in the execution of various cellular processes and form the basis of biological mechanisms. Although large amount of PPIs data for different species has been generated by high-throughput experimental techniques, current PPI pairs obtained with experimental methods cover only a fraction of the complete PPI networks, and further, the experimental methods for identifying PPIs are both time-consuming and expensive. Hence, it is urgent and challenging to develop automated computational methods to efficiently and accurately predict PPIs.'),
(95678, 2013, 'bmc', 'Diagnostic prediction of complex diseases using phase-only correlation based on virtual sample template', 'Wang, Shu-Lin|Fang, Yaping|Fang, Jianwen', 'Applied Bioinformatics Laboratory, the University of Kansas, 2034 Becker Drive, Lawrence, KS 66047, USA.|Applied Bioinformatics Laboratory, the University of Kansas, 2034 Becker Drive, Lawrence, KS 66047, USA.|Applied Bioinformatics Laboratory, the University of Kansas, 2034 Becker Drive, Lawrence, KS 66047, USA.', 'United States|United States|United States', 8, 'Algorithms|Gene Expression Profiling|Humans|Neoplasms|Neurodegenerative Diseases|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-14-S8-S11', '23815677', 'Complex diseases induce perturbations to interaction and regulation networks in living systems, resulting in dynamic equilibrium states that differ for different diseases and also normal states. Thus identifying gene expression patterns corresponding to different equilibrium states is of great benefit to the diagnosis and treatment of complex diseases. However, it remains a major challenge to deal with the high dimensionality and small size of available complex disease gene expression datasets currently used for discovering gene expression patterns.'),
(95679, 2013, 'bmc', 'Locating tandem repeats in weighted sequences in proteins', 'Zhang, Hui|Guo, Qing|Iliopoulos, Costas S', 'College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China.|College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China.|College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China.', 'China|China|China', 6, 'Algorithms|Proteins|Sequence Alignment|Tandem Repeat Sequences', 'Journal Article', '10.1186/1471-2105-14-S8-S2', '23815711', 'A weighted biological sequence is a string in which a set of characters may appear at each position with respective probabilities of occurrence. We attempt to locate all the tandem repeats in a weighted sequence. A repeated substring is called a tandem repeat if each occurrence of the substring is directly adjacent to each other. By introducing the idea of equivalence classes in weighted sequences, we identify the tandem repeats of every possible length using an iterative partitioning technique. We also present the algorithm for recording the tandem repeats, and prove that the problem can be solved in O(n²) time.'),
(95680, 2013, 'bmc', 'PFClust: a novel parameter free clustering algorithm', 'Mavridis, Lazaros|Nath, Neetika|Mitchell, John B O', 'Biomedical Sciences Research Complex and EaStCHEM School of Chemistry, Purdie Building, University of St Andrews, North Haugh, St Andrews, KY16 9ST, Scotland, UK. lazaros.mavridis.lm@gmail.com|Biomedical Sciences Research Complex and EaStCHEM School of Chemistry, Purdie Building, University of St Andrews, North Haugh, St Andrews, KY16 9ST, Scotland, UK. lazaros.mavridis.lm@gmail.com|Biomedical Sciences Research Complex and EaStCHEM School of Chemistry, Purdie Building, University of St Andrews, North Haugh, St Andrews, KY16 9ST, Scotland, UK. lazaros.mavridis.lm@gmail.com', 'United Kingdom|United Kingdom|United Kingdom', 13, 'Algorithms|Cluster Analysis|Protein Folding|Protein Structure, Tertiary', 'Journal Article', '10.1186/1471-2105-14-213', '23819480', 'We present the algorithm PFClust (Parameter Free Clustering), which is able automatically to cluster data and identify a suitable number of clusters to group them into without requiring any parameters to be specified by the user. The algorithm partitions a dataset into a number of clusters that share some common attributes, such as their minimum expectation value and variance of intra-cluster similarity. A set of n objects can be clustered into any number of clusters from one to n, and there are many different hierarchical and partitional, agglomerative and divisive, clustering methodologies available that can be used to do this. Nonetheless, automatically determining the number of clusters present in a dataset constitutes a significant challenge for clustering algorithms. Identifying a putative optimum number of clusters to group the objects into involves computing and evaluating a range of clusterings with different numbers of clusters. However, there is no agreed or unique definition of optimum in this context. Thus, we test PFClust on datasets for which an external gold standard of correct cluster definitions exists, noting that this division into clusters may be suboptimal according to other reasonable criteria. PFClust is heuristic in the sense that it cannot be described in terms of optimising any single simply-expressed metric over the space of possible clusterings.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95681, 2013, 'bmc', 'Comprehensive analysis of correlation coefficients estimated from pooling heterogeneous microarray data', 'Almeida-de-Macedo, Mxc3xa1rcia M|Ransom, Nick|Feng, Yaping|Hurst, Jonathan|Wurtele, Eve Syrkin', 'Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA. marcia.almeida_de_macedo@syngenta.com.|Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA. marcia.almeida_de_macedo@syngenta.com.|Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA. marcia.almeida_de_macedo@syngenta.com.|Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA. marcia.almeida_de_macedo@syngenta.com.|Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA. marcia.almeida_de_macedo@syngenta.com.', 'United States|United States|United States|United States|United States', 9, 'Computer Simulation|Female|Gene Expression Profiling|Genetics, Population|Humans|Male|Meta-Analysis as Topic|Models, Theoretical|Oligonucleotide Array Sequence Analysis|Research Design', 'Journal Article', '10.1186/1471-2105-14-214', '23822712', 'The synthesis of information across microarray studies has been performed by combining statistical results of individual studies (as in a mosaic), or by combining data from multiple studies into a large pool to be analyzed as a single data set (as in a melting pot of data). Specific issues relating to data heterogeneity across microarray studies, such as differences within and between labs or differences among experimental conditions, could lead to equivocal results in a melting pot approach.'),
(95682, 2013, 'bmc', 'Gel2DE - a software tool for correlation analysis of 2D gel electrophoresis data', 'Øye, Ola Kristoffer|Jørgensen, Katarina M|Hjelle, Sigrun M|Sulen, André|Ulvang, Dag Magne|Gjertsen, Bjørn Tore', 'Christian Michelsen Research AS, P.O. Box 60315892 Bergen, Norway.|Christian Michelsen Research AS, P.O. Box 60315892 Bergen, Norway.|Christian Michelsen Research AS, P.O. Box 60315892 Bergen, Norway.|Christian Michelsen Research AS, P.O. Box 60315892 Bergen, Norway.|Christian Michelsen Research AS, P.O. Box 60315892 Bergen, Norway.|Christian Michelsen Research AS, P.O. Box 60315892 Bergen, Norway.', 'Norway|Norway|Norway|Norway|Norway|Norway', 8, 'Electrophoresis, Gel, Two-Dimensional|Protein Isoforms|Protein Processing, Post-Translational|Software|Tumor Suppressor Protein p53', 'Journal Article', '10.1186/1471-2105-14-215', '23829206', 'Two-dimensional gel electrophoresis (2DE) is a powerful technique for studying protein isoforms and their modifications. Existing commercial 2D image analysis tools rely on spot detection that limits analysis of complex protein profiles, e.g. spot appearance/disappearance or overlapping spots. Pixel-by-pixel correlation analysis, an analysis technique for identifying relations between protein patterns in gel images and external variables, can overcome such limitations in spot analysis.'),
(95683, 2013, 'bmc', 'Rapid forward-in-time simulation at the chromosome and genome level', 'Aberer, Andre J|Stamatakis, Alexandros', 'The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, Heidelberg D-69118, Germany. andre.aberer@h-its.org|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, Heidelberg D-69118, Germany. andre.aberer@h-its.org', 'Germany|Germany', 10, 'Algorithms|Base Sequence|Biological Evolution|Chromosome Breakpoints|Computer Simulation|DNA Breaks, Double-Stranded|Genetics, Population|Genome|Mutation|Recombination, Genetic|Selection, Genetic|Software|Time', 'Journal Article', '10.1186/1471-2105-14-216', '23834340', 'In population genetics, simulation is a fundamental tool for analyzing how basic evolutionary forces such as natural selection, recombination, and mutation shape the genetic landscape of a population. Forward simulation represents the most powerful, but, at the same time, most compute-intensive approach for simulating the genetic material of a population.'),
(95684, 2013, 'bmc', 'RCytoscape: tools for exploratory network analysis', 'Shannon, Paul T|Grimes, Mark|Kutlu, Burak|Bot, Jan J|Galas, David J', 'Fred Hutchison Cancer Research Institute, Seattle, WA, USA. pshannon@fhcrc.org|Fred Hutchison Cancer Research Institute, Seattle, WA, USA. pshannon@fhcrc.org|Fred Hutchison Cancer Research Institute, Seattle, WA, USA. pshannon@fhcrc.org|Fred Hutchison Cancer Research Institute, Seattle, WA, USA. pshannon@fhcrc.org|Fred Hutchison Cancer Research Institute, Seattle, WA, USA. pshannon@fhcrc.org', 'United States|United States|United States|United States|United States', 61, 'Chromosome Mapping|Computational Biology|Genome, Human|Glioblastoma|Humans|Models, Genetic|Software', 'Journal Article', '10.1186/1471-2105-14-217', '23837656', 'Biomolecular pathways and networks are dynamic and complex, and the perturbations to them which cause disease are often multiple, heterogeneous and contingent. Pathway and network visualizations, rendered on a computer or published on paper, however, tend to be static, lacking in detail, and ill-equipped to explore the variety and quantities of data available today, and the complex causes we seek to understand.'),
(95685, 2013, 'bmc', 'Software LS-MIDA for efficient mass isotopomer distribution analysis in metabolic modelling', 'Ahmed, Zeeshan|Zeeshan, Saman|Huber, Claudia|Hensel, Michael|Schomburg, Dietmar|Mxc3xbcnch, Richard|Eisenreich, Wolfgang|Dandekar, Thomas', 'Department of Bioinformatics, Biocenter, University of Wxc3xbcrzburg, Wxc3xbcrzburg, Germany.|Department of Bioinformatics, Biocenter, University of Wxc3xbcrzburg, Wxc3xbcrzburg, Germany.|Department of Bioinformatics, Biocenter, University of Wxc3xbcrzburg, Wxc3xbcrzburg, Germany.|Department of Bioinformatics, Biocenter, University of Wxc3xbcrzburg, Wxc3xbcrzburg, Germany.|Department of Bioinformatics, Biocenter, University of Wxc3xbcrzburg, Wxc3xbcrzburg, Germany.|Department of Bioinformatics, Biocenter, University of Wxc3xbcrzburg, Wxc3xbcrzburg, Germany.|Department of Bioinformatics, Biocenter, University of Wxc3xbcrzburg, Wxc3xbcrzburg, Germany.|Department of Bioinformatics, Biocenter, University of Wxc3xbcrzburg, Wxc3xbcrzburg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 10, 'Algorithms|Isotopes|Least-Squares Analysis|Mass Spectrometry|Metabolic Networks and Pathways|Models, Biological|Software', 'Journal Article', '10.1186/1471-2105-14-218', '23837681', 'The knowledge of metabolic pathways and fluxes is important to understand the adaptation of organisms to their biotic and abiotic environment. The specific distribution of stable isotope labelled precursors into metabolic products can be taken as fingerprints of the metabolic events and dynamics through the metabolic networks. An open-source software is required that easily and rapidly calculates from mass spectra of labelled metabolites, derivatives and their fragments global isotope excess and isotopomer distribution.'),
(95686, 2013, 'bmc', 'TCC: an R package for comparing tag count data with robust normalization strategies', 'Sun, Jianqiang|Nishiyama, Tomoaki|Shimizu, Kentaro|Kadota, Koji', 'Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.|Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.|Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.|Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.', 'Japan|Japan|Japan|Japan', 189, 'Animals|Arabidopsis Proteins|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Mice|RNA Replicase|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-14-219', '23837715', 'Differential expression analysis based on next-generation sequencing technologies is a fundamental means of studying RNA expression. We recently developed a multi-step normalization method (called TbT) for two-group RNA-seq data with replicates and demonstrated that the statistical methods available in four R packages (edgeR, DESeq, baySeq, and NBPSeq) together with TbT can produce a well-ranked gene list in which true differentially expressed genes (DEGs) are top-ranked and non-DEGs are bottom ranked. However, the advantages of the current TbT method come at the cost of a huge computation time. Moreover, the R packages did not have normalization methods based on such a multi-step strategy.'),
(95687, 2013, 'bmc', 'NURD: an implementation of a new method to estimate isoform expression from non-uniform RNA-seq data', 'Ma, Xinyun|Zhang, Xuegong', 'MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, TNLIST and Department of Automation, Tsinghua University, Beijing 100084, China.|MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, TNLIST and Department of Automation, Tsinghua University, Beijing 100084, China.', 'China|China', 10, 'Algorithms|Alternative Splicing|Gene Expression Profiling|RNA Isoforms|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-14-220', '23837734', 'RNA-Seq technology has been used widely in transcriptome study, and one of the most important applications is to estimate the expression level of genes and their alternative splicing isoforms. There have been several algorithms published to estimate the expression based on different models. Recently Wu et al. published a method that can accurately estimate isoform level expression by considering position-related sequencing biases using nonparametric models. The method has advantages in handling different read distributions, but there hasnt been an efficient program to implement this algorithm.'),
(95688, 2013, 'bmc', 'Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis', 'Schmidt, Brian J|Casey, Fergal P|Paterson, Thomas|Chan, Jason R', 'Entelos Holding Corporation, 2121 South El Camino Real, Suite 600, San Mateo, CA 94403, USA.|Entelos Holding Corporation, 2121 South El Camino Real, Suite 600, San Mateo, CA 94403, USA.|Entelos Holding Corporation, 2121 South El Camino Real, Suite 600, San Mateo, CA 94403, USA.|Entelos Holding Corporation, 2121 South El Camino Real, Suite 600, San Mateo, CA 94403, USA.', 'United States|United States|United States|United States', 27, 'Algorithms|Antibodies, Monoclonal, Murine-Derived|Antirheumatic Agents|Arthritis, Rheumatoid|B-Lymphocytes|Biomarkers|Computer Simulation|Female|Fibroblasts|Humans|Immunologic Factors|Interferon-beta|Macrophages|Male|Rituximab', 'Journal Article', '10.1186/1471-2105-14-221', '23841912', 'Mechanistic biosimulation can be used in drug development to form testable hypotheses, develop predictions of efficacy before clinical trial results are available, and elucidate clinical response to therapy. However, there is a lack of tools to simultaneously (1) calibrate the prevalence of mechanistically distinct, large sets of virtual patients so their simulated responses statistically match phenotypic variability reported in published clinical trial outcomes, and (2) explore alternate hypotheses of those prevalence weightings to reflect underlying uncertainty in population biology. Here, we report the development of an algorithm, MAPEL (Mechanistic Axes Population Ensemble Linkage), which utilizes a mechanistically-based weighting method to match clinical trial statistics. MAPEL is the first algorithm for developing weighted virtual populations based on biosimulation results that enables the rapid development of an ensemble of alternate virtual population hypotheses, each validated by a composite goodness-of-fit criterion.'),
(95689, 2013, 'bmc', 'Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and clustering', 'Ji, Shuiwang', 'Department of Computer Science, Old Dominion University, 4700 Elkhorn Avenue, Suite 3300, Norfolk, VA 23529-0162, USA. sji@cs.odu.edu', 'United States', 21, 'Animals|Brain|Cluster Analysis|Computational Biology|Computer Graphics|Gene Expression Profiling|In Situ Hybridization|Mice', 'Journal Article', '10.1186/1471-2105-14-222', '23845024', 'The structured organization of cells in the brain plays a key role in its functional efficiency. This delicate organization is the consequence of unique molecular identity of each cell gradually established by precise spatiotemporal gene expression control during development. Currently, studies on the molecular-structural association are beginning to reveal how the spatiotemporal gene expression patterns are related to cellular differentiation and structural development.'),
(95690, 2013, 'bmc', 'A decision theory paradigm for evaluating identifier mapping and filtering methods using data integration', 'Day, Roger S|McDade, Kevin K', 'Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. day01@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. day01@pitt.edu', 'United States|United States', 4, 'Decision Theory|Endometrial Neoplasms|Female|Gene Dosage|Gene Expression Profiling|Humans|MicroRNAs|Proteins|Proteomics|RNA, Messenger|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-14-223', '23855655', 'In bioinformatics, we pre-process raw data into a format ready for answering medical and biological questions. A key step in processing is labeling the measured features with the identities of the molecules purportedly assayed: molecular identification (MI). Biological meaning comes from identifying these molecular measurements correctly with actual molecular species. But MI can be incorrect. Identifier filtering (IDF) selects features with more trusted MI, leaving a smaller, but more correct dataset. Identifier mapping (IDM) is needed when an analyst is combining two high-throughput (HT) measurement platforms on the same samples. IDM produces ID pairs, one ID from each platform, where the mapping declares that the two analytes are associated through a causal path, direct or indirect (example: pairing an ID for an mRNA species with an ID for a protein species that is its putative translation). Many competing solutions for IDF and IDM exist. Analysts need a rigorous method for evaluating and comparing all these choices.'),
(95691, 2013, 'bmc', 'SLiMScape: a protein short linear motif analysis plugin for Cytoscape', 'OBrien, Kevin T|Haslam, Niall J|Shields, Denis C', 'UCD Conway, Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin, Ireland.|UCD Conway, Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin, Ireland.|UCD Conway, Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin, Ireland.', 'Ireland|Ireland|Ireland', 0, 'Amino Acid Motifs|Protein Interaction Maps|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-14-224', '23855714', 'Computational protein short linear motif discovery can use protein interaction information to search for motifs among proteins which share a common interactor. Cytoscape provides a visual interface for protein networks but there is no streamlined way to rapidly visualize motifs in a network of proteins, or to integrate computational discovery with such visualizations.'),
(95692, 2013, 'bmc', 'SNVHMM: predicting single nucleotide variants from next generation sequencing', 'Bian, Jiawen|Liu, Chenglin|Wang, Hongyan|Xing, Jing|Kachroo, Priyanka|Zhou, Xiaobo', 'UCD Conway, Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin, Ireland.|UCD Conway, Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin, Ireland.|UCD Conway, Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin, Ireland.|UCD Conway, Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin, Ireland.|UCD Conway, Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin, Ireland.|UCD Conway, Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin, Ireland.', 'Ireland|Ireland|Ireland|Ireland|Ireland|Ireland', 4, 'Algorithms|Bayes Theorem|Chromosome Mapping|Genetic Variation|Genome, Human|Genomics|Genotype|High-Throughput Nucleotide Sequencing|Humans|Markov Chains|Predictive Value of Tests|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-14-225', '23855743', 'The rapid development of next generation sequencing (NGS) technology provides a novel avenue for genomic exploration and research. Single nucleotide variants (SNVs) inferred from next generation sequencing are expected to reveal gene mutations in cancer. However, NGS has lower sequence coverage and poor SNVs detection capability in the regulatory regions of the genome. Post probabilistic based methods are efficient for detection of SNVs in high coverage regions or sequencing data with high depth. However, for data with low sequencing depth, the efficiency of such algorithms remains poor and needs to be improved.'),
(95693, 2013, 'bmc', 'Fast online and index-based algorithms for approximate search of RNA sequence-structure patterns', 'Meyer, Fernando|Kurtz, Stefan|Beckstette, Michael', 'Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, Hamburg 20146, Germany. beckstette@zbh.uni-hamburg.de.|Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, Hamburg 20146, Germany. beckstette@zbh.uni-hamburg.de.|Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, Hamburg 20146, Germany. beckstette@zbh.uni-hamburg.de.', 'Germany|Germany|Germany', 6, 'Algorithms|Base Pairing|Base Sequence|Computational Biology|Computer Simulation|Databases, Factual|RNA|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-14-226', '23865810', 'It is well known that the search for homologous RNAs is more effective if both sequence and structure information is incorporated into the search. However, current tools for searching with RNA sequence-structure patterns cannot fully handle mutations occurring on both these levels or are simply not fast enough for searching large sequence databases because of the high computational costs of the underlying sequence-structure alignment problem.'),
(95694, 2013, 'bmc', 'A fast weak motif-finding algorithm based on community detection in graphs', 'Jia, Caiyan|Carson, Matthew B|Yu, Jian', 'School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China. cyjia@bjtu.edu.cn|School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China. cyjia@bjtu.edu.cn|School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China. cyjia@bjtu.edu.cn', 'China|China|China', 0, 'Algorithms|Animals|Binding Sites|DNA|Escherichia coli K12|Mice|Nucleotide Motifs|Promoter Regions, Genetic|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-14-227', '23865838', 'Identification of transcription factor binding sites (also called motif discovery) in DNA sequences is a basic step in understanding genetic regulation. Although many successful programs have been developed, the problem is far from being solved on account of diversity in gene expression/regulation and the low specificity of binding sites. State-of-the-art algorithms have their own constraints (e.g., high time or space complexity for finding long motifs, low precision in identification of weak motifs, or the OOPS constraint: one occurrence of the motif instance per sequence) which limit their scope of application.'),
(95695, 2013, 'bmc', 'CELDA -- an ontology for the comprehensive representation of cells in complex systems', 'Seltmann, Stefanie|Stachelscheid, Harald|Damaschun, Alexander|Jansen, Ludger|Lekschas, Fritz|Fontaine, Jean-Fred|Nguyen-Dobinsky, Throng Nghia|Leser, Ulf|Kurtz, Andreas', 'Charité-Universitätsmedizin Berlin, Berlin Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany.|Charité-Universitätsmedizin Berlin, Berlin Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany.|Charité-Universitätsmedizin Berlin, Berlin Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany.|Charité-Universitätsmedizin Berlin, Berlin Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany.|Charité-Universitätsmedizin Berlin, Berlin Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany.|Charité-Universitätsmedizin Berlin, Berlin Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany.|Charité-Universitätsmedizin Berlin, Berlin Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany.|Charité-Universitätsmedizin Berlin, Berlin Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany.|Charité-Universitätsmedizin Berlin, Berlin Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Cells|Cellular Structures|Embryonic Stem Cells|Gene Expression|Humans|Kidney|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-228', '23865855', 'The need for detailed description and modeling of cells drives the continuous generation of large and diverse datasets. Unfortunately, there exists no systematic and comprehensive way to organize these datasets and their information. CELDA (Cell: Expression, Localization, Development, Anatomy) is a novel ontology for the association of primary experimental data and derived knowledge to various types of cells of organisms.'),
(95696, 2013, 'bmc', 'Classification of protein motifs based on subcellular localization uncovers evolutionary relationships at both sequence and functional levels', 'Parras-Moltó, Marcos|Campos-Laborie, Francisco J|García-Diéguez, Juan|Rodríguez-Griñolo, M Rosario|Pérez-Pulido, Antonio J', 'Centro Andaluz de Biologia del Desarrollo (CABD, UPO-CSIC-JA), Facultad de Ciencias Experimentales (Área de Genética), Universidad Pablo de Olavide, 41013, Sevilla, Spain.|Centro Andaluz de Biologia del Desarrollo (CABD, UPO-CSIC-JA), Facultad de Ciencias Experimentales (Área de Genética), Universidad Pablo de Olavide, 41013, Sevilla, Spain.|Centro Andaluz de Biologia del Desarrollo (CABD, UPO-CSIC-JA), Facultad de Ciencias Experimentales (Área de Genética), Universidad Pablo de Olavide, 41013, Sevilla, Spain.|Centro Andaluz de Biologia del Desarrollo (CABD, UPO-CSIC-JA), Facultad de Ciencias Experimentales (Área de Genética), Universidad Pablo de Olavide, 41013, Sevilla, Spain.|Centro Andaluz de Biologia del Desarrollo (CABD, UPO-CSIC-JA), Facultad de Ciencias Experimentales (Área de Genética), Universidad Pablo de Olavide, 41013, Sevilla, Spain.', 'Spain|Spain|Spain|Spain|Spain', 0, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Computational Biology|Databases, Protein|Evolution, Molecular|Multigene Family|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1186/1471-2105-14-229', '23865897', 'Most proteins have evolved in specific cellular compartments that limit their functions and potential interactions. On the other hand, motifs define amino acid arrangements conserved between protein family members and represent powerful tools for assigning function to protein sequences. The ideal motif would identify all members of a protein family but in practice many motifs identify both family members and unrelated proteins, referred to as True Positive (TP) and False Positive (FP) sequences, respectively.'),
(95697, 2013, 'bmc', 'Mining differential top-k co-expression patterns from time course comparative gene expression datasets', 'Liu, Yu-Cheng|Cheng, Chun-Pei|Tseng, Vincent S', 'Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan R.O.C.|Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan R.O.C.|Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan R.O.C.', '-|-|-', 0, 'Algorithms|Databases, Factual|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Genes|Oligonucleotide Array Sequence Analysis', 'Comparative Study', '10.1186/1471-2105-14-230', '23870110', 'Frequent pattern mining analysis applied on microarray dataset appears to be a promising strategy for identifying relationships between gene expression levels. Unfortunately, too many itemsets (co-expressed genes) are identified by this analysis method since it does not consider the importance of each gene within biological processes to a cellular response and does not take into account temporal properties under biological treatment-control matched conditions in a microarray dataset.'),
(95698, 2013, 'bmc', 'Empirical assessment of competitive hybridization and noise in ultra high density canine tiling arrays', 'Willet, Cali E|Bunbury-Cruickshank, Laura|van Rooy, Diane|Child, Georgina|Shariflou, Mohammad R|Thomson, Peter C|Wade, Claire M', 'Faculty of Veterinary Science, The University of Sydney, Sydney, NSW 2006, Australia. cali.willet@sydney.edu.au|Faculty of Veterinary Science, The University of Sydney, Sydney, NSW 2006, Australia. cali.willet@sydney.edu.au|Faculty of Veterinary Science, The University of Sydney, Sydney, NSW 2006, Australia. cali.willet@sydney.edu.au|Faculty of Veterinary Science, The University of Sydney, Sydney, NSW 2006, Australia. cali.willet@sydney.edu.au|Faculty of Veterinary Science, The University of Sydney, Sydney, NSW 2006, Australia. cali.willet@sydney.edu.au|Faculty of Veterinary Science, The University of Sydney, Sydney, NSW 2006, Australia. cali.willet@sydney.edu.au|Faculty of Veterinary Science, The University of Sydney, Sydney, NSW 2006, Australia. cali.willet@sydney.edu.au', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Animals|DNA|Dogs|Genome|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes', 'Journal Article', '10.1186/1471-2105-14-231', '23870167', 'In addition to probe sequence characteristics, noise in hybridization array data is thought to be influenced by competitive hybridization between probes tiled at high densities. Empirical evaluation of competitive hybridization and an estimation of what other non-sequence related features might affect noisy data is currently lacking.'),
(95699, 2013, 'bmc', 'Automatic landmark annotation and dense correspondence registration for 3D human facial images', 'Guo, Jianya|Mei, Xi|Tang, Kun', 'CAS-MPG Partner Institute and Key Laboratory for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.|CAS-MPG Partner Institute and Key Laboratory for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.|CAS-MPG Partner Institute and Key Laboratory for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.', 'China|China|China', 0, 'Artificial Intelligence|Computational Biology|Face|Humans|Imaging, Three-Dimensional|Software', 'Journal Article', '10.1186/1471-2105-14-232', '23870191', 'Traditional anthropometric studies of human face rely on manual measurements of simple features, which are labor intensive and lack of full comprehensive inference. Dense surface registration of three-dimensional (3D) human facial images holds great potential for high throughput quantitative analyses of complex facial traits. However there is a lack of automatic high density registration method for 3D faical images. Furthermore, current approaches of landmark recognition require further improvement in accuracy to support anthropometric applications.'),
(95700, 2013, 'bmc', 'A strategy to select suitable physicochemical attributes of amino acids for protein fold recognition', 'Sharma, Alok|Paliwal, Kuldip K|Dehzangi, Abdollah|Lyons, James|Imoto, Seiya|Miyano, Satoru', 'Laboratory of DNA Information Analysis, University of Tokyo, Minato-ku, Tokyo, Japan. aloks@ims.u-tokyo.ac.jp|Laboratory of DNA Information Analysis, University of Tokyo, Minato-ku, Tokyo, Japan. aloks@ims.u-tokyo.ac.jp|Laboratory of DNA Information Analysis, University of Tokyo, Minato-ku, Tokyo, Japan. aloks@ims.u-tokyo.ac.jp|Laboratory of DNA Information Analysis, University of Tokyo, Minato-ku, Tokyo, Japan. aloks@ims.u-tokyo.ac.jp|Laboratory of DNA Information Analysis, University of Tokyo, Minato-ku, Tokyo, Japan. aloks@ims.u-tokyo.ac.jp|Laboratory of DNA Information Analysis, University of Tokyo, Minato-ku, Tokyo, Japan. aloks@ims.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan', 33, 'Algorithms|Amino Acid Sequence|Amino Acids|Chemical Phenomena|Computational Biology|Drug Design|Protein Folding|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/1471-2105-14-233', '23879571', 'Assigning a protein into one of its folds is a transitional step for discovering three dimensional protein structure, which is a challenging task in bimolecular (biological) science. The present research focuses on: 1) the development of classifiers, and 2) the development of feature extraction techniques based on syntactic and/or physicochemical properties.'),
(95701, 2013, 'bmc', 'Automatic extraction of biomolecular interactions: an empirical approach', 'Zhang, Lifeng|Berleant, Daniel|Ding, Jing|Wurtele, Eve Syrkin', 'Siemens Corporate Research, Princeton, NJ, USA.|Siemens Corporate Research, Princeton, NJ, USA.|Siemens Corporate Research, Princeton, NJ, USA.|Siemens Corporate Research, Princeton, NJ, USA.', 'United States|United States|United States|United States', 2, 'Algorithms|Data Mining|Databases, Factual|MEDLINE|Software', 'Journal Article', '10.1186/1471-2105-14-234', '23883165', 'We describe a method for extracting data about how biomolecule pairs interact from texts. This method relies on empirically determined characteristics of sentences. The characteristics are efficient to compute, making this approach to extraction of biomolecular interactions scalable. The results of such interaction mining can support interaction network annotation, question answering, database construction, and other applications.'),
(95702, 2013, 'bmc', 'The KUPNetViz: a biological network viewer for multiple -omics datasets in kidney diseases', 'Moulos, Panagiotis|Klein, Julie|Jupp, Simon|Stevens, Robert|Bascands, Jean-Loup|Schanstra, Joost P', 'Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institute of Cardiovascular and Metabolic Disease, 1 avenue Jean Poulhès, 31432 Toulouse, France. pmoulos@eie.gr|Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institute of Cardiovascular and Metabolic Disease, 1 avenue Jean Poulhès, 31432 Toulouse, France. pmoulos@eie.gr|Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institute of Cardiovascular and Metabolic Disease, 1 avenue Jean Poulhès, 31432 Toulouse, France. pmoulos@eie.gr|Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institute of Cardiovascular and Metabolic Disease, 1 avenue Jean Poulhès, 31432 Toulouse, France. pmoulos@eie.gr|Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institute of Cardiovascular and Metabolic Disease, 1 avenue Jean Poulhès, 31432 Toulouse, France. pmoulos@eie.gr|Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institute of Cardiovascular and Metabolic Disease, 1 avenue Jean Poulhès, 31432 Toulouse, France. pmoulos@eie.gr', 'France|France|France|France|France|France', 9, 'Animals|Computational Biology|Disease Models, Animal|Humans|Internet|Polycystic Kidney Diseases|Search Engine|Software', 'Journal Article', '10.1186/1471-2105-14-235', '23883183', 'Constant technological advances have allowed scientists in biology to migrate from conventional single-omics to multi-omics experimental approaches, challenging bioinformatics to bridge this multi-tiered information. Ongoing research in renal biology is no exception. The results of large-scale and/or high throughput experiments, presenting a wealth of information on kidney disease are scattered across the web. To tackle this problem, we recently presented the KUPKB, a multi-omics data repository for renal diseases.'),
(95703, 2013, 'bmc', 'svapls: an R package to correct for hidden factors of variability in gene expression studies', 'Chakraborty, Sutirtha|Datta, Somnath|Datta, Susmita', 'Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY-40202, USA. s0chak10@louisville.edu|Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY-40202, USA. s0chak10@louisville.edu|Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY-40202, USA. s0chak10@louisville.edu', 'United States|United States|United States', 2, 'Gene Expression Profiling|Genome|Least-Squares Analysis|Software', 'Journal Article', '10.1186/1471-2105-14-236', '23883280', 'Hidden variability is a fundamentally important issue in the context of gene expression studies. Collected tissue samples may have a wide variety of hidden effects that may alter their transcriptional landscape significantly. As a result their actual differential expression pattern can be potentially distorted, leading to inaccurate results from a genome-wide testing for the important transcripts.'),
(95704, 2013, 'bmc', 'DepthTools: an R package for a robust analysis of gene expression data', 'Torrente, Aurora|López-Pintado, Sara|Romo, Juan', 'Functional Genomics Team, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK. aurora@ebi.ac.uk|Functional Genomics Team, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK. aurora@ebi.ac.uk|Functional Genomics Team, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK. aurora@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 4, 'Algorithms|Gene Expression Profiling|Genome|Humans|Male|Oligonucleotide Array Sequence Analysis|Prostatic Neoplasms|Software', 'Journal Article', '10.1186/1471-2105-14-237', '23885712', 'The use of DNA microarrays and oligonucleotide chips of high density in modern biomedical research provides complex, high dimensional data which have been proven to convey crucial information about gene expression levels and to play an important role in disease diagnosis. Therefore, there is a need for developing new, robust statistical techniques to analyze these data.'),
(95705, 2013, 'bmc', 'Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping', 'Paulus, Stefan|Dupuis, Jan|Mahlein, Anne-Katrin|Kuhlmann, Heiner', 'Institute of Geodesy and Geoinformation - Professorship of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany. paulus@igg.uni-bonn.de|Institute of Geodesy and Geoinformation - Professorship of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany. paulus@igg.uni-bonn.de|Institute of Geodesy and Geoinformation - Professorship of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany. paulus@igg.uni-bonn.de|Institute of Geodesy and Geoinformation - Professorship of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany. paulus@igg.uni-bonn.de', 'Germany|Germany|Germany|Germany', 111, 'Imaging, Three-Dimensional|Lasers|Phenotype|Plant Leaves|Plant Stems|Plant Structures|Triticum', 'Journal Article', '10.1186/1471-2105-14-238', '23890277', 'Laserscanning recently has become a powerful and common method for plant parameterization and plant growth observation on nearly every scale range. However, 3D measurements with high accuracy, spatial resolution and speed result in a multitude of points that require processing and analysis. The primary objective of this research has been to establish a reliable and fast technique for high throughput phenotyping using differentiation, segmentation and classification of single plants by a fully automated system. In this report, we introduce a technique for automated classification of point clouds of plants and present the applicability for plant parameterization.'),
(95706, 2013, 'bmc', 'A new approach for prediction of tumor sensitivity to targeted drugs based on functional data', 'Berlow, Noah|Davis, Lara E|Cantor, Emma L|Séguin, Bernard|Keller, Charles|Pal, Ranadip', 'Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.|Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.|Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.|Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.|Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.|Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.', 'United States|United States|United States|United States|United States|United States', 30, 'Algorithms|Animals|Antineoplastic Agents|Cell Survival|Dogs|Drug Screening Assays, Antitumor|Gene Regulatory Networks|Humans|Molecular Targeted Therapy|Neoplasms|Tumor Cells, Cultured', 'Journal Article', '10.1186/1471-2105-14-239', '23890326', 'The success of targeted anti-cancer drugs are frequently hindered by the lack of knowledge of the individual pathway of the patient and the extreme data requirements on the estimation of the personalized genetic network of the patients tumor. The prediction of tumor sensitivity to targeted drugs remains a major challenge in the design of optimal therapeutic strategies. The current sensitivity prediction approaches are primarily based on genetic characterizations of the tumor sample. We propose a novel sensitivity prediction approach based on functional perturbation data that incorporates the drug protein interaction information and sensitivities to a training set of drugs with known targets.'),
(95707, 2013, 'bmc', 'Robust flux balance analysis of multiscale biochemical reaction networks', 'Sun, Yuekai|Fleming, Ronan M T|Thiele, Ines|Saunders, Michael A', 'Institute for Computational and Mathematical Engineering, Stanford University, Stanford, USA. yuekai@stanford.edu|Institute for Computational and Mathematical Engineering, Stanford University, Stanford, USA. yuekai@stanford.edu|Institute for Computational and Mathematical Engineering, Stanford University, Stanford, USA. yuekai@stanford.edu|Institute for Computational and Mathematical Engineering, Stanford University, Stanford, USA. yuekai@stanford.edu', 'United States|United States|United States|United States', 16, 'Biochemical Phenomena|Escherichia coli|Metabolic Networks and Pathways|Software', 'Journal Article', '10.1186/1471-2105-14-240', '23899245', 'Biological processes such as metabolism, signaling, and macromolecular synthesis can be modeled as large networks of biochemical reactions. Large and comprehensive networks, like integrated networks that represent metabolism and macromolecular synthesis, are inherently multiscale because reaction rates can vary over many orders of magnitude. They require special methods for accurate analysis because naive use of standard optimization systems can produce inaccurate or erroneously infeasible results.'),
(95708, 2013, 'bmc', 'Identification of highly synchronized subnetworks from gene expression data', 'Gao, Shouguo|Wang, Xujing', 'Department of Physics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.|Department of Physics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.', 'United States|United States', 3, 'Algorithms|Cell Cycle|Computational Biology|Computer Simulation|Gene Expression Profiling|Gene Regulatory Networks|Nonlinear Dynamics|Protein Interaction Mapping|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-14-S9-S5', '23901792', 'There has been a growing interest in identifying context-specific active protein-protein interaction (PPI) subnetworks through integration of PPI and time course gene expression data. However the interaction dynamics during the biological process under study has not been sufficiently considered previously.'),
(95709, 2013, 'bmc', 'MGC: a metagenomic gene caller', 'El Allali, Achraf|Rose, John R', 'Department of Computer Science and Engineering, University of South Carolina, 315 Main Street, Columbia, SC 29208, USA. eachraf@gmail.com|Department of Computer Science and Engineering, University of South Carolina, 315 Main Street, Columbia, SC 29208, USA. eachraf@gmail.com', 'United States|United States', 10, 'Algorithms|Base Composition|Base Sequence|Codon|Computational Biology|Genome, Archaeal|Genome, Bacterial|Metagenomics|Neural Networks (Computer)|Open Reading Frames', 'Comparative Study', '10.1186/1471-2105-14-S9-S6', '23901840', 'Computational gene finding algorithms have proven their robustness in identifying genes in complete genomes. However, metagenomic sequencing has presented new challenges due to the incomplete and fragmented nature of the data. During the last few years, attempts have been made to extract complete and incomplete open reading frames (ORFs) directly from short reads and identify the coding ORFs, bypassing other challenging tasks such as the assembly of the metagenome.'),
(95710, 2013, 'bmc', 'Solving the molecular distance geometry problem with inaccurate distance data', 'Souza, Michael|Lavor, Carlile|Muritiba, Albert|Maculan, Nelson', 'Department of Statistics and Applied Mathematics, Federal University of Ceará, Ceará, 60455-760, Brazil. michael@ufc.br|Department of Statistics and Applied Mathematics, Federal University of Ceará, Ceará, 60455-760, Brazil. michael@ufc.br|Department of Statistics and Applied Mathematics, Federal University of Ceará, Ceará, 60455-760, Brazil. michael@ufc.br|Department of Statistics and Applied Mathematics, Federal University of Ceará, Ceará, 60455-760, Brazil. michael@ufc.br', 'Brazil|Brazil|Brazil|Brazil', 18, 'Algorithms|Computational Biology|Least-Squares Analysis|Models, Molecular|Nonlinear Dynamics|Proteins', 'Journal Article', '10.1186/1471-2105-14-S9-S7', '23901894', 'We present a new iterative algorithm for the molecular distance geometry problem with inaccurate and sparse data, which is based on the solution of linear systems, maximum cliques, and a minimization of nonlinear least-squares function. Computational results with real protein structures are presented in order to validate our approach. '),
(95711, 2013, 'bmc', 'Parallel comparison of Illumina RNA-Seq and Affymetrix microarray platforms on transcriptomic profiles generated from 5-aza-deoxy-cytidine treated HT-29 colon cancer cells and simulated datasets', 'Xu, Xiao|Zhang, Yuanhao|Williams, Jennie|Antoniou, Eric|McCombie, W Richard|Wu, Song|Zhu, Wei|Davidson, Nicholas O|Denoya, Paula|Li, Ellen', 'School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA.|School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA.|School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA.|School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA.|School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA.|School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA.|School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA.|School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA.|School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA.|School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 48, 'Algorithms|Azacitidine|Colonic Neoplasms|Gene Expression Profiling|HT29 Cells|Humans|Oligonucleotide Array Sequence Analysis|RNA, Neoplasm|Regression Analysis|Sensitivity and Specificity|Sequence Analysis, RNA', 'Comparative Study', '10.1186/1471-2105-14-S9-S1', '23902433', 'High throughput parallel sequencing, RNA-Seq, has recently emerged as an appealing alternative to microarray in identifying differentially expressed genes (DEG) between biological groups. However, there still exists considerable discrepancy on gene expression measurements and DEG results between the two platforms. The objective of this study was to compare parallel paired-end RNA-Seq and microarray data generated on 5-azadeoxy-cytidine (5-Aza) treated HT-29 colon cancer cells with an additional simulation study.'),
(95712, 2013, 'bmc', 'Reconstruction of viral population structure from next-generation sequencing data using multicommodity flows', 'Skums, Pavel|Mancuso, Nicholas|Artyomenko, Alexander|Tork, Bassam|Mandoiu, Ion|Khudyakov, Yury|Zelikovsky, Alex', 'Laboratory of Molecular Epidemiology and Bioinformatics, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road NE, 30333 Atlanta, GA, USA. kki8@cdc.gov|Laboratory of Molecular Epidemiology and Bioinformatics, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road NE, 30333 Atlanta, GA, USA. kki8@cdc.gov|Laboratory of Molecular Epidemiology and Bioinformatics, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road NE, 30333 Atlanta, GA, USA. kki8@cdc.gov|Laboratory of Molecular Epidemiology and Bioinformatics, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road NE, 30333 Atlanta, GA, USA. kki8@cdc.gov|Laboratory of Molecular Epidemiology and Bioinformatics, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road NE, 30333 Atlanta, GA, USA. kki8@cdc.gov|Laboratory of Molecular Epidemiology and Bioinformatics, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road NE, 30333 Atlanta, GA, USA. kki8@cdc.gov|Laboratory of Molecular Epidemiology and Bioinformatics, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road NE, 30333 Atlanta, GA, USA. kki8@cdc.gov', 'United States|United States|United States|United States|United States|United States|United States', 14, 'Algorithms|Genetic Variation|Genome, Viral|Hepacivirus|RNA Viruses|Sequence Analysis, RNA', 'Comparative Study', '10.1186/1471-2105-14-S9-S2', '23902469', 'Highly mutable RNA viruses exist in infected hosts as heterogeneous populations of genetically close variants known as quasispecies. Next-generation sequencing (NGS) allows for analysing a large number of viral sequences from infected patients, presenting a novel opportunity for studying the structure of a viral population and understanding virus evolution, drug resistance and immune escape. Accurate reconstruction of genetic composition of intra-host viral populations involves assembling the NGS short reads into whole-genome sequences and estimating frequencies of individual viral variants. Although a few approaches were developed for this task, accurate reconstruction of quasispecies populations remains greatly unresolved.'),
(95713, 2013, 'bmc', 'Optimal assembly for high throughput shotgun sequencing', 'Bresler, Guy|Bresler, Maayan|Tse, David', 'Department of EECS, UC Berkeley, California, USA. gbresler@eecs.berkeley.edu.|Department of EECS, UC Berkeley, California, USA. gbresler@eecs.berkeley.edu.|Department of EECS, UC Berkeley, California, USA. gbresler@eecs.berkeley.edu.', 'United States|United States|United States', 31, 'Algorithms|Contig Mapping|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-14-S5-S18', '23902516', 'We present a framework for the design of optimal assembly algorithms for shotgun sequencing under the criterion of complete reconstruction. We derive a lower bound on the read length and the coverage depth required for reconstruction in terms of the repeat statistics of the genome. Building on earlier works, we design a de Brujin graph based assembly algorithm which can achieve very close to the lower bound for repeat statistics of a wide range of sequenced genomes, including the GAGE datasets. The results are based on a set of necessary and sufficient conditions on the DNA sequence and the reads for reconstruction. The conditions can be viewed as the shotgun sequencing analogue of Ukkonen-Pevzners necessary and sufficient conditions for Sequencing by Hybridization. ');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95714, 2013, 'bmc', 'PoPLAR: Portal for Petascale Lifescience Applications and Research', 'Rekapalli, Bhanu|Giblock, Paul|Reardon, Christopher', 'Joint Institute for Computational Sciences, The University of Tennessee, Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge, TN 37831-6173, USA. brekapal@utk.edu|Joint Institute for Computational Sciences, The University of Tennessee, Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge, TN 37831-6173, USA. brekapal@utk.edu|Joint Institute for Computational Sciences, The University of Tennessee, Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge, TN 37831-6173, USA. brekapal@utk.edu', 'United States|United States|United States', 7, 'Computational Biology|Computing Methodologies|Databases, Genetic|Electronic Data Processing|Information Storage and Retrieval|Internet|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-14-S9-S3', '23902523', 'We are focusing specifically on fast data analysis and retrieval in bioinformatics that will have a direct impact on the quality of human health and the environment. The exponential growth of data generated in biology research, from small atoms to big ecosystems, necessitates an increasingly large computational component to perform analyses. Novel DNA sequencing technologies and complementary high-throughput approaches--such as proteomics, genomics, metabolomics, and meta-genomics--drive data-intensive bioinformatics. While individual research centers or universities could once provide for these applications, this is no longer the case. Today, only specialized national centers can deliver the level of computing resources required to meet the challenges posed by rapid data growth and the resulting computational demand. Consequently, we are developing massively parallel applications to analyze the growing flood of biological data and contribute to the rapid discovery of novel knowledge.'),
(95715, 2013, 'bmc', 'Classification and assessment tools for structural motif discovery algorithms', 'Badr, Ghada|Al-Turaiki, Isra|Mathkour, Hassan', 'King Saud University, College of Computer and Information Sciences, Riyadh, Kingdom of Saudi Arabia. badrghada@hotmail.com|King Saud University, College of Computer and Information Sciences, Riyadh, Kingdom of Saudi Arabia. badrghada@hotmail.com|King Saud University, College of Computer and Information Sciences, Riyadh, Kingdom of Saudi Arabia. badrghada@hotmail.com', 'Saudi Arabia|Saudi Arabia|Saudi Arabia', 2, 'Algorithms|Computational Biology|Conserved Sequence|Models, Statistical|Nucleotide Motifs|Sequence Analysis, RNA', 'Comparative Study', '10.1186/1471-2105-14-S9-S4', '23902564', 'Motif discovery is the problem of finding recurring patterns in biological data. Patterns can be sequential, mainly when discovered in DNA sequences. They can also be structural (e.g. when discovering RNA motifs). Finding common structural patterns helps to gain a better understanding of the mechanism of action (e.g. post-transcriptional regulation). Unlike DNA motifs, which are sequentially conserved, RNA motifs exhibit conservation in structure, which may be common even if the sequences are different. Over the past few years, hundreds of algorithms have been developed to solve the sequential motif discovery problem, while less work has been done for the structural case.'),
(95716, 2013, 'bmc', 'Proceedings of the Seventh International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, September 16-18, 2010, Palermo, Italy', '-', '-', '-', 2, 'Animals|Artificial Intelligence|Computational Biology|Humans', 'Congress', '10.1186/1471-2105-14-S9-S4', '23908986', '-'),
(95717, 2013, 'bmc', 'MatrixCatch--a novel tool for the recognition of composite regulatory elements in promoters', 'Deyneko, Igor V|Kel, Alexander E|Kel-Margoulis, Olga V|Deineko, Elena V|Wingender, Edgar|Weiss, Siegfried', 'Department of Molecular Immunology, Helmholtz Centre for Infection Research, Braunschweig, Germany. Igor.Deyneko@helmholtz-hzi.de|Department of Molecular Immunology, Helmholtz Centre for Infection Research, Braunschweig, Germany. Igor.Deyneko@helmholtz-hzi.de|Department of Molecular Immunology, Helmholtz Centre for Infection Research, Braunschweig, Germany. Igor.Deyneko@helmholtz-hzi.de|Department of Molecular Immunology, Helmholtz Centre for Infection Research, Braunschweig, Germany. Igor.Deyneko@helmholtz-hzi.de|Department of Molecular Immunology, Helmholtz Centre for Infection Research, Braunschweig, Germany. Igor.Deyneko@helmholtz-hzi.de|Department of Molecular Immunology, Helmholtz Centre for Infection Research, Braunschweig, Germany. Igor.Deyneko@helmholtz-hzi.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 13, 'Algorithms|Computational Biology|Gene Expression Regulation|Genomics|Nucleotide Motifs|Promoter Regions, Genetic|Regulatory Elements, Transcriptional|Regulatory Sequences, Nucleic Acid', 'Journal Article', '10.1186/1471-2105-14-241', '23924163', 'Accurate recognition of regulatory elements in promoters is an essential prerequisite for understanding the mechanisms of gene regulation at the level of transcription. Composite regulatory elements represent a particular type of such transcriptional regulatory elements consisting of pairs of individual DNA motifs. In contrast to the present approach, most available recognition techniques are based purely on statistical evaluation of the occurrence of single motifs. Such methods are limited in application, since the accuracy of recognition is greatly dependent on the size and quality of the sequence dataset. Methods that exploit available knowledge and have broad applicability are evidently needed.'),
(95718, 2013, 'bmc', 'GOParGenPy: a high throughput method to generate gene ontology data matrices', 'Kumar, Ajay Anand|Holm, Liisa|Toronen, Petri', 'Institute of Biotechnology, University of Helsinki, PO Box 56, (Viikinkaari 5), Helsinki 00014, Finland. ajay.kumar@helsinki.fi|Institute of Biotechnology, University of Helsinki, PO Box 56, (Viikinkaari 5), Helsinki 00014, Finland. ajay.kumar@helsinki.fi|Institute of Biotechnology, University of Helsinki, PO Box 56, (Viikinkaari 5), Helsinki 00014, Finland. ajay.kumar@helsinki.fi', 'Finland|Finland|Finland', 4, 'Artificial Intelligence|Gene Ontology|Genes|Molecular Sequence Annotation|Proteins|Search Engine|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-242', '23927037', 'Gene Ontology (GO) is a popular standard in the annotation of gene products and provides information related to genes across all species. The structure of GO is dynamic and is updated on a daily basis. However, the popular existing methods use outdated versions of GO. Moreover, these tools are slow to process large datasets consisting of more than 20,000 genes.'),
(95719, 2013, 'bmc', '4273xcfx80: bioinformatics education on low cost ARM hardware', 'Barker, Daniel|Ferrier, David Ek|Holland, Peter Wh|Mitchell, John Bo|Plaisier, Heleen|Ritchie, Michael G|Smart, Steven D', 'Sir Harold Mitchell Building, School of Biology, University of St Andrews, St Andrews, Fife KY16 9TH, UK. db60@st-andrews.ac.uk|Sir Harold Mitchell Building, School of Biology, University of St Andrews, St Andrews, Fife KY16 9TH, UK. db60@st-andrews.ac.uk|Sir Harold Mitchell Building, School of Biology, University of St Andrews, St Andrews, Fife KY16 9TH, UK. db60@st-andrews.ac.uk|Sir Harold Mitchell Building, School of Biology, University of St Andrews, St Andrews, Fife KY16 9TH, UK. db60@st-andrews.ac.uk|Sir Harold Mitchell Building, School of Biology, University of St Andrews, St Andrews, Fife KY16 9TH, UK. db60@st-andrews.ac.uk|Sir Harold Mitchell Building, School of Biology, University of St Andrews, St Andrews, Fife KY16 9TH, UK. db60@st-andrews.ac.uk|Sir Harold Mitchell Building, School of Biology, University of St Andrews, St Andrews, Fife KY16 9TH, UK. db60@st-andrews.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 11, 'Biology|Computational Biology|Computers|Humans|Software|Students|Teaching Materials|Textbooks as Topic|Universities', 'Journal Article', '10.1186/1471-2105-14-243', '23937194', 'Teaching bioinformatics at universities is complicated by typical computer classroom settings. As well as running software locally and online, students should gain experience of systems administration. For a future career in biology or bioinformatics, the installation of software is a useful skill. We propose that this may be taught by running the course on GNU/Linux running on inexpensive Raspberry Pi computer hardware, for which students may be granted full administrator access.'),
(95720, 2013, 'bmc', 'RCircos: an R package for Circos 2D track plots', 'Zhang, Hongen|Meltzer, Paul|Davis, Sean', 'Genetics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 37, Room 6138, 37 Convent Drive, Bethesda, MD 20892-4265, USA. sdavis2@mail.nih.gov.|Genetics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 37, Room 6138, 37 Convent Drive, Bethesda, MD 20892-4265, USA. sdavis2@mail.nih.gov.|Genetics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 37, Room 6138, 37 Convent Drive, Bethesda, MD 20892-4265, USA. sdavis2@mail.nih.gov.', 'United States|United States|United States', 153, 'Animals|Computer Graphics|Gene Expression Profiling|Genome|Genomics|Image Processing, Computer-Assisted|Mice|Rats|Software', 'Journal Article', '10.1186/1471-2105-14-244', '23937229', 'Circos is a Perl language based software package for visualizing similarities and differences of genome structure and positional relationships between genomic intervals. Running Circos requires extra data processing procedures to prepare plot data files and configure files from datasets, which limits its capability of integrating directly with other software tools such as R. Recently published R Bioconductor package ggbio provides a function to display genomic data in circular layout based on multiple other packages, which increases its complexity of usage and decreased the flexibility in integrating with other R pipelines.'),
(95721, 2013, 'bmc', 'Group sparse canonical correlation analysis for genomic data integration', 'Lin, Dongdong|Zhang, Jigang|Li, Jingyao|Calhoun, Vince D|Deng, Hong-Wen|Wang, Yu-Ping', 'Biomedical Engineering Department, Tulane University, New Orleans, LA, USA.|Biomedical Engineering Department, Tulane University, New Orleans, LA, USA.|Biomedical Engineering Department, Tulane University, New Orleans, LA, USA.|Biomedical Engineering Department, Tulane University, New Orleans, LA, USA.|Biomedical Engineering Department, Tulane University, New Orleans, LA, USA.|Biomedical Engineering Department, Tulane University, New Orleans, LA, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|DNA Copy Number Variations|Genetic Markers|Genome, Human|Genome-Wide Association Study|Genomics|Humans|Polymorphism, Single Nucleotide|Systems Integration', 'Journal Article', '10.1186/1471-2105-14-245', '23937249', 'The emergence of high-throughput genomic datasets from different sources and platforms (e.g., gene expression, single nucleotide polymorphisms (SNP), and copy number variation (CNV)) has greatly enhanced our understandings of the interplay of these genomic factors as well as their influences on the complex diseases. It is challenging to explore the relationship between these different types of genomic data sets. In this paper, we focus on a multivariate statistical method, canonical correlation analysis (CCA) method for this problem. Conventional CCA method does not work effectively if the number of data samples is significantly less than that of biomarkers, which is a typical case for genomic data (e.g., SNPs). Sparse CCA (sCCA) methods were introduced to overcome such difficulty, mostly using penalizations with l-1 norm (CCA-l1) or the combination of l-1and l-2 norm (CCA-elastic net). However, they overlook the structural or group effect within genomic data in the analysis, which often exist and are important (e.g., SNPs spanning a gene interact and work together as a group).'),
(95722, 2013, 'bmc', 'ArkMAP: integrating genomic maps across species and data sources', 'Paterson, Trevor|Law, Andy', 'Division of Genetics and Genomics, The Roslin Institute, Royal-Dick School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian EH25 9RG, UK. trevor.paterson@roslin.ed.ac.uk|Division of Genetics and Genomics, The Roslin Institute, Royal-Dick School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian EH25 9RG, UK. trevor.paterson@roslin.ed.ac.uk', 'United Kingdom|United Kingdom', 0, 'Chromosome Mapping|Computational Biology|Databases, Factual|Genetic Variation|Genome|Genomics|Internet|Software|Species Specificity|Synteny', 'Journal Article', '10.1186/1471-2105-14-246', '23941167', 'The visualisation of genetic and genomic maps aligned within and between species and across data sources can be used to inform studies of genome evolution, assist genome assembly projects and aid gene discovery and identification. Whilst annotation, integration and exploration of assembled genome sequences is well supported, there are fewer tools available which can display genetic maps for less well-characterized species, and integrate these maps with annotated reference genomes to support cross species comparisons.'),
(95723, 2013, 'bmc', 'PKIS: computational identification of protein kinases for experimentally discovered protein phosphorylation sites', 'Zou, Liang|Wang, Mang|Shen, Yi|Liao, Jie|Li, Ao|Wang, Minghui', 'Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China. mhwang@ustc.edu.cn.|Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China. mhwang@ustc.edu.cn.|Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China. mhwang@ustc.edu.cn.|Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China. mhwang@ustc.edu.cn.|Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China. mhwang@ustc.edu.cn.|Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China. mhwang@ustc.edu.cn.', 'China|China|China|China|China|China', 36, 'Animals|CDC2 Protein Kinase|Casein Kinase II|Computational Biology|Humans|Internet|Intracellular Signaling Peptides and Proteins|Phosphorylation|Predictive Value of Tests|Protein-Tyrosine Kinases|Proto-Oncogene Proteins c-met|Signal Transduction|Substrate Specificity|Support Vector Machine|Syk Kinase', 'Journal Article', '10.1186/1471-2105-14-247', '23941207', 'Dynamic protein phosphorylation is an essential regulatory mechanism in various organisms. In this capacity, it is involved in a multitude of signal transduction pathways. Kinase-specific phosphorylation data lay the foundation for reconstruction of signal transduction networks. For this reason, precise annotation of phosphorylated proteins is the first step toward simulating cell signaling pathways. However, the vast majority of kinase-specific phosphorylation data remain undiscovered and existing experimental methods and computational phosphorylation site (P-site) prediction tools have various limitations with respect to addressing this problem.'),
(95724, 2013, 'bmc', 'kClust: fast and sensitive clustering of large protein sequence databases', 'Hauser, Maria|Mayer, Christian E|Söding, Johannes', 'Gene Center and Center for Integrated Protein Science (CIPSM), Ludwig-Maximilians-Universität München, Feodor-Lynen-Str, 25, Munich 81377, Germany. soeding@genzentrum.lmu.de.|Gene Center and Center for Integrated Protein Science (CIPSM), Ludwig-Maximilians-Universität München, Feodor-Lynen-Str, 25, Munich 81377, Germany. soeding@genzentrum.lmu.de.|Gene Center and Center for Integrated Protein Science (CIPSM), Ludwig-Maximilians-Universität München, Feodor-Lynen-Str, 25, Munich 81377, Germany. soeding@genzentrum.lmu.de.', 'Germany|Germany|Germany', 0, 'Algorithms|Cluster Analysis|Databases, Factual|Databases, Protein|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-14-248', '23945046', 'Fueled by rapid progress in high-throughput sequencing, the size of public sequence databases doubles every two years. Searching the ever larger and more redundant databases is getting increasingly inefficient. Clustering can help to organize sequences into homologous and functionally similar groups and can improve the speed, sensitivity, and readability of homology searches. However, because the clustering time is quadratic in the number of sequences, standard sequence search methods are becoming impracticable.'),
(95725, 2013, 'bmc', 'Gentrepid V20: a web server for candidate disease gene prediction', 'Ballouz, Sara|Liu, Jason Y|George, Richard A|Bains, Naresh|Liu, Arthur|Oti, Martin|Gaeta, Bruno|Fatkin, Diane|Wouters, Merridee A', 'School of Medicine, Deakin University, Geelong, VIC 3217, Australia. m.wouters@deakin.edu.au.|School of Medicine, Deakin University, Geelong, VIC 3217, Australia. m.wouters@deakin.edu.au.|School of Medicine, Deakin University, Geelong, VIC 3217, Australia. m.wouters@deakin.edu.au.|School of Medicine, Deakin University, Geelong, VIC 3217, Australia. m.wouters@deakin.edu.au.|School of Medicine, Deakin University, Geelong, VIC 3217, Australia. m.wouters@deakin.edu.au.|School of Medicine, Deakin University, Geelong, VIC 3217, Australia. m.wouters@deakin.edu.au.|School of Medicine, Deakin University, Geelong, VIC 3217, Australia. m.wouters@deakin.edu.au.|School of Medicine, Deakin University, Geelong, VIC 3217, Australia. m.wouters@deakin.edu.au.|School of Medicine, Deakin University, Geelong, VIC 3217, Australia. m.wouters@deakin.edu.au.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 4, 'Computational Biology|Databases, Genetic|Genetic Predisposition to Disease|Genome-Wide Association Study|Humans|Internet|Phenotype', 'Journal Article', '10.1186/1471-2105-14-249', '23947436', 'Candidate disease gene prediction is a rapidly developing area of bioinformatics research with the potential to deliver great benefits to human health. As experimental studies detecting associations between genetic intervals and disease proliferate, better bioinformatic techniques that can expand and exploit the data are required.'),
(95726, 2013, 'bmc', 'Conversion of KEGG metabolic pathways to SBGN maps including automatic layout', 'Czauderna, Tobias|Wybrow, Michael|Marriott, Kim|Schreiber, Falk', 'Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. czauderna@ipk-gatersleben.de.|Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. czauderna@ipk-gatersleben.de.|Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. czauderna@ipk-gatersleben.de.|Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. czauderna@ipk-gatersleben.de.', 'Germany|Germany|Germany|Germany', 6, 'Computer Graphics|Databases, Genetic|Genome|Humans|Metabolic Networks and Pathways|Systems Biology', 'Journal Article', '10.1186/1471-2105-14-250', '23953132', 'Biologists make frequent use of databases containing large and complex biological networks. One popular database is the Kyoto Encyclopedia of Genes and Genomes (KEGG) which uses its own graphical representation and manual layout for pathways. While some general drawing conventions exist for biological networks, arbitrary graphical representations are very common. Recently, a new standard has been established for displaying biological processes, the Systems Biology Graphical Notation (SBGN), which aims to unify the look of such maps. Ideally, online repositories such as KEGG would automatically provide networks in a variety of notations including SBGN. Unfortunately, this is non-trivial, since converting between notations may add, remove or otherwise alter map elements so that the existing layout cannot be simply reused.'),
(95727, 2013, 'bmc', 'GRank: a middleware search engine for ranking genes by relevance to given genes', 'Taha, Kamal|Homouz, Dirar|Al Muhairi, Hassan|Al Mahmoud, Zaid', 'Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi, UAE. kamal.taha@kustar.ac.ae.|Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi, UAE. kamal.taha@kustar.ac.ae.|Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi, UAE. kamal.taha@kustar.ac.ae.|Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi, UAE. kamal.taha@kustar.ac.ae.', '-|-|-|-', 0, 'Algorithms|Animals|Computational Biology|Humans|Molecular Sequence Annotation|Multigene Family|Proteins|Search Engine|Semantics|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-251', '23957362', 'Biologists may need to know the set of genes that are semantically related to a given set of genes. For instance, a biologist may need to know the set of genes related to another set of genes known to be involved in a specific disease. Some works use the concept of gene clustering in order to identify semantically related genes. Others propose tools that return the set of genes that are semantically related to a given set of genes. Most of these gene similarity measures determine the semantic similarities among the genes based solely on the proximity to each other of the GO terms annotating the genes, while overlook the structural dependencies among these GO terms, which may lead to low recall and precision of results.'),
(95728, 2013, 'bmc', 'Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters', 'Hensman, James|Lawrence, Neil D|Rattray, Magnus', 'Department of Computer Science, The University of Sheffield, Sheffield, UK. james.hensman@sheffield.ac.uk.|Department of Computer Science, The University of Sheffield, Sheffield, UK. james.hensman@sheffield.ac.uk.|Department of Computer Science, The University of Sheffield, Sheffield, UK. james.hensman@sheffield.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Bayes Theorem|Cluster Analysis|Computational Biology|Drosophila|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Models, Genetic|Normal Distribution|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-14-252', '23962281', 'Time course data from microarrays and high-throughput sequencing experiments require simple, computationally efficient and powerful statistical models to extract meaningful biological signal, and for tasks such as data fusion and clustering. Existing methodologies fail to capture either the temporal or replicated nature of the experiments, and often impose constraints on the data collection process, such as regularly spaced samples, or similar sampling schema across replications.'),
(95729, 2013, 'bmc', 'WholeCellViz: data visualization for whole-cell models', 'Lee, Ruby|Karr, Jonathan R|Covert, Markus W', 'Department of Bioengineering, Stanford University, Stanford, CA 94025, USA. mcovert@stanford.edu.|Department of Bioengineering, Stanford University, Stanford, CA 94025, USA. mcovert@stanford.edu.|Department of Bioengineering, Stanford University, Stanford, CA 94025, USA. mcovert@stanford.edu.', 'United States|United States|United States', 0, 'Cell Cycle|Chromosome Mapping|Computational Biology|Computer Simulation|Databases, Factual|Databases, Genetic|Internet|Models, Biological|Software', 'Journal Article', '10.1186/1471-2105-14-253', '23964998', 'Whole-cell models promise to accelerate biomedical science and engineering. However, discovering new biology from whole-cell models and other high-throughput technologies requires novel tools for exploring and analyzing complex, high-dimensional data.'),
(95730, 2013, 'bmc', 'A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments', 'Esnaola, Mikel|Puig, Pedro|Gonzalez, David|Castelo, Robert|Gonzalez, Juan R', 'Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain. robert.castelo@upf.edu.|Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain. robert.castelo@upf.edu.|Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain. robert.castelo@upf.edu.|Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain. robert.castelo@upf.edu.|Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain. robert.castelo@upf.edu.', 'Spain|Spain|Spain|Spain|Spain', 0, 'Computational Biology|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Models, Statistical|Reproducibility of Results|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-14-254', '23965047', 'High-throughput RNA sequencing (RNA-seq) offers unprecedented power to capture the real dynamics of gene expression. Experimental designs with extensive biological replication present a unique opportunity to exploit this feature and distinguish expression profiles with higher resolution. RNA-seq data analysis methods so far have been mostly applied to data sets with few replicates and their default settings try to provide the best performance under this constraint. These methods are based on two well-known count data distributions: the Poisson and the negative binomial. The way to properly calibrate them with large RNA-seq data sets is not trivial for the non-expert bioinformatics user.'),
(95731, 2013, 'bmc', 'A multiple-alignment based primer design algorithm for genetically highly variable DNA targets', 'Brodin, Johanna|Krishnamoorthy, Mohan|Athreya, Gayathri|Fischer, Will|Hraber, Peter|Gleasner, Cheryl|Green, Lance|Korber, Bette|Leitner, Thomas', 'Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. tkl@lanl.gov.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. tkl@lanl.gov.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. tkl@lanl.gov.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. tkl@lanl.gov.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. tkl@lanl.gov.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. tkl@lanl.gov.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. tkl@lanl.gov.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. tkl@lanl.gov.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. tkl@lanl.gov.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 20, 'Algorithms|DNA Primers|HIV Infections|HIV-1|High-Throughput Nucleotide Sequencing|Humans|Models, Genetic|Polymerase Chain Reaction|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-14-255', '23965160', 'Primer design for highly variable DNA sequences is difficult, and experimental success requires attention to many interacting constraints. The advent of next-generation sequencing methods allows the investigation of rare variants otherwise hidden deep in large populations, but requires attention to population diversity and primer localization in relatively conserved regions, in addition to recognized constraints typically considered in primer design.'),
(95732, 2013, 'bmc', 'The GMOseek matrix: a decision support tool for optimizing the detection of genetically modified plants', 'Block, Annette|Debode, Frédéric|Grohmann, Lutz|Hulin, Julie|Taverniers, Isabel|Kluga, Linda|Barbau-Piednoir, Elodie|Broeders, Sylvia|Huber, Ingrid|Van den Bulcke, Marc|Heinze, Petra|Berben, Gilbert|Busch, Ulrich|Roosens, Nancy|Janssen, Eric|Žel, Jana|Gruden, Kristina|Morisset, Dany', 'Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium. debode@cra.wallonie.be.|Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium. debode@cra.wallonie.be.|Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium. debode@cra.wallonie.be.|Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium. debode@cra.wallonie.be.|Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium. debode@cra.wallonie.be.|Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium. debode@cra.wallonie.be.|Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium. debode@cra.wallonie.be.|Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium. debode@cra.wallonie.be.|Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium. debode@cra.wallonie.be.|Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium. debode@cra.wallonie.be.|Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium. debode@cra.wallonie.be.|Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium. debode@cra.wallonie.be.|Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium. debode@cra.wallonie.be.|Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium. debode@cra.wallonie.be.|Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium. debode@cra.wallonie.be.|Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium. debode@cra.wallonie.be.|Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium. debode@cra.wallonie.be.|Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium. debode@cra.wallonie.be.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belg', 0, 'Base Sequence|Decision Support Techniques|Genes, Plant|Plants, Genetically Modified|Polymerase Chain Reaction', 'Journal Article', '10.1186/1471-2105-14-256', '23965170', 'Since their first commercialization, the diversity of taxa and the genetic composition of transgene sequences in genetically modified plants (GMOs) are constantly increasing. To date, the detection of GMOs and derived products is commonly performed by PCR-based methods targeting specific DNA sequences introduced into the host genome. Information available regarding the GMOs molecular characterization is dispersed and not appropriately organized. For this reason, GMO testing is very challenging and requires more complex screening strategies and decision making schemes, demanding in return the use of efficient bioinformatics tools relying on reliable information.'),
(95733, 2013, 'bmc', 'Differential expression analysis with global network adjustment', 'Gelfond, Jonathan A|Ibrahim, Joseph G|Gupta, Mayetri|Chen, Ming-Hui|Cody, Jannine D', 'Department of Epidemiology and Biostatistics, University of Texas Health Science Center San Antonio, San Antonio, Texas, USA. gelfondjal@uthscsa.edu.|Department of Epidemiology and Biostatistics, University of Texas Health Science Center San Antonio, San Antonio, Texas, USA. gelfondjal@uthscsa.edu.|Department of Epidemiology and Biostatistics, University of Texas Health Science Center San Antonio, San Antonio, Texas, USA. gelfondjal@uthscsa.edu.|Department of Epidemiology and Biostatistics, University of Texas Health Science Center San Antonio, San Antonio, Texas, USA. gelfondjal@uthscsa.edu.|Department of Epidemiology and Biostatistics, University of Texas Health Science Center San Antonio, San Antonio, Texas, USA. gelfondjal@uthscsa.edu.', 'United States|United States|United States|United States|United States', 0, 'Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Models, Genetic|Regression Analysis|Reproducibility of Results|Signal-To-Noise Ratio', 'Journal Article', '10.1186/1471-2105-14-258', '23968143', 'Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a genes expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.'),
(95734, 2013, 'bmc', 'MethyQA: a pipeline for bisulfite-treated methylation sequencing quality assessment', 'Sun, Shuying|Noviski, Aaron|Yu, Xiaoqing', 'Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland 44106, Ohio, USA. ssun5211@yahoo.com.|Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland 44106, Ohio, USA. ssun5211@yahoo.com.|Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland 44106, Ohio, USA. ssun5211@yahoo.com.', 'United States|United States|United States', 12, 'DNA|DNA Methylation|High-Throughput Nucleotide Sequencing|Polymerase Chain Reaction|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1186/1471-2105-14-259', '23968174', 'DNA methylation is an epigenetic event that adds a methyl-group to the 5 cytosine. This epigenetic modification can significantly affect gene expression in both normal and diseased cells. Hence, it is important to study methylation signals at the single cytosine site level, which is now possible utilizing bisulfite conversion technique (i.e., converting unmethylated Cs to Us and then to Ts after PCR amplification) and next generation sequencing (NGS) technologies. Despite the advances of NGS technologies, certain quality issues remain. Some of the more prevalent quality issues involve low per-base sequencing quality at the 3 end, PCR amplification bias, and bisulfite conversion rates. Therefore, it is important to conduct quality assessment before downstream analysis. To the best of our knowledge, no existing software packages can generally assess the quality of methylation sequencing data generated based on different bisulfite-treated protocols.'),
(95735, 2013, 'bmc', 'A novel method to assess collagen architecture in skin', 'Osman, Osman S|Selway, Joanne L|Harikumar, Parvathy E|Stocker, Claire J|Wargent, Edward T|Cawthorne, Michael A|Jassim, Sabah|Langlands, Kenneth', 'The Clore Laboratory, The University of Buckingham, Hunter Street, Buckingham MK18 1EG, UK. joanne.selway@buckingham.ac.uk.|The Clore Laboratory, The University of Buckingham, Hunter Street, Buckingham MK18 1EG, UK. joanne.selway@buckingham.ac.uk.|The Clore Laboratory, The University of Buckingham, Hunter Street, Buckingham MK18 1EG, UK. joanne.selway@buckingham.ac.uk.|The Clore Laboratory, The University of Buckingham, Hunter Street, Buckingham MK18 1EG, UK. joanne.selway@buckingham.ac.uk.|The Clore Laboratory, The University of Buckingham, Hunter Street, Buckingham MK18 1EG, UK. joanne.selway@buckingham.ac.uk.|The Clore Laboratory, The University of Buckingham, Hunter Street, Buckingham MK18 1EG, UK. joanne.selway@buckingham.ac.uk.|The Clore Laboratory, The University of Buckingham, Hunter Street, Buckingham MK18 1EG, UK. joanne.selway@buckingham.ac.uk.|The Clore Laboratory, The University of Buckingham, Hunter Street, Buckingham MK18 1EG, UK. joanne.selway@buckingham.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 34, 'Animals|Collagen|Dermis|Diabetes Mellitus, Experimental|Fourier Analysis|Humans|Male|Mice|Mice, Inbred C57BL|Mice, Mutant Strains|Microscopy, Polarization|Skin|Skin Aging', 'Journal Article', '10.1186/1471-2105-14-260', '23971965', 'Texture within biological specimens may reveal critical insights, while being very difficult to quantify. This is a particular problem in histological analysis. For example, cross-polar images of picrosirius stained skin reveal exquisite structure, allowing changes in the basketweave conformation of healthy collagen to be assessed. Existing techniques measure gross pathological changes, such as fibrosis, but are not sufficiently sensitive to detect more subtle and progressive pathological changes in the dermis, such as those seen in ageing. Moreover, screening methods for cutaneous therapeutics require accurate, unsupervised and high-throughput image analysis techniques.'),
(95736, 2013, 'bmc', 'NPEBseq: nonparametric empirical bayesian-based procedure for differential expression analysis of RNA-seq data', 'Bi, Yingtao|Davuluri, Ramana V', 'Center for Systems and Computational Biology, Molecular and Cellular Oncogenesis Program, The Wistar Institute, 19104 Philadelphia, PA, USA. rdavuluri@wistar.org.|Center for Systems and Computational Biology, Molecular and Cellular Oncogenesis Program, The Wistar Institute, 19104 Philadelphia, PA, USA. rdavuluri@wistar.org.', 'United States|United States', 0, 'Bayes Theorem|Computational Biology|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|RNA|Sequence Alignment|Sequence Analysis, RNA|Software|Statistics, Nonparametric', 'Journal Article', '10.1186/1471-2105-14-262', '23981227', 'RNA-seq, a massive parallel-sequencing-based transcriptome profiling method, provides digital data in the form of aligned sequence read counts. The comparative analyses of the data require appropriate statistical methods to estimate the differential expression of transcript variants across different cell/tissue types and disease conditions.'),
(95737, 2013, 'bmc', 'A balanced iterative random forest for gene selection from microarray data', 'Anaissi, Ali|Kennedy, Paul J|Goyal, Madhu|Catchpoole, Daniel R', 'Centre for Quantum Computation & Intelligent Systems (QCIS), Faculty of Engineering and Information Technology (FEIT), University of Technology, Sydney (UTS), Broadway New South Wales 2007, Australia. ali.anaissi@uts.edu.au.|Centre for Quantum Computation & Intelligent Systems (QCIS), Faculty of Engineering and Information Technology (FEIT), University of Technology, Sydney (UTS), Broadway New South Wales 2007, Australia. ali.anaissi@uts.edu.au.|Centre for Quantum Computation & Intelligent Systems (QCIS), Faculty of Engineering and Information Technology (FEIT), University of Technology, Sydney (UTS), Broadway New South Wales 2007, Australia. ali.anaissi@uts.edu.au.|Centre for Quantum Computation & Intelligent Systems (QCIS), Faculty of Engineering and Information Technology (FEIT), University of Technology, Sydney (UTS), Broadway New South Wales 2007, Australia. ali.anaissi@uts.edu.au.', 'Australia|Australia|Australia|Australia', 0, 'Bayes Theorem|Child|Computational Biology|Female|Gene Expression Profiling|Genetic Markers|Humans|Models, Genetic|Neoplasms|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-14-261', '23981907', 'The wealth of gene expression values being generated by high throughput microarray technologies leads to complex high dimensional datasets. Moreover, many cohorts have the problem of imbalanced classes where the number of patients belonging to each class is not the same. With this kind of dataset, biologists need to identify a small number of informative genes that can be used as biomarkers for a disease.'),
(95738, 2013, 'bmc', 'inTB - a data integration platform for molecular and clinical epidemiological analysis of tuberculosis', 'Soares, Patrícia|Alves, Renato J|Abecasis, Ana B|Penha-Gonçalves, Carlos|Gomes, M Gabriela M|Pereira-Leal, José B', 'Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Apartado 14, Oeiras P-2781-901, Portugal. jleal@igc.gulbenkian.pt.|Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Apartado 14, Oeiras P-2781-901, Portugal. jleal@igc.gulbenkian.pt.|Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Apartado 14, Oeiras P-2781-901, Portugal. jleal@igc.gulbenkian.pt.|Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Apartado 14, Oeiras P-2781-901, Portugal. jleal@igc.gulbenkian.pt.|Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Apartado 14, Oeiras P-2781-901, Portugal. jleal@igc.gulbenkian.pt.|Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Apartado 14, Oeiras P-2781-901, Portugal. jleal@igc.gulbenkian.pt.', 'Portugal|Portugal|Portugal|Portugal|Portugal|Portugal', 7, 'Biomedical Research|Computational Biology|Database Management Systems|Humans|Internet|Molecular Epidemiology|Molecular Typing|Mycobacterium tuberculosis|Tuberculosis|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-14-264', '24001185', 'Tuberculosis is currently the second highest cause of death from infectious diseases worldwide. The emergence of multi and extensive drug resistance is threatening to make tuberculosis incurable. There is growing evidence that the genetic diversity of Mycobacterium tuberculosis may have important clinical consequences. Therefore, combining genetic, clinical and socio-demographic data is critical to understand the epidemiology of this infectious disease, and how virulence and other phenotypic traits evolve over time. This requires dedicated bioinformatics platforms, capable of integrating and enabling analyses of this heterogeneous data.'),
(95739, 2013, 'bmc', 'Ontology based molecular signatures for immune cell types via gene expression analysis', 'Meehan, Terrence F|Vasilevsky, Nicole A|Mungall, Christopher J|Dougall, David S|Haendel, Melissa A|Blake, Judith A|Diehl, Alexander D', 'Department of Neurology, University at Buffalo School of Medicine and Biomedical Sciences, 100 High Street, Buffalo, NY 14203, USA. addiehl@buffalo.edu.|Department of Neurology, University at Buffalo School of Medicine and Biomedical Sciences, 100 High Street, Buffalo, NY 14203, USA. addiehl@buffalo.edu.|Department of Neurology, University at Buffalo School of Medicine and Biomedical Sciences, 100 High Street, Buffalo, NY 14203, USA. addiehl@buffalo.edu.|Department of Neurology, University at Buffalo School of Medicine and Biomedical Sciences, 100 High Street, Buffalo, NY 14203, USA. addiehl@buffalo.edu.|Department of Neurology, University at Buffalo School of Medicine and Biomedical Sciences, 100 High Street, Buffalo, NY 14203, USA. addiehl@buffalo.edu.|Department of Neurology, University at Buffalo School of Medicine and Biomedical Sciences, 100 High Street, Buffalo, NY 14203, USA. addiehl@buffalo.edu.|Department of Neurology, University at Buffalo School of Medicine and Biomedical Sciences, 100 High Street, Buffalo, NY 14203, USA. addiehl@buffalo.edu.', 'United States|United States|United States|United States|United States|United States|United States', 8, 'Biomarkers|Cells|Computer Simulation|Gene Expression Profiling|Gene Ontology|Genetic Markers|Genomics|Humans', 'Journal Article', '10.1186/1471-2105-14-263', '24004649', 'New technologies are focusing on characterizing cell types to better understand their heterogeneity. With large volumes of cellular data being generated, innovative methods are needed to structure the resulting data analyses. Here, we describe an Ontologically BAsed Molecular Signature (OBAMS) method that identifies novel cellular biomarkers and infers biological functions as characteristics of particular cell types. This method finds molecular signatures for immune cell types based on mapping biological samples to the Cell Ontology (CL) and navigating the space of all possible pairwise comparisons between cell types to find genes whose expression is core to a particular cell types identity.'),
(95740, 2013, 'bmc', 'Two Pfam protein families characterized by a crystal structure of protein lpg2210 from Legionella pneumophila', 'Coggill, Penelope|Eberhardt, Ruth Y|Finn, Robert D|Chang, Yuanyuan|Jaroszewski, Lukasz|Godzik, Adam|Das, Debanu|Xu, Qingping|Axelrod, Herbert L|Aravind, L|Murzin, Alexey G|Bateman, Alex', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK. pcc@sanger.ac.uk.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK. pcc@sanger.ac.uk.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK. pcc@sanger.ac.uk.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK. pcc@sanger.ac.uk.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK. pcc@sanger.ac.uk.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK. pcc@sanger.ac.uk.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK. pcc@sanger.ac.uk.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK. pcc@sanger.ac.uk.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK. pcc@sanger.ac.uk.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK. pcc@sanger.ac.uk.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK. pcc@sanger.ac.uk.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK. pcc@sanger.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', 3, 'Amino Acid Sequence|Bacterial Proteins|Databases, Protein|Legionella pneumophila|Molecular Sequence Data|Protein Structure, Tertiary|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-14-265', '24004689', 'Every genome contains a large number of uncharacterized proteins that may encode entirely novel biological systems. Many of these uncharacterized proteins fall into related sequence families. By applying sequence and structural analysis we hope to provide insight into novel biology.'),
(95741, 2013, 'bmc', 'xcexbcHEM for identification of differentially expressed miRNAs using hypercuboid equivalence partition matrix', 'Paul, Sushmita|Maji, Pradipta', 'Biomedical Imaging and Bioinformatics Lab, Indian Statistical Institute, 203, B, T, Road, Kolkata, 700108, India. pmaji@isical.ac.in.|Biomedical Imaging and Bioinformatics Lab, Indian Statistical Institute, 203, B, T, Road, Kolkata, 700108, India. pmaji@isical.ac.in.', 'India|India', 0, 'Cluster Analysis|Computational Biology|Gene Expression Profiling|Humans|Lung Neoplasms|MicroRNAs|Models, Genetic|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-14-266', '24006840', 'The miRNAs, a class of short approximately 22-nucleotide non-coding RNAs, often act post-transcriptionally to inhibit mRNA expression. In effect, they control gene expression by targeting mRNA. They also help in carrying out normal functioning of a cell as they play an important role in various cellular processes. However, dysregulation of miRNAs is found to be a major cause of a disease. It has been demonstrated that miRNA expression is altered in many human cancers, suggesting that they may play an important role as disease biomarkers. Multiple reports have also noted the utility of miRNAs for the diagnosis of cancer. Among the large number of miRNAs present in a microarray data, a modest number might be sufficient to classify human cancers. Hence, the identification of differentially expressed miRNAs is an important problem particularly for the data sets with large number of miRNAs and small number of samples.');
