INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96699, 2015, 'bmc', 'Field phenotyping of grapevine growth using dense stereo reconstruction', 'Klodt, Maria|Herzog, Katja|Töpfer, Reinhard|Cremers, Daniel', 'Department of Informatics, Technische Universität München, Boltzmannstraße 3, 85748, Garching, Germany. klodt@tum.de.|Julius Kühn-Institute - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, 76833, Siebeldingen, Germany. katja.herzog@jki.bund.de.|Julius Kühn-Institute - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, 76833, Siebeldingen, Germany. Reinhard.toepfer@jki.bund.de.|Department of Informatics, Technische Universität München, Boltzmannstraße 3, 85748, Garching, Germany. cremers@tum.de.', 'Germany|Germany|Germany|Germany', 0, 'Color|Fruit|Image Interpretation, Computer-Assisted|Light|Models, Statistical|Phenotype|Plant Leaves|Plant Stems|Vitis', 'Journal Article', '10.1186/s12859-015-0560-x', '25943369', 'The demand for high-throughput and objective phenotyping in plant research has been increasing during the last years due to large experimental sites. Sensor-based, non-invasive and automated processes are needed to overcome the phenotypic bottleneck, which limits data volumes on account of manual evaluations. A major challenge for sensor-based phenotyping in vineyards is the distinction between the grapevine in the foreground and the field in the background - this is especially the case for red-green-blue (RGB) images, where similar color distributions occur both in the foreground plant and in the field and background plants. However, RGB cameras are a suitable tool in the field because they provide high-resolution data at fast acquisition rates with robustness to outdoor illumination.'),
(96700, 2015, 'bmc', 'Controlling false discoveries in high-dimensional situations: boosting with stability selection', 'Hofner, Benjamin|Boccuto, Luigi|Gxc3xb6ker, Markus', 'Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-University Erlangen-Nuremberg, Waldstraxc3x9fe 6, Erlangen, 91054, Germany. benjamin.hofner@fau.de.|Greenwood Genetic Center, 113 Gregor Mendel Circle, Greenwood, 29646, SC, USA. lboccuto@ggc.org.|Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures, Inhoffenstraxc3x9fe 7b, Braunschweig, 38124, Germany. markus.goeker@dsmz.de.', 'Germany|United States|Germany', 38, 'Algorithms|Biomarkers|Case-Control Studies|Child Development Disorders, Pervasive|Computer Simulation|False Positive Reactions|Gene Expression Profiling|Humans|Models, Statistical|Nonlinear Dynamics|Phenotype', 'Comparative Study', '10.1186/s12859-015-0575-3', '25943565', 'Modern biotechnologies often result in high-dimensional data sets with many more variables than observations (nxe2x89xaap). These data sets pose new challenges to statistical analysis: Variable selection becomes one of the most important tasks in this setting. Similar challenges arise if in modern data sets from observational studies, e.g., in ecology, where flexible, non-linear models are fitted to high-dimensional data. We assess the recently proposed flexible framework for variable selection called stability selection. By the use of resampling procedures, stability selection adds a finite sample error control to high-dimensional variable selection procedures such as Lasso or boosting. We consider the combination of boosting and stability selection and present results from a detailed simulation study that provide insights into the usefulness of this combination. The interpretation of the used error bounds is elaborated and insights for practical data analysis are given.'),
(96701, 2015, 'bmc', 'Estimation of the methylation pattern distribution from deep sequencing data', 'Lin, Peijie|Forêt, Sylvain|Wilson, Susan R|Burden, Conrad J', 'Mathematical Sciences Institute, Australian National University, Canberra, ACT 2601, Australia. paul.lin@unsw.edu.au.|Research School of Biology, Australian National University, Canberra, ACT 2601, Australia. Sylvain.Foret@anu.edu.au.|Mathematical Sciences Institute, Australian National University, Canberra, ACT 2601, Australia. Sue.Wilson@anu.edu.au.|Mathematical Sciences Institute, Australian National University, Canberra, ACT 2601, Australia. Conrad.Burden@anu.edu.au.', 'Australia|Australia|Australia|Australia', 0, 'Algorithms|Animals|Bees|Brain|Cytosine|DNA Methylation|Documentation|High-Throughput Nucleotide Sequencing|Models, Statistical|Programming Languages|Sulfites', 'Journal Article', '10.1186/s12859-015-0600-6', '25943746', 'Bisulphite sequencing enables the detection of cytosine methylation. The sequence of the methylation states of cytosines on any given read forms a methylation pattern that carries substantially more information than merely studying the average methylation level at individual positions. In order to understand better the complexity of DNA methylation landscapes in biological samples, it is important to study the diversity of these methylation patterns. However, the accurate quantification of methylation patterns is subject to sequencing errors and spurious signals due to incomplete bisulphite conversion of cytosines.'),
(96702, 2015, 'bmc', 'Determining minimum set of driver nodes in protein-protein interaction networks', 'Zhang, Xiao-Fei|Ou-Yang, Le|Zhu, Yuan|Wu, Meng-Yun|Dai, Dao-Qing', 'School of Mathematics and Statistics, Central China Normal University, Luoyu Road, Wuhan, 430079, China. zhangxf@mail.ccnu.edu.cn.|Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, Guangzhou, 510275, China. ouyangle@mail2.sysu.edu.cn.|School of Mathematics and Statistics, Guangdong University of Finance and Economics, ChiSha Road, Guangzhou, 510320, China. zhuyuan7@mail2.sysu.edu.cn.|School of Statistics and Management, Shanghai University of Finance and Economics, Guoding Road, Shanghai, 200433, China. wu.mengyun@mail.shufe.edu.cn.|Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, Guangzhou, 510275, China. stsddq@mail.sysu.edu.cn.', 'China|China|China|China|China', 38, 'Algorithms|Gene Regulatory Networks|Humans|Models, Theoretical|Protein Interaction Maps|Proteins|Software', 'Journal Article', '10.1186/s12859-015-0591-3', '25947063', 'Recently, several studies have drawn attention to the determination of a minimum set of driver proteins that are important for the control of the underlying protein-protein interaction (PPI) networks. In general, the minimum dominating set (MDS) model is widely adopted. However, because the MDS model does not generate a unique MDS configuration, multiple different MDSs would be generated when using different optimization algorithms. Therefore, among these MDSs, it is difficult to find out the one that represents the true driver set of proteins.'),
(96703, 2015, 'bmc', 'Deep convolutional neural networks for annotating gene expression patterns in the mouse brain', 'Zeng, Tao|Li, Rongjian|Mukkamala, Ravi|Ye, Jieping|Ji, Shuiwang', 'Department of Computer Science, Old Dominion University, Norfolk, 23529, VA, USA. tzeng@cs.odu.edu.|Department of Computer Science, Old Dominion University, Norfolk, 23529, VA, USA. rli@cs.odu.edu.|Department of Computer Science, Old Dominion University, Norfolk, 23529, VA, USA. mukka@cs.odu.edu.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, 48109, MI, USA. jpye@umich.edu.|Department of Computer Science, Old Dominion University, Norfolk, 23529, VA, USA. sji@cs.odu.edu.', 'United States|United States|United States|United States|United States', 30, 'Animals|Brain|Gene Expression Profiling|Gene Expression Regulation, Developmental|Image Processing, Computer-Assisted|In Situ Hybridization|Mice|Molecular Sequence Annotation|Neural Networks (Computer)|Pattern Recognition, Automated', 'Journal Article', '10.1186/s12859-015-0553-9', '25948335', 'Profiling gene expression in brain structures at various spatial and temporal scales is essential to understanding how genes regulate the development of brain structures. The Allen Developing Mouse Brain Atlas provides high-resolution 3-D in situ hybridization (ISH) gene expression patterns in multiple developing stages of the mouse brain. Currently, the ISH images are annotated with anatomical terms manually. In this paper, we propose a computational approach to annotate gene expression pattern images in the mouse brain at various structural levels over the course of development.'),
(96704, 2015, 'bmc', 'A coherent mathematical characterization of isotope trace extraction, isotopic envelope extraction, and LC-MS correspondence', 'Smith, Rob|Prince, John T|Ventura, Dan', 'Department of Computer Science, Old Dominion University, Norfolk, 23529, VA, USA. sji@cs.odu.edu.|Department of Computer Science, Old Dominion University, Norfolk, 23529, VA, USA. sji@cs.odu.edu.|Department of Computer Science, Old Dominion University, Norfolk, 23529, VA, USA. sji@cs.odu.edu.', 'United States|United States|United States', 0, 'Algorithms|Chromatography, Liquid|Humans|Isotopes|Mass Spectrometry|Models, Theoretical|Peptides|Proteomics', 'Journal Article', '10.1186/1471-2105-16-S7-S1', '25951936', 'Liquid chromatography-mass spectrometry is a popular technique for high-throughput protein, lipid, and metabolite comparative analysis. Such statistical comparison of millions of data points requires the generation of an inter-run correspondence. Though many techniques for generating this correspondence exist, few if any, address certain well-known run-to-run LC-MS behaviors such as elution order swaps, unbounded retention time swaps, missing data, and significant differences in abundance. Moreover, not all extant correspondence methods leverage the rich discriminating information offered by isotope envelope extraction informed by isotope trace extraction. To date, no attempt has been made to create a formal generalization of extant algorithms for these problems.'),
(96705, 2015, 'bmc', 'Performance of a blockwise approach in variable selection using linkage disequilibrium information', 'Dehman, Alia|Ambroise, Christophe|Neuvial, Pierre', 'Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. alia.dehman@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. christophe.ambroise@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.', 'France|France|France', 0, 'Algorithms|Genetic Markers|Genome-Wide Association Study|Haplotypes|Humans|Linkage Disequilibrium|Models, Theoretical|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-015-0556-6', '25951947', 'Genome-wide association studies (GWAS) aim at finding genetic markers that are significantly associated with a phenotype of interest. Single nucleotide polymorphism (SNP) data from the entire genome are collected for many thousands of SNP markers, leading to high-dimensional regression problems where the number of predictors greatly exceeds the number of observations. Moreover, these predictors are statistically dependent, in particular due to linkage disequilibrium (LD). We propose a three-step approach that explicitly takes advantage of the grouping structure induced by LD in order to identify common variants which may have been missed by single marker analyses (SMA). In the first step, we perform a hierarchical clustering of SNPs with an adjacency constraint using LD as a similarity measure. In the second step, we apply a model selection approach to the obtained hierarchy in order to define LD blocks. Finally, we perform Group Lasso regression on the inferred LD blocks. We investigate the efficiency of this approach compared to state-of-the art regression methods: haplotype association tests, SMA, and Lasso and Elastic-Net regressions.'),
(96706, 2015, 'bmc', 'MICA: A fast short-read aligner that takes full advantage of Many Integrated Core Architecture (MIC)', 'Luo, Ruibang|Cheung, Jeanno|Wu, Edward|Wang, Heng|Chan, Sze-Hang|Law, Wai-Chun|He, Guangzhu|Yu, Chang|Liu, Chi-Man|Zhou, Dazong|Li, Yingrui|Li, Ruiqiang|Wang, Jun|Zhu, Xiaoqian|Peng, Shaoliang|Lam, Tak-Wah', 'Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.', 'France|France|France|France|France|France|France|France|France|France|France|France|France|France|Fr', 11, 'Algorithms|Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Programming Languages|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-16-S7-S10', '25952019', 'Short-read aligners have recently gained a lot of speed by exploiting the massive parallelism of GPU. An uprising alterative to GPU is Intel MIC, supercomputers like Tianhe-2, currently top of TOP500, is built with 48,000 MIC boards to offer ~55 PFLOPS. The CPU-like architecture of MIC allows CPU-based software to be parallelized easily, however, the performance is often inferior to GPU counterparts as an MIC card contains only ~60 cores (while a GPU card typically has over a thousand cores).'),
(96707, 2015, 'bmc', 'Current controlled vocabularies are insufficient to uniquely map molecular entities to mass spectrometry signal', 'Smith, Rob|Taylor, Ryan M|Prince, John T', 'Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.', 'France|France|France', 0, 'Electronic Data Processing|Humans|Mass Spectrometry|Natural Language Processing|Peptides|Programming Languages|Proteomics|Reproducibility of Results|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-16-S7-S2', '25952148', 'The comparison of analyte mass spectrometry precursor (MS1) signal is central to many proteomic (and other -omic) workflows. Standard vocabularies for mass spectrometry exist and provide good coverage for most experimental applications yet are insufficient for concise and unambiguous description of data concepts spanning the range of signal provenance from a molecular perspective (e.g. from charged peptides down to fine isotopes). Without a standard unambiguous nomenclature, literature searches, algorithm reproducibility and algorithm evaluation for MS-omics data processing are nearly impossible.'),
(96708, 2015, 'bmc', 'Psychoactive pharmaceuticals as environmental contaminants may disrupt highly inter-connected nodes in an Autism-associated protein-protein interaction network', 'Kaushik, Gaurav|Thomas, Michael A|Aho, Ken A', 'Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.', 'France|France|France', 0, 'Animals|Anticonvulsants|Antidepressive Agents, Second-Generation|Autistic Disorder|Brain|Carbamazepine|Cyclohexanols|Cyprinidae|Environmental Pollutants|Female|Gene Regulatory Networks|Humans|Maternal Exposure|Nerve Tissue Proteins|Protein Interaction Maps|Valproic Acid|Venlafaxine Hydrochloride', 'Journal Article', '10.1186/1471-2105-16-S7-S3', '25952302', 'Most cases of idiopathic autism spectrum disorder (ASD) likely result from unknown environmental triggers in genetically susceptible individuals. These triggers may include maternal exposure of a fetus to minute concentrations of pharmaceuticals, such as carbamazepine (CBZ), venlafaxine (VNX) and fluoxetine (FLX). Unmetabolized pharmaceuticals reach drinking water through a variety of routes, including ineffectively treated sewage. Previous studies in our laboratory examined the extent to which gene sets were enriched in minnow brains treated with pharmaceuticals. Here, we tested the hypothesis that genes in fish brains and human cell cultures, significantly enriched by pharmaceuticals, would have distinct characteristics in an ASD-associated protein interaction network. We accomplished this by comparing these groups using 10 network indices.'),
(96709, 2015, 'bmc', 'A method of searching for related literature on protein structure analysis by considering a users intention', 'Ito, Azusa|Ohkawa, Takenao', 'Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.', 'France|France', 0, 'Databases, Protein|Evaluation Studies as Topic|Humans|Information Storage and Retrieval|Intention|Periodicals as Topic|Protein Conformation|Proteins|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-16-S7-S4', '25952498', 'In recent years, with advances in techniques for protein structure analysis, the knowledge about protein structure and function has been published in a vast number of articles. A method to search for specific publications from such a large pool of articles is needed. In this paper, we propose a method to search for related articles on protein structure analysis by using an article itself as a query.'),
(96710, 2015, 'bmc', 'Heterozygous genome assembly via binary classification of homologous sequence', 'Bodily, Paul M|Fujimoto, M|Ortega, Cameron|Okuda, Nozomu|Price, Jared C|Clement, Mark J|Snell, Quinn', 'Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.', 'France|France|France|France|France|France|France', 7, 'Algorithms|Artificial Intelligence|Contig Mapping|Diploidy|Genome, Human|Heterozygote|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Sequence Homology|Software', 'Journal Article', '10.1186/1471-2105-16-S7-S5', '25952609', 'Genome assemblers to date have predominantly targeted haploid reference reconstruction from homozygous data. When applied to diploid genome assembly, these assemblers perform poorly, owing to the violation of assumptions during both the contigging and scaffolding phases. Effective tools to overcome these problems are in growing demand. Increasing parameter stringency during contigging is an effective solution to obtaining haplotype-specific contigs, however, effective algorithms for scaffolding such contigs are lacking.'),
(96711, 2015, 'bmc', 'Self-training in significance space of support vectors for imbalanced biomedical event data', 'Munkhdalai, Tsendsuren|Namsrai, Oyun-Erdene|Ryu, Keun', 'Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.', 'France|France|France', 0, 'Algorithms|Data Mining|Databases, Bibliographic|Humans|Information Storage and Retrieval|Models, Theoretical|Natural Language Processing|Periodicals as Topic|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-16-S7-S6', '25952719', 'Pairwise relationships extracted from biomedical literature are insufficient in formulating biomolecular interactions. Extraction of complex relations (namely, biomedical events) has become the main focus of the text-mining community. However, there are two critical issues that are seldom dealt with by existing systems. First, an annotated corpus for training a prediction model is highly imbalanced. Second, supervised models trained on only a single annotated corpus can limit system performance. Fortunately, there is a large pool of unlabeled data containing much of the domain background that one can exploit.'),
(96712, 2015, 'bmc', 'A novel procedure for statistical inference and verification of gene regulatory subnetwork', 'Gong, Haijun|Klinger, Jakob|Damazyn, Kevin|Li, Xiangrui|Huang, Shiyang', 'Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.', 'France|France|France|France|France', 0, 'Algorithms|Computational Biology|Data Interpretation, Statistical|Gene Expression Profiling|Gene Expression Regulation, Fungal|Gene Regulatory Networks|Genome, Fungal|Humans|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Software|Time Factors', 'Journal Article', '10.1186/1471-2105-16-S7-S7', '25952938', 'The reconstruction of gene regulatory network from time course microarray data can help us comprehensively understand the biological system and discover the pathogenesis of cancer and other diseases. But how to correctly and efficiently decifer the gene regulatory network from high-throughput gene expression data is a big challenge due to the relatively small amount of observations and curse of dimensionality. Computational biologists have developed many statistical inference and machine learning algorithms to analyze the microarray data. In the previous studies, the correctness of an inferred regulatory network is manually checked through comparing with public database or an existing model.'),
(96713, 2015, 'bmc', 'A practical guideline for intracranial volume estimation in patients with Alzheimers disease', 'Sargolzaei, Saman|Sargolzaei, Arman|Cabrerizo, Mercedes|Chen, Gang|Goryawala, Mohammed|Noei, Shirin|Zhou, Qi|Duara, Ranjan|Barker, Warren|Adjouadi, Malek', 'Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.', 'France|France|France|France|France|France|France|France|France|France', 0, 'Adult|Aged|Aged, 80 and over|Alzheimer Disease|Brain|Case-Control Studies|Female|Humans|Image Interpretation, Computer-Assisted|Magnetic Resonance Imaging|Male|Organ Size|Software', 'Comparative Study', '10.1186/1471-2105-16-S7-S8', '25953026', 'Intracranial volume (ICV) is an important normalization measure used in morphometric analyses to correct for head size in studies of Alzheimer Disease (AD). Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation in patients with Alzheimer disease in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and the type of software most suitable for use in estimating the ICV measure.'),
(96714, 2015, 'bmc', 'A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks', 'Sargolzaei, Saman|Cabrerizo, Mercedes|Sargolzaei, Arman|Noei, Shirin|Eddin, Anas|Rajaei, Hoda|Pinzon-Ardila, Alberto|Gonzalez-Arias, Sergio M|Jayakar, Prasanna|Adjouadi, Malek', 'Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.', 'France|France|France|France|France|France|France|France|France|France', 0, 'Adolescent|Brain|Brain Mapping|Child|Child, Preschool|Electroencephalography|Epilepsy|Female|Humans|Male|Models, Theoretical|Nerve Net|Predictive Value of Tests|Scalp|Sensitivity and Specificity|Signal Processing, Computer-Assisted', 'Comparative Study', '10.1186/1471-2105-16-S7-S9', '25953124', 'The lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment.'),
(96715, 2014, 'bmc', 'Highlights from the 1st ISCB Latin American Student Council Symposium 2014 Introduction', 'Parra, R Gonzalo|Simonetti, Franco L|Hasenahuer, Marcia A|Olguin-Orellana, Gabriel J|Shanmugam, Avinash K', 'Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.|Laboratoire de Mathématiques et Modélisation dEvry (LaMME), Université dEvry-Val-dEssonne/UMR CNRS 8071/ENSIIE/USC INRA, Evry, France. pierre.neuvial@genopole.cnrs.fr.', 'France|France|France|France|France', 0, 'Computational Biology|Education, Professional|Students|United States', 'Congress', '10.1186/1471-2105-16-S7-S9', '25955751', 'This report summarizes the scientific content and activities of the first edition of the Latin American Symposium organized by the Student Council of the International Society for Computational Biology (ISCB), held in conjunction with the Third Latin American conference from the International Society for Computational Biology (ISCB-LA 2014) in Belo Horizonte, Brazil, on October 27, 2014.'),
(96716, 2015, 'bmc', 'Bilingual term alignment from comparable corpora in English discharge summary and Chinese discharge summary', 'Xu, Yan|Chen, Luoxin|Wei, Junsheng|Ananiadou, Sophia|Fan, Yubo|Qian, Yi|Chang, Eric I-Chao|Tsujii, Junichi', 'State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, China. xuyan04@gmail.com.|State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, China. arcduke7@163.com.|State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, China. weijunsheng90@gmail.com.|The National Centre for Text Mining, School of Computer Science, The University of Manchester, Manchester, UK. sophia.ananiadou@manchester.ac.uk.|State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, China. yubofan@buaa.edu.cn.|Jinhua Peoples Hospital, Jinhua, China. qianyicosta@163.com.|Microsoft Research Asia, Beijing, China. eric.chang@microsoft.com.|Microsoft Research Asia, Beijing, China. jtsujii@microsoft.com.', 'China|China|China|United Kingdom|China|China|China|China', 9, 'Asian Continental Ancestry Group|England|Humans|Information Storage and Retrieval|Medical Informatics|Multilingualism|Natural Language Processing|Patient Discharge|Software|Translating', 'Comparative Study', '10.1186/s12859-015-0606-0', '25956056', 'Electronic medical record (EMR) systems have become widely used throughout the world to improve the quality of healthcare and the efficiency of hospital services. A bilingual medical lexicon of Chinese and English is needed to meet the demand for the multi-lingual and multi-national treatment. We make efforts to extract a bilingual lexicon from English and Chinese discharge summaries with a small seed lexicon. The lexical terms can be classified into two categories: single-word terms (SWTs) and multi-word terms (MWTs). For SWTs, we use a label propagation (LP, context-based) method to extract candidates of translation pairs. For MWTs, which are pervasive in the medical domain, we propose a term alignment method, which firstly obtains translation candidates for each component word of a Chinese MWT, and then generates their combinations, from which the system selects a set of plausible translation candidates.'),
(96717, 2015, 'bmc', 'Is this the right normalization? A diagnostic tool for ChIP-seq normalization', 'Angelini, Claudia|Heller, Ruth|Volkinshtein, Rita|Yekutieli, Daniel', 'Istituto per le Applicazioni del Calcolo Mauro Picone, Via Pietro Castellino, 111, Naples, 80131, Italy. c.angelini@iac.cnr.it.|Department of Statistics and Operations Research Tel Aviv University, Ramat Aviv, Tel Aviv, 69978, Israel. ruheller@post.tau.ac.il.|Department of Statistics and Operations Research Tel Aviv University, Ramat Aviv, Tel Aviv, 69978, Israel. spirita123@gmail.com.|Department of Statistics and Operations Research Tel Aviv University, Ramat Aviv, Tel Aviv, 69978, Israel. yekutiel@post.tau.ac.il.', 'Italy|Israel|Israel|Israel', 5, 'Algorithms|Binding Sites|Chromatin Immunoprecipitation|Data Interpretation, Statistical|Genome, Fungal|High-Throughput Nucleotide Sequencing|Histones|RNA Polymerase II|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/s12859-015-0579-z', '25957089', 'Chip-seq experiments are becoming a standard approach for genome-wide profiling protein-DNA interactions, such as detecting transcription factor binding sites, histone modification marks and RNA Polymerase II occupancy. However, when comparing a ChIP sample versus a control sample, such as Input DNA, normalization procedures have to be applied in order to remove experimental source of biases. Despite the substantial impact that the choice of the normalization method can have on the results of a ChIP-seq data analysis, their assessment is not fully explored in the literature. In particular, there are no diagnostic tools that show whether the applied normalization is indeed appropriate for the data being analyzed.'),
(96718, 2015, 'bmc', 'A multi-view genomic data simulator', 'Fratello, Michele|Serra, Angela|Fortino, Vittorio|Raiconi, Giancarlo|Tagliaferri, Roberto|Greco, Dario', 'Department of Medical, Surgical, Neurological, Metabolic and Ageing Sciences, Second University of Napoli, Napoli, Italy. michele.fratello@unina2.it.|Department of Computer Science, Fisciano, Italy. aserra@unisa.it.|Unit of Systems Toxicology and Nanosafety Research Centre, Finnish Institute of Occupational Health, FIOH, Helsinki, Finland. vittorio.fortino@ttl.fi.|Department of Computer Science, Fisciano, Italy. gianni@unisa.it.|Department of Computer Science, Fisciano, Italy. rtagliaferr@unisa.it.|Unit of Systems Toxicology and Nanosafety Research Centre, Finnish Institute of Occupational Health, FIOH, Helsinki, Finland. dario.greco@ttl.fi.', 'Italy|Italy|Finland|Italy|Italy|Finland', 0, 'Algorithms|Computational Biology|Computer Simulation|DNA Copy Number Variations|DNA Methylation|Datasets as Topic|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Genomics|Humans|MicroRNAs', 'Journal Article', '10.1186/s12859-015-0577-1', '25962835', 'OMICs technologies allow to assay the state of a large number of different features (e.g., mRNA expression, miRNA expression, copy number variation, DNA methylation, etc.) from the same samples. The objective of these experiments is usually to find a reduced set of significant features, which can be used to differentiate the conditions assayed. In terms of development of novel feature selection computational methods, this task is challenging for the lack of fully annotated biological datasets to be used for benchmarking. A possible way to tackle this problem is generating appropriate synthetic datasets, whose composition and behaviour are fully controlled and known a priori.'),
(96719, 2015, 'bmc', 'MSCA: a spectral comparison algorithm between time series to identify protein-protein interactions', 'Arenas, Ailan F|Salcedo, Gladys E|Montoya, Andrey M|Gomez-Marin, Jorge E', 'Gepamol, Universidad del Quindío, Carrera 15 Calle 12N, Armenia, Colombia. aylanfarid@yahoo.com.|Grupo de Investigación y Asesoría en Estadística, Carrera 15 Calle 12N, 460, Armenia, Colombia. gsalcedo@uniquindio.edu.co.|Grupo de Investigación y Asesoría en Estadística, Carrera 15 Calle 12N, 460, Armenia, Colombia. ammontoya@uniquindio.edu.co.|Gepamol, Universidad del Quindío, Carrera 15 Calle 12N, Armenia, Colombia. gepamol2@uniquindio.edu.co.', 'Armenia|Armenia|Armenia|Armenia', 3, 'Algorithms|GTP Phosphohydrolases|Host-Parasite Interactions|Humans|Protein Interaction Mapping|Protozoan Proteins|Signal Transduction|Time Factors|Toxoplasma|Toxoplasmosis|Virulence', 'Journal Article', '10.1186/s12859-015-0599-8', '25963052', 'The interactions between pathogen proteins and their hosts allow pathogens to manipulate host cellular mechanisms to their advantage. The identification of host proteins that are targeted by virulent pathogen proteins is crucial to increase our understanding of infection mechanisms and to propose new therapeutics that target pathogens. Understanding the virulence mechanisms of pathogens requires a detailed molecular description of the proteins involved, but acquiring this knowledge is time consuming and prohibitively expensive. Therefore, we develop a statistical method based on hypothesis testing to compare the time series obtained from conversion of the physicochemical characteristics of the amino acids that form the primary structure of proteins and thus to propose potential functional relation between proteins. We called this algorithm the multiple spectral comparison algorithm (MSCA), the MSCA was inspired by the BLASTP tool and was implemented in R code. The algorithm compares and relates multiple time series according to their spectral similarities, and the biological relation between them could be interpreted as either a similar function or protein-protein interaction (PPI).'),
(96720, 2015, 'bmc', 'Domain similarity based orthology detection', 'Bitard-Feildel, Tristan|Kemena, Carsten|Greenwood, Jenny M|Bornberg-Bauer, Erich', 'Institute for Evolution and Biodiversity, University of Münster, Hüfferstr. 1, Münster, Germany. t.bitard.feildel@uni-muenster.de.|Institute for Evolution and Biodiversity, University of Münster, Hüfferstr. 1, Münster, Germany. c.kemena@uni-muenster.de.|Institute for Evolution and Biodiversity, University of Münster, Hüfferstr. 1, Münster, Germany. j.greenwood@uni-muenster.de.|Institute for Evolution and Biodiversity, University of Münster, Hüfferstr. 1, Münster, Germany. ebb@uni-muenster.de.', 'Germany|Germany|Germany|Germany', 9, 'Computational Biology|Humans|Protein Interaction Domains and Motifs|Proteins|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/s12859-015-0570-8', '25968113', 'Orthologous protein detection software mostly uses pairwise comparisons of amino-acid sequences to assert whether two proteins are orthologous or not. Accordingly, when the number of sequences for comparison increases, the number of comparisons to compute grows in a quadratic order. A current challenge of bioinformatic research, especially when taking into account the increasing number of sequenced organisms available, is to make this ever-growing number of comparisons computationally feasible in a reasonable amount of time. We propose to speed up the detection of orthologous proteins by using strings of domains to characterize the proteins.'),
(96721, 2015, 'bmc', 'DisCons: a novel tool to quantify and classify evolutionary conservation of intrinsic protein disorder', 'Varadi, Mihaly|Guharoy, Mainak|Zsolyomi, Fruzsina|Tompa, Peter', 'VIB Structural Biology Research Center (SBRC), Brussels, Belgium. mvaradi@vub.ac.be.|VIB Structural Biology Research Center (SBRC), Brussels, Belgium. mainak.guharoy@vib-vub.be.|Vrije Universiteit Brussel, Brussels, Belgium. fzsolyom@vub.ac.be.|VIB Structural Biology Research Center (SBRC), Brussels, Belgium. ptompa@vub.ac.be.', 'Belgium|Belgium|Belgium|Belgium', 0, 'Amino Acid Sequence|Amino Acid Substitution|Conserved Sequence|Evolution, Molecular|Humans|Models, Molecular|Molecular Sequence Data|Protein Conformation|Sequence Analysis, Protein|Software|Tumor Suppressor Protein p53', 'Journal Article', '10.1186/s12859-015-0592-2', '25968230', 'Analyzing the amino acid sequence of an intrinsically disordered protein (IDP) in an evolutionary context can yield novel insights on the functional role of disordered regions and sequence element(s). However, in the case of many IDPs, the lack of evolutionary conservation of the primary sequence can hamper the study of functionality, because the conservation of their disorder profile and ensuing function(s) may not appear in a traditional analysis of the evolutionary history of the protein.'),
(96722, 2015, 'bmc', 'PhyTB: Phylogenetic tree visualisation and sample positioning for M tuberculosis', 'Benavente, Ernest D|Coll, Francesc|Furnham, Nick|McNerney, Ruth|Glynn, Judith R|Campino, Susana|Pain, Arnab|Mohareb, Fady R|Clark, Taane G', 'Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, Keppel St, London, UK. ernest.diezbenavente@lshtm.ac.uk.|Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, Keppel St, London, UK. francesc.coll@lshtm.ac.uk.|Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, Keppel St, London, UK. nick.furnham@lshtm.ac.uk.|Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, Keppel St, London, UK. ruth.mcnerney@lshtm.ac.uk.|Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel St, London, UK. judith.glynn@lshtm.ac.uk.|Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. sc11@sanger.ac.uk.|Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia. arnab.pain@kaust.edu.sa.|Engineering Sciences Division, School of Engineering, Cranfield University, Cranfield, UK. f.mohareb@cranfield.ac.uk.|Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, Keppel St, London, UK. taane.clark@lshtm.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Saudi Arabia|United Kingdom|United Kingdom', 15, 'Computer Graphics|Genome, Bacterial|Geography|Mycobacterium tuberculosis|Phylogeny|Polymorphism, Genetic|Software|Tuberculosis', 'Journal Article', '10.1186/s12859-015-0603-3', '25968323', 'Phylogenetic-based classification of M. tuberculosis and other bacterial genomes is a core analysis for studying evolutionary hypotheses, disease outbreaks and transmission events. Whole genome sequencing is providing new insights into the genomic variation underlying intra- and inter-strain diversity, thereby assisting with the classification and molecular barcoding of the bacteria. One roadblock to strain investigation is the lack of user-interactive solutions to interrogate and visualise variation within a phylogenetic tree setting.'),
(96723, 2015, 'bmc', 'In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology', 'Yusuf, Nurlisa|Zakaria, Ammar|Omar, Mohammad Iqbal|Shakaff, Ali Yeon Md|Masnan, Maz Jamilah|Kamarudin, Latifah Munirah|Abdul Rahim, Norasmadi|Zakaria, Nur Zawatil Isqi|Abdullah, Azian Azamimi|Othman, Amizah|Yasin, Mohd Sadek', 'Centre of Excellence for Advanced Sensor Technology, Universiti Malaysia Perlis, Perlis, Malaysia. nurlisayusuf@gmail.com.|Centre of Excellence for Advanced Sensor Technology, Universiti Malaysia Perlis, Perlis, Malaysia. ammarzakaria@unimap.edu.my.|Centre of Excellence for Advanced Sensor Technology, Universiti Malaysia Perlis, Perlis, Malaysia. iqbalomar@unimap.edu.my.|Centre of Excellence for Advanced Sensor Technology, Universiti Malaysia Perlis, Perlis, Malaysia. aliyeon@unimap.edu.my.|Institute for Engineering Mathematics, Universiti Malaysia Perlis, Perlis, Malaysia. mazjamilah@unimap.edu.my.|Centre of Excellence for Advanced Sensor Technology, Universiti Malaysia Perlis, Perlis, Malaysia. latifahmunirah@unimap.edu.my.|Centre of Excellence for Advanced Sensor Technology, Universiti Malaysia Perlis, Perlis, Malaysia. norasmadi@unimap.edu.my.|Centre of Excellence for Advanced Sensor Technology, Universiti Malaysia Perlis, Perlis, Malaysia. nurishqi@gmail.com.|Nara Institute of Science and Technology, Takayama-cho, Ikoma-shi, Nara, Japan. azian.azamimi.zt2@is.naist.jp.|Department of Microbiology|, Hospital Tuanku Fauziah, Jalan Kolam, Kangar, Perlis, Malaysia. amizah74@gmail.com.|Department of Microbiology|, Hospital Tuanku Fauziah, Jalan Kolam, Kangar, Perlis, Malaysia. mohdsadekyasin@yahoo.com.', 'Malaysia|Malaysia|Malaysia|Malaysia|Malaysia|Malaysia|Malaysia|Malaysia|Japan|-|Malaysia|-|Malaysia', 0, 'Algorithms|Bacteria|Biosensing Techniques|Data Mining|Diabetic Foot|Discriminant Analysis|Electronic Nose|Gas Chromatography-Mass Spectrometry|Humans|In Vitro Techniques|Neural Networks (Computer)|Odorants|Support Vector Machine', 'Journal Article', '10.1186/s12859-015-0601-5', '25971258', 'Effective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) technique will provide a diagnostic tool that will allow for rapid and accurate identification of a pathogen.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96724, 2015, 'bmc', 'A Long Fragment Aligner called ALFALFA', 'Vyverman, Michaël|Baets, Bernard De|Fack, Veerle|Dawyndt, Peter', 'Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 Building S9, Ghent, B-9000, Belgium. Michael.Vyverman@UGent.be.|Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure links 653, Ghent, B-9000, Belgium. Bernard.DeBaets@UGent.be.|Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 Building S9, Ghent, B-9000, Belgium. Veerle.Fack@UGent.be.|Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 Building S9, Ghent, B-9000, Belgium. Peter.Dawyndt@UGent.be.', 'Belgium|Belgium|Belgium|Belgium', 7, 'Algorithms|Biological Evolution|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Sequence Analysis, DNA|Software|Workflow', 'Journal Article', '10.1186/s12859-015-0533-0', '25971785', 'Rapid evolutions in sequencing technology force read mappers into flexible adaptation to longer reads, changing error models, memory barriers and novel applications.'),
(96725, 2015, 'bmc', 'KnowLife: a versatile approach for constructing a large knowledge graph for biomedical sciences', 'Ernst, Patrick|Siu, Amy|Weikum, Gerhard', 'Max-Planck-Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany. pernst@mpi-inf.mpg.de.|Max-Planck-Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany. siu@mpi-inf.mpg.de.|Max-Planck-Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany. weikum@mpi-inf.mpg.de.', 'Germany|Germany|Germany', 0, 'Biomedical Research|Humans|Information Storage and Retrieval|Knowledge Bases|Natural Language Processing|Pattern Recognition, Automated|Software', 'Journal Article', '10.1186/s12859-015-0549-5', '25971816', 'Biomedical knowledge bases (KBs) have become important assets in life sciences. Prior work on KB construction has three major limitations. First, most biomedical KBs are manually built and curated, and cannot keep up with the rate at which new findings are published. Second, for automatic information extraction (IE), the text genre of choice has been scientific publications, neglecting sources like health portals and online communities. Third, most prior work on IE has focused on the molecular level or chemogenomics only, like protein-protein interactions or gene-drug relationships, or solely address highly specific topics such as drug effects.'),
(96726, 2015, 'bmc', 'GENLIB: an R package for the analysis of genealogical data', 'Gauvin, Héloïse|Lefebvre, Jean-François|Moreau, Claudia|Lavoie, Eve-Marie|Labuda, Damian|Vézina, Hélène|Roy-Gagnon, Marie-Hélène', 'Département de médecine sociale et préventive, Université de Montréal, Montréal, Québec, Canada. heloise.gauvin@umontreal.ca.|Centre de recherche, Centre hospitalier universitaire Sainte-Justine, Montréal, Québec, Canada. jflefebvre27@gmail.com.|Centre de recherche, Centre hospitalier universitaire Sainte-Justine, Montréal, Québec, Canada. claudiamoreau1@gmail.com.|BALSAC Project, Université du Québec à Chicoutimi, Chicoutimi, Québec, Canada. eve-marie_lavoie@uqac.ca.|Centre de recherche, Centre hospitalier universitaire Sainte-Justine, Montréal, Québec, Canada. damian.labuda@umontreal.ca.|BALSAC Project, Université du Québec à Chicoutimi, Chicoutimi, Québec, Canada. helene_vezina@uqac.ca.|Centre de recherche, Centre hospitalier universitaire Sainte-Justine, Montréal, Québec, Canada. mroygagn@uottawa.ca.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 7, 'Alleles|Biological Evolution|Databases, Factual|Demography|Genealogy and Heraldry|Genetics, Population|Humans|Molecular Epidemiology|Pedigree|Population Groups|Quebec|Software', 'Journal Article', '10.1186/s12859-015-0581-5', '25971991', 'Founder populations have an important role in the study of genetic diseases. Access to detailed genealogical records is often one of their advantages. These genealogical data provide unique information for researchers in evolutionary and population genetics, demography and genetic epidemiology. However, analyzing large genealogical datasets requires specialized methods and software. The GENLIB software was developed to study the large genealogies of the French Canadian population of Quebec, Canada. These genealogies are accessible through the BALSAC database, which contains over 3 million records covering the whole province of Quebec over four centuries. Using this resource, extended pedigrees of up to 17 generations can be constructed from a sample of present-day individuals.'),
(96727, 2015, 'bmc', 'ISOpureR: an R implementation of a computational purification algorithm of mixed tumour profiles', 'Anghel, Catalina V|Quon, Gerald|Haider, Syed|Nguyen, Francis|Deshwar, Amit G|Morris, Quaid D|Boutros, Paul C', 'Informatics and Biocomputing Program, Ontario Institute for Cancer Research, 661 University Avenue, Toronto, Suite 510, M5G 0A3, ON, Canada. Catalina.Anghel@oicr.on.ca.|Department of Computer Science, University of Toronto, 10 Kings College Road, Room 3303, M5S 3G4, Toronto, ON, Canada. gquon@mit.edu.|Informatics and Biocomputing Program, Ontario Institute for Cancer Research, 661 University Avenue, Toronto, Suite 510, M5G 0A3, ON, Canada. Syed.Haider@oicr.on.ca.|Informatics and Biocomputing Program, Ontario Institute for Cancer Research, 661 University Avenue, Toronto, Suite 510, M5G 0A3, ON, Canada. F-Nguyen@hotmail.com.|Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 Kings College, Room SFB540, Toronto, M5S 3G4, ON, Canada. Amit.Deshwar@utoronto.ca.|Department of Computer Science, University of Toronto, 10 Kings College Road, Room 3303, M5S 3G4, Toronto, ON, Canada. Quaid.Morris@utoronto.ca.|Informatics and Biocomputing Program, Ontario Institute for Cancer Research, 661 University Avenue, Toronto, Suite 510, M5G 0A3, ON, Canada. Paul.Boutros@oicr.on.ca.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 13, 'Algorithms|Computational Biology|Gene Expression Profiling|Humans|Male|Models, Theoretical|Prognosis|Prostatic Neoplasms|Software', 'Journal Article', '10.1186/s12859-015-0597-x', '25972088', 'Tumour samples containing distinct sub-populations of cancer and normal cells present challenges in the development of reproducible biomarkers, as these biomarkers are based on bulk signals from mixed tumour profiles. ISOpure is the only mRNA computational purification method to date that does not require a paired tumour-normal sample, provides a personalized cancer profile for each patient, and has been tested on clinical data. Replacing mixed tumour profiles with ISOpure-preprocessed cancer profiles led to better prognostic gene signatures for lung and prostate cancer.'),
(96728, 2015, 'bmc', 'Hierarchical decomposition of dynamically evolving regulatory networks', 'Ay, Ahmet|Gong, Dihong|Kahveci, Tamer', 'Departments of Biology and Mathematics, Colgate University, Hamilton, 13346, NY, USA. aay@colgate.edu.|Department of Computer and Information Science and Engineering, University of Florida, Gainesville, 32611, FL, USA. dihong@cise.ufl.edu.|Department of Computer and Information Science and Engineering, University of Florida, Gainesville, 32611, FL, USA. tamer@cise.ufl.edu.', 'United States|United States|United States', 0, 'Algorithms|Cell Lineage|Cell Physiological Phenomena|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Humans|Lymphocytes|Stem Cells', 'Journal Article', '10.1186/s12859-015-0529-9', '25976669', 'Gene regulatory networks describe the interplay between genes and their products. These networks control almost every biological activity in the cell through interactions. The hierarchy of genes in these networks as defined by their interactions gives important insights into how these functions are governed. Accurately determining the hierarchy of genes is however a computationally difficult problem. This problem is further complicated by the fact that an intrinsic characteristic of regulatory networks is that the wiring of interactions can change over time. Determining how the hierarchy in the gene regulatory networks changes with dynamically evolving network topology remains to be an unsolved challenge.'),
(96729, 2015, 'bmc', 'NetCooperate: a network-based tool for inferring host-microbe and microbe-microbe cooperation', 'Levy, Roie|Carr, Rogan|Kreimer, Anat|Freilich, Shiri|Borenstein, Elhanan', 'Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA. roie@uw.edu.|Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA. rogan@uw.edu.|Department of Electrical Engineering & Computer Science, Center for Computational Biology, UC Berkeley, Berkeley, CA, 94720, USA. anat.kreimer@berkeley.edu.|Newe Yaar Research Center, Agricultural Research Organization, Ramat Yishay, 30095, Israel. shiri@agri.gov.il.|Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA. elbo@uw.edu.', 'United States|United States|United States|Israel|United States', 0, 'Animals|Bacteria|Computational Biology|Host-Parasite Interactions|Humans|Internet|Metabolic Networks and Pathways|Microbial Interactions|Models, Biological|Software', 'Journal Article', '10.1186/s12859-015-0588-y', '25980407', 'Host-microbe and microbe-microbe interactions are often governed by the complex exchange of metabolites. Such interactions play a key role in determining the way pathogenic and commensal species impact their host and in the assembly of complex microbial communities. Recently, several studies have demonstrated how such interactions are reflected in the organization of the metabolic networks of the interacting species, and introduced various graph theory-based methods to predict host-microbe and microbe-microbe interactions directly from network topology. Using these methods, such studies have revealed evolutionary and ecological processes that shape species interactions and community assembly, highlighting the potential of this reverse-ecology research paradigm.'),
(96730, 2015, 'bmc', 'PathRings: a web-based tool for exploration of ortholog and expression data in biological pathways', 'Zhu, Yongnan|Sun, Liang|Garbarino, Alexander|Schmidt, Carl|Fang, Jinglong|Chen, Jian', 'Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD, USA. yongnan@umbc.edu.|Department of Animal & Food Sciences, University of Delaware, Newark, DE, USA. sunliang@udel.edu.|Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD, USA. garba1@umbc.edu.|Department of Animal & Food Sciences, University of Delaware, Newark, DE, USA. schmidtc@udel.edu.|Department of Computer Science, Hangzhou Dianzi University, Hangzhou, Zhejiang Province, P.R. China. fjl@hdu.edu.cn.|Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD, USA. jichen@umbc.edu.', 'United States|United States|United States|United States|China|United States', 5, 'Computer Graphics|Computer Simulation|Gene Expression|Genomics|Humans|Internet|Metabolic Networks and Pathways|Proteins|Proteomics|Signal Transduction|Software|Species Specificity', 'Journal Article', '10.1186/s12859-015-0585-1', '25982732', 'High-throughput methods are generating biological data on a vast scale. In many instances, genomic, transcriptomic, and proteomic data must be interpreted in the context of signaling and metabolic pathways to yield testable hypotheses. Since humans can interpret visual information rapidly, a means for interactive visual exploration that lets biologists interpret such data in a comprehensive and exploratory manner would be invaluable. However, humans have limited memory capacity. Current visualization tools have limited viewing and manipulation capabilities to address complex data analysis problems, and visual exploratory tools are needed to reduce the high mental workload imposed on biologists.'),
(96731, 2015, 'bmc', 'ProtDCal: A program to compute general-purpose-numerical descriptors for sequences and 3D-structures of proteins', 'Ruiz-Blanco, Yasser B|Paz, Waldo|Green, James|Marrero-Ponce, Yovani', 'Unit of Computer-Aided Molecular Biosilico Discovery and Bioinformatic Research (CAMD-BIR Unit), Facultad de Química y Farmacia, Universidad Central Marta Abreu de Las Villas, Road to Camajuani km 5 ½, Santa Clara, CP: 54830, Villa Clara, Cuba. yasserrb@uclv.edu.cu.|Unit of Computer-Aided Molecular Biosilico Discovery and Bioinformatic Research (CAMD-BIR Unit), Facultad de Química y Farmacia, Universidad Central Marta Abreu de Las Villas, Road to Camajuani km 5 ½, Santa Clara, CP: 54830, Villa Clara, Cuba. waldopaz@uclv.cu.|Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada. jrgreen@sce.carleton.ca.|Unit of Computer-Aided Molecular Biosilico Discovery and Bioinformatic Research (CAMD-BIR Unit), Facultad de Química y Farmacia, Universidad Central Marta Abreu de Las Villas, Road to Camajuani km 5 ½, Santa Clara, CP: 54830, Villa Clara, Cuba. ymarrero77@yahoo.es.', 'Cuba|Cuba|Canada|Cuba', 0, 'Glycosylation|Humans|Principal Component Analysis|Protein Processing, Post-Translational|Protein Structure, Secondary|Proteins|Software', 'Journal Article', '10.1186/s12859-015-0586-0', '25982853', 'The exponential growth of protein structural and sequence databases is enabling multifaceted approaches to understanding the long sought sequence-structure-function relationship. Advances in computation now make it possible to apply well-established data mining and pattern recognition techniques to these data to learn models that effectively relate structure and function. However, extracting meaningful numerical descriptors of protein sequence and structure is a key issue that requires an efficient and widely available solution.'),
(96732, 2015, 'bmc', 'Optimal programs of pathway control: dissecting the influence of pathway topology and feedback inhibition on pathway regulation', 'de Hijas-Liste, Gundián M|Balsa-Canto, Eva|Ewald, Jan|Bartl, Martin|Li, Pu|Banga, Julio R|Kaleta, Christoph', 'Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC, C/Eduardo Cabello 6, Vigo, 36208, Spain. gundian@iim.csic.es.|Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC, C/Eduardo Cabello 6, Vigo, 36208, Spain. ebalsa@iim.csic.es.|esearch Group Theoretical Systems Biology, Friedrich Schiller University Jena, Leutragraben 1,, Jena, 07743, Germany. jan.ewald@uni-jena.de.|esearch Group Theoretical Systems Biology, Friedrich Schiller University Jena, Leutragraben 1,, Jena, 07743, Germany. ebalsa@iim.csic.es.|Simulation and Optimal Processes Group, Ilmenau University of Technology, P.O.Box 100565, Ilmenau, 98684, Germany. ebalsa@iim.csic.es.|Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC, C/Eduardo Cabello 6, Vigo, 36208, Spain. julio@iim.csic.es.|esearch Group Theoretical Systems Biology, Friedrich Schiller University Jena, Leutragraben 1,, Jena, 07743, Germany. c.kaleta@iem.uni-kiel.de.', 'Spain|Spain|Germany|Germany|Germany|Spain|Germany', 6, 'Algorithms|Allosteric Regulation|Computational Biology|Escherichia coli|Feedback, Physiological|Gene Expression Regulation, Bacterial|Metabolic Networks and Pathways|Models, Theoretical|Proteins|Proteomics', 'Journal Article', '10.1186/s12859-015-0587-z', '25982966', 'Adjusting the capacity of metabolic pathways in response to rapidly changing environmental conditions is an important component of microbial adaptation strategies to stochastic environments. In this work, we use advanced dynamic optimization techniques combined with theoretical models to study which reactions in pathways are optimally targeted by regulatory interactions in order to minimize the regulatory effort that is required to adjust the flux through a complex metabolic network. Moreover, we analyze how constraints in the speed at which an organism can respond on a proteomic level influences these optimal targets of pathway control.'),
(96733, 2015, 'bmc', 'Multi-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancer', 'Sachnev, Vasily|Saraswathi, Saras|Niaz, Rashid|Kloczkowski, Andrzej|Suresh, Sundaram', 'Department of Information, Communication and Electronics Engineering, Catholic University of Korea, Bucheon, Republic of Korea. bassvasys@hotmail.com.|Battelle Center for Mathematical Medicine at The Research Institute at Nationwide Childrens Hospital, currently at Sidra, Medical and Research Center, Doha, Qatar. ssundararajan@sidra.org.|Department of Medical Informatics, Sidra Medical and Research Center, Doha, Qatar. rniaz@sidra.org.|Battelle Center for Mathematical Medicine at The Research Institute at Nationwide Childrens Hospital, Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, USA. andrzej.kloczkowski@nationwidechildrens.org.|School of Computer Science, Nanyang Technological University, Nanyang, Singapore. ssundaram@ntu.edu.sg.', 'United States|Qatar|Qatar|United States|Singapore', 0, 'Algorithms|Artificial Intelligence|Biomarkers, Tumor|Gene Expression Regulation, Neoplastic|Humans|Neoplasms|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1186/s12859-015-0565-5', '25986937', 'Traditional cancer treatments have centered on cytotoxic drugs and general purpose chemotherapy that may not be tailored to treat specific cancers. Identification of molecular markers that are related to different types of cancers might lead to discovery of drugs that are patient and disease specific. This study aims to use microarray gene expression cancer data to identify biomarkers that are indicative of different types of cancers. Our aim is to provide a multi-class cancer classifier that can simultaneously differentiate between cancers and identify type-specific biomarkers, through the application of the Binary Coded Genetic Algorithm (BCGA) and a neural network based Extreme Learning Machine (ELM) algorithm.'),
(96734, 2015, 'bmc', 'Software for the analysis and visualization of deep mutational scanning data', 'Bloom, Jesse D', 'Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, 98109, WA, USA. jbloom@fredhutch.org.', 'United States', 0, 'Amino Acid Sequence|Computational Biology|Computer Graphics|DNA Mutational Analysis|High-Throughput Nucleotide Sequencing|Humans|Molecular Sequence Data|Mutation|Proteins|Selection, Genetic|Software', 'Journal Article', '10.1186/s12859-015-0590-4', '25990960', 'Deep mutational scanning is a technique to estimate the impacts of mutations on a gene by using deep sequencing to count mutations in a library of variants before and after imposing a functional selection. The impacts of mutations must be inferred from changes in their counts after selection.'),
(96735, 2015, 'bmc', 'affy2sv: an R package to pre-process Affymetrix CytoScan HD and 750K arrays for SNP, CNV, inversion and mosaicism calling', 'Hernandez-Ferrer, Carles|Quintela Garcia, Ines|Danielski, Katharina|Carracedo, Ángel|Pérez-Jurado, Luis A|González, Juan R', 'Center for Research in Environmental Epidemiology (CREAL), Doctor Aiguader 88, 08003, Barcelona, Spain. chernandez@creal.cat.|Grupo de Medicina Xenómica - Universidade de Santiago de Compostela, Santiago de Compostela, Spain. ines.quintela@usc.es.|Affymetrix, UK Ltd, High Wycombe, UK. katharina_danielski@affymetrix.com.|Grupo de Medicina Xenómica - Universidade de Santiago de Compostela, Santiago de Compostela, Spain. angel.carracedo@usc.es.|CIBER Enfermedades Raras (CIBERER), Madrid, Spain. luis.perez@upf.edu.|Center for Research in Environmental Epidemiology (CREAL), Doctor Aiguader 88, 08003, Barcelona, Spain. jrgonzalez@creal.cat.', 'Spain|Spain|United Kingdom|Spain|Spain|Spain', 1, 'Algorithms|Chromosome Inversion|DNA Copy Number Variations|Genetic Association Studies|Genome, Human|Genome-Wide Association Study|Humans|In Situ Hybridization, Fluorescence|Mosaicism|Oligonucleotide Array Sequence Analysis|Paraffin Embedding|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/s12859-015-0608-y', '25991004', 'The well-known Genome-Wide Association Studies (GWAS) had led to many scientific discoveries using SNP data. Even so, they were not able to explain the full heritability of complex diseases. Now, other structural variants like copy number variants or DNA inversions, either germ-line or in mosaicism events, are being studies. We present the R package affy2sv to pre-process Affymetrix CytoScan HD/750k array (also for Genome-Wide SNP 5.0/6.0 and Axiom) in structural variant studies.'),
(96736, 2015, 'bmc', 'InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams', 'Heberle, Henry|Meirelles, Gabriela Vaz|da Silva, Felipe R|Telles, Guilherme P|Minghim, Rosane', 'Universidade de São Paulo, Instituto de Ciências Matemáticas e de Computação, Av. Trabalhador São-carlense, 400, São Carlos SP, Brazil. henry@icmc.usp.br.|Laboratório Nacional de Biociências, Campinas SP, Caixa Postal 6192, Brazil. gabriela.meirelles@lnbio.cnpem.br.|Embrapa Informática Agropecuária, Av. André Tosello, 209, Campinas SP, Brazil. felipe.silva@embrapa.br.|Universidade Estadual de Campinas, Instituto de Computação, Av. Albert Einstein, 1251, Campinas SP, Brazil. gpt@ic.unicamp.br.|Universidade de São Paulo, Instituto de Ciências Matemáticas e de Computação, Av. Trabalhador São-carlense, 400, São Carlos SP, Brazil. rminghim@icmc.usp.br.', 'Brazil|Brazil|Brazil|Brazil|Brazil', 0, 'Biomarkers, Tumor|Computational Biology|Computer Graphics|Data Interpretation, Statistical|Genome, Plant|Humans|Internet|Male|Musa|Plant Proteins|Prostatic Neoplasms|Proteomics|Software|Tumor Cells, Cultured', 'Journal Article', '10.1186/s12859-015-0611-3', '25994840', 'Set comparisons permeate a large number of data analysis workflows, in particular workflows in biological sciences. Venn diagrams are frequently employed for such analysis but current tools are limited.'),
(96737, 2015, 'bmc', 'Probing long-range interactions by extracting free energies from genome-wide chromosome conformation capture data', 'Saberi, Saeed|Farré, Pau|Cuvier, Olivier|Emberly, Eldon', 'Physics Department, Simon Fraser University, 8888 University Drive, Burnaby, V5A 1S6, BC, Canada. saied.sabery.m@gmail.com.|Physics Department, Simon Fraser University, 8888 University Drive, Burnaby, V5A 1S6, BC, Canada. pau557@gmail.com.|Laboratoire de Biologie Moléculaire des Eucaryotes (LBME), Toulouse, France. olivier.cuvier@ibcg.biotoul.fr.|Physics Department, Simon Fraser University, 8888 University Drive, Burnaby, V5A 1S6, BC, Canada. eemberly@sfu.ca.', 'Canada|Canada|France|Canada', 0, 'Animals|Chromatin|Chromosome Mapping|Chromosomes|DNA|Drosophila melanogaster|Genome|Molecular Conformation|Nucleic Acid Conformation|Principal Component Analysis', 'Journal Article', '10.1186/s12859-015-0584-2', '26001583', 'A variety of DNA binding proteins are involved in regulating and shaping the packing of chromatin. They aid the formation of loops in the DNA that function to isolate different structural domains. A recent experimental technique, Hi-C, provides a method for determining the frequency of such looping between all distant parts of the genome. Given that the binding locations of many chromatin associated proteins have also been measured, it has been possible to make estimates for their influence on the long-range interactions as measured by Hi-C. However, a challenge in this analysis is the predominance of non-specific contacts that mask out the specific interactions of interest.'),
(96738, 2015, 'bmc', 'HTJoinSolver: Human immunoglobulin VDJ partitioning using approximate dynamic programming constrained by conserved motifs', 'Russ, Daniel E|Ho, Kwan-Yuet|Longo, Nancy S', 'Division of Computational Bioscience, Center for Information Technology, NIH, 12 South Drive, Bethesda, MD, 20892, USA. druss@mail.nih.gov.|Division of Computational Bioscience, Center for Information Technology, NIH, 12 South Drive, Bethesda, MD, 20892, USA. hok@mail.nih.gov.|Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, 40 Convent Drive, Bethesda, MD, 20892, USA. longon@mail.nih.gov.', 'United States|United States|United States', 0, 'Algorithms|Base Sequence|Computational Biology|Conserved Sequence|Gene Rearrangement|Humans|Immunoglobulin Joining Region|Immunoglobulin Variable Region|Molecular Sequence Data|Mutation|Software', 'Journal Article', '10.1186/s12859-015-0589-x', '26001675', 'Partitioning the human immunoglobulin variable region into variable (V), diversity (D), and joining (J) segments is a common sequence analysis step. We introduce a novel approximate dynamic programming method that uses conserved immunoglobulin gene motifs to improve performance of aligning V-segments of rearranged immunoglobulin (Ig) genes. Our new algorithm enhances the former JOINSOLVER algorithm by processing sequences with insertions and/or deletions (indels) and improves the efficiency for large datasets provided by high throughput sequencing.'),
(96739, 2015, 'bmc', 'NetworkPainter: dynamic intracellular pathway animation in Cytobank', 'Karr, Jonathan R|Guturu, Harendra|Chen, Edward Y|Blair, Stuart L|Irish, Jonathan M|Kotecha, Nikesh|Covert, Markus W', 'Graduate Program in Biophysics, Stanford University, 443 Via Ortega, MC 4245, Stanford, CA, 94305, USA. jkarr@stanfordalumni.org.|Department of Electrical Engineering, Stanford University, 279 Campus Drive West, MC 5329, Stanford, CA, 94305, USA. hguturu@stanford.edu.|Department of Bioengineering, Stanford University, 443 Via Ortega, MC 4245, Stanford, CA, 94305, USA. edchen@alumni.stanford.edu.|Cytobank Inc, 821 West El Camino Real, Mountain View, CA, 94040, USA. stu@cytobank.org.|Department of Medicine, Stanford University, 269 Campus Drive West, MC 5175, Stanford, CA, 94305, USA. jonathan.irish@vanderbilt.edu.|Graduate Program in Biomedical Informatics, Stanford University, 269 Campus Drive West, MC 5175, Stanford, CA, 94305, USA. nikesh@cytobank.org.|Department of Bioengineering, Stanford University, 443 Via Ortega, MC 4245, Stanford, CA, 94305, USA. mcovert@stanford.edu.', 'United States|United States|United States|United States|United States|United States|United States', 3, 'Computational Biology|Computer Simulation|Cytoplasm|Database Management Systems|Databases, Factual|Flow Cytometry|Humans|Internet|Leukocytes, Mononuclear|Signal Transduction|Software', 'Journal Article', '10.1186/s12859-015-0602-4', '26003204', 'High-throughput technologies such as flow and mass cytometry have the potential to illuminate cellular networks. However, analyzing the data produced by these technologies is challenging. Visualization is needed to help researchers explore this data.'),
(96740, 2015, 'bmc', 'Weighted protein residue networks based on joint recurrences between residues', 'Karain, Wael I|Qaraeen, Nael I', 'Department of Physics, Birzeit University, Birzeit, Palestine. wqaran@birzeit.edu.|Department of Computer Science, Birzeit University, Birzeit, Palestine. nqaraeen@birzeit.edu.', '-|-', 11, 'Bacterial Proteins|Computational Biology|Molecular Dynamics Simulation|Protein Interaction Domains and Motifs|Protein Structure, Secondary|Solvents|beta-Lactamases', 'Journal Article', '10.1186/s12859-015-0621-1', '26003989', 'Weighted and un-weighted protein residue networks can predict key functional residues in proteins based on the closeness centrality C and betweenness centrality B values for each residue. A static snapshot of the protein structure, and a cutoff distance, are used to define edges between the network nodes. In this work we apply the weighted network approach to study the xcexb2-Lactamase Inhibitory Protein (BLIP). Joint recurrences extracted from molecular dynamics MD trajectory positions of the protein residue carbon alpha atoms are used to define edge weights between nodes, and no cutoff distance is used. The results for B and C from our approach are compared with those extracted from an un-weighted network, and a weighted network that uses interatomic contacts to define edge weights between nodes, respectively.'),
(96741, 2015, 'bmc', 'A density-based clustering approach for identifying overlapping protein complexes with functional preferences', 'Hu, Lun|Chan, Keith C C', 'Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China. cslhu@comp.polyu.edu.hk.|Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China. keith.chan@polyu.edu.hk.', 'China|China', 0, 'Cluster Analysis|Computational Biology|Humans|Multiprotein Complexes|Protein Interaction Mapping|Proteins|Proteomics|Saccharomyces cerevisiae', 'Journal Article', '10.1186/s12859-015-0583-3', '26013799', 'Identifying protein complexes is an essential task for understanding the mechanisms of proteins in cells. Many computational approaches have thus been developed to identify protein complexes in protein-protein interaction (PPI) networks. Regarding the information that can be adopted by computational approaches to identify protein complexes, in addition to the graph topology of PPI network, the consideration of functional information of proteins has been becoming popular recently. Relevant approaches perform their tasks by relying on the idea that proteins in the same protein complex may be associated with similar functional information. However, we note from our previous researches that for most protein complexes their proteins are only similar in specific subsets of categories of functional information instead of the entire set. Hence, if the preference of each functional category can also be taken into account when identifying protein complexes, the accuracy will be improved.'),
(96742, 2015, 'bmc', 'Ambivalent covariance models', 'Janssen, Stefan|Giegerich, Robert', 'Practical Computer Science, Faculty of Technology, Bielefeld University, Universitätsstraße 25, Bielefeld, 33615, Germany. sjanssen@techfak.uni-bielefeld.de.|Practical Computer Science, Faculty of Technology, Bielefeld University, Universitätsstraße 25, Bielefeld, 33615, Germany. robert@techfak.uni-bielefeld.de.', 'Germany|Germany', 0, 'Databases, Factual|Humans|Models, Statistical|Position-Specific Scoring Matrices|RNA|RNA, Transfer|RNA, Untranslated|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-015-0569-1', '26017195', 'Evolutionary variations let us define a set of similar nucleic acid sequences as a family if these different molecules execute a common function. Capturing their sequence variation by using e. g. position specific scoring matrices significantly improves sensitivity of detection tools. Members of a functional (non-coding) RNA family are affected by these variations not only on the sequence, but also on the structural level. For example, some transfer-RNAs exhibit a fifth helix in addition to the typical cloverleaf structure. Current covariance models - the unrivaled homology search approach for structured RNA - do not benefit from structural variation within a family, but rather penalize it. This leads to artificial subdivision of families and loss of information in the RFAM database.'),
(96743, 2015, 'bmc', 'MANTIS: an R package that simulates multilocus models of pathogen evolution', 'Lourenço, José|Wikramaratna, Paul S|Gupta, Sunetra', 'Department of Zoology, University of Oxford, South Parks Road, Oxford, UK. jose.lourenco@zoo.ox.ac.uk.|Department of Zoology, University of Oxford, South Parks Road, Oxford, UK. paul.wikramaratna@ed.ac.uk.|Department of Zoology, University of Oxford, South Parks Road, Oxford, UK. sunetra.gupta@zoo.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 5, 'Biological Evolution|Communicable Diseases|Disease Transmission, Infectious|Host-Pathogen Interactions|Humans|Models, Statistical|Software', 'Journal Article', '10.1186/s12859-015-0598-9', '26017358', 'In host-pathogen systems the development of immunity by the host places pressure on pathogens, by setting up competition between genetic variants due to the establishment of cross-protective responses. These pressures can lead to pathogen-specific, ubiquitous dynamic behaviours. Understanding the evolutionary forces that shape these patterns is one of the key goals of computationally simulated epidemiological models. Despite the contribution of such research methods in recent years to our current understanding of pathogen evolution, the availability of free software tools for the general public remains scarce.'),
(96744, 2015, 'bmc', 'tcR: an R package for T cell receptor repertoire advanced data analysis', 'Nazarov, Vadim I|Pogorelyy, Mikhail V|Komech, Ekaterina A|Zvyagin, Ivan V|Bolotin, Dmitry A|Shugay, Mikhail|Chudakov, Dmitry M|Lebedev, Yury B|Mamedov, Ilgar Z', 'Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 16/10 Miklukho-Maklaya, Moscow, 117997, Russia. vdm.nazarov@gmail.com.|Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 16/10 Miklukho-Maklaya, Moscow, 117997, Russia. m.pogorely@gmail.com.|Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 16/10 Miklukho-Maklaya, Moscow, 117997, Russia. ekomech@gmail.com.|Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 16/10 Miklukho-Maklaya, Moscow, 117997, Russia. izvyagin@gmail.com.|Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 16/10 Miklukho-Maklaya, Moscow, 117997, Russia. bolotin.dmitriy@gmail.com.|Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 16/10 Miklukho-Maklaya, Moscow, 117997, Russia. mikhail.shugay@gmail.com.|Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 16/10 Miklukho-Maklaya, Moscow, 117997, Russia. chudakovdm@mail.ru.|Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 16/10 Miklukho-Maklaya, Moscow, 117997, Russia. lebedev_yb@ibch.ru.|Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 16/10 Miklukho-Maklaya, Moscow, 117997, Russia. imamedov78@gmail.com.', '-|-|-|-|-|-|-|-|-', 0, 'High-Throughput Nucleotide Sequencing|Humans|Immunoglobulins|Programming Languages|Receptors, Antigen, T-Cell|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-015-0613-1', '26017500', 'The Immunoglobulins (IG) and the T cell receptors (TR) play the key role in antigen recognition during the adaptive immune response. Recent progress in next-generation sequencing technologies has provided an opportunity for the deep T cell receptor repertoire profiling. However, a specialised software is required for the rational analysis of massive data generated by next-generation sequencing.'),
(96745, 2015, 'bmc', 'Learning-guided automatic three dimensional synapse quantification for drosophila neurons', 'Sanders, Jonathan|Singh, Anil|Sterne, Gabriella|Ye, Bing|Zhou, Jie', 'Department of Computer Science, Northern Illinois University, DeKalb, IL, 60115, USA.|Department of Computer Science, Northern Illinois University, DeKalb, IL, 60115, USA.|Life Sciences Institute and Department of Cell and Developmental Biology University of Michigan, Ann Arbor, MI, 48109, USA.|Life Sciences Institute and Department of Cell and Developmental Biology University of Michigan, Ann Arbor, MI, 48109, USA.|Department of Computer Science, Northern Illinois University, DeKalb, IL, 60115, USA. jzhou@niu.edu.', 'United States|United States|United States|United States|United States', 6, 'Algorithms|Animals|Brain|Drosophila melanogaster|Imaging, Three-Dimensional|Neurons|Pattern Recognition, Automated|Synapses', 'Journal Article', '10.1186/s12859-015-0616-y', '26017624', 'The subcellular distribution of synapses is fundamentally important for the assembly, function, and plasticity of the nervous system. Automated and effective quantification tools are a prerequisite to large-scale studies of the molecular mechanisms of subcellular synapse distribution. Common practices for synapse quantification in neuroscience labs remain largely manual or semi-manual. This is mainly due to computational challenges in automatic quantification of synapses, including large volume, high dimensions and staining artifacts. In the case of confocal imaging, optical limit and xy-z resolution disparity also require special considerations to achieve the necessary robustness.'),
(96746, 2015, 'bmc', 'Mirinho: An efficient and general plant and animal pre-miRNA predictor for genomic and deep sequencing data', 'Higashi, Susan|Fournier, Cyril|Gautier, Christian|Gaspin, Christine|Sagot, Marie-France', 'ERABLE team, Inria Grenoble Rhône-Alpes, Montbonnot Saint-Martin, 38330, France. susan.higashi@inria.fr.|Université de Lyon, F-69000, Lyon, Université Lyon 1, CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, Villeurbanne, F-69622, France. cyril.fournier@insa-lyon.fr.|ERABLE team, Inria Grenoble Rhône-Alpes, Montbonnot Saint-Martin, 38330, France. christian.gautier@univ-lyon1.fr.|INRA, UBIA & Plateforme Bioinformatique, 24 Chemin de Borde Rouge, Auzeville, POBOX 5627, Castanet Tolosan, 31326, France. christine.gaspin@toulouse.inra.fr.|ERABLE team, Inria Grenoble Rhône-Alpes, Montbonnot Saint-Martin, 38330, France. marie-france.sagot@inria.fr.', 'France|France|France|France|France', 7, 'Algorithms|Animals|Arabidopsis|Base Pairing|Base Sequence|Genome|Genomics|High-Throughput Nucleotide Sequencing|Insecta|MicroRNAs|Molecular Sequence Data|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1186/s12859-015-0594-0', '26022464', 'Several methods exist for the prediction of precursor miRNAs (pre-miRNAs) in genomic or sRNA-seq (small RNA sequences) data produced by NGS (Next Generation Sequencing). One key information used for this task is the characteristic hairpin structure adopted by pre-miRNAs, that in general are identified using RNA folders whose complexity is cubic in the size of the input. The vast majority of pre-miRNA predictors then rely on further information learned from previously validated miRNAs from the same or a closely related genome for the final prediction of new miRNAs. With this paper, we wished to address three main issues. The first was methodological and aimed at obtaining a more time-efficient predictor, however without losing in accuracy which represented a second issue. We indeed aimed at better predicting miRNAs at a genome scale, but also from sRNAseq data where in some cases, notably of plants, the current folding methods often infer the wrong structure. The third issue is related to the fact that it is important to rely as little as possible on previously recorded examples of miRNAs. We therefore also sought a method that is less dependent on previous miRNA records.'),
(96747, 2015, 'bmc', 'Discrimination of cell cycle phases in PCNA-immunolabeled cells', 'Schönenberger, Felix|Deutzmann, Anja|Ferrando-May, Elisa|Merhof, Dorit', 'Bioimaging Center (BIC), University of Konstanz, Universitätsstraße 10, Konstanz, Germany. Felix.Schoenenberger@uni-konstanz.de.|Bioimaging Center (BIC), University of Konstanz, Universitätsstraße 10, Konstanz, Germany. Anja.Deutzmann@stanford.edu.|Bioimaging Center (BIC), University of Konstanz, Universitätsstraße 10, Konstanz, Germany. Elisa.May@uni-konstanz.de.|Institute of Imaging & Computer Vision, RWTH Aachen University, Templergraben 55, Aachen, 52074, Germany. Dorit.Merhof@lfb.rwth-aachen.de.', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Antibodies, Monoclonal|Cell Cycle|Cell Nucleus|Cell Proliferation|DNA Replication|HeLa Cells|Humans|Image Processing, Computer-Assisted|Microscopy, Confocal|Microscopy, Fluorescence|Proliferating Cell Nuclear Antigen|Support Vector Machine', 'Journal Article', '10.1186/s12859-015-0618-9', '26022740', 'Protein function in eukaryotic cells is often controlled in a cell cycle-dependent manner. Therefore, the correct assignment of cellular phenotypes to cell cycle phases is a crucial task in cell biology research. Nuclear proteins whose localization varies during the cell cycle are valuable and frequently used markers of cell cycle progression. Proliferating cell nuclear antigen (PCNA) is a protein which is involved in DNA replication and has cell cycle dependent properties. In this work, we present a tool to identify cell cycle phases and in particular, sub-stages of the DNA replication phase (S-phase) based on the characteristic patterns of PCNA distribution. Single time point images of PCNA-immunolabeled cells are acquired using confocal and widefield fluorescence microscopy. In order to discriminate different cell cycle phases, an optimized processing pipeline is proposed. For this purpose, we provide an in-depth analysis and selection of appropriate features for classification, an in-depth evaluation of different classification algorithms, as well as a comparative analysis of classification performance achieved with confocal versus widefield microscopy images.'),
(96748, 2015, 'bmc', 'Navigating 3D electron microscopy maps with EM-SURFER', 'Esquivel-Rodríguez, Juan|Xiong, Yi|Han, Xusi|Guang, Shuomeng|Christoffer, Charles|Kihara, Daisuke', 'Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA. jesquivel@purdue.edu.|Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA. xiongyi128@gmail.com.|Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA. han273@purdue.edu.|Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA. sguang@purdue.edu.|Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA. christ35@purdue.edu.|Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA. dkihara@purdue.edu.', 'United States|United States|United States|United States|United States|United States', 11, 'Computational Biology|Databases, Protein|Humans|Imaging, Three-Dimensional|Internet|Microscopy, Electron|Models, Molecular|Proteins|Software', 'Journal Article', '10.1186/s12859-015-0580-6', '26025554', 'The Electron Microscopy DataBank (EMDB) is growing rapidly, accumulating biological structural data obtained mainly by electron microscopy and tomography, which are emerging techniques for determining large biomolecular complex and subcellular structures. Together with the Protein Data Bank (PDB), EMDB is becoming a fundamental resource of the tertiary structures of biological macromolecules. To take full advantage of this indispensable resource, the ability to search the database by structural similarity is essential. However, unlike high-resolution structures stored in PDB, methods for comparing low-resolution electron microscopy (EM) density maps in EMDB are not well established.'),
(96749, 2015, 'bmc', 'DECODE: an integrated differential co-expression and differential expression analysis of gene expression data', 'Lui, Thomas W H|Tsui, Nancy B Y|Chan, Lawrence W C|Wong, Cesar S C|Siu, Parco M F|Yung, Benjamin Y M', 'Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. tlui27@yahoo.com.|Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. nancytsui.cuhk@gmail.com.|Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. wing.chi.chan@polyu.edu.hk.|Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. cesar.wong@polyu.edu.hk.|Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. parco.siu@polyu.edu.hk.|Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. ben.yung@polyu.edu.hk.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong', 18, 'Algorithms|Breast Neoplasms|Computational Biology|Female|Gene Expression Profiling|Gene Regulatory Networks|High-Throughput Nucleotide Sequencing|Humans|Protein Interaction Maps|Software', 'Journal Article', '10.1186/s12859-015-0582-4', '26026612', 'Both differential expression (DE) and differential co-expression (DC) analyses are appreciated as useful tools in understanding gene regulation related to complex diseases. The performance of integrating DE and DC, however, remains unexplored.'),
(96750, 2015, 'bmc', 'Optimizing agent-based transmission models for infectious diseases', 'Willem, Lander|Stijven, Sean|Tijskens, Engelbert|Beutels, Philippe|Hens, Niel|Broeckhove, Jan', 'Centre for Health Economics Research & Modeling of Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium. lander.willem@uantwerp.be.|Modeling of Systems And Internet Communication, Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium. sean.stijven@uantwerp.be.|HPC core facility CalcUA, Computational Mathematics, University of Antwerp, Antwerp, Belgium. engelbert.tijskens@uantwerp.be.|Centre for Health Economics Research & Modeling of Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium. philippe.beutels@uantwerp.be.|Centre for Health Economics Research & Modeling of Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium. niel.hens@uhasselt.be.|Modeling of Systems And Internet Communication, Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium. jan.broeckhove@uantwerp.be.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Communicable Diseases|Computer Simulation|Computers|Disease Transmission, Infectious|Epidemiologic Methods|Humans|Incidence|Models, Theoretical|Software', 'Journal Article', '10.1186/s12859-015-0612-2', '26031500', 'Infectious disease modeling and computational power have evolved such that large-scale agent-based models (ABMs) have become feasible. However, the increasing hardware complexity requires adapted software designs to achieve the full potential of current high-performance workstations.'),
(96751, 2015, 'bmc', 'UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets', 'Abu-Jamous, Basel|Fa, Rui|Roberts, David J|Nandi, Asoke K', 'Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, Middlesex, UB8 3PH, UK. basel.abujamous@brunel.ac.uk.|Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, Middlesex, UB8 3PH, UK. rui.fa@brunel.ac.uk.|National Health Service Blood and Transplant, Oxford, OX3 9BQ, UK. david.roberts@ndcls.ox.ac.uk.|Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, Middlesex, UB8 3PH, UK. asoke.nandi@brunel.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 9, 'Cell Cycle|Cluster Analysis|Gene Expression Profiling|Genes, Fungal|Genome, Fungal|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae', 'Journal Article', '10.1186/s12859-015-0614-0', '26040489', 'Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96752, 2015, 'bmc', 'Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature', 'Ravikumar, Komandur Elayavilli|Wagholikar, Kavishwar B|Li, Dingcheng|Kocher, Jean-Pierre|Liu, Hongfang', 'Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First St SW, Harvick 3rd, Rochester, MN, 55905, USA. KomandurElayavilli.Ravikumar@mayo.edu.|Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First St SW, Harvick 3rd, Rochester, MN, 55905, USA. Wagholikar.Kavishwar@mayo.edu.|Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First St SW, Harvick 3rd, Rochester, MN, 55905, USA. Li.DingCheng@mayo.edu.|Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First St SW, Harvick 3rd, Rochester, MN, 55905, USA. Kocher.Jeanpierre@mayo.edu.|Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First St SW, Harvick 3rd, Rochester, MN, 55905, USA. Liu.Hongfang@mayo.edu.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Data Mining|Databases, Factual|Disease|Genetic Variation|High-Throughput Nucleotide Sequencing|Humans|Medical Subject Headings|Mutation|Natural Language Processing|Publications', 'Journal Article', '10.1186/s12859-015-0609-x', '26047637', 'Advances in the next generation sequencing technology has accelerated the pace of individualized medicine (IM), which aims to incorporate genetic/genomic information into medicine. One immediate need in interpreting sequencing data is the assembly of information about genetic variants and their corresponding associations with other entities (e.g., diseases or medications). Even with dedicated effort to capture such information in biological databases, much of this information remains locked in the unstructured text of biomedical publications. There is a substantial lag between the publication and the subsequent abstraction of such information into databases. Multiple text mining systems have been developed, but most of them focus on the sentence level association extraction with performance evaluation based on gold standard text annotations specifically prepared for text mining systems.'),
(96753, 2015, 'bmc', 'Using Gene Ontology to describe the role of the neurexin-neuroligin-SHANK complex in human, mouse and rat and its relevance to autism', 'Patel, Sejal|Roncaglia, Paola|Lovering, Ruth C', 'Institute of Medical Science, University of Toronto, Medical Sciences Building, 1 Kings College Circle, Toronto, M5S 1A8, Canada. sejalr.patel@mail.utoronto.ca.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. paola@ebi.ac.uk.|Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, Rayne Building, 5 University Street, London, WC1E 6JF, UK. r.lovering@ucl.ac.uk.', 'Canada|United Kingdom|United Kingdom', 0, 'Animals|Autistic Disorder|Cell Adhesion Molecules, Neuronal|Cell Physiological Phenomena|Gene Ontology|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Membrane Potentials|Mice|Models, Molecular|Nerve Tissue Proteins|Phenotype|Rats|Social Behavior|Synapses|Synaptic Potentials', 'Journal Article', '10.1186/s12859-015-0622-0', '26047810', 'People with an autistic spectrum disorder (ASD) display a variety of characteristic behavioral traits, including impaired social interaction, communication difficulties and repetitive behavior. This complex neurodevelopment disorder is known to be associated with a combination of genetic and environmental factors. Neurexins and neuroligins play a key role in synaptogenesis and neurexin-neuroligin adhesion is one of several processes that have been implicated in autism spectrum disorders.'),
(96754, 2015, 'bmc', 'Robust and automated three-dimensional segmentation of densely packed cell nuclei in different biological specimens with Lines-of-Sight decomposition', 'Mathew, B|Schmitz, A|Muxc3xb1oz-Descalzo, S|Ansari, N|Pampaloni, F|Stelzer, E H K|Fischer, S C', 'Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. biena.mathew@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. alexander.schmitz@physikalischebiologie.de.|Department of Biology and Biochemistry, University of Bath, Bath, BA2 7AY, UK. s.munoz-descalzo@bath.ac.uk.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. nariman.ansari@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. francesco.pampaloni@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. ernst.stelzer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.', 'Germany|Germany|United Kingdom|Germany|Germany|Germany|Germany', 25, 'Algorithms|Animals|Breast Neoplasms|Cell Nucleus|Computational Biology|Embryo, Mammalian|Female|Image Interpretation, Computer-Assisted|Imaging, Three-Dimensional|Mice|Microscopy, Fluorescence|Pancreatic Neoplasms|Pattern Recognition, Automated|Spheroids, Cellular', 'Journal Article', '10.1186/s12859-015-0617-x', '26049713', 'Due to the large amount of data produced by advanced microscopy, automated image analysis is crucial in modern biology. Most applications require reliable cell nuclei segmentation. However, in many biological specimens cell nuclei are densely packed and appear to touch one another in the images. Therefore, a major difficulty of three-dimensional cell nuclei segmentation is the decomposition of cell nuclei that apparently touch each other. Current methods are highly adapted to a certain biological specimen or a specific microscope. They do not ensure similarly accurate segmentation performance, i.e. their robustness for different datasets is not guaranteed. Hence, these methods require elaborate adjustments to each dataset.'),
(96755, 2015, 'bmc', 'Preface: BITS2014, the annual meeting of the Italian Society of Bioinformatics', 'Facchiano, Angelo|Angelini, Claudia|Bosotti, Roberta|Guffanti, Alessandro|Marabotti, Anna|Marangoni, Roberto|Pascarella, Stefano|Romano, Paolo|Zanzoni, Andreas|Helmer-Citterich, Manuela', 'Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Computational Biology|Humans|Societies, Scientific', 'Introductory Journal Article', '10.1186/1471-2105-16-S9-S1', '26050789', 'This Preface introduces the content of the BioMed Central journal Supplements related to BITS2014 meeting, held in Rome, Italy, from the 26th to the 28th of February, 2014. '),
(96756, 2015, 'bmc', 'Alternative splicing detection workflow needs a careful combination of sample prep and bioinformatics analysis', 'Carrara, Matteo|Lum, Josephine|Cordero, Francesca|Beccuti, Marco|Poidinger, Michael|Donatelli, Susanna|Calogero, Raffaele Adolfo|Zolezzi, Francesca', 'Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 12, 'Alternative Splicing|Computational Biology|Exons|Gene Library|Humans|RNA|RNA, Ribosomal|Sequence Analysis, RNA|Workflow', 'Journal Article', '10.1186/1471-2105-16-S9-S2', '26050971', 'RNA-Seq provides remarkable power in the area of biomarkers discovery and disease characterization. Two crucial steps that affect RNA-Seq experiment results are Library Sample Preparation (LSP) and Bioinformatics Analysis (BA). This work describes an evaluation of the combined effect of LSP methods and BA tools in the detection of splice variants.'),
(96757, 2015, 'bmc', 'Differential diagnosis of pleural mesothelioma using Logic Learning Machine', 'Parodi, Stefano|Filiberti, Rosa|Marroni, Paola|Libener, Roberta|Ivaldi, Giovanni Paolo|Mussap, Michele|Ferrari, Enrico|Manneschi, Chiara|Montani, Erika|Muselli, Marco', 'Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.|Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universitxc3xa4t Frankfurt am Main, Max-von-Laue-Straxc3x9fe 15, 60438, Frankfurt am Main, Germany. sabine.fischer@physikalischebiologie.de.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Artificial Intelligence|Biomarkers, Tumor|Cohort Studies|Decision Trees|Diagnosis, Differential|Female|Humans|Logic|Lung Neoplasms|Male|Mesothelioma|Middle Aged|Neoplasm Metastasis|Neural Networks (Computer)|Pleural Neoplasms', 'Journal Article', '10.1186/1471-2105-16-S9-S3', '26051106', 'Tumour markers are standard tools for the differential diagnosis of cancer. However, the occurrence of nonspecific symptoms and different malignancies involving the same cancer site may lead to a high proportion of misclassifications. Classification accuracy can be improved by combining information from different markers using standard data mining techniques, like Decision Tree (DT), Artificial Neural Network (ANN), and k-Nearest Neighbour (KNN) classifier. Unfortunately, each method suffers from some unavoidable limitations. DT, in general, tends to show a low classification performance, whereas ANN and KNN produce a black-box classification that does not provide biological information useful for clinical purposes.'),
(96758, 2015, 'bmc', 'WormGUIDES: an interactive single cell developmental atlas and tool for collaborative multidimensional data exploration', 'Santella, Anthony|Catena, Raúl|Kovacevic, Ismar|Shah, Pavak|Yu, Zidong|Marquina-Solis, Javier|Kumar, Abhishek|Wu, Yicong|Schaff, James|Colón-Ramos, Daniel|Shroff, Hari|Mohler, William A|Bao, Zhirong', 'Developmental Biology Program, Sloan-Kettering Institute, New York, NY, USA. santella@mskcc.org.|Developmental Biology Program, Sloan-Kettering Institute, New York, NY, USA. raulcatena@gmail.com.|Developmental Biology Program, Sloan-Kettering Institute, New York, NY, USA. ismar.kovacevic@gmail.com.|Developmental Biology Program, Sloan-Kettering Institute, New York, NY, USA. shahp2@mskcc.org.|Developmental Biology Program, Sloan-Kettering Institute, New York, NY, USA. yuzidong@just.edu.cn.|Program in Cellular Neuroscience, Neurodegeneration and Repair, Department of Cell Biology, Yale University School of Medicine, New Haven, CT, USA. javier.marquina@yale.edu.|Program in Cellular Neuroscience, Neurodegeneration and Repair, Department of Cell Biology, Yale University School of Medicine, New Haven, CT, USA. abhishek.kumar@nih.gov.|Section on High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering NIH, Bethesda, MD, USA. wuy7@mail.nih.gov.|Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT, USA. Schaff@uchc.edu.|Program in Cellular Neuroscience, Neurodegeneration and Repair, Department of Cell Biology, Yale University School of Medicine, New Haven, CT, USA. daniel.colon-ramos@yale.edu.|Section on High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering NIH, Bethesda, MD, USA. hari.shroff@nih.gov.|Dept. of Genetics & Dev Biology, University of Connecticut Health Center, Farmington, CT, USA. mohler.william@gmail.com.|Developmental Biology Program, Sloan-Kettering Institute, New York, NY, USA. baoz@mskcc.org.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Animals|Caenorhabditis elegans|Databases, Factual|Nervous System|Single-Cell Analysis|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0627-8', '26051157', 'Imaging and image analysis advances are yielding increasingly complete and complicated records of cellular events in tissues and whole embryos. The ability to follow hundreds to thousands of cells at the individual level demands a spatio-temporal data infrastructure: tools to assemble and collate knowledge about development spatially in a manner analogous to geographic information systems (GIS). Just as GIS indexes items or events based on their spatio-temporal or 4D location on the Earth these tools would organize knowledge based on location within the tissues or embryos. Developmental processes are highly context-specific, but the complexity of the 4D environment in which they unfold is a barrier to assembling an understanding of any particular process from diverse sources of information. In the same way that GIS aids the understanding and use of geo-located large data sets, software can, with a proper frame of reference, allow large biological data sets to be understood spatially. Intuitive tools are needed to navigate the spatial structure of complex tissue, collate large data sets and existing knowledge with this spatial structure and help users derive hypotheses about developmental mechanisms.'),
(96759, 2015, 'bmc', 'Light-weight reference-based compression of FASTQ data', 'Zhang, Yongpeng|Li, Linsen|Yang, Yanli|Yang, Xiao|He, Shan|Zhu, Zexuan', 'College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. 2120230303@email.szu.edu.cn.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. li5linsen@163.com.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. yylszu@163.com.|The Broad Institute, Cambridge, MA, 02142, USA. xiaoyang@broadinstitute.org.|School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK. S.He@cs.bham.ac.uk.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.', 'China|China|China|United Kingdom|United Kingdom|China', 20, 'Algorithms|Data Compression|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-015-0628-7', '26051252', 'The exponential growth of next generation sequencing (NGS) data has posed big challenges to data storage, management and archive. Data compression is one of the effective solutions, where reference-based compression strategies can typically achieve superior compression ratios compared to the ones not relying on any reference.'),
(96760, 2015, 'bmc', 'Fast randomized approximate string matching with succinct hash data structures', 'Policriti, Alberto|Prezza, Nicola', 'College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.', 'China|China', 7, 'Algorithms|Computer Simulation|Genome, Human|Genome, Plant|Humans|Sequence Analysis, DNA|Vitis', 'Journal Article', '10.1186/1471-2105-16-S9-S4', '26051265', 'The high throughput of modern NGS sequencers coupled with the huge sizes of genomes currently analysed, poses always higher algorithmic challenges to align short reads quickly and accurately against a reference sequence. A crucial, additional, requirement is that the data structures used should be light. The available modern solutions usually are a compromise between the mentioned constraints: in particular, indexes based on the Burrows-Wheeler transform offer reduced memory requirements at the price of lower sensitivity, while hash-based text indexes guarantee high sensitivity at the price of significant memory consumption.'),
(96761, 2015, 'bmc', 'adLIMS: a customized open source software that allows bridging clinical and basic molecular research studies', 'Calabria, Andrea|Spinozzi, Giulio|Benedicenti, Fabrizio|Tenderini, Erika|Montini, Eugenio', 'College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.', 'China|China|China|China|China', 0, 'Algorithms|Automation|Clinical Laboratory Information Systems|Data Collection|Databases, Genetic|Genomics|High-Throughput Nucleotide Sequencing|Humans|Polymerase Chain Reaction|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-16-S9-S5', '26051409', 'Many biological laboratories that deal with genomic samples are facing the problem of sample tracking, both for pure laboratory management and for efficiency. Our laboratory exploits PCR techniques and Next Generation Sequencing (NGS) methods to perform high-throughput integration site monitoring in different clinical trials and scientific projects. Because of the huge amount of samples that we process every year, which result in hundreds of millions of sequencing reads, we need to standardize data management and tracking systems, building up a scalable and flexible structure with web-based interfaces, which are usually called Laboratory Information Management System (LIMS).'),
(96762, 2015, 'bmc', 'Kissing loop interaction in adenine riboswitch: insights from umbrella sampling simulations', 'Di Palma, Francesco|Bottaro, Sandro|Bussi, Giovanni', 'College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.', 'China|China|China', 17, 'Adenine|Humans|Molecular Dynamics Simulation|Nucleic Acid Conformation|RNA|Riboswitch|Thermodynamics', 'Journal Article', '10.1186/1471-2105-16-S9-S6', '26051557', 'Riboswitches are cis-acting regulatory RNA elements prevalently located in the leader sequences of bacterial mRNA. An adenine sensing riboswitch cis-regulates adeninosine deaminase gene (add) in Vibrio vulnificus. The structural mechanism regulating its conformational changes upon ligand binding mostly remains to be elucidated. In this open framework it has been suggested that the ligand stabilizes the interaction of the distal kissing loop complex. Using accurate full-atom molecular dynamics with explicit solvent in combination with enhanced sampling techniques and advanced analysis methods it could be possible to provide a more detailed perspective on the formation of these tertiary contacts.'),
(96763, 2015, 'bmc', 'ComiRNet: a web-based system for the analysis of miRNA-gene regulatory networks', 'Pio, Gianvito|Ceci, Michelangelo|Malerba, Donato|DElia, Domenica', 'College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.', 'China|China|China|China', 28, 'Algorithms|Gene Regulatory Networks|High-Throughput Nucleotide Sequencing|Humans|Internet|MicroRNAs|RNA, Messenger', 'Journal Article', '10.1186/1471-2105-16-S9-S7', '26051695', 'The understanding of mechanisms and functions of microRNAs (miRNAs) is fundamental for the study of many biological processes and for the elucidation of the pathogenesis of many human diseases. Technological advances represented by high-throughput technologies, such as microarray and next-generation sequencing, have significantly aided miRNA research in the last decade. Nevertheless, the identification of true miRNA targets and the complete elucidation of the rules governing their functional targeting remain nebulous. Computational tools have been proven to be fundamental for guiding experimental validations for the discovery of new miRNAs, for the identification of their targets and for the elucidation of their regulatory mechanisms.'),
(96764, 2015, 'bmc', 'Automatising the analysis of stochastic biochemical time-series', 'Caravagna, Giulio|De Sano, Luca|Antoniotti, Marco', 'College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.', 'China|China|China', 1, 'Animals|Automation|Bacteria|Computational Biology|Computer Simulation|Models, Biological|Models, Statistical|Predatory Behavior|Software|Stochastic Processes', 'Journal Article', '10.1186/1471-2105-16-S9-S8', '26051821', 'Mathematical and computational modelling of biochemical systems has seen a lot of effort devoted to the definition and implementation of high-performance mechanistic simulation frameworks. Within these frameworks it is possible to analyse complex models under a variety of configurations, eventually selecting the best setting of, e.g., parameters for a target system.'),
(96765, 2015, 'bmc', 'Optimization of sequence alignments according to the number of sequences vs number of sites trade-off', 'Dutheil, Julien Y|Figuet, Emeric', 'Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Biology, August-Thienemann-Str. 2, Plön, 24306, Germany. dutheil@evolbio.mpg.de.|Institut des Sciences de lÉvolution - Montpellier, Place Eugène Bataillon - C.C. 065 -, Montpellier, 34095, France. emeric.figuet@univ-montp2.fr.', 'Germany|France', 2, 'Algorithms|Computational Biology|Databases, Factual|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-015-0619-8', '26055961', 'Comparative analysis of homologous sequences enables the understanding of evolutionary patterns at the molecular level, unraveling the functional constraints that shaped the underlying genes. Bioinformatic pipelines for comparative sequence analysis typically include procedures for (i) alignment quality assessment and (ii) control of sequence redundancy. An additional, underassessed step is the control of the amount and distribution of missing data in sequence alignments. While the number of sequences available for a given gene typically increases with time, the site-specific coverage of each alignment position remains highly variable because of differences in sequencing and annotation quality, or simply because of biological variation. For any given alignment-based analysis, the selection of sequences thus defines a trade-off between the species representation and the quantity of sites with sufficient coverage to be included in the subsequent analyses.'),
(96766, 2015, 'bmc', 'Clotho: addressing the scalability of forward time population genetic simulation', 'Putnam, Patrick P|Wilsey, Philip A|Zhang, Ge', 'Human Genetics, Cincinnati Childrens Hospital Medical Center, 3333 Burnet Ave, Cincinnati, 45229-3026, OH, USA. putnampp@gmail.com.|Department of Electrical Engineering and Computing Systems, University of Cincinnati, PO Box 210030, Cincinnati, 45221-0030, OH, USA. wilseypa@gmail.com.|Human Genetics, Cincinnati Childrens Hospital Medical Center, 3333 Burnet Ave, Cincinnati, 45229-3026, OH, USA. zhangge.uc@gmail.com.', 'United States|United States|United States', 0, 'Algorithms|Biological Evolution|Computer Simulation|Genetics, Population|Humans|Mutation Rate|Software|Time Factors', 'Journal Article', '10.1186/s12859-015-0631-z', '26056047', 'Forward Time Population Genetic Simulations offer a flexible framework for modeling the various evolutionary processes occurring in nature. Often this model expressibility is countered by an increased memory usage or computational overhead. With the complexity of simulation scenarios continuing to increase, addressing the scalability of the underlying simulation framework is a growing consideration.'),
(96767, 2015, 'bmc', 'A composite genome approach to identify phylogenetically informative data from next-generation sequencing', 'Schwartz, Rachel S|Harkins, Kelly M|Stone, Anne C|Cartwright, Reed A', 'The Biodesign Institute, Arizona State University, Tempe, AZ, USA. Rachel.Schwartz@asu.edu.|School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA. kmharkin@ucsc.edu.|School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA. acstone@asu.edu.|The Biodesign Institute, Arizona State University, Tempe, AZ, USA. cartwright@asu.edu.', 'United States|United States|United States|United States', 8, 'Animals|Genome|Genomics|High-Throughput Nucleotide Sequencing|Hominidae|Mammals|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-015-0632-y', '26062548', 'Improvements in sequencing technology now allow easy acquisition of large datasets, however, analyzing these data for phylogenetics can be challenging. We have developed a novel method to rapidly obtain homologous genomic data for phylogenetics directly from next-generation sequencing reads without the use of a reference genome. This software, called SISRS, avoids the time consuming steps of de novo whole genome assembly, multiple genome alignment, and annotation.'),
(96768, 2015, 'bmc', 'BitMapper: an efficient all-mapper based on bit-vector computing', 'Cheng, Haoyu|Jiang, Huaipan|Yang, Jiaoyun|Xu, Yun|Shang, Yi', 'Key Laboratory on High Performance Computing, Hefei, Anhui230027, P.R. China. chhy@mail.ustc.edu.cn.|Key Laboratory on High Performance Computing, Hefei, Anhui230027, P.R. China. jhp9650@mail.ustc.edu.cn.|Hefei University of Technology, Hefei, 230009, China. jiaoyun@hfut.edu.cn.|Key Laboratory on High Performance Computing, Hefei, Anhui230027, P.R. China. xuyun@ustc.edu.cn.|Department of Computer Science, University of Missouri-Columbia, Columbia MO, 65203, USA. ShangY@missouri.edu.', 'China|China|China|China|United States', 16, 'Algorithms|Animals|Arabidopsis|Caenorhabditis elegans|Computing Methodologies|Genome|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Software', 'Journal Article', '10.1186/s12859-015-0626-9', '26063651', 'As the next-generation sequencing (NGS) technologies producing hundreds of millions of reads every day, a tremendous computational challenge is to map NGS reads to a given reference genome efficiently. However, existing methods of all-mappers, which aim at finding all mapping locations of each read, are very time consuming. The majority of existing all-mappers consist of 2 main parts, filtration and verification. This work significantly reduces verification time, which is the dominant part of the running time.'),
(96769, 2015, 'bmc', 'AllelicImbalance: an R/bioconductor package for detecting, managing, and visualizing allele expression imbalance data from RNA sequencing', 'Gådin, Jesper R|vant Hooft, Ferdinand M|Eriksson, Per|Folkersen, Lasse', 'Atherosclerosis Research Unit, Karolinska University Hospital Solna, Center for Molecular Medicine, Bldg L8:03, S-171 76, Stockholm, Sweden. Jesper.r.gadin@ki.se.|Atherosclerosis Research Unit, Karolinska University Hospital Solna, Center for Molecular Medicine, Bldg L8:03, S-171 76, Stockholm, Sweden. Ferdinand.vant.Hooft@ki.se.|Atherosclerosis Research Unit, Karolinska University Hospital Solna, Center for Molecular Medicine, Bldg L8:03, S-171 76, Stockholm, Sweden. Per.Eriksson@ki.se.|Atherosclerosis Research Unit, Karolinska University Hospital Solna, Center for Molecular Medicine, Bldg L8:03, S-171 76, Stockholm, Sweden. lassefolkersen@gmail.com.', 'Sweden|Sweden|Sweden|Sweden', 6, 'Alleles|Allelic Imbalance|Aorta|Computer Graphics|Exons|High-Throughput Nucleotide Sequencing|Humans|Liver|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-015-0620-2', '26066318', 'One aspect in which RNA sequencing is more valuable than microarray-based methods is the ability to examine the allelic imbalance of the expression of a gene. This process is often a complex task that entails quality control, alignment, and the counting of reads over heterozygous single-nucleotide polymorphisms. Allelic imbalance analysis is subject to technical biases, due to differences in the sequences of the measured alleles. Flexible bioinformatics tools are needed to ease the workflow while retaining as much RNA sequencing information as possible throughout the analysis to detect and address the possible biases.'),
(96770, 2015, 'bmc', 'Integrated web visualizations for protein-protein interaction databases', 'Jeanquartier, Fleur|Jean-Quartier, Claire|Holzinger, Andreas', 'Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, Graz, 8036, Austria. f.jeanquartier@hci-kdd.org.|Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, Graz, 8036, Austria. c.jeanquartier@hci-kdd.org.|Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, Graz, 8036, Austria. a.holzinger@tugraz.at.', 'Austria|Austria|Austria', 24, 'Computational Biology|Computer Graphics|Computer Simulation|Databases, Protein|Humans|Internet|Protein Interaction Maps|Proteins|Software|Systems Biology|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0615-z', '26077899', 'Understanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks.'),
(96771, 2015, 'bmc', 'Alignment of time course gene expression data and the classification of developmentally driven genes with hidden Markov models', 'Robinson, Sean|Glonek, Garique|Koch, Inge|Thomas, Mark|Davies, Christopher', 'School of Mathematical Sciences, University of Adelaide, Adelaide, Australia. sean.robinson@alumni.adelaide.edu.au.|School of Mathematical Sciences, University of Adelaide, Adelaide, Australia. gary.glonek@adelaide.edu.au.|School of Mathematical Sciences, University of Adelaide, Adelaide, Australia. inge.koch@adelaide.edu.au.|CSIRO Agriculture, Adelaide, Australia. mark.r.thomas@csiro.au.|CSIRO Agriculture, Adelaide, Australia. christopher.davies@csiro.au.', 'Australia|Australia|Australia|Australia|Australia', 4, 'Algorithms|Gene Expression Profiling|Gene Expression Regulation, Developmental|Genes, Plant|Genome, Plant|Humans|Likelihood Functions|Markov Chains|Time Factors|Vitis|Wine', 'Journal Article', '10.1186/s12859-015-0634-9', '26084333', 'We consider data from a time course microarray experiment that was conducted on grapevines over the development cycle of the grape berries at two different vineyards in South Australia. Although the underlying biological process of berry development is the same at both vineyards, there are differences in the timing of the development due to local conditions. We aim to align the data from the two vineyards to enable an integrated analysis of the gene expression and use the alignment of the expression profiles to classify likely developmental function.'),
(96772, 2015, 'bmc', 'RDML-Ninja and RDMLdb for standardized exchange of qPCR data', 'Ruijter, Jan M|Lefever, Steve|Anckaert, Jasper|Hellemans, Jan|Pfaffl, Michael W|Benes, Vladimir|Bustin, Stephen A|Vandesompele, Jo|Untergasser, Andreas|RDML consortium', 'Department of Anatomy, Embryology & Physiology, Academic Medical Center, Amsterdam, 1100AZ, Netherlands. j.m.ruijter@amc.uva.nl.|Center for Medical Genetics, Ghent University, Gent, B-9000, Belgium. steve.lefever@UGent.be.|Center for Medical Genetics, Ghent University, Gent, B-9000, Belgium. jasper.anckaert@UGent.be.|Biogazelle, Zwijnaarde, 9052, Belgium. Jan.Hellemans@Biogazelle.com.|Physiology, Center of Life and Food Sciences, Technical University of Munich, Freising Weihenstephan, Munich, 85354, Germany. michael.pfaffl@wzw.tum.de.|Genomics Core Facility, European Molecular Biology Laboratory, Heidelberg, 69117, Germany. benes@embl.de.|Postgraduate Medical Institute, Anglia Ruskin University, Chelmsford, CM1 1SQ, UK. stephen.bustin@anglia.ac.uk.|Center for Medical Genetics, Ghent University, Gent, B-9000, Belgium. Joke.Vandesompele@UGent.be.|Genomics Core Facility, European Molecular Biology Laboratory, Heidelberg, 69117, Germany. andreas@untergasser.de.|Genomics Core Facility, European Molecular Biology Laboratory, Heidelberg, 69117, Germany. andreas@untergasser.de.', 'Netherlands|Belgium|Belgium|Belgium|Germany|Germany|United Kingdom|Belgium|Germany|Germany', 8, 'Computer Communication Networks|Databases, Factual|Humans|Polymerase Chain Reaction|Software', 'Journal Article', '10.1186/s12859-015-0637-6', '26087842', 'The universal qPCR data exchange file format RDML is today well accepted by the scientific community, part of the MIQE guidelines and implemented in many qPCR instruments. With the increased use of RDML new challenges emerge. The flexibility of the RDML format resulted in some implementations that did not meet the expectations of the consortium in the level of support or the use of elements.'),
(96773, 2015, 'bmc', 'Ferret: a sentence-based literature scanning system', 'Srinivasan, Padmini|Zhang, Xiao-Ning|Bouten, Roxane|Chang, Caren', 'Computer Science, University of Iowa, Iowa City, IA, USA. padmini-srinivasan@uiowa.edu.|Department of Biology, St Bonaventure University, St Bonaventure, NY, USA. xzhang@sbu.edu.|Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA. roxane@bouten.com.|Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA. carenc@umd.edu.', 'United States|United States|United States|United States', 2, 'Arabidopsis|Arabidopsis Proteins|Databases, Bibliographic|Ethylenes|Flowers|Information Storage and Retrieval|Phenotype|PubMed|Salicylic Acid|Semantics|Software|Stress, Physiological', 'Journal Article', '10.1186/s12859-015-0630-0', '26091670', 'The rapid pace of bioscience research makes it very challenging to track relevant articles in ones area of interest. MEDLINE, a primary source for biomedical literature, offers access to more than 20 million citations with three-quarters of a million new ones added each year. Thus it is not surprising to see active research in building new document retrieval and sentence retrieval systems. We present Ferret, a prototype retrieval system, designed to retrieve and rank sentences (and their documents) conveying gene-centric relationships of interest to a scientist. The prototype has several features. For example, it is designed to handle gene name ambiguity and perform query expansion. Inputs can be a list of genes with an optional list of keywords. Sentences are retrieved across species but the species discussed in the records are identified. Results are presented in the form of a heat map and sentences corresponding to specific cells of the heat map may be selected for display. Ferret is designed to assist bio scientists at different stages of research from early idea exploration to advanced analysis of results from bench experiments.'),
(96774, 2015, 'bmc', 'Factors affecting the accuracy of a class prediction model in gene expression data', 'Novianti, Putri W|Jong, Victor L|Roes, Kit C B|Eijkemans, Marinus J C', 'Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508, GA, Utrecht, The Netherlands. Novianti-3@umcutrecht.nl.|Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508, GA, Utrecht, The Netherlands. Jong@umcutecht.nl.|Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508, GA, Utrecht, The Netherlands. Roes@umcutrecht.nl.|Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508, GA, Utrecht, The Netherlands. M.J.C.Eijkemans@umcutrecht.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands', 14, 'Bayes Theorem|Biomarkers|Cell Lineage|Communicable Diseases|Discriminant Analysis|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Theoretical|Sample Size|Support Vector Machine', 'Journal Article', '10.1186/s12859-015-0610-4', '26093633', 'Class prediction models have been shown to have varying performances in clinical gene expression datasets. Previous evaluation studies, mostly done in the field of cancer, showed that the accuracy of class prediction models differs from dataset to dataset and depends on the type of classification function. While a substantial amount of information is known about the characteristics of classification functions, little has been done to determine which characteristics of gene expression data have impact on the performance of a classifier. This study aims to empirically identify data characteristics that affect the predictive accuracy of classification models, outside of the field of cancer.'),
(96775, 2015, 'bmc', 'Multiobjective triclustering of time-series transcriptome data reveals key genes of biological processes', 'Bhar, Anirban|Haubrock, Martin|Mukhopadhyay, Anirban|Wingender, Edgar', 'Institute of Bioinformatics, University Medical Center, Georg August University, Goettingen, Goldschmidtstrasse 1, Goettingen, D-37077, Germany. anirban.bhar@bioinf.med.uni-goettingen.de.|Institute of Bioinformatics, University Medical Center, Georg August University, Goettingen, Goldschmidtstrasse 1, Goettingen, D-37077, Germany. martin.haubrock@bioinf.med.uni-goettingen.de.|Department of Computer Science and Engineering, University of Kalyani, Kalyani, -741235, India. anirban@klyuniv.ac.in.|Institute of Bioinformatics, University Medical Center, Georg August University, Goettingen, Goldschmidtstrasse 1, Goettingen, D-37077, Germany. edgar.wingender@bioinf.med.uni-goettingen.de.', 'Germany|Germany|India|Germany', 8, 'Algorithms|Biological Phenomena|Biomarkers|Cell Differentiation|Cluster Analysis|Datasets as Topic|Gene Expression Profiling|Gene Regulatory Networks|Humans|Induced Pluripotent Stem Cells|Myocytes, Cardiac|Oligonucleotide Array Sequence Analysis|Time Factors|Transcriptome', 'Journal Article', '10.1186/s12859-015-0635-8', '26108437', 'Exploratory analysis of multi-dimensional high-throughput datasets, such as microarray gene expression time series, may be instrumental in understanding the genetic programs underlying numerous biological processes. In such datasets, variations in the gene expression profiles are usually observed across replicates and time points. Thus mining the temporal expression patterns in such multi-dimensional datasets may not only provide insights into the key biological processes governing organs to grow and develop but also facilitate the understanding of the underlying complex gene regulatory circuits.'),
(96776, 2015, 'bmc', 'Error correction and statistical analyses for intra-host comparisons of feline immunodeficiency virus diversity from high-throughput sequencing data', 'Liu, Yang|Chiaromonte, Francesca|Ross, Howard|Malhotra, Raunaq|Elleder, Daniel|Poss, Mary', 'Department of Statistics, The Pennsylvania State University, University Park, PA, 16802, USA. ywl5222@psu.edu.|Department of Statistics, The Pennsylvania State University, University Park, PA, 16802, USA. chiaro@stat.psu.edu.|Bioinformatics Institute, School of Biological Sciences, University of Auckland, Auckland, 1142, New Zealand. h.ross@auckland.ac.nz.|Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA, 16802, USA. raunaq@psu.edu.|Department of Biology, The Pennsylvania State University, University Park, PA, 16802, USA. Daniel.Elleder@img.cas.cz.|Department of Biology, The Pennsylvania State University, University Park, PA, 16802, USA. mposs@bx.psu.edu.', 'United States|United States|New Zealand|United States|United States|United States', 0, 'Algorithms|Animals|Cat Diseases|Cats|DNA, Viral|Data Interpretation, Statistical|Feline Acquired Immunodeficiency Syndrome|High-Throughput Nucleotide Sequencing|Immunodeficiency Virus, Feline|Models, Statistical', 'Journal Article', '10.1186/s12859-015-0607-z', '26123018', 'Infection with feline immunodeficiency virus (FIV) causes an immunosuppressive disease whose consequences are less severe if cats are co-infected with an attenuated FIV strain (PLV). We use virus diversity measurements, which reflect replication ability and the virus response to various conditions, to test whether diversity of virulent FIV in lymphoid tissues is altered in the presence of PLV. Our data consisted of the 3 half of the FIV genome from three tissues of animals infected with FIV alone, or with FIV and PLV, sequenced by 454 technology.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96777, 2015, 'bmc', 'TMFoldRec: a statistical potential-based transmembrane protein fold recognition tool', 'Kozma, Dániel|Tusnády, Gábor E', 'Momentum Membrane Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, PO Box 7, , H 1518, Budapest, Hungary. kozma.daniel@ttk.mta.hu.|Momentum Membrane Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, PO Box 7, , H 1518, Budapest, Hungary. tusnady.gabor@ttk.mta.hu.', 'Hungary|Hungary', 0, 'Algorithms|Computational Biology|Databases, Protein|Humans|Membrane Proteins|Models, Statistical|Protein Conformation|Protein Folding', 'Journal Article', '10.1186/s12859-015-0638-5', '26123059', 'Transmembrane proteins (TMPs) are the key components of signal transduction, cell-cell adhesion and energy and material transport into and out from the cells. For the deep understanding of these processes, structure determination of transmembrane proteins is indispensable. However, due to technical difficulties, only a few transmembrane protein structures have been determined experimentally. Large-scale genomic sequencing provides increasing amounts of sequence information on the proteins and whole proteomes of living organisms resulting in the challenge of bioinformatics, how the structural information should be gained from a sequence.'),
(96778, 2015, 'bmc', 'BioMaS: a modular pipeline for Bioinformatic analysis of Metagenomic AmpliconS', 'Fosso, Bruno|Santamaria, Monica|Marzano, Marinella|Alonso-Alemany, Daniel|Valiente, Gabriel|Donvito, Giacinto|Monaco, Alfonso|Notarangelo, Pasquale|Pesole, Graziano', 'Institute of Biomembranes and Bioenergetics, Consiglio Nazionale delle Ricerche, via Amendola 165/A, Bari, 70126, Italy. b.fosso@ibbe.cnr.it.|Institute of Biomembranes and Bioenergetics, Consiglio Nazionale delle Ricerche, via Amendola 165/A, Bari, 70126, Italy. m.santamaria@ibbe.cnr.it.|Institute of Biomembranes and Bioenergetics, Consiglio Nazionale delle Ricerche, via Amendola 165/A, Bari, 70126, Italy. marzano.marinella@gmail.com.|Algorithms, Bioinformatics, Complexity and Formal Methods Research Group, Technical University of Catalonia, E-08034, Barcelona, Spain. dalonso@cs.upc.edu.|Algorithms, Bioinformatics, Complexity and Formal Methods Research Group, Technical University of Catalonia, E-08034, Barcelona, Spain. valiente@cs.upc.edu.|National Institute of Nuclear Physics, via E. Orabona 4, Bari, 70125, Italy. giacinto.donvito@ba.infn.it.|National Institute of Nuclear Physics, via E. Orabona 4, Bari, 70125, Italy. Alfonso.Monaco@ba.infn.it.|National Institute of Nuclear Physics, via E. Orabona 4, Bari, 70125, Italy. Pasquale.Notarangelo@ba.infn.it.|Institute of Biomembranes and Bioenergetics, Consiglio Nazionale delle Ricerche, via Amendola 165/A, Bari, 70126, Italy. graziano.pesole@uniba.it.', 'Italy|Italy|Italy|Spain|Spain|Italy|Italy|Italy|Italy', 22, 'Bacteria|Biodiversity|Computational Biology|Fungi|High-Throughput Nucleotide Sequencing|Metagenomics|Software', 'Journal Article', '10.1186/s12859-015-0595-z', '26130132', 'Substantial advances in microbiology, molecular evolution and biodiversity have been carried out in recent years thanks to Metagenomics, which allows to unveil the composition and functions of mixed microbial communities in any environmental niche. If the investigation is aimed only at the microbiome taxonomic structure, a target-based metagenomic approach, here also referred as Meta-barcoding, is generally applied. This approach commonly involves the selective amplification of a species-specific genetic marker (DNA meta-barcode) in the whole taxonomic range of interest and the exploration of its taxon-related variants through High-Throughput Sequencing (HTS) technologies. The accessibility to proper computational systems for the large-scale bioinformatic analysis of HTS data represents, currently, one of the major challenges in advanced Meta-barcoding projects.'),
(96779, 2015, 'bmc', 'YBYRÁ facilitates comparison of large phylogenetic trees', 'Machado, Denis Jacob', 'Inter-institutional Grad Program on Bioinformatics, University of São Paulo, Rua do Matão, tv. 14, no. 101, sala 137, São Paulo, 05508-090, Brazil. machadodj@usp.br.', 'Brazil', 16, 'Biological Evolution|Computer Simulation|Humans|Phylogeny|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1186/s12859-015-0642-9', '26130249', 'The number and size of tree topologies that are being compared by phylogenetic systematists is increasing due to technological advancements in high-throughput DNA sequencing. However, we still lack tools to facilitate comparison among phylogenetic trees with a large number of terminals.'),
(96780, 2015, 'bmc', 'Comparing K-mer based methods for improved classification of 16S sequences', 'Vinje, Hilde|Liland, Kristian Hovde|Almøy, Trygve|Snipen, Lars', 'Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Oslo, N-1432 Ås, Norway. hilde.vinje@nmbu.no.|Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Oslo, N-1432 Ås, Norway. kristian.liland@nmbu.no.|Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Oslo, N-1432 Ås, Norway. trygve.almoy@nmbu.no.|Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Oslo, N-1432 Ås, Norway. lars.snipen@nmbu.no.', 'Norway|Norway|Norway|Norway', 0, 'Algorithms|Bacteria|Bayes Theorem|Biodiversity|Classification|RNA, Ribosomal, 16S', 'Journal Article', '10.1186/s12859-015-0647-4', '26130333', 'The need for precise and stable taxonomic classification is highly relevant in modern microbiology. Parallel to the explosion in the amount of sequence data accessible, there has also been a shift in focus for classification methods. Previously, alignment-based methods were the most applicable tools. Now, methods based on counting K-mers by sliding windows are the most interesting classification approach with respect to both speed and accuracy. Here, we present a systematic comparison on five different K-mer based classification methods for the 16S rRNA gene. The methods differ from each other both in data usage and modelling strategies. We have based our study on the commonly known and well-used naïve Bayes classifier from the RDP project, and four other methods were implemented and tested on two different data sets, on full-length sequences as well as fragments of typical read-length.'),
(96781, 2015, 'bmc', 'MetaDiff: differential isoform expression analysis using random-effects meta-regression', 'Jia, Cheng|Guan, Weihua|Yang, Amy|Xiao, Rui|Tang, W H Wilson|Moravec, Christine S|Margulies, Kenneth B|Cappola, Thomas P|Li, Mingyao|Li, Chun', 'Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA. jiacheng@mail.med.upenn.edu.|Division of Biostatistics, School of Public Health, University of Minnesota School of Public Health, Minneapolis, MN, 55455, USA. wguan@umn.edu.|Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA. sheepsheep.yang@gmail.com.|Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA. rxiao@mail.med.upenn.edu.|Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, OH, 44195, USA. TANGW@ccf.org.|Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, OH, 44195, USA. MORAVEC@ccf.org.|Cardiovascular Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA. Kenneth.Margulies@uphs.upenn.edu.|Cardiovascular Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA. thomas.cappola@uphs.upenn.edu.|Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA. mingyao@mail.med.upenn.edu.|Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, 44106, USA. cxl791@case.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Gene Expression Profiling|Gene Expression Regulation|Heart Failure|High-Throughput Nucleotide Sequencing|Humans|Models, Statistical|Protein Isoforms|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-015-0623-z', '26134005', 'RNA sequencing (RNA-Seq) allows an unbiased survey of the entire transcriptome in a high-throughput manner. A major application of RNA-Seq is to detect differential isoform expression across experimental conditions, which is of great biological interest due to its direct relevance to protein function and disease pathogenesis. Detection of differential isoform expression is challenging because of uncertainty in isoform expression estimation owing to ambiguous reads and variability in precision of the estimates across samples. It is desirable to have a method that can account for these issues and is flexible enough to allow adjustment for covariates.'),
(96782, 2015, 'bmc', 'GPCRtm: An amino acid substitution matrix for the transmembrane region of class A G Protein-Coupled Receptors', 'Rios, Santiago|Fernandez, Marta F|Caltabiano, Gianluigi|Campillo, Mercedes|Pardo, Leonardo|Gonzalez, Angel', 'Laboratori de Medicina Computacional, Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, 08193, Bellaterra, Barcelona, Spain.|Laboratori de Medicina Computacional, Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, 08193, Bellaterra, Barcelona, Spain.|Laboratori de Medicina Computacional, Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, 08193, Bellaterra, Barcelona, Spain.|Laboratori de Medicina Computacional, Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, 08193, Bellaterra, Barcelona, Spain.|Laboratori de Medicina Computacional, Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, 08193, Bellaterra, Barcelona, Spain.|Laboratori de Medicina Computacional, Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, 08193, Bellaterra, Barcelona, Spain. Angel.Gonzalez@uab.es.', 'Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Amino Acid Sequence|Amino Acid Substitution|Amino Acids|Computational Biology|Humans|Membrane Proteins|Models, Molecular|Molecular Sequence Data|Phylogeny|Receptors, G-Protein-Coupled|Rhodopsin|Sequence Alignment|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/s12859-015-0639-4', '26134144', 'Protein sequence alignments and database search methods use standard scoring matrices calculated from amino acid substitution frequencies in general sets of proteins. These general-purpose matrices are not optimal to align accurately sequences with marked compositional biases, such as hydrophobic transmembrane regions found in membrane proteins. In this work, an amino acid substitution matrix (GPCRtm) is calculated for the membrane spanning segments of the G protein-coupled receptor (GPCR) rhodopsin family, one of the largest transmembrane protein family in humans with great importance in health and disease.'),
(96783, 2015, 'bmc', 'Capturing coevolutionary signals inrepeat proteins', 'Espada, Rocío|Parra, R Gonzalo|Mora, Thierry|Walczak, Aleksandra M|Ferreiro, Diego U', 'Protein Physiology Lab, Dep de Química Biológica, Facultad de Ciencias Exactas y Naturales, UBA-CONICET-IQUIBICEN, Buenos Aires, Argentina.|Protein Physiology Lab, Dep de Química Biológica, Facultad de Ciencias Exactas y Naturales, UBA-CONICET-IQUIBICEN, Buenos Aires, Argentina.|Laboratoire de physique statistique, CNRS, UPMC and École normale supérieure, 24 rue Lhomond, Paris, 75005, France.|, 24 rue Lhomond, Paris, 75005, France.|Protein Physiology Lab, Dep de Química Biológica, Facultad de Ciencias Exactas y Naturales, UBA-CONICET-IQUIBICEN, Buenos Aires, Argentina.', 'Argentina|Argentina|France|France|Argentina', 2, 'Amino Acid Motifs|Amino Acids|Evolution, Molecular|Humans|Models, Molecular|Protein Folding|Protein Multimerization|Proteins', 'Journal Article', '10.1186/s12859-015-0648-3', '26134293', 'The analysis of correlations of amino acid occurrences in globular domains has led to the development of statistical tools that can identify native contacts - portions of the chains that come to close distance in folded structural ensembles. Here we introduce a direct coupling analysis for repeat proteins - natural systems for which the identification of folding domains remains challenging.'),
(96784, 2015, 'bmc', 'Topological characterization of neuronal arbor morphology via sequence representation: II--global alignment', 'Gillette, Todd A|Hosseini, Parsa|Ascoli, Giorgio A', 'Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study (MS2A1), George Mason University, Fairfax, VA, USA. todd.gillette@gmail.com.|Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study (MS2A1), George Mason University, Fairfax, VA, USA. phosseini13@gmail.com.|Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study (MS2A1), George Mason University, Fairfax, VA, USA. ascoli@gmu.edu.', 'United States|United States|United States', 0, 'Algorithms|Animals|Axons|Base Sequence|Dendrites|Drosophila|Mice|Molecular Sequence Data|Neurons|Phylogeny|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1186/s12859-015-0605-1', '26141505', 'The increasing abundance of neuromorphological data provides both the opportunity and the challenge to compare massive numbers of neurons from a wide diversity of sources efficiently and effectively. We implemented a modified global alignment algorithm representing axonal and dendritic bifurcations as strings of characters. Sequence alignment quantifies neuronal similarity by identifying branch-level correspondences between trees.'),
(96785, 2015, 'bmc', 'PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides', 'Islam, S M Ashiqul|Sajed, Tanvir|Kearney, Christopher Michel|Baker, Erich J', 'Institute of Biomedical Studies, Baylor University, Waco, TX, USA. S_Islam@Baylor.edu.|Department of Computer Science, University of Alberta, Edmonton, AB, Canada. Tsajed@ualberta.ca.|Institute of Biomedical Studies, Baylor University, Waco, TX, USA. Chris_Kearney@Baylor.edu.|Institute of Biomedical Studies, Baylor University, Waco, TX, USA. Erich_Baker@Baylor.edu.', 'United States|Canada|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Animals|Cystine|Disulfides|Models, Statistical|Molecular Sequence Data|Peptide Fragments|Support Vector Machine', 'Journal Article', '10.1186/s12859-015-0633-x', '26142484', 'Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation. Their effective interstitial and macro-environmental use requires energetic and structural stability. One successful group of these peptides includes a tri-disulfide domain arrangement that offers toxicity and high stability. Sequential tri-disulfide connectivity variants create highly compact disulfide folds capable of withstanding a variety of environmental stresses. Their combination of toxicity and stability make these peptides remarkably valuable for their potential as bio-insecticides, antimicrobial peptides and peptide drug candidates. However, the wide sequence variation, sources and modalities of group members impose serious limitations on our ability to rapidly identify potential members. As a result, there is a need for automated high-throughput member classification approaches that leverage their demonstrated tertiary and functional homology.'),
(96786, 2015, 'bmc', 'The UniFrac significance test is sensitive to tree topology', 'Lozupone, Catherine A|Knight, Rob', 'Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, 80045, USA. Catherine.Lozupone@ucdenver.edu.|Departments of Pediatrics and Computer Science & Engineering, University of California San Diego, La Jolla, CA, 92093, USA. robknight@ucsd.edu.', 'United States|United States', 10, 'Bacteria|Computational Biology|Metagenome|Microbiology|Phylogeny|Probability', 'Journal Article', '10.1186/s12859-015-0640-y', '26150095', 'Long et al. (BMC Bioinformatics 2014, 15(1):278) describe a discrepancy in using UniFrac to assess statistical significance of community differences. Specifically, they find that weighted UniFrac results differ between input trees where (a) replicate sequences each have their own tip, or (b) all replicates are assigned to one tip with an associated count. We argue that these are two distinct cases that differ in the probability distribution on which the statistical test is based, because of the differences in tree topology. Further study is needed to understand which randomization procedure best detects different aspects of community dissimilarities. '),
(96787, 2015, 'bmc', 'VISA--Vector Integration Site Analysis server: a web-based server to rapidly identify retroviral integration sites from next-generation sequencing', 'Hocum, Jonah D|Battrell, Logan R|Maynard, Ryan|Adair, Jennifer E|Beard, Brian C|Rawlings, David J|Kiem, Hans-Peter|Miller, Daniel G|Trobridge, Grant D', 'Department of Pharmaceutical Sciences, Washington State University, Spokane, WA, 99210, USA. jhocum@wsu.edu.|Department of Pharmaceutical Sciences, Washington State University, Spokane, WA, 99210, USA. logan_battrell@wsu.edu.|Department of Pharmaceutical Sciences, Washington State University, Spokane, WA, 99210, USA. ryan.maynard@wsu.edu.|Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA. jadair@fhcrc.org.|Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA. bbeard@fhcrc.org.|Department of Pediatrics, University of Washington, Seattle, WA, 98195, USA. drawling@uw.edu.|Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA. hkiem@fhcrc.org.|Department of Pediatrics, University of Washington, Seattle, WA, 98195, USA. dgmiller@uw.edu.|Department of Pharmaceutical Sciences, Washington State University, Spokane, WA, 99210, USA. grant.trobridge@wsu.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 28, 'Genetic Vectors|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Internet|Retroviridae|Software|Virus Integration', 'Journal Article', '10.1186/s12859-015-0653-6', '26150117', 'Analyzing the integration profile of retroviral vectors is a vital step in determining their potential genotoxic effects and developing safer vectors for therapeutic use. Identifying retroviral vector integration sites is also important for retroviral mutagenesis screens.'),
(96788, 2015, 'bmc', 'Deciding when to stop: efficient experimentation to learn to predict drug-target interactions', 'Temerinac-Ott, Maja|Naik, Armaghan W|Murphy, Robert F', 'Freiburg Institute for Advanced Studies, University of Freiburg, Freiburg, Germany. temerina@frias.uni-freiburg.de.|Computational Biology Department, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, 15213, PA, USA. rumi@cmu.edu.|Freiburg Institute for Advanced Studies, University of Freiburg, Freiburg, Germany. murphy@cmu.edu.', 'Germany|United States|Germany', 0, 'Algorithms|Drug Discovery|Humans|Models, Biological|Models, Statistical|Pharmaceutical Preparations|Proteins|Research Design', 'Journal Article', '10.1186/s12859-015-0650-9', '26153434', 'Active learning is a powerful tool for guiding an experimentation process. Instead of doing all possible experiments in a given domain, active learning can be used to pick the experiments that will add the most knowledge to the current model. Especially, for drug discovery and development, active learning has been shown to reduce the number of experiments needed to obtain high-confidence predictions. However, in practice, it is crucial to have a method to evaluate the quality of the current predictions and decide when to stop the experimentation process. Only by applying reliable stopping criteria to active learning can time and costs in the experimental process actually be saved.'),
(96789, 2015, 'bmc', 'Machine learning approach for pooled DNA sample calibration', 'Hellicar, Andrew D|Rahman, Ashfaqur|Smith, Daniel V|Henshall, John M', 'CSIRO Computational Informatics, Castray Esplanade, Hobart, Australia. andrew.hellicar@csiro.au.|CSIRO Computational Informatics, Castray Esplanade, Hobart, Australia. ashfaqur.rahman@csiro.au.|CSIRO Computational Informatics, Castray Esplanade, Hobart, Australia. daniel.v.smith@csiro.au.|CSIRO Agriculture Flagship, Armidale, Australia. john.henshall@csiro.au.', 'Australia|Australia|Australia|Australia', 0, 'Calibration|Cluster Analysis|DNA|Gene Frequency|Genomics|Genotype|Humans|Machine Learning|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-015-0593-1', '26156142', 'Despite ongoing reduction in genotyping costs, genomic studies involving large numbers of species with low economic value (such as Black Tiger prawns) remain cost prohibitive. In this scenario DNA pooling is an attractive option to reduce genotyping costs. However, genotyping of pooled samples comprising DNA from many individuals is challenging due to the presence of errors that exceed the allele frequency quantisation size and therefore cannot be simply corrected by clustering techniques. The solution to the calibration problem is a correction to the allele frequency to mitigate errors incurred in the measurement process. We highlight the limitations of the existing calibration solutions such as the fact they impose assumptions on the variation between allele frequencies 0, 0.5, and 1.0, and address a limited set of error types. We propose a novel machine learning method to address the limitations identified.'),
(96790, 2015, 'bmc', 'MONALISA for stochastic simulations of Petri net models of biochemical systems', 'Balazki, Pavel|Lindauer, Klaus|Einloft, Jens|Ackermann, Jörg|Koch, Ina', 'Department of Molecular Bioinformatics, Institute of Computer Science, Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Straße 11-15, Frankfurt am Main, 60325, Germany. Pavel.Balazki@gmail.com.|Sanofi Aventis Deutschland GmbH, Industriepark Höchst H831, Frankfurt am Main, 65926, Germany. Klaus.Lindauer@sanofi-aventis.com.|Department of Molecular Bioinformatics, Institute of Computer Science, Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Straße 11-15, Frankfurt am Main, 60325, Germany. Einloft@bioinformatik.uni-frankfurt.de.|Department of Molecular Bioinformatics, Institute of Computer Science, Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Straße 11-15, Frankfurt am Main, 60325, Germany. J.Ackermann@bioinformatik.uni-frankfurt.de.|Department of Molecular Bioinformatics, Institute of Computer Science, Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Straße 11-15, Frankfurt am Main, 60325, Germany. Ina.koch@bioinformatik.uni-frankfurt.de.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Computer Simulation|Gene Regulatory Networks|Humans|Kinetics|Models, Theoretical|Receptor, Insulin|Signal Transduction|Software|Stochastic Processes|Systems Biology', 'Journal Article', '10.1186/s12859-015-0596-y', '26156221', 'The concept of Petri nets (PN) is widely used in systems biology and allows modeling of complex biochemical systems like metabolic systems, signal transduction pathways, and gene expression networks. In particular, PN allows the topological analysis based on structural properties, which is important and useful when quantitative (kinetic) data are incomplete or unknown. Knowing the kinetic parameters, the simulation of time evolution of such models can help to study the dynamic behavior of the underlying system. If the number of involved entities (molecules) is low, a stochastic simulation should be preferred against the classical deterministic approach of solving ordinary differential equations. The Stochastic Simulation Algorithm (SSA) is a common method for such simulations. The combination of the qualitative and semi-quantitative PN modeling and stochastic analysis techniques provides a valuable approach in the field of systems biology.'),
(96791, 2015, 'bmc', 'Topological characterization of neuronal arbor morphology via sequence representation: I--motif analysis', 'Gillette, Todd A|Ascoli, Giorgio A', 'Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study (MS2A1), George Mason University, Fairfax, VA, USA. todd.gillette@gmail.com.|Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study (MS2A1), George Mason University, Fairfax, VA, USA. ascoli@gmu.edu.', 'United States|United States', 0, 'Amino Acid Motifs|Animals|Axons|Base Sequence|Dendrites|Mice|Molecular Sequence Data|Neurites|Neurons|Pyramidal Tracts', 'Journal Article', '10.1186/s12859-015-0604-2', '26156313', 'The morphology of neurons offers many insights into developmental processes and signal processing. Numerous reports have focused on metrics at the level of individual branches or whole arbors, however, no studies have attempted to quantify repeated morphological patterns within neuronal trees. We introduce a novel sequential encoding of neurite branching suitable to explore topological patterns.'),
(96792, 2015, 'bmc', 'An evaluation of statistical methods for DNA methylation microarray data analysis', 'Li, Dongmei|Xie, Zidian|Pape, Marc Le|Dye, Timothy', 'Clinical and Translational Science Institute, School of Medicine and Dentistry, University of Rochester, 265 Crittenden Boulevard CU 420708, Rochester, 14642, NY, USA. dongmei_li@urmc.rochester.edu.|Department of Biostatistics and Computational Biology, University of Rochester, 265 Crittenden Boulevard CU 420708, Rochester, 14642, NY, USA. xzd482@yahoo.com.|John A. Burns School of Medicine, University of Hawaii, 651 Ilalo Street 101, Honolulu, 96813, HI, USA. lepape@hawaii.edu.|Department of Obstetrics and Gynecology, University of Rochester, 500 Red Creek Drive Suite 220, Rochester, 14623, NY, USA. Tim_Dye@URMC.Rochester.edu.', 'United States|United States|United States|United States', 20, 'Arthritis, Rheumatoid|Bayes Theorem|Computer Simulation|DNA Methylation|Female|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Ovarian Neoplasms|Sample Size|Statistics, Nonparametric', 'Evaluation Studies', '10.1186/s12859-015-0641-x', '26156501', 'DNA methylation offers an excellent example for elucidating how epigenetic information affects gene expression. xcexb2 values and M values are commonly used to quantify DNA methylation. Statistical methods applicable to DNA methylation data analysis span a number of approaches such as Wilcoxon rank sum test, t-test, Kolmogorov-Smirnov test, permutation test, empirical Bayes method, and bump hunting method. Nonetheless, selection of an optimal statistical method can be challenging when different methods generate inconsistent results from the same data set.'),
(96793, 2015, 'bmc', 'Optimal combination of feature selection and classification via local hyperplane based learning strategy', 'Cheng, Xiaoping|Cai, Hongmin|Zhang, Yue|Xu, Bo|Su, Weifeng', 'School of Computer Science& Engineering, South China University of Technology, Guangdong, China. c.xp01@mail.scut.edu.cn.|School of Computer Science& Engineering, South China University of Technology, Guangdong, China. hmcai@scut.edu.cn.|Electrical And Information College of Jinan University, Guangdong, China. yuezhang@comp.hkbu.edu.hk.|School of Computer Science& Engineering, South China University of Technology, Guangdong, China. xubo_2009@163.com.|BNU-HKBU United International College, Hong Kong, China. wfsu@uic.edu.hk.', 'China|China|China|China|China', 0, 'Cluster Analysis|Discriminant Analysis|Gene Expression Profiling|Gene Regulatory Networks|Humans|Machine Learning|Neoplasms|Support Vector Machine', 'Journal Article', '10.1186/s12859-015-0629-6', '26159165', 'Classifying cancers by gene selection is among the most important and challenging procedures in biomedicine. A major challenge is to design an effective method that eliminates irrelevant, redundant, or noisy genes from the classification, while retaining all of the highly discriminative genes.'),
(96794, 2015, 'bmc', 'Evaluation of BLAST-based edge-weighting metrics used for homology inference with the Markov Clustering algorithm', 'Gibbons, Theodore R|Mount, Stephen M|Cooper, Endymion D|Delwiche, Charles F', 'Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Baltimore, 20742, Maryland. trgibbons@gmail.com.|Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Baltimore, 20742, Maryland. smount@umd.edu.|Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Baltimore, 20742, Maryland. endymion.dante.cooper@gmail.com.|Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Baltimore, 20742, Maryland. delwiche@umd.edu.', '-|-|-|-', 8, 'Algorithms|Amino Acid Sequence|Cluster Analysis|Computational Biology|Databases, Factual|Humans|Markov Chains|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/s12859-015-0625-x', '26160651', 'Clustering protein sequences according to inferred homology is a fundamental step in the analysis of many large data sets. Since the publication of the Markov Clustering (MCL) algorithm in 2002, it has been the centerpiece of several popular applications. Each of these approaches generates an undirected graph that represents sequences as nodes connected to each other by edges weighted with a BLAST-based metric. MCL is then used to infer clusters of homologous proteins by analyzing these graphs. The various approaches differ only by how they weight the edges, yet there has been very little direct examination of the relative performance of alternative edge-weighting metrics. This study compares the performance of four BLAST-based edge-weighting metrics: the bit score, bit score ratio (BSR), bit score over anchored length (BAL), and negative common log of the expectation value (NLE). Performance is tested using the Extended CEGMA KOGs (ECK) database, which we introduce here.'),
(96795, 2015, 'bmc', 'BSPAT: a fast online tool for DNA methylation co-occurrence pattern analysis based on high-throughput bisulfite sequencing data', 'Hu, Ke|Ting, Angela H|Li, Jing', 'Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, 44106, Ohio, USA. jingli@cwru.edu.|Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, 44195, Ohio, USA. jingli@cwru.edu.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, 44106, Ohio, USA. jingli@cwru.edu.', 'United States|United States|United States', 0, 'Base Sequence|Cell Line, Tumor|DNA Methylation|Genomics|High-Throughput Nucleotide Sequencing|Humans|Male|Molecular Sequence Data|Prostatic Neoplasms|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|Sulfites', 'Journal Article', '10.1186/s12859-015-0649-2', '26163275', 'Bisulfite sequencing is one of the most widely used technologies in analyzing DNA methylation patterns, which are important in understanding and characterizing the mechanism of DNA methylation and its functions in disease development. Efficient and user-friendly tools are critical in carrying out such analysis on high-throughput bisulfite sequencing data. However, existing tools are either not scalable well, or inadequate in providing visualization and other desirable functionalities.'),
(96796, 2015, 'bmc', 'Epitopemap: a web application for integrated whole proteome epitope prediction', 'Farrell, Damien|Gordon, Stephen V', 'School of Veterinary Medicine, University College Dublin, Belfield, Dublin, Ireland. farrell.damien@gmail.com.|School of Veterinary Medicine, University College Dublin, Belfield, Dublin, Ireland. stephen.gordon@ucd.ie.', 'Ireland|Ireland', 1, 'Databases, Protein|Epitope Mapping|Epitopes, T-Lymphocyte|Histocompatibility Antigens Class I|Humans|Immunogenetic Phenomena|Protein Binding|Proteins|Proteome|Software', 'Journal Article', '10.1186/s12859-015-0659-0', '26169908', 'Predictions of MHC binding affinity are commonly used in immunoinformatics for T cell epitope prediction. There are multiple available methods, some of which provide web access. However there is currently no convenient way to access the results from multiple methods at the same time or to execute predictions for an entire proteome at once.'),
(96797, 2015, 'bmc', 'groHMM: a computational tool for identifying unannotated and cell type-specific transcription units from global run-on sequencing data', 'Chae, Minho|Danko, Charles G|Kraus, W Lee', 'Laboratory of Signaling and Gene Regulation, Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, 75390, Dallas, TX, USA. minho.chae@utsouthwestern.edu.|Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, 14853, Ithaca, NY, USA. dankoc@gmail.com.|Laboratory of Signaling and Gene Regulation, Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, 75390, Dallas, TX, USA. lee.kraus@utsouthwestern.edu.', 'United States|United States|United States', 0, 'Animals|Cell Line|Computational Biology|High-Throughput Nucleotide Sequencing|Humans|MCF-7 Cells|Markov Chains|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1186/s12859-015-0656-3', '26173492', 'Global run-on coupled with deep sequencing (GRO-seq) provides extensive information on the location and function of coding and non-coding transcripts, including primary microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and enhancer RNAs (eRNAs), as well as yet undiscovered classes of transcripts. However, few computational tools tailored toward this new type of sequencing data are available, limiting the applicability of GRO-seq data for identifying novel transcription units.'),
(96798, 2015, 'bmc', 'Joint haplotype assembly and genotype calling via sequential Monte Carlo algorithm', 'Ahn, Soyeon|Vikalo, Haris', 'Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, 78712, Texas, USA. soyeon.ahn@utexas.edu.|Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, 78712, Texas, USA. hvikalo@ece.utexas.edu.', 'United States|United States', 1, 'Algorithms|Animals|Genome|Genotype|Haplotypes|High-Throughput Nucleotide Sequencing|Humans|Internet|Monte Carlo Method|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0651-8', '26178880', 'Genetic variations predispose individuals to hereditary diseases, play important role in the development of complex diseases, and impact drug metabolism. The full information about the DNA variations in the genome of an individual is given by haplotypes, the ordered lists of single nucleotide polymorphisms (SNPs) located on chromosomes. Affordable high-throughput DNA sequencing technologies enable routine acquisition of data needed for the assembly of single individual haplotypes. However, state-of-the-art high-throughput sequencing platforms generate data that is erroneous, which induces uncertainty in the SNP and genotype calling procedures and, ultimately, adversely affect the accuracy of haplotyping. When inferring haplotype phase information, the vast majority of the existing techniques for haplotype assembly assume that the genotype information is correct. This motivates the development of methods capable of joint genotype calling and haplotype assembly.'),
(96799, 2015, 'bmc', 'QoRTs: a comprehensive toolset for quality control and data processing of RNA-Seq experiments', 'Hartley, Stephen W|Mullikin, James C', 'Comparative Genomics Analysis Unit, Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA. stephen.hartley@nih.gov.|Comparative Genomics Analysis Unit, Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA.', 'United States|United States', 0, 'High-Throughput Nucleotide Sequencing|Internet|Quality Control|RNA|RNA Splicing|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-015-0670-5', '26187896', 'High-throughput next-generation RNA sequencing has matured into a viable and powerful method for detecting variations in transcript expression and regulation. Proactive quality control is of critical importance as unanticipated biases, artifacts, or errors can potentially drive false associations and lead to flawed results.'),
(96800, 2015, 'bmc', 'Distribution Analyzer, a methodology for identifying and clustering outlier conditions from single-cell distributions, and its application to a Nanog reporter RNAi screen', 'Gingold, Julian A|Coakley, Ed S|Su, Jie|Lee, Dung-Fang|Lau, Zerlina|Zhou, Hongwei|Felsenfeld, Dan P|Schaniel, Christoph|Lemischka, Ihor R', 'The Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. julian.gingold@mssm.edu.|Program in Applied Mathematics, Yale University, New Haven, CT, 06511, USA. ed.s.coakley@gmail.com.|The Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. sujiepkubio@gmail.com.|The Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. dung-fang.lee@mssm.edu.|Integrated Screening Core, Experimental Therapeutics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. wingshan.lau@mssm.edu.|The Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. hongwei.zhou@mssm.edu.|The Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. dan@felsenfeld.com.|The Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. christoph.schaniel@mssm.edu.|The Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. ihor.lemischka@mssm.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 4, 'Animals|Cell Differentiation|Cell Line|Cluster Analysis|Computational Biology|Genes, Reporter|Genome|Homeodomain Proteins|Mediator Complex|Mice|Mouse Embryonic Stem Cells|Nanog Homeobox Protein|Promoter Regions, Genetic|RNA Interference|RNA, Small Interfering|Tretinoin', 'Journal Article', '10.1186/s12859-015-0636-7', '26198214', 'Chemical or small interfering (si) RNA screens measure the effects of many independent experimental conditions, each applied to a population of cells (e.g., all of the cells in a well). High-content screens permit a readout (e.g., fluorescence, luminescence, cell morphology) from each cell in the population. Most analysis approaches compare the average effect on each population, precluding identification of outliers that affect the distribution of the reporter in the population but not its average. Other approaches only measure changes to the distribution with a single parameter, precluding accurate distinction and clustering of interesting outlier distributions.'),
(96801, 2015, 'bmc', 'Detection and categorization of bacteria habitats using shallow linguistic analysis', 'Karadeniz, xc4xb0lknur|xc3x96zgxc3xbcr, Arzucan', 'The Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. ihor.lemischka@mssm.edu.|The Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. ihor.lemischka@mssm.edu.', 'United States|United States', 4, 'Bacteria|Ecosystem|Environmental Microbiology|Humans|Linguistics|Machine Learning|Natural Language Processing|Software', 'Journal Article', '10.1186/1471-2105-16-S10-S5', '26201262', 'Information regarding bacteria biotopes is important for several research areas including health sciences, microbiology, and food processing and preservation. One of the challenges for scientists in these domains is the huge amount of information buried in the text of electronic resources. Developing methods to automatically extract bacteria habitat relations from the text of these electronic resources is crucial for facilitating research in these areas.'),
(96802, 2015, 'bmc', 'The contribution of co-reference resolution to supervised relation detection between bacteria and biotopes entities', 'Lavergne, Thomas|Grouin, Cyril|Zweigenbaum, Pierre', 'The Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. ihor.lemischka@mssm.edu.|The Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. ihor.lemischka@mssm.edu.|The Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. ihor.lemischka@mssm.edu.', 'United States|United States|United States', 0, 'Bacteria|Computational Biology|Data Mining|Ecosystem|Environmental Microbiology|Humans|Information Storage and Retrieval|Natural Language Processing|Semantics', 'Journal Article', '10.1186/1471-2105-16-S10-S6', '26201352', 'The acquisition of knowledge about relations between bacteria and their locations (habitats and geographical locations) in short texts about bacteria, as defined in the BioNLP-ST 2013 Bacteria Biotope task, depends on the detection of co-reference links between mentions of entities of each of these three types. To our knowledge, no participant in this task has investigated this aspect of the situation. The present work specifically addresses issues raised by this situation: (i) how to detect these co-reference links and associated co-reference chains, (ii) how to use them to prepare positive and negative examples to train a supervised system for the detection of relations between entity mentions, (iii) what context around which entity mentions contributes to relation detection when co-reference chains are provided.'),
(96803, 2015, 'bmc', 'Adaptable, high recall, event extraction system with minimal configuration', 'Miwa, Makoto|Ananiadou, Sophia', 'The Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. ihor.lemischka@mssm.edu.|The Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. ihor.lemischka@mssm.edu.', 'United States|United States', 0, 'Gene Regulatory Networks|Genes|Humans|Information Storage and Retrieval|Knowledge Bases|Models, Theoretical|Natural Language Processing|Neoplasms', 'Journal Article', '10.1186/1471-2105-16-S10-S7', '26201408', 'Biomedical event extraction has been a major focus of biomedical natural language processing (BioNLP) research since the first BioNLP shared task was held in 2009. Accordingly, a large number of event extraction systems have been developed. Most such systems, however, have been developed for specific tasks and/or incorporated task specific settings, making their application to new corpora and tasks problematic without modification of the systems themselves. There is thus a need for event extraction systems that can achieve high levels of accuracy when applied to corpora in new domains, without the need for exhaustive tuning or modification, whilst retaining competitive levels of performance.'),
(96804, 2015, 'bmc', 'Extracting biomedical events from pairs of text entities', 'Liu, Xiao|Bordes, Antoine|Grandvalet, Yves', 'The Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. ihor.lemischka@mssm.edu.|The Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. ihor.lemischka@mssm.edu.|The Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. ihor.lemischka@mssm.edu.', 'United States|United States|United States', 5, 'Biomedical Research|Computational Biology|Data Mining|Databases, Factual|Humans|Knowledge Bases|Models, Theoretical|Natural Language Processing|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-16-S10-S8', '26201478', 'Huge amounts of electronic biomedical documents, such as molecular biology reports or genomic papers are generated daily. Nowadays, these documents are mainly available in the form of unstructured free texts, which require heavy processing for their registration into organized databases. This organization is instrumental for information retrieval, enabling to answer the advanced queries of researchers and practitioners in biology, medicine, and related fields. Hence, the massive data flow calls for efficient automatic methods of text-mining that extract high-level information, such as biomedical events, from biomedical text. The usual computational tools of Natural Language Processing cannot be readily applied to extract these biomedical events, due to the peculiarities of the domain. Indeed, biomedical documents contain highly domain-specific jargon and syntax. These documents also describe distinctive dependencies, making text-mining in molecular biology a specific discipline.'),
(96805, 2015, 'bmc', 'Knowledge transfer via classification rules using functional mapping for integrative modeling of gene expression data', 'Ogoe, Henry A|Visweswaran, Shyam|Lu, Xinghua|Gopalakrishnan, Vanathi', 'Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. hao9@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. shv3@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. xinghua@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.', 'United States|United States|United States|United States', 0, 'Algorithms|Biomarkers|Databases, Factual|Gene Expression|Humans|Models, Genetic|Neoplasms', 'Journal Article', '10.1186/s12859-015-0643-8', '26202217', 'Most transcriptomic data from microarrays are generated from small sample sizes compared to the large number of measured biomarkers, making it very difficult to build accurate and generalizable disease state classification models. Integrating information from different, but related, transcriptomic data may help build better classification models. However, most proposed methods for integrative analysis of transcriptomic data cannot incorporate domain knowledge, which can improve model performance. To this end, we have developed a methodology that leverages transfer rule learning and functional modules, which we call TRL-FM, to capture and abstract domain knowledge in the form of classification rules to facilitate integrative modeling of multiple gene expression data. TRL-FM is an extension of the transfer rule learner (TRL) that we developed previously. The goal of this study was to test our hypothesis that an integrative model obtained via the TRL-FM approach outperforms traditional models based on single gene expression data sources.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96806, 2015, 'bmc', 'Overview of the gene regulation network and the bacteria biotope tasks in BioNLP13 shared task', 'Bossy, Robert|Golik, Wiktoria|Ratkovic, Zorana|Valsamou, Dialekti|Bessières, Philippe|Nédellec, Claire', 'Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.', 'United States|United States|United States|United States|United States|United States', 12, 'Bacteria|Environmental Microbiology|Epistasis, Genetic|Gene Regulatory Networks|Genes, Bacterial|Humans|Information Storage and Retrieval|Natural Language Processing', 'Journal Article', '10.1186/1471-2105-16-S10-S1', '26202448', 'We present the two Bacteria Track tasks of BioNLP 2013 Shared Task (ST): Gene Regulation Network (GRN) and Bacteria Biotope (BB). These tasks were previously introduced in the 2011 BioNLP-ST Bacteria Track as Bacteria Gene Interaction (BI) and Bacteria Biotope (BB). The Bacteria Track was motivated by a need to develop specific BioNLP tools for fine-grained event extraction in bacteria biology. The 2013 tasks expand on the 2011 version by better addressing the biological knowledge modeling needs. New evaluation metrics were designed for the new goals. Moving beyond a list of gene interactions, the goal of the GRN task is to build a gene regulation network from the extracted gene interactions. BB13 is dedicated to the extraction of bacteria biotopes, i.e. bacterial environmental information, as was BB11. BB13 extends the typology of BB11 to a large diversity of biotopes, as defined by the OntoBiotope ontology. The detection of entities and events is tackled by distinct subtasks in order to measure the progress achieved by the participant systems since 2011.'),
(96807, 2015, 'bmc', 'Overview of the Cancer Genetics and Pathway Curation tasks of BioNLP Shared Task 2013', 'Pyysalo, Sampo|Ohta, Tomoko|Rak, Rafal|Rowley, Andrew|Chun, Hong-Woo|Jung, Sung-Jae|Choi, Sung-Pil|Tsujii, Junichi|Ananiadou, Sophia', 'Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Gene Regulatory Networks|Genes|Humans|Information Storage and Retrieval|Knowledge Bases|Models, Theoretical|Natural Language Processing|Neoplasms', 'Journal Article', '10.1186/1471-2105-16-S10-S2', '26202570', 'Since their introduction in 2009, the BioNLP Shared Task events have been instrumental in advancing the development of methods and resources for the automatic extraction of information from the biomedical literature. In this paper, we present the Cancer Genetics (CG) and Pathway Curation (PC) tasks, two event extraction tasks introduced in the BioNLP Shared Task 2013. The CG task focuses on cancer, emphasizing the extraction of physiological and pathological processes at various levels of biological organization, and the PC task targets reactions relevant to the development of biomolecular pathway models, defining its extraction targets on the basis of established pathway representations and ontologies.'),
(96808, 2015, 'bmc', 'Extending the evaluation of Genia Event task toward knowledge base construction and comparison to Gene Regulation Ontology task', 'Kim, Jin-Dong|Kim, Jung-Jae|Han, Xu|Rebholz-Schuhmann, Dietrich', 'Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.', 'United States|United States|United States|United States', 0, 'Databases, Factual|Gene Expression Regulation|Gene Ontology|Gene Regulatory Networks|Genes|Humans|Information Storage and Retrieval|Knowledge Bases|Natural Language Processing|Semantics', 'Evaluation Studies', '10.1186/1471-2105-16-S10-S3', '26202680', 'The third edition of the BioNLP Shared Task was held with the grand theme knowledge base construction (KB). The Genia Event (GE) task was re-designed and implemented in light of this theme. For its final report, the participating systems were evaluated from a perspective of annotation. To further explore the grand theme, we extended the evaluation from a perspective of KB construction. Also, the Gene Regulation Ontology (GRO) task was newly introduced in the third edition. The final evaluation of the participating systems resulted in relatively low performance. The reason was attributed to the large size and complex semantic representation of the ontology. To investigate potential benefits of resource exchange between the presumably similar tasks, we measured the overlap between the datasets of the two tasks, and tested whether the dataset for one task can be used to enhance performance on the other.'),
(96809, 2015, 'bmc', 'Semi-supervised Learning for the BioNLP Gene Regulation Network', 'Provoost, Thomas|Moens, Marie-Francine', 'Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.', 'United States|United States', 0, 'Computational Biology|Data Mining|Gene Regulatory Networks|Genes|Humans|Information Storage and Retrieval|Supervised Machine Learning', 'Journal Article', '10.1186/1471-2105-16-S10-S4', '26202824', 'The BioNLP Gene Regulation Task has attracted a diverse collection of submissions showcasing state-of-the-art systems. However, a principal challenge remains in obtaining a significant amount of recall. We argue that this is an important quality for Information Extraction tasks in this field. We propose a semi-supervised framework, leveraging a large corpus of unannotated data available to us. In this framework, the annotated data is used to find plausible candidates for positive data points, which are included in the machine learning process. As this is a method principally designed for gaining recall, we further explore additional methods to improve precision on top of this. These are: weighted regularisation in the SVM framework, and filtering out unlabelled examples based on a probabilistic rule-finding method. The latter method also allows us to add candidates for negatives from unlabelled data, a method not viable in the unfiltered approach.'),
(96810, 2015, 'bmc', 'Red: an intelligent, rapid, accurate tool for detecting repeats de-novo on the genomic scale', 'Girgis, Hani Z', 'Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, 20894, MD, USA. girgishz@mail.nih.gov.', 'United States', 0, 'DNA|Genome, Bacterial|Genome, Human|Genome, Plant|Genomics|Humans|Interspersed Repetitive Sequences|Markov Chains|Plants|Software', 'Journal Article', '10.1186/s12859-015-0654-5', '26206263', 'With rapid advancements in technology, the sequences of thousands of species genomes are becoming available. Within the sequences are repeats that comprise significant portions of genomes. Successful annotations thus require accurate discovery of repeats. As species-specific elements, repeats in newly sequenced genomes are likely to be unknown. Therefore, annotating newly sequenced genomes requires tools to discover repeats de-novo. However, the currently available de-novo tools have limitations concerning the size of the input sequence, ease of use, sensitivities to major types of repeats, consistency of performance, speed, and false positive rate.'),
(96811, 2015, 'bmc', 'GESPA: classifying nsSNPs to predict disease association', 'Khurana, Jay K|Reeder, Jay E|Shrimpton, Antony E|Thakar, Juilee', 'Department of Urology, SUNY Upstate Medical University, Syracuse, NY, USA. jaykk128@yahoo.com.|Department of Obstetrics and Gynecology, University of Rochester, Rochester, NY, USA. jay_reeder@urmc.rochester.edu.|Department of Pathology, SUNY Upstate Medical University, Syracuse, NY, USA. shrimpta@upstate.edu.|Department of Microbiology and Immunology, University of Rochester, Rochester, NY, USA. Juilee_Thakar@urmc.rochester.edu.', 'United States|United States|United States|United States', 3, 'DNA|Genomics|High-Throughput Nucleotide Sequencing|Humans|Internet|Phenotype|Polymorphism, Single Nucleotide|Proteins|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-015-0673-2', '26206375', 'Non-synonymous single nucleotide polymorphisms (nsSNPs) are the most common DNA sequence variation associated with disease in humans. Thus determining the clinical significance of each nsSNP is of great importance. Potential detrimental nsSNPs may be identified by genetic association studies or by functional analysis in the laboratory, both of which are expensive and time consuming. Existing computational methods lack accuracy and features to facilitate nsSNP classification for clinical use. We developed the GESPA (GEnomic Single nucleotide Polymorphism Analyzer) program to predict the pathogenicity and disease phenotype of nsSNPs.'),
(96812, 2015, 'bmc', 'Object-based representation and analysis of light and electron microscopic volume data using Blender', 'Asadulina, Albina|Conzelmann, Markus|Williams, Elizabeth A|Panzera, Aurora|Jékely, Gáspár', 'Max Planck Institute for Developmental Biology, Spemannstrasse 35, 72076, Tübingen, Germany. albina.asadulina@tuebingen.mpg.de.|Max Planck Institute for Developmental Biology, Spemannstrasse 35, 72076, Tübingen, Germany. markus.conzelmann@tuebingen.mpg.de.|Max Planck Institute for Developmental Biology, Spemannstrasse 35, 72076, Tübingen, Germany. elizabeth.williams@tuebingen.mpg.de.|Max Planck Institute for Developmental Biology, Spemannstrasse 35, 72076, Tübingen, Germany. aurora.panzera@tuebingen.mpg.de.|Max Planck Institute for Developmental Biology, Spemannstrasse 35, 72076, Tübingen, Germany. gaspar.jekely@tuebingen.mpg.de.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Animals|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Internet|Larva|Microscopy, Confocal|Microscopy, Electron|Models, Biological|Polychaeta|RNA|Transcriptome|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0652-7', '26208945', 'Rapid improvements in light and electron microscopy imaging techniques and the development of 3D anatomical atlases necessitate new approaches for the visualization and analysis of image data. Pixel-based representations of raw light microscopy data suffer from limitations in the number of channels that can be visualized simultaneously. Complex electron microscopic reconstructions from large tissue volumes are also challenging to visualize and analyze.'),
(96813, 2015, 'bmc', 'Sealer: a scalable gap-closing application for finishing draft genomes', 'Paulino, Daniel|Warren, René L|Vandervalk, Benjamin P|Raymond, Anthony|Jackman, Shaun D|Birol, Inanç', 'Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, V5Z 4S6, Canada. dpaulino@bcgsc.ca.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, V5Z 4S6, Canada. rwarren@bcgsc.ca.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, V5Z 4S6, Canada. benv@bcgsc.ca.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, V5Z 4S6, Canada. traymond@bcgsc.ca.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, V5Z 4S6, Canada. sjackman@bcgsc.ca.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, V5Z 4S6, Canada. ibirol@bcgsc.ca.', 'Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Algorithms|Computational Biology|Genome, Human|Genome, Plant|High-Throughput Nucleotide Sequencing|Humans|Internet|Pinaceae|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0663-4', '26209068', 'While next-generation sequencing technologies have made sequencing genomes faster and more affordable, deciphering the complete genome sequence of an organism remains a significant bioinformatics challenge, especially for large genomes. Low sequence coverage, repetitive elements and short read length make de novo genome assembly difficult, often resulting in sequence and/or fragment gaps - uncharacterized nucleotide (N) stretches of unknown or estimated lengths. Some of these gaps can be closed by re-processing latent information in the raw reads. Even though there are several tools for closing gaps, they do not easily scale up to processing billion base pair genomes.'),
(96814, 2015, 'bmc', 'Mapping side chain interactions at protein helix termini', 'Newell, Nicholas E', 'Independent researcher, Reading, MA, USA. nenewell@comcast.net.', 'United States', 0, 'Algorithms|Amino Acid Motifs|Cluster Analysis|Computational Biology|Hydrogen Bonding|Hydrophobic and Hydrophilic Interactions|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins|Static Electricity', 'Journal Article', '10.1186/s12859-015-0671-4', '26209176', 'Interactions that involve one or more amino acid side chains near the ends of protein helices stabilize helix termini and shape the geometry of the adjacent loops, making a substantial contribution to overall protein structure. Previous work has identified key helix-terminal motifs, such as Asx/ST N-caps, the capping box, and hydrophobic and electrostatic interactions, but important questions remain, including: 1) What loop backbone geometries are favoured by each motif? 2) To what extent are multi-amino acid motifs likely to represent genuine cooperative interactions? 3) Can new motifs be identified in a large, recent dataset using the latest bioinformatics tools?'),
(96815, 2015, 'bmc', 'Odintifier--A computational method for identifying insertions of organellar origin from modern and ancient high-throughput sequencing data based on haplotype phasing', 'Samaniego Castruita, Jose Alfredo|Zepeda Mendoza, Marie Lisandra|Barnett, Ross|Wales, Nathan|Gilbert, M Thomas P', 'Centre for GeoGenetics, The Natural History Museum of Denmark, University of Copenhagen, Øster Voldgade 5-7, Copenhagen, DK-1350, Denmark. jose.samaniego@snm.ku.dk.|Centre for GeoGenetics, The Natural History Museum of Denmark, University of Copenhagen, Øster Voldgade 5-7, Copenhagen, DK-1350, Denmark. lisandracady@gmail.com.|Centre for GeoGenetics, The Natural History Museum of Denmark, University of Copenhagen, Øster Voldgade 5-7, Copenhagen, DK-1350, Denmark. drrossbarnett@gmail.com.|Centre for GeoGenetics, The Natural History Museum of Denmark, University of Copenhagen, Øster Voldgade 5-7, Copenhagen, DK-1350, Denmark. wales.nathan@gmail.com.|Centre for GeoGenetics, The Natural History Museum of Denmark, University of Copenhagen, Øster Voldgade 5-7, Copenhagen, DK-1350, Denmark. mtpgilbert@gmail.com.', 'Denmark|Denmark|Denmark|Denmark|Denmark', 3, 'Ajuga|Algorithms|Animals|Computational Biology|DNA|Genome, Chloroplast|Genome, Mitochondrial|Genomics|Haplotypes|High-Throughput Nucleotide Sequencing|Lions|Mitochondria|Plastids|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Vitis', 'Journal Article', '10.1186/s12859-015-0682-1', '26216337', 'Cellular organelles with genomes of their own (e.g. plastids and mitochondria) can pass genetic sequences to other organellar genomes within the cell in many species across the eukaryote phylogeny. The extent of the occurrence of these organellar-derived inserted sequences (odins) is still unknown, but if not accounted for in genomic and phylogenetic studies, they can be a source of error. However, if correctly identified, these inserted sequences can be used for evolutionary and comparative genomic studies. Although such insertions can be detected using various laboratory and bioinformatic strategies, there is currently no straightforward way to apply them as a standard organellar genome assembly on next-generation sequencing data. Furthermore, most current methods for identification of such insertions are unsuitable for use on non-model organisms or ancient DNA datasets.'),
(96816, 2015, 'bmc', 'Evaluation of variant detection software for pooled next-generation sequence data', 'Huang, Howard W|NISC Comparative Sequencing Program|Mullikin, James C|Hansen, Nancy F', 'National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. hhuang58@jhu.edu.|National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. hhuang58@jhu.edu.|National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. mullikin@mail.nih.gov.|National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. nhansen@mail.nih.gov.', 'United States|United States|United States|United States', 0, 'Algorithms|DNA|Genetic Variation|Genomics|High-Throughput Nucleotide Sequencing|Humans|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1186/s12859-015-0624-y', '26220471', 'Despite the tremendous drop in the cost of nucleotide sequencing in recent years, many research projects still utilize sequencing of pools containing multiple samples for the detection of sequence variants as a cost saving measure. Various software tools exist to analyze these pooled sequence data, yet little has been reported on the relative accuracy and ease of use of these different programs.'),
(96817, 2015, 'bmc', 'Inferring 3D chromatin structure using a multiscale approach based on quaternions', 'Caudai, Claudia|Salerno, Emanuele|Zoppè, Monica|Tonazzini, Anna', 'National Research Council of Italy, Institute of Information Science and Technologies, Via Moruzzi, 1, Pisa, 56124, Italy. claudia.caudai@isti.cnr.it.|National Research Council of Italy, Institute of Information Science and Technologies, Via Moruzzi, 1, Pisa, 56124, Italy. emanuele.salerno@isti.cnr.it.|National Research Council of Italy, Institute of Clinical Physiology, Via Moruzzi, 1, 56124, Pisa, Italy. mzoppe@ifc.cnr.it.|National Research Council of Italy, Institute of Information Science and Technologies, Via Moruzzi, 1, Pisa, 56124, Italy. anna.tonazzini@isti.cnr.it.', 'Italy|Italy|Italy|Italy', 0, 'Algorithms|Chromatin|DNA|Genomics|Humans|Internet|Molecular Conformation|Monte Carlo Method|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0667-0', '26220581', 'The knowledge of the spatial organisation of the chromatin fibre in cell nuclei helps researchers to understand the nuclear machinery that regulates DNA activity. Recent experimental techniques of the type Chromosome Conformation Capture (3C, or similar) provide high-resolution, high-throughput data consisting in the number of times any possible pair of DNA fragments is found to be in contact, in a certain population of cells. As these data carry information on the structure of the chromatin fibre, several attempts have been made to use them to obtain high-resolution 3D reconstructions of entire chromosomes, or even an entire genome. The techniques proposed treat the data in different ways, possibly exploiting physical-geometric chromatin models. One popular strategy is to transform contact data into Euclidean distances between pairs of fragments, and then solve a classical distance-to-geometry problem.'),
(96818, 2015, 'bmc', 'SpirPro: A Spirulina proteome database and web-based tools for the analysis of protein-protein interactions at the metabolic level in Spirulina (Arthrospira) platensis C1', 'Senachak, Jittisak|Cheevadhanarak, Supapon|Hongsthong, Apiradee', 'Biochemical Engineering and Pilot Plant Research and Development (BEC) Unit, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency at King Mongkuts University of Technology Thonburi, 49 Soi Thian Thalae 25, Bang Khun Thian Chai Thalae Rd., Tha Kham, Bang Khun Thian, Bangkok, 10150, Thailand. jittisak.sen@biotec.or.th.|School of Bioresources and Technology, King Mongkuts University of Technology Thonburi, 49 Soi Thian Thalae 25, Bang Khun Thian Chai Thalae Rd., Tha Kham, Bang Khun Thian, Bangkok, 10150, Thailand. supaponche@gmail.com.|Biochemical Engineering and Pilot Plant Research and Development (BEC) Unit, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency at King Mongkuts University of Technology Thonburi, 49 Soi Thian Thalae 25, Bang Khun Thian Chai Thalae Rd., Tha Kham, Bang Khun Thian, Bangkok, 10150, Thailand. apiradee@biotec.or.th.', 'Thailand|Thailand|Thailand', 10, 'Databases, Factual|Internet|Metabolic Networks and Pathways|Protein Interaction Maps|Proteome|Proteomics|Spirulina|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0676-z', '26220682', 'Spirulina (Arthrospira) platensis is the only cyanobacterium that in addition to being studied at the molecular level and subjected to gene manipulation, can also be mass cultivated in outdoor ponds for commercial use as a food supplement. Thus, encountering environmental changes, including temperature stresses, is common during the mass production of Spirulina. The use of cyanobacteria as an experimental platform, especially for photosynthetic gene manipulation in plants and bacteria, is becoming increasingly important. Understanding the mechanisms and protein-protein interaction networks that underlie low- and high-temperature responses is relevant to Spirulina mass production. To accomplish this goal, high-throughput techniques such as OMICs analyses are used. Thus, large datasets must be collected, managed and subjected to information extraction. Therefore, databases including (i) proteomic analysis and protein-protein interaction (PPI) data and (ii) domain/motif visualization tools are required for potential use in temperature response models for plant chloroplasts and photosynthetic bacteria.'),
(96819, 2015, 'bmc', 'Computing all hybridization networks for multiple binary phylogenetic input trees', 'Albrecht, Benjamin', 'Institut für Informatik, Ludwig-Maximilians-Universität, 80333München, Amalienstr. 17, Germany. benjamin.albrecht@bio.ifi.lmu.de.', 'Germany', 0, 'Algorithms|Genomics|Phylogeny|Poaceae|Software', 'Journal Article', '10.1186/s12859-015-0660-7', '26223230', 'The computation of phylogenetic trees on the same set of species that are based on different orthologous genes can lead to incongruent trees. One possible explanation for this behavior are interspecific hybridization events recombining genes of different species. An important approach to analyze such events is the computation of hybridization networks.'),
(96820, 2015, 'bmc', 'Equalizer reduces SNP bias in Affymetrix microarrays', 'Quigley, David', 'Helen Diller Family Comprehensive Cancer Center, University of California at San Francisco, San Francisco, CA, 94158, USA. David.Quigley@ucsf.edu.', 'United States', 0, 'Animals|Cell Line|Female|Gene Expression Profiling|Genome|Humans|Mammary Glands, Animal|Mice|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Quantitative Trait Loci|RNA, Messenger|Skin|Software', 'Journal Article', '10.1186/s12859-015-0669-y', '26223252', 'Gene expression microarrays measure the levels of messenger ribonucleic acid (mRNA) in a sample using probe sequences that hybridize with transcribed regions. These probe sequences are designed using a reference genome for the relevant species. However, most model organisms and all humans have genomes that deviate from their reference. These variations, which include single nucleotide polymorphisms, insertions of additional nucleotides, and nucleotide deletions, can affect the microarrays performance. Genetic experiments comparing individuals bearing different population-associated single nucleotide polymorphisms that intersect microarray probes are therefore subject to systemic bias, as the reduction in binding efficiency due to a technical artifact is confounded with genetic differences between parental strains. This problem has been recognized for some time, and earlier methods of compensation have attempted to identify probes affected by genome variants using statistical models. These methods may require replicate microarray measurement of gene expression in the relevant tissue in inbred parental samples, which are not always available in model organisms and are never available in humans.'),
(96821, 2015, 'bmc', 'SuRankCo: supervised ranking of contigs in de novo assemblies', 'Kuhring, Mathias|Dabrowski, Piotr Wojtek|Piro, Vitor C|Nitsche, Andreas|Renard, Bernhard Y', 'Central Administration 4 (IT), Robert Koch Institute, Berlin, Germany. KuhringK@rki.de.|Research Group Bioinformatics (NG4), Robert Koch Institute, Berlin, Germany. DabrowskiW@rki.de.|Centre for Biological Threats and Special Pathogens (ZBS 1), Robert Koch Institute, Berlin, Germany. PiroV@rki.de.|Research Group Bioinformatics (NG4), Robert Koch Institute, Berlin, Germany. NitscheA@rki.de.|Research Group Bioinformatics (NG4), Robert Koch Institute, Berlin, Germany. RenardB@rki.de.', 'Germany|Germany|Germany|Germany|Germany', 4, 'Algorithms|Contig Mapping|Escherichia coli|ROC Curve|Software', 'Journal Article', '10.1186/s12859-015-0644-7', '26224355', 'Evaluating the quality and reliability of a de novo assembly and of single contigs in particular is challenging since commonly a ground truth is not readily available and numerous factors may influence results. Currently available procedures provide assembly scores but lack a comparative quality ranking of contigs within an assembly.'),
(96822, 2015, 'bmc', 'Comparison of phosphorylation patterns across eukaryotes by discriminative N-gram analysis', 'Frades, Itziar|Resjö, Svante|Andreasson, Erik', 'Department of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp, SE-230 53, Sweden. itziar.frades@gmail.com.|Department of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp, SE-230 53, Sweden. svante.resjo@slu.se.|Department of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp, SE-230 53, Sweden. erik.andreasson@slu.se.', 'Sweden|Sweden|Sweden', 0, 'Algorithms|Amino Acid Motifs|Animals|Arabidopsis|Cluster Analysis|Discriminant Analysis|Eukaryota|Evolution, Molecular|Humans|Phosphopeptides|Phosphorylation|Phytophthora|Protein Structure, Secondary|Proteins|Proteomics|Saccharomyces cerevisiae', 'Comparative Study', '10.1186/s12859-015-0657-2', '26224486', 'How protein phosphorylation relates to kingdom/phylum divergence is largely unknown and the amino acid residues surrounding the phosphorylation site have profound importance on protein kinase-substrate interactions. Standard motif analysis is not adequate for large scale comparative analysis because each phophopeptide is assigned to a unique motif and perform poorly with the unbalanced nature of the input datasets.'),
(96823, 2015, 'bmc', 'Erratum to: Improving protein order-disorder classification using charge-hydropathy plots', 'Huang, Fei|Oldfield, Christopher J|Xue, Bin|Hsu, Wei-Lun|Meng, Jingwei|Liu, Xiaowen|Shen, Li|Romero, Pedro|Uversky, Vladimir N|Dunker, A Keith', 'Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA. huangfei@iu.edu.|Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA. cjoldfie@umail.iu.edu.|Department of Cell Biology, Microbiology, and Molecular Biology, University of South Florida, Tampa, Florida, USA. binxue@usf.edu.|Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA. hsu20@iu.edu.|Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA. mengj@iupui.edu.|Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA. liuxiaowencs@gmail.com.|Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA. shenli@iu.edu.|Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA. promero@wisc.edu.|Department of Molecular Medicine, University of South Florida, Tampa, Florida, USA. vuversky@health.usf.edu.|Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA. kedunker@iupui.edu.', 'India|India|United States|India|India|India|India|United States|United States|India', 0, '-', 'Published Erratum', '10.1186/s12859-015-0646-5', '26227315', '-'),
(96824, 2015, 'bmc', 'HaploPOP: a software that improves population assignment by combining markers into haplotypes', 'Duforet-Frebourg, Nicolas|Gattepaille, Lucie M|Blum, Michael G B|Jakobsson, Mattias', 'Univ. Grenoble Alpes, TIMC-IMAG, Grenoble, F-38000, France. duforetn@berkeley.edu.|Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden. lucie.gattepaille@gmail.com.|Univ. Grenoble Alpes, TIMC-IMAG, Grenoble, F-38000, France. michael.blum@imag.fr.|Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden. mattias.jakobsson@ebc.uu.se.', 'France|Sweden|France|Sweden', 5, 'Algorithms|Genetics, Population|Genomics|Genotype|Haplotypes|Humans|Internet|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Principal Component Analysis|Software', 'Journal Article', '10.1186/s12859-015-0661-6', '26227424', 'In ecology and forensics, some population assignment techniques use molecular markers to assign individuals to known groups. However, assigning individuals to known populations can be difficult if the level of genetic differentiation among populations is small. Most assignment studies handle independent markers, often by pruning markers in Linkage Disequilibrium (LD), ignoring the information contained in the correlation among markers due to LD.'),
(96825, 2015, 'bmc', 'Simulated unbound structures for benchmarking of protein docking in the DOCKGROUND resource', 'Kirys, Tatsiana|Ruvinsky, Anatoly M|Singla, Deepak|Tuzikov, Alexander V|Kundrotas, Petras J|Vakser, Ilya A', 'Center for Computational Biology, The University of Kansas, Lawrence, KS, 66047, USA. tatsiana.bylund@gmail.com.|Center for Computational Biology, The University of Kansas, Lawrence, KS, 66047, USA. aruvinsky@yahoo.com.|Center for Computational Biology, The University of Kansas, Lawrence, KS, 66047, USA. deepkumar1983@gmail.com.|United Institute of Informatics Problems, National Academy of Sciences, 220012, Minsk, Belarus. tuzikov@newman.bas-net.by.|Center for Computational Biology, The University of Kansas, Lawrence, KS, 66047, USA. pkundro@ku.edu.|Center for Computational Biology, The University of Kansas, Lawrence, KS, 66047, USA. vakser@ku.edu.', 'United States|United States|United States|Belarus|United States|United States', 7, 'Algorithms|Benchmarking|Binding Sites|Computational Biology|Computer Simulation|Internet|Molecular Docking Simulation|Protein Interaction Domains and Motifs|Protein Structure, Tertiary|Proteins|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0672-3', '26227548', 'Proteins play an important role in biological processes in living organisms. Many protein functions are based on interaction with other proteins. The structural information is important for adequate description of these interactions. Sets of protein structures determined in both bound and unbound states are essential for benchmarking of the docking procedures. However, the number of such proteins in PDB is relatively small. A radical expansion of such sets is possible if the unbound structures are computationally simulated.'),
(96826, 2015, 'bmc', 'An investigation into inter- and intragenomic variations of graphic genomic signatures', 'Karamichalis, Rallis|Kari, Lila|Konstantinidis, Stavros|Kopecki, Steffen', 'Department of Computer Science, University of Western Ontario, London, ON, Canada. rkaramic@uwo.ca.|Department of Computer Science, University of Western Ontario, London, ON, Canada. lila.kari@uwo.ca.|Department of Mathematics and Computing Science, Saint Marys University, Halifax, NS, Canada. s.konstantinidis@smu.ca.|Department of Computer Science, University of Western Ontario, London, ON, Canada. steffen@csd.uwo.ca.', 'Canada|Canada|Canada|Canada', 10, 'Algorithms|Animals|Arabidopsis|Computer Graphics|DNA|Escherichia coli|Genome|Genomics|Humans|Plasmodium falciparum|Saccharomyces cerevisiae|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-015-0655-4', '26249837', 'Motivated by the general need to identify and classify species based on molecular evidence, genome comparisons have been proposed that are based on measuring mostly Euclidean distances between Chaos Game Representation (CGR) patterns of genomic DNA sequences.'),
(96827, 2015, 'bmc', 'Comparison of data mining and allometric model in estimation of tree biomass', 'Sanquetta, Carlos R|Wojciechowski, Jaime|Dalla Corte, Ana P|Behling, Alexandre|Péllico Netto, Sylvio|Rodrigues, Aurélio L|Sanquetta, Mateus N I', 'Forest Science Department, Federal University of Paraná, 900 Lothário Meissner Avenue, Curitiba, Paraná, Brazil. carlos_sanquetta@hotmail.com.|Forest Science Department, Federal University of Paraná, 900 Lothário Meissner Avenue, Curitiba, Paraná, Brazil. jaimewo@gmail.com.|Forest Science Department, Federal University of Paraná, 900 Lothário Meissner Avenue, Curitiba, Paraná, Brazil. anapaulacorte@gmail.com.|Forest Science Department, Federal University of Paraná, 900 Lothário Meissner Avenue, Curitiba, Paraná, Brazil. alexandre.behling@yahoo.com.br.|Forest Science Department, Federal University of Paraná, 900 Lothário Meissner Avenue, Curitiba, Paraná, Brazil. sylviopelliconetto@gmail.com.|Forest Science Department, Federal University of Paraná, 900 Lothário Meissner Avenue, Curitiba, Paraná, Brazil. alourencorodrigues@gmail.com.|Forest Science Department, Federal University of Paraná, 900 Lothário Meissner Avenue, Curitiba, Paraná, Brazil. mateus.sanquetta@hotmail.com.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 10, 'Biomass|Data Mining|Environmental Monitoring|Forests|Models, Theoretical|Population Dynamics|Trees|Tropical Climate|Uncertainty', 'Journal Article', '10.1186/s12859-015-0662-5', '26250142', 'The traditional method used to estimate tree biomass is allometry. In this method, models are tested and equations fitted by regression usually applying ordinary least squares, though other analogous methods are also used for this purpose. Due to the nature of tree biomass data, the assumptions of regression are not always accomplished, bringing uncertainties to the inferences. This article demonstrates that the Data Mining (DM) technique can be used as an alternative to traditional regression approach to estimate tree biomass in the Atlantic Forest, providing better results than allometry, and demonstrating simplicity, versatility and flexibility to apply to a wide range of conditions.'),
(96828, 2015, 'bmc', 'Confident difference criterion: a new Bayesian differentially expressed gene selection algorithm with applications', 'Yu, Fang|Chen, Ming-Hui|Kuo, Lynn|Talbott, Heather|Davis, John S', 'Department of Biostatistics, University of Nebraska Medical Center, Omaha, 68198-4350, NE, USA. fangyu@unmc.edu.|Department of Statistics, University of Connecticut, Storrs, 06269-4120, CT, USA. ming-hui.chen@uconn.edu.|Department of Statistics, University of Connecticut, Storrs, 06269-4120, CT, USA. lynn.kuo@uconn.edu.|Department of Biochemistry and Molecular Biology and Department of Obstetrics and Gynecology, University of Nebraska Medical Center, Omaha, 68198-5870, NE, USA. heather.talbott@unmc.edu.|VA Nebraska-Western Iowa Health Care System and Department of Obstetrics and Gynecology, University of Nebraska Medical Center, Omaha, 68198-3255, NE, USA. jsdavis@unmc.edu.', 'United States|United States|United States|United States|United States', 2, 'Algorithms|Bayes Theorem|Dinoprost|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Humans|Oligonucleotide Array Sequence Analysis|Signal Transduction|Time Factors', 'Journal Article', '10.1186/s12859-015-0664-3', '26250443', 'Recently, the Bayesian method becomes more popular for analyzing high dimensional gene expression data as it allows us to borrow information across different genes and provides powerful estimators for evaluating gene expression levels. It is crucial to develop a simple but efficient gene selection algorithm for detecting differentially expressed (DE) genes based on the Bayesian estimators.'),
(96829, 2015, 'bmc', 'InteMAP: Integrated metagenomic assembly pipeline for NGS short reads', 'Lai, Binbin|Wang, Fumeng|Wang, Xiaoqi|Duan, Liping|Zhu, Huaiqiu', 'State Key Lab for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China. binbinlai2012@gmail.com.|State Key Lab for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China. wang_fumeng@126.com.|State Key Lab for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China. xiaoqiwang@pku.edu.cn.|Department of Gastroenterology, Peking University Third Hospital, Beijing, 100191, China. duanlp@bjmu.edu.cn.|State Key Lab for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China. hqzhu@pku.edu.cn.', 'China|China|China|China|China', 11, 'Algorithms|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-015-0686-x', '26250558', 'Next-generation sequencing (NGS) has greatly facilitated metagenomic analysis but also raised new challenges for metagenomic DNA sequence assembly, owing to its high-throughput nature and extremely short reads generated by sequencers such as Illumina. To date, how to generate a high-quality draft assembly for metagenomic sequencing projects has not been fully addressed.'),
(96830, 2015, 'bmc', 'Automated interpretation of 3D laserscanned point clouds for plant organ segmentation', 'Wahabzada, Mirwaes|Paulus, Stefan|Kersting, Kristian|Mahlein, Anne-Katrin', 'INRES-Phytomedicine, University of Bonn, Meckenheimer Allee 166a, Bonn, 53115, Germany. mirwaes@uni-bonn.de.|IGG-Geodesy, University of Bonn, Nussallee 17, Bonn, 53115, Germany. paulus@igg.uni-bonn.de.|Computer Science Department, TU Dortmund University, Otto-Hahn-Str. 14, Dortmund, 44227, Germany. kristian.kersting@cs.tu-dortmund.de.|INRES-Phytomedicine, University of Bonn, Meckenheimer Allee 166a, Bonn, 53115, Germany. amahlein@uni-bonn.de.', 'Germany|Germany|Germany|Germany', 38, 'Algorithms|Cluster Analysis|Hordeum|Imaging, Three-Dimensional|Lasers|Phenotype|Plant Structures|Triticum|Vitis', 'Journal Article', '10.1186/s12859-015-0665-2', '26253564', 'Plant organ segmentation from 3D point clouds is a relevant task for plant phenotyping and plant growth observation. Automated solutions are required to increase the efficiency of recent high-throughput plant phenotyping pipelines. However, plant geometrical properties vary with time, among observation scales and different plant types. The main objective of the present research is to develop a fully automated, fast and reliable data driven approach for plant organ segmentation.'),
(96831, 2015, 'bmc', 'RNA-binding residues prediction using structural features', 'Ren, Huizhu|Shen, Ying', '2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Key Laboratory of Hormones and Development (Ministry of Health), Metabolic Diseases Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300070, China. huizhuren@gmail.com.|School of Software Engineering, Tongji University, Shanghai, 201804, China. yingshen@tongji.edu.cn.', 'China|China', 19, 'Algorithms|Binding Sites|Computational Biology|Humans|Models, Molecular|RNA|RNA-Binding Proteins|Software', 'Journal Article', '10.1186/s12859-015-0691-0', '26254826', 'RNA-protein complexes play an essential role in many biological processes. To explore potential functions of RNA-protein complexes, its important to identify RNA-binding residues in proteins.'),
(96832, 2015, 'bmc', 'Breaking the computational barriers of pairwise genome comparison', 'Torreno, Oscar|Trelles, Oswaldo', 'Advanced Computing Technologies Unit, RISC Software GmbH, Softwarepark 35, Hagenberg, 4232, Austria.|Computer Architecture Department, University of Malaga, Bulevar Luis Pasteur 35, Malaga, 29071, Spain. ortrelles@uma.es.', 'Austria|Spain', 13, 'Algorithms|Animals|Bacteria|Computational Biology|Datasets as Topic|Drosophila|Genome|Humans|Mammals|Software|Synteny|Viruses', 'Comparative Study', '10.1186/s12859-015-0679-9', '26260162', 'Conventional pairwise sequence comparison software algorithms are being used to process much larger datasets than they were originally designed for. This can result in processing bottlenecks that limit software capabilities or prevent full use of the available hardware resources. Overcoming the barriers that limit the efficient computational analysis of large biological sequence datasets by retrofitting existing algorithms or by creating new applications represents a major challenge for the bioinformatics community.'),
(96833, 2015, 'bmc', 'ImmunExplorer (IMEX): a software framework for diversity and clonality analyses of immunoglobulins and T cell receptors on the basis of IMGT/HighV-QUEST preprocessed NGS data', 'Schaller, Susanne|Weinberger, Johannes|Jimenez-Heredia, Raul|Danzer, Martin|Oberbauer, Rainer|Gabriel, Christian|Winkler, Stephan M', 'University of Applied Sciences Upper Austria, Hagenberg Campus, Bioinformatics Research Group, Softwarepark 13, Hagenberg, 4232, Austria. susanne.schaller@fh-hagenberg.at.|Red Cross Transfusion Service of Upper Austria, Krankenhausstrasse 7, Linz, 4020, Austria. johannes.weinberger@o.roteskreuz.at.|Red Cross Transfusion Service of Upper Austria, Krankenhausstrasse 7, Linz, 4020, Austria. raul.jimenezHeredia@o.roteskreuz.at.|Red Cross Transfusion Service of Upper Austria, Krankenhausstrasse 7, Linz, 4020, Austria. martin.danzer@o.roteskreuz.at.|Elisabethinen Hospital, Fadingerstrasse 1, Linz, 4020, Austria. rainer.oberbauer@meduniwien.ac.at.|Red Cross Transfusion Service of Upper Austria, Krankenhausstrasse 7, Linz, 4020, Austria. christian.gabriel@o.roteskreuz.at.|University of Applied Sciences Upper Austria, Hagenberg Campus, Bioinformatics Research Group, Softwarepark 13, Hagenberg, 4232, Austria. stephan.winkler@fh-hagenberg.at.', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria', 18, 'Algorithms|DNA|DNA Primers|Gene Rearrangement|Humans|Immunoglobulins|Polymerase Chain Reaction|Receptors, Antigen, T-Cell|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-015-0687-9', '26264428', 'Todays modern research of B and T cell antigen receptors (the immunoglobulins (IG) or antibodies and T cell receptors (TR)) forms the basis for detailed analyses of the human adaptive immune system. For instance, insights in the state of the adaptive immune system provide information that is essentially important in monitoring transplantation processes and the regulation of immune suppressiva. In this context, algorithms and tools are necessary for analyzing the IG and TR diversity on nucleotide as well as on amino acid sequence level, identifying highly proliferated clonotypes, determining the diversity of the cell repertoire found in a sample, comparing different states of the human immune system, and visualizing all relevant information.'),
(96834, 2015, 'bmc', 'leBIBIQBPP: a set of databases and a webtool for automatic phylogenetic analysis of prokaryotic sequences', 'Flandrois, Jean-Pierre|Perrière, Guy|Gouy, Manolo', 'Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 5558, Université Claude Bernard - Lyon 1, 43 bd. du 11 Novembre 1918, Villeurbanne, 69622, France. jean-pierre.flandrois@univ-lyon1.fr.|Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 5558, Université Claude Bernard - Lyon 1, 43 bd. du 11 Novembre 1918, Villeurbanne, 69622, France. guy.perriere@univ-lyon1.fr.|Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 5558, Université Claude Bernard - Lyon 1, 43 bd. du 11 Novembre 1918, Villeurbanne, 69622, France. manolo.gouy@univ-lyon1.fr.', 'France|France|France', 23, 'Archaea|Bacteria|Computational Biology|Databases, Nucleic Acid|Internet|Likelihood Functions|Phylogeny|RNA, Archaeal|RNA, Bacterial|RNA, Ribosomal|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-015-0692-z', '26264559', 'Estimating the phylogenetic position of bacterial and archaeal organisms by genetic sequence comparisons is considered as the gold-standard in taxonomy. This is also a way to identify the species of origin of the sequence. The quality of the reference database used in such analyses is crucial: the database must reflect the up-to-date bacterial nomenclature and accurately indicate the species of origin of its sequences.'),
(96835, 2015, 'bmc', 'A simplicial complex-based approach to unmixing tumor progression data', 'Roman, Theodore|Nayyeri, Amir|Fasy, Brittany Terese|Schwartz, Russell', 'Computatational Biology Department, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, USA. troman@andrew.cmu.edu.|Computer Science Department, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, USA. amirn@cmu.edu.|Department of Computer Science, Tulane University, 6834 St. Charles St., New Orleans, USA. bfasy@tulane.edu.|Computatational Biology Department, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, USA. russells@andrew.cmu.edu.', 'United States|United States|United States|United States', 3, 'Breast Neoplasms|Computational Biology|Computer Simulation|Disease Progression|Female|Gene Expression Profiling|Gene Regulatory Networks|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Neoplasm Proteins', 'Journal Article', '10.1186/s12859-015-0694-x', '26264682', 'Tumorigenesis is an evolutionary process by which tumor cells acquire mutations through successive diversification and differentiation. There is much interest in reconstructing this process of evolution due to its relevance to identifying drivers of mutation and predicting future prognosis and drug response. Efforts are challenged by high tumor heterogeneity, though, both within and among patients. In prior work, we showed that this heterogeneity could be turned into an advantage by computationally reconstructing models of cell populations mixed to different degrees in distinct tumors. Such mixed membership model approaches, however, are still limited in their ability to dissect more than a few well-conserved cell populations across a tumor data set.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96836, 2015, 'bmc', 'MultiSETTER: web server for multiple RNA structure comparison', 'xc4x8cech, Petr|Hoksza, David|Svozil, Daniel', 'Laboratory of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technickxc3xa1 5, CZ-166 28, Prague, Czech Republic.|Laboratory of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technickxc3xa1 5, CZ-166 28, Prague, Czech Republic. hoksza@ksi.mff.cuni.cz.|Laboratory of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technickxc3xa1 5, CZ-166 28, Prague, Czech Republic. svozild@vscht.cz.', '-|-|-', 8, 'Algorithms|Internet|Nucleic Acid Conformation|RNA|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-015-0696-8', '26264783', 'Understanding the architecture and function of RNA molecules requires methods for comparing and analyzing their tertiary and quaternary structures. While structural superposition of short RNAs is achievable in a reasonable time, large structures represent much bigger challenge. Therefore, we have developed a fast and accurate algorithm for RNA pairwise structure superposition called SETTER and implemented it in the SETTER web server. However, though biological relationships can be inferred by a pairwise structure alignment, key features preserved by evolution can be identified only from a multiple structure alignment. Thus, we extended the SETTER algorithm to the alignment of multiple RNA structures and developed the MultiSETTER algorithm.'),
(96837, 2015, 'bmc', 'Amplitude spectrum distance: measuring the global shape divergence of protein fragments', 'Galiez, Clovis|Coste, Franxc3xa7ois', 'Inria Rennes - Bretagne Atlantique, Rennes, France. clovis.galiez@inria.fr.|Inria Rennes - Bretagne Atlantique, Rennes, France. francois.coste@inria.fr.', 'France|France', 0, 'Algorithms|Cluster Analysis|Computational Biology|Humans|Peptide Fragments|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/s12859-015-0693-y', '26268224', 'In structural bioinformatics, there is an increasing interest in identifying and understanding the evolution of local protein structures regarded as key structural or functional protein building blocks. A central need is then to compare these, possibly short, fragments by measuring efficiently and accurately their (dis)similarity. Progress towards this goal has given rise to scores enabling to assess the strong similarity of fragments. Yet, there is still a lack of more progressive scores, with meaningful intermediate values, for the comparison, retrieval or clustering of distantly related fragments.'),
(96838, 2015, 'bmc', 'Robust sequence alignment using evolutionary rates coupled with an amino acid substitution matrix', 'Ndhlovu, Andrew|Hazelhurst, Scott|Durand, Pierre M', 'Evolutionary Medicine Laboratory, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. andrew.ndhlovu@students.wits.ac.za.|School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa. scott.hazelhurst@wits.ac.za.|Evolutionary Medicine Laboratory, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. pierre.durand@wits.ac.za.', 'South Africa|South Africa|South Africa', 0, 'Algorithms|Amino Acid Sequence|Amino Acid Substitution|Codon|Databases, Factual|Evolution, Molecular|Humans|Molecular Sequence Data|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/s12859-015-0688-8', '26269100', 'Selective pressures at the DNA level shape genes into profiles consisting of patterns of rapidly evolving sites and sites withstanding change. These profiles remain detectable even when protein sequences become extensively diverged. A common task in molecular biology is to infer functional, structural or evolutionary relationships by querying a database using an algorithm. However, problems arise when sequence similarity is low. This study presents an algorithm that uses the evolutionary rate at codon sites, the dN/dS (xcfx89) parameter, coupled to a substitution matrix as an alignment metric for detecting distantly related proteins. The algorithm, called BLOSUM-FIRE couples a newer and improved version of the original FIRE (Functional Inference using Rates of Evolution) algorithm with an amino acid substitution matrix in a dynamic scoring function. The enigmatic hepatitis B virus X protein was used as a test case for BLOSUM-FIRE and its associated database EvoDB.'),
(96839, 2015, 'bmc', 'SW1PerS: Sliding windows and 1-persistence scoring, discovering periodicity in gene expression time series data', 'Perea, Jose A|Deckard, Anastasia|Haase, Steve B|Harer, John', 'Department of Mathematics, Duke University, Science Dr, Durham, 27708, NC, USA. joperea@math.duke.edu.|Program in Computational Biology and Bioinformatics, Duke University, Durham, 27708, NC, USA. anastasia@deckard.edu.|Center for Systems Biology, Institute for Genome Sciences & Policy, Duke University, Durham, 27708, NC, USA. shaase@duke.edu.|Department of Mathematics, Duke University, Science Dr, Durham, 27708, NC, USA. harer@math.duke.edu.', 'United States|United States|United States|United States', 34, 'Algorithms|Area Under Curve|Cell Division|Circadian Rhythm|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|ROC Curve|Saccharomyces cerevisiae', 'Journal Article', '10.1186/s12859-015-0645-6', '26277424', 'Identifying periodically expressed genes across different processes (e.g. the cell and metabolic cycles, circadian rhythms, etc) is a central problem in computational biology. Biological time series may contain (multiple) unknown signal shapes of systemic relevance, imperfections like noise, damping, and trending, or limited sampling density. While there exist methods for detecting periodicity, their design biases (e.g. toward a specific signal shape) can limit their applicability in one or more of these situations.'),
(96840, 2015, 'bmc', 'MVDA: a multi-view genomic data integration methodology', 'Serra, Angela|Fratello, Michele|Fortino, Vittorio|Raiconi, Giancarlo|Tagliaferri, Roberto|Greco, Dario', 'NeuRoNe Lab, Department of Computer Science, University of Salerno, Fisciano, Italy. aserra@unisa.it.|Department of Medical, Surgical, Neurological, Metabolic and Ageing Sciences, Second University of Napoli, Napoli, Italy. michele.fratello@unina2.it.|Unit of Systems Toxicology and Nanosafety Research Centre, Finnish Institute of Occupational Health, FIOH, Helsinki, Finland. vittorio.fortino@ttl.fi.|NeuRoNe Lab, Department of Computer Science, University of Salerno, Fisciano, Italy. gianni@unisa.it.|NeuRoNe Lab, Department of Computer Science, University of Salerno, Fisciano, Italy. rtagliaferri@unisa.it.|Unit of Systems Toxicology and Nanosafety Research Centre, Finnish Institute of Occupational Health, FIOH, Helsinki, Finland. dario.greco@ttl.fi.', 'Italy|Italy|Finland|Italy|Italy|Finland', 37, 'Algorithms|Cluster Analysis|Genomics|MicroRNAs|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-015-0680-3', '26283178', 'Multiple high-throughput molecular profiling by omics technologies can be collected for the same individuals. Combining these data, rather than exploiting them separately, can significantly increase the power of clinically relevant patients subclassifications.'),
(96841, 2015, 'bmc', 'SIMAT: GC-SIM-MS data analysis tool', 'Ranjbar, Mohammad R Nezami|Di Poto, Cristina|Wang, Yue|Ressom, Habtom W', 'Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA, USA. nranjbar@vt.edu.|Department of Oncology, Georgetown University, Washington DC, USA. cd329@georgetown.edu.|Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA, USA. yuewang@vt.edu.|Department of Oncology, Georgetown University, Washington DC, USA. hwr@georgetown.edu.', 'United States|United States|United States|United States', 1, 'Algorithms|Gas Chromatography-Mass Spectrometry|Internet|Metabolomics|Software', 'Journal Article', '10.1186/s12859-015-0681-2', '26283310', 'Gas chromatography coupled with mass spectrometry (GC-MS) is one of the technologies widely used for qualitative and quantitative analysis of small molecules. In particular, GC coupled to single quadrupole MS can be utilized for targeted analysis by selected ion monitoring (SIM). However, to our knowledge, there are no software tools specifically designed for analysis of GC-SIM-MS data. In this paper, we introduce a new R/Bioconductor package called SIMAT for quantitative analysis of the levels of targeted analytes. SIMAT provides guidance in choosing fragments for a list of targets. This is accomplished through an optimization algorithm that has the capability to select the most appropriate fragments from overlapping chromatographic peaks based on a pre-specified library of background analytes. The tool also allows visualization of the total ion chromatograms (TIC) of runs and extracted ion chromatograms (EIC) of analytes of interest. Moreover, retention index (RI) calibration can be performed and raw GC-SIM-MS data can be imported in netCDF or NIST mass spectral library (MSL) formats.'),
(96842, 2015, 'bmc', 'diffHic: a Bioconductor package to detect differential genomic interactions in Hi-C data', 'Lun, Aaron T L|Smyth, Gordon K', 'The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC, 3052, Melbourne, Australia. alun@wehi.edu.au.|The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC, 3052, Melbourne, Australia. smyth@wehi.edu.au.', 'Australia|Australia', 57, 'Chromatin|Genomics|High-Throughput Nucleotide Sequencing|Internet|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-015-0683-0', '26283514', 'Chromatin conformation capture with high-throughput sequencing (Hi-C) is a technique that measures the in vivo intensity of interactions between all pairs of loci in the genome. Most conventional analyses of Hi-C data focus on the detection of statistically significant interactions. However, an alternative strategy involves identifying significant changes in the interaction intensity (i.e., differential interactions) between two or more biological conditions. This is more statistically rigorous and may provide more biologically relevant results.'),
(96843, 2015, 'bmc', 'Efficient test for nonlinear dependence of two continuous variables', 'Wang, Yi|Li, Yi|Cao, Hongbao|Xiong, Momiao|Shugart, Yin Yao|Jin, Li', 'Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200433, China. godspeed.wang@gmail.com.|Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200433, China. liyistat@gmail.com.|Unit on Statistical Genomics, Division of Intramural Division Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA. hongbao.cao@nih.gov.|Human Genetics Center, School of Public Health, University of Texas Houston Health Sciences Center, Houston, TX, USA. momiao.xiong@gmail.com.|Unit on Statistical Genomics, Division of Intramural Division Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA. yin.yao@nih.gov.|Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200433, China. lijin@fudan.edu.cn.', 'China|China|United States|United States|United States|China', 12, 'Humans|Kidney Neoplasms|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-015-0697-7', '26283601', 'Testing dependence/correlation of two variables is one of the fundamental tasks in statistics. In this work, we proposed a new way of testing nonlinear dependence between two continuous variables (X and Y).'),
(96844, 2015, 'bmc', 'A cascade computer model for mocrobicide diffusivity from mucoadhesive formulations', 'Lee, Yugyung|Khemka, Alok|Acharya, Gayathri|Giri, Namita|Lee, Chi H', 'School of Computing and Engineering, Kansas City, USA. Leeyu@umkc.edu.|School of Computing and Engineering, Kansas City, USA. alokkhemka2@gmail.com.|Division of Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, 2464 Charlotte Street, Kansas City, MO, 64108, USA. gaya3acharya@gmail.com.|Division of Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, 2464 Charlotte Street, Kansas City, MO, 64108, USA. ngbm4@mail.umkc.edu.|Division of Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, 2464 Charlotte Street, Kansas City, MO, 64108, USA. leech@umkc.edu.', 'United States|United States|United States|United States|United States', 1, 'Anti-Infective Agents|Chemistry, Pharmaceutical|Computer Simulation|Diffusion|Drug Carriers|Female|Gels|Humans|Neural Networks (Computer)|Sexually Transmitted Diseases', 'Journal Article', '10.1186/s12859-015-0684-z', '26286552', 'The cascade computer model (CCM) was designed as a machine-learning feature platform for prediction of drug diffusivity from the mucoadhesive formulations. Three basic models (the statistical regression model, the K nearest neighbor model and the modified version of the back propagation neural network) in CCM operate sequentially in close collaboration with each other, employing the estimated value obtained from the afore-positioned base model as an input value to the next-positioned base model in the cascade. The effects of various parameters on the pharmacological efficacy of a female controlled drug delivery system (FcDDS) intended for prevention of women from HIV-1 infection were evaluated using an in vitro apparatus Simulant Vaginal System (SVS). We used computer simulations to explicitly examine the changes in drug diffusivity from FcDDS and determine the prognostic potency of each variable for in vivo prediction of formulation efficacy. The results obtained using the CCM approach were compared with those from individual multiple regression model.'),
(96845, 2015, 'bmc', 'A convex formulation for joint RNA isoform detection and quantification from multiple RNA-seq samples', 'Bernard, Elsa|Jacob, Laurent|Mairal, Julien|Viara, Eric|Vert, Jean-Philippe', 'MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Fontainebleau, 77300, France. Elsa.Bernard@mines-paristech.fr.|Laboratoire Biométrie et Biologie Evolutive, Université de Lyon, Université Lyon 1, CNRS, INRA, UMR5558, Villeurbanne, France. laurent.jacob@univ-lyon1.fr.|Inria, LEAR Team, Laboratoire Jean Kuntzmann, CNRS, Université Grenoble Alpes, 655, Avenue de lEurope, Montbonnot, 38330, France. julien.mairal.spam@m4x.org.|Sysra, 91330, Yerres, France. viara@sysra.com.|MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Fontainebleau, 77300, France. Jean-Philippe.Vert@mines-paristech.fr.', 'France|France|France|France|France', 1, 'Algorithms|Alternative Splicing|Humans|Internet|RNA|RNA Isoforms|Sequence Analysis, RNA|Transcriptome|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0695-9', '26286719', 'Detecting and quantifying isoforms from RNA-seq data is an important but challenging task. The problem is often ill-posed, particularly at low coverage. One promising direction is to exploit several samples simultaneously.'),
(96846, 2015, 'bmc', 'POPE: post optimization posterior evaluation of likelihood free models', 'Meeds, Edward|Chiang, Michael|Lee, Mary|Cinquin, Olivier|Lowengrub, John|Welling, Max', 'Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands. E.W.F.Meeds@uva.nl.|School of Biological Sciences, University of California, Irvine, USA. mcah5a@gmail.com.|Department of Mathematics, University of California, Irvine, USA. mary.lee@uci.edu.|School of Biological Sciences, University of California, Irvine, USA. olivier.cinquin@uci.edu.|Department of Mathematics, University of California, Irvine, USA. lowengrb@math.uci.edu.|Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands. welling.max@gmail.com.', 'Netherlands|United States|United States|United States|United States|Netherlands', 0, 'Algorithms|Bayes Theorem|Colonic Neoplasms|Computer Simulation|Humans|Models, Theoretical|Neoplastic Stem Cells|Probability|Signal Transduction|Stem Cell Niche|Stochastic Processes', 'Evaluation Studies', '10.1186/s12859-015-0658-1', '26289041', 'In many domains, scientists build complex simulators of natural phenomena that encode their hypotheses about the underlying processes. These simulators can be deterministic or stochastic, fast or slow, constrained or unconstrained, and so on. Optimizing the simulators with respect to a set of parameter values is common practice, resulting in a single parameter setting that minimizes an objective subject to constraints.'),
(96847, 2015, 'bmc', 'Reliable scaling of position weight matrices for binding strength comparisons between transcription factors', 'Ma, Xiaoyan|Ezer, Daphne|Navarro, Carmen|Adryan, Boris', 'Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK. xm227@cam.ac.uk.|Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK. de276@cam.ac.uk.|Cambridge Systems Biology Center, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK. cnluzon@decsai.ugr.es.|Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK. ba255@cam.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 3, 'Algorithms|Animals|Binding Sites|Chromatin Immunoprecipitation|Computational Biology|DNA|Drosophila melanogaster|Humans|Position-Specific Scoring Matrices|Protein Binding|Saccharomyces cerevisiae|Sequence Analysis, DNA|Transcription Factors|Vertebrates', 'Comparative Study', '10.1186/s12859-015-0666-1', '26289072', 'Scoring DNA sequences against Position Weight Matrices (PWMs) is a widely adopted method to identify putative transcription factor binding sites. While common bioinformatics tools produce scores that can reflect the binding strength between a specific transcription factor and the DNA, these scores are not directly comparable between different transcription factors. Other methods, including p-value associated approaches (Touzet H, Varrxc3xa9 J-S. Efficient and accurate p-value computation for position weight matrices. Algorithms Mol Biol. 2007,2(1510.1186):1748-7188), provide more rigorous ways to identify potential binding sites, but their results are difficult to interpret in terms of binding energy, which is essential for the modeling of transcription factor binding dynamics and enhancer activities.'),
(96848, 2015, 'bmc', 'TMalphaDB and TMbetaDB: web servers to study the structural role of sequence motifs in xcexb1-helix and xcexb2-barrel domains of membrane proteins', 'Perea, Marc|Lugtenburg, Ivar|Mayol, Eduardo|Cordomxc3xad, Arnau|Deupxc3xad, Xavier|Pardo, Leonardo|Olivella, Mireia', 'Laboratori de Medicina Computacional, Unitat de Bioestadxc3xadstica, Facultat de Medicina, Universitat Autxc3xb2noma de Barcelona, Bellaterra, Spain.|Laboratori de Medicina Computacional, Unitat de Bioestadxc3xadstica, Facultat de Medicina, Universitat Autxc3xb2noma de Barcelona, Bellaterra, Spain.|Laboratori de Medicina Computacional, Unitat de Bioestadxc3xadstica, Facultat de Medicina, Universitat Autxc3xb2noma de Barcelona, Bellaterra, Spain.|Laboratori de Medicina Computacional, Unitat de Bioestadxc3xadstica, Facultat de Medicina, Universitat Autxc3xb2noma de Barcelona, Bellaterra, Spain.|Laboratori de Medicina Computacional, Unitat de Bioestadxc3xadstica, Facultat de Medicina, Universitat Autxc3xb2noma de Barcelona, Bellaterra, Spain.|Laboratori de Medicina Computacional, Unitat de Bioestadxc3xadstica, Facultat de Medicina, Universitat Autxc3xb2noma de Barcelona, Bellaterra, Spain. Leonardo.pardo@uab.es.|Department de Biologia de Sistemes, Universitat de Vic, Vic, Barcelona, Spain. mireia.olivella@uvic.cat.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 4, 'Amino Acid Motifs|Databases, Protein|Humans|Internet|Membrane Proteins|Protein Conformation|Protein Structure, Tertiary|Sequence Analysis, Protein', 'Journal Article', '10.1186/s12859-015-0699-5', '26289158', 'Membrane proteins represent over 25 % of human protein genes and account for more than 60 % of drug targets due to their accessibility from the extracellular environment. The increasing number of available crystal structures of these proteins in the Protein Data Bank permits an initial estimation of their structural properties.'),
(96849, 2015, 'bmc', 'Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment', 'Bohler, Anwesha|Eijssen, Lars M T|van Iersel, Martijn P|Leemans, Christ|Willighagen, Egon L|Kutmon, Martina|Jaillard, Magali|Evelo, Chris T', 'Department of Bioinformatics - BiGCaT, Maastricht University, P.O. Box 616, UNS 50 Box 19, 6200, MD, Maastricht, The Netherlands. anwesha.dutta@maastrichtuniversity.nl.|Department of Bioinformatics - BiGCaT, Maastricht University, P.O. Box 616, UNS 50 Box 19, 6200, MD, Maastricht, The Netherlands. l.eijssen@maastrichtuniversity.nl.|General Bioinformatics, Reading, Berkshire, RG4 7RT, UK. mvaniersel@gmail.com.|Department of Bioinformatics - BiGCaT, Maastricht University, P.O. Box 616, UNS 50 Box 19, 6200, MD, Maastricht, The Netherlands. christleemans@gmail.com.|Department of Bioinformatics - BiGCaT, Maastricht University, P.O. Box 616, UNS 50 Box 19, 6200, MD, Maastricht, The Netherlands. egon.willighagen@maastrichtuniversity.nl.|Department of Bioinformatics - BiGCaT, Maastricht University, P.O. Box 616, UNS 50 Box 19, 6200, MD, Maastricht, The Netherlands. martina.kutmon@maastrichtuniversity.nl.|Bioinformatics Research Department, BioMérieux S.A, 69280, Marcy lEtoile, France. magali.dancette@biomerieux.com.|Department of Bioinformatics - BiGCaT, Maastricht University, P.O. Box 616, UNS 50 Box 19, 6200, MD, Maastricht, The Netherlands. chris.evelo@maastrichtuniversity.nl.', 'Netherlands|Netherlands|United Kingdom|Netherlands|Netherlands|Netherlands|France|Netherlands', 10, 'Animals|Automation|Biomarkers, Tumor|Computer Graphics|Cyclophosphamide|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Genomics|Mice|Neoplasms|Signal Transduction|Software|Workflow', 'Journal Article', '10.1186/s12859-015-0708-8', '26298294', 'Biological pathways are descriptive diagrams of biological processes widely used for functional analysis of differentially expressed genes or proteins. Primary data analysis, such as quality control, normalisation, and statistical analysis, is often performed in scripting languages like R, Perl, and Python. Subsequent pathway analysis is usually performed using dedicated external applications. Workflows involving manual use of multiple environments are time consuming and error prone. Therefore, tools are needed that enable pathway analysis directly within the same scripting languages used for primary data analyses. Existing tools have limited capability in terms of available pathway content, pathway editing and visualisation options, and export file formats. Consequently, making the full-fledged pathway analysis tool PathVisio available from various scripting languages will benefit researchers.'),
(96850, 2015, 'bmc', 'Coevolutionary analyses require phylogenetically deep alignments and better null models to accurately detect inter-protein contacts within and between species', 'Avila-Herrera, Aram|Pollard, Katherine S', 'Bioinformatics Graduate Program, University of California, San Francisco, USA. aram.avilaherrera@ucsf.edu.|Bioinformatics Graduate Program, University of California, San Francisco, USA. kpollard@gladstone.ucsf.edu.', 'United States|United States', 0, 'APOBEC-3G Deaminase|Cytidine Deaminase|Evolution, Molecular|Host-Pathogen Interactions|Humans|Phylogeny|Selection, Genetic|Ubiquitination|vif Gene Products, Human Immunodeficiency Virus', 'Journal Article', '10.1186/s12859-015-0677-y', '26303588', 'When biomolecules physically interact, natural selection operates on them jointly. Contacting positions in protein and RNA structures exhibit correlated patterns of sequence evolution due to constraints imposed by the interaction, and molecular arms races can develop between interacting proteins in pathogens and their hosts. To evaluate how well methods developed to detect coevolving residues within proteins can be adapted for cross-species, inter-protein analysis, we used statistical criteria to quantify the performance of these methods in detecting inter-protein residues within 8 angstroms of each other in the co-crystal structures of 33 bacterial protein interactions. We also evaluated their performance for detecting known residues at the interface of a host-virus protein complex with a partially solved structure.'),
(96851, 2015, 'bmc', 'OD-seq: outlier detection in multiple sequence alignments', 'Jehl, Peter|Sievers, Fabian|Higgins, Desmond G', 'UCD Conway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin, 4, Ireland. peter.jehl@ucdconnect.ie.|UCD Conway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin, 4, Ireland. fabian.sievers@ucd.ie.|UCD Conway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin, 4, Ireland. des.higgins@ucd.ie.', 'Ireland|Ireland|Ireland', 0, 'ATP-Binding Cassette Transporters|Algorithms|Amino Acid Sequence|Humans|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/s12859-015-0702-1', '26303676', 'Multiple sequence alignments (MSA) are widely used in sequence analysis for a variety of tasks. Outlier sequences can make downstream analyses unreliable or make the alignments less accurate while they are being constructed. This paper describes a simple method for automatically detecting outliers and accompanying software called OD-seq. It is based on finding sequences whose average distance to the rest of the sequences in a dataset, is anomalous.'),
(96852, 2015, 'bmc', 'GelJ--a tool for analyzing DNA fingerprint gel images', 'Heras, Jónathan|Domínguez, César|Mata, Eloy|Pascual, Vico|Lozano, Carmen|Torres, Carmen|Zarazaga, Myriam', 'Department of Mathematics and Computer Science, University of La Rioja, Ed. Vives. C/ Luís de Ulloa 2, Logroño, 26004, Spain. jonathan.heras@unirioja.es.|Department of Mathematics and Computer Science, University of La Rioja, Ed. Vives. C/ Luís de Ulloa 2, Logroño, 26004, Spain. cedomin@unirioja.es.|Department of Mathematics and Computer Science, University of La Rioja, Ed. Vives. C/ Luís de Ulloa 2, Logroño, 26004, Spain. eloy.mata@unirioja.es.|Department of Mathematics and Computer Science, University of La Rioja, Ed. Vives. C/ Luís de Ulloa 2, Logroño, 26004, Spain. vico.pascual@unirioja.es.|Biochemistry and Molecular Biology Area, University of La Rioja, Ed. Científico Tecnológico-CCT. C/ Madre de Dios, 53, Logroño, 26006, Spain. carmen.lozano@unirioja.es.|Biochemistry and Molecular Biology Area, University of La Rioja, Ed. Científico Tecnológico-CCT. C/ Madre de Dios, 53, Logroño, 26006, Spain. carmen.torres@unirioja.es.|Biochemistry and Molecular Biology Area, University of La Rioja, Ed. Científico Tecnológico-CCT. C/ Madre de Dios, 53, Logroño, 26006, Spain. myriam.zarazaga@unirioja.es.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 86, 'Cluster Analysis|DNA|DNA Fingerprinting|Electrophoresis, Gel, Pulsed-Field|Internet|Software', 'Journal Article', '10.1186/s12859-015-0703-0', '26307353', 'DNA fingerprinting is a technique for comparing DNA patterns that has applications in a wide variety of contexts. Several commercial and freely-available tools can be used to analyze DNA fingerprint gel images, however, commercial tools are expensive and usually difficult to use, and, free tools support the basic functionality for DNA fingerprint analysis, but lack some instrumental features to obtain accurate results.'),
(96853, 2015, 'bmc', 'Predicting protein function via downward random walks on a gene ontology', 'Yu, Guoxian|Zhu, Hailong|Domeniconi, Carlotta|Liu, Jiming', 'College of Computer and Information Sciences, Southwest University, Beibei, Chongqing, China. gxyu@swu.edu.cn.|Department of Computer Science, Hong Kong Baptist University, Hong Kong, Hong Kong. hlzhu@comp.hkbu.edu.hk.|Department of Computer Science, George Mason University, Fairfax, VA, USA. carlotta@cs.gmu.edu.|Department of Computer Science, Hong Kong Baptist University, Hong Kong, Hong Kong. jiming@comp.hkbu.edu.hk.', 'China|Hong Kong|United States|Hong Kong', 0, 'Algorithms|Gene Ontology|Humans|Molecular Sequence Annotation|Proteins|Proteomics|Yeasts', 'Journal Article', '10.1186/s12859-015-0713-y', '26310806', 'High-throughput bio-techniques accumulate ever-increasing amount of genomic and proteomic data. These data are far from being functionally characterized, despite the advances in gene (or genes product proteins) functional annotations. Due to experimental techniques and to the research bias in biology, the regularly updated functional annotation databases, i.e., the Gene Ontology (GO), are far from being complete. Given the importance of protein functions for biological studies and drug design, proteins should be more comprehensively and precisely annotated.'),
(96854, 2015, 'bmc', 'Erratum to: Evaluation of BLAST-based edge-weighting metrics used for homology inference with the Markov Clustering algorithm', 'Gibbons, Theodore R|Mount, Stephen M|Cooper, Endymion D|Delwiche, Charles F', 'Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, 20742, Maryland. trgibbons@gmail.com.|Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, 20742, Maryland. smount@umd.edu.|Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, 20742, Maryland. endymion.dante.cooper@gmail.com.|Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, 20742, Maryland. delwiche@umd.edu.', '-|-|-|-', 2, '-', 'Published Erratum', '10.1186/s12859-015-0690-1', '26315999', '-'),
(96855, 2015, 'bmc', 'Quantitative gene set analysis generalized for repeated measures, confounder adjustment, and continuous covariates', 'Turner, Jacob A|Bolen, Christopher R|Blankenship, Derek M', 'Baylor Research Institute, 3310 Live Oak, Dallas, 75204, TX, USA. jacob.turner1@baylorhealth.edu.|Department of Microbiology and Immunology, Stanford University School, Stanford, 94305, CA, USA. cbolen@stanford.edu.|Baylor Research Institute, 3310 Live Oak, Dallas, 75204, TX, USA. DerekBl@BaylorHealth.edu.', 'United States|United States|United States', 0, 'Age Factors|Algorithms|Female|Gene Expression|Genomics|Humans|Influenza A Virus, H3N2 Subtype|Influenza, Human|Linear Models|Longitudinal Studies|Male|Sex Factors', 'Journal Article', '10.1186/s12859-015-0707-9', '26316107', 'Gene set analysis (GSA) of gene expression data can be highly powerful when the biological signal is weak compared to other sources of variability in the data. However, many gene set analysis approaches utilize permutation tests which are not appropriate for complex study designs. For example, the correlation of subjects is broken when comparing time points within a longitudinal study. Linear mixed models provide a method to analyze longitudinal studies as well as adjust for potential confounding factors and account for sources of variability that are not of primary interest. Currently, there are no known gene set analysis approaches that fully account for these study design and analysis aspects. In order to do so, we generalize the QuSAGE gene set analysis algorithm, denoted Q-Gen, and provide the necessary estimation adjustments to incorporate linear mixed model analyses.'),
(96856, 2015, 'bmc', 'Prior knowledge driven Granger causality analysis on gene regulatory network discovery', 'Yao, Shun|Yoo, Shinjae|Yu, Dantong', 'Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, 11790, NY, USA. yaoshun88@gmail.com.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. sjyoo@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.', 'United States|United States|United States', 0, 'Gene Expression Profiling|Gene Regulatory Networks|Regression Analysis', 'Journal Article', '10.1186/s12859-015-0710-1', '26316173', 'Our study focuses on discovering gene regulatory networks from time series gene expression data using the Granger causality (GC) model. However, the number of available time points (T) usually is much smaller than the number of target genes (n) in biological datasets. The widely applied pairwise GC model (PGC) and other regularization strategies can lead to a significant number of false identifications when n>T.'),
(96857, 2015, 'bmc', 'VisRseq: R-based visual framework for analysis of sequencing data', 'Younesy, Hamid|Möller, Torsten|Lorincz, Matthew C|Karimi, Mohammad M|Jones, Steven J M', 'Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.', 'United States|United States|United States|United States|United States', 19, 'Animals|Computational Biology|Computer Graphics|Databases, Nucleic Acid|Embryonic Stem Cells|Gene Expression Profiling|Gene Frequency|Genome|Germ Cells|High-Throughput Nucleotide Sequencing|Internet|Mice|Mice, Inbred C57BL|Sequence Analysis, DNA|Software|Trophoblasts|User-Computer Interface|Workflow', 'Journal Article', '10.1186/1471-2105-16-S11-S2', '26328469', 'Several tools have been developed to enable biologists to perform initial browsing and exploration of sequencing data. However the computational tool set for further analyses often requires significant computational expertise to use and many of the biologists with the knowledge needed to interpret these data must rely on programming experts.'),
(96858, 2015, 'bmc', 'Pan-Tetris: an interactive visualisation for Pan-genomes', 'Hennig, André|Bernhardt, Jörg|Nieselt, Kay', 'Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.', 'United States|United States|United States', 11, 'Algorithms|Bacterial Proteins|Computational Biology|Computer Graphics|Genome, Bacterial|Genomics|Software|Staphylococcus aureus', 'Journal Article', '10.1186/1471-2105-16-S11-S3', '26328606', 'Large-scale genome projects have paved the way to microbial pan-genome analyses. Pan-genomes describe the union of all genes shared by all members of the species or taxon under investigation. They offer a framework to assess the genomic diversity of a given collection of individual genomes and moreover they help to consolidate gene predictions and annotations. The computation of pan-genomes is often a challenge, and many techniques that use a global alignment-independent approach run the risk of not separating paralogs from orthologs. Also alignment-based approaches which take the gene neighbourhood into account often need additional manual curation of the results. This is quite time consuming and so far there is no visualisation tool available that offers an interactive GUI for the pan-genome to support curating pan-genomic computations or annotations of orthologous genes.'),
(96859, 2015, 'bmc', 'Epiviz: a view inside the design of an integrated visual analysis software for genomics', 'Chelaru, Florin|Corrada Bravo, Héctor', 'Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.', 'United States|United States', 0, 'Algorithms|Computational Biology|Computer Graphics|Genome, Human|Genomics|Humans|Information Storage and Retrieval|Proteins|Software|Workflow', 'Journal Article', '10.1186/1471-2105-16-S11-S4', '26328750', 'Computational and visual data analysis for genomics has traditionally involved a combination of tools and resources, of which the most ubiquitous consist of genome browsers, focused mainly on integrative visualization of large numbers of big datasets, and computational environments, focused on data modeling of a small number of moderately sized datasets. Workflows that involve the integration and exploration of multiple heterogeneous data sources, small and large, public and user specific have been poorly addressed by these tools. In our previous work, we introduced Epiviz, which bridges the gap between the two types of tools, simplifying these workflows.'),
(96860, 2015, 'bmc', 'XCluSim: a visual analytics tool for interactively comparing multiple clustering results of bioinformatics data', 'LYi, Sehi|Ko, Bongkyung|Shin, DongHwa|Cho, Young-Joon|Lee, Jaeyong|Kim, Bohyoung|Seo, Jinwook', 'Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.', 'United States|United States|United States|United States|United States|United States|United States', 17, 'Algorithms|Cluster Analysis|Computational Biology|Computer Graphics|Genome, Human|Humans|Software', 'Journal Article', '10.1186/1471-2105-16-S11-S5', '26328893', 'Though cluster analysis has become a routine analytic task for bioinformatics research, it is still arduous for researchers to assess the quality of a clustering result. To select the best clustering method and its parameters for a dataset, researchers have to run multiple clustering algorithms and compare them. However, such a comparison task with multiple clustering results is cognitively demanding and laborious.'),
(96861, 2015, 'bmc', 'BactoGeNIE: a large-scale comparative genome visualization for big displays', 'Aurisano, Jillian|Reda, Khairi|Johnson, Andrew|Marai, Elisabeta G|Leigh, Jason', 'Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.', 'United States|United States|United States|United States|United States', 0, 'Computational Biology|Computer Graphics|Escherichia coli|Escherichia coli Proteins|Genome, Bacterial|Genomics|Software', 'Journal Article', '10.1186/1471-2105-16-S11-S6', '26329021', 'The volume of complete bacterial genome sequence data available to comparative genomics researchers is rapidly increasing. However, visualizations in comparative genomics--which aim to enable analysis tasks across collections of genomes--suffer from visual scalability issues. While large, multi-tiled and high-resolution displays have the potential to address scalability issues, new approaches are needed to take advantage of such environments, in order to enable the effective visual analysis of large genomics datasets.'),
(96862, 2015, 'bmc', 'Integrated visual analysis of protein structures, sequences, and feature data', 'Stolte, Christian|Sabir, Kenneth S|Heinrich, Julian|Hammang, Christopher J|Schafferhans, Andrea|ODonoghue, Seán I', 'Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.', 'United States|United States|United States|United States|United States|United States', 4, 'Amyloid beta-Protein Precursor|B-Lymphocytes|Computational Biology|Computer Graphics|Databases, Protein|Humans|Protein Conformation|Protein Processing, Post-Translational|Sequence Analysis, Protein|Software|src-Family Kinases', 'Journal Article', '10.1186/1471-2105-16-S11-S7', '26329268', 'To understand the molecular mechanisms that give rise to a proteins function, biologists often need to (i) find and access all related atomic-resolution 3D structures, and (ii) map sequence-based features (e.g., domains, single-nucleotide polymorphisms, post-translational modifications) onto these structures.'),
(96863, 2015, 'bmc', 'Physically-based in silico light sheet microscopy for visualizing fluorescent brain models', 'Abdellah, Marwan|Bilgili, Ahmet|Eilemann, Stefan|Markram, Henry|Schürmann, Felix', 'Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.', 'United States|United States|United States|United States|United States', 9, 'Animals|Brain|Brain Mapping|Computer Simulation|Fluorescence|Fluorescent Dyes|Image Processing, Computer-Assisted|Lasers|Microscopy, Fluorescence|Models, Theoretical|Monte Carlo Method|Neuroimaging|Rats', 'Journal Article', '10.1186/1471-2105-16-S11-S8', '26329404', 'We present a physically-based computational model of the light sheet fluorescence microscope (LSFM). Based on Monte Carlo ray tracing and geometric optics, our method simulates the operational aspects and image formation process of the LSFM. This simulated, in silico LSFM creates synthetic images of digital fluorescent specimens that can resemble those generated by a real LSFM, as opposed to established visualization methods producing visually-plausible images. We also propose an accurate fluorescence rendering model which takes into account the intrinsic characteristics of fluorescent dyes to simulate the light interaction with fluorescent biological specimen.'),
(96864, 2015, 'bmc', 'Visual parameter optimisation for biomedical image processing', 'Pretorius, A J|Zhou, Y|Ruddle, R A', 'Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.', 'United States|United States|United States', 0, 'Algorithms|Cell Nucleus|Cells, Cultured|Colonic Neoplasms|Computer Graphics|Computer Simulation|Humans|Image Processing, Computer-Assisted|Liver|Software', 'Journal Article', '10.1186/1471-2105-16-S11-S9', '26329538', 'Biomedical image processing methods require users to optimise input parameters to ensure high-quality output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships between input and output.'),
(96865, 2015, 'bmc', 'Multi-location gram-positive and gram-negative bacterial protein subcellular localization using gene ontology and multi-label classifier ensemble', 'Wang, Xiao|Zhang, Jun|Li, Guo-Zheng', 'Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.|Computational Science Center, Brookhaven National Laboratory, Upton, 11793, NY, USA. dtyu@bnl.gov.', 'United States|United States|United States', 19, 'Bacterial Proteins|Gene Ontology|Gram-Negative Bacteria|Gram-Positive Bacteria|Intracellular Space|Protein Transport', 'Journal Article', '10.1186/1471-2105-16-S12-S1', '26329681', 'It has become a very important and full of challenge task to predict bacterial protein subcellular locations using computational methods. Although there exist a lot of prediction methods for bacterial proteins, the majority of these methods can only deal with single-location proteins. But unfortunately many multi-location proteins are located in the bacterial cells. Moreover, multi-location proteins have special biological functions capable of helping the development of new drugs. So it is necessary to develop new computational methods for accurately predicting subcellular locations of multi-location bacterial proteins.'),
(96866, 2015, 'bmc', 'CompGO: an R package for comparing and visualizing Gene Ontology enrichment differences between DNA binding experiments', 'Waardenberg, Ashley J|Basset, Samuel D|Bouveret, Romaric|Harvey, Richard P', 'Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia. awaardenberg@cmri.org.au.|Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia. sam.d.bassett@gmail.com.|Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia. r.bouveret@victorchang.edu.au.|Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia. r.harvey@victorchang.edu.au.', 'Australia|Australia|Australia|Australia', 0, 'Computational Biology|DNA|Gene Ontology|Genes, Homeobox|Genomics|Humans', 'Journal Article', '10.1186/s12859-015-0701-2', '26329719', 'Gene ontology (GO) enrichment is commonly used for inferring biological meaning from systems biology experiments. However, determining differential GO and pathway enrichment between DNA-binding experiments or using the GO structure to classify experiments has received little attention.'),
(96867, 2015, 'bmc', 'Multiscale modeling of mucosal immune responses', 'Mei, Yongguo|Abedi, Vida|Carbo, Adria|Zhang, Xiaoying|Lu, Pinyi|Philipson, Casandra|Hontecillas, Raquel|Hoops, Stefan|Liles, Nathan|Bassaganya-Riera, Josep', 'Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia. r.harvey@victorchang.edu.au.|Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia. r.harvey@victorchang.edu.au.|Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia. r.harvey@victorchang.edu.au.|Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia. r.harvey@victorchang.edu.au.|Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia. r.harvey@victorchang.edu.au.|Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia. r.harvey@victorchang.edu.au.|Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia. r.harvey@victorchang.edu.au.|Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia. r.harvey@victorchang.edu.au.|Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia. r.harvey@victorchang.edu.au.|Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia. r.harvey@victorchang.edu.au.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 0, 'CD4-Positive T-Lymphocytes|Computer Simulation|Humans|Immunity, Mucosal|Models, Biological|Signal Transduction', 'Journal Article', '10.1186/1471-2105-16-S12-S2', '26329787', 'Computational techniques are becoming increasingly powerful and modeling tools for biological systems are of greater needs. Biological systems are inherently multiscale, from molecules to tissues and from nano-seconds to a lifespan of several years or decades. ENISI MSM integrates multiple modeling technologies to understand immunological processes from signaling pathways within cells to lesion formation at the tissue level. This paper examines and summarizes the technical details of ENISI, from its initial version to its latest cutting-edge implementation.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96868, 2015, 'bmc', 'New in silico approach to assessing RNA secondary structures with non-canonical base pairs', 'Rybarczyk, Agnieszka|Szostak, Natalia|Antczak, Maciej|Zok, Tomasz|Popenda, Mariusz|Adamiak, Ryszard|Blazewicz, Jacek|Szachniuk, Marta', 'Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. arybarczyk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. nszostak@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mantczak@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. tzok@cs.put.poznan.pl.|Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704, Poznan, Poland. mariusz.popenda@ibch.poznan.pl.|Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704, Poznan, Poland. ryszard.adamiak@ibch.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. jblazewicz@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.', 'Poland|Poland|Poland|Poland|Poland|Poland|Poland|Poland', 16, 'Base Pairing|Computer Simulation|Protein Structure, Secondary|RNA', 'Journal Article', '10.1186/s12859-015-0718-6', '26329823', 'The function of RNA is strongly dependent on its structure, so an appropriate recognition of this structure, on every level of organization, is of great importance. One particular concern is the assessment of base-base interactions, described as the secondary structure, the knowledge of which greatly facilitates an interpretation of RNA function and allows for structure analysis on the tertiary level. The RNA secondary structure can be predicted from a sequence using in silico methods often adjusted with experimental data, or assessed from 3D structure atom coordinates. Computational approaches typically consider only canonical, Watson-Crick and wobble base pairs. Handling of non-canonical interactions, important for a full description of RNA structure, is still very difficult.'),
(96869, 2015, 'bmc', 'Protein complex detection in PPI networks based on data integration and supervised learning method', 'Yu, Feng|Yang, Zhi|Hu, Xiao|Sun, Yuan|Lin, Hong|Wang, Jian', 'Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.', 'Poland|Poland|Poland|Poland|Poland|Poland', 8, 'Gene Ontology|Protein Interaction Maps|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Supervised Machine Learning', 'Journal Article', '10.1186/1471-2105-16-S12-S3', '26329886', 'Revealing protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to predict protein complexes from protein-protein interaction (PPI) networks. However, the small amount of known physical interactions may limit protein complex detection.'),
(96870, 2015, 'bmc', 'Ensemble learning for prediction of the bioactivity capacity of herbal medicines from chromatographic fingerprints', 'Chen, Hao|Poon, Josiah|Poon, Simon K|Cui, Lizhi|Fan, Kei|Sze, Daniel', 'Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.', 'Poland|Poland|Poland|Poland|Poland|Poland', 3, 'Algorithms|Astragalus Plant|Chromatography, High Pressure Liquid|Drugs, Chinese Herbal|Gene Expression Regulation|Linear Models|Multivariate Analysis|Plant Extracts|Plants, Medicinal', 'Journal Article', '10.1186/1471-2105-16-S12-S4', '26329995', 'Recent quality control of complex mixtures, including herbal medicines, is not limited to chemical chromatographic definition of one or two selected compounds, multivariate linear regression methods with dimension reduction or regularisation have been used to predict the bioactivity capacity from the chromatographic fingerprints of the herbal extracts. The challenge of this type of analysis requires a multi-dimensional approach at two levels: firstly each herb comprises complex mixtures of active and non-active chemical components, and secondly there are many factors relating to the growth, production, and processing of the herbal products. All these factors result in the significantly diverse concentrations of bioactive compounds in the herbal products. Therefore, it is imminent to have a predictive model with better generalisation that can accurately predict the bioactivity capacity of samples when only the chemical fingerprints data are available.'),
(96871, 2015, 'bmc', 'Dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks', 'Shen, Xianjun|Yi, Li|Yi, Yang|Yang, Jincai|He, Tingting|Hu, Xiaohua', 'Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.', 'Poland|Poland|Poland|Poland|Poland|Poland', 0, 'Algorithms|Gene Ontology|Protein Interaction Maps|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Supervised Machine Learning', 'Journal Article', '10.1186/1471-2105-16-S12-S5', '26330105', 'The identification of protein functional modules would be a great aid in furthering our knowledge of the principles of cellular organization. Most existing algorithms for identifying protein functional modules have a common defect -- once a protein node is assigned to a functional module, there is no chance to move the protein to the other functional modules during the follow-up processes, which lead the erroneous partitioning occurred at previous step to accumulate till to the end.'),
(96872, 2015, 'bmc', 'Highlights from the 5th Symposium on Biological Data Visualization: Part 1', 'Aerts, Jan|Marai, G Elisabeta|Nieselt, Kay|Nielsen, Cydney|Streit, Marc|Weiskopf, Daniel', 'Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.', 'Poland|Poland|Poland|Poland|Poland|Poland', 0, '-', 'Introductory Journal Article', '10.1186/1471-2105-16-S11-S1', '26330192', '-'),
(96873, 2015, 'bmc', 'GRAPHIE: graph based histology image explorer', 'Ding, Hao|Wang, Chao|Huang, Kun|Machiraju, Raghu', 'Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.|Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. mszachniuk@cs.put.poznan.pl.', 'Poland|Poland|Poland|Poland', 7, 'Algorithms|Animals|Computational Biology|Computer Graphics|Computer Simulation|Databases, Factual|Image Processing, Computer-Assisted|Information Storage and Retrieval|Models, Biological|Retina|Software|User-Computer Interface|Zebrafish', 'Journal Article', '10.1186/1471-2105-16-S11-S10', '26330277', 'Histology images comprise one of the important sources of knowledge for phenotyping studies in systems biology. However, the annotation and analyses of histological data have remained a manual, subjective and relatively low-throughput process.'),
(96874, 2015, 'bmc', 'HiCdat: a fast and easy-to-use Hi-C data analysis tool', 'Schmid, Marc W|Grob, Stefan|Grossniklaus, Ueli', 'Institute of Plant Biology, University of Zurich, Zollikerstrasse 107, Zürich, 8008, Switzerland. marcschmid@gmx.ch.|Institute of Plant Biology, University of Zurich, Zollikerstrasse 107, Zürich, 8008, Switzerland. sgrob@botinst.uzh.ch.|Institute of Plant Biology, University of Zurich, Zollikerstrasse 107, Zürich, 8008, Switzerland. grossnik@botinst.uzh.ch.', 'Switzerland|Switzerland|Switzerland', 0, 'Chromosomes|Genomics|Humans|RNA|Software|Statistics as Topic', 'Journal Article', '10.1186/s12859-015-0678-x', '26334796', 'The study of nuclear architecture using Chromosome Conformation Capture (3C) technologies is a novel frontier in biology. With further reduction in sequencing costs, the potential of Hi-C in describing nuclear architecture as a phenotype is only about to unfold. To use Hi-C for phenotypic comparisons among different cell types, conditions, or genetic backgrounds, Hi-C data processing needs to be more accessible to biologists.'),
(96875, 2015, 'bmc', 'A reverse-engineering approach to dissect post-translational modulators of transcription factors activity from transcriptional data', 'Gambardella, Gennaro|Peluso, Ivana|Montefusco, Sandro|Bansal, Mukesh|Medina, Diego L|Lawrence, Neil|di Bernardo, Diego', 'The Telethon Institute of Genetics and Medicine, Naples, Italy. gennaro.1.gambardella@kcl.ac.uk.|The Telethon Institute of Genetics and Medicine, Naples, Italy. peluso@tigem.it.|The Telethon Institute of Genetics and Medicine, Naples, Italy. montefusco@tigem.it.|Columbia Initiative in Systems Biology and Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA. mb3113@c2b2.columbia.edu.|The Telethon Institute of Genetics and Medicine, Naples, Italy. medina@tigem.it.|Department of Computer Science, University of Sheffield, Sheffield, UK. N.Lawrence@dcs.sheffield.ac.uk.|The Telethon Institute of Genetics and Medicine, Naples, Italy. dibernardo@tigem.it.', 'Italy|Italy|Italy|United Kingdom|Italy|United Kingdom|Italy', 0, 'Gene Expression Regulation|Protein Processing, Post-Translational|Signal Transduction|Transcription Factors', 'Journal Article', '10.1186/s12859-015-0700-3', '26334955', 'Transcription factors (TFs) act downstream of the major signalling pathways functioning as master regulators of cell fate. Their activity is tightly regulated at the transcriptional, post-transcriptional and post-translational level. Proteins modifying TF activity are not easily identified by experimental high-throughput methods.'),
(96876, 2015, 'bmc', 'EMSAR: estimation of transcript abundance from RNA-seq data by mappability-based segmentation and reclustering', 'Lee, Soohyun|Seo, Chae Hwa|Alver, Burak Han|Lee, Sanghyuk|Park, Peter J', 'Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.|Emerging Technology Center, DNA link, Seoul, South Korea.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.|Emerging Technology Center, DNA link, Seoul, South Korea.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. peter_park@hms.harvard.edu.', 'United States|United States|United States|United States|United States', 10, 'Base Sequence|Gene Expression Profiling|Genome|Protein Isoforms|RNA|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1186/s12859-015-0704-z', '26335049', 'RNA-seq has been widely used for genome-wide expression profiling. RNA-seq data typically consists of tens of millions of short sequenced reads from different transcripts. However, due to sequence similarity among genes and among isoforms, the source of a given read is often ambiguous. Existing approaches for estimating expression levels from RNA-seq reads tend to compromise between accuracy and computational cost.'),
(96877, 2015, 'bmc', 'WiseScaffolder: an algorithm for the semi-automatic scaffolding of Next Generation Sequencing data', 'Farrant, Gregory K|Hoebeke, Mark|Partensky, Frédéric|Andres, Gwendoline|Corre, Erwan|Garczarek, Laurence', 'Sorbonne Universités, UPMC Univ. Paris 06, UMR 7144, Station Biologique, CS 90074, 29688, Roscoff cedex, France.|CNRS, FR 2424, ABiMS Platform, Station Biologique, Place Georges Teissier, CS 90074, 29688, Roscoff cedex, France.|Sorbonne Universités, UPMC Univ. Paris 06, UMR 7144, Station Biologique, CS 90074, 29688, Roscoff cedex, France.|CNRS, FR 2424, ABiMS Platform, Station Biologique, Place Georges Teissier, CS 90074, 29688, Roscoff cedex, France.|CNRS, FR 2424, ABiMS Platform, Station Biologique, Place Georges Teissier, CS 90074, 29688, Roscoff cedex, France.|Sorbonne Universités, UPMC Univ. Paris 06, UMR 7144, Station Biologique, CS 90074, 29688, Roscoff cedex, France. laurence.garczarek@sb-roscoff.fr.', 'France|France|France|France|France|France', 0, 'Chromosome Mapping|Genome|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Synechococcus', 'Journal Article', '10.1186/s12859-015-0705-y', '26335184', 'The sequencing depth provided by high-throughput sequencing technologies has allowed a rise in the number of de novo sequenced genomes that could potentially be closed without further sequencing. However, genome scaffolding and closure require costly human supervision that often results in genomes being published as drafts. A number of automatic scaffolders were recently released, which improved the global quality of genomes published in the last few years. Yet, none of them reach the efficiency of manual scaffolding.'),
(96878, 2015, 'bmc', 'Multi-objective optimization for RNA design with multiple target secondary structures', 'Taneda, Akito', 'Graduate School of Science and Technology, Hirosaki University, 3 Bunkyo-cho, Hirosaki, Aomori, Japan. taneda@eit.hirosaki-u.ac.jp.', 'Japan', 17, 'Algorithms|Base Sequence|Nucleic Acid Conformation|Protein Structure, Secondary|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-015-0706-x', '26335276', 'RNAs are attractive molecules as the biological parts for synthetic biology. In particular, the ability of conformational changes, which can be encoded in designer RNAs, enables us to create multistable molecular switches that function in biological circuits. Although various algorithms for designing such RNA switches have been proposed, the previous algorithms optimize the RNA sequences against the weighted sum of objective functions, where empirical weights among objective functions are used. In addition, an RNA design algorithm for multiple pseudoknot targets is currently not available.'),
(96879, 2015, 'bmc', 'SFESA: a web server for pairwise alignment refinement by secondary structure shifts', 'Tong, Jing|Pei, Jimin|Grishin, Nick V', 'Department of Biophysics and Department of Biochemistry, University of Texas Southwestern Medical Center at Dallas, 6001 Forest Park Road, Dallas, TX, 75390-9050, USA. jing.tong@utsouthwestern.edu.|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center at Dallas, 6001 Forest Park Road, Dallas, TX, 75390-9050, USA. jpei@chop.swmed.edu.|Department of Biophysics and Department of Biochemistry, University of Texas Southwestern Medical Center at Dallas, 6001 Forest Park Road, Dallas, TX, 75390-9050, USA. grishin@chop.swmed.edu.', 'United States|United States|United States', 0, 'Amino Acid Sequence|Computational Biology|Databases, Nucleic Acid|Protein Structure, Secondary|Proteins|Sequence Alignment', 'Journal Article', '10.1186/s12859-015-0711-0', '26335387', 'Protein sequence alignment is essential for a variety of tasks such as homology modeling and active site prediction. Alignment errors remain the main cause of low-quality structure models. A bioinformatics tool to refine alignments is needed to make protein alignments more accurate.'),
(96880, 2015, 'bmc', 'Scientific workflow optimization for improved peptide and protein identification', 'Holl, Sonja|Mohammed, Yassene|Zimmermann, Olav|Palmblad, Magnus', 'Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, 52425, Jülich, Germany. s.holl@fz-juelich.de.|Center for Proteomics and Metabolomics, Leiden University Medical Center, PO Box 9600, 2300, RC, Leiden, The Netherlands. y.mohammed@lumc.nl.|Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, 52425, Jülich, Germany. olav.zimmermann@fz-juelich.de.|Center for Proteomics and Metabolomics, Leiden University Medical Center, PO Box 9600, 2300, RC, Leiden, The Netherlands. n.m.palmblad@lumc.nl.', 'Germany|Netherlands|Germany|Netherlands', 5, 'Algorithms|Computational Biology|Databases, Protein|Humans|Mass Spectrometry|Models, Statistical|Peptide Fragments|Programming Languages|Proteins|Proteomics|Software|User-Computer Interface|Workflow', 'Journal Article', '10.1186/s12859-015-0714-x', '26335531', 'Peptide-spectrum matching is a common step in most data processing workflows for mass spectrometry-based proteomics. Many algorithms and software packages, both free and commercial, have been developed to address this task. However, these algorithms typically require the user to select instrument- and sample-dependent parameters, such as mass measurement error tolerances and number of missed enzymatic cleavages. In order to select the best algorithm and parameter set for a particular dataset, in-depth knowledge about the data as well as the algorithms themselves is needed. Most researchers therefore tend to use default parameters, which are not necessarily optimal.'),
(96881, 2015, 'bmc', 'Enabling network inference methods to handle missing data and outliers', 'Folch-Fortuny, Abel|Villaverde, Alejandro F|Ferrer, Alberto|Banga, Julio R', 'Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Camino de Vera s/n, Valencia, 46022, Spain. abfolfor@upv.es.|BioProcess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain.|Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Camino de Vera s/n, Valencia, 46022, Spain.|BioProcess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain.', 'Spain|Spain|Spain|Spain', 0, 'Gene Regulatory Networks|Humans|Information Theory|Statistics as Topic', 'Journal Article', '10.1186/s12859-015-0717-7', '26335628', 'The inference of complex networks from data is a challenging problem in biological sciences, as well as in a wide range of disciplines such as chemistry, technology, economics, or sociology. The quantity and quality of the data greatly affect the results. While many methodologies have been developed for this task, they seldom take into account issues such as missing data or outlier detection and correction, which need to be properly addressed before network inference.'),
(96882, 2015, 'bmc', 'CoSREM: a graph mining algorithm for the discovery of combinatorial splicing regulatory elements', 'Badr, Eman|Heath, Lenwood S', 'Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA.|Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA. heath@vt.edu.', 'United States|United States', 3, 'Algorithms|Computer Graphics|Exons|Gene Expression Regulation, Neoplastic|Humans|Introns|Neoplasm Proteins|Neoplasms|RNA Splicing|Regulatory Sequences, Nucleic Acid', 'Journal Article', '10.1186/s12859-015-0698-6', '26337677', 'Alternative splicing (AS) is a post-transcriptional regulatory mechanism for gene expression regulation. Splicing decisions are affected by the combinatorial behavior of different splicing factors that bind to multiple binding sites in exons and introns. These binding sites are called splicing regulatory elements (SREs). Here we develop CoSREM (Combinatorial SRE Miner), a graph mining algorithm to discover combinatorial SREs in human exons. Our model does not assume a fixed length of SREs and incorporates experimental evidence as well to increase accuracy. CoSREM is able to identify sets of SREs and is not limited to SRE pairs as are current approaches.'),
(96883, 2015, 'bmc', 'Seq-ing improved gene expression estimates from microarrays using machine learning', 'Korir, Paul K|Geeleher, Paul|Seoighe, Cathal', 'School of Biochemistry and Cell Biology, University College Cork, Western Road, Cork, Ireland. paul.korir@gmail.com.|Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL-60637, USA. paul.geeleher@gmail.com.|School of Mathematics, Statistics and Applied Mathematics, University Road, Galway, Ireland. cathal.seoighe@nuigalway.ie.', 'Ireland|United States|Ireland', 0, 'Computational Biology|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Machine Learning|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1186/s12859-015-0712-z', '26338512', 'Quantifying gene expression by RNA-Seq has several advantages over microarrays, including greater dynamic range and gene expression estimates on an absolute, rather than a relative scale. Nevertheless, microarrays remain in widespread use, demonstrated by the ever-growing numbers of samples deposited in public repositories.'),
(96884, 2015, 'bmc', 'Statistical analysis of a Bayesian classifier based on the expression of miRNAs', 'Ricci, Leonardo|Del Vescovo, Valerio|Cantaloni, Chiara|Grasso, Margherita|Barbareschi, Mattia|Denti, Michela Alessandra', 'Department of Physics, University of Trento, Trento, I-38123, Italy. leonardo.ricci@unitn.it.|Centre for Integrative Biology, University of Trento, Trento, I-38123, Italy. valerio.delvescovo@unitn.it.|Unit of Surgical Pathology, Trento, I-38122, Italy. cantaloni.chiara@gmail.com.|Centre for Integrative Biology, University of Trento, Trento, I-38123, Italy. margherita.grasso@unitn.it.|Unit of Surgical Pathology, Trento, I-38122, Italy. mattia.barbareschi@apss.tn.it.|Centre for Integrative Biology, University of Trento, Trento, I-38123, Italy. michela.denti@unitn.it.', 'Italy|Italy|Italy|Italy|Italy|Italy', 4, 'Adenocarcinoma|Bayes Theorem|Carcinoma, Squamous Cell|Gene Expression Regulation, Neoplastic|Genetic Markers|Humans|Lung Neoplasms|MicroRNAs|Models, Statistical|Prognosis|Software', 'Journal Article', '10.1186/s12859-015-0715-9', '26338526', 'During the last decade, many scientific works have concerned the possible use of miRNA levels as diagnostic and prognostic tools for different kinds of cancer. The development of reliable classifiers requires tackling several crucial aspects, some of which have been widely overlooked in the scientific literature: the distribution of the measured miRNA expressions and the statistical uncertainty that affects the parameters that characterize a classifier. In this paper, these topics are analysed in detail by discussing a model problem, i.e. the development of a Bayesian classifier that, on the basis of the expression of miR-205, miR-21 and snRNA U6, discriminates samples into two classes of pulmonary tumors: adenocarcinomas and squamous cell carcinomas.'),
(96885, 2015, 'bmc', 'Finite mixture clustering of human tissues with different levels of IGF-1 splice variants mRNA transcripts', 'Pelosi, Michele|Alfò, Marco|Martella, Francesca|Pappalardo, Elisa|Musarò, Antonio', 'Institute Pasteur Cenci-Bolognetti, DAHFMO-Unit of Histology and Medical Embryology, IIM, Sapienza University of Rome, Via A. Scarpa 16, 00161, Rome, Italy. michi_pelo@libero.it.|Dipartimento di Scienze Statistiche, Sapienza University of Rome, P. le A. Moro 5, 00185, Rome, Italy. marco.alfo@uniroma1.it.|Dipartimento di Scienze Statistiche, Sapienza University of Rome, P. le A. Moro 5, 00185, Rome, Italy. francesca.martella@uniroma1.it.|Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford, OX1 3RF, UK. elisa.pappalardo@path.ox.ac.uk.|Institute Pasteur Cenci-Bolognetti, DAHFMO-Unit of Histology and Medical Embryology, IIM, Sapienza University of Rome, Via A. Scarpa 16, 00161, Rome, Italy. antonio.musaro@uniroma1.it.', 'Italy|Italy|Italy|United Kingdom|Italy', 6, 'Algorithms|Alternative Splicing|Cluster Analysis|Female|Gene Expression Profiling|Humans|Insulin-Like Growth Factor I|Male|Normal Distribution|Protein Isoforms|RNA Isoforms|RNA, Messenger|Real-Time Polymerase Chain Reaction', 'Journal Article', '10.1186/s12859-015-0689-7', '26370240', 'This study addresses a recurrent biological problem, that is to define a formal clustering structure for a set of tissues on the basis of the relative abundance of multiple alternatively spliced isoforms mRNAs generated by the same gene. To this aim, we have used a model-based clustering approach, based on a finite mixture of multivariate Gaussian densities. However, given we had more technical replicates from the same tissue for each quantitative measurement, we also employed a finite mixture of linear mixed models, with tissue-specific random effects.'),
(96886, 2015, 'bmc', 'Reference-free compression of high throughput sequencing data with a probabilistic de Bruijn graph', 'Benoit, Gaëtan|Lemaitre, Claire|Lavenier, Dominique|Drezen, Erwan|Dayris, Thibault|Uricaru, Raluca|Rizk, Guillaume', 'INRIA/IRISA/GenScale, Campus de Beaulieu, Rennes, 35042, France. gaetan.benoit@inria.fr.|INRIA/IRISA/GenScale, Campus de Beaulieu, Rennes, 35042, France. claire.lemaitre@inria.fr.|INRIA/IRISA/GenScale, Campus de Beaulieu, Rennes, 35042, France. dominique.lavenier@irisa.fr.|INRIA/IRISA/GenScale, Campus de Beaulieu, Rennes, 35042, France. erwan.drezen@inria.fr.|University of Bordeaux, CNRS/LaBRI, Talence, F-33405, France. thibault.dayris@outlook.fr.|University of Bordeaux, CNRS/LaBRI, Talence, F-33405, France. ruricaru@labri.fr.|INRIA/IRISA/GenScale, Campus de Beaulieu, Rennes, 35042, France. guillaume.rizk@gmail.com.', 'France|France|France|France|France|France|France', 0, 'Algorithms|Animals|Caenorhabditis elegans|Caenorhabditis elegans Proteins|Computational Biology|Computer Graphics|Computer Simulation|Data Compression|High-Throughput Nucleotide Sequencing|Metagenomics|Probability|Software', 'Journal Article', '10.1186/s12859-015-0709-7', '26370285', 'Data volumes generated by next-generation sequencing (NGS) technologies is now a major concern for both data storage and transmission. This triggered the need for more efficient methods than general purpose compression tools, such as the widely used gzip method.'),
(96887, 2015, 'bmc', 'Development and validation of an epitope prediction tool for swine (PigMatrix) based on the pocket profile method', 'Gutiérrez, Andres H|Martin, William D|Bailey-Kellogg, Chris|Terry, Frances|Moise, Leonard|De Groot, Anne S', 'Institute for Immunology and Informatics, CMB/CELS, University of Rhode Island, Providence, RI, 02903, USA. anunez.uri@gmail.com.|EpiVax, Inc., Providence, RI, 02860, USA. martinb@epivax.com.|Department of Computer Science, Dartmouth College, Hanover, NH, 03755, USA. cbk@cs.dartmouth.edu.|EpiVax, Inc., Providence, RI, 02860, USA. fterry@epivax.com.|Institute for Immunology and Informatics, CMB/CELS, University of Rhode Island, Providence, RI, 02903, USA. lmoise@epivax.com.|Institute for Immunology and Informatics, CMB/CELS, University of Rhode Island, Providence, RI, 02903, USA. dr.annie.degroot@gmail.com.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Alleles|Animals|Catalytic Domain|Computational Biology|Epitope Mapping|Epitopes, T-Lymphocyte|Histocompatibility Antigens Class I|Histocompatibility Antigens Class II|Humans|Models, Theoretical|Molecular Docking Simulation|Peptide Fragments|Protein Binding|Protein Conformation|Protein Structure, Tertiary|ROC Curve|Swine|Vaccines', 'Journal Article', '10.1186/s12859-015-0724-8', '26370412', 'T cell epitope prediction tools and associated vaccine design algorithms have accelerated the development of vaccines for humans. Predictive tools for swine and other food animals are not as well developed, primarily because the data required to develop the tools are lacking. Here, we overcome a lack of T cell epitope data to construct swine epitope predictors by systematically leveraging available human information. Applying the pocket profile method, we use sequence and structural similarities in the binding pockets of human and swine major histocompatibility complex proteins to infer Swine Leukocyte Antigen (SLA) peptide binding preferences. We developed epitope-prediction matrices (PigMatrices), for three SLA class I alleles (SLA-1*0401, 2*0401 and 3*0401) and one class II allele (SLA-DRB1*0201), based on the binding preferences of the best-matched Human Leukocyte Antigen (HLA) pocket for each SLA pocket. The contact residues involved in the binding pockets were defined for class I based on crystal structures of either SLA (SLA-specific contacts, Ssc) or HLA supertype alleles (HLA contacts, Hc), for class II, only Hc was possible. Different substitution matrices were evaluated (PAM and BLOSUM) for scoring pocket similarity and identifying the best human match. The accuracy of the PigMatrices was compared to available online swine epitope prediction tools such as PickPocket and NetMHCpan.'),
(96888, 2015, 'bmc', 'Hybrid-Lambda: simulation of multiple merger and Kingman gene genealogies in species networks and species trees', 'Zhu, Sha|Degnan, James H|Goldstien, Sharyn J|Eldon, Bjarki', 'Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. joe.zhu@well.ox.ac.uk.|Department of Mathematics and Statistics, University of New Mexico, Albuquerque, New Mexico, USA. jamdeg@unm.edu.|Department of Biology, University of Canterbury, Christchurch, New Zealand. sharyn.goldstien@canterbury.ac.nz.|Institut für Mathematik, Technische Universität Berlin, Berlin, Germany. eldon@math.tu-berlin.de.', 'United Kingdom|Mexico|New Zealand|Germany', 0, 'Gene Frequency|Genealogy and Heraldry|Genetic Speciation|Genetics, Population|Humans|Models, Genetic|Polymorphism, Genetic|Population Density|Software', 'Journal Article', '10.1186/s12859-015-0721-y', '26373308', 'There has been increasing interest in coalescent models which admit multiple mergers of ancestral lineages, and to model hybridization and coalescence simultaneously.'),
(96889, 2015, 'bmc', 'A methodology for exploring biomarker--phenotype associations: application to flow cytometry data and systemic sclerosis clinical manifestations', 'Huang, Hongtai|Fava, Andrea|Guhr, Tara|Cimbro, Raffaello|Rosen, Antony|Boin, Francesco|Ellis, Hugh', 'Department of Geography and Environmental Engineering, GWC Whiting School of Engineering, The Johns Hopkins University, Baltimore, MD, USA. Hongtai@jhu.edu.|Division of Rheumatology, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. afava1@jhmi.edu.|Division of Rheumatology, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. tguhr@jhmi.edu.|Division of Rheumatology, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. rcimbro1@jhmi.edu.|Division of Rheumatology, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. arosen@jhmi.edu.|Division of Rheumatology, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. francesco.boin@ucsf.edu.|Department of Geography and Environmental Engineering, GWC Whiting School of Engineering, The Johns Hopkins University, Baltimore, MD, USA. hugh.ellis@jhu.edu.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Biomarkers|Cohort Studies|Disease Progression|Flow Cytometry|Humans|Lung Diseases, Interstitial|Machine Learning|Phenotype|ROC Curve|Scleroderma, Systemic|Support Vector Machine', 'Journal Article', '10.1186/s12859-015-0722-x', '26373409', 'This work seeks to develop a methodology for identifying reliable biomarkers of disease activity, progression and outcome through the identification of significant associations between high-throughput flow cytometry (FC) data and interstitial lung disease (ILD) - a systemic sclerosis (SSc, or scleroderma) clinical phenotype which is the leading cause of morbidity and mortality in SSc. A specific aim of the work involves developing a clinically useful screening tool that could yield accurate assessments of disease state such as the risk or presence of SSc-ILD, the activity of lung involvement and the likelihood to respond to therapeutic intervention. Ultimately this instrument could facilitate a refined stratification of SSc patients into clinically relevant subsets at the time of diagnosis and subsequently during the course of the disease and thus help in preventing bad outcomes from disease progression or unnecessary treatment side effects. The methods utilized in the work involve: (1) clinical and peripheral blood flow cytometry data (Immune Response In Scleroderma, IRIS) from consented patients followed at the Johns Hopkins Scleroderma Center. (2) machine learning (Conditional Random Forests - CRF) coupled with Gene Set Enrichment Analysis (GSEA) to identify subsets of FC variables that are highly effective in classifying ILD patients, and (3) stochastic simulation to design, train and validate ILD risk screening tools.'),
(96890, 2015, 'bmc', 'A weighting approach for judging the effect of patient strata on high-dimensional risk prediction signatures', 'Weyer, Veronika|Binder, Harald', 'Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center Mainz, Johannes Gutenberg-University Mainz, Obere Zahlbacher Strasse 69, Mainz, Germany. weyer@uni-mainz.de.|Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center Mainz, Johannes Gutenberg-University Mainz, Obere Zahlbacher Strasse 69, Mainz, Germany. binderh@uni-mainz.de.', 'Germany|Germany', 0, 'Gene Expression|Humans|Proportional Hazards Models|Regression Analysis|Risk Assessment', 'Journal Article', '10.1186/s12859-015-0716-8', '26374641', 'High-dimensional molecular measurements, e.g. gene expression data, can be linked to clinical time-to-event endpoints by Cox regression models and regularized estimation approaches, such as componentwise boosting, and can incorporate a large number of covariates as well as provide variable selection. If there is heterogeneity due to known patient subgroups, a stratified Cox model allows for separate baseline hazards in each subgroup. Variable selection will still depend on the relative stratum sizes in the data, which might be a convenience sample and not representative for future applications. Such effects need to be systematically investigated and could even help to more reliably identify components of risk prediction signatures.'),
(96891, 2015, 'bmc', 'mAPKL: R/ Bioconductor package for detecting gene exemplars and revealing their characteristics', 'Sakellariou, Argiris|Spyrou, George', 'Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Street, Athens, 115 27, Greece. argisake@gmail.gr.|Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Street, Athens, 115 27, Greece. gspyrou@bioacademy.gr.', 'Greece|Greece', 3, 'Algorithms|Cluster Analysis|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Humans|Molecular Sequence Annotation|Software', 'Journal Article', '10.1186/s12859-015-0719-5', '26374744', 'So far many algorithms have been proposed towards the detection of significant genes in microarray analysis problems. Several of those approaches are freely available as R-packages though their engagement in gene expression analysis by non-bioinformaticians is usually a frustrating task. Besides, only some of those packages offer a complete suite of tools starting from initial data import and ending to analysis report. Here we present an R/Bioconductor package that implements a hybrid gene selection method along with a bunch of functions to facilitate a thorough and convenient gene expression profiling analysis.'),
(96892, 2015, 'bmc', 'SMRT sequencing only de novo assembly of the sugar beet (Beta vulgaris) chloroplast genome', 'Stadermann, Kai Bernd|Weisshaar, Bernd|Holtgräwe, Daniela', 'Chair of Genome Research, Faculty of Biology, Bielefeld University, Bielefeld, Germany. kstaderm@CeBiTec.Uni-Bielefeld.DE.|Chair of Genome Research, Faculty of Biology, Bielefeld University, Bielefeld, Germany. bernd.weisshaar@Uni-Bielefeld.DE.|Chair of Genome Research, Faculty of Biology, Bielefeld University, Bielefeld, Germany. dholtgra@CeBiTec.Uni-Bielefeld.DE.', 'Germany|Germany|Germany', 0, 'Beta vulgaris|Genome, Chloroplast|High-Throughput Nucleotide Sequencing|Molecular Sequence Data|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-015-0726-6', '26377912', 'Third generation sequencing methods, like SMRT (Single Molecule, Real-Time) sequencing developed by Pacific Biosciences, offer much longer read length in comparison to Next Generation Sequencing (NGS) methods. Hence, they are well suited for de novo- or re-sequencing projects. Sequences generated for these purposes will not only contain reads originating from the nuclear genome, but also a significant amount of reads originating from the organelles of the target organism. These reads are usually discarded but they can also be used for an assembly of organellar replicons. The long read length supports resolution of repetitive regions and repeats within the organelles genome which might be problematic when just using short read data. Additionally, SMRT sequencing is less influenced by GC rich areas and by long stretches of the same base.'),
(96893, 2015, 'bmc', 'Phylogenetic network analysis as a parsimony optimization problem', 'Wheeler, Ward C', 'Division of Invertebrate Zoology, American Museum of Natural History, Central Park West @ 79th Street, New York, 10024-5192, NY, USA. wheeler@amnh.org.', 'United States', 0, 'Computational Biology|Influenza A Virus, H1N1 Subtype|Neural Networks (Computer)|Phylogeny|Proteins', 'Journal Article', '10.1186/s12859-015-0675-0', '26382078', 'Many problems in comparative biology are, or are thought to be, best expressed as phylogenetic networks as opposed to trees. In trees, vertices may have only a single parent (ancestor), while networks allow for multiple parent vertices. There are two main interpretive types of networks, softwired and hardwired. The parsimony cost of hardwired networks is based on all changes over all edges, hence must be greater than or equal to the best tree cost contained (displayed) by the network. This is in contrast to softwired, where each character follows the lowest parsimony cost tree displayed by the network, resulting in costs which are less than or equal to the best display tree. Neither situation is ideal since hard-wired networks are not generally biologically attractive (since individual heritable characters can have more than one parent) and softwired networks can be trivially optimized (containing the best tree for each character). Furthermore, given the alternate cost scenarios of trees and these two flavors of networks, hypothesis testing among these explanatory scenarios is impossible.'),
(96894, 2015, 'bmc', 'Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles', 'Inman, Keith|Rudin, Norah|Cheng, Ken|Robinson, Chris|Kirschner, Adam|Inman-Semerau, Luke|Lohmueller, Kirk E', 'Department of Criminal Justice Administration, California State University, East Bay, 25800 Carlos Bee Boulevard, Hayward, CA, 94542, USA. keith.inman@csueastbay.edu.|, 650 Castro Street, Suite 120-404, Mountain View, CA, 94041, USA. norah@forensicdna.com.|, 1224 Burnham Dr, San Jose, CA, 95132, USA. ken.lin.cheng@gmail.com.|, 2854 Marsh St, Los Angeles, CA, 90039, USA. crob@coderbarn.com.|, 401 East 86th St., Apt 12H, New York, NY, 10028, USA. me@adamskirschner.com.|, 604 Lochmoor Ct, Danville, CA, 94526, USA. luke.semerau@gmail.com.|Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 621 Charles E. Young Dr. South, Los Angeles, CA, 90095-1606, USA. klohmueller@ucla.edu.', 'United States|United States|United States|United States|United States|United States|United States', 24, 'DNA|DNA Fingerprinting|Forensic Genetics|Humans|Internet|Likelihood Functions|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0740-8', '26384762', 'Technological advances have enabled the analysis of very small amounts of DNA in forensic cases. However, the DNA profiles from such evidence are frequently incomplete and can contain contributions from multiple individuals. The complexity of such samples confounds the assessment of the statistical weight of such evidence. One approach to account for this uncertainty is to use a likelihood ratio framework to compare the probability of the evidence profile under different scenarios. While researchers favor the likelihood ratio framework, few open-source software solutions with a graphical user interface implementing these calculations are available for practicing forensic scientists.'),
(96895, 2015, 'bmc', 'Genome-wide prediction of prokaryotic two-component system networks using a sequence-based meta-predictor', 'Kara, Altan|Vickers, Martin|Swain, Martin|Whitworth, David E|Fernandez-Fuentes, Narcis', 'Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3EB, UK. karaltn@gmail.com.|Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3EB, UK. mjv08@aber.ac.uk.|Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3EB, UK. mts11@aber.ac.uk.|Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3EB, UK. dew@aber.ac.uk.|Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3EB, UK. narcis.fernandez@gmail.com.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, 'Area Under Curve|Bacteria|Bacterial Proteins|Genome, Bacterial|Histidine Kinase|Protein Interaction Maps|Protein Kinases|ROC Curve|Support Vector Machine', 'Journal Article', '10.1186/s12859-015-0741-7', '26384938', 'Two component systems (TCS) are signalling complexes manifested by a histidine kinase (receptor) and a response regulator (effector). They are the most abundant signalling pathways in prokaryotes and control a wide range of biological processes. The pairing of these two components is highly specific, often requiring costly and time-consuming experimental characterisation. Therefore, there is considerable interest in developing accurate prediction tools to lessen the burden of experimental work and cope with the ever-increasing amount of genomic information.'),
(96896, 2015, 'bmc', 'Reconstruction of novel transcription factor regulons through inference of their binding sites', 'Elmas, Abdulkadir|Wang, Xiaodong|Samoilov, Michael S', 'Department of Electrical Engineering, Columbia University, 500W 120th Street, New York, 10027, NY, USA. elmas@ee.columbia.edu.|Department of Electrical Engineering, Columbia University, 500W 120th Street, New York, 10027, NY, USA. wangx@ee.columbia.edu.|Department of Bioengineering, QB3 California Institute for Quantitative Biosciences UC Berkeley, 1700 4th St #214, Berkeley, 94720, California, USA. mssamoilov@lbl.gov.', 'United States|United States|United States', 0, 'Algorithms|Bacterial Proteins|Base Sequence|Binding Sites|Escherichia coli|Escherichia coli Proteins|Protein Binding|Regulon|Repressor Proteins|Serine Endopeptidases|Transcription Factors', 'Journal Article', '10.1186/s12859-015-0685-y', '26388177', 'In most sequenced organisms the number of known regulatory genes (e.g., transcription factors (TFs)) vastly exceeds the number of experimentally-verified regulons that could be associated with them. At present, identification of TF regulons is mostly done through comparative genomics approaches. Such methods could miss organism-specific regulatory interactions and often require expensive and time-consuming experimental techniques to generate the underlying data.'),
(96897, 2015, 'bmc', 'Boosting for high-dimensional two-class prediction', 'Blagus, Rok|Lusa, Lara', 'Institute for Biostatistics and Medical Informatics, University of Ljubljana, Vrazov trg 2, Ljubljana, Slovenia. rok.blagus@mf.uni-lj.si.|Institute for Biostatistics and Medical Informatics, University of Ljubljana, Vrazov trg 2, Ljubljana, Slovenia. lara.lusa@mf.uni-lj.si.', 'Slovenia|Slovenia', 14, 'Algorithms|Computer Simulation|Databases, Factual|Gene Expression Profiling|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/s12859-015-0723-9', '26390865', 'In clinical research prediction models are used to accurately predict the outcome of the patients based on some of their characteristics. For high-dimensional prediction models (the number of variables greatly exceeds the number of samples) the choice of an appropriate classifier is crucial as it was observed that no single classification algorithm performs optimally for all types of data. Boosting was proposed as a method that combines the classification results obtained using base classifiers, where the sample weights are sequentially adjusted based on the performance in previous iterations. Generally boosting outperforms any individual classifier, but studies with high-dimensional data showed that the most standard boosting algorithm, AdaBoost.M1, cannot significantly improve the performance of its base classier. Recently other boosting algorithms were proposed (Gradient boosting, Stochastic Gradient boosting, LogitBoost), they were shown to perform better than AdaBoost.M1 but their performance was not evaluated for high-dimensional data.'),
(96898, 2015, 'bmc', 'Statistical significance approximation in local trend analysis of high-throughput time-series data using the theory of Markov chains', 'Xia, Li C|Ai, Dongmei|Cram, Jacob A|Liang, Xiaoyi|Fuhrman, Jed A|Sun, Fengzhu', 'Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, 94305-5151, CA, USA.|School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China.|Marine and Environmental Biology, Department of Biological Sciences, University of Southern California, Los Angeles, 90089-0371, CA, USA.|School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China.|Marine and Environmental Biology, Department of Biological Sciences, University of Southern California, Los Angeles, 90089-0371, CA, USA.|Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, 90089-2910, CA, USA. fsun@usc.edu.', 'United States|China|United States|China|United States|United States', 7, 'Algorithms|Bacteria|High-Throughput Nucleotide Sequencing|Markov Chains|Probability|Software|Time Factors', 'Journal Article', '10.1186/s12859-015-0732-8', '26390921', 'Local trend (i.e. shape) analysis of time series data reveals co-changing patterns in dynamics of biological systems. However, slow permutation procedures to evaluate the statistical significance of local trend scores have limited its applications to high-throughput time series data analysis, e.g., data from the next generation sequencing technology based studies.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96899, 2015, 'bmc', 'Phylodynamic reconstruction of the spatiotemporal transmission and demographic history of coxsackievirus B2', 'Huang, Hui-Wen|Chen, Yao-Shen|Chen, Jeff Yi-Fu|Lu, Po-Liang|Lin, Yung-Cheng|Chen, Bao-Chen|Chou, Li-Chiu|Wang, Chu-Feng|Su, Hui-Ju|Huang, Yi-Chien|Shi, Yong-Ying|Chen, Hsiu-Lin|Sanno-Duanda, Bintou|Huang, Tsi-Shu|Lin, Kuei-Hsiang|Tyan, Yu-Chang|Chu, Pei-Yu', 'Department of Anesthesiology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, ROC, Taiwan. july25wen@gmail.com.|Division of Infectious Diseases, Kaohsiung Veterans General Hospital, Kaohsiung, ROC, Taiwan. yschen@isca.vghks.gov.tw.|Department of Biotechnology, Kaohsiung Medical University, Kaohsiung, ROC, Taiwan. yifuc@kmu.edu.tw.|School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, ROC, Taiwan. idpaul@gmail.com.|Institute of Bioscience and Biotechnology, National Taiwan Ocean University, Keelung, ROC, Taiwan. lin65mt@gmail.com.|Division of Microbiology, Department of Pathology and Laboratory Medicine, Kaohsiung Veterans General Hospital, Kaohsiung, ROC, Taiwan. metapneumonvirus83@gmail.com.|Department of Laboratory Medicine, Kaohsiung Medical University Hospital, Kaohsiung, ROC, Taiwan. chou9566@yahoo.com.tw.|Department of Laboratory Medicine, Kaohsiung Medical University Hospital, Kaohsiung, ROC, Taiwan. virus7047@yahoo.com.tw.|Department of Laboratory Medicine, Kaohsiung Medical University Hospital, Kaohsiung, ROC, Taiwan. ruth780124@yahoo.com.tw.|Department of Medical Laboratory Science and Biotechnology, College of Health Sciences, Kaohsiung Medical University, Kaohsiung, ROC, Taiwan. kimo21835@yahoo.com.tw.|Department of Medical Laboratory Science and Biotechnology, College of Health Sciences, Kaohsiung Medical University, Kaohsiung, ROC, Taiwan. wing_282@hotmail.com.|Department of Pediatrics, Kaohsiung Medical University Hospital, Kaohsiung, ROC, Taiwan. 840062@ms.kmuh.org.tw.|Department of Medical Laboratory Science and Biotechnology, College of Health Sciences, Kaohsiung Medical University, Kaohsiung, ROC, Taiwan. nenneht@gmail.com.|Division of Microbiology, Department of Pathology and Laboratory Medicine, Kaohsiung Veterans General Hospital, Kaohsiung, ROC, Taiwan. tsishuhuang@gmail.com.|School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, ROC, Taiwan. kuhsli@gmail.com.|Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan. yctyan@cc.kmu.edu.tw.|Department of Laboratory Medicine, Kaohsiung Medical University Hospital, Kaohsiung, ROC, Taiwan. peiyuchu@kmu.edu.tw.', '-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-', 0, 'Bayes Theorem|Child|Child, Preschool|Coxsackievirus Infections|Demography|Disease Outbreaks|Enterovirus|Genotype|Humans|Infant|Phylogeny|Phylogeography|RNA, Viral|Taiwan|Viral Proteins', 'Journal Article', '10.1186/s12859-015-0738-2', '26390997', 'Studies regarding coxsackievirus (CV) tend to focus on epidemic outbreaks, an imbalanced topology is considered to be an indication of acute infection with partial cross-immunity. In enteroviruses, a clear understanding of the characteristics of tree topology, transmission, and its demographic dynamics in viral succession and circulation are essential for identifying prevalence trends in endemic pathogens such as coxsackievirus B2 (CV-B2). This study applied a novel Bayesian evolutionary approach to elucidate the phylodynamic characteristics of CV-B2. A dataset containing 51 VP1 sequences and a dataset containing 34 partial 3D(pol) sequencing were analyzed, where each dataset included Taiwan sequences isolated during 1988-2013.'),
(96900, 2015, 'bmc', 'ATGme: Open-source web application for rare codon identification and custom DNA sequence optimization', 'Daniel, Edward|Onwukwe, Goodluck U|Wierenga, Rik K|Quaggin, Susan E|Vainio, Seppo J|Krause, Mirja', 'Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, Structural Biochemistry, University of Oulu, Oulu, Finland. edward.daniel@oulu.fi.|Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, Structural Biochemistry, University of Oulu, Oulu, Finland. goodluck.onwukwe@oulu.fi.|Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, Structural Biochemistry, University of Oulu, Oulu, Finland. rik.wierenga@oulu.fi.|Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA. quaggin@northwestern.edu.|Biocenter Oulu, Laboratory of Developmental Biology, InfoTech Oulu, Center for Cell Matrix Research, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Aapistie 5A, FIN-90220, Oulu, Finland. seppo.vainio@oulu.fi.|Biocenter Oulu, Laboratory of Developmental Biology, InfoTech Oulu, Center for Cell Matrix Research, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Aapistie 5A, FIN-90220, Oulu, Finland. mirja.krause@oulu.fi.', 'Finland|Finland|Finland|United States|Finland|Finland', 12, 'Base Sequence|Codon|DNA|Escherichia coli|Gene Expression Regulation|Humans|Internet|Molecular Sequence Data|Recombinant Proteins|Saccharomyces cerevisiae|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1186/s12859-015-0743-5', '26391121', 'Codon usage plays a crucial role when recombinant proteins are expressed in different organisms. This is especially the case if the codon usage frequency of the organism of origin and the target host organism differ significantly, for example when a human gene is expressed in E. coli. Therefore, to enable or enhance efficient gene expression it is of great importance to identify rare codons in any given DNA sequence and subsequently mutate these to codons which are more frequently used in the expression host.'),
(96901, 2015, 'bmc', 'Group-based variant calling leveraging next-generation supercomputing for large-scale whole-genome sequencing studies', 'Standish, Kristopher A|Carland, Tristan M|Lockwood, Glenn K|Pfeiffer, Wayne|Tatineni, Mahidhar|Huang, C Chris|Lamberth, Sarah|Cherkas, Yauheniya|Brodmerkel, Carrie|Jaeger, Ed|Smith, Lance|Rajagopal, Gunaretnam|Curran, Mark E|Schork, Nicholas J', 'Biomedical Sciences Graduate Program, University of California, San Diego, Gilman Drive, La Jolla, 92092, CA, USA. kstandis@ucsd.edu.|Human Biology, J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, 92092, CA, USA. carlandt@gmail.com.|San Diego Supercomputer Center, University of California, San Diego, Gilman Drive, La Jolla, 92092, CA, USA. glennklockwood@gmail.com.|San Diego Supercomputer Center, University of California, San Diego, Gilman Drive, La Jolla, 92092, CA, USA. pfeiffer@sdsc.edu.|San Diego Supercomputer Center, University of California, San Diego, Gilman Drive, La Jolla, 92092, CA, USA. mahidhar@sdsc.edu.|Systems Pharmacology & Biomarkers (Immunology), Janssen R&D LLC, Springhouse, PA, USA. CHuang4@its.jnj.com.|Systems Pharmacology & Biomarkers (Immunology), Janssen R&D LLC, Springhouse, PA, USA. slambert@its.jnj.com.|Systems Pharmacology & Biomarkers (Immunology), Janssen R&D LLC, Springhouse, PA, USA. ycherka@its.jnj.com.|Systems Pharmacology & Biomarkers (Immunology), Janssen R&D LLC, Springhouse, PA, USA. cbrodmer@its.jnj.com.|Systems Pharmacology & Biomarkers (Immunology), Janssen R&D LLC, Springhouse, PA, USA. ejaeger@its.jnj.com.|Systems Pharmacology & Biomarkers (Immunology), Janssen R&D LLC, Springhouse, PA, USA. lsmith10@its.jnj.com.|Systems Pharmacology & Biomarkers (Immunology), Janssen R&D LLC, Springhouse, PA, USA. grajagop@its.jnj.com.|Systems Pharmacology & Biomarkers (Immunology), Janssen R&D LLC, Springhouse, PA, USA. mcurran3@its.jnj.com.|Human Biology, J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, 92092, CA, USA. nschork@jcvi.org.', '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', 0, 'Computers|Data Interpretation, Statistical|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-015-0736-4', '26395405', 'Next-generation sequencing (NGS) technologies have become much more efficient, allowing whole human genomes to be sequenced faster and cheaper than ever before. However, processing the raw sequence reads associated with NGS technologies requires care and sophistication in order to draw compelling inferences about phenotypic consequences of variation in human genomes. It has been shown that different approaches to variant calling from NGS data can lead to different conclusions. Ensuring appropriate accuracy and quality in variant calling can come at a computational cost.'),
(96902, 2015, 'bmc', 'htsint: a Python library for sequencing pipelines that combines data through gene set generation', 'Richards, Adam J|Herrel, Anthony|Bonneaud, Camille', 'Station dEcologie Expérimentale du CNRS, USR 2936, Route du CNRS, Moulis, 09200, France. adamricha@gmail.com.|UMR 7179 CNRS/MNHN, Département dEcologie et de Gestion de la Biodiversité 57 rue Cuvier, Case postale 55, Paris, 75231, France. anthony.herrel@mnhn.fr.|Station dEcologie Expérimentale du CNRS, USR 2936, Route du CNRS, Moulis, 09200, France. C.Bonneaud@exeter.ac.uk.', 'France|France|France', 0, 'Computational Biology|Databases, Genetic|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Programming Languages|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-015-0729-3', '26399714', 'Sequencing technologies provide a wealth of details in terms of genes, expression, splice variants, polymorphisms, and other features. A standard for sequencing analysis pipelines is to put genomic or transcriptomic features into a context of known functional information, but the relationships between ontology terms are often ignored. For RNA-Seq, considering genes and their genetic variants at the group level enables a convenient way to both integrate annotation data and detect small coordinated changes between experimental conditions, a known caveat of gene level analyses.'),
(96903, 2015, 'bmc', 'Inference of regulatory networks with a convergence improved MCMC sampler', 'Agostinho, Nilzair B|Machado, Karina S|Werhli, Adriano V', 'Centro de Ciências Computacionais - C3 Universidade Federal do Rio Grande- FURG, Campus Carreiros, Rio Grande, Brazil. nilzairmb@gmail.com.|Centro de Ciências Computacionais - C3 Universidade Federal do Rio Grande- FURG, Campus Carreiros, Rio Grande, Brazil. karinaecomp@gmail.com.|Centro de Ciências Computacionais - C3 Universidade Federal do Rio Grande- FURG, Campus Carreiros, Rio Grande, Brazil. werhli@gmail.com.', 'Brazil|Brazil|Brazil', 5, 'Algorithms|Bayes Theorem|Gene Regulatory Networks|Humans|Markov Chains|Models, Genetic|Models, Theoretical|Monte Carlo Method|Normal Distribution|Signal Transduction|Systems Biology', 'Journal Article', '10.1186/s12859-015-0734-6', '26399857', 'One of the goals of the Systems Biology community is to have a detailed map of all biological interactions in an organism. One small yet important step in this direction is the creation of biological networks from post-genomic data. Bayesian networks are a very promising model for the inference of regulatory networks in Systems Biology. Usually, Bayesian networks are sampled with a Markov Chain Monte Carlo (MCMC) sampler in the structure space. Unfortunately, conventional MCMC sampling schemes are often slow in mixing and convergence. To improve MCMC convergence, an alternative method is proposed and tested with different sets of data. Moreover, the proposed method is compared with the traditional MCMC sampling scheme.'),
(96904, 2015, 'bmc', 'Subtype prediction in pediatric acute myeloid leukemia: classification using differential network rank conservation revisited', 'Obulkasim, Askar|Fornerod, Maarten|Zwaan, Michel C|Reinhardt, Dirk|van den Heuvel-Eibrink, Marry M', 'Department of Pediatric Oncology/Hematology, Erasmus-MC Sophia Childrens Hospital, Rotterdam, The Netherlands. a.obulkasim@erasmusmc.nl.|Department of Pediatric Oncology/Hematology, Erasmus-MC Sophia Childrens Hospital, Rotterdam, The Netherlands. m.fornerod@erasmusmc.nl.|Department of Pediatric Oncology/Hematology, Erasmus-MC Sophia Childrens Hospital, Rotterdam, The Netherlands.|AML-BFM Study Group, Pediatric Hematology/Oncology, Essen, Germany.|Department of Pediatric Oncology/Hematology, Erasmus-MC Sophia Childrens Hospital, Rotterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Germany|Netherlands', 0, 'Algorithms|Child|Computational Biology|Databases, Factual|Gene Expression Profiling|Gene Regulatory Networks|Humans|Leukemia, Myeloid, Acute', 'Journal Article', '10.1186/s12859-015-0737-3', '26399969', 'One of the most important application spectrums of transcriptomic data is cancer phenotype classification. Many characteristics of transcriptomic data, such as redundant features and technical artifacts, make over-fitting commonplace. Promising classification results often fail to generalize across datasets with different sources, platforms, or preprocessing. Recently a novel differential network rank conservation (DIRAC) algorithm to characterize cancer phenotypes using transcriptomic data. DIRAC is a member of a family of algorithms that have shown useful for disease classification based on the relative expression of genes. Combining the robustness of this familys simple decision rules with known biological relationships, this systems approach identifies interpretable, yet highly discriminate networks. While DIRAC has been briefly employed for several classification problems in the original paper, the potentials of DIRAC in cancer phenotype classification, and especially robustness against artifacts in transcriptomic data have not been fully characterized yet.'),
(96905, 2015, 'bmc', '2D and 3D similarity landscape analysis identifies PARP as a novel off-target for the drug Vatalanib', 'Gohlke, Bjoern-Oliver|Overkamp, Tim|Richter, Anja|Richter, Antje|Daniel, Peter T|Gillissen, Bernd|Preissner, Robert', 'Structural Bioinformatics Group, Charite - University Medicine Berlin & ECRC, Lindenberger Weg 80, 13125, Berlin, Germany. bjoern-oliver.gohlke@charite.de.|Department of Hematology, Oncology and Tumor Immunology, University Medical Center Charité, Campus Berlin-Buch, Humboldt University Berlin, Berlin, Germany. tim@overkamp.de.|German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany. anja.richter@charite.de.|Department of Hematology, Oncology and Tumor Immunology, University Medical Center Charité, Campus Berlin-Buch, Humboldt University Berlin, Berlin, Germany. antje.richter@charite.de.|Department of Hematology, Oncology and Tumor Immunology, University Medical Center Charité, Campus Berlin-Buch, Humboldt University Berlin, Berlin, Germany. peter.daniel@charite.de.|Department of Hematology, Oncology and Tumor Immunology, University Medical Center Charité, Campus Berlin-Buch, Humboldt University Berlin, Berlin, Germany. bernhard.gillissen@charite.de.|Structural Bioinformatics Group, Charite - University Medicine Berlin & ECRC, Lindenberger Weg 80, 13125, Berlin, Germany. robert.preissner@charite.de.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 6, 'Binding Sites|Colorimetry|Computational Biology|Drug Discovery|Humans|MCF-7 Cells|Microscopy, Fluorescence|Molecular Docking Simulation|Phthalazines|Poly(ADP-ribose) Polymerase Inhibitors|Poly(ADP-ribose) Polymerases|Protein Binding|Protein Structure, Tertiary|Pyridines|Vascular Endothelial Growth Factor A', 'Journal Article', '10.1186/s12859-015-0730-x', '26403354', 'Searching for two-dimensional (2D) structural similarities is a useful tool to identify new active compounds in drug-discovery programs. However, as 2D similarity measures neglect important structural and functional features, similarity by 2D might be underestimated. In the present study, we used combined 2D and three-dimensional (3D) similarity comparisons to reveal possible new functions and/or side-effects of known bioactive compounds.'),
(96906, 2015, 'bmc', 'Systematic noise degrades gene co-expression signals but can be corrected', 'Freytag, Saskia|Gagnon-Bartsch, Johann|Speed, Terence P|Bahlo, Melanie', 'Bioinformatics Division, Walter + Eliza Hall Institute, 1G Royal Parade, Melbourne, 3050, Australia. freytag.s@wehi.edu.au.|Department of Statistics, University of California, 367 Evans Hall, Berkeley, 94720, USA. johann@berkeley.edu.|Bioinformatics Division, Walter + Eliza Hall Institute, 1G Royal Parade, Melbourne, 3050, Australia. terry@wehi.edu.au.|Bioinformatics Division, Walter + Eliza Hall Institute, 1G Royal Parade, Melbourne, 3050, Australia. bahlo@wehi.edu.au.', 'Australia|United States|Australia|Australia', 22, 'Brain|Epilepsy|Gene Expression|Humans|Linear Models|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/s12859-015-0745-3', '26403471', 'In the past decade, the identification of gene co-expression has become a routine part of the analysis of high-dimensional microarray data. Gene co-expression, which is mostly detected via the Pearson correlation coefficient, has played an important role in the discovery of molecular pathways and networks. Unfortunately, the presence of systematic noise in high-dimensional microarray datasets corrupts estimates of gene co-expression. Removing systematic noise from microarray data is therefore crucial. Many cleaning approaches for microarray data exist, however these methods are aimed towards improving differential expression analysis and their performances have been primarily tested for this application. To our knowledge, the performances of these approaches have never been systematically compared in the context of gene co-expression estimation.'),
(96907, 2015, 'bmc', 'PROKARYO: an illustrative and interactive computational model of the lactose operon in the bacterium Escherichia coli', 'Esmaeili, Afshin|Davison, Timothy|Wu, Andrew|Alcantara, Joenel|Jacob, Christian', 'Department of Computer Science, Faculty of Science, University of Calgary, 2500 University Drive NW, Calgary, T2N 1N4, Canada. afshin@invistaware.com.|Department of Computer Science, Faculty of Science, University of Calgary, 2500 University Drive NW, Calgary, T2N 1N4, Canada. tbdaviso@ucalgary.ca.|Department of Computer Science, Faculty of Science, University of Calgary, 2500 University Drive NW, Calgary, T2N 1N4, Canada. agkwu@ucalgary.ca.|Department of Microbiology, Immunology and Infectious Diseases, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, T2N 4N1, Canada. jalcanta@ucalgary.ca.|Department of Computer Science, Faculty of Science, University of Calgary, 2500 University Drive NW, Calgary, T2N 1N4, Canada. cjacob@ucalgary.ca.', 'Canada|Canada|Canada|Canada|Canada', 0, 'Algorithms|DNA-Directed RNA Polymerases|Escherichia coli|Escherichia coli Proteins|Gene Expression Regulation|Lac Operon|Membrane Transport Proteins|Models, Biological|RNA, Messenger|Software', 'Journal Article', '10.1186/s12859-015-0720-z', '26415599', 'We are creating software for agent-based simulation and visualization of bio-molecular processes in bacterial and eukaryotic cells. As a first example, we have built a 3-dimensional, interactive computer model of an Escherichia coli bacterium and its associated biomolecular processes. Our illustrative model focuses on the gene regulatory processes that control the expression of genes involved in the lactose operon. Prokaryo, our agent-based cell simulator, incorporates cellular structures, such as plasma membranes and cytoplasm, as well as elements of the molecular machinery, including RNA polymerase, messenger RNA, lactose permease, and ribosomes.'),
(96908, 2015, 'bmc', 'CATCHing putative causative variants in consanguineous families', 'Santoni, Federico Andrea|Makrythanasis, Periklis|Antonarakis, Stylianos E', 'Department of Genetic Medicine and Development, University of Geneva, Rue Michel Servet 1, Geneva, Switzerland. Federico.santoni@unige.ch.|Department of Genetic Medicine and Development, University of Geneva, Rue Michel Servet 1, Geneva, Switzerland. Periklis.makrythanasis@unige.ch.|Department of Genetic Medicine and Development, University of Geneva, Rue Michel Servet 1, Geneva, Switzerland. Stylianos.antonarakis@unige.ch.', 'Switzerland|Switzerland|Switzerland', 11, 'Algorithms|Consanguinity|Genotype|High-Throughput Nucleotide Sequencing|Homozygote|Humans|Internet|Musculoskeletal Diseases|Pedigree|Phenotype|Polymorphism, Single Nucleotide|Receptor, Fibroblast Growth Factor, Type 3|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0727-5', '26415661', 'Consanguinity is an important risk factor for autosomal recessive (AR) disorders. Extended genomic regions identical by descent (IBD) in the offspring of consanguineous parents give rise to recessive disorders with identical (homozygous) pathogenic variants in both alleles. However, many clinical phenotypes presenting in the offspring of consanguineous couples are still of unknown etiology. Nowadays advances in High Throughput Sequencing provide an excellent opportunity to achieve a molecular diagnosis or to identify novel candidate genes.'),
(96909, 2015, 'bmc', 'NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference', 'Bellot, Pau|Olsen, Catharina|Salembier, Philippe|Oliveras-Vergés, Albert|Meyer, Patrick E', 'Universitat Politecnica de Catalunya BarcelonaTECH, Department of Signal Theory and Communications, UPC-Campus Nord, C/ Jordi Girona, 1-3, Barcelona, 08034, Spain. pau.bellot@upc.edu.|Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium. colsen@ulb.ac.be.|Universitat Politecnica de Catalunya BarcelonaTECH, Department of Signal Theory and Communications, UPC-Campus Nord, C/ Jordi Girona, 1-3, Barcelona, 08034, Spain. philippe.salembier@upc.edu.|Universitat Politecnica de Catalunya BarcelonaTECH, Department of Signal Theory and Communications, UPC-Campus Nord, C/ Jordi Girona, 1-3, Barcelona, 08034, Spain. albert.oliveras@upc.edu.|Bioinformatics and Systems Biology (BioSys), Faculty of Sciences, Université de Liège (ULg), 27 Blvd du Rectorat, Liège, 4000, Belgium. patrick.meyer@ulg.ac.be.', 'Spain|Belgium|Spain|Spain|Belgium', 22, 'Algorithms|Area Under Curve|Benchmarking|Gene Regulatory Networks|Humans|ROC Curve|Software', 'Journal Article', '10.1186/s12859-015-0728-4', '26415849', 'In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods.'),
(96910, 2015, 'bmc', 'Label noise in subtype discrimination of class C G protein-coupled receptors: A systematic approach to the analysis of classification errors', 'König, Caroline|Cárdenas, Martha I|Giraldo, Jesús|Alquézar, René|Vellido, Alfredo', 'Dept. of Computer Science, Univ. Politècnica de Catalunya, C. Jordi Girona, 1-3, Barcelona, 08034, Spain. ckonig@cs.upc.edu.|Dept. of Computer Science, Univ. Politècnica de Catalunya, C. Jordi Girona, 1-3, Barcelona, 08034, Spain. mic@cs.upc.edu.|Institut de Neurociències, Unitat de Bioestadística, Univ. Autònoma de Barcelona, Cerdanyola del Vallès, Barcelona, 08193, Spain. Jesus.Giraldo@uab.es.|Dept. of Computer Science, Univ. Politècnica de Catalunya, C. Jordi Girona, 1-3, Barcelona, 08034, Spain. alquezar@cs.upc.edu.|Dept. of Computer Science, Univ. Politècnica de Catalunya, C. Jordi Girona, 1-3, Barcelona, 08034, Spain. avellido@cs.upc.edu.', 'Spain|Spain|Spain|Spain|Spain', 0, 'Amino Acid Sequence|Computational Biology|Phylogeny|Receptors, G-Protein-Coupled|Support Vector Machine', 'Journal Article', '10.1186/s12859-015-0731-9', '26415951', 'The characterization of proteins in families and subfamilies, at different levels, entails the definition and use of class labels. When the adscription of a protein to a family is uncertain, or even wrong, this becomes an instance of what has come to be known as a label noise problem. Label noise has a potentially negative effect on any quantitative analysis of proteins that depends on label information. This study investigates class C of G protein-coupled receptors, which are cell membrane proteins of relevance both to biology in general and pharmacology in particular. Their supervised classification into different known subtypes, based on primary sequence data, is hampered by label noise. The latter may stem from a combination of expert knowledge limitations and the lack of a clear correspondence between labels that mostly reflect GPCR functionality and the different representations of the protein primary sequences.'),
(96911, 2015, 'bmc', 'methylPipe and compEpiTools: a suite of R packages for the integrative analysis of epigenomics data', 'Kishore, Kamal|de Pretis, Stefano|Lister, Ryan|Morelli, Marco J|Bianchi, Valerio|Amati, Bruno|Ecker, Joseph R|Pelizzola, Mattia', 'Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Milano, 20139, Italy. kamal.kishore@iit.it.|Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Milano, 20139, Italy. stefano.depretis@iit.it.|Australian Research Council Centre of Excellence in Plant Energy Biology, The University of Western Australia, Perth, WA, 6009, Australia. ryan.lister@uwa.edu.au.|Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Milano, 20139, Italy. marco.morelli@iit.it.|Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Milano, 20139, Italy. valerio.bianchi@iit.it.|Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Milano, 20139, Italy. bruno.amati@iit.it.|Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA. ecker@salk.edu.|Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Milano, 20139, Italy. mattia.pelizzola@iit.it.', 'Italy|Italy|Australia|Italy|Italy|Italy|United States|Italy', 30, 'CpG Islands|DNA|DNA Methylation|Epigenomics|High-Throughput Nucleotide Sequencing|Histone Code|Internet|RNA|Sequence Analysis, DNA|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0742-6', '26415965', 'Numerous methods are available to profile several epigenetic marks, providing data with different genome coverage and resolution. Large epigenomic datasets are then generated, and often combined with other high-throughput data, including RNA-seq, ChIP-seq for transcription factors (TFs) binding and DNase-seq experiments. Despite the numerous computational tools covering specific steps in the analysis of large-scale epigenomics data, comprehensive software solutions for their integrative analysis are still missing. Multiple tools must be identified and combined to jointly analyze histone marks, TFs binding and other -omics data together with DNA methylation data, complicating the analysis of these data and their integration with publicly available datasets.'),
(96912, 2015, 'bmc', 'VEGAWES: variational segmentation on whole exome sequencing for copy number detection', 'Anjum, Samreen|Morganella, Sandro|DAngelo, Fulvio|Iavarone, Antonio|Ceccarelli, Michele', 'Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. sshaikh2@qf.org.qa.|European Molecular Biology Laboratory, European Bioinformatics Institute, (EMBL -EBI), Wellcome Trust Genome Campus, Cambridge, CB10 1SD, UK. sandro@ebi.ac.uk.|BIOGEM, Ariano Irpino, 83031, Italy. fulvio.dan13@gmail.com.|Institute for Cancer Genetics, Columbia University, New York, 10027, USA. ai2102@columbia.edu.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.', 'Qatar|United Kingdom|Italy|United Kingdom|Qatar', 0, 'Algorithms|Area Under Curve|Brain Neoplasms|DNA|DNA Copy Number Variations|Glioblastoma|High-Throughput Nucleotide Sequencing|Humans|Polymorphism, Single Nucleotide|ROC Curve|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-015-0748-0', '26416038', 'Copy number variations are important in the detection and progression of significant tumors and diseases. Recently, Whole Exome Sequencing is gaining popularity with copy number variations detection due to low cost and better efficiency. In this work, we developed VEGAWES for accurate and robust detection of copy number variations on WES data. VEGAWES is an extension to a variational based segmentation algorithm, VEGA: Variational estimator for genomic aberrations, which has previously outperformed several algorithms on segmenting array comparative genomic hybridization data.'),
(96913, 2015, 'bmc', 'Detrimental effects of duplicate reads and low complexity regions on RNA- and ChIP-seq data', 'Dozmorov, Mikhail G|Adrianto, Indra|Giles, Cory B|Glass, Edmund|Glenn, Stuart B|Montgomery, Courtney|Sivils, Kathy L|Olson, Lorin E|Iwayama, Tomoaki|Freeman, Willard M|Lessard, Christopher J|Wren, Jonathan D', 'Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.', 'Qatar|Qatar|Qatar|Qatar|Qatar|Qatar|Qatar|Qatar|Qatar|Qatar|Qatar|Qatar', 10, 'High-Throughput Nucleotide Sequencing|Humans|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-16-S13-S10', '26423047', 'Adapter trimming and removal of duplicate reads are common practices in next-generation sequencing pipelines. Sequencing reads ambiguously mapped to repetitive and low complexity regions can also be problematic for accurate assessment of the biological signal, yet their impact on sequencing data has not received much attention. We investigate how trimming the adapters, removing duplicates, and filtering out reads overlapping low complexity regions influence the significance of biological signal in RNA- and ChIP-seq experiments.'),
(96914, 2015, 'bmc', 'Global multiple protein-protein interaction network alignment by combining pairwise network alignments', 'Dohrmann, Jakob|Puchin, Juris|Singh, Rahul', 'Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.', 'Qatar|Qatar|Qatar', 7, 'Computational Biology|Protein Interaction Maps|Proteins|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-16-S13-S11', '26423128', 'A wealth of protein interaction data has become available in recent years, creating an urgent need for powerful analysis techniques. In this context, the problem of finding biologically meaningful correspondences between different protein-protein interaction networks (PPIN) is of particular interest. The PPIN of a species can be compared with that of other species through the process of PPIN alignment. Such an alignment can provide insight into basic problems like species evolution and network component function determination, as well as translational problems such as target identification and elucidation of mechanisms of disease spread. Furthermore, multiple PPINs can be aligned simultaneously, expanding the analytical implications of the result. While there are several pairwise network alignment algorithms, few methods are capable of multiple network alignment.'),
(96915, 2015, 'bmc', 'Computational identification of genetic subnetwork modules associated with maize defense response to Fusarium verticillioides', 'Kim, Mansuck|Zhang, Huan|Woloshuk, Charles|Shim, Won-Bo|Yoon, Byung-Jun', 'Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.', 'Qatar|Qatar|Qatar|Qatar|Qatar', 6, 'Base Sequence|Fusarium|Gene Regulatory Networks|Host-Pathogen Interactions|Zea mays', 'Journal Article', '10.1186/1471-2105-16-S13-S12', '26423221', 'Maize, a crop of global significance, is vulnerable to a variety of biotic stresses resulting in economic losses. Fusarium verticillioides (teleomorph Gibberella moniliformis) is one of the key fungal pathogens of maize, causing ear rots and stalk rots. To better understand the genetic mechanisms involved in maize defense as well as F. verticillioides virulence, a systematic investigation of the host-pathogen interaction is needed. The aim of this study was to computationally identify potential maize subnetwork modules associated with its defense response against F. verticillioides.'),
(96916, 2015, 'bmc', 'A Bayesian approach for inducing sparsity in generalized linear models with multi-category response', 'Madahian, Behrouz|Roy, Sujoy|Bowman, Dale|Deng, Lih Y|Homayouni, Ramin', 'Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.', 'Qatar|Qatar|Qatar|Qatar|Qatar', 0, 'Bayes Theorem|Gene Expression Profiling|Humans|Linear Models|Male|Neoplasms|Prostatic Neoplasms', 'Journal Article', '10.1186/1471-2105-16-S13-S13', '26423345', 'The dimension and complexity of high-throughput gene expression data create many challenges for downstream analysis. Several approaches exist to reduce the number of variables with respect to small sample sizes. In this study, we utilized the Generalized Double Pareto (GDP) prior to induce sparsity in a Bayesian Generalized Linear Model (GLM) setting. The approach was evaluated using a publicly available microarray dataset containing 99 samples corresponding to four different prostate cancer subtypes.'),
(96917, 2015, 'bmc', 'Efficient experimental design for uncertainty reduction in gene regulatory networks', 'Dehghannasiri, Roozbeh|Yoon, Byung-Jun|Dougherty, Edward R', 'Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.', 'Qatar|Qatar|Qatar', 15, 'Gene Regulatory Networks|Genomics|Humans|Research Design|Uncertainty', 'Journal Article', '10.1186/1471-2105-16-S13-S2', '26423515', 'An accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene regulatory networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first.'),
(96918, 2015, 'bmc', 'Dynamical modeling of uncertain interaction-based genomic networks', 'Mohsenizadeh, Daniel N|Hua, Jianping|Bittner, Michael|Dougherty, Edward R', 'Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.', 'Qatar|Qatar|Qatar|Qatar', 0, 'Genomics|Models, Molecular|Molecular Dynamics Simulation|Uncertainty', 'Journal Article', '10.1186/1471-2105-16-S13-S3', '26423606', 'Most dynamical models for genomic networks are built upon two current methodologies, one process-based and the other based on Boolean-type networks. Both are problematic when it comes to experimental design purposes in the laboratory. The first approach requires a comprehensive knowledge of the parameters involved in all biological processes a priori, whereas the results from the second method may not have a biological correspondence and thus cannot be tested in the laboratory. Moreover, the current methods cannot readily utilize existing curated knowledge databases and do not consider uncertainty in the knowledge. Therefore, a new methodology is needed that can generate a dynamical model based on available biological data, assuming uncertainty, while the results from experimental design can be examined in the laboratory.'),
(96919, 2015, 'bmc', 'DMAP: a connectivity map database to enable identification of novel drug repositioning candidates', 'Huang, Hui|Nguyen, Thanh|Ibrahim, Sara|Shantharam, Sandeep|Yue, Zongliang|Chen, Jake Y', 'Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.', 'Qatar|Qatar|Qatar|Qatar|Qatar|Qatar', 0, 'Databases, Factual|Drug Repositioning|Proteins', 'Journal Article', '10.1186/1471-2105-16-S13-S4', '26423722', 'Drug repositioning is a cost-efficient and time-saving process to drug development compared to traditional techniques. A systematic method to drug repositioning is to identify candidate drugs gene expression profiles on target disease models and determine how similar these profiles are to approved drugs. Databases such as the CMAP have been developed recently to help with systematic drug repositioning.'),
(96920, 2015, 'bmc', 'Segmental HOG: new descriptor for glomerulus detection in kidney microscopy image', 'Kato, Tsuyoshi|Relator, Raissa|Ngouv, Hayliang|Hirohashi, Yoshihiro|Takaki, Osamu|Kakimoto, Tetsuhiro|Okada, Kinya', 'Faculty of Science and Engineering, Gunma University, Kiryu-shi, Gunma, 376-8515, Japan. katotsu@cs.gunma-u.ac.jp.|Faculty of Science and Engineering, Gunma University, Kiryu-shi, Gunma, 376-8515, Japan.|Faculty of Science and Engineering, Gunma University, Kiryu-shi, Gunma, 376-8515, Japan.|Faculty of Science and Engineering, Gunma University, Kiryu-shi, Gunma, 376-8515, Japan.|Gunma University Hospital, Maebashi-shi, Gunma, 371-8511, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Desmin|Humans|Kidney Glomerulus|Microscopy|Support Vector Machine', 'Journal Article', '10.1186/s12859-015-0739-1', '26423821', 'The detection of the glomeruli is a key step in the histopathological evaluation of microscopic images of the kidneys. However, the task of automatic detection of the glomeruli poses challenges owing to the differences in their sizes and shapes in renal sections as well as the extensive variations in their intensities due to heterogeneity in immunohistochemistry staining. Although the rectangular histogram of oriented gradients (Rectangular HOG) is a widely recognized powerful descriptor for general object detection, it shows many false positives owing to the aforementioned difficulties in the context of glomeruli detection.'),
(96921, 2015, 'bmc', 'Density-based parallel skin lesion border detection with webCL', 'Lemon, James|Kockara, Sinan|Halic, Tansel|Mete, Mutlu', 'Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.', 'Japan|Japan|Japan|Japan', 3, 'Dermoscopy|Humans|Image Interpretation, Computer-Assisted|Melanoma|Pattern Recognition, Automated|Skin|Skin Neoplasms', 'Journal Article', '10.1186/1471-2105-16-S13-S5', '26423836', 'Dermoscopy is a highly effective and noninvasive imaging technique used in diagnosis of melanoma and other pigmented skin lesions. Many aspects of the lesion under consideration are defined in relation to the lesion border. This makes border detection one of the most important steps in dermoscopic image analysis. In current practice, dermatologists often delineate borders through a hand drawn representation based upon visual inspection. Due to the subjective nature of this technique, intra- and inter-observer variations are common. Because of this, the automated assessment of lesion borders in dermoscopic images has become an important area of study.'),
(96922, 2015, 'bmc', 'An adverse event capture and management system for cancer studies', 'Lencioni, Alex|Hutchins, Laura|Annis, Sandy|Chen, Wanchi|Ermisoglu, Emre|Feng, Zhidan|Mack, Karen|Simpson, Kacie|Lane, Cheryl|Topaloglu, Umit', 'Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Data Collection|Drug-Related Side Effects and Adverse Reactions|Humans|National Cancer Institute (U.S.)|Neoplasms|United States', 'Journal Article', '10.1186/1471-2105-16-S13-S6', '26424052', 'Comprehensive capture of Adverse Events (AEs) is crucial for monitoring for side effects of a therapy while assessing efficacy. For cancer studies, the National Cancer Institute has developed the Common Terminology Criteria for Adverse Events (CTCAE) as a required standard for recording attributes and grading AEs. The AE assessments should be part of the Electronic Health Record (EHR) system, yet, due to patient-centric EHR design and implementation, many EHRs dont provide straightforward functions to assess ongoing AEs to indicate a resolution or a grade change for clinical trials.'),
(96923, 2015, 'bmc', 'Enhancing cancer clonality analysis with integrative genomics', 'Peterson, Erich A|Bauer, Michael A|Chavan, Shweta S|Ashby, Cody|Weinhold, Niels|Heuck, Christoph J|Morgan, Gareth J|Johann, Donald J', 'Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 4, 'Clonal Evolution|Epigenesis, Genetic|Epigenomics|Genomics|Humans|Neoplasms', 'Journal Article', '10.1186/1471-2105-16-S13-S7', '26424171', 'It is understood that cancer is a clonal disease initiated by a single cell, and that metastasis, which is the spread of cancer from the primary site, is also initiated by a single cell. The seemingly natural capability of cancer to adapt dynamically in a Darwinian manner is a primary reason for therapeutic failures. Survival advantages may be induced by cancer therapies and also occur as a result of inherent cell and microenvironmental factors. The selected more fit clones outmatch their competition and then become dominant in the tumor via propagation of progeny. This clonal expansion leads to relapse, therapeutic resistance and eventually death. The goal of this study is to develop and demonstrate a more detailed clonality approach by utilizing integrative genomics.'),
(96924, 2015, 'bmc', 'A heuristic approach to determine an appropriate number of topics in topic modeling', 'Zhao, Weizhong|Chen, James J|Perkins, Roger|Liu, Zhichao|Ge, Weigong|Ding, Yijun|Zou, Wen', 'Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Computational Biology|Data Mining|Databases, Factual|Heuristics|High-Throughput Nucleotide Sequencing', 'Journal Article', '10.1186/1471-2105-16-S13-S8', '26424364', 'Topic modelling is an active research field in machine learning. While mainly used to build models from unstructured textual data, it offers an effective means of data mining where samples represent documents, and different biological endpoints or omics data represent words. Latent Dirichlet Allocation (LDA) is the most commonly used topic modelling method across a wide number of technical fields. However, model development can be arduous and tedious, and requires burdensome and systematic sensitivity studies in order to find the best set of model parameters. Often, time-consuming subjective evaluations are needed to compare models. Currently, research has yielded no easy way to choose the proper number of topics in a model beyond a major iterative approach.');
