INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95262, 2012, 'bmc', 'EpicCapo: epitope prediction using combined information of amino acid pairwise contact potentials and HLA-peptide contact site information', 'Saethang, Thammakorn|Hirose, Osamu|Kimkong, Ingorn|Tran, Vu Anh|Dang, Xuan Tho|Nguyen, Lan Anh T|Le, Tu Kien T|Kubo, Mamoru|Yamada, Yoichi|Satou, Kenji', 'Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan. thammakorn.kmutt@gmail.com|Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan. thammakorn.kmutt@gmail.com|Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan. thammakorn.kmutt@gmail.com|Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan. thammakorn.kmutt@gmail.com|Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan. thammakorn.kmutt@gmail.com|Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan. thammakorn.kmutt@gmail.com|Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan. thammakorn.kmutt@gmail.com|Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan. thammakorn.kmutt@gmail.com|Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan. thammakorn.kmutt@gmail.com|Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan. thammakorn.kmutt@gmail.com', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 6, 'Algorithms|Amino Acids|Epitopes|HLA Antigens|Humans|Influenza A virus|Influenza Vaccines|Protein Binding|Support Vector Machine|T-Lymphocytes, Cytotoxic', 'Journal Article', '10.1186/1471-2105-13-313', '23176036', 'Epitope identification is an essential step toward synthetic vaccine development since epitopes play an important role in activating immune response. Classical experimental approaches are laborious and time-consuming, and therefore computational methods for generating epitope candidates have been actively studied. Most of these methods, however, are based on sophisticated nonlinear techniques for achieving higher predictive performance. The use of these techniques tend to diminish their interpretability with respect to binding potential: that is, they do not provide much insight into binding mechanisms.'),
(95263, 2012, 'bmc', 'Local functional descriptors for surface comparison based binding prediction', 'Cipriano, Gregory M|Phillips, George N|Gleicher, Michael', 'Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA.|Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA.|Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA.', 'United States|United States|United States', 3, 'Algorithms|Binding Sites|Ligands|Models, Molecular|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-13-314', '23176080', 'Molecular recognition in proteins occurs due to appropriate arrangements of physical, chemical, and geometric properties of an atomic surface. Similar surface regions should create similar binding interfaces. Effective methods for comparing surface regions can be used in identifying similar regions, and to predict interactions without regard to the underlying structural scaffold that creates the surface.'),
(95264, 2012, 'bmc', 'A statistical model-building perspective to identification of MS/MS spectra with PeptideProphet', 'Ma, Kelvin|Vitek, Olga|Nesvizhskii, Alexey I', 'Department of Statistics, Purdue University, 250 N. University Street, West Lafayette, Indiana, USA.|Department of Statistics, Purdue University, 250 N. University Street, West Lafayette, Indiana, USA.|Department of Statistics, Purdue University, 250 N. University Street, West Lafayette, Indiana, USA.', 'India|India|India', 44, 'Algorithms|Humans|Models, Statistical|Peptides|Proteomics|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-13-S16-S1', '23176103', 'PeptideProphet is a post-processing algorithm designed to evaluate the confidence in identifications of MS/MS spectra returned by a database search. In this manuscript we describe the what and how of PeptideProphet in a manner aimed at statisticians and life scientists who would like to gain a more in-depth understanding of the underlying statistical modeling. The theory and rationale behind the mixture-modeling approach taken by PeptideProphet is discussed from a statistical model-building perspective followed by a description of how a model can be used to express confidence in the identification of individual peptides or sets of peptides. We also demonstrate how to evaluate the quality of model fit and select an appropriate model from several available alternatives. We illustrate the use of PeptideProphet in association with the Trans-Proteomic Pipeline, a free suite of software used for protein identification.'),
(95265, 2012, 'bmc', 'A tutorial in displaying mass spectrometry-based proteomic data using heat maps', 'Key, Melissa', 'Center for Computational Diagnostics, IU School of Medicine, Indianapolis, IN, USA. melkey@iupui.edu', 'India', 36, 'Data Display|Mass Spectrometry|Proteins|Proteomics', 'Journal Article', '10.1186/1471-2105-13-S16-S10', '23176119', 'Data visualization plays a critical role in interpreting experimental results of proteomic experiments. Heat maps are particularly useful for this task, as they allow us to find quantitative patterns across proteins and biological samples simultaneously. The quality of a heat map can be vastly improved by understanding the options available to display and organize the data in the heat map. This tutorial illustrates how to optimize heat maps for proteomics data by incorporating known characteristics of the data into the image. First, the concepts used to guide the creating of heat maps are demonstrated. Then, these concepts are applied to two types of analysis: visualizing spectral features across biological samples, and presenting the results of tests of statistical significance. For all examples we provide details of computer code in the open-source statistical programming language R, which can be used for biologists and clinicians with little statistical background. Heat maps are a useful tool for presenting quantitative proteomic data organized in a matrix format. Understanding and optimizing the parameters used to create the heat map can vastly improve both the appearance and the interoperation of heat map data.'),
(95266, 2012, 'bmc', 'MALDI imaging mass spectrometry: statistical data analysis and current computational challenges', 'Alexandrov, Theodore', 'Center for Industrial Mathematics, University of Bremen, Bibliothekstr, 1, 28359 Bremen, Germany. theodore@uni-bremen.de', 'Germany', 117, 'Biomarkers|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1186/1471-2105-13-S16-S11', '23176142', 'Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) imaging mass spectrometry, also called MALDI-imaging, is a label-free bioanalytical technique used for spatially-resolved chemical analysis of a sample. Usually, MALDI-imaging is exploited for analysis of a specially prepared tissue section thaw mounted onto glass slide. A tremendous development of the MALDI-imaging technique has been observed during the last decade. Currently, it is one of the most promising innovative measurement techniques in biochemistry and a powerful and versatile tool for spatially-resolved chemical analysis of diverse sample types ranging from biological and plant tissues to bio and polymer thin films. In this paper, we outline computational methods for analyzing MALDI-imaging data with the emphasis on multivariate statistical methods, discuss their pros and cons, and give recommendations on their application. The methods of unsupervised data mining as well as supervised classification methods for biomarker discovery are elucidated. We also present a high-throughput computational pipeline for interpretation of MALDI-imaging data using spatial segmentation. Finally, we discuss current challenges associated with the statistical analysis of MALDI-imaging data.'),
(95267, 2012, 'bmc', '1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data', 'Cox, Juergen|Mann, Matthias', 'Department for Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany. cox@biochem.mpg.de|Department for Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany. cox@biochem.mpg.de', 'Germany|Germany', 0, 'Algorithms|Data Interpretation, Statistical|Genes|Proteins|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-13-S16-S12', '23176165', 'Quantitative proteomics now provides abundance ratios for thousands of proteins upon perturbations. These need to be functionally interpreted and correlated to other types of quantitative genome-wide data such as the corresponding transcriptome changes. We describe a new method, 2D annotation enrichment, which compares quantitative data from any two omics types in the context of categorical annotation of the proteins or genes. Suitable genome-wide categories are membership of proteins in biochemical pathways, their annotation with gene ontology terms, sub-cellular localization, presence of protein domains or membership in protein complexes. 2D annotation enrichment detects annotation terms whose members show consistent behavior in one or both of the data dimensions. This consistent behavior can be a correlation between the two data types, such as simultaneous up- or down-regulation in both data dimensions, or a lack thereof, such as regulation in one dimension but no change in the other. For the statistical formulation of the test we introduce a two-dimensional generalization of the nonparametric two-sample test. The false discovery rate is stringently controlled by correcting for multiple hypothesis testing. We also describe one-dimensional annotation enrichment, which can be applied to single omics data. The 1D and 2D annotation enrichment algorithms are freely available as part of the Perseus software.'),
(95268, 2012, 'bmc', 'Knowledge-based analysis of proteomics data', 'Bessarabova, Marina|Ishkin, Alexander|JeBailey, Lellean|Nikolskaya, Tatiana|Nikolsky, Yuri', 'Thomson Reuters, IP & Science, 5901 Priestly Dr., #200, Carlsbad, CA 92008, USA.|Thomson Reuters, IP & Science, 5901 Priestly Dr., #200, Carlsbad, CA 92008, USA.|Thomson Reuters, IP & Science, 5901 Priestly Dr., #200, Carlsbad, CA 92008, USA.|Thomson Reuters, IP & Science, 5901 Priestly Dr., #200, Carlsbad, CA 92008, USA.|Thomson Reuters, IP & Science, 5901 Priestly Dr., #200, Carlsbad, CA 92008, USA.', 'United States|United States|United States|United States|United States', 0, 'Databases, Protein|Humans|Knowledge Bases|Proteins|Proteomics', 'Journal Article', '10.1186/1471-2105-13-S16-S13', '23176192', 'As it is the case with any OMICs technology, the value of proteomics data is defined by the degree of its functional interpretation in the context of phenotype. Functional analysis of proteomics profiles is inherently complex, as each of hundreds of detected proteins can belong to dozens of pathways, be connected in different context-specific groups by protein interactions and regulated by a variety of one-step and remote regulators. Knowledge-based approach deals with this complexity by creating a structured database of protein interactions, pathways and protein-disease associations from experimental literature and a set of statistical tools to compare the proteomics profiles with this rich source of accumulated knowledge. Here we describe the main methods of ontology enrichment, interactome topology and network analysis applied on a comprehensive, manually curated and semantically consistent knowledge source MetaBase and demonstrate several case studies in different disease areas.'),
(95269, 2012, 'bmc', 'False discovery rates in spectral identification', 'Jeong, Kyowon|Kim, Sangtae|Bandeira, Nuno', 'Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA, USA.|Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA, USA.|Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA, USA.', 'United States|United States|United States', 0, 'Algorithms|Databases, Protein|Peptides|Proteins|Proteomics|Search Engine|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-13-S16-S2', '23176207', 'Automated database search engines are one of the fundamental engines of high-throughput proteomics enabling daily identifications of hundreds of thousands of peptides and proteins from tandem mass (MS/MS) spectrometry data. Nevertheless, this automation also makes it humanly impossible to manually validate the vast lists of resulting identifications from such high-throughput searches. This challenge is usually addressed by using a Target-Decoy Approach (TDA) to impose an empirical False Discovery Rate (FDR) at a pre-determined threshold x% with the expectation that at most x% of the returned identifications would be false positives. But despite the fundamental importance of FDR estimates in ensuring the utility of large lists of identifications, there is surprisingly little consensus on exactly how TDA should be applied to minimize the chances of biased FDR estimates. In fact, since less rigorous TDA/FDR estimates tend to result in more identifications (at higher true FDR), there is often little incentive to enforce strict TDA/FDR procedures in studies where the major metric of success is the size of the list of identifications and there are no follow up studies imposing hard cost constraints on the number of reported false positives. Here we address the problem of the accuracy of TDA estimates of empirical FDR. Using MS/MS spectra from samples where we were able to define a factual FDR estimator of true FDR we evaluate several popular variants of the TDA procedure in a variety of database search contexts. We show that the fraction of false identifications can sometimes be over 10× higher than reported and may be unavoidably high for certain types of searches. In addition, we further report that the two-pass search strategy seems the most promising database search strategy. While unavoidably constrained by the particulars of any specific evaluation dataset, our observations support a series of recommendations towards maximizing the number of resulting identifications while controlling database searches with robust and reproducible TDA estimation of empirical FDR.'),
(95270, 2012, 'bmc', 'A cross-validation scheme for machine learning algorithms in shotgun proteomics', 'Granholm, Viktor|Noble, William Stafford|Käll, Lukas', 'Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden.|Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden.|Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden.', 'Sweden|Sweden|Sweden', 16, 'Algorithms|Artificial Intelligence|Databases, Protein|Mass Spectrometry|Peptides|Proteomics', 'Journal Article', '10.1186/1471-2105-13-S16-S3', '23176259', 'Peptides are routinely identified from mass spectrometry-based proteomics experiments by matching observed spectra to peptides derived from protein databases. The error rates of these identifications can be estimated by target-decoy analysis, which involves matching spectra to shuffled or reversed peptides. Besides estimating error rates, decoy searches can be used by semi-supervised machine learning algorithms to increase the number of confidently identified peptides. As for all machine learning algorithms, however, the results must be validated to avoid issues such as overfitting or biased learning, which would produce unreliable peptide identifications. Here, we discuss how the target-decoy method is employed in machine learning for shotgun proteomics, focusing on how the results can be validated by cross-validation, a frequently used validation scheme in machine learning. We also use simulated data to demonstrate the proposed cross-validation schemes ability to detect overfitting.'),
(95271, 2012, 'bmc', 'Computational approaches to protein inference in shotgun proteomics', 'Li, Yong Fuga|Radivojac, Predrag', 'School of Informatics and Computing, Indiana University, Bloomington 150 S, Woodlawn Avenue, Bloomington, Indiana 47405, USA.|School of Informatics and Computing, Indiana University, Bloomington 150 S, Woodlawn Avenue, Bloomington, Indiana 47405, USA.', 'India|India', 36, 'Chromatography, Liquid|Computational Biology|Databases, Protein|Peptides|Proteins|Proteome|Proteomics|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-13-S16-S4', '23176300', 'Shotgun proteomics has recently emerged as a powerful approach to characterizing proteomes in biological samples. Its overall objective is to identify the form and quantity of each protein in a high-throughput manner by coupling liquid chromatography with tandem mass spectrometry. As a consequence of its high throughput nature, shotgun proteomics faces challenges with respect to the analysis and interpretation of experimental data. Among such challenges, the identification of proteins present in a sample has been recognized as an important computational task. This task generally consists of (1) assigning experimental tandem mass spectra to peptides derived from a protein database, and (2) mapping assigned peptides to proteins and quantifying the confidence of identified proteins. Protein identification is fundamentally a statistical inference problem with a number of methods proposed to address its challenges. In this review we categorize current approaches into rule-based, combinatorial optimization and probabilistic inference techniques, and present them using integer programming and Bayesian inference frameworks. We also discuss the main challenges of protein identification and propose potential solutions with the goal of spurring innovative research in this area.'),
(95272, 2012, 'bmc', 'Normalization and missing value imputation for label-free LC-MS analysis', 'Karpievitch, Yuliya V|Dabney, Alan R|Smith, Richard D', 'School of Mathematics and Physics, University of Tasmania, Hobart, Tasmania, Australia. yuliya.karpievitch@utas.edu.au|School of Mathematics and Physics, University of Tasmania, Hobart, Tasmania, Australia. yuliya.karpievitch@utas.edu.au|School of Mathematics and Physics, University of Tasmania, Hobart, Tasmania, Australia. yuliya.karpievitch@utas.edu.au', 'Australia|Australia|Australia', 0, 'Bias|Chromatography, Liquid|Mass Spectrometry|Proteomics|Research Design', 'Journal Article', '10.1186/1471-2105-13-S16-S5', '23176322', 'Shotgun proteomic data are affected by a variety of known and unknown systematic biases as well as high proportions of missing values. Typically, normalization is performed in an attempt to remove systematic biases from the data before statistical inference, sometimes followed by missing value imputation to obtain a complete matrix of intensities. Here we discuss several approaches to normalization and dealing with missing values, some initially developed for microarray data and some developed specifically for mass spectrometry-based data.'),
(95273, 2012, 'bmc', 'Statistical protein quantification and significance analysis in label-free LC-MS experiments with complex designs', 'Clough, Timothy|Thaminy, Safia|Ragg, Susanne|Aebersold, Ruedi|Vitek, Olga', 'Department of Statistics, Purdue University, West Lafayette, IN, USA. ovitek@stat.purdue.edu|Department of Statistics, Purdue University, West Lafayette, IN, USA. ovitek@stat.purdue.edu|Department of Statistics, Purdue University, West Lafayette, IN, USA. ovitek@stat.purdue.edu|Department of Statistics, Purdue University, West Lafayette, IN, USA. ovitek@stat.purdue.edu|Department of Statistics, Purdue University, West Lafayette, IN, USA. ovitek@stat.purdue.edu', 'United States|United States|United States|United States|United States', 0, 'Chromatography, Liquid|Models, Statistical|Peptides|Proteins|Proteomics|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-13-S16-S6', '23176351', 'Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is widely used for quantitative proteomic investigations. The typical output of such studies is a list of identified and quantified peptides. The biological and clinical interest is, however, usually focused on quantitative conclusions at the protein level. Furthermore, many investigations ask complex biological questions by studying multiple interrelated experimental conditions. Therefore, there is a need in the field for generic statistical models to quantify protein levels even in complex study designs.'),
(95274, 2012, 'bmc', 'Statistical methods for quantitative mass spectrometry proteomic experiments with labeling', 'Oberg, Ann L|Mahoney, Douglas W', 'Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA. oberg.ann@mayo.edu|Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA. oberg.ann@mayo.edu', 'United States|United States', 0, 'Biometry|Humans|Isotope Labeling|Male|Mass Spectrometry|Prostatic Neoplasms|Proteins|Proteomics|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-13-S16-S7', '23176383', 'Mass Spectrometry utilizing labeling allows multiple specimens to be subjected to mass spectrometry simultaneously. As a result, between-experiment variability is reduced. Here we describe use of fundamental concepts of statistical experimental design in the labeling framework in order to minimize variability and avoid biases. We demonstrate how to export data in the format that is most efficient for statistical analysis. We demonstrate how to assess the need for normalization, perform normalization, and check whether it worked. We describe how to build a model explaining the observed values and test for differential protein abundance along with descriptive statistics and measures of reliability of the findings. Concepts are illustrated through the use of three case studies utilizing the iTRAQ 4-plex labeling protocol.'),
(95275, 2012, 'bmc', 'In silico design of targeted SRM-based experiments', 'Nahnsen, Sven|Kohlbacher, Oliver', 'Center for Bioinformatics, Quantitative Biology Center, and Department of Computer Science, University of Tübingen, Germany. sven.nahnsen@uni-tuebingen.de|Center for Bioinformatics, Quantitative Biology Center, and Department of Computer Science, University of Tübingen, Germany. sven.nahnsen@uni-tuebingen.de', 'Germany|Germany', 8, 'Algorithms|Computer Simulation|Humans|Mass Spectrometry|Peptides|Proteins|Proteomics|Research Design|Software', 'Journal Article', '10.1186/1471-2105-13-S16-S8', '23176520', 'Selected reaction monitoring (SRM)-based proteomics approaches enable highly sensitive and reproducible assays for profiling of thousands of peptides in one experiment. The development of such assays involves the determination of retention time, detectability and fragmentation properties of peptides, followed by an optimal selection of transitions. If those properties have to be identified experimentally, the assay development becomes a time-consuming task. We introduce a computational framework for the optimal selection of transitions for a given set of proteins based on their sequence information alone or in conjunction with already existing transition databases. The presented method enables the rapid and fully automated initial development of assays for targeted proteomics. We introduce the relevant methods, report and discuss a step-wise and generic protocol and we also show that we can reach an ad hoc coverage of 80 % of the targeted proteins. The presented algorithmic procedure is implemented in the open-source software package OpenMS/TOPP.'),
(95276, 2012, 'bmc', 'Statistical characterization of multiple-reaction monitoring mass spectrometry (MRM-MS) assays for quantitative proteomics', 'Mani, D R|Abbatiello, Susan E|Carr, Steven A', 'The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA. manidr@broad.mit.edu|The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA. manidr@broad.mit.edu|The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA. manidr@broad.mit.edu', 'United States|United States|United States', 0, 'Biomarkers|Data Interpretation, Statistical|Indicator Dilution Techniques|Isotopes|Limit of Detection|Mass Spectrometry|Peptides|Proteins|Proteomics', 'Journal Article', '10.1186/1471-2105-13-S16-S9', '23176545', 'Multiple reaction monitoring mass spectrometry (MRM-MS) with stable isotope dilution (SID) is increasingly becoming a widely accepted assay for the quantification of proteins and peptides. These assays have shown great promise in relatively high throughput verification of candidate biomarkers. While the use of MRM-MS assays is well established in the small molecule realm, their introduction and use in proteomics is relatively recent. As such, statistical and computational methods for the analysis of MRM-MS data from proteins and peptides are still being developed. Based on our extensive experience with analyzing a wide range of SID-MRM-MS data, we set forth a methodology for analysis that encompasses significant aspects ranging from data quality assessment, assay characterization including calibration curves, limits of detection (LOD) and quantification (LOQ), and measurement of intra- and interlaboratory precision. We draw upon publicly available seminal datasets to illustrate our methods and algorithms.'),
(95277, 2012, 'bmc', 'CloudMan as a platform for tool, data, and analysis distribution', 'Afgan, Enis|Chapman, Brad|Taylor, James', 'Center for Informatics and Computing (CIR), Ruxc4x91er Boxc5xa1kovixc4x87 Institute (RBI), Zagreb, Croatia. enis.afgan@irb.hr|Center for Informatics and Computing (CIR), Ruxc4x91er Boxc5xa1kovixc4x87 Institute (RBI), Zagreb, Croatia. enis.afgan@irb.hr|Center for Informatics and Computing (CIR), Ruxc4x91er Boxc5xa1kovixc4x87 Institute (RBI), Zagreb, Croatia. enis.afgan@irb.hr', 'Croatia|Croatia|Croatia', 39, 'Information Storage and Retrieval|Software', 'Journal Article', '10.1186/1471-2105-13-315', '23181507', 'Cloud computing provides an infrastructure that facilitates large scale computational analysis in a scalable, democratized fashion, However, in this context it is difficult to ensure sharing of an analysis environment and associated data in a scalable and precisely reproducible way.'),
(95278, 2012, 'bmc', 'TeraStitcher - a tool for fast automatic 3D-stitching of teravoxel-sized microscopy images', 'Bria, Alessandro|Iannello, Giulio', 'Center of Integrated Research, University Campus Bio-Medico di Roma, v, Alvaro del Portillo 21, 00128 Roma (RM), Italy. a.bria@alice.it|Center of Integrated Research, University Campus Bio-Medico di Roma, v, Alvaro del Portillo 21, 00128 Roma (RM), Italy. a.bria@alice.it', 'Italy|Italy', 0, 'Algorithms|Animals|Imaging, Three-Dimensional|Mice|Microscopy|Software', 'Journal Article', '10.1186/1471-2105-13-316', '23181553', 'Further advances in modern microscopy are leading to teravoxel-sized tiled 3D images at high resolution, thus increasing the dimension of the stitching problem of at least two orders of magnitude. The existing software solutions do not seem adequate to address the additional requirements arising from these datasets, such as the minimization of memory usage and the need to process just a small portion of data.'),
(95279, 2012, 'bmc', 'A Monte Carlo-based framework enhances the discovery and interpretation of regulatory sequence motifs', 'Seitzer, Phillip|Wilbanks, Elizabeth G|Larsen, David J|Facciotti, Marc T', 'Department of Biomedical Engineering, One Shields Ave, University of California, Davis, CA 95616, USA.|Department of Biomedical Engineering, One Shields Ave, University of California, Davis, CA 95616, USA.|Department of Biomedical Engineering, One Shields Ave, University of California, Davis, CA 95616, USA.|Department of Biomedical Engineering, One Shields Ave, University of California, Davis, CA 95616, USA.', 'United States|United States|United States|United States', 13, 'Algorithms|Amino Acid Motifs|Bacterial Proteins|Chromatin Immunoprecipitation|Chromosomal Proteins, Non-Histone|Computational Biology|DNA|DNA-Binding Proteins|Escherichia coli|Halobacterium|Monte Carlo Method|Nucleotide Motifs|Proteins|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Serine Endopeptidases|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-13-317', '23181585', 'Discovery of functionally significant short, statistically overrepresented subsequence patterns (motifs) in a set of sequences is a challenging problem in bioinformatics. Oftentimes, not all sequences in the set contain a motif. These non-motif-containing sequences complicate the algorithmic discovery of motifs. Filtering the non-motif-containing sequences from the larger set of sequences while simultaneously determining the identity of the motif is, therefore, desirable and a non-trivial problem in motif discovery research.'),
(95280, 2012, 'bmc', 'On the contributions of topological features to transcriptional regulatory network robustness', 'Zamal, Faiyaz Al|Ruths, Derek', 'School of Computer Science, McGill University, Montreal, Canada. faiyaz.zamal@mail.mcgill.ca|School of Computer Science, McGill University, Montreal, Canada. faiyaz.zamal@mail.mcgill.ca', 'Canada|Canada', 0, 'Computational Biology|Escherichia coli|Escherichia coli Proteins|Fungal Proteins|Gene Regulatory Networks|Transcription Factors|Yeasts', 'Journal Article', '10.1186/1471-2105-13-318', '23194062', 'Because biological networks exhibit a high-degree of robustness, a systemic understanding of their architecture and function requires an appraisal of the network design principles that confer robustness. In this project, we conduct a computational study of the contribution of three degree-based topological properties (transcription factor-target ratio, degree distribution, cross-talk suppression) and their combinations on the robustness of transcriptional regulatory networks. We seek to quantify the relative degree of robustness conferred by each property (and combination) and also to determine the extent to which these properties alone can explain the robustness observed in transcriptional networks.'),
(95281, 2012, 'bmc', 'Multiple-platform data integration method with application to combined analysis of microarray and proteomic data', 'Wu, Shicheng|Xu, Yawen|Feng, Zeny|Yang, Xiaojian|Wang, Xiaogang|Gao, Xin', 'Department of Mathematics and Statistics, York University, 4700 Keele, Street, Toronto, Ontario M3J 1P3, Canada.|Department of Mathematics and Statistics, York University, 4700 Keele, Street, Toronto, Ontario M3J 1P3, Canada.|Department of Mathematics and Statistics, York University, 4700 Keele, Street, Toronto, Ontario M3J 1P3, Canada.|Department of Mathematics and Statistics, York University, 4700 Keele, Street, Toronto, Ontario M3J 1P3, Canada.|Department of Mathematics and Statistics, York University, 4700 Keele, Street, Toronto, Ontario M3J 1P3, Canada.|Department of Mathematics and Statistics, York University, 4700 Keele, Street, Toronto, Ontario M3J 1P3, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 8, 'Biomarkers|Gene Expression Profiling|Genetic Markers|Oligonucleotide Array Sequence Analysis|Proteomics|Research Design', 'Journal Article', '10.1186/1471-2105-13-320', '23198695', 'It is desirable in genomic studies to select biomarkers that differentiate between normal and diseased populations based on related data sets from different platforms, including microarray expression and proteomic data. Most recently developed integration methods focus on correlation analyses between gene and protein expression profiles. The correlation methods select biomarkers with concordant behavior across two platforms but do not directly select differentially expressed biomarkers. Other integration methods have been proposed to combine statistical evidence in terms of ranks and p-values, but they do not account for the dependency relationships among the data across platforms.'),
(95282, 2012, 'bmc', 'Learning virulent proteins from integrated query networks', 'Cadag, Eithon|Tarczy-Hornoch, Peter|Myler, Peter J', 'Ayasdi Inc, Palo Alto, CA 94301, USA. ecadag@uw.edu|Ayasdi Inc, Palo Alto, CA 94301, USA. ecadag@uw.edu|Ayasdi Inc, Palo Alto, CA 94301, USA. ecadag@uw.edu', 'United States|United States|United States', 0, 'Data Interpretation, Statistical|Databases, Protein|Proteins|Sequence Analysis, Protein|Virulence|Virulence Factors', 'Journal Article', '10.1186/1471-2105-13-321', '23198735', 'Methods of weakening and attenuating pathogens abilities to infect and propagate in a host, thus allowing the natural immune system to more easily decimate invaders, have gained attention as alternatives to broad-spectrum targeting approaches. The following work describes a technique to identifying proteins involved in virulence by relying on latent information computationally gathered across biological repositories, applicable to both generic and specific virulence categories.'),
(95283, 2012, 'bmc', 'SNPEVG: a graphical tool for GWAS graphing with mouse clicks', 'Wang, Shengwen|Dvorkin, Daniel|Da, Yang', 'Department of Animal Science, University of Minnesota, St. Paul, Minnesota, USA.|Department of Animal Science, University of Minnesota, St. Paul, Minnesota, USA.|Department of Animal Science, University of Minnesota, St. Paul, Minnesota, USA.', 'United States|United States|United States', 42, 'Computer Graphics|Computer Peripherals|Data Interpretation, Statistical|Genome-Wide Association Study|Phenotype|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-13-319', '23199373', 'Genome-wide association studies (GWAS) using single nucleotide polymorphism (SNP) markers generate large quantities of tests results. Global and local graphical viewing of the test results is an effective approach to digest and interpret GWAS results.'),
(95284, 2012, 'bmc', 'Finding quasi-modules of human and viral miRNAs: a case study of human cytomegalovirus (HCMV)', 'Veksler-Lublinsky, Isana|Shemer-Avni, Yonat|Meiri, Eti|Bentwich, Zvi|Kedem, Klara|Ziv-Ukelson, Michal', 'Department of Computer Science, Ben-Gurion University, Beer-Sheva 84105, Israel.|Department of Computer Science, Ben-Gurion University, Beer-Sheva 84105, Israel.|Department of Computer Science, Ben-Gurion University, Beer-Sheva 84105, Israel.|Department of Computer Science, Ben-Gurion University, Beer-Sheva 84105, Israel.|Department of Computer Science, Ben-Gurion University, Beer-Sheva 84105, Israel.|Department of Computer Science, Ben-Gurion University, Beer-Sheva 84105, Israel.', 'Israel|Israel|Israel|Israel|Israel|Israel', 0, 'Algorithms|Cytomegalovirus|Down-Regulation|Gene Expression Regulation|Humans|MicroRNAs|RNA, Viral', 'Journal Article', '10.1186/1471-2105-13-322', '23206407', 'MicroRNAs (miRNAs) are important regulators of gene expression encoded by a variety of organisms, including viruses. Although the function of most of the viral miRNAs is currently unknown, there is evidence that both viral and host miRNAs contribute to the interactions between viruses and their hosts. miRNAs constitute a complex combinatorial network, where one miRNA may target many genes and one gene may be targeted by multiple miRNAs. In particular, viral and host miRNAs may also have mutual target genes. Based on published evidence linking viral and host miRNAs there are three modes of mutual regulation: competing, cooperating, and compensating modes.'),
(95285, 2012, 'bmc', 'CLAG: an unsupervised non hierarchical clustering algorithm handling biological data', 'Dib, Linda|Carbone, Alessandra', 'UPMC, UMR7238, Génomique Analytique, 15 rue de lEcole de Médecine, F-75006 Paris, France.|UPMC, UMR7238, Génomique Analytique, 15 rue de lEcole de Médecine, F-75006 Paris, France.', 'France|France', 13, 'Algorithms|Brain Neoplasms|Breast Neoplasms|Cluster Analysis|Female|Gene Expression|Humans|MicroRNAs|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-13-194', '23216858', 'Searching for similarities in a set of biological data is intrinsically difficult due to possible data points that should not be clustered, or that should group within several clusters. Under these hypotheses, hierarchical agglomerative clustering is not appropriate. Moreover, if the dataset is not known enough, like often is the case, supervised classification is not appropriate either.'),
(95286, 2012, 'bmc', 'Flexible and efficient genome tiling design with penalized uniqueness score', 'Du, Yang|Murani, Eduard|Ponsuksili, Siriluck|Wimmers, Klaus', 'Research Unit Molecular Biology, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.|Research Unit Molecular Biology, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.|Research Unit Molecular Biology, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.|Research Unit Molecular Biology, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Animals|DNA Probes|Genome|Humans|Oligonucleotide Array Sequence Analysis|Sensitivity and Specificity|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-13-323', '23216884', 'As a powerful tool in whole genome analysis, tiling array has been widely used in the answering of many genomic questions. Now it could also serve as a capture device for the library preparation in the popular high throughput sequencing experiments. Thus, a flexible and efficient tiling array design approach is still needed and could assist in various types and scales of transcriptomic experiment.'),
(95287, 2012, 'bmc', 'Hydra: a scalable proteomic search engine which utilizes the Hadoop distributed computing framework', 'Lewis, Steven|Csordas, Attila|Killcoyne, Sarah|Hermjakob, Henning|Hoopmann, Michael R|Moritz, Robert L|Deutsch, Eric W|Boyle, John', 'Institute for Systems Biology, Seattle, WA, USA. steven.lewis@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. steven.lewis@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. steven.lewis@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. steven.lewis@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. steven.lewis@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. steven.lewis@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. steven.lewis@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. steven.lewis@systemsbiology.org', 'United States|United States|United States|United States|United States|United States|United States|United States', 40, 'Algorithms|Databases, Factual|Mass Spectrometry|Peptides|Protein Processing, Post-Translational|Proteomics|Search Engine|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-13-324', '23216909', 'For shotgun mass spectrometry based proteomics the most computationally expensive step is in matching the spectra against an increasingly large database of sequences and their post-translational modifications with known masses. Each mass spectrometer can generate data at an astonishingly high rate, and the scope of what is searched for is continually increasing. Therefore solutions for improving our ability to perform these searches are needed.'),
(95288, 2012, 'bmc', 'A novel approach for biomarker selection and the integration of repeated measures experiments from two assays', 'Liquet, Benoit|Lê Cao, Kim-Anh|Hocini, Hakim|Thiébaut, Rodolphe', 'Univ, Bordeaux, ISPED, centre INSERM U-897-Epidémiologie-Biostatistique, Bordeaux, F-33000, France. benoit.liquet@isped.u-bordeaux2.fr|Univ, Bordeaux, ISPED, centre INSERM U-897-Epidémiologie-Biostatistique, Bordeaux, F-33000, France. benoit.liquet@isped.u-bordeaux2.fr|Univ, Bordeaux, ISPED, centre INSERM U-897-Epidémiologie-Biostatistique, Bordeaux, F-33000, France. benoit.liquet@isped.u-bordeaux2.fr|Univ, Bordeaux, ISPED, centre INSERM U-897-Epidémiologie-Biostatistique, Bordeaux, F-33000, France. benoit.liquet@isped.u-bordeaux2.fr', 'France|France|France|France', 63, 'AIDS Vaccines|Biomarkers|Clinical Trials as Topic|Cluster Analysis|Computer Simulation|Cytokines|Discriminant Analysis|Gene Expression|Gene Expression Profiling|Humans|Multivariate Analysis|Transcriptome', 'Journal Article', '10.1186/1471-2105-13-325', '23216942', 'High throughput omics experiments are usually designed to compare changes observed between different conditions (or interventions) and to identify biomarkers capable of characterizing each condition. We consider the complex structure of repeated measurements from different assays where different conditions are applied on the same subjects.'),
(95289, 2012, 'bmc', 'A computational pipeline for the development of multi-marker bio-signature panels and ensemble classifiers', 'Günther, Oliver P|Chen, Virginia|Freue, Gabriela Cohen|Balshaw, Robert F|Tebbutt, Scott J|Hollander, Zsuzsanna|Takhar, Mandeep|McMaster, W Robert|McManus, Bruce M|Keown, Paul A|Ng, Raymond T', 'NCE CECR Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, BC, V6Z 1Y6, Canada.|NCE CECR Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, BC, V6Z 1Y6, Canada.|NCE CECR Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, BC, V6Z 1Y6, Canada.|NCE CECR Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, BC, V6Z 1Y6, Canada.|NCE CECR Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, BC, V6Z 1Y6, Canada.|NCE CECR Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, BC, V6Z 1Y6, Canada.|NCE CECR Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, BC, V6Z 1Y6, Canada.|NCE CECR Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, BC, V6Z 1Y6, Canada.|NCE CECR Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, BC, V6Z 1Y6, Canada.|NCE CECR Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, BC, V6Z 1Y6, Canada.|NCE CECR Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, BC, V6Z 1Y6, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Acute Disease|Algorithms|Area Under Curve|Biomarkers|Female|Genomics|Graft Rejection|Humans|Kidney Transplantation|Male|Proteomics|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-13-326', '23216969', 'Biomarker panels derived separately from genomic and proteomic data and with a variety of computational methods have demonstrated promising classification performance in various diseases. An open question is how to create effective proteo-genomic panels. The framework of ensemble classifiers has been applied successfully in various analytical domains to combine classifiers so that the performance of the ensemble exceeds the performance of individual classifiers. Using blood-based diagnosis of acute renal allograft rejection as a case study, we address the following question in this paper: Can acute rejection classification performance be improved by combining individual genomic and proteomic classifiers in an ensemble?'),
(95290, 2012, 'bmc', 'Improving stability and understandability of genotype-phenotype mapping in Saccharomyces using regularized variable selection in L-PLS regression', 'Mehmood, Tahir|Warringer, Jonas|Snipen, Lars|Sæbø, Solve', 'Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway. tahir.mehmood@umb.no|Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway. tahir.mehmood@umb.no|Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway. tahir.mehmood@umb.no|Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway. tahir.mehmood@umb.no', 'Norway|Norway|Norway|Norway', 4, 'Genetic Association Studies|Genotype|Least-Squares Analysis|Phenotype|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-13-327', '23216988', 'Multivariate approaches have been successfully applied to genome wide association studies. Recently, a Partial Least Squares (PLS) based approach was introduced for mapping yeast genotype-phenotype relations, where background information such as gene function classification, gene dispensability, recent or ancient gene copy number variations and the presence of premature stop codons or frameshift mutations in reading frames, were used post hoc to explain selected genes. One of the latest advancement in PLS named L-Partial Least Squares (L-PLS), where L presents the used data structure, enables the use of background information at the modeling level. Here, a modification of L-PLS with variable importance on projection (VIP) was implemented using a stepwise regularized procedure for gene and background information selection. Results were compared to PLS-based procedures, where no background information was used.'),
(95291, 2012, 'bmc', 'Comparison of co-expression measures: mutual information, correlation, and model based indices', 'Song, Lin|Langfelder, Peter|Horvath, Steve', 'Human Genetics, David Geffen School of Medicine, University of California, California, Los Angeles, USA.|Human Genetics, David Geffen School of Medicine, University of California, California, Los Angeles, USA.|Human Genetics, David Geffen School of Medicine, University of California, California, Los Angeles, USA.', 'United States|United States|United States', 0, 'Algorithms|Gene Expression Profiling|Gene Regulatory Networks|Humans|Models, Genetic|Multigene Family|Statistics as Topic', 'Comparative Study', '10.1186/1471-2105-13-328', '23217028', 'Co-expression measures are often used to define networks among genes. Mutual information (MI) is often used as a generalized correlation measure. It is not clear how much MI adds beyond standard (robust) correlation measures or regression model based association measures. Further, it is important to assess what transformations of these and other co-expression measures lead to biologically meaningful modules (clusters of genes).'),
(95292, 2012, 'bmc', 'Generalization of the normal-exponential model: exploration of a more accurate parametrisation for the signal distribution on Illumina BeadArrays', 'Plancade, Sandra|Rozenholc, Yves|Lund, Eiliv', 'Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, 9037 Tromsø, Norway. sandra.c.plancade@uit.no|Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, 9037 Tromsø, Norway. sandra.c.plancade@uit.no|Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, 9037 Tromsø, Norway. sandra.c.plancade@uit.no', 'Norway|Norway|Norway', 5, 'Models, Chemical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-13-329', '23231059', 'Illumina BeadArray technology includes non specific negative control features that allow a precise estimation of the background noise. As an alternative to the background subtraction proposed in BeadStudio which leads to an important loss of information by generating negative values, a background correction method modeling the observed intensities as the sum of the exponentially distributed signal and normally distributed noise has been developed. Nevertheless, Wang and Ye (2012) display a kernel-based estimator of the signal distribution on Illumina BeadArrays and suggest that a gamma distribution would represent a better modeling of the signal density. Hence, the normal-exponential modeling may not be appropriate for Illumina data and background corrections derived from this model may lead to wrong estimation.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95293, 2012, 'bmc', 'Fast detection of de novo copy number variants from SNP arrays for case-parent trios', 'Scharpf, Robert B|Beaty, Terri H|Schwender, Holger|Younkin, Samuel G|Scott, Alan F|Ruczinski, Ingo', 'Department of Oncology, Johns Hopkins University, Baltimore, MD, USA. rscharp1@jhmi.edu|Department of Oncology, Johns Hopkins University, Baltimore, MD, USA. rscharp1@jhmi.edu|Department of Oncology, Johns Hopkins University, Baltimore, MD, USA. rscharp1@jhmi.edu|Department of Oncology, Johns Hopkins University, Baltimore, MD, USA. rscharp1@jhmi.edu|Department of Oncology, Johns Hopkins University, Baltimore, MD, USA. rscharp1@jhmi.edu|Department of Oncology, Johns Hopkins University, Baltimore, MD, USA. rscharp1@jhmi.edu', 'United States|United States|United States|United States|United States|United States', 9, 'Algorithms|Child|Cleft Palate|DNA Copy Number Variations|Genome, Human|Humans|Oligonucleotide Array Sequence Analysis|Parents|Polymorphism, Single Nucleotide|Regression Analysis|Software', 'Journal Article', '10.1186/1471-2105-13-330', '23234608', 'In studies of case-parent trios, we define copy number variants (CNVs) in the offspring that differ from the parental copy numbers as de novo and of interest for their potential functional role in disease. Among the leading array-based methods for discovery of de novo CNVs in case-parent trios is the joint hidden Markov model (HMM) implemented in the PennCNV software. However, the computational demands of the joint HMM are substantial and the extent to which false positive identifications occur in case-parent trios has not been well described. We evaluate these issues in a study of oral cleft case-parent trios.'),
(95294, 2012, 'bmc', 'Prediction of novel long non-coding RNAs based on RNA-Seq data of mouse Klf1 knockout study', 'Sun, Lei|Zhang, Zhihua|Bailey, Timothy L|Perkins, Andrew C|Tallack, Michael R|Xu, Zhao|Liu, Hui', 'School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, JiangSu 221008, PR China.|School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, JiangSu 221008, PR China.|School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, JiangSu 221008, PR China.|School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, JiangSu 221008, PR China.|School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, JiangSu 221008, PR China.|School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, JiangSu 221008, PR China.|School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, JiangSu 221008, PR China.', 'China|China|China|China|China|China|China', 81, 'Animals|Base Sequence|Computational Biology|Exons|Genome|High-Throughput Nucleotide Sequencing|Kruppel-Like Transcription Factors|Mice|Mice, Knockout|Open Reading Frames|RNA, Long Noncoding|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1186/1471-2105-13-331', '23237380', 'Study on long non-coding RNAs (lncRNAs) has been promoted by high-throughput RNA sequencing (RNA-Seq). However, it is still not trivial to identify lncRNAs from the RNA-Seq data and it remains a challenge to uncover their functions.'),
(95295, 2012, 'bmc', 'Searching for transcription factor binding sites in vector spaces', 'Lee, Chih|Huang, Chun-Hsi', 'Department of Computer Science and Engineering, University of Connecticut, Fairfield Road, Storrs, CT 06269, USA.|Department of Computer Science and Engineering, University of Connecticut, Fairfield Road, Storrs, CT 06269, USA.', 'United States|United States', 0, 'Algorithms|Animals|Binding Sites|Computational Biology|Escherichia coli|Humans|Likelihood Functions|Mice|Position-Specific Scoring Matrices|Programming Languages|Protein Binding|Transcription Factors', 'Comparative Study', '10.1186/1471-2105-13-215', '23244338', 'Computational approaches to transcription factor binding site identification have been actively researched in the past decade. Learning from known binding sites, new binding sites of a transcription factor in unannotated sequences can be identified. A number of search methods have been introduced over the years. However, one can rarely find one single method that performs the best on all the transcription factors. Instead, to identify the best method for a particular transcription factor, one usually has to compare a handful of methods. Hence, it is highly desirable for a method to perform automatic optimization for individual transcription factors.'),
(95296, 2012, 'bmc', 'CYPSI: a structure-based interface for cytochrome P450s and ligands in Arabidopsis thaliana', 'Zhang, Gaihua|Zhang, Yijing|Su, Zhen', 'State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100094, Peoples Republic of China.|State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100094, Peoples Republic of China.|State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100094, Peoples Republic of China.', 'China|China|China', 0, 'Algorithms|Arabidopsis|Cytochrome P-450 Enzyme System|Ligands|Protein Conformation|Software', 'Journal Article', '10.1186/1471-2105-13-332', '23256889', 'The cytochrome P450 (CYP) superfamily enables terrestrial plants to adapt to harsh environments. CYPs are key enzymes involved in a wide range of metabolic pathways. It is particularly useful to be able to analyse the three-dimensional (3D) structure when investigating the interactions between CYPs and their substrates. However, only two plant CYP structures have been resolved. In addition, no currently available databases contain structural information on plant CYPs and ligands. Fortunately, the 3D structure of CYPs is highly conserved and this has made it possible to obtain structural information from template-based modelling (TBM).'),
(95297, 2012, 'bmc', 'RNAmap2D - calculation, visualization and analysis of contact and distance maps for RNA and protein-RNA complex structures', 'Pietal, Michal J|Szostak, Natalia|Rother, Kristian M|Bujnicki, Janusz M', 'Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland.|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland.|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland.|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland.', 'Poland|Poland|Poland|Poland', 0, 'Algorithms|Base Pairing|Computational Biology|Models, Molecular|Nucleic Acid Conformation|Protein Conformation|Proteins|RNA|Software', 'Journal Article', '10.1186/1471-2105-13-333', '23259794', 'The structures of biological macromolecules provide a framework for studying their biological functions. Three-dimensional structures of proteins, nucleic acids, or their complexes, are difficult to visualize in detail on flat surfaces, and algorithms for their spatial superposition and comparison are computationally costly. Molecular structures, however, can be represented as 2D maps of interactions between the individual residues, which are easier to visualize and compare, and which can be reconverted to 3D structures with reasonable precision. There are many visualization tools for maps of protein structures, but few for nucleic acids.'),
(95298, 2012, 'bmc', 'Protein interface classification by evolutionary analysis', 'Duarte, Jose M|Srebniak, Adam|Schärer, Martin A|Capitani, Guido', 'Paul Scherrer Institut, Villigen, CH-5232, Switzerland.|Paul Scherrer Institut, Villigen, CH-5232, Switzerland.|Paul Scherrer Institut, Villigen, CH-5232, Switzerland.|Paul Scherrer Institut, Villigen, CH-5232, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Biological Evolution|Computational Biology|Entropy|Internet|Proteins|Sequence Homology|Software', 'Journal Article', '10.1186/1471-2105-13-334', '23259833', 'Distinguishing biologically relevant interfaces from lattice contacts in protein crystals is a fundamental problem in structural biology. Despite efforts towards the computational prediction of interface character, many issues are still unresolved.'),
(95299, 2012, 'bmc', 'Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages', 'Taminau, Jonatan|Meganck, Stijn|Lazar, Cosmin|Steenhoff, David|Coletta, Alain|Molter, Colin|Duque, Robin|de Schaetzen, Virginie|Weiss Solís, David Y|Bersini, Hugues|Nowé, Ann', 'AI (CoMo), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. jtaminau@vub.ac.be|AI (CoMo), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. jtaminau@vub.ac.be|AI (CoMo), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. jtaminau@vub.ac.be|AI (CoMo), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. jtaminau@vub.ac.be|AI (CoMo), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. jtaminau@vub.ac.be|AI (CoMo), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. jtaminau@vub.ac.be|AI (CoMo), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. jtaminau@vub.ac.be|AI (CoMo), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. jtaminau@vub.ac.be|AI (CoMo), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. jtaminau@vub.ac.be|AI (CoMo), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. jtaminau@vub.ac.be|AI (CoMo), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. jtaminau@vub.ac.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 52, 'Access to Information|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-13-335', '23259851', 'With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining multiple existing data sets. In this new context, analysis itself is no longer the problem, but retrieving and consistently integrating all this data before delivering it to the wide variety of existing analysis tools becomes the new bottleneck.'),
(95300, 2012, 'bmc', 'A computational model for compressed sensing RNAi cellular screening', 'Tan, Hua|Fan, Jing|Bao, Jiguang|Dy, Jennifer G|Zhou, Xiaobo', 'Department of Radiology, The Methodist Hospital Research Institute, Weill Medical College of Cornell University, Houston, TX 77030, USA.|Department of Radiology, The Methodist Hospital Research Institute, Weill Medical College of Cornell University, Houston, TX 77030, USA.|Department of Radiology, The Methodist Hospital Research Institute, Weill Medical College of Cornell University, Houston, TX 77030, USA.|Department of Radiology, The Methodist Hospital Research Institute, Weill Medical College of Cornell University, Houston, TX 77030, USA.|Department of Radiology, The Methodist Hospital Research Institute, Weill Medical College of Cornell University, Houston, TX 77030, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Computer Simulation|Gene Library|Humans|Nucleotide Motifs|RNA Interference|RNA, Messenger|RNA, Small Interfering', 'Journal Article', '10.1186/1471-2105-13-337', '23270311', 'RNA interference (RNAi) becomes an increasingly important and effective genetic tool to study the function of target genes by suppressing specific genes of interest. This system approach helps identify signaling pathways and cellular phase types by tracking intensity and/or morphological changes of cells. The traditional RNAi screening scheme, in which one siRNA is designed to knockdown one specific mRNA target, needs a large library of siRNAs and turns out to be time-consuming and expensive.'),
(95301, 2012, 'bmc', 'Copy number variation signature to predict human ancestry', 'Pronold, Melissa|Vali, Marzieh|Pique-Regi, Roger|Asgharzadeh, Shahab', 'Department of Pediatrics, Childrens Hospital Los Angeles and The Saban Research Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.|Department of Pediatrics, Childrens Hospital Los Angeles and The Saban Research Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.|Department of Pediatrics, Childrens Hospital Los Angeles and The Saban Research Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.|Department of Pediatrics, Childrens Hospital Los Angeles and The Saban Research Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.', 'United States|United States|United States|United States', 7, 'African Continental Ancestry Group|Algorithms|Asian Continental Ancestry Group|Case-Control Studies|DNA Copy Number Variations|Ethnic Groups|European Continental Ancestry Group|Genetics, Population|Genome, Human|Humans', 'Journal Article', '10.1186/1471-2105-13-336', '23270563', 'Copy number variations (CNVs) are genomic structural variants that are found in healthy populations and have been observed to be associated with disease susceptibility. Existing methods for CNV detection are often performed on a sample-by-sample basis, which is not ideal for large datasets where common CNVs must be estimated by comparing the frequency of CNVs in the individual samples. Here we describe a simple and novel approach to locate genome-wide CNVs common to a specific population, using human ancestry as the phenotype.'),
(95302, 2012, 'bmc', 'Linearization of ancestral multichromosomal genomes', 'Maxc5x88uch, Jxc3xa1n|Patterson, Murray|Wittler, Roland|Chauve, Cedric|Tannier, Eric', 'INRIA Rhxc3xb4ne-Alpes, 655 avenue de IEurope, F-38344 Montbonnot, France. Murray.Patterson@inria.fr|INRIA Rhxc3xb4ne-Alpes, 655 avenue de IEurope, F-38344 Montbonnot, France. Murray.Patterson@inria.fr|INRIA Rhxc3xb4ne-Alpes, 655 avenue de IEurope, F-38344 Montbonnot, France. Murray.Patterson@inria.fr|INRIA Rhxc3xb4ne-Alpes, 655 avenue de IEurope, F-38344 Montbonnot, France. Murray.Patterson@inria.fr|INRIA Rhxc3xb4ne-Alpes, 655 avenue de IEurope, F-38344 Montbonnot, France. Murray.Patterson@inria.fr', 'France|France|France|France|France', 19, 'Algorithms|Chromosomes|Evolution, Molecular|Genome|Genomics', 'Journal Article', '10.1186/1471-2105-13-S19-S11', '23281593', 'Recovering the structure of ancestral genomes can be formalized in terms of properties of binary matrices such as the Consecutive-Ones Property (C1P). The Linearization Problem asks to extract, from a given binary matrix, a maximum weight subset of rows that satisfies such a property. This problem is in general intractable, and in particular if the ancestral genome is expected to contain only linear chromosomes or a unique circular chromosome. In the present work, we consider a relaxation of this problem, which allows ancestral genomes that can contain several chromosomes, each either linear or circular.'),
(95303, 2012, 'bmc', 'An automatic device for detection and classification of malaria parasite species in thick blood film', 'Kaewkamnerd, Saowaluck|Uthaipibull, Chairat|Intarapanich, Apichart|Pannarut, Montri|Chaotheing, Sastra|Tongsima, Sissades', 'National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Thailand Science Park, Pathumthani 12120, Thailand.|National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Thailand Science Park, Pathumthani 12120, Thailand.|National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Thailand Science Park, Pathumthani 12120, Thailand.|National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Thailand Science Park, Pathumthani 12120, Thailand.|National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Thailand Science Park, Pathumthani 12120, Thailand.|National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Thailand Science Park, Pathumthani 12120, Thailand.', 'Thailand|Thailand|Thailand|Thailand|Thailand|Thailand', 0, 'Animals|Chromatin|Erythrocytes|Humans|Image Processing, Computer-Assisted|Malaria|Malaria, Falciparum|Microscopy|Parasitemia|Plasmodium|Plasmodium falciparum|Plasmodium vivax', 'Journal Article', '10.1186/1471-2105-13-S17-S18', '23281600', 'Current malaria diagnosis relies primarily on microscopic examination of Giemsa-stained thick and thin blood films. This method requires vigorously trained technicians to efficiently detect and classify the malaria parasite species such as Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) for an appropriate drug administration. However, accurate classification of parasite species is difficult to achieve because of inherent technical limitations and human inconsistency. To improve performance of malaria parasite classification, many researchers have proposed automated malaria detection devices using digital image analysis. These image processing tools, however, focus on detection of parasites on thin blood films, which may not detect the existence of parasites due to the parasite scarcity on the thin blood film. The problem is aggravated with low parasitemia condition. Automated detection and classification of parasites on thick blood films, which contain more numbers of parasite per detection area, would address the previous limitation.'),
(95304, 2012, 'bmc', 'Inference of reticulate evolutionary histories by maximum likelihood: the performance of information criteria', 'Park, Hyun Jung|Nakhleh, Luay', 'Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA. hjpark@bcm.edu|Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA. hjpark@bcm.edu', 'United States|United States', 6, 'Bayes Theorem|Evolution, Molecular|Genes|Likelihood Functions|Models, Genetic|Phylogeny', 'Journal Article', '10.1186/1471-2105-13-S19-S12', '23281614', 'Maximum likelihood has been widely used for over three decades to infer phylogenetic trees from molecular data. When reticulate evolutionary events occur, several genomic regions may have conflicting evolutionary histories, and a phylogenetic network may provide a more adequate model for representing the evolutionary history of the genomes or species. A maximum likelihood (ML) model has been proposed for this case and accounts for both mutation within a genomic region and reticulation across the regions. However, the performance of this model in terms of inferring information about reticulate evolution and properties that affect this performance have not been studied.'),
(95305, 2012, 'bmc', 'A linear-time algorithm for reconstructing zero-recombinant haplotype configuration on a pedigree', 'Lai, En-Yu|Wang, Wei-Bung|Jiang, Tao|Wu, Kun-Pin', 'Institute of Biomedical Informatics, National Yang Ming University, Taipei 112, Taiwan.|Institute of Biomedical Informatics, National Yang Ming University, Taipei 112, Taiwan.|Institute of Biomedical Informatics, National Yang Ming University, Taipei 112, Taiwan.|Institute of Biomedical Informatics, National Yang Ming University, Taipei 112, Taiwan.', '-|-|-|-', 2, 'Algorithms|Alleles|Child|Computer Simulation|Genetic Diseases, Inborn|Genetic Variation|Genotype|Haplotypes|Humans|Models, Genetic|Pedigree|Recombination, Genetic', 'Journal Article', '10.1186/1471-2105-13-S17-S19', '23281626', 'When studying genetic diseases in which genetic variations are passed on to offspring, the ability to distinguish between paternal and maternal alleles is essential. Determining haplotypes from genotype data is called haplotype inference. Most existing computational algorithms for haplotype inference have been designed to use genotype data collected from individuals in the form of a pedigree. A haplotype is regarded as a hereditary unit and therefore input pedigrees are preferred that are free of mutational events and have a minimum number of genetic recombinational events. These ideas motivated the zero-recombinant haplotype configuration (ZRHC) problem, which strictly follows the Mendelian law of inheritance, namely that one haplotype of each child is inherited from the father and the other haplotype is inherited from the mother, both without any mutation. So far no linear-time algorithm for ZRHC has been proposed for general pedigrees, even though the number of mating loops in a human pedigree is usually very small and can be regarded as constant.'),
(95306, 2012, 'bmc', 'Restricted DCJ-indel model: sorting linear genomes with DCJ and indels', 'da Silva, Poly H|Machado, Raphael|Dantas, Simone|Braga, Marília D V', 'IME, Universidade Federal Fluminense, Niterói, Brazil. poly.hannah@gmail.com|IME, Universidade Federal Fluminense, Niterói, Brazil. poly.hannah@gmail.com|IME, Universidade Federal Fluminense, Niterói, Brazil. poly.hannah@gmail.com|IME, Universidade Federal Fluminense, Niterói, Brazil. poly.hannah@gmail.com', 'Brazil|Brazil|Brazil|Brazil', 4, 'Algorithms|Chromosomes|DNA Breaks, Double-Stranded|Genome|Genomics|INDEL Mutation|Models, Genetic|Mutation|Translocation, Genetic', 'Journal Article', '10.1186/1471-2105-13-S19-S14', '23281630', 'The double-cut-and-join (DCJ) is a model that is able to efficiently sort a genome into another, generalizing the typical mutations (inversions, fusions, fissions, translocations) to which genomes are subject, but allowing the existence of circular chromosomes at the intermediate steps. In the general model many circular chromosomes can coexist in some intermediate step. However, when the compared genomes are linear, it is more plausible to use the so-called restricted DCJ model, in which we proceed the reincorporation of a circular chromosome immediately after its creation. These two consecutive DCJ operations, which create and reincorporate a circular chromosome, mimic a transposition or a block-interchange. When the compared genomes have the same content, it is known that the genomic distance for the restricted DCJ model is the same as the distance for the general model. If the genomes have unequal contents, in addition to DCJ it is necessary to consider indels, which are insertions and deletions of DNA segments. Linear time algorithms were proposed to compute the distance and to find a sorting scenario in a general, unrestricted DCJ-indel model that considers DCJ and indels.'),
(95307, 2012, 'bmc', 'Functional relevance of dynamic properties of Dimeric NADP-dependent Isocitrate Dehydrogenases', 'Vinekar, Rithvik|Verma, Chandra|Ghosh, Indira', 'School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Mehrauli Road, New Delhi 110067, India.|School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Mehrauli Road, New Delhi 110067, India.|School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Mehrauli Road, New Delhi 110067, India.', 'India|India|India', 11, 'Amino Acid Sequence|Bacterial Proteins|Escherichia coli|Isocitrate Dehydrogenase|Molecular Sequence Data|Mycobacterium tuberculosis|Phylogeny|Protein Conformation|Protein Multimerization|Sequence Alignment|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-13-S17-S2', '23281650', 'Isocitrate Dehydrogenases (IDHs) are important enzymes present in all living cells. Three subfamilies of functionally dimeric IDHs (subfamilies I, II, III) are known. Subfamily I are well-studied bacterial IDHs, like that of Escherischia coli. Subfamily II has predominantly eukaryotic members, but it also has several bacterial members, many being pathogens or endosymbionts. subfamily III IDHs are NAD-dependent. The eukaryotic-like subfamily II IDH from pathogenic bacteria such as Mycobacterium tuberculosis IDH1 are expected to have regulation similar to that of bacteria which use the glyoxylate bypass to survive starvation. Yet they are structurally different from IDHs of subfamily I, such as the E. coli IDH.'),
(95308, 2012, 'bmc', 'Gene trees and species trees: irreconcilable differences', 'Swenson, Krister M|El-Mabrouk, Nadia', 'Département dInformatique, DIRO, Université de Montréal, H3C 3J7 Canada. swensonk@iro.umontreal.ca|Département dInformatique, DIRO, Université de Montréal, H3C 3J7 Canada. swensonk@iro.umontreal.ca', 'Canada|Canada', 0, 'Algorithms|Animals|Evolution, Molecular|Gene Duplication|Genes|Genome|Interleukin-1|Phylogeny|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-13-S19-S15', '23281654', 'Reconciliation is the classical method for inferring a duplication and loss history from a set of extant genes. It is based upon the notion of embedding the gene tree into the species tree, the incongruence between the two indicating evidence for duplication and loss. However, results obtained by this method are highly dependent upon the considered species and gene trees. Thus, painstaking attention has been given to the development of methods for reconstructing accurate gene trees.'),
(95309, 2012, 'bmc', 'A graph-theoretic approach for inparalog detection', 'Tremblay-Savard, Olivier|Swenson, Krister M', 'Département dInformatique, DIRO, Université de Montréal, H3C 3J7, Canada. swensonk@iro.umontreal.ca|Département dInformatique, DIRO, Université de Montréal, H3C 3J7, Canada. swensonk@iro.umontreal.ca', 'Canada|Canada', 0, 'Animals|Evolution, Molecular|Gene Duplication|Genomics|Humans|Mice|Multigene Family|Phylogeny|Rats|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-13-S19-S16', '23281701', 'Understanding the history of a gene family that evolves through duplication, speciation, and loss is a fundamental problem in comparative genomics. Features such as function, position, and structural similarity between genes are intimately connected to this history, relationships between genes such as orthology (genes related through a speciation event) or paralogy (genes related through a duplication event) are usually correlated with these features. For example, recent work has shown that in human and mouse there is a strong connection between function and inparalogs, the paralogs that were created since the speciation event separating the human and mouse lineages. Methods exist for detecting inparalogs that either use information from only two species, or consider a set of species but rely on clustering methods. In this paper we present a graph-theoretic approach for finding lower bounds on the number of inparalogs for a given set of species, we pose an edge covering problem on the similarity graph and give an efficient 2/3-approximation as well as a faster heuristic. Since the physical position of inparalogs corresponding to recent speciations is not likely to have changed since the duplication, we also use our predictions to estimate the types of duplications that have occurred in some vertebrates and drosophila.'),
(95310, 2012, 'bmc', 'ARG-based genome-wide analysis of cacao cultivars', 'Utro, Filippo|Cornejo, Omar Eduardo|Livingstone, Donald|Motamayor, Juan Carlos|Parida, Laxmi', 'Computational Biology Center, IBM TJ Watson Research, Yorktown Heights, NY 10598, USA.|Computational Biology Center, IBM TJ Watson Research, Yorktown Heights, NY 10598, USA.|Computational Biology Center, IBM TJ Watson Research, Yorktown Heights, NY 10598, USA.|Computational Biology Center, IBM TJ Watson Research, Yorktown Heights, NY 10598, USA.|Computational Biology Center, IBM TJ Watson Research, Yorktown Heights, NY 10598, USA.', 'United States|United States|United States|United States|United States', 6, 'Base Sequence|Cacao|Evolution, Molecular|Genetic Linkage|Genome, Plant|Genomics|Polymorphism, Single Nucleotide|Recombination, Genetic', 'Journal Article', '10.1186/1471-2105-13-S19-S17', '23281769', 'Ancestral recombinations graph (ARG) is a topological structure that captures the relationship between the extant genomic sequences in terms of genetic events including recombinations. IRiS is a system that estimates the ARG on sequences of individuals, at genomic scales, capturing the relationship between these individuals of the species. Recently, this system was used to estimate the ARG of the recombining X Chromosome of a collection of human populations using relatively dense, bi-allelic SNP data.'),
(95311, 2012, 'bmc', 'TIGER: tiled iterative genome assembler', 'Wu, Xiao-Long|Heo, Yun|El Hajj, Izzat|Hwu, Wen-Mei|Chen, Deming|Ma, Jian', 'Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.', 'United States|United States|United States|United States|United States|United States', 8, 'Animals|Contig Mapping|Genome|Genomics|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-13-S19-S18', '23281792', 'With the cost reduction of the next-generation sequencing (NGS) technologies, genomics has provided us with an unprecedented opportunity to understand fundamental questions in biology and elucidate human diseases. De novo genome assembly is one of the most important steps to reconstruct the sequenced genome. However, most de novo assemblers require enormous amount of computational resource, which is not accessible for most research groups and medical personnel.'),
(95312, 2012, 'bmc', 'Exploiting ancestral mammalian genomes for the prediction of human transcription factor binding sites', 'Blanchette, Mathieu', 'McGill Centre for Bioinformatics and School of Computer Science, McGill University, H3C 2B4 Québec, Canada. blanchem@cs.mcgill.ca', 'Canada', 0, 'Animals|Binding Sites|Chromosomes, Human, Pair 1|Genome, Human|Genomics|Humans|Protein Binding|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-13-S19-S2', '23281809', 'The computational prediction of Transcription Factor Binding Sites (TFBS) remains a challenge due to their short length and low information content. Comparative genomics approaches that simultaneously consider several related species and favor sites that have been conserved throughout evolution improve the accuracy (specificity) of the predictions but are limited due to a phenomenon called binding site turnover, where sequence evolution causes one TFBS to replace another in the same region. In parallel to this development, an increasing number of mammalian genomes are now sequenced and it is becoming possible to infer, to a surprisingly high degree of accuracy, ancestral mammalian sequences.'),
(95313, 2012, 'bmc', 'Gene family assignment-free comparative genomics', 'Doerr, Daniel|Thxc3xa9venin, Annelyse|Stoye, Jens', 'Faculty of Technology, Center for Biotechnology, Bielefeld University, Germany. ddoerr@cebitec.uni-bielefeld.de|Faculty of Technology, Center for Biotechnology, Bielefeld University, Germany. ddoerr@cebitec.uni-bielefeld.de|Faculty of Technology, Center for Biotechnology, Bielefeld University, Germany. ddoerr@cebitec.uni-bielefeld.de', 'Germany|Germany|Germany', 12, 'Algorithms|Gammaproteobacteria|Gene Order|Genome, Bacterial|Genomics|Multigene Family|Sequence Analysis, DNA', 'Comparative Study', '10.1186/1471-2105-13-S19-S3', '23281826', 'The comparison of relative gene orders between two genomes offers deep insights into functional correlations of genes and the evolutionary relationships between the corresponding organisms. Methods for gene order analyses often require prior knowledge of homologies between all genes of the genomic dataset. Since such information is hard to obtain, it is common to predict homologous groups based on sequence similarity. These hypothetical groups of homologous genes are called gene families.'),
(95314, 2012, 'bmc', 'B-cell epitope prediction through a graph model', 'Zhao, Liang|Wong, Limsoon|Lu, Lanyuan|Hoi, Steven C H|Li, Jinyan', 'Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore.|Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore.|Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore.|Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore.|Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore', 14, 'Algorithms|Antibodies|Antigen-Antibody Complex|Antigens|Computer Graphics|Computer Simulation|Epitope Mapping|Epitopes, B-Lymphocyte|Humans|Markov Chains|Models, Immunological|Protein Conformation', 'Journal Article', '10.1186/1471-2105-13-S17-S20', '23281855', 'Prediction of B-cell epitopes from antigens is useful to understand the immune basis of antibody-antigen recognition, and is helpful in vaccine design and drug development. Tremendous efforts have been devoted to this long-studied problem, however, existing methods have at least two common limitations. One is that they only favor prediction of those epitopes with protrusive conformations, but show poor performance in dealing with planar epitopes. The other limit is that they predict all of the antigenic residues of an antigen as belonging to one single epitope even when multiple non-overlapping epitopes of an antigen exist.'),
(95315, 2012, 'bmc', 'A flexible ancestral genome reconstruction method based on gapped adjacencies', 'Gagnon, Yves|Blanchette, Mathieu|El-Mabrouk, Nadia', 'Département dInformatique, DIRO, Université de Montréal, Canada.|Département dInformatique, DIRO, Université de Montréal, Canada.|Département dInformatique, DIRO, Université de Montréal, Canada.', 'Canada|Canada|Canada', 0, 'Algorithms|Contig Mapping|Evolution, Molecular|Genome|Phylogeny|Synteny', 'Journal Article', '10.1186/1471-2105-13-S19-S4', '23281872', 'The small phylogeny problem consists in inferring ancestral genomes associated with each internal node of a phylogenetic tree of a set of extant species. Existing methods can be grouped into two main categories: the distance-based methods aiming at minimizing a total branch length, and the synteny-based (or mapping) methods that first predict a collection of relations between ancestral markers in term of synteny, and then assemble this collection into a set of Contiguous Ancestral Regions (CARs). The predicted CARs are likely to be more reliable as they are more directly deduced from observed conservations in extant species. However the challenge is to end up with a completely assembled genome.'),
(95316, 2012, 'bmc', 'Mathematical modeling of the immune system recognition to mammary carcinoma antigen', 'Bianca, Carlo|Chiacchio, Ferdinando|Pappalardo, Francesco|Pennisi, Marzio', 'Dipartimento di Matematica e Informatica, Università di Catania, Catania, Italy.|Dipartimento di Matematica e Informatica, Università di Catania, Catania, Italy.|Dipartimento di Matematica e Informatica, Università di Catania, Catania, Italy.|Dipartimento di Matematica e Informatica, Università di Catania, Catania, Italy.', 'Italy|Italy|Italy|Italy', 0, 'Animals|Antigen-Presenting Cells|Antigens, Neoplasm|B-Lymphocytes|Cancer Vaccines|Computer Simulation|Immune System|Mice|Mice, Transgenic|Models, Immunological|Receptor, ErbB-2|T-Lymphocytes', 'Journal Article', '10.1186/1471-2105-13-S17-S21', '23281916', 'The definition of artificial immunity, realized through vaccinations, is nowadays a practice widely developed in order to eliminate cancer disease. The present paper deals with an improved version of a mathematical model recently analyzed and related to the competition between immune system cells and mammary carcinoma cells under the action of a vaccine (Triplex). The model describes in detail both the humoral and cellular response of the immune system to the tumor associate antigen and the recognition process between B cells, T cells and antigen presenting cells. The control of the tumor cells growth occurs through the definition of different vaccine protocols. The performed numerical simulations of the model are in agreement with in vivo experiments on transgenic mice.'),
(95317, 2012, 'bmc', 'Medians seek the corners, and other conjectures', 'Haghighi, Maryam|Sankoff, David', 'Department of Mathematics and Statistics, University of Ottawa, 585 King Edward Avenue, Ottawa, Canada K1N 6N5.|Department of Mathematics and Statistics, University of Ottawa, 585 King Edward Avenue, Ottawa, Canada K1N 6N5.', 'Canada|Canada', 0, 'Computer Simulation|Gene Order|Genome|Genomics|Phylogeny', 'Journal Article', '10.1186/1471-2105-13-S19-S5', '23281922', 'Median construction is at the heart of several approaches to gene-order phylogeny. It has been observed that the solution to a median problem is generally not unique, and that alternate solutions may be quite different. Another concern has to do with a tendency for medians to fall on or near one of the three input orders, and hence to contain no information about the other two.'),
(95318, 2012, 'bmc', 'InCoB2012 Conference: from biological data to knowledge to technological breakthroughs', 'Schönbach, Christian|Tongsima, Sissades|Chan, Jonathan|Brusic, Vladimir|Tan, Tin Wee|Ranganathan, Shoba', 'Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka 820-8502, Japan. schoen@bio.kyutech.ac.jp|Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka 820-8502, Japan. schoen@bio.kyutech.ac.jp|Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka 820-8502, Japan. schoen@bio.kyutech.ac.jp|Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka 820-8502, Japan. schoen@bio.kyutech.ac.jp|Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka 820-8502, Japan. schoen@bio.kyutech.ac.jp|Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka 820-8502, Japan. schoen@bio.kyutech.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Biomedical Research|Computational Biology|Humans|Thailand', 'Congress', '10.1186/1471-2105-13-S17-S1', '23281929', 'Ten years ago when Asia-Pacific Bioinformatics Network held the first International Conference on Bioinformatics (InCoB) in Bangkok its theme was North-South Networking. At that time InCoB aimed to provide biologists and bioinformatics researchers in the Asia-Pacific region a forum to meet, interact with, and disseminate knowledge about the burgeoning field of bioinformatics. Meanwhile InCoB has evolved into a major regional bioinformatics conference that attracts not only talented and established scientists from the region but increasingly also from East Asia, North America and Europe. Since 2006 InCoB yielded 114 articles in BMC Bioinformatics supplement issues that have been cited nearly 1,000 times to date. In part, these developments reflect the success of bioinformatics education and continuous efforts to integrate and utilize bioinformatics in biotechnology and biosciences in the Asia-Pacific region. A cross-section of research leading from biological data to knowledge and to technological applications, the InCoB2012 theme, is introduced in this editorial. Other highlights included sessions organized by the Pan-Asian Pacific Genome Initiative and a Machine Learning in Immunology competition. InCoB2013 is scheduled for September 18-21, 2013 at Suzhou, China.'),
(95319, 2012, 'bmc', 'Personalized cloud-based bioinformatics services for research and education: use cases and the elasticHPC package', 'El-Kalioby, Mohamed|Abouelhoda, Mohamed|Krüger, Jan|Giegerich, Robert|Sczyrba, Alexander|Wall, Dennis P|Tonellato, Peter', 'Centre for Informatics Sciences, Nile University, Giza, Egypt. mohamed.elkalioby@nileu.edu.eg|Centre for Informatics Sciences, Nile University, Giza, Egypt. mohamed.elkalioby@nileu.edu.eg|Centre for Informatics Sciences, Nile University, Giza, Egypt. mohamed.elkalioby@nileu.edu.eg|Centre for Informatics Sciences, Nile University, Giza, Egypt. mohamed.elkalioby@nileu.edu.eg|Centre for Informatics Sciences, Nile University, Giza, Egypt. mohamed.elkalioby@nileu.edu.eg|Centre for Informatics Sciences, Nile University, Giza, Egypt. mohamed.elkalioby@nileu.edu.eg|Centre for Informatics Sciences, Nile University, Giza, Egypt. mohamed.elkalioby@nileu.edu.eg', 'Egypt|Egypt|Egypt|Egypt|Egypt|Egypt|Egypt', 0, 'Computational Biology|Education|Information Services|Internet|Research|Software', 'Journal Article', '10.1186/1471-2105-13-S17-S22', '23281941', 'Bioinformatics services have been traditionally provided in the form of a web-server that is hosted at institutional infrastructure and serves multiple users. This model, however, is not flexible enough to cope with the increasing number of users, increasing data size, and new requirements in terms of speed and availability of service. The advent of cloud computing suggests a new service model that provides an efficient solution to these problems, based on the concepts of resources-on-demand and pay-as-you-go. However, cloud computing has not yet been introduced within bioinformatics servers due to the lack of usage scenarios and software layers that address the requirements of the bioinformatics domain.'),
(95320, 2012, 'bmc', 'Multiple genome comparison based on overlap regions of pairwise local alignments', 'Jahn, Katharina|Sudek, Henner|Stoye, Jens', 'Department of Mathematics and Statistics, University of Ottawa, Canada. kjahn@cebitec.uni-bielefeld.de|Department of Mathematics and Statistics, University of Ottawa, Canada. kjahn@cebitec.uni-bielefeld.de|Department of Mathematics and Statistics, University of Ottawa, Canada. kjahn@cebitec.uni-bielefeld.de', 'Canada|Canada|Canada', 2, 'Algorithms|Genome|Humans|Reference Standards|Sequence Alignment', 'Comparative Study', '10.1186/1471-2105-13-S19-S7', '23281942', 'Mancheron, Uricaru and Rivals (Nucleic Acids Res. 39:e101, 2011) recently introduced a new approach in the context of multiple genome comparison that allows to detect regions of strong overlaps in a set of pairwise local alignments between several reference genomes and one target genome. Such overlap regions are an important source of information in genome annotation.'),
(95321, 2012, 'bmc', 'A hybrid method for the exact planted (l, d) motif finding problem and its parallelization', 'Abbas, Mostafa M|Abouelhoda, Mohamed|Bahig, Hazem M', 'Department of Basic Sciences, Faculty of Engineering, Sinai University, El-Arish, Egypt. mabbas@su.edu.eg|Department of Basic Sciences, Faculty of Engineering, Sinai University, El-Arish, Egypt. mabbas@su.edu.eg|Department of Basic Sciences, Faculty of Engineering, Sinai University, El-Arish, Egypt. mabbas@su.edu.eg', 'Egypt|Egypt|Egypt', 9, 'Computational Biology|Nucleotide Motifs|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-13-S17-S10', '23281969', 'Given a set of DNA sequences s1, ..., st, the (l, d) motif problem is to find an l-length motif sequence M , not necessary existing in any of the input sequences, such that for each sequence si, 1 xe2x89xa4 i xe2x89xa4 t, there is at least one subsequence differing with at most d mismatches from M. Many exact algorithms have been developed to solve the motif finding problem in the last three decades. However, the problem is still challenging and its solution is limited to small values of l and d.'),
(95322, 2012, 'bmc', 'CFMDS: CUDA-based fast multidimensional scaling for genome-scale data', 'Park, Sungin|Shin, Soo-Yong|Hwang, Kyu-Baek', 'School of Computer Science and Engineering, Soongsil University, Seoul 156-743, Korea.|School of Computer Science and Engineering, Soongsil University, Seoul 156-743, Korea.|School of Computer Science and Engineering, Soongsil University, Seoul 156-743, Korea.', '-|-|-', 0, 'Algorithms|Animals|Genome|Genomics|Humans|Mice|Multivariate Analysis|Software', 'Journal Article', '10.1186/1471-2105-13-S17-S23', '23282007', 'Multidimensional scaling (MDS) is a widely used approach to dimensionality reduction. It has been applied to feature selection and visualization in various areas. Among diverse MDS methods, the classical MDS is a simple and theoretically sound solution for projecting data objects onto a low dimensional space while preserving the original distances among them as much as possible. However, it is not trivial to apply it to genome-scale data (e.g., microarray gene expression profiles) on regular desktop computers, because of its high computational complexity.'),
(95323, 2012, 'bmc', 'A consolidation algorithm for genomes fractionated after higher order polyploidization', 'Jahn, Katharina|Zheng, Chunfang|Kovxc3xa1xc4x8d, Jakub|Sankoff, David', 'Department of Mathematics and Statistics, University of Ottawa, 585 King Edward Avenue, Ottawa, Canada K1N 6N5. sankoff@uottawa.ca|Department of Mathematics and Statistics, University of Ottawa, 585 King Edward Avenue, Ottawa, Canada K1N 6N5. sankoff@uottawa.ca|Department of Mathematics and Statistics, University of Ottawa, 585 King Edward Avenue, Ottawa, Canada K1N 6N5. sankoff@uottawa.ca|Department of Mathematics and Statistics, University of Ottawa, 585 King Edward Avenue, Ottawa, Canada K1N 6N5. sankoff@uottawa.ca', 'Canada|Canada|Canada|Canada', 0, 'Algorithms|Gene Dosage|Gene Duplication|Gene Order|Genome|Genome, Plant|Polyploidy|Sequence Analysis, DNA|Vitis', 'Journal Article', '10.1186/1471-2105-13-S19-S8', '23282012', 'It has recently been shown that fractionation, the random loss of excess gene copies after a whole genome duplication event, is a major cause of gene order disruption. When estimating evolutionary distances between genomes based on chromosomal rearrangement, fractionation inevitably leads to significant overestimation of classic rearrangement distances. This bias can be largely avoided when genomes are preprocessed by consolidation, a procedure that identifies and accounts for regions of fractionation.'),
(95324, 2012, 'bmc', 'TranSeqAnnotator: large-scale analysis of transcriptomic data', 'Menon, Ranjeeta|Garg, Gagan|Gasser, Robin B|Ranganathan, Shoba', 'Department of Chemistry and Biomolecular Sciences and ARC Centre of Excellence, Macquarie University, Sydney, NSW 2109, Australia.|Department of Chemistry and Biomolecular Sciences and ARC Centre of Excellence, Macquarie University, Sydney, NSW 2109, Australia.|Department of Chemistry and Biomolecular Sciences and ARC Centre of Excellence, Macquarie University, Sydney, NSW 2109, Australia.|Department of Chemistry and Biomolecular Sciences and ARC Centre of Excellence, Macquarie University, Sydney, NSW 2109, Australia.', 'Australia|Australia|Australia|Australia', 3, 'Expressed Sequence Tags|Gene Expression Profiling|Molecular Sequence Annotation|Software|Transcriptome', 'Journal Article', '10.1186/1471-2105-13-S17-S24', '23282024', 'The transcriptome of an organism can be studied with the analysis of expressed sequence tag (EST) data sets that offers a rapid and cost effective approach with several new and updated bioinformatics approaches and tools for assembly and annotation. The comprehensive analyses comprehend an organism along with the genome and proteome analysis. With the advent of large-scale sequencing projects and generation of sequence data at protein and cDNA levels, automated analysis pipeline is necessary to store, organize and annotate ESTs.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95325, 2012, 'bmc', 'Mapping small molecule binding data to structural domains', 'Kruger, Felix A|Rostom, Raghd|Overington, John P', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Binding Sites|Computational Biology|Drug Discovery|Humans|Ligands|Protein Structure, Tertiary|Proteins|Small Molecule Libraries|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-13-S17-S11', '23282026', 'Large-scale bioactivity/SAR Open Data has recently become available, and this has allowed new analyses and approaches to be developed to help address the productivity and translational gaps of current drug discovery. One of the current limitations of these data is the relative sparsity of reported interactions per protein target, and complexities in establishing clear relationships between bioactivity and targets using bioinformatics tools. We detail in this paper the indexing of targets by the structural domains that bind (or are likely to bind) the ligand within a full-length protein. Specifically, we present a simple heuristic to map small molecule binding to Pfam domains. This profiling can be applied to all proteins within a genome to give some indications of the potential pharmacological modulation and regulation of all proteins.'),
(95326, 2012, 'bmc', 'Reconstructing genome mixtures from partial adjacencies', 'Mahmoody, Ahmad|Kahn, Crystal L|Raphael, Benjamin J', 'Department of Computer Science, Brown University, Providence, RI, USA. ahmad@cs.brown.edu|Department of Computer Science, Brown University, Providence, RI, USA. ahmad@cs.brown.edu|Department of Computer Science, Brown University, Providence, RI, USA. ahmad@cs.brown.edu', 'United States|United States|United States', 4, 'Algorithms|Base Sequence|Contig Mapping|Gene Rearrangement|Genome, Human|Humans|Mutation|Neoplasms|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-13-S19-S9', '23282028', 'Many cancer genome sequencing efforts are underway with the goal of identifying the somatic mutations that drive cancer progression. A major difficulty in these studies is that tumors are typically heterogeneous, with individual cells in a tumor having different complements of somatic mutations. However, nearly all DNA sequencing technologies sequence DNA from multiple cells, thus resulting in measurement of mutations from a mixture of genomes. Genome rearrangements are a major class of somatic mutations in many tumors, and the novel adjacencies (i.e. breakpoints) resulting from these rearrangements are readily detected from DNA sequencing reads. However, the assignment of each rearrangement, or adjacency, to an individual cancer genome in the mixture is not known. Moreover, the quantity of DNA sequence reads may be insufficient to measure all rearrangements in all genomes in the tumor. Motivated by this application, we formulate the k-minimum completion problem (k-MCP). In this problem, we aim to reconstruct k genomes derived from a single reference genome, given partial information about the adjacencies present in the mixture of these genomes. We show that the 1-MCP is solvable in linear time in the cases where: (i) the measured, incomplete genome has a single circular or linear chromosome, (ii) there are no restrictions on the chromosomal content of the measured, incomplete genome. We also show that the k-MCP problem, for k xe2x89xa5 3 in general, and the 2-MCP problem with the double-cut-and-join (DCJ) distance are NP-complete, when there are no restriction on the chromosomal structure of the measured, incomplete genome. These results lay the foundation for future algorithmic studies of the k-MCP and the application of these algorithms to real cancer sequencing data.'),
(95327, 2012, 'bmc', 'A preliminary study on automated freshwater algae recognition and classification system', 'Mosleh, Mogeeb A A|Manssor, Hayat|Malek, Sorayya|Milow, Pozi|Salleh, Aishah', 'Artificial Intelligent Department, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia. MogeebMosleh@um.edu.my.|Artificial Intelligent Department, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia. MogeebMosleh@um.edu.my.|Artificial Intelligent Department, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia. MogeebMosleh@um.edu.my.|Artificial Intelligent Department, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia. MogeebMosleh@um.edu.my.|Artificial Intelligent Department, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia. MogeebMosleh@um.edu.my.', 'Malaysia|Malaysia|Malaysia|Malaysia|Malaysia', 0, 'Algorithms|Chrysophyta|Cyanobacteria|Diatoms|Environmental Monitoring|Fresh Water|Image Processing, Computer-Assisted|Principal Component Analysis|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-13-S17-S25', '23282059', 'Freshwater algae can be used as indicators to monitor freshwater ecosystem condition. Algae react quickly and predictably to a broad range of pollutants. Thus they provide early signals of worsening environment. This study was carried out to develop a computer-based image processing technique to automatically detect, recognize, and identify algae genera from the divisions Bacillariophyta, Chlorophyta and Cyanobacteria in Putrajaya Lake. Literature shows that most automated analyses and identification of algae images were limited to only one type of algae. Automated identification system for tropical freshwater algae is even non-existent and this study is partly to fill this gap.'),
(95328, 2012, 'bmc', 'A probabilistic coevolutionary biclustering algorithm for discovering coherent patterns in gene expression dataset', 'Joung, Je-Gun|Kim, Soo-Jin|Shin, Soo-Yong|Zhang, Byoung-Tak', 'Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, Korea.|Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, Korea.|Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, Korea.|Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, Korea.', '-|-|-|-', 9, 'Algorithms|Cluster Analysis|Evolution, Molecular|Gene Expression|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-13-S17-S12', '23282075', 'Biclustering has been utilized to find functionally important patterns in biological problem. Here a bicluster is a submatrix that consists of a subset of rows and a subset of columns in a matrix, and contains homogeneous patterns. The problem of finding biclusters is still challengeable due to computational complex trying to capture patterns from two-dimensional features.'),
(95329, 2012, 'bmc', 'Phylogenetic reconstruction in the order Nymphaeales: ITS2 secondary structure analysis and in silico testing of maturase k (matK) as a potential marker for DNA bar coding', 'Biswal, Devendra Kumar|Debnath, Manish|Kumar, Shakti|Tandon, Pramod', 'Bioinformatics Centre, North Eastern Hill University, Shillong 793022, Meghalaya, India.|Bioinformatics Centre, North Eastern Hill University, Shillong 793022, Meghalaya, India.|Bioinformatics Centre, North Eastern Hill University, Shillong 793022, Meghalaya, India.|Bioinformatics Centre, North Eastern Hill University, Shillong 793022, Meghalaya, India.', 'India|India|India|India', 0, 'Bayes Theorem|Chloroplasts|Computer Simulation|DNA Barcoding, Taxonomic|DNA, Chloroplast|DNA, Intergenic|DNA, Plant|Endoribonucleases|Fossils|Genetic Markers|Likelihood Functions|Nucleic Acid Conformation|Nucleotidyltransferases|Nymphaeaceae|Phylogeny|Protein Structure, Secondary|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-13-S17-S26', '23282079', 'The Nymphaeales (waterlilly and relatives) lineage has diverged as the second branch of basal angiosperms and comprises of two families: Cabombaceae and Nymphaceae. The classification of Nymphaeales and phylogeny within the flowering plants are quite intriguing as several systems (Thorne system, Dahlgren system, Cronquist system, Takhtajan system and APG III system (Angiosperm Phylogeny Group III system) have attempted to redefine the Nymphaeales taxonomy. There have been also fossil records consisting especially of seeds, pollen, stems, leaves and flowers as early as the lower Cretaceous. Here we present an in silico study of the order Nymphaeales taking maturaseK (matK) and internal transcribed spacer (ITS2) as biomarkers for phylogeny reconstruction (using character-based methods and Bayesian approach) and identification of motifs for DNA barcoding.'),
(95330, 2012, 'bmc', 'Prediction of nuclear proteins using nuclear translocation signals proposed by probabilistic latent semantic indexing', 'Su, Emily Chia-Yu|Chang, Jia-Ming|Cheng, Cheng-Wei|Sung, Ting-Yi|Hsu, Wen-Lian', 'Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan. emilysu@tmu.edu.tw|Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan. emilysu@tmu.edu.tw|Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan. emilysu@tmu.edu.tw|Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan. emilysu@tmu.edu.tw|Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan. emilysu@tmu.edu.tw', '-|-|-|-|-', 0, 'Active Transport, Cell Nucleus|Amino Acid Sequence|Computational Biology|Dipeptides|Molecular Sequence Data|Nuclear Localization Signals|Nuclear Proteins|Position-Specific Scoring Matrices|Protein Structure, Tertiary|Semantics|Sequence Analysis, Protein|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-13-S17-S13', '23282098', 'Identification of subcellular localization in proteins is crucial to elucidate cellular processes and molecular functions in a cell. However, given a tremendous amount of sequence data generated in the post-genomic era, determining protein localization based on biological experiments can be expensive and time-consuming. Therefore, developing prediction systems to analyze uncharacterised proteins efficiently has played an important role in high-throughput protein analyses. In a eukaryotic cell, many essential biological processes take place in the nucleus. Nuclear proteins shuttle between nucleus and cytoplasm based on recognition of nuclear translocation signals, including nuclear localization signals (NLSs) and nuclear export signals (NESs). Currently, only a few approaches have been developed specifically to predict nuclear localization using sequence features, such as putative NLSs. However, it has been shown that prediction coverage based on the NLSs is very low. In addition, most existing approaches only attained prediction accuracy and Matthews correlation coefficient (MCC) around 54%~70% and 0.250~0.380 on independent test set, respectively. Moreover, no predictor can generate sequence motifs to characterize features of potential NESs, in which biological properties are not well understood from existing experimental studies.'),
(95331, 2012, 'bmc', 'Prediction and analysis of protein solubility using a novel scoring card method with dipeptide composition', 'Huang, Hui-Ling|Charoenkwan, Phasit|Kao, Te-Fen|Lee, Hua-Chin|Chang, Fang-Lin|Huang, Wen-Lin|Ho, Shinn-Jang|Shu, Li-Sun|Chen, Wen-Liang|Ho, Shinn-Ying', 'Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.', '-|-|-|-|-|-|-|-|-|-', 0, 'Amino Acids|Databases, Protein|Dipeptides|Escherichia coli|Recombinant Proteins|Solubility|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-13-S17-S3', '23282103', 'Existing methods for predicting protein solubility on overexpression in Escherichia coli advance performance by using ensemble classifiers such as two-stage support vector machine (SVM) based classifiers and a number of feature types such as physicochemical properties, amino acid and dipeptide composition, accompanied with feature selection. It is desirable to develop a simple and easily interpretable method for predicting protein solubility, compared to existing complex SVM-based methods.'),
(95332, 2012, 'bmc', 'The study of muscle remodeling in Drosophila metamorphosis using in vivo microscopy and bioimage informatics', 'Chinta, Rambabu|Tan, Joo Huang|Wasser, Martin', 'Live-Cell Imaging and Automation of Image Analysis Group, Imaging Informatics Division, Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore.|Live-Cell Imaging and Automation of Image Analysis Group, Imaging Informatics Division, Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore.|Live-Cell Imaging and Automation of Image Analysis Group, Imaging Informatics Division, Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore.', 'Singapore|Singapore|Singapore', 0, 'Adult|Animals|Cell Nucleus|Drosophila|Humans|Imaging, Three-Dimensional|Larva|Metamorphosis, Biological|Microscopy, Confocal|Muscle, Skeletal|RNA Interference', 'Journal Article', '10.1186/1471-2105-13-S17-S14', '23282138', 'Metamorphosis in insects transforms the larval into an adult body plan and comprises the destruction and remodeling of larval and the generation of adult tissues. The remodeling of larval into adult muscles promises to be a genetic model for human atrophy since it is associated with dramatic alteration in cell size. Furthermore, muscle development is amenable to 3D in vivo microscopy at high cellular resolution. However, multi-dimensional image acquisition leads to sizeable amounts of data that demand novel approaches in image processing and analysis.'),
(95333, 2012, 'bmc', 'Discovery of a new class of inhibitors for the protein arginine deiminase type 4 (PAD4) by structure-based virtual screening', 'Teo, Chian Ying|Shave, Steven|Chor, Adam Leow Thean|Salleh, Abu Bakar|Rahman, Mohd Basyaruddin Bin Abdul|Walkinshaw, Malcolm D|Tejo, Bimo A', 'Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Malaysia.|Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Malaysia.|Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Malaysia.|Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Malaysia.|Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Malaysia.|Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Malaysia.|Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Malaysia.', 'Malaysia|Malaysia|Malaysia|Malaysia|Malaysia|Malaysia|Malaysia', 12, 'Antirheumatic Agents|Arthritis, Rheumatoid|Computational Biology|Drug Discovery|Enzyme Inhibitors|Escherichia coli|Humans|Hydrolases|Ligands|Protein Conformation|Protein-Arginine Deiminases|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-13-S17-S4', '23282142', 'Rheumatoid arthritis (RA) is an autoimmune disease with unknown etiology. Anticitrullinated protein autoantibody has been documented as a highly specific autoantibody associated with RA. Protein arginine deiminase type 4 (PAD4) is the enzyme responsible for catalyzing the conversion of peptidylarginine into peptidylcitrulline. PAD4 is a new therapeutic target for RA treatment. In order to search for inhibitors of PAD4, structure-based virtual screening was performed using LIDAEUS (Ligand discovery at Edinburgh university). Potential inhibitors were screened experimentally by inhibition assays.'),
(95334, 2012, 'bmc', 'Improved statistical model checking methods for pathway analysis', 'Koh, Chuan Hock|Palaniappan, Sucheendra K|Thiagarajan, P S|Wong, Limsoon', 'NUS Graduate School for Integrative Sciences and Engineering, Singapore. kohchuanhock@nus.edu.sg|NUS Graduate School for Integrative Sciences and Engineering, Singapore. kohchuanhock@nus.edu.sg|NUS Graduate School for Integrative Sciences and Engineering, Singapore. kohchuanhock@nus.edu.sg|NUS Graduate School for Integrative Sciences and Engineering, Singapore. kohchuanhock@nus.edu.sg', 'Singapore|Singapore|Singapore|Singapore', 8, 'Algorithms|Animals|Caenorhabditis elegans|Cell Differentiation|Models, Biological|Models, Statistical|Neural Pathways|Neurons|Systems Biology|Taste', 'Journal Article', '10.1186/1471-2105-13-S17-S15', '23282174', 'Statistical model checking techniques have been shown to be effective for approximate model checking on large stochastic systems, where explicit representation of the state space is impractical. Importantly, these techniques ensure the validity of results with statistical guarantees on errors. There is an increasing interest in these classes of algorithms in computational systems biology since analysis using traditional model checking techniques does not scale well. In this context, we present two improvements to existing statistical model checking algorithms. Firstly, we construct an algorithm which removes the need of the user to define the indifference region, a critical parameter in previous sequential hypothesis testing algorithms. Secondly, we extend the algorithm to account for the case when there may be a limit on the computational resources that can be spent on verifying a property, i.e, if the original algorithm is not able to make a decision even after consuming the available amount of resources, we resort to a p-value based approach to make a decision. We demonstrate the improvements achieved by our algorithms in comparison to current algorithms first with a straightforward yet representative example, followed by a real biological model on cell fate of gustatory neurons with microRNAs.'),
(95335, 2012, 'bmc', 'Multiple virtual screening approaches for finding new hepatitis C virus RNA-dependent RNA polymerase inhibitors: structure-based screens and molecular dynamics for the pursue of new poly pharmacological inhibitors', 'Elhefnawi, Mahmoud|ElGamacy, Mohammad|Fares, Mohamed', 'Informatics and Systems Department, Division of Engineering Research and Centre of Excellence for Advanced Sciences, National Research Centre, Tahrir Street, 12311 Cairo, Egypt. mahef@aucegypt.edu|Informatics and Systems Department, Division of Engineering Research and Centre of Excellence for Advanced Sciences, National Research Centre, Tahrir Street, 12311 Cairo, Egypt. mahef@aucegypt.edu|Informatics and Systems Department, Division of Engineering Research and Centre of Excellence for Advanced Sciences, National Research Centre, Tahrir Street, 12311 Cairo, Egypt. mahef@aucegypt.edu', 'Egypt|Egypt|Egypt', 0, 'Allosteric Site|Antiviral Agents|Binding Sites|Drug Evaluation, Preclinical|Enzyme Inhibitors|Hepacivirus|Humans|Inhibitory Concentration 50|Molecular Dynamics Simulation|Protein Conformation|RNA Replicase|Structure-Activity Relationship|Viral Nonstructural Proteins|Virus Replication', 'Journal Article', '10.1186/1471-2105-13-S17-S5', '23282180', 'The RNA polymerase NS5B of Hepatitis C virus (HCV) is a well-characterised drug target with an active site and four allosteric binding sites. This work presents a workflow for virtual screening and its application to Drug Bank screening targeting the Hepatitis C Virus (HCV) RNA polymerase non-nucleoside binding sites. Potential polypharmacological drugs are sought with predicted active inhibition on viral replication, and with proven positive pharmaco-clinical profiles. The approach adopted was receptor-based. Docking screens, guided with contact pharmacophores and neural-network activity prediction models on all allosteric binding sites and MD simulations, constituted our analysis workflow for identification of potential hits. Steps included: 1) using a two-phase docking screen with Surflex and Glide Xp. 2) Ranking based on scores, and important H interactions. 3) a machine-learning target-trained artificial neural network PIC prediction model used for ranking. This provided a better correlation of IC50 values of the training sets for each site with different docking scores and sub-scores. 4) interaction pharmacophores-through retrospective analysis of protein-inhibitor complex X-ray structures for the interaction pharmacophore (common interaction modes) of inhibitors for the five non-nucleoside binding sites were constructed. These were used for filtering the hits according to the critical binding feature of formerly reported inhibitors. This filtration process resulted in identification of potential new inhibitors as well as formerly reported ones for the thumb II and Palm I sites (HCV-81) NS5B binding sites. Eventually molecular dynamics simulations were carried out, confirming the binding hypothesis and resulting in 4 hits.'),
(95336, 2012, 'bmc', 'Temporal dynamics of protein complexes in PPI networks: a case study using yeast cell cycle dynamics', 'Srihari, Sriganesh|Leong, Hon Wai', 'Department of Computer Science, National University of Singapore, Singapore.|Department of Computer Science, National University of Singapore, Singapore.', 'Singapore|Singapore', 35, 'Algorithms|Cell Cycle|Models, Biological|Multiprotein Complexes|Protein Interaction Mapping|Proteins|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-13-S17-S16', '23282200', 'Complexes of physically interacting proteins are one of the fundamental functional units responsible for driving key biological mechanisms within the cell. With the advent of high-throughput techniques, significant amount of protein interaction (PPI) data has been catalogued for organisms such as yeast, which has in turn fueled computational methods for systematic identification and study of protein complexes. However, many complexes are dynamic entities - their subunits are known to assemble at a particular cellular space and time to perform a particular function and disassemble after that - and while current computational analyses have concentrated on studying the dynamics of individual or pairs of proteins in PPI networks, a crucial aspect overlooked is the dynamics of whole complex formations. In this work, using yeast as our model, we incorporate time in the form of cell-cycle phases into the prediction of complexes from PPI networks and study the temporal phenomena of complex assembly and disassembly across phases. We hypothesize that staticness (constitutive expression) of proteins might be related to their temporal reusability across complexes, and test this hypothesis using complexes predicted from large-scale PPI networks across the yeast cell cycle phases. Our results hint towards a biological design principle underlying cellular mechanisms - cells maintain generic proteins as static to enable their reusability across multiple temporal complexes. We also demonstrate that these findings provide additional support and alternative explanations to findings from existing works on the dynamics in PPI networks.'),
(95337, 2012, 'bmc', 'Large-scale reverse docking profiles and their applications', 'Lee, Minho|Kim, Dongsup', 'Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Korea.|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Korea.', '-|-', 0, 'Cycloheximide|Drug Discovery|Fluorouracil|Humans|Ligands|Protein Binding|Protein Conformation|Proteins|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/1471-2105-13-S17-S6', '23282219', 'Reverse docking approaches have been explored in previous studies on drug discovery to overcome some problems in traditional virtual screening. However, current reverse docking approaches are problematic in that the target spaces of those studies were rather small, and their applications were limited to identifying new drug targets. In this study, we expanded the scope of target space to a set of all protein structures currently available and developed several new applications of reverse docking method.'),
(95338, 2012, 'bmc', 'CMPF: class-switching minimized pathfinding in metabolic networks', 'Lim, Kevin|Wong, Limsoon', 'School of Computing, National University of Singapore, Singapore. kevinl@comp.nus.edu.sg.|School of Computing, National University of Singapore, Singapore. kevinl@comp.nus.edu.sg.', 'Singapore|Singapore', 0, 'Algorithms|Computational Biology|Glycolysis|Metabolic Networks and Pathways|Software', 'Journal Article', '10.1186/1471-2105-13-S17-S17', '23282238', 'The metabolic network is an aggregation of enzyme catalyzed reactions that converts one compound to another. Paths in a metabolic network are a sequence of enzymes that describe how a chemical compound of interest can be produced in a biological system. As the number of such paths is quite large, many methods have been developed to score paths so that the k-shortest paths represent the set of paths that are biologically meaningful or efficient. However, these approaches do not consider whether the sequence of enzymes can be manufactured in the same pathway/species/localization. As a result, a predicted sequence might consist of groups of enzymes that operate in distinct pathway/species/localization and may not truly reflect the events occurring within cell.'),
(95339, 2012, 'bmc', 'Sanjeevini: a freely accessible web-server for target directed lead molecule discovery', 'Jayaram, B|Singh, Tanya|Mukherjee, Goutam|Mathur, Abhinav|Shekhar, Shashank|Shekhar, Vandana', 'Department of Chemistry, Indian Institute of Technology, Hauz Khas, New Delhi-110016, India. bjayaram@chemistry.iitd.ac.in|Department of Chemistry, Indian Institute of Technology, Hauz Khas, New Delhi-110016, India. bjayaram@chemistry.iitd.ac.in|Department of Chemistry, Indian Institute of Technology, Hauz Khas, New Delhi-110016, India. bjayaram@chemistry.iitd.ac.in|Department of Chemistry, Indian Institute of Technology, Hauz Khas, New Delhi-110016, India. bjayaram@chemistry.iitd.ac.in|Department of Chemistry, Indian Institute of Technology, Hauz Khas, New Delhi-110016, India. bjayaram@chemistry.iitd.ac.in|Department of Chemistry, Indian Institute of Technology, Hauz Khas, New Delhi-110016, India. bjayaram@chemistry.iitd.ac.in', 'India|India|India|India|India|India', 62, 'Drug Discovery|Internet|Proteins|Software', 'Journal Article', '10.1186/1471-2105-13-S17-S7', '23282245', 'Computational methods utilizing the structural and functional information help to understand specific molecular recognition events between the target biomolecule and candidate hits and make it possible to design improved lead molecules for the target.'),
(95340, 2012, 'bmc', 'An efficient algorithm for de novo predictions of biochemical pathways between chemical compounds', 'Nakamura, Masaomi|Hachiya, Tsuyoshi|Saito, Yutaka|Sato, Kengo|Sakakibara, Yasubumi', 'Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Yokohama 223-8522, Japan.|Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Yokohama 223-8522, Japan.|Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Yokohama 223-8522, Japan.|Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Yokohama 223-8522, Japan.|Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Yokohama 223-8522, Japan.', 'Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Anthocyanins|Benzopyrans|Computational Biology|Computer Simulation|Enzymes|Glucosides|Lutein|Metabolic Networks and Pathways|Organic Chemicals', 'Journal Article', '10.1186/1471-2105-13-S17-S8', '23282285', 'Prediction of biochemical (metabolic) pathways has a wide range of applications, including the optimization of drug candidates, and the elucidation of toxicity mechanisms. Recently, several methods have been developed for pathway prediction to derive a goal compound from a start compound. However, these methods require high computational costs, and cannot perform comprehensive prediction of novel metabolic pathways. Our aim of this study is to develop a de novo prediction method for reconstructions of metabolic pathways and predictions of unknown biosynthetic pathways in the sense that it does not require any initial network such as KEGG metabolic network to be explored.'),
(95341, 2012, 'bmc', 'ChemEx: information extraction system for chemical data curation', 'Tharatipyakul, Atima|Numnark, Somrak|Wichadakul, Duangdao|Ingsriswang, Supawadee', 'Information Systems Laboratory, National Center for Genetic Engineering and Biotechnology (BIOTEC), 113 Thailand Science Park, Phaholyothin Road, Klong 1, Klong Luang, Pathumthani, Thailand.|Information Systems Laboratory, National Center for Genetic Engineering and Biotechnology (BIOTEC), 113 Thailand Science Park, Phaholyothin Road, Klong 1, Klong Luang, Pathumthani, Thailand.|Information Systems Laboratory, National Center for Genetic Engineering and Biotechnology (BIOTEC), 113 Thailand Science Park, Phaholyothin Road, Klong 1, Klong Luang, Pathumthani, Thailand.|Information Systems Laboratory, National Center for Genetic Engineering and Biotechnology (BIOTEC), 113 Thailand Science Park, Phaholyothin Road, Klong 1, Klong Luang, Pathumthani, Thailand.', 'Thailand|Thailand|Thailand|Thailand', 11, 'Data Mining|Information Systems|Small Molecule Libraries|Software', 'Journal Article', '10.1186/1471-2105-13-S17-S9', '23282330', 'Manual chemical data curation from publications is error-prone, time consuming, and hard to maintain up-to-date data sets. Automatic information extraction can be used as a tool to reduce these problems. Since chemical structures usually described in images, information extraction needs to combine structure image recognition and text mining together.'),
(95342, 2012, 'bmc', 'Abstracts of the Eighth International Society for Computational Biology (ISCB) Student Council Symposium 2012 Long Beach, California, USA July 13-14, 2012', '-', '-', '-', 11, 'Computational Biology', 'Congress', '10.1186/1471-2105-13-S17-S9', '23282369', '-'),
(95343, 2012, 'bmc', 'On pairwise distances and median score of three genomes under DCJ', 'Aganezov, Sergey|Alekseyev, Max A', 'Algorithmic Biology Laboratory, St Petersburg Academic University, St, Petersburg, Russia.|Algorithmic Biology Laboratory, St Petersburg Academic University, St, Petersburg, Russia.', '-|-', 4, 'Computer Simulation|DNA Breaks, Double-Stranded|Evolution, Molecular|Gene Rearrangement|Genome|Models, Genetic', 'Journal Article', '10.1186/1471-2105-13-S19-S1', '23282410', 'In comparative genomics, the rearrangement distance between two genomes (equal the minimal number of genome rearrangements required to transform them into a single genome) is often used for measuring their evolutionary remoteness. Generalization of this measure to three genomes is known as the median score (while a resulting genome is called median genome). In contrast to the rearrangement distance between two genomes which can be computed in linear time, computing the median score for three genomes is NP-hard. This inspires a quest for simpler and faster approximations for the median score, the most natural of which appears to be the halved sum of pairwise distances which in fact represents a lower bound for the median score.In this work, we study relationship and interplay of pairwise distances between three genomes and their median score under the model of Double-Cut-and-Join (DCJ) rearrangements. Most remarkably we show that while a rearrangement may change the sum of pairwise distances by at most 2 (and thus change the lower bound by at most 1), even the most powerful rearrangements in this respect that increase the lower bound by 1 (by moving one genome farther away from each of the other two genomes), which we call strong, do not necessarily affect the median score. This observation implies that the two measures are not as well-correlated as ones intuition may suggest.We further prove that the median score attains the lower bound exactly on the triples of genomes that can be obtained from a single genome with strong rearrangements. While the sum of pairwise distances with the factor 2/3 represents an upper bound for the median score, its tightness remains unclear. Nonetheless, we show that the difference of the median score and its lower bound is not bounded by a constant.'),
(95344, 2012, 'bmc', 'Compareads: comparing huge metagenomic experiments', 'Maillet, Nicolas|Lemaitre, Claire|Chikhi, Rayan|Lavenier, Dominique|Peterlongo, Pierre', 'INRIA Rennes - Bretagne Atlantique/IRISA, EPI GenScale, Rennes, France. nicolas.maillet@inria.fr|INRIA Rennes - Bretagne Atlantique/IRISA, EPI GenScale, Rennes, France. nicolas.maillet@inria.fr|INRIA Rennes - Bretagne Atlantique/IRISA, EPI GenScale, Rennes, France. nicolas.maillet@inria.fr|INRIA Rennes - Bretagne Atlantique/IRISA, EPI GenScale, Rennes, France. nicolas.maillet@inria.fr|INRIA Rennes - Bretagne Atlantique/IRISA, EPI GenScale, Rennes, France. nicolas.maillet@inria.fr', 'France|France|France|France|France', 0, 'Algorithms|Information Storage and Retrieval|Metagenomics', 'Comparative Study', '10.1186/1471-2105-13-S19-S10', '23282463', 'Nowadays, metagenomic sample analyses are mainly achieved by comparing them with a priori knowledge stored in data banks. While powerful, such approaches do not allow to exploit unknown and/or unculturable species, for instance estimated at 99% for Bacteria.'),
(95345, 2012, 'bmc', 'Identification of markers associated with global changes in DNA methylation regulation in cancers', 'Qiu, Peng|Zhang, Li', 'Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.', 'United States|United States', 0, 'Biomarkers, Tumor|Cluster Analysis|DNA Methylation|Female|Gene Expression Regulation, Neoplastic|Gene Library|Genomics|Humans|Neoplasms', 'Journal Article', '10.1186/1471-2105-13-S13-S7', '23320390', 'DNA methylation exhibits different patterns in different cancers. DNA methylation rates at different genomic loci appear to be highly correlated in some samples but not in others. We call such phenomena conditional concordant relationships (CCRs). In this study, we explored DNA methylation patterns in 12 common cancers using data of 2434 patient samples collected by The Cancer Genome Atlas project. We developed an exploratory method to characterize CCRs in the methylation data and identified the 200 gene markers whose on-and-off statuses in DNA methylation are most significantly associated with drastic changes in CCRs throughout the genome. Clustering analysis of the methylation data of the 200 markers showed that they are tightly associated with cancer subtypes. We also generated a library of the significant CCRs that may be of interest to future studies of the regulation network of DNA methylation in cancer.'),
(95346, 2012, 'bmc', 'Phylogenetic search through partial tree mixing', 'Sundberg, Kenneth|Clement, Mark|Snell, Quinn|Ventura, Dan|Whiting, Michael|Crandall, Keith', 'Department of Computer Science, Brigham Young University, Provo, UT 84602, USA.|Department of Computer Science, Brigham Young University, Provo, UT 84602, USA.|Department of Computer Science, Brigham Young University, Provo, UT 84602, USA.|Department of Computer Science, Brigham Young University, Provo, UT 84602, USA.|Department of Computer Science, Brigham Young University, Provo, UT 84602, USA.|Department of Computer Science, Brigham Young University, Provo, UT 84602, USA.', 'United States|United States|United States|United States|United States|United States', 1, 'Algorithms|Humans|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-13-S13-S8', '23320449', 'Recent advances in sequencing technology have created large data sets upon which phylogenetic inference can be performed. Current research is limited by the prohibitive time necessary to perform tree search on a reasonable number of individuals. This research develops new phylogenetic algorithms that can operate on tens of thousands of species in a reasonable amount of time through several innovative search techniques.'),
(95347, 2012, 'bmc', 'Physicochemical property consensus sequences for functional analysis, design of multivalent antigens and targeted antivirals', 'Schein, Catherine H|Bowen, David M|Lewis, Jessica A|Choi, Kyung|Paul, Aniko|van der Heden van Noort, Gerbrand J|Lu, Wenzhe|Filippov, Dmitri V', 'Institute for Translational Sciences, Computational Biology, Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, Texas 77555-0857, USA. chschein@utmb.edu|Institute for Translational Sciences, Computational Biology, Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, Texas 77555-0857, USA. chschein@utmb.edu|Institute for Translational Sciences, Computational Biology, Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, Texas 77555-0857, USA. chschein@utmb.edu|Institute for Translational Sciences, Computational Biology, Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, Texas 77555-0857, USA. chschein@utmb.edu|Institute for Translational Sciences, Computational Biology, Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, Texas 77555-0857, USA. chschein@utmb.edu|Institute for Translational Sciences, Computational Biology, Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, Texas 77555-0857, USA. chschein@utmb.edu|Institute for Translational Sciences, Computational Biology, Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, Texas 77555-0857, USA. chschein@utmb.edu|Institute for Translational Sciences, Computational Biology, Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, Texas 77555-0857, USA. chschein@utmb.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 9, 'Amino Acid Sequence|Antigens, Viral|Antiviral Agents|Consensus Sequence|Dengue|Drug Design|Flavivirus|Gene Products, env|Humans|Molecular Sequence Data|Sequence Analysis, Protein|Viral Envelope Proteins', 'Journal Article', '10.1186/1471-2105-13-S13-S9', '23320474', 'Analysis of large sets of biological sequence data from related strains or organisms is complicated by superficial redundancy in the set, which may contain many members that are identical except at one or two positions. Thus a new method, based on deriving physicochemical property (PCP)-consensus sequences, was tested for its ability to generate reference sequences and distinguish functionally significant changes from background variability.'),
(95348, 2012, 'bmc', 'Sources of variation in false discovery rate estimation include sample size, correlation, and inherent differences between groups', 'Zhang, Jiexin|Coombes, Kevin R', 'Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.', 'United States|United States', 0, 'Algorithms|Biometry|Computer Simulation|Gene Expression Profiling|Humans|Lymphoma, Follicular|Male|Oligonucleotide Array Sequence Analysis|Prostatic Neoplasms|Regression Analysis|Sample Size|Software', 'Journal Article', '10.1186/1471-2105-13-S13-S1', '23320794', 'High-throughtput technologies enable the testing of tens of thousands of measurements simultaneously. Identification of genes that are differentially expressed or associated with clinical outcomes invokes the multiple testing problem. False Discovery Rate (FDR) control is a statistical method used to correct for multiple comparisons for independent or weakly dependent test statistics. Although FDR control is frequently applied to microarray data analysis, gene expression is usually correlated, which might lead to inaccurate estimates. In this paper, we evaluate the accuracy of FDR estimation.'),
(95349, 2012, 'bmc', 'Model averaging strategies for structure learning in Bayesian networks with limited data', 'Broom, Bradley M|Do, Kim-Anh|Subramanian, Devika', 'Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, Texas 77030, USA. bmbroom@mdanderson.org|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, Texas 77030, USA. bmbroom@mdanderson.org|Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, Texas 77030, USA. bmbroom@mdanderson.org', 'United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Bayes Theorem|Cluster Analysis|Female|Gene Expression Profiling|Glioblastoma|Humans|Male|Models, Statistical', 'Journal Article', '10.1186/1471-2105-13-S13-S10', '23320818', 'Considerable progress has been made on algorithms for learning the structure of Bayesian networks from data. Model averaging by using bootstrap replicates with feature selection by thresholding is a widely used solution for learning features with high confidence. Yet, in the context of limited data many questions remain unanswered. What scoring functions are most effective for model averaging? Does the bias arising from the discreteness of the bootstrap significantly affect learning performance? Is it better to pick the single best network or to average multiple networks learnt from each bootstrap resample? How should thresholds for learning statistically significant features be selected?'),
(95350, 2012, 'bmc', 'The Genboree Microbiome Toolset and the analysis of 16S rRNA microbial sequences', 'Riehle, Kevin|Coarfa, Cristian|Jackson, Andrew|Ma, Jun|Tandon, Arpit|Paithankar, Sameer|Raghuraman, Sriram|Mistretta, Toni-Ann|Saulnier, Delphine|Raza, Sabeen|Diaz, Maria Alejandra|Shulman, Robert|Aagaard, Kjersti|Versalovic, James|Milosavljevic, Aleksandar', 'Molecular & Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Molecular & Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Molecular & Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Molecular & Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Molecular & Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Molecular & Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Molecular & Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Molecular & Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Molecular & Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Molecular & Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Molecular & Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Molecular & Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Molecular & Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Molecular & Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.|Molecular & Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 25, 'Child|Computational Biology|Gastrointestinal Tract|Humans|Irritable Bowel Syndrome|Metagenome|Metagenomics|Obesity|Phylogeny|RNA, Ribosomal, 16S|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-13-S13-S11', '23320832', 'Microbial metagenomic analyses rely on an increasing number of publicly available tools. Installation, integration, and maintenance of the tools poses significant burden on many researchers and creates a barrier to adoption of microbiome analysis, particularly in translational settings.'),
(95351, 2012, 'bmc', 'Graph-based signal integration for high-throughput phenotyping', 'Herskovic, Jorge R|Subramanian, Devika|Cohen, Trevor|Bozzo-Silva, Pamela A|Bearden, Charles F|Bernstam, Elmer V', 'School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA. jorge.r.herskovic@uth.tmc.edu|School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA. jorge.r.herskovic@uth.tmc.edu|School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA. jorge.r.herskovic@uth.tmc.edu|School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA. jorge.r.herskovic@uth.tmc.edu|School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA. jorge.r.herskovic@uth.tmc.edu|School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA. jorge.r.herskovic@uth.tmc.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Breast Neoplasms|Computer Simulation|Electronic Health Records|Female|Humans|Models, Biological|Phenotype|Unified Medical Language System', 'Evaluation Studies', '10.1186/1471-2105-13-S13-S2', '23320851', 'Electronic Health Records aggregated in Clinical Data Warehouses (CDWs) promise to revolutionize Comparative Effectiveness Research and suggest new avenues of research. However, the effectiveness of CDWs is diminished by the lack of properly labeled data. We present a novel approach that integrates knowledge from the CDW, the biomedical literature, and the Unified Medical Language System (UMLS) to perform high-throughput phenotyping. In this paper, we automatically construct a graphical knowledge model and then use it to phenotype breast cancer patients. We compare the performance of this approach to using MetaMap when labeling records.'),
(95352, 2012, 'bmc', 'Self consistency grouping: a stringent clustering method', 'Kim, Bong-Hyun|Chitturi, Bhadrachalam|Grishin, Nick V', 'Biochemistry Department, UT Southwestern Medical Center, Dallas, TX, USA.|Biochemistry Department, UT Southwestern Medical Center, Dallas, TX, USA.|Biochemistry Department, UT Southwestern Medical Center, Dallas, TX, USA.', 'United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computational Biology|Protein Structure, Tertiary', 'Journal Article', '10.1186/1471-2105-13-S13-S3', '23320864', 'Numerous types of clustering like single linkage and K-means have been widely studied and applied to a variety of scientific problems. However, the existing methods are not readily applicable for the problems that demand high stringency.'),
(95353, 2012, 'bmc', 'An effective method for network module extraction from microarray data', 'Mahanta, Priyakshi|Ahmed, Hasin A|Bhattacharyya, Dhruba K|Kalita, Jugal K', 'Department of Computer Science, and Engg, Tezpur University, Napaam, Tezpur, India.|Department of Computer Science, and Engg, Tezpur University, Napaam, Tezpur, India.|Department of Computer Science, and Engg, Tezpur University, Napaam, Tezpur, India.|Department of Computer Science, and Engg, Tezpur University, Napaam, Tezpur, India.', 'India|India|India|India', 18, 'Algorithms|Computational Biology|Data Interpretation, Statistical|Databases, Genetic|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-13-S13-S4', '23320896', 'The development of high-throughput Microarray technologies has provided various opportunities to systematically characterize diverse types of computational biological networks. Co-expression network have become popular in the analysis of microarray data, such as for detecting functional gene modules.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95354, 2012, 'bmc', 'Accuracy of RNA-Seq and its dependence on sequencing depth', 'Cai, Guoshuai|Li, Hua|Lu, Yue|Huang, Xuelin|Lee, Juhee|Müller, Peter|Ji, Yuan|Liang, Shoudan', 'Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 15, 'Gene Expression Profiling|Gene Library|Genome|High-Throughput Nucleotide Sequencing|Models, Statistical|RNA, Messenger|Sequence Analysis, DNA|Sequence Analysis, RNA', 'Evaluation Studies', '10.1186/1471-2105-13-S13-S5', '23320920', 'The cost of DNA sequencing has undergone a dramatical reduction in the past decade. As a result, sequencing technologies have been increasingly applied to genomic research. RNA-Seq is becoming a common technique for surveying gene expression based on DNA sequencing. As it is not clear how increased sequencing capacity has affected measurement accuracy of mRNA, we sought to investigate that relationship.'),
(95355, 2012, 'bmc', 'BM-BC: a Bayesian method of base calling for Solexa sequence data', 'Ji, Yuan|Mitra, Riten|Quintana, Fernando|Jara, Alejandro|Mueller, Peter|Liu, Ping|Lu, Yue|Liang, Shoudan', 'Center for Clinical and Research Informatics, Northshore University HealthSystem, Evanston, IL 60091, USA. yji@northshore.org|Center for Clinical and Research Informatics, Northshore University HealthSystem, Evanston, IL 60091, USA. yji@northshore.org|Center for Clinical and Research Informatics, Northshore University HealthSystem, Evanston, IL 60091, USA. yji@northshore.org|Center for Clinical and Research Informatics, Northshore University HealthSystem, Evanston, IL 60091, USA. yji@northshore.org|Center for Clinical and Research Informatics, Northshore University HealthSystem, Evanston, IL 60091, USA. yji@northshore.org|Center for Clinical and Research Informatics, Northshore University HealthSystem, Evanston, IL 60091, USA. yji@northshore.org|Center for Clinical and Research Informatics, Northshore University HealthSystem, Evanston, IL 60091, USA. yji@northshore.org|Center for Clinical and Research Informatics, Northshore University HealthSystem, Evanston, IL 60091, USA. yji@northshore.org', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Bayes Theorem|Genome|Genomics|RNA|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-13-S13-S6', '23320938', 'Base calling is a critical step in the Solexa next-generation sequencing procedure. It compares the position-specific intensity measurements that reflect the signal strength of four possible bases (A, C, G, T) at each genomic position, and outputs estimates of the true sequences for short reads of DNA or RNA. We present a Bayesian method of base calling, BM-BC, for Solexa-GA sequencing data. The Bayesian method builds on a hierarchical model that accounts for three sources of noise in the data, which are known to affect the accuracy of the base calls: fading, phasing, and cross-talk between channels. We show that the new method improves the precision of base calling compared with currently leading methods. Furthermore, the proposed method provides a probability score that measures the confidence of each base call. This probability score can be used to estimate the false discovery rate of the base calling or to rank the precision of the estimated DNA sequences, which in turn can be useful for downstream analysis such as sequence alignment.'),
(95356, 2013, 'bmc', 'SRAdb: query and use public next-generation sequencing data from within R', 'Zhu, Yuelin|Stephens, Robert M|Meltzer, Paul S|Davis, Sean R', 'Genetics Branch, National Cancer Institute, National Institutes of HealthBethesda, MD 20892, USA.|Genetics Branch, National Cancer Institute, National Institutes of HealthBethesda, MD 20892, USA.|Genetics Branch, National Cancer Institute, National Institutes of HealthBethesda, MD 20892, USA.|Genetics Branch, National Cancer Institute, National Institutes of HealthBethesda, MD 20892, USA.', 'United States|United States|United States|United States', 51, 'Databases, Nucleic Acid|Genomics|High-Throughput Nucleotide Sequencing|Software', 'Journal Article', '10.1186/1471-2105-14-19', '23323543', 'The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Illumina (Genome Analyzer, HiSeq, MiSeq, .etc), Roche 454 GS System, Applied Biosystems SOLiD System, Helicos Heliscope, PacBio RS, and others.'),
(95357, 2013, 'bmc', 'BioCause: Annotating and analysing causality in the biomedical domain', 'Mihxc4x83ilxc4x83, Claudiu|Ohta, Tomoko|Pyysalo, Sampo|Ananiadou, Sophia', 'The National Centre for Text Mining, School of Computer Science, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK. claudiu.mihaila@cs.man.ac.uk|The National Centre for Text Mining, School of Computer Science, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK. claudiu.mihaila@cs.man.ac.uk|The National Centre for Text Mining, School of Computer Science, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK. claudiu.mihaila@cs.man.ac.uk|The National Centre for Text Mining, School of Computer Science, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK. claudiu.mihaila@cs.man.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 33, 'Data Interpretation, Statistical|Data Mining|Software', 'Journal Article', '10.1186/1471-2105-14-2', '23323613', 'Biomedical corpora annotated with event-level information represent an important resource for domain-specific information extraction (IE) systems. However, bio-event annotation alone cannot cater for all the needs of biologists. Unlike work on relation and event extraction, most of which focusses on specific events and named entities, we aim to build a comprehensive resource, covering all statements of causal association present in discourse. Causality lies at the heart of biomedical knowledge, such as diagnosis, pathology or systems biology, and, thus, automatic causality recognition can greatly reduce the human workload by suggesting possible causal connections and aiding in the curation of pathway models. A biomedical text corpus annotated with such relations is, hence, crucial for developing and evaluating biomedical text mining.'),
(95358, 2013, 'bmc', 'Testing robustness of relative complexity measure method constructing robust phylogenetic trees for Galanthus L using the relative complexity measure', 'Bakxc4xb1xc5x9f, Yasin|Otu, Hasan H|Taxc5x9fxc3xa7xc4xb1, Nivart|Meydan, Cem|Bilgin, Nexc5x9fe|Yxc3xbczbaxc5x9fxc4xb1oxc4x9flu, Sxc4xb1rrxc4xb1|Sezerman, O Uxc4x9fur', 'Department of Biology, Abant xc4xb0zzet Baysal University, Bolu 14280, Turkey. bakis_y@ibu.edu.tr|Department of Biology, Abant xc4xb0zzet Baysal University, Bolu 14280, Turkey. bakis_y@ibu.edu.tr|Department of Biology, Abant xc4xb0zzet Baysal University, Bolu 14280, Turkey. bakis_y@ibu.edu.tr|Department of Biology, Abant xc4xb0zzet Baysal University, Bolu 14280, Turkey. bakis_y@ibu.edu.tr|Department of Biology, Abant xc4xb0zzet Baysal University, Bolu 14280, Turkey. bakis_y@ibu.edu.tr|Department of Biology, Abant xc4xb0zzet Baysal University, Bolu 14280, Turkey. bakis_y@ibu.edu.tr|Department of Biology, Abant xc4xb0zzet Baysal University, Bolu 14280, Turkey. bakis_y@ibu.edu.tr', 'Turkey|Turkey|Turkey|Turkey|Turkey|Turkey|Turkey', 0, 'DNA, Chloroplast|DNA, Plant|DNA, Ribosomal Spacer|Galanthus|Phylogeny|Polymerase Chain Reaction|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-14-20', '23323678', 'Most phylogeny analysis methods based on molecular sequences use multiple alignment where the quality of the alignment, which is dependent on the alignment parameters, determines the accuracy of the resulting trees. Different parameter combinations chosen for the multiple alignment may result in different phylogenies. A new non-alignment based approach, Relative Complexity Measure (RCM), has been introduced to tackle this problem and proven to work in fungi and mitochondrial DNA.'),
(95359, 2013, 'bmc', 'Cophenetic metrics for phylogenetic trees, after Sokal and Rohlf', 'Cardona, Gabriel|Mir, Arnau|Rossellxc3xb3, Francesc|Rotger, Lucxc3xada|Sxc3xa1nchez, David', 'Department of Mathematics and Computer Science, University of the Balearic Islands, E-07122 Palma de Mallorca, Spain.|Department of Mathematics and Computer Science, University of the Balearic Islands, E-07122 Palma de Mallorca, Spain.|Department of Mathematics and Computer Science, University of the Balearic Islands, E-07122 Palma de Mallorca, Spain.|Department of Mathematics and Computer Science, University of the Balearic Islands, E-07122 Palma de Mallorca, Spain.|Department of Mathematics and Computer Science, University of the Balearic Islands, E-07122 Palma de Mallorca, Spain.', 'Spain|Spain|Spain|Spain|Spain', 0, 'Biological Evolution|Phylogeny', 'Journal Article', '10.1186/1471-2105-14-3', '23323711', 'Phylogenetic tree comparison metrics are an important tool in the study of evolution, and hence the definition of such metrics is an interesting problem in phylogenetics. In a paper in Taxon fifty years ago, Sokal and Rohlf proposed to measure quantitatively the difference between a pair of phylogenetic trees by first encoding them by means of their half-matrices of cophenetic values, and then comparing these matrices. This idea has been used several times since then to define dissimilarity measures between phylogenetic trees but, to our knowledge, no proper metric on weighted phylogenetic trees with nested taxa based on this idea has been formally defined and studied yet. Actually, the cophenetic values of pairs of different taxa alone are not enough to single out phylogenetic trees with weighted arcs or nested taxa.'),
(95360, 2013, 'bmc', 'SyntTax: a web server linking synteny to prokaryotic taxonomy', 'Oberto, Jacques', 'Université Paris-Sud 11, CNRS, UMR8621, Institut de Génétique et Microbiologie, 91405, Orsay, France. jacques.oberto@igmors.u-psud.fr', 'France', 79, 'Algorithms|Archaea|Archaeal Proteins|Bacteria|Bacterial Proteins|Classification|Cluster Analysis|Databases, Nucleic Acid|Evolution, Molecular|Gene Fusion|Gene Order|Genome, Archaeal|Genome, Bacterial|Genomics|Internet|Multigene Family|Phylogeny|Software|Synteny', 'Journal Article', '10.1186/1471-2105-14-4', '23323735', 'The study of the conservation of gene order or synteny constitutes a powerful methodology to assess the orthology of genomic regions and to predict functional relationships between genes. The exponential growth of microbial genomic databases is expected to improve synteny predictions significantly. Paradoxically, this genomic data plethora, without information on organisms relatedness, could impair the performance of synteny analysis programs.'),
(95361, 2013, 'bmc', 'Random generalized linear model: a highly accurate and interpretable ensemble predictor', 'Song, Lin|Langfelder, Peter|Horvath, Steve', 'Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California, USA.|Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California, USA.|Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California, USA.', 'United States|United States|United States', 0, 'Animals|Artificial Intelligence|Disease|Gene Expression Profiling|Genomics|Humans|Linear Models|Mice|Regression Analysis|Software', 'Journal Article', '10.1186/1471-2105-14-5', '23323760', 'Ensemble predictors such as the random forest are known to have superior accuracy but their black-box predictions are difficult to interpret. In contrast, a generalized linear model (GLM) is very interpretable especially when forward feature selection is used to construct the model. However, forward feature selection tends to overfit the data and leads to low predictive accuracy. Therefore, it remains an important research goal to combine the advantages of ensemble predictors (high accuracy) with the advantages of forward regression modeling (interpretability). To address this goal several articles have explored GLM based ensemble predictors. Since limited evaluations suggested that these ensemble predictors were less accurate than alternative predictors, they have found little attention in the literature.'),
(95362, 2013, 'bmc', 'Mojo Hand, a TALEN design tool for genome editing applications', 'Neff, Kevin L|Argue, David P|Ma, Alvin C|Lee, Han B|Clark, Karl J|Ekker, Stephen C', 'Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN, USA.|Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN, USA.|Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN, USA.|Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN, USA.|Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN, USA.|Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Binding Sites|Consensus Sequence|DNA Restriction Enzymes|DNA-Binding Proteins|Endonucleases|Genome|Genomics|Humans|Internet|Recombinant Fusion Proteins|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-14-1', '23323762', 'Recent studies of transcription activator-like (TAL) effector domains fused to nucleases (TALENs) demonstrate enormous potential for genome editing. Effective design of TALENs requires a combination of selecting appropriate genetic features, finding pairs of binding sites based on a consensus sequence, and, in some cases, identifying endogenous restriction sites for downstream molecular genetic applications.'),
(95363, 2013, 'bmc', 'More powerful significant testing for time course gene expression data using functional principal component analysis approaches', 'Wu, Shuang|Wu, Hulin', 'Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, NY 14642, USA. hulin_wu@urmc.rochester.edu|Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, NY 14642, USA. hulin_wu@urmc.rochester.edu', 'United States|United States', 29, 'Cell Cycle|Cyclins|Gene Expression Profiling|Kinetics|Principal Component Analysis|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-14-6', '23323795', 'One of the fundamental problems in time course gene expression data analysis is to identify genes associated with a biological process or a particular stimulus of interest, like a treatment or virus infection. Most of the existing methods for this problem are designed for data with longitudinal replicates. But in reality, many time course gene experiments have no replicates or only have a small number of independent replicates.'),
(95364, 2013, 'bmc', 'Redundancy in electronic health record corpora: analysis, impact on text mining performance and mitigation strategies', 'Cohen, Raphael|Elhadad, Michael|Elhadad, Noémie', 'Department of Computer Science, Ben-Gurion University in the Negev, Beer-Sheva, Israel. cohenrap@bgu.ac.il|Department of Computer Science, Ben-Gurion University in the Negev, Beer-Sheva, Israel. cohenrap@bgu.ac.il|Department of Computer Science, Ben-Gurion University in the Negev, Beer-Sheva, Israel. cohenrap@bgu.ac.il', 'Israel|Israel|Israel', 0, 'Algorithms|Data Mining|Electronic Health Records|Health Records, Personal|Humans|Semantics', 'Journal Article', '10.1186/1471-2105-14-10', '23323800', 'The increasing availability of Electronic Health Record (EHR) data and specifically free-text patient notes presents opportunities for phenotype extraction. Text-mining methods in particular can help disease modeling by mapping named-entities mentions to terminologies and clustering semantically related terms. EHR corpora, however, exhibit specific statistical and linguistic characteristics when compared with corpora in the biomedical literature domain. We focus on copy-and-paste redundancy: clinicians typically copy and paste information from previous notes when documenting a current patient encounter. Thus, within a longitudinal patient record, one expects to observe heavy redundancy. In this paper, we ask three research questions: (i) How can redundancy be quantified in large-scale text corpora? (ii) Conventional wisdom is that larger corpora yield better results in text mining. But how does the observed EHR redundancy affect text mining? Does such redundancy introduce a bias that distorts learned models? Or does the redundancy introduce benefits by highlighting stable and important subsets of the corpus? (iii) How can one mitigate the impact of redundancy on text mining?'),
(95365, 2013, 'bmc', 'GSVA: gene set variation analysis for microarray and RNA-seq data', 'Hänzelmann, Sonja|Castelo, Robert|Guinney, Justin', 'Research Program on Biomedical Informatics, Hospital del Mar Medical Research Institute, Barcelona, Catalonia, Spain.|Research Program on Biomedical Informatics, Hospital del Mar Medical Research Institute, Barcelona, Catalonia, Spain.|Research Program on Biomedical Informatics, Hospital del Mar Medical Research Institute, Barcelona, Catalonia, Spain.', 'Spain|Spain|Spain', 822, 'Analysis of Variance|Female|Gene Expression Profiling|Genetic Variation|Humans|Leukemia, Biphenotypic, Acute|Oligonucleotide Array Sequence Analysis|Ovarian Neoplasms|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Sequence Analysis, RNA|Software|Statistics, Nonparametric|Survival Analysis', 'Journal Article', '10.1186/1471-2105-14-7', '23323831', 'Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets.'),
(95366, 2013, 'bmc', 'An improved method to detect correct protein folds using partial clustering', 'Zhou, Jianjun|Wishart, David S', 'JHK Co., Ltd., 2049 Heping Road, Shenzhen, Guangdong 518010, China.|JHK Co., Ltd., 2049 Heping Road, Shenzhen, Guangdong 518010, China.', 'China|China', 14, 'Algorithms|Cluster Analysis|Protein Conformation|Protein Folding', 'Journal Article', '10.1186/1471-2105-14-11', '23323835', 'Structure-based clustering is commonly used to identify correct protein folds among candidate folds (also called decoys) generated by protein structure prediction programs. However, traditional clustering methods exhibit a poor runtime performance on large decoy sets. We hypothesized that a more efficient partial clustering approach in combination with an improved scoring scheme could significantly improve both the speed and performance of existing candidate selection methods.'),
(95367, 2013, 'bmc', 'XperimentR: painless annotation of a biological experiment for the laboratory scientist', 'Tomlinson, Chris D|Barton, Geraint R|Woodbridge, Mark|Butcher, Sarah A', 'Centre for Integrated Systems Biology and Bioinformatics, Imperial College London, Sir Ernst Chain Building, South Kensington Campus, London SW7 2AZ, UK. chris.tomlinson@imperial.ac.uk|Centre for Integrated Systems Biology and Bioinformatics, Imperial College London, Sir Ernst Chain Building, South Kensington Campus, London SW7 2AZ, UK. chris.tomlinson@imperial.ac.uk|Centre for Integrated Systems Biology and Bioinformatics, Imperial College London, Sir Ernst Chain Building, South Kensington Campus, London SW7 2AZ, UK. chris.tomlinson@imperial.ac.uk|Centre for Integrated Systems Biology and Bioinformatics, Imperial College London, Sir Ernst Chain Building, South Kensington Campus, London SW7 2AZ, UK. chris.tomlinson@imperial.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 3, 'Information Systems|Internet|Laboratory Personnel|Research|Research Design|Research Personnel|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-8', '23323856', 'Todays biological experiments often involve the collaboration of multidisciplinary researchers utilising several high throughput omics platforms. There is a requirement for the details of the experiment to be adequately described using standardised ontologies to enable data preservation, the analysis of the data and to facilitate the export of the data to public repositories. However there are a bewildering number of ontologies, controlled vocabularies, and minimum standards available for use to describe experiments. There is a need for user-friendly software tools to aid laboratory scientists in capturing the experimental information.'),
(95368, 2013, 'bmc', 'A detailed error analysis of 13 kernel methods for protein-protein interaction extraction', 'Tikk, Domonkos|Solt, Illés|Thomas, Philippe|Leser, Ulf', 'Knowledge Management in Bioinformatics, Computer Science Department, Humboldt-Universität zu Berlin, 10099 Berlin, Germany. tikk@informatik.hu-berlin.de|Knowledge Management in Bioinformatics, Computer Science Department, Humboldt-Universität zu Berlin, 10099 Berlin, Germany. tikk@informatik.hu-berlin.de|Knowledge Management in Bioinformatics, Computer Science Department, Humboldt-Universität zu Berlin, 10099 Berlin, Germany. tikk@informatik.hu-berlin.de|Knowledge Management in Bioinformatics, Computer Science Department, Humboldt-Universität zu Berlin, 10099 Berlin, Germany. tikk@informatik.hu-berlin.de', 'Germany|Germany|Germany|Germany', 17, 'Algorithms|Protein Interaction Mapping', 'Evaluation Studies', '10.1186/1471-2105-14-12', '23323857', 'Kernel-based classification is the current state-of-the-art for extracting pairs of interacting proteins (PPIs) from free text. Various proposals have been put forward, which diverge especially in the specific kernel function, the type of input representation, and the feature sets. These proposals are regularly compared to each other regarding their overall performance on different gold standard corpora, but little is known about their respective performance on the instance level.'),
(95369, 2013, 'bmc', 'MotifLab: a tools and data integration workbench for motif discovery and regulatory sequence analysis', 'Klepper, Kjetil|Drabløs, Finn', 'Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway. kjetil.klepper@ntnu.no|Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway. kjetil.klepper@ntnu.no', 'Norway|Norway', 21, 'Algorithms|Binding Sites|Colforsin|Gene Expression Regulation|Humans|Nucleotide Motifs|Phylogeny|Regulatory Elements, Transcriptional|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-14-9', '23323883', 'Traditional methods for computational motif discovery often suffer from poor performance. In particular, methods that search for sequence matches to known binding motifs tend to predict many non-functional binding sites because they fail to take into consideration the biological state of the cell. In recent years, genome-wide studies have generated a lot of data that has the potential to improve our ability to identify functional motifs and binding sites, such as information about chromatin accessibility and epigenetic states in different cell types. However, it is not always trivial to make use of this data in combination with existing motif discovery tools, especially for researchers who are not skilled in bioinformatics programming.'),
(95370, 2013, 'bmc', 'FUSIM: a software tool for simulating fusion transcripts', 'Bruno, Andrew E|Miecznikowski, Jeffrey C|Qin, Maochun|Wang, Jianmin|Liu, Song', 'Department of Biostatistics, SUNY at Buffalo, Buffalo, NY 14214, USA. aebruno2@buffalo.edu|Department of Biostatistics, SUNY at Buffalo, Buffalo, NY 14214, USA. aebruno2@buffalo.edu|Department of Biostatistics, SUNY at Buffalo, Buffalo, NY 14214, USA. aebruno2@buffalo.edu|Department of Biostatistics, SUNY at Buffalo, Buffalo, NY 14214, USA. aebruno2@buffalo.edu|Department of Biostatistics, SUNY at Buffalo, Buffalo, NY 14214, USA. aebruno2@buffalo.edu', 'United States|United States|United States|United States|United States', 10, 'Computer Simulation|Gene Expression Profiling|Gene Fusion|High-Throughput Nucleotide Sequencing|Humans|Mutant Chimeric Proteins|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-14-13', '23323884', 'Gene fusions are the result of chromosomal aberrations and encode chimeric RNA (fusion transcripts) that play an important role in cancer genesis. Recent advances in high throughput transcriptome sequencing have given rise to computational methods for new fusion discovery. The ability to simulate fusion transcripts is essential for testing and improving those tools.'),
(95371, 2013, 'bmc', 'Negated bio-events: analysis and identification', 'Nawaz, Raheel|Thompson, Paul|Ananiadou, Sophia', 'National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK. raheel.nawaz@cs.man.ac.uk|National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK. raheel.nawaz@cs.man.ac.uk|National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK. raheel.nawaz@cs.man.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 20, 'Algorithms|Artificial Intelligence|Data Mining', 'Journal Article', '10.1186/1471-2105-14-14', '23323936', 'Negation occurs frequently in scientific literature, especially in biomedical literature. It has previously been reported that around 13% of sentences found in biomedical research articles contain negation. Historically, the main motivation for identifying negated events has been to ensure their exclusion from lists of extracted interactions. However, recently, there has been a growing interest in negative results, which has resulted in negation detection being identified as a key challenge in biomedical relation extraction. In this article, we focus on the problem of identifying negated bio-events, given gold standard event annotations.'),
(95372, 2013, 'bmc', 'xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data', 'Uppal, Karan|Soltow, Quinlyn A|Strobel, Frederick H|Pittard, W Stephen|Gernert, Kim M|Yu, Tianwei|Jones, Dean P', 'BimCore, School of Medicine, Emory University, Atlanta, GA, USA.|BimCore, School of Medicine, Emory University, Atlanta, GA, USA.|BimCore, School of Medicine, Emory University, Atlanta, GA, USA.|BimCore, School of Medicine, Emory University, Atlanta, GA, USA.|BimCore, School of Medicine, Emory University, Atlanta, GA, USA.|BimCore, School of Medicine, Emory University, Atlanta, GA, USA.|BimCore, School of Medicine, Emory University, Atlanta, GA, USA.', 'United States|United States|United States|United States|United States|United States|United States', 139, 'Algorithms|Chromatography, Liquid|Mass Spectrometry|Metabolomics|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-14-15', '23323971', 'Detection of low abundance metabolites is important for de novo mapping of metabolic pathways related to diet, microbiome or environmental exposures. Multiple algorithms are available to extract m/z features from liquid chromatography-mass spectral data in a conservative manner, which tends to preclude detection of low abundance chemicals and chemicals found in small subsets of samples. The present study provides software to enhance such algorithms for feature detection, quality assessment, and annotation.'),
(95373, 2013, 'bmc', 'The taxonomic name resolution service: an online tool for automated standardization of plant names', 'Boyle, Brad|Hopkins, Nicole|Lu, Zhenyuan|Raygoza Garay, Juan Antonio|Mozzherin, Dmitry|Rees, Tony|Matasci, Naim|Narro, Martha L|Piel, William H|McKay, Sheldon J|Lowry, Sonya|Freeland, Chris|Peet, Robert K|Enquist, Brian J', 'Department of Ecology and Evolutionary Biology, University of Arizona Tucson, P,O, Box 210088, Tucson, AZ 85721, USA. bboyle@email.arizona.edu|Department of Ecology and Evolutionary Biology, University of Arizona Tucson, P,O, Box 210088, Tucson, AZ 85721, USA. bboyle@email.arizona.edu|Department of Ecology and Evolutionary Biology, University of Arizona Tucson, P,O, Box 210088, Tucson, AZ 85721, USA. bboyle@email.arizona.edu|Department of Ecology and Evolutionary Biology, University of Arizona Tucson, P,O, Box 210088, Tucson, AZ 85721, USA. bboyle@email.arizona.edu|Department of Ecology and Evolutionary Biology, University of Arizona Tucson, P,O, Box 210088, Tucson, AZ 85721, USA. bboyle@email.arizona.edu|Department of Ecology and Evolutionary Biology, University of Arizona Tucson, P,O, Box 210088, Tucson, AZ 85721, USA. bboyle@email.arizona.edu|Department of Ecology and Evolutionary Biology, University of Arizona Tucson, P,O, Box 210088, Tucson, AZ 85721, USA. bboyle@email.arizona.edu|Department of Ecology and Evolutionary Biology, University of Arizona Tucson, P,O, Box 210088, Tucson, AZ 85721, USA. bboyle@email.arizona.edu|Department of Ecology and Evolutionary Biology, University of Arizona Tucson, P,O, Box 210088, Tucson, AZ 85721, USA. bboyle@email.arizona.edu|Department of Ecology and Evolutionary Biology, University of Arizona Tucson, P,O, Box 210088, Tucson, AZ 85721, USA. bboyle@email.arizona.edu|Department of Ecology and Evolutionary Biology, University of Arizona Tucson, P,O, Box 210088, Tucson, AZ 85721, USA. bboyle@email.arizona.edu|Department of Ecology and Evolutionary Biology, University of Arizona Tucson, P,O, Box 210088, Tucson, AZ 85721, USA. bboyle@email.arizona.edu|Department of Ecology and Evolutionary Biology, University of Arizona Tucson, P,O, Box 210088, Tucson, AZ 85721, USA. bboyle@email.arizona.edu|Department of Ecology and Evolutionary Biology, University of Arizona Tucson, P,O, Box 210088, Tucson, AZ 85721, USA. bboyle@email.arizona.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 211, 'Algorithms|Classification|Databases, Factual|Internet|Names|Plants|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-14-16', '23324024', 'The digitization of biodiversity data is leading to the widespread application of taxon names that are superfluous, ambiguous or incorrect, resulting in mismatched records and inflated species numbers. The ultimate consequences of misspelled names and bad taxonomy are erroneous scientific conclusions and faulty policy decisions. The lack of tools for correcting this names problem has become a fundamental obstacle to integrating disparate data sources and advancing the progress of biodiversity science.'),
(95374, 2013, 'bmc', 'CySBGN: a Cytoscape plug-in to integrate SBGN maps', 'Gonçalves, Emanuel|van Iersel, Martijn|Saez-Rodriguez, Julio', 'EMBL - European Bioinformatics Institute, Cambridge, UK.|EMBL - European Bioinformatics Institute, Cambridge, UK.|EMBL - European Bioinformatics Institute, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom', 9, 'Computer Graphics|Metabolic Networks and Pathways|Plants|Software|Systems Biology|Systems Integration', 'Journal Article', '10.1186/1471-2105-14-17', '23324051', 'A standard graphical notation is essential to facilitate exchange of network representations of biological processes. Towards this end, the Systems Biology Graphical Notation (SBGN) has been proposed, and it is already supported by a number of tools. However, support for SBGN in Cytoscape, one of the most widely used platforms in biology to visualise and analyse networks, is limited, and in particular it is not possible to import SBGN diagrams.'),
(95375, 2013, 'bmc', 'JContextExplorer: a tree-based approach to facilitate cross-species genomic context comparison', 'Seitzer, Phillip|Huynh, Tu Anh|Facciotti, Marc T', 'Department of Biomedical Engineering, One Shields Ave, University of California, Davis, CA 95616, USA.|Department of Biomedical Engineering, One Shields Ave, University of California, Davis, CA 95616, USA.|Department of Biomedical Engineering, One Shields Ave, University of California, Davis, CA 95616, USA.', 'United States|United States|United States', 7, 'Algorithms|Alphaproteobacteria|Cluster Analysis|Gammaproteobacteria|Genes, Bacterial|Genomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-14-18', '23324080', 'Cross-species comparisons of gene neighborhoods (also called genomic contexts) in microbes may provide insight into determining functionally related or co-regulated sets of genes, suggest annotations of previously un-annotated genes, and help to identify horizontal gene transfer events across microbial species. Existing tools to investigate genomic contexts, however, lack features for dynamically comparing and exploring genomic regions from multiple species. As DNA sequencing technologies improve and the number of whole sequenced microbial genomes increases, a user-friendly genome context comparison platform designed for use by a broad range of users promises to satisfy a growing need in the biological community.'),
(95376, 2013, 'bmc', 'Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides', 'Stanislawski, Jerzy|Kotulska, Malgorzata|Unold, Olgierd', 'Institute of Computer Engineering, Control and Robotics, Wroclaw University of Technology, 50-370 Wroclaw, Poland.|Institute of Computer Engineering, Control and Robotics, Wroclaw University of Technology, 50-370 Wroclaw, Poland.|Institute of Computer Engineering, Control and Robotics, Wroclaw University of Technology, 50-370 Wroclaw, Poland.', 'Poland|Poland|Poland', 16, 'Algorithms|Amyloid|Artificial Intelligence|Decision Trees|Humans|Neural Networks (Computer)|Peptides|ROC Curve', 'Journal Article', '10.1186/1471-2105-14-21', '23327628', 'Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods.'),
(95377, 2013, 'bmc', 'Kerfuffle: a web tool for multi-species gene colocalization analysis', 'Aboukhalil, Robert|Fendler, Bernard|Atwal, Gurinder S', 'Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA. raboukha@cshl.edu|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA. raboukha@cshl.edu|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA. raboukha@cshl.edu', 'United States|United States|United States', 0, 'Base Sequence|Cluster Analysis|Genes|Genomics|Humans|Internet|Software|Synteny', 'Journal Article', '10.1186/1471-2105-14-22', '23327649', 'The evolutionary pressures that underlie the large-scale functional organization of the genome are not well understood in eukaryotes. Recent evidence suggests that functionally similar genes may colocalize (cluster) in the eukaryotic genome, suggesting the role of chromatin-level gene regulation in shaping the physical distribution of coordinated genes. However, few of the bioinformatic tools currently available allow for a systematic study of gene colocalization across several, evolutionarily distant species. Furthermore, most tools require the user to input manually curated lists of gene position information, DNA sequence or gene homology relations between species. With the growing number of sequenced genomes, there is a need to provide new comparative genomics tools that can address the analysis of multi-species gene colocalization.'),
(95378, 2013, 'bmc', 'Genecentric: a package to uncover graph-theoretic structure in high-throughput epistasis data', 'Gallant, Andrew|Leiserson, Mark D M|Kachalov, Maxim|Cowen, Lenore J|Hescott, Benjamin J', 'Department of Computer Science, Tufts University, Medford, MA 02155, USA.|Department of Computer Science, Tufts University, Medford, MA 02155, USA.|Department of Computer Science, Tufts University, Medford, MA 02155, USA.|Department of Computer Science, Tufts University, Medford, MA 02155, USA.|Department of Computer Science, Tufts University, Medford, MA 02155, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Epistasis, Genetic|Genes, Fungal|Models, Genetic|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1186/1471-2105-14-23', '23331614', 'New technology has resulted in high-throughput screens for pairwise genetic interactions in yeast and other model organisms. For each pair in a collection of non-essential genes, an epistasis score is obtained, representing how much sicker (or healthier) the double-knockout organism will be compared to what would be expected from the sickness of the component single knockouts. Recent algorithmic work has identified graph-theoretic patterns in this data that can indicate functional modules, and even sets of genes that may occur in compensatory pathways, such as a BPM-type schema first introduced by Kelley and Ideker. However, to date, any algorithms for finding such patterns in the data were implemented internally, with no software being made publically available.'),
(95379, 2013, 'bmc', 'MICAN: a protein structure alignment algorithm that can handle Multiple-chains, Inverse alignments, C(xcexb1) only models, Alternative alignments, and Non-sequential alignments', 'Minami, Shintaro|Sawada, Kengo|Chikenji, George', 'Department of Computational Science and Engineering, Nagoya University, Nagoya 464-8603, Japan.|Department of Computational Science and Engineering, Nagoya University, Nagoya 464-8603, Japan.|Department of Computational Science and Engineering, Nagoya University, Nagoya 464-8603, Japan.', 'Japan|Japan|Japan', 0, 'Algorithms|Models, Molecular|Protein Structure, Secondary|Proteins|Structural Homology, Protein', 'Evaluation Studies', '10.1186/1471-2105-14-24', '23331634', 'Protein pairs that have the same secondary structure packing arrangement but have different topologies have attracted much attention in terms of both evolution and physical chemistry of protein structures. Further investigation of such protein relationships would give us a hint as to how proteins can change their fold in the course of evolution, as well as a insight into physico-chemical properties of secondary structure packing. For this purpose, highly accurate sequence order independent structure comparison methods are needed.'),
(95380, 2013, 'bmc', 'Extending the BEAGLE library to a multi-FPGA platform', 'Jin, Zheming|Bakos, Jason D', 'Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA.|Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA.', 'United States|United States', 3, 'Algorithms|Likelihood Functions|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-14-25', '23331707', 'Maximum Likelihood (ML)-based phylogenetic inference using Felsensteins pruning algorithm is a standard method for estimating the evolutionary relationships amongst a set of species based on DNA sequence data, and is used in popular applications such as RAxML, PHYLIP, GARLI, BEAST, and MrBayes. The Phylogenetic Likelihood Function (PLF) and its associated scaling and normalization steps comprise the computational kernel for these tools. These computations are data intensive but contain fine grain parallelism that can be exploited by coprocessor architectures such as FPGAs and GPUs. A general purpose API called BEAGLE has recently been developed that includes optimized implementations of Felsensteins pruning algorithm for various data parallel architectures. In this paper, we extend the BEAGLE API to a multiple Field Programmable Gate Array (FPGA)-based platform called the Convey HC-1.'),
(95381, 2013, 'bmc', 'A Parzen window-based approach for the detection of locally enriched transcription factor binding sites', 'Vandenbon, Alexis|Kumagai, Yutaro|Teraguchi, Shunsuke|Amada, Karlou Mar|Akira, Shizuo|Standley, Daron M', 'Laboratory of Systems Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan. alexisvdb@ifrec.osaka-u.ac.jp|Laboratory of Systems Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan. alexisvdb@ifrec.osaka-u.ac.jp|Laboratory of Systems Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan. alexisvdb@ifrec.osaka-u.ac.jp|Laboratory of Systems Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan. alexisvdb@ifrec.osaka-u.ac.jp|Laboratory of Systems Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan. alexisvdb@ifrec.osaka-u.ac.jp|Laboratory of Systems Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan. alexisvdb@ifrec.osaka-u.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Base Composition|Binding Sites|DNA|Gene Expression Regulation|Nucleotide Motifs|Promoter Regions, Genetic|Sequence Analysis, DNA|Toll-Like Receptors|Transcription Factors', 'Journal Article', '10.1186/1471-2105-14-26', '23331723', 'Identification of cis- and trans-acting factors regulating gene expression remains an important problem in biology. Bioinformatics analyses of regulatory regions are hampered by several difficulties. One is that binding sites for regulatory proteins are often not significantly over-represented in the set of DNA sequences of interest, because of high levels of false positive predictions, and because of positional restrictions on functional binding sites with regard to the transcription start site.'),
(95382, 2013, 'bmc', 'Predicting PDZ domain mediated protein interactions from structure', 'Hui, Shirley|Xing, Xiang|Bader, Gary D', 'The Donnelly Centre, University of Toronto, Toronto, ON, Canada.|The Donnelly Centre, University of Toronto, Toronto, ON, Canada.|The Donnelly Centre, University of Toronto, Toronto, ON, Canada.', 'Canada|Canada|Canada', 0, 'Humans|Ligands|Models, Molecular|PDZ Domains|Peptides|Protein Interaction Mapping|Protein Interaction Maps|Proteome|Sequence Analysis, Protein|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-14-27', '23336252', 'PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors.'),
(95383, 2013, 'bmc', 'A fast least-squares algorithm for population inference', 'Parry, R Mitchell|Wang, May D', 'The Wallace H, Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.|The Wallace H, Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.', 'Georgia|Georgia', 3, 'Algorithms|Gene Frequency|Genetics, Population|Genome-Wide Association Study|Genotype|Genotyping Techniques|HapMap Project|Humans|Least-Squares Analysis|Likelihood Functions|Markov Chains|Models, Statistical|Monte Carlo Method', 'Journal Article', '10.1186/1471-2105-14-28', '23343408', 'Population inference is an important problem in genetics used to remove population stratification in genome-wide association studies and to detect migration patterns or shared ancestry. An individuals genotype can be modeled as a probabilistic function of ancestral population memberships, Q, and the allele frequencies in those populations, P. The parameters, P and Q, of this binomial likelihood model can be inferred using slow sampling methods such as Markov Chain Monte Carlo methods or faster gradient based approaches such as sequential quadratic programming. This paper proposes a least-squares simplification of the binomial likelihood model motivated by a Euclidean interpretation of the genotype feature space. This results in a faster algorithm that easily incorporates the degree of admixture within the sample of individuals and improves estimates without requiring trial-and-error tuning.'),
(95384, 2013, 'bmc', 'Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion', 'Babaei, Sepideh|Hulsman, Marc|Reinders, Marcel|de Ridder, Jeroen', 'Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.|Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.|Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.|Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 25, 'Algorithms|Animals|Artificial Intelligence|Cluster Analysis|Genes, Neoplasm|Genomics|Humans|Leukemia|Mice|Mutagenesis, Insertional|Mutation|Mutation Rate|Neoplasms|Protein Interaction Mapping|Software', 'Journal Article', '10.1186/1471-2105-14-29', '23343428', 'Delineating the molecular drivers of cancer, i.e. determining cancer genes and the pathways which they deregulate, is an important challenge in cancer research. In this study, we aim to identify pathways of frequently mutated genes by exploiting their network neighborhood encoded in the protein-protein interaction network. To this end, we introduce a multi-scale diffusion kernel and apply it to a large collection of murine retroviral insertional mutagenesis data. The diffusion strength plays the role of scale parameter, determining the size of the network neighborhood that is taken into account. As a result, in addition to detecting genes with frequent mutations in their genomic vicinity, we find genes that harbor frequent mutations in their interaction network context.'),
(95385, 2012, 'bmc', 'Proceedings of the The 2011 International Conference on Intelligent Computing (ICIC 2011), August 11-14, 2011, Zhengzhou, China', '-', '-', '-', 0, 'Artificial Intelligence|Computational Biology|Humans', 'Congress', '10.1186/1471-2105-14-29', '23346590', '-');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95386, 2013, 'bmc', 'Seq2Ref: a web server to facilitate functional interpretation', 'Li, Wenlin|Cong, Qian|Kinch, Lisa N|Grishin, Nick V', 'Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390-9050, USA.|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390-9050, USA.|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390-9050, USA.|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390-9050, USA.', 'United States|United States|United States|United States', 4, 'Databases, Protein|Internet|Molecular Sequence Annotation|Proteins|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/1471-2105-14-30', '23356573', 'The size of the protein sequence database has been exponentially increasing due to advances in genome sequencing. However, experimentally characterized proteins only constitute a small portion of the database, such that the majority of sequences have been annotated by computational approaches. Current automatic annotation pipelines inevitably introduce errors, making the annotations unreliable. Instead of such error-prone automatic annotations, functional interpretation should rely on annotations of reference proteins that have been experimentally characterized or manually curated.'),
(95387, 2013, 'bmc', 'Estimation of data-specific constitutive exons with RNA-Seq data', 'Patrick, Ellis|Buckley, Michael|Yang, Yee Hwa', 'School of Mathematics and Statistics, University of Sydney, Sydney NSW 2006, Australia.|School of Mathematics and Statistics, University of Sydney, Sydney NSW 2006, Australia.|School of Mathematics and Statistics, University of Sydney, Sydney NSW 2006, Australia.', 'Australia|Australia|Australia', 0, 'Algorithms|Alternative Splicing|Cluster Analysis|Exons|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-14-31', '23360225', 'RNA-Seq has the potential to answer many diverse and interesting questions about the inner workings of cells. Estimating changes in the overall transcription of a gene is not straightforward. Changes in overall gene transcription can easily be confounded with changes in exon usage which alter the lengths of transcripts produced by a gene. Measuring the expression of constitutive exons--xons which are consistently conserved after splicing--ffers an unbiased estimation of the overall transcription of a gene.'),
(95388, 2013, 'bmc', 'Mechanistic analysis of multi-omics datasets to generate kinetic parameters for constraint-based metabolic models', 'Cotten, Cameron|Reed, Jennifer L', 'Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Dr, Madison, WI 53706, USA.|Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Dr, Madison, WI 53706, USA.', 'United States|United States', 28, 'Biomass|Escherichia coli|Kinetics|Metabolomics|Models, Biological|Proteomics|Thermodynamics', 'Journal Article', '10.1186/1471-2105-14-32', '23360254', 'Constraint-based modeling uses mass balances, flux capacity, and reaction directionality constraints to predict fluxes through metabolism. Although transcriptional regulation and thermodynamic constraints have been integrated into constraint-based modeling, kinetic rate laws have not been extensively used.'),
(95389, 2013, 'bmc', 'HTQC: a fast quality control toolkit for Illumina sequencing data', 'Yang, Xi|Liu, Di|Liu, Fei|Wu, Jun|Zou, Jing|Xiao, Xue|Zhao, Fangqing|Zhu, Baoli', 'CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, NO,1 West Beichen Road, Chaoyang District, Beijing, China.|CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, NO,1 West Beichen Road, Chaoyang District, Beijing, China.|CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, NO,1 West Beichen Road, Chaoyang District, Beijing, China.|CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, NO,1 West Beichen Road, Chaoyang District, Beijing, China.|CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, NO,1 West Beichen Road, Chaoyang District, Beijing, China.|CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, NO,1 West Beichen Road, Chaoyang District, Beijing, China.|CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, NO,1 West Beichen Road, Chaoyang District, Beijing, China.|CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, NO,1 West Beichen Road, Chaoyang District, Beijing, China.', 'China|China|China|China|China|China|China|China', 68, 'Genomics|Humans|Quality Control|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-14-33', '23363224', 'Illumina sequencing platform is widely used in genome research. Sequence reads quality assessment and control are needed for downstream analysis. However, software that provides efficient quality assessment and versatile filtration methods is still lacking.'),
(95390, 2013, 'bmc', 'A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits', 'Hayashi, Takeshi|Iwata, Hiroyoshi', 'Agroinformatics Division, National Agriculture and Food Research Organization, Agricultural Research Center, Kannondai, Tsukuba, Ibaraki, 305-8666, Japan. hayatk@affrc.go.jp|Agroinformatics Division, National Agriculture and Food Research Organization, Agricultural Research Center, Kannondai, Tsukuba, Ibaraki, 305-8666, Japan. hayatk@affrc.go.jp', 'Japan|Japan', 37, 'Bayes Theorem|Breeding|Genomics|Genotype|Models, Genetic|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-14-34', '23363272', 'Genomic selection is an effective tool for animal and plant breeding, allowing effective individual selection without phenotypic records through the prediction of genomic breeding value (GBV). To date, genomic selection has focused on a single trait. However, actual breeding often targets multiple correlated traits, and, therefore, joint analysis taking into consideration the correlation between traits, which might result in more accurate GBV prediction than analyzing each trait separately, is suitable for multi-trait genomic selection. This would require an extension of the prediction model for single-trait GBV to multi-trait case. As the computational burden of multi-trait analysis is even higher than that of single-trait analysis, an effective computational method for constructing a multi-trait prediction model is also needed.'),
(95391, 2013, 'bmc', 'Inferring homologous protein-protein interactions through pair position specific scoring matrix', 'Lin, Chun-Yu|Chen, Yung-Chiang|Lo, Yu-Shu|Yang, Jinn-Moon', 'Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.', '-|-|-|-', 4, 'Algorithms|Position-Specific Scoring Matrices|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins|Saccharomyces cerevisiae|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-14-S2-S11', '23367879', 'The protein-protein interaction (PPI) is one of the most important features to understand biological processes. For a PPI, the physical domain-domain interaction (DDI) plays the key role for biology functions. In the post-genomic era, to rapidly identify homologous PPIs for analyzing the contact residue pairs of their interfaces within DDIs on a genomic scale is essential to determine PPI networks and the PPI interface evolution across multiple species.'),
(95392, 2013, 'bmc', 'Discovery and analysis of consistent active sub-networks in cancers', 'Gaire, Raj K|Smith, Lorey|Humbert, Patrick|Bailey, James|Stuckey, Peter J|Haviv, Izhak', 'NICTA, Victoria Laboratory and Department of Computing and Information Systems, University of Melbourne, Parkville, Vic 3010, Australia. rgaire@csse.unimelb.edu.au|NICTA, Victoria Laboratory and Department of Computing and Information Systems, University of Melbourne, Parkville, Vic 3010, Australia. rgaire@csse.unimelb.edu.au|NICTA, Victoria Laboratory and Department of Computing and Information Systems, University of Melbourne, Parkville, Vic 3010, Australia. rgaire@csse.unimelb.edu.au|NICTA, Victoria Laboratory and Department of Computing and Information Systems, University of Melbourne, Parkville, Vic 3010, Australia. rgaire@csse.unimelb.edu.au|NICTA, Victoria Laboratory and Department of Computing and Information Systems, University of Melbourne, Parkville, Vic 3010, Australia. rgaire@csse.unimelb.edu.au|NICTA, Victoria Laboratory and Department of Computing and Information Systems, University of Melbourne, Parkville, Vic 3010, Australia. rgaire@csse.unimelb.edu.au', 'Australia|Australia|Australia|Australia|Australia|Australia', 8, 'Breast Neoplasms|Computer Simulation|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Glioblastoma|Humans|Protein Interaction Maps', 'Comparative Study', '10.1186/1471-2105-14-S2-S7', '23368093', 'Gene expression profiles can show significant changes when genetically diseased cells are compared with non-diseased cells. Biological networks are often used to identify active subnetworks (ASNs) of the diseases from the expression profiles to understand the reason behind the observed changes. Current methodologies for discovering ASNs mostly use undirected PPI networks and node centric approaches. This can limit their ability to find the meaningful ASNs when using integrated networks having comprehensive information than the traditional protein-protein interaction networks. Using appropriate scoring functions to assess both genes and their interactions may allow the discovery of better ASNs. In this paper, we present CASNet, which aims to identify better ASNs using (i) integrated interaction networks (mixed graphs), (ii) directions of regulations of genes, and (iii) combined node and edge scores. We simplify and extend previous methodologies to incorporate edge evaluations and lessen their sensitivity to significance thresholds. We formulate our objective functions using mixed integer programming (MIP) and show that optimal solutions may be obtained. We compare the ASNs obtained by CASNet and similar other approaches to show that CASNet can often discover more meaningful and stable regulatory ASNs. Our analysis of a breast cancer dataset finds that the positive feedback loops across 7 genes, AR, ESR1, MYC, E2F2, PGR, BCL2 and CCND1 are conserved across the basal/triple negative subtypes in multiple datasets that could potentially explain the aggressive nature of this cancer subtype. Furthermore, comparison of the basal subtype of breast cancer and the mesenchymal subtype of glioblastoma ASNs shows that an ASN in the vicinity of IL6 is conserved across the two subtypes. This result suggests that subtypes of different cancers can show molecular similarities indicating that the therapeutic approaches in different types of cancers may be shared.'),
(95393, 2013, 'bmc', 'Reconstruction of phyletic trees by global alignment of multiple metabolic networks', 'Ma, Cheng-Yu|Lin, Shu-Hsi|Lee, Chi-Ching|Tang, Chuan Yi|Berger, Bonnie|Liao, Chung-Shou', 'Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan.|Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan.|Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan.|Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan.|Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan.|Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan.', '-|-|-|-|-|-', 9, 'Algorithms|Cluster Analysis|Fermentation|Genomics|Lactobacillus|Light-Harvesting Protein Complexes|Metabolic Networks and Pathways|Photosynthesis|Phylogeny|Prochlorococcus|Sulfur-Reducing Bacteria|Synechococcus', 'Journal Article', '10.1186/1471-2105-14-S2-S12', '23368411', 'In the last decade, a considerable amount of research has been devoted to investigating the phylogenetic properties of organisms from a systems-level perspective. Most studies have focused on the classification of organisms based on structural comparison and local alignment of metabolic pathways. In contrast, global alignment of multiple metabolic networks complements sequence-based phylogenetic analyses and provides more comprehensive information.'),
(95394, 2013, 'bmc', 'Building Markov state models with solvent dynamics', 'Gu, Chen|Chang, Huang-Wei|Maibaum, Lutz|Pande, Vijay S|Carlsson, Gunnar E|Guibas, Leonidas J', 'Department of Computer Science, Stanford University, Stanford, CA 94305, USA.|Department of Computer Science, Stanford University, Stanford, CA 94305, USA.|Department of Computer Science, Stanford University, Stanford, CA 94305, USA.|Department of Computer Science, Stanford University, Stanford, CA 94305, USA.|Department of Computer Science, Stanford University, Stanford, CA 94305, USA.|Department of Computer Science, Stanford University, Stanford, CA 94305, USA.', 'United States|United States|United States|United States|United States|United States', 10, 'Algorithms|Computer Simulation|Markov Chains|Models, Molecular|Solutions|Solvents', 'Comparative Study', '10.1186/1471-2105-14-S2-S8', '23368418', 'Markov state models have been widely used to study conformational changes of biological macromolecules. These models are built from short timescale simulations and then propagated to extract long timescale dynamics. However, the solvent information in molecular simulations are often ignored in current methods, because of the large number of solvent molecules in a system and the indistinguishability of solvent molecules upon their exchange.'),
(95395, 2013, 'bmc', 'A multispecies polyadenylation site model', 'Ho, Eric S|Gunderson, Samuel I|Duffy, Siobain', 'Department of Molecular Genetics, Microbiology and Immunology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, Piscataway, New Jersey, USA. eric.ho@umdnj.edu|Department of Molecular Genetics, Microbiology and Immunology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, Piscataway, New Jersey, USA. eric.ho@umdnj.edu|Department of Molecular Genetics, Microbiology and Immunology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, Piscataway, New Jersey, USA. eric.ho@umdnj.edu', 'Jersey|Jersey|Jersey', 0, 'Animals|Artificial Intelligence|Conserved Sequence|Discriminant Analysis|Logistic Models|Models, Theoretical|Nucleosomes|Phylogeny|Plants|Poly A|Polyadenylation|Principal Component Analysis|RNA, Messenger|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-14-S2-S9', '23368518', 'Polyadenylation is present in all three domains of life, making it the most conserved post-transcriptional process compared with splicing and 5-capping. Even though most mammalian poly(A) sites contain a highly conserved hexanucleotide in the upstream region and a far less conserved U/GU-rich sequence in the downstream region, there are many exceptions. Furthermore, poly(A) sites in other species, such as plants and invertebrates, exhibit high deviation from this genomic structure, making the construction of a general poly(A) site recognition model challenging. We surveyed nine poly(A) site prediction methods published between 1999 and 2011. All methods exploit the skewed nucleotide profile across the poly(A) sites, and the highly conserved poly(A) signal as the primary features for recognition. These methods typically use a large number of features, which increases the dimensionality of the models to crippling degrees, and typically are not validated against many kinds of genomes.'),
(95396, 2013, 'bmc', 'Prediction of peptides binding to MHC class I and II alleles by temporal motif mining', 'Meydan, Cem|Otu, Hasan H|Sezerman, Osman Uxc4x9fur', 'Bioengineering Department, Sabanci University, 34956, Istanbul, Turkey.|Bioengineering Department, Sabanci University, 34956, Istanbul, Turkey.|Bioengineering Department, Sabanci University, 34956, Istanbul, Turkey.', 'Turkey|Turkey|Turkey', 22, 'Algorithms|Alleles|Area Under Curve|Artificial Intelligence|Data Mining|Databases, Protein|Genes, MHC Class I|Genes, MHC Class II|Humans|Neural Networks (Computer)|Peptides|Protein Binding|Protein Interaction Domains and Motifs', 'Journal Article', '10.1186/1471-2105-14-S2-S13', '23368521', 'MHC (Major Histocompatibility Complex) is a key player in the immune response of most vertebrates. The computational prediction of whether a given antigenic peptide will bind to a specific MHC allele is important in the development of vaccines for emerging pathogens, the creation of possibilities for controlling immune response, and for the applications of immunotherapy. One of the problems that make this computational prediction difficult is the detection of the binding core region in peptides, coupled with the presence of bulges and loops causing variations in the total sequence length. Most machine learning methods require the sequences to be of the same length to successfully discover the binding motifs, ignoring the length variance in both motif mining and prediction steps. In order to overcome this limitation, we propose the use of time-based motif mining methods that work position-independently.'),
(95397, 2013, 'bmc', 'A model of the circadian clock in the cyanobacterium Cyanothece sp ATCC 51142', 'Vinh, Nguyen Xuan|Chetty, Madhu|Coppel, Ross|Gaudana, Sandeep|Wangikar, Pramod P', 'Gippsland School of Information Technology, Monash University, Australia. vinh.nguyen@monash.edu|Gippsland School of Information Technology, Monash University, Australia. vinh.nguyen@monash.edu|Gippsland School of Information Technology, Monash University, Australia. vinh.nguyen@monash.edu|Gippsland School of Information Technology, Monash University, Australia. vinh.nguyen@monash.edu|Gippsland School of Information Technology, Monash University, Australia. vinh.nguyen@monash.edu', 'Australia|Australia|Australia|Australia|Australia', 3, 'Biofuels|Biomass|Chromosome Mapping|Circadian Clocks|Computational Biology|Cyanothece|Gene Regulatory Networks|Models, Biological|Nitrogen Fixation|Oligonucleotide Array Sequence Analysis|Photosynthesis|Synechococcus', 'Journal Article', '10.1186/1471-2105-14-S2-S14', '23368635', 'The over consumption of fossil fuels has led to growing concerns over climate change and global warming. Increasing research activities have been carried out towards alternative viable biofuel sources. Of several different biofuel platforms, cyanobacteria possess great potential, for their ability to accumulate biomass tens of times faster than traditional oilseed crops. The cyanobacterium Cyanothece sp. ATCC 51142 has recently attracted lots of research interest as a model organism for such research. Cyanothece can perform efficiently both photosynthesis and nitrogen fixation within the same cell, and has been recently shown to produce biohydrogen--a byproduct of nitrogen fixation--at very high rates of several folds higher than previously described hydrogen-producing photosynthetic microbes. Since the key enzyme for nitrogen fixation is very sensitive to oxygen produced by photosynthesis, Cyanothece employs a sophisticated temporal separation scheme, where nitrogen fixation occurs at night and photosynthesis at day. At the core of this temporal separation scheme is a robust clocking mechanism, which so far has not been thoroughly studied. Understanding how this circadian clock interacts with and harmonizes global transcription of key cellular processes is one of the keys to realize the inherent potential of this organism.'),
(95398, 2013, 'bmc', 'Identifying cross-category relations in gene ontology and constructing genome-specific term association networks', 'Peng, Jiajie|Chen, Jin|Wang, Yadong', 'School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.', 'China|China|China', 12, 'Algorithms|Computational Biology|Gene Regulatory Networks|Humans|Molecular Sequence Annotation|Saccharomyces cerevisiae|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-S2-S15', '23368677', 'Gene Ontology (GO) has been widely used in biological databases, annotation projects, and computational analyses. Although the three GO categories are structured as independent ontologies, the biological relationships across the categories are not negligible for biological reasoning and knowledge integration. However, the existing cross-category ontology term similarity measures are either developed by utilizing the GO data only or based on manually curated term name similarities, ignoring the fact that GO is evolving quickly and the gene annotations are far from complete.'),
(95399, 2013, 'bmc', 'Spiral search: a hydrophobic-core directed local search for simplified PSP on 3D FCC lattice', 'Rashid, Mahmood A|Newton, M A Hakim|Hoque, Md Tamjidul|Shatabda, Swakkhar|Pham, Duc Nghia|Sattar, Abdul', 'Institute for Integrated & Intelligent Systems, Griffith University, QLD, Australia. mahmood.rashid@nicta.com.au|Institute for Integrated & Intelligent Systems, Griffith University, QLD, Australia. mahmood.rashid@nicta.com.au|Institute for Integrated & Intelligent Systems, Griffith University, QLD, Australia. mahmood.rashid@nicta.com.au|Institute for Integrated & Intelligent Systems, Griffith University, QLD, Australia. mahmood.rashid@nicta.com.au|Institute for Integrated & Intelligent Systems, Griffith University, QLD, Australia. mahmood.rashid@nicta.com.au|Institute for Integrated & Intelligent Systems, Griffith University, QLD, Australia. mahmood.rashid@nicta.com.au', 'Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Algorithms|Amino Acid Sequence|Hydrophobic and Hydrophilic Interactions|Models, Theoretical|Protein Conformation|Proteins', 'Journal Article', '10.1186/1471-2105-14-S2-S16', '23368706', 'Protein structure prediction is an important but unsolved problem in biological science. Predicted structures vary much with energy functions and structure-mapping spaces. In our simplified ab initio protein structure prediction methods, we use hydrophobic-polar (HP) energy model for structure evaluation, and 3-dimensional face-centred-cubic lattice for structure mapping. For HP energy model, developing a compact hydrophobic-core (H-core) is essential for the progress of the search. The H-core helps find a stable structure with the lowest possible free energy.'),
(95400, 2013, 'bmc', 'Adaptive bi-level programming for optimal gene knockouts for targeted overproduction under phenotypic constraints', 'Ren, Shaogang|Zeng, Bo|Qian, Xiaoning', 'Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA.|Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA.|Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA.', 'United States|United States|United States', 28, 'Algorithms|Computational Biology|Escherichia coli|Gene Knockout Techniques|Metabolic Engineering|Metabolic Networks and Pathways|Models, Biological', 'Journal Article', '10.1186/1471-2105-14-S2-S17', '23368729', 'Optimization procedures to identify gene knockouts for targeted biochemical overproduction have been widely in use in modern metabolic engineering. Flux balance analysis (FBA) framework has provided conceptual simplifications for genome-scale dynamic analysis at steady states. Based on FBA, many current optimization methods for targeted bio-productions have been developed under the maximum cell growth assumption. The optimization problem to derive gene knockout strategies recently has been formulated as a bi-level programming problem in OptKnock for maximum targeted bio-productions with maximum growth rates. However, it has been shown that knockout mutants in fact reach the steady states with the minimization of metabolic adjustment (MOMA) from the corresponding wild-type strains instead of having maximal growth rates after genetic or metabolic intervention. In this work, we propose a new bi-level computational framework--MOMAKnock--which can derive robust knockout strategies under the MOMA flux distribution approximation.'),
(95401, 2013, 'bmc', 'A practical O(n log2 n) time algorithm for computing the triplet distance on binary trees', 'Sand, Andreas|Brodal, Gerth Stølting|Fagerberg, Rolf|Pedersen, Christian N S|Mailund, Thomas', 'Bioinformatics Research Center, Aarhus University, Denmark.|Bioinformatics Research Center, Aarhus University, Denmark.|Bioinformatics Research Center, Aarhus University, Denmark.|Bioinformatics Research Center, Aarhus University, Denmark.|Bioinformatics Research Center, Aarhus University, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark', 0, 'Algorithms|Computational Biology|Computer Simulation|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-14-S2-S18', '23368759', 'The triplet distance is a distance measure that compares two rooted trees on the same set of leaves by enumerating all sub-sets of three leaves and counting how often the induced topologies of the tree are equal or different. We present an algorithm that computes the triplet distance between two rooted binary trees in time O (n log2 n). The algorithm is related to an algorithm for computing the quartet distance between two unrooted binary trees in time O (n log n). While the quartet distance algorithm has a very severe overhead in the asymptotic time complexity that makes it impractical compared to O (n2) time algorithms, we show through experiments that the triplet distance algorithm can be implemented to give a competitive wall-time running time.'),
(95402, 2013, 'bmc', 'Seventh International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2010) Palermo, Italy, 16-18 September 2010 Introduction', 'Rizzo, Riccardo|Lisboa, Paulo J G', 'Bioinformatics Research Center, Aarhus University, Denmark.|Bioinformatics Research Center, Aarhus University, Denmark.', 'Denmark|Denmark', 0, 'Artificial Intelligence|Biometry|Biostatistics|Computational Biology|Italy', 'Introductory Journal Article', '10.1186/1471-2105-14-S1-I1', '23368761', '-'),
(95403, 2013, 'bmc', 'The road not taken: retreat and diverge in local search for simplified protein structure prediction', 'Shatabda, Swakkhar|Newton, M A Hakim|Rashid, Mahmood A|Pham, Duc Nghia|Sattar, Abdul', 'Institute of Intelligent and Integrated Systems, Griffith University, Queensland, Australia. swakkhar.shatabda@nicta.com.au|Institute of Intelligent and Integrated Systems, Griffith University, Queensland, Australia. swakkhar.shatabda@nicta.com.au|Institute of Intelligent and Integrated Systems, Griffith University, Queensland, Australia. swakkhar.shatabda@nicta.com.au|Institute of Intelligent and Integrated Systems, Griffith University, Queensland, Australia. swakkhar.shatabda@nicta.com.au|Institute of Intelligent and Integrated Systems, Griffith University, Queensland, Australia. swakkhar.shatabda@nicta.com.au', 'Australia|Australia|Australia|Australia|Australia', 14, 'Algorithms|Computational Biology|Hydrophobic and Hydrophilic Interactions|Models, Theoretical|Protein Conformation|Protein Folding|Proteins', 'Journal Article', '10.1186/1471-2105-14-S2-S19', '23368768', 'Given a proteins amino acid sequence, the protein structure prediction problem is to find a three dimensional structure that has the native energy level. For many decades, it has been one of the most challenging problems in computational biology. A simplified version of the problem is to find an on-lattice self-avoiding walk that minimizes the interaction energy among the amino acids. Local search methods have been preferably used in solving the protein structure prediction problem for their efficiency in finding very good solutions quickly. However, they suffer mainly from two problems: re-visitation and stagnancy.'),
(95404, 2013, 'bmc', 'CoNVEX: copy number variation estimation in exome sequencing data using HMM', 'Amarasinghe, Kaushalya C|Li, Jason|Halgamuge, Saman K', 'Department of Mechanical Engineering, University of Melbourne, Parkville, VIC 3010, Australia. kca@student.unimelb.edu.au|Department of Mechanical Engineering, University of Melbourne, Parkville, VIC 3010, Australia. kca@student.unimelb.edu.au|Department of Mechanical Engineering, University of Melbourne, Parkville, VIC 3010, Australia. kca@student.unimelb.edu.au', 'Australia|Australia|Australia', 35, 'Comparative Genomic Hybridization|DNA Copy Number Variations|Exome|Exons|Genomics|Humans|Markov Chains|Models, Statistical|Neoplasms', 'Journal Article', '10.1186/1471-2105-14-S2-S2', '23368785', 'One of the main types of genetic variations in cancer is Copy Number Variations (CNV). Whole exome sequencing (WES) is a popular alternative to whole genome sequencing (WGS) to study disease specific genomic variations. However, finding CNV in Cancer samples using WES data has not been fully explored.'),
(95405, 2013, 'bmc', 'Visualization of protein interaction networks: problems and solutions', 'Agapito, Giuseppe|Guzzi, Pietro Hiram|Cannataro, Mario', 'Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy.|Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy.|Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy.', 'Italy|Italy|Italy', 42, 'Algorithms|Cluster Analysis|Computer Simulation|Databases, Protein|Protein Interaction Mapping|Protein Interaction Maps|Software|Systems Biology', 'Journal Article', '10.1186/1471-2105-14-S1-S1', '23368786', 'Visualization concerns the representation of data visually and is an important task in scientific research. Protein-protein interactions (PPI) are discovered using either wet lab techniques, such mass spectrometry, or in silico predictions tools, resulting in large collections of interactions stored in specialized databases. The set of all interactions of an organism forms a protein-protein interaction network (PIN) and is an important tool for studying the behaviour of the cell machinery. Since graphic representation of PINs may highlight important substructures, e.g. protein complexes, visualization is more and more used to study the underlying graph structure of PINs. Although graphs are well known data structures, there are different open problems regarding PINs visualization: the high number of nodes and connections, the heterogeneity of nodes (proteins) and edges (interactions), the possibility to annotate proteins and interactions with biological information extracted by ontologies (e.g. Gene Ontology) that enriches the PINs with semantic information, but complicates their visualization.'),
(95406, 2013, 'bmc', 'Protein disulfide topology determination through the fusion of mass spectrometric analysis and sequence-based prediction using Dempster-Shafer theory', 'Singh, Rahul|Murad, William', 'Department of Computer Science, San Francisco State University, San Francisco, CA 94132, USA. rahul@sfsu.edu|Department of Computer Science, San Francisco State University, San Francisco, CA 94132, USA. rahul@sfsu.edu', 'United States|United States', 3, 'Algorithms|Computer Simulation|Disulfides|Mass Spectrometry|Models, Theoretical|Protein Conformation|Proteins', 'Journal Article', '10.1186/1471-2105-14-S2-S20', '23368815', 'Disulfide bonds constitute one of the most important cross-linkages in proteins and significantly influence protein structure and function. At the state-of-the-art, various methodological frameworks have been proposed for identification of disulfide bonds. These include among others, mass spectrometry-based methods, sequence-based predictive approaches, as well as techniques like crystallography and NMR. Each of these frameworks has its advantages and disadvantages in terms of pre-requisites for applicability, throughput, and accuracy. Furthermore, the results from different methods may concur or conflict in parts.'),
(95407, 2013, 'bmc', 'Prediction of disulfide connectivity in proteins with machine-learning methods and correlated mutations', 'Savojardo, Castrense|Fariselli, Piero|Martelli, Pier Luigi|Casadio, Rita', 'Department of Computer Science and Engineering, University of Bologna, Via Mura Anteo Zamboni 7, 41029 Bologna, Italy.|Department of Computer Science and Engineering, University of Bologna, Via Mura Anteo Zamboni 7, 41029 Bologna, Italy.|Department of Computer Science and Engineering, University of Bologna, Via Mura Anteo Zamboni 7, 41029 Bologna, Italy.|Department of Computer Science and Engineering, University of Bologna, Via Mura Anteo Zamboni 7, 41029 Bologna, Italy.', 'Italy|Italy|Italy|Italy', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Cysteine|Disulfides|Mutation|Proteins|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-14-S1-S10', '23368835', 'Recently, information derived by correlated mutations in proteins has regained relevance for predicting protein contacts. This is due to new forms of mutual information analysis that have been proven to be more suitable to highlight direct coupling between pairs of residues in protein structures and to the large number of protein chains that are currently available for statistical validation. It was previously discussed that disulfide bond topology in proteins is also constrained by correlated mutations.'),
(95408, 2013, 'bmc', 'Molecular docking analysis of 2009-H1N1 and 2004-H5N1 influenza virus HLA-B*4405-restricted HA epitope candidates: implications for TCR cross-recognition and vaccine development', 'Su, Chinh T T|Schönbach, Christian|Kwoh, Chee-Keong', 'Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore.|Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore.|Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore.', 'Singapore|Singapore|Singapore', 8, 'Cross Reactions|Epitopes, T-Lymphocyte|HLA Antigens|Influenza A Virus, H1N1 Subtype|Influenza A Virus, H5N1 Subtype|Molecular Docking Simulation|Protein Structure, Tertiary|Receptors, Antigen, T-Cell', 'Journal Article', '10.1186/1471-2105-14-S2-S21', '23368875', 'The pandemic 2009-H1N1 influenza virus circulated in the human population and caused thousands deaths worldwide. Studies on pandemic influenza vaccines have shown that T cell recognition to conserved epitopes and cross-reactive T cell responses are important when new strains emerge, especially in the absence of antibody cross-reactivity. In this work, using HLA-B*4405 and DM1-TCR structure model, we systematically generated high confidence conserved 2009-H1N1 T cell epitope candidates and investigated their potential cross-reactivity against H5N1 avian flu virus.'),
(95409, 2013, 'bmc', 'Accurate prediction of protein enzymatic class by N-to-1 Neural Networks', 'Volpato, Viola|Adelfio, Alessandro|Pollastri, Gianluca', 'School of Computer Science and Informatics, University College Dublin, Ireland.|School of Computer Science and Informatics, University College Dublin, Ireland.|School of Computer Science and Informatics, University College Dublin, Ireland.', 'Ireland|Ireland|Ireland', 21, 'Algorithms|Amino Acid Motifs|Animals|Databases, Protein|Enzymes|Neural Networks (Computer)|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Evaluation Studies', '10.1186/1471-2105-14-S1-S11', '23368876', 'We present a novel ab initio predictor of protein enzymatic class. The predictor can classify proteins, solely based on their sequences, into one of six classes extracted from the enzyme commission (EC) classification scheme and is trained on a large, curated database of over 6,000 non-redundant proteins which we have assembled in this work. The predictor is powered by an ensemble of N-to-1 Neural Network, a novel architecture which we have recently developed. N-to-1 Neural Networks operate on the full sequence and not on predefined features. All motifs of a predefined length (31 residues in this work) are considered and are compressed by an N-to-1 Neural Network into a feature vector which is automatically determined during training. We test our predictor in 10-fold cross-validation and obtain state of the art results, with a 96% correct classification and 86% generalized correlation. All six classes are predicted with a specificity of at least 80% and false positive rates never exceeding 7%. We are currently investigating enhanced input encoding schemes which include structural information, and are analyzing trained networks to mine motifs that are most informative for the prediction, hence, likely, functionally relevant.'),
(95410, 2013, 'bmc', 'SymGRASS: a database of sugarcane orthologous genes involved in arbuscular mycorrhiza and root nodule symbiosis', 'Belarmino, Luis Carlos|Silva, Roberta Lane de Oliveira|Cavalcanti, Nina da Mota Soares|Krezdorn, Nicolas|Kido, Ederson Akio|Horres, Ralf|Winter, Peter|Kahl, Günter|Benko-Iseppon, Ana Maria', 'Universidade Federal de Pernambuco, Centro de Ciências Biológicas, Departamento de Genética, Recife, PE, Brazil. lucabellarmino@gmail.com|Universidade Federal de Pernambuco, Centro de Ciências Biológicas, Departamento de Genética, Recife, PE, Brazil. lucabellarmino@gmail.com|Universidade Federal de Pernambuco, Centro de Ciências Biológicas, Departamento de Genética, Recife, PE, Brazil. lucabellarmino@gmail.com|Universidade Federal de Pernambuco, Centro de Ciências Biológicas, Departamento de Genética, Recife, PE, Brazil. lucabellarmino@gmail.com|Universidade Federal de Pernambuco, Centro de Ciências Biológicas, Departamento de Genética, Recife, PE, Brazil. lucabellarmino@gmail.com|Universidade Federal de Pernambuco, Centro de Ciências Biológicas, Departamento de Genética, Recife, PE, Brazil. lucabellarmino@gmail.com|Universidade Federal de Pernambuco, Centro de Ciências Biológicas, Departamento de Genética, Recife, PE, Brazil. lucabellarmino@gmail.com|Universidade Federal de Pernambuco, Centro de Ciências Biológicas, Departamento de Genética, Recife, PE, Brazil. lucabellarmino@gmail.com|Universidade Federal de Pernambuco, Centro de Ciências Biológicas, Departamento de Genética, Recife, PE, Brazil. lucabellarmino@gmail.com', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 0, 'Databases, Genetic|Expressed Sequence Tags|Genes, Plant|Mycorrhizae|Root Nodules, Plant|Saccharum|Software|Symbiosis|Transcriptome', 'Journal Article', '10.1186/1471-2105-14-S1-S2', '23368899', 'The rationale for gathering information from plants procuring nitrogen through symbiotic interactions controlled by a common genetic program for a sustainable biofuel production is the high energy demanding application of synthetic nitrogen fertilizers. We curated sequence information publicly available for the biofuel plant sugarcane, performed an analysis of the common SYM pathway known to control symbiosis in other plants, and provide results, sequences and literature links as an online database.'),
(95411, 2013, 'bmc', 'Characterising RNA secondary structure space using information entropy', 'Sükösd, Zsuzsanna|Knudsen, Bjarne|Anderson, James W J|Novák, Adám|Kjems, Jørgen|Pedersen, Christian N S', 'Bioinformatics Research Center, Aarhus University, Aarhus, Denmark. zs@birc.au.dk|Bioinformatics Research Center, Aarhus University, Aarhus, Denmark. zs@birc.au.dk|Bioinformatics Research Center, Aarhus University, Aarhus, Denmark. zs@birc.au.dk|Bioinformatics Research Center, Aarhus University, Aarhus, Denmark. zs@birc.au.dk|Bioinformatics Research Center, Aarhus University, Aarhus, Denmark. zs@birc.au.dk|Bioinformatics Research Center, Aarhus University, Aarhus, Denmark. zs@birc.au.dk', 'Denmark|Denmark|Denmark|Denmark|Denmark|Denmark', 0, 'Algorithms|Base Sequence|Computational Biology|Entropy|Models, Theoretical|Nucleic Acid Conformation|Probability|RNA|Software', 'Journal Article', '10.1186/1471-2105-14-S2-S22', '23368905', 'Comparative methods for RNA secondary structure prediction use evolutionary information from RNA alignments to increase prediction accuracy. The model is often described in terms of stochastic context-free grammars (SCFGs), which generate a probability distribution over secondary structures. It is, however, unclear how this probability distribution changes as a function of the input alignment. As prediction programs typically only return a single secondary structure, better characterisation of the underlying probability space of RNA secondary structures is of great interest. In this work, we show how to efficiently compute the information entropy of the probability distribution over RNA secondary structures produced for RNA alignments by a phylo-SCFG, and implement it for the PPfold model. We also discuss interpretations and applications of this quantity, including how it can clarify reasons for low prediction reliability scores. PPfold and its source code are available from http://birc.au.dk/software/ppfold/.'),
(95412, 2013, 'bmc', 'A negative selection heuristic to predict new transcriptional targets', 'Cerulo, Luigi|Paduano, Vincenzo|Zoppoli, Pietro|Ceccarelli, Michele', 'Department of Science, University of Sannio, Benevento, Italy. lcerulo@unisannio.it|Department of Science, University of Sannio, Benevento, Italy. lcerulo@unisannio.it|Department of Science, University of Sannio, Benevento, Italy. lcerulo@unisannio.it|Department of Science, University of Sannio, Benevento, Italy. lcerulo@unisannio.it', 'Italy|Italy|Italy|Italy', 0, 'Artificial Intelligence|B-Lymphocytes|Binding Sites|Escherichia coli|Gene Regulatory Networks|Humans|Regulatory Elements, Transcriptional|Transcription Factors', 'Journal Article', '10.1186/1471-2105-14-S1-S3', '23368951', 'Supervised machine learning approaches have been recently adopted in the inference of transcriptional targets from high throughput trascriptomic and proteomic data showing major improvements from with respect to the state of the art of reverse gene regulatory network methods. Beside traditional unsupervised techniques, a supervised classifier learns, from known examples, a function that is able to recognize new relationships for new data. In the context of gene regulatory inference a supervised classifier is coerced to learn from positive and unlabeled examples, as the counter negative examples are unavailable or hard to collect. Such a condition could limit the performance of the classifier especially when the amount of training examples is low.'),
(95413, 2013, 'bmc', 'A novel subgradient-based optimization algorithm for blockmodel functional module identification', 'Wang, Yijie|Qian, Xiaoning', 'Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA. yijie@mail.usf.edu|Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA. yijie@mail.usf.edu', 'United States|United States', 4, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Protein|Humans|Models, Theoretical|Protein Interaction Mapping|Protein Interaction Maps|Proteins|Saccharomyces cerevisiae', 'Comparative Study', '10.1186/1471-2105-14-S2-S23', '23368964', 'Functional module identification in biological networks may provide new insights into the complex interactions among biomolecules for a better understanding of cellular functional organization. Most of existing functional module identification methods are based on the optimization of network modularity and cluster networks into groups of nodes within which there are a higher-than-expectation number of edges. However, module identification simply based on this topological criterion may not discover certain kinds of biologically meaningful modules within which nodes are sparsely connected but have similar interaction patterns with the rest of the network. In order to unearth more biologically meaningful functional modules, we propose a novel efficient convex programming algorithm based on the subgradient method with heuristic path generation to solve the problem in a recently proposed framework of blockmodel module identification. We have implemented our algorithm for large-scale protein-protein interaction (PPI) networks, including Saccharomyces cerevisia and Homo sapien PPI networks collected from the Database of Interaction Proteins (DIP) and Human Protein Reference Database (HPRD). Our experimental results have shown that our algorithm achieves comparable network clustering performance in comparison to the more time-consuming simulated annealing (SA) optimization. Furthermore, preliminary results for identifying fine-grained functional modules in both biological networks and the comparison with the commonly adopted Markov Clustering (MCL) algorithm have demonstrated the potential of our algorithm to discover new types of modules, within which proteins are sparsely connected but with significantly enriched biological functionalities.'),
(95414, 2013, 'bmc', 'An extensible six-step methodology to automatically generate fuzzy DSSs for diagnostic applications', 'dAcierno, Antonio|Esposito, Massimo|De Pietro, Giuseppe', 'Institute of Food Sciences - National Research Council of Italy, Via Roma 64, Avellino, Italy. dacierno.a@isa.cnr.it|Institute of Food Sciences - National Research Council of Italy, Via Roma 64, Avellino, Italy. dacierno.a@isa.cnr.it|Institute of Food Sciences - National Research Council of Italy, Via Roma 64, Avellino, Italy. dacierno.a@isa.cnr.it', 'Italy|Italy|Italy', 8, 'Breast Neoplasms|Decision Support Systems, Clinical|Diagnosis, Computer-Assisted|Female|Fuzzy Logic|Humans', 'Journal Article', '10.1186/1471-2105-14-S1-S4', '23368970', 'The diagnosis of many diseases can be often formulated as a decision problem, uncertainty affects these problems so that many computerized Diagnostic Decision Support Systems (in the following, DDSSs) have been developed to aid the physician in interpreting clinical data and thus to improve the quality of the whole process. Fuzzy logic, a well established attempt at the formalization and mechanization of human capabilities in reasoning and deciding with noisy information, can be profitably used. Recently, we informally proposed a general methodology to automatically build DDSSs on the top of fuzzy knowledge extracted from data.'),
(95415, 2013, 'bmc', 'A knowledge-based decision support system in bioinformatics: an application to protein complex extraction', 'Fiannaca, Antonino|La Rosa, Massimo|Urso, Alfonso|Rizzo, Riccardo|Gaglio, Salvatore', 'ICAR-CNR, National Research Council of Italy, Viale delle Scienze Ed, 11, Palermo, 90128, Italy.|ICAR-CNR, National Research Council of Italy, Viale delle Scienze Ed, 11, Palermo, 90128, Italy.|ICAR-CNR, National Research Council of Italy, Viale delle Scienze Ed, 11, Palermo, 90128, Italy.|ICAR-CNR, National Research Council of Italy, Viale delle Scienze Ed, 11, Palermo, 90128, Italy.|ICAR-CNR, National Research Council of Italy, Viale delle Scienze Ed, 11, Palermo, 90128, Italy.', 'Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Computational Biology|Decision Support Techniques|Knowledge Bases|Multiprotein Complexes|Protein Interaction Mapping|Saccharomyces cerevisiae Proteins|Software|Workflow', 'Journal Article', '10.1186/1471-2105-14-S1-S5', '23368995', 'We introduce a Knowledge-based Decision Support System (KDSS) in order to face the Protein Complex Extraction issue. Using a Knowledge Base (KB) coding the expertise about the proposed scenario, our KDSS is able to suggest both strategies and tools, according to the features of input dataset. Our system provides a navigable workflow for the current experiment and furthermore it offers support in the configuration and running of every processing component of that workflow. This last feature makes our system a crossover between classical DSS and Workflow Management Systems.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95416, 2013, 'bmc', 'Mass spectrometry-based protein identification by integrating de novo sequencing with database searching', 'Wang, Penghao|Wilson, Susan R', 'Prince of Wales Clinical School, University of New South Wales, Australia. penghao.wang@unsw.edu.au|Prince of Wales Clinical School, University of New South Wales, Australia. penghao.wang@unsw.edu.au', 'Australia|Australia', 5, 'Algorithms|Amino Acid Sequence|Computational Biology|Databases, Protein|Mass Spectrometry|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-14-S2-S24', '23369017', 'Mass spectrometry-based protein identification is a very challenging task. The main identification approaches include de novo sequencing and database searching. Both approaches have shortcomings, so an integrative approach has been developed. The integrative approach firstly infers partial peptide sequences, known as tags, directly from tandem spectra through de novo sequencing, and then puts these sequences into a database search to see if a close peptide match can be found. However the current implementation of this integrative approach has several limitations. Firstly, simplistic de novo sequencing is applied and only very short sequence tags are used. Secondly, most integrative methods apply an algorithm similar to BLAST to search for exact sequence matches and do not accommodate sequence errors well. Thirdly, by applying these methods the integrated de novo sequencing makes a limited contribution to the scoring model which is still largely based on database searching.'),
(95417, 2013, 'bmc', 'A methodology to assess the intrinsic discriminative ability of a distance function and its interplay with clustering algorithms for microarray data analysis', 'Giancarlo, Raffaele|Lo Bosco, Giosué|Pinello, Luca|Utro, Filippo', 'Dipartimento di Matematica ed Informatica, Universitá di Palermo, Via Archirafi 34, 90123 Palermo, Italy.|Dipartimento di Matematica ed Informatica, Universitá di Palermo, Via Archirafi 34, 90123 Palermo, Italy.|Dipartimento di Matematica ed Informatica, Universitá di Palermo, Via Archirafi 34, 90123 Palermo, Italy.|Dipartimento di Matematica ed Informatica, Universitá di Palermo, Via Archirafi 34, 90123 Palermo, Italy.', 'Italy|Italy|Italy|Italy', 7, 'Algorithms|Cluster Analysis|Oligonucleotide Array Sequence Analysis|Transcriptome', 'Journal Article', '10.1186/1471-2105-14-S1-S6', '23369037', 'Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from statistics to computer science. Following Handl et al., it can be summarized as a three step process: (1) choice of a distance function, (2) choice of a clustering algorithm, (3) choice of a validation method. Although such a purist approach to clustering is hardly seen in many areas of science, genomic data require that level of attention, if inferences made from cluster analysis have to be of some relevance to biomedical research.'),
(95418, 2013, 'bmc', 'Efficient known ncRNA search including pseudoknots', 'Yuan, Cheng|Sun, Yanni', 'Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA. yannisun@msu.edu|Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA. yannisun@msu.edu', 'United States|United States', 0, 'Algorithms|Base Sequence|Computational Biology|Computer Simulation|Nucleic Acid Conformation|RNA, Untranslated|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-14-S2-S25', '23369049', 'Searching for members of characterized ncRNA families containing pseudoknots is an important component of genome-scale ncRNA annotation. However, the state-of-the-art known ncRNA search is based on context-free grammar (CFG), which cannot effectively model pseudoknots. Thus, existing CFG-based ncRNA identification tools usually ignore pseudoknots during search. As a result, dozens of sequences that do not contain the native pseudoknots are reported by these tools. When pseudoknot structures are vital to the functions of the ncRNAs, these sequences may not be true members.'),
(95419, 2013, 'bmc', 'Expression dynamics and genome distribution of osmoprotectants in soybean: identifying important components to face abiotic stress', 'Kido, Ederson A|Ferreira Neto, Josxc3xa9 R C|Silva, Roberta L O|Belarmino, Luis C|Bezerra Neto, Joxc3xa3o P|Soares-Cavalcanti, Nina M|Pandolfi, Valesca|Silva, Manassxc3xa9s D|Nepomuceno, Alexandre L|Benko-Iseppon, Ana M', 'Departament of Genetics/Biological Sciences Center, Federal University of Pernambuco, Recife, Pernambuco, CEP 50,670-420, Brazil.|Departament of Genetics/Biological Sciences Center, Federal University of Pernambuco, Recife, Pernambuco, CEP 50,670-420, Brazil.|Departament of Genetics/Biological Sciences Center, Federal University of Pernambuco, Recife, Pernambuco, CEP 50,670-420, Brazil.|Departament of Genetics/Biological Sciences Center, Federal University of Pernambuco, Recife, Pernambuco, CEP 50,670-420, Brazil.|Departament of Genetics/Biological Sciences Center, Federal University of Pernambuco, Recife, Pernambuco, CEP 50,670-420, Brazil.|Departament of Genetics/Biological Sciences Center, Federal University of Pernambuco, Recife, Pernambuco, CEP 50,670-420, Brazil.|Departament of Genetics/Biological Sciences Center, Federal University of Pernambuco, Recife, Pernambuco, CEP 50,670-420, Brazil.|Departament of Genetics/Biological Sciences Center, Federal University of Pernambuco, Recife, Pernambuco, CEP 50,670-420, Brazil.|Departament of Genetics/Biological Sciences Center, Federal University of Pernambuco, Recife, Pernambuco, CEP 50,670-420, Brazil.|Departament of Genetics/Biological Sciences Center, Federal University of Pernambuco, Recife, Pernambuco, CEP 50,670-420, Brazil.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 22, 'Cluster Analysis|Expressed Sequence Tags|Gene Expression Profiling|Genes, Plant|Genome, Plant|High-Throughput Nucleotide Sequencing|Osmotic Pressure|Seeds|Soybeans|Stress, Physiological|Synteny', 'Journal Article', '10.1186/1471-2105-14-S1-S7', '23369061', 'Despite the importance of osmoprotectants, no previous in silico evaluation of high throughput data is available for higher plants. The present approach aimed at the identification and annotation of osmoprotectant-related sequences applied to short transcripts from a soybean HT-SuperSAGE (High Throughput Super Serial Analysis of Gene Expression, 26-bp tags) database, and also its comparison with other transcriptomic and genomic data available from different sources.'),
(95420, 2013, 'bmc', 'Widespread evidence of viral miRNAs targeting host pathways', 'Carl, Joseph W|Trgovcich, Joanne|Hannenhalli, Sridhar', 'Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA.|Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA.|Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA.', 'United States|United States|United States', 15, 'Animals|Computational Biology|Cytomegalovirus|Databases, Nucleic Acid|Gene Expression Regulation, Viral|Humans|Mice|MicroRNAs|RNA, Viral|Viruses', 'Journal Article', '10.1186/1471-2105-14-S2-S3', '23369080', 'MicroRNAs (miRNA) are regulatory genes that target and repress other RNA molecules via sequence-specific binding. Several biological processes are regulated across many organisms by evolutionarily conserved miRNAs. Plants and invertebrates employ their miRNA in defense against viruses by targeting and degrading viral products. Viruses also encode miRNAs and there is evidence to suggest that virus-encoded miRNAs target specific host genes and pathways that may be beneficial for their infectivity and/or proliferation. However, it is not clear whether there are general patterns underlying cellular targets of viral miRNAs.'),
(95421, 2013, 'bmc', 'Finding reproducible cluster partitions for the k-means algorithm', 'Lisboa, Paulo J G|Etchells, Terence A|Jarman, Ian H|Chambers, Simon J', 'School of Computing and Mathematical Sciences, Byrom Street, Liverpool John Moores University, Liverpool L3 3AF, UK.|School of Computing and Mathematical Sciences, Byrom Street, Liverpool John Moores University, Liverpool L3 3AF, UK.|School of Computing and Mathematical Sciences, Byrom Street, Liverpool John Moores University, Liverpool L3 3AF, UK.|School of Computing and Mathematical Sciences, Byrom Street, Liverpool John Moores University, Liverpool L3 3AF, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 23, 'Algorithms|Breast Neoplasms|Cardiotocography|Cluster Analysis|Computational Biology|Female|Humans|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-14-S1-S8', '23369085', 'K-means clustering is widely used for exploratory data analysis. While its dependence on initialisation is well-known, it is common practice to assume that the partition with lowest sum-of-squares (SSQ) total i.e. within cluster variance, is both reproducible under repeated initialisations and also the closest that k-means can provide to true structure, when applied to synthetic data. We show that this is generally the case for small numbers of clusters, but for values of k that are still of theoretical and practical interest, similar values of SSQ can correspond to markedly different cluster partitions. This paper extends stability measures previously presented in the context of finding optimal values of cluster number, into a component of a 2-d map of the local minima found by the k-means algorithm, from which not only can values of k be identified for further analysis but, more importantly, it is made clear whether the best SSQ is a suitable solution or whether obtaining a consistently good partition requires further application of the stability index. The proposed method is illustrated by application to five synthetic datasets replicating a real world breast cancer dataset with varying data density, and a large bioinformatics dataset.'),
(95422, 2013, 'bmc', 'SNPranker 20: a gene-centric data mining tool for diseases associated SNP prioritization in GWAS', 'Merelli, Ivan|Calabria, Andrea|Cozzi, Paolo|Viti, Federica|Mosca, Ettore|Milanesi, Luciano', 'Consiglio Nazionale delle Ricerche - Istituto di Tecnologie Biomediche (CNR-ITB), Via F,lli Cervi 93, 20090 Segrate (MI), Italy.|Consiglio Nazionale delle Ricerche - Istituto di Tecnologie Biomediche (CNR-ITB), Via F,lli Cervi 93, 20090 Segrate (MI), Italy.|Consiglio Nazionale delle Ricerche - Istituto di Tecnologie Biomediche (CNR-ITB), Via F,lli Cervi 93, 20090 Segrate (MI), Italy.|Consiglio Nazionale delle Ricerche - Istituto di Tecnologie Biomediche (CNR-ITB), Via F,lli Cervi 93, 20090 Segrate (MI), Italy.|Consiglio Nazionale delle Ricerche - Istituto di Tecnologie Biomediche (CNR-ITB), Via F,lli Cervi 93, 20090 Segrate (MI), Italy.|Consiglio Nazionale delle Ricerche - Istituto di Tecnologie Biomediche (CNR-ITB), Via F,lli Cervi 93, 20090 Segrate (MI), Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 19, 'Computational Biology|Data Mining|Disease|Genes|Genome-Wide Association Study|Genotype|Humans|Internet|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-14-S1-S9', '23369106', 'The capability of correlating specific genotypes with human diseases is a complex issue in spite of all advantages arisen from high-throughput technologies, such as Genome Wide Association Studies (GWAS). New tools for genetic variants interpretation and for Single Nucleotide Polymorphisms (SNPs) prioritization are actually needed. Given a list of the most relevant SNPs statistically associated to a specific pathology as result of a genotype study, a critical issue is the identification of genes that are effectively related to the disease by re-scoring the importance of the identified genetic variations. Vice versa, given a list of genes, it can be of great importance to predict which SNPs can be involved in the onset of a particular disease, in order to focus the research on their effects.'),
(95423, 2013, 'bmc', 'An enhanced computational platform for investigating the roles of regulatory RNA and for identifying functional RNA motifs', 'Chang, Tzu-Hao|Huang, Hsi-Yuan|Hsu, Justin Bo-Kai|Weng, Shun-Long|Horng, Jorng-Tzong|Huang, Hsien-Da', 'Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan.', '-|-|-|-|-|-', 170, 'Base Sequence|Computer Graphics|Gene Expression Regulation|Internet|Nucleotide Motifs|Regulatory Sequences, Nucleic Acid|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-14-S2-S4', '23369107', 'Functional RNA molecules participate in numerous biological processes, ranging from gene regulation to protein synthesis. Analysis of functional RNA motifs and elements in RNA sequences can obtain useful information for deciphering RNA regulatory mechanisms. Our previous work, RegRNA, is widely used in the identification of regulatory motifs, and this work extends it by incorporating more comprehensive and updated data sources and analytical approaches into a new platform.'),
(95424, 2013, 'bmc', 'Shape and secondary structure prediction for ncRNAs including pseudoknots based on linear SVM', 'Achawanantakun, Rujira|Sun, Yanni', 'Department of Computer Science and Engineering, Michigan State University, Michigan, USA.|Department of Computer Science and Engineering, Michigan State University, Michigan, USA.', 'United States|United States', 6, 'Computational Biology|Nucleic Acid Conformation|RNA, Untranslated|Software|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-14-S2-S1', '23369147', 'Accurate secondary structure prediction provides important information to undefirstafinding the tertiary structures and thus the functions of ncRNAs. However, the accuracy of the native structure derivation of ncRNAs is still not satisfactory, especially on sequences containing pseudoknots. It is recently shown that using the abstract shapes, which retain adjacency and nesting of structural features but disregard the length details of helix and loop regions, can improve the performance of structure prediction. In this work, we use SVM-based feature selection to derive the consensus abstract shape of homologous ncRNAs and apply the predicted shape to structure prediction including pseudoknots.'),
(95425, 2013, 'bmc', 'iStable: off-the-shelf predictor integration for predicting protein stability changes', 'Chen, Chi-Wei|Lin, Jerome|Chu, Yen-Wei', 'Institute of Genomics and Bioinformatics, National Chung Hsing University 250, Kuo Kuang Rd., Taichung 402, Taiwan.|Institute of Genomics and Bioinformatics, National Chung Hsing University 250, Kuo Kuang Rd., Taichung 402, Taiwan.|Institute of Genomics and Bioinformatics, National Chung Hsing University 250, Kuo Kuang Rd., Taichung 402, Taiwan.', '-|-|-', 77, 'Amino Acids|Mutation|Protein Stability|Protein Structure, Secondary|Proteins|Software|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-14-S2-S5', '23369171', 'Mutation of a single amino acid residue can cause changes in a protein, which could then lead to a loss of protein function. Predicting the protein stability changes can provide several possible candidates for the novel protein designing. Although many prediction tools are available, the conflicting prediction results from different tools could cause confusion to users.'),
(95426, 2013, 'bmc', 'Sequence-only evolutionary and predicted structural features for the prediction of stability changes in protein mutants', 'Folkman, Lukas|Stantic, Bela|Sattar, Abdul', 'Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia. lukas.folkman@griffithuni.edu.au|Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia. lukas.folkman@griffithuni.edu.au|Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia. lukas.folkman@griffithuni.edu.au', 'Australia|Australia|Australia', 11, 'Algorithms|Amino Acids|Artificial Intelligence|Databases, Protein|Evolution, Molecular|Likelihood Functions|Mutant Proteins|Protein Stability|Protein Structure, Secondary', 'Comparative Study', '10.1186/1471-2105-14-S2-S6', '23369338', 'Even a single amino acid substitution in a protein sequence may result in significant changes in protein stability, structure, and therefore in protein function as well. In the post-genomic era, computational methods for predicting stability changes from only the sequence of a protein are of importance. While evolutionary relationships of protein mutations can be extracted from large protein databases holding millions of protein sequences, relevant evolutionary features for the prediction of stability changes have not been proposed. Also, the use of predicted structural features in situations when a protein structure is not available has not been explored.'),
(95427, 2013, 'bmc', 'An integrated pharmacokinetics ontology and corpus for text mining', 'Wu, Heng-Yi|Karnik, Shreyas|Subhadarshini, Abhinita|Wang, Zhiping|Philips, Santosh|Han, Xu|Chiang, Chienwei|Liu, Lei|Boustani, Malaz|Rocha, Luis M|Quinney, Sara K|Flockhart, David|Li, Lang', 'Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN, USA.|Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN, USA.|Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN, USA.|Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN, USA.|Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN, USA.|Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN, USA.|Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN, USA.|Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN, USA.|Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN, USA.|Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN, USA.|Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN, USA.|Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN, USA.|Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN, USA.', 'India|India|India|India|India|India|India|India|India|India|India|India|India', 26, 'Cytochrome P-450 Enzyme System|Data Mining|Databases, Factual|Drug Interactions|Ketoconazole|Midazolam|Pharmacokinetics|Tamoxifen', 'Journal Article', '10.1186/1471-2105-14-35', '23374886', 'Drug pharmacokinetics parameters, drug interaction parameters, and pharmacogenetics data have been unevenly collected in different databases and published extensively in the literature. Without appropriate pharmacokinetics ontology and a well annotated pharmacokinetics corpus, it will be difficult to develop text mining tools for pharmacokinetics data collection from the literature and pharmacokinetics data integration from multiple databases.'),
(95428, 2013, 'bmc', 'The evolutionary rate of antibacterial drug targets', 'Gladki, Arkadiusz|Kaczanowski, Szymon|Szczesny, Pawel|Zielenkiewicz, Piotr', 'Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawinskiego 5A, Warsaw, Poland.|Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawinskiego 5A, Warsaw, Poland.|Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawinskiego 5A, Warsaw, Poland.|Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawinskiego 5A, Warsaw, Poland.', 'Poland|Poland|Poland|Poland', 11, 'Anti-Bacterial Agents|Bacteria|Bacterial Proteins|Enterobacteriaceae|Evolution, Molecular|Genes, Essential', 'Journal Article', '10.1186/1471-2105-14-36', '23374913', 'One of the major issues in the fight against infectious diseases is the notable increase in multiple drug resistance in pathogenic species. For that reason, newly acquired high-throughput data on virulent microbial agents attract the attention of many researchers seeking potential new drug targets. Many approaches have been used to evaluate proteins from infectious pathogens, including, but not limited to, similarity analysis, reverse docking, statistical 3D structure analysis, machine learning, topological properties of interaction networks or a combination of the aforementioned methods. From a biological perspective, most essential proteins (knockout lethal for bacteria) or highly conserved proteins (broad spectrum activity) are potential drug targets. Ribosomal proteins comprise such an example. Many of them are well-known drug targets in bacteria. It is intuitive that we should learn from nature how to design good drugs. Firstly, known antibiotics are mainly originating from natural products of microorganisms targeting other microorganisms. Secondly, paleontological data suggests that antibiotics have been used by microorganisms for million years. Thus, we have hypothesized that good drug targets are evolutionary constrained and are subject of evolutionary selection. This means that mutations in such proteins are deleterious and removed by selection, which makes them less susceptible to random development of resistance. Analysis of the speed of evolution seems to be good approach to test this hypothesis.'),
(95429, 2013, 'bmc', 'A novel application of motion analysis for detecting stress responses in embryos at different stages of development', 'Tills, Oliver|Bitterli, Tabitha|Culverhouse, Phil|Spicer, John I|Rundle, Simon', 'Marine Biology and Ecology Research Centre, School of Marine Science and Engineering, Plymouth University, Plymouth, Devon PL4 8AA, UK. oliver.tills@plymouth.ac.uk|Marine Biology and Ecology Research Centre, School of Marine Science and Engineering, Plymouth University, Plymouth, Devon PL4 8AA, UK. oliver.tills@plymouth.ac.uk|Marine Biology and Ecology Research Centre, School of Marine Science and Engineering, Plymouth University, Plymouth, Devon PL4 8AA, UK. oliver.tills@plymouth.ac.uk|Marine Biology and Ecology Research Centre, School of Marine Science and Engineering, Plymouth University, Plymouth, Devon PL4 8AA, UK. oliver.tills@plymouth.ac.uk|Marine Biology and Ecology Research Centre, School of Marine Science and Engineering, Plymouth University, Plymouth, Devon PL4 8AA, UK. oliver.tills@plymouth.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 7, 'Animals|Embryonic Development|Ethanol|Motion|Optical Imaging|Salinity|Snails|Stress, Physiological|Xenopus laevis|Zebrafish', 'Journal Article', '10.1186/1471-2105-14-37', '23374982', 'Motion analysis is one of the tools available to biologists to extract biologically relevant information from image datasets and has been applied to a diverse range of organisms. The application of motion analysis during early development presents a challenge, as embryos often exhibit complex, subtle and diverse movement patterns. A method of motion analysis able to holistically quantify complex embryonic movements could be a powerful tool for fields such as toxicology and developmental biology to investigate whole organism stress responses. Here we assessed whether motion analysis could be used to distinguish the effects of stressors on three early developmental stages of each of three species: (i) the zebrafish Danio rerio (stages 19 h, 21.5 h and 33 h exposed to 1.5% ethanol and a salinity of 5), (ii) the African clawed toad Xenopus laevis (stages 24, 32 and 34 exposed to a salinity of 20), and iii) the pond snail Radix balthica (stages E3, E4, E6, E9 and E11 exposed to salinities of 5, 10 and 15). Image sequences were analysed using Sparse Optic Flow and the resultant frame-to-frame motion parameters were analysed using Discrete Fourier Transform to quantify the distribution of energy at different frequencies. This spectral frequency dataset was then used to construct a Bray-Curtis similarity matrix and differences in movement patterns between embryos in this matrix were tested for using ANOSIM.'),
(95430, 2013, 'bmc', 'FANTOM: Functional and taxonomic analysis of metagenomes', 'Sanli, Kemal|Karlsson, Fredrik H|Nookaew, Intawat|Nielsen, Jens', 'Department of Chemical and Biological Engineering, Chalmers University of Technology, Kemivägen 10, Gothenburg SE 412 96, Sweden.|Department of Chemical and Biological Engineering, Chalmers University of Technology, Kemivägen 10, Gothenburg SE 412 96, Sweden.|Department of Chemical and Biological Engineering, Chalmers University of Technology, Kemivägen 10, Gothenburg SE 412 96, Sweden.|Department of Chemical and Biological Engineering, Chalmers University of Technology, Kemivägen 10, Gothenburg SE 412 96, Sweden.', 'Sweden|Sweden|Sweden|Sweden', 30, 'Bacteria|Classification|Databases, Factual|Databases, Genetic|Humans|Inflammatory Bowel Diseases|Metagenome|Metagenomics|Obesity|Software', 'Journal Article', '10.1186/1471-2105-14-38', '23375020', 'Interpretation of quantitative metagenomics data is important for our understanding of ecosystem functioning and assessing differences between various environmental samples. There is a need for an easy to use tool to explore the often complex metagenomics data in taxonomic and functional context.'),
(95431, 2013, 'bmc', 'puma 30: improved uncertainty propagation methods for gene and transcript expression analysis', 'Liu, Xuejun|Gao, Zhenzhu|Zhang, Li|Rattray, Magnus', 'College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Yudao St., Nanjing 210016, China. xuejun.liu@nuaa.edu.cn|College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Yudao St., Nanjing 210016, China. xuejun.liu@nuaa.edu.cn|College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Yudao St., Nanjing 210016, China. xuejun.liu@nuaa.edu.cn|College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Yudao St., Nanjing 210016, China. xuejun.liu@nuaa.edu.cn', 'China|China|China|China', 8, 'Carcinoma, Squamous Cell|Cluster Analysis|Exons|Gene Expression Profiling|Genomics|Head and Neck Neoplasms|Humans|Oligonucleotide Array Sequence Analysis|RNA Isoforms|Software|Squamous Cell Carcinoma of Head and Neck|Uncertainty', 'Journal Article', '10.1186/1471-2105-14-39', '23379655', 'Microarrays have been a popular tool for gene expression profiling at genome-scale for over a decade due to the low cost, short turn-around time, excellent quantitative accuracy and ease of data generation. The Bioconductor package puma incorporates a suite of analysis methods for determining uncertainties from Affymetrix GeneChip data and propagating these uncertainties to downstream analysis. As isoform level expression profiling receives more and more interest within genomics in recent years, exon microarray technology offers an important tool to quantify expression level of the majority of exons and enables the possibility of measuring isoform level expression. However, puma does not include methods for the analysis of exon array data. Moreover, the current expression summarisation method for Affymetrix 3 GeneChip data suffers from instability for low expression genes. For the downstream analysis, the method for differential expression detection is computationally intensive and the original expression clustering method does not consider the variance across the replicated technical and biological measurements. It is therefore necessary to develop improved uncertainty propagation methods for gene and transcript expression analysis.'),
(95432, 2013, 'bmc', 'ACG: rapid inference of population history from recombining nucleotide sequences', 'OFallon, Brendan D', 'ARUP Labs, 500 Chipeta Way, Salt Lake City, UT 84108, USA. brendan.d.ofallon@aruplab.com', 'United States', 13, 'Bayes Theorem|Evolution, Molecular|Markov Chains|Models, Genetic|Monte Carlo Method|Pedigree|Population Density|Recombination, Genetic|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-14-40', '23379678', 'Reconstruction of population history from genetic data often requires Monte Carlo integration over the genealogy of the samples. Among tools that perform such computations, few are able to consider genetic histories including recombination events, precluding their use on most alignments of nuclear DNA. Explicit consideration of recombinations requires modeling the history of the sequences with an Ancestral Recombination Graph (ARG) in place of a simple tree, which presents significant computational challenges.'),
(95433, 2013, 'bmc', 'Elucidating the crosstalk mechanism between IFN-gamma and IL-6 via mathematical modelling', 'Qi, Yun-feng|Huang, Yan-xin|Wang, Hong-yan|Zhang, Yu|Bao, Yong-li|Sun, Lu-guo|Wu, Yin|Yu, Chun-lei|Song, Zhen-bo|Zheng, Li-hua|Sun, Ying|Wang, Guan-nan|Li, Yu-xin', 'National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China.|National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China.|National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China.|National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China.|National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China.|National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China.|National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China.|National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China.|National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China.|National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China.|National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China.|National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China.|National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China.', 'China|China|China|China|China|China|China|China|China|China|China|China|China', 10, 'Interferon-gamma|Interleukin-6|Janus Kinases|Models, Biological|STAT1 Transcription Factor|STAT3 Transcription Factor|Signal Transduction|Suppressor of Cytokine Signaling Proteins', 'Journal Article', '10.1186/1471-2105-14-41', '23384097', 'Interferon-gamma (IFN-gamma) and interleukin-6 (IL-6) are multifunctional cytokines that regulate immune responses, cell proliferation, and tumour development and progression, which frequently have functionally opposing roles. The cellular responses to both cytokines are activated via the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. During the past 10 years, the crosstalk mechanism between the IFN-gamma and IL-6 pathways has been studied widely and several biological hypotheses have been proposed, but the kinetics and detailed crosstalk mechanism remain unclear.'),
(95434, 2013, 'bmc', 'A new unsupervised gene clustering algorithm based on the integration of biological knowledge into expression data', 'Verbanck, Marie|Lê, Sébastien|Pagès, Jérôme', 'Applied Mathematics Department, Agrocampus Ouest, 65, rue de Saint-Brieuc, Rennes, France. marie.verbanck@agrocampus-ouest.fr|Applied Mathematics Department, Agrocampus Ouest, 65, rue de Saint-Brieuc, Rennes, France. marie.verbanck@agrocampus-ouest.fr|Applied Mathematics Department, Agrocampus Ouest, 65, rue de Saint-Brieuc, Rennes, France. marie.verbanck@agrocampus-ouest.fr', 'France|France|France', 12, 'Algorithms|Cluster Analysis|Gene Expression Profiling|Humans|Molecular Sequence Annotation', 'Journal Article', '10.1186/1471-2105-14-42', '23387364', 'Gene clustering algorithms are massively used by biologists when analysing omics data. Classical gene clustering strategies are based on the use of expression data only, directly as in Heatmaps, or indirectly as in clustering based on coexpression networks for instance. However, the classical strategies may not be sufficient to bring out all potential relationships amongst genes.'),
(95435, 2013, 'bmc', 'M-pick, a modularity-based method for OTU picking of 16S rRNA sequences', 'Wang, Xiaoyu|Yao, Jin|Sun, Yijun|Mai, Volker', 'Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, Emerging Pathogens Institute, University of Florida, Gainesville, FL 32610, USA.|Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, Emerging Pathogens Institute, University of Florida, Gainesville, FL 32610, USA.|Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, Emerging Pathogens Institute, University of Florida, Gainesville, FL 32610, USA.|Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, Emerging Pathogens Institute, University of Florida, Gainesville, FL 32610, USA.', 'United States|United States|United States|United States', 26, 'Algorithms|Classification|Cluster Analysis|Gastrointestinal Tract|Genetic Variation|Humans|RNA, Ribosomal, 16S|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-14-43', '23387433', 'Binning 16S rRNA sequences into operational taxonomic units (OTUs) is an initial crucial step in analyzing large sequence datasets generated to determine microbial community compositions in various environments including that of the human gut. Various methods have been developed, but most suffer from either inaccuracies or from being unable to handle millions of sequences generated in current studies. Furthermore, existing binning methods usually require a priori decisions regarding binning parameters such as a distance level for defining an OTU.'),
(95436, 2013, 'bmc', 'Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information', 'Panwar, Bharat|Gupta, Sudheer|Raghava, Gajendra P S', 'Bioinformatics Centre, Institute of Microbial Technology (CSIR), Chandigarh, India.|Bioinformatics Centre, Institute of Microbial Technology (CSIR), Chandigarh, India.|Bioinformatics Centre, Institute of Microbial Technology (CSIR), Chandigarh, India.', 'India|India|India', 15, 'Amino Acids|Binding Sites|Carrier Proteins|Evolution, Molecular|Ligands|Position-Specific Scoring Matrices|Pyridoxal Phosphate|Sequence Analysis, Protein|Support Vector Machine|Vitamin A|Vitamin B Complex|Vitamins', 'Journal Article', '10.1186/1471-2105-14-44', '23387468', 'The vitamins are important cofactors in various enzymatic-reactions. In past, many inhibitors have been designed against vitamin binding pockets in order to inhibit vitamin-protein interactions. Thus, it is important to identify vitamin interacting residues in a protein. It is possible to detect vitamin-binding pockets on a protein, if its tertiary structure is known. Unfortunately tertiary structures of limited proteins are available. Therefore, it is important to develop in-silico models for predicting vitamin interacting residues in protein from its primary structure.'),
(95437, 2013, 'bmc', 'Snpdat: easy and rapid annotation of results from de novo snp discovery projects for model and non-model organisms', 'Doran, Anthony G|Creevey, Christopher J', 'Teagasc Animal and Bioscience Research Department, Animal & Grassland Research and Innovation Centre, Teagasc, Grange, Dunsany, Co, Meath, Ireland.|Teagasc Animal and Bioscience Research Department, Animal & Grassland Research and Innovation Centre, Teagasc, Grange, Dunsany, Co, Meath, Ireland.', 'Ireland|Ireland', 21, 'Animals|Cattle|Databases, Nucleic Acid|Genome|High-Throughput Nucleotide Sequencing|Models, Animal|Molecular Sequence Annotation|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-14-45', '23390980', 'Single nucleotide polymorphisms (SNPs) are the most abundant genetic variant found in vertebrates and invertebrates. SNP discovery has become a highly automated, robust and relatively inexpensive process allowing the identification of many thousands of mutations for model and non-model organisms. Annotating large numbers of SNPs can be a difficult and complex process. Many tools available are optimised for use with organisms densely sampled for SNPs, such as humans. There are currently few tools available that are species non-specific or support non-model organism data.'),
(95438, 2013, 'bmc', 'Multiple consensus trees: a method to separate divergent genes', 'Guénoche, Alain', 'Institut de Mathématiques de Luminy, 163 Av, de Luminy, 13009 Marseille, France. guenoche@iml.univ-mrs.fr', 'France', 4, 'Algorithms|Escherichia coli|Evolution, Molecular|Genes|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-14-46', '23394478', 'It is generally admitted that the species tree cannot be inferred from the genetic sequences of a single gene because the evolution of different genes, and thus the gene tree topologies, may vary substantially. Gene trees can differ, for example, because of horizontal transfer events or because some of them correspond to paralogous instead of orthologous sequences. A variety of methods has been proposed to tackle the problem of the reconciliation of gene trees in order to reconstruct a species tree. When the taxa in all the trees are identical, the problem can be stated as a consensus tree problem.'),
(95439, 2013, 'bmc', 'A survey of protein interaction data and multigenic inherited disorders', 'Mora, Antonio|Michalickova, Katerina|Donaldson, Ian M', 'Department for Molecular Biosciences, University of Oslo, P.O. Box 1041 Blindern, 0316, Oslo, Norway.|Department for Molecular Biosciences, University of Oslo, P.O. Box 1041 Blindern, 0316, Oslo, Norway.|Department for Molecular Biosciences, University of Oslo, P.O. Box 1041 Blindern, 0316, Oslo, Norway.', 'Norway|Norway|Norway', 4, 'Algorithms|Databases, Genetic|Databases, Protein|Genetic Diseases, Inborn|Humans|Hyperglycinemia, Nonketotic|Liddle Syndrome|Multiprotein Complexes|Nephritis, Hereditary|Protein Interaction Mapping', 'Journal Article', '10.1186/1471-2105-14-47', '23398688', 'Multigenic diseases are often associated with protein complexes or interactions involved in the same pathway. We wanted to estimate to what extent this is true given a consolidated protein interaction data set. The study stresses data integration and data representation issues.'),
(95440, 2013, 'bmc', 'Semantic representation of monogenean haptoral Bar image annotation', 'Abu, Arpah|Susan, Lim Lee Hong|Sidhu, Amandeep Singh|Dhillon, Sarinder Kaur', 'Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia.|Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia.|Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia.|Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia.', 'Malaysia|Malaysia|Malaysia|Malaysia', 0, 'Animals|Fishes|Image Processing, Computer-Assisted|Knowledge Bases|Semantics|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-48', '23398696', 'Digitised monogenean images are usually stored in file system directories in an unstructured manner. In this paper we propose a semantic representation of these images in the form of a Monogenean Haptoral Bar Image (MHBI) ontology, which are annotated with taxonomic classification, diagnostic hard part and image properties. The data we used are basically of the monogenean species found in fish, thus we built a simple Fish ontology to demonstrate how the host (fish) ontology can be linked to the MHBI ontology. This will enable linking of information from the monogenean ontology to the host species found in the fish ontology without changing the underlying schema for either of the ontologies.'),
(95441, 2013, 'bmc', 'MultiAlign: a multiple LC-MS analysis tool for targeted omics analysis', 'LaMarche, Brian L|Crowell, Kevin L|Jaitly, Navdeep|Petyuk, Vladislav A|Shah, Anuj R|Polpitiya, Ashoka D|Sandoval, John D|Kiebel, Gary R|Monroe, Matthew E|Callister, Stephen J|Metz, Thomas O|Anderson, Gordon A|Smith, Richard D', 'Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Carcinoma, Hepatocellular|Chromatography, Liquid|Cluster Analysis|Humans|Liver Neoplasms|Mass Spectrometry|Metabolomics|Peptides|Proteome|Proteomics|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-14-49', '23398735', 'MultiAlign is a free software tool that aligns multiple liquid chromatography-mass spectrometry datasets to one another by clustering mass and chromatographic elution features across datasets. Applicable to both label-free proteomics and metabolomics comparative analyses, the software can be operated in several modes. For example, clustered features can be matched to a reference database to identify analytes, used to generate abundance profiles, linked to tandem mass spectra based on parent precursor masses, and culled for targeted liquid chromatography-tandem mass spectrometric analysis. MultiAlign is also capable of tandem mass spectral clustering to describe proteome structure and find similarity in subsequent sample runs.'),
(95442, 2013, 'bmc', 'Estimation of CpG coverage in whole methylome next-generation sequencing studies', 'van den Oord, Edwin J C G|Bukszar, Jozsef|Rudolf, Gábor|Nerella, Srilaxmi|McClay, Joseph L|Xie, Lin Y|Aberg, Karolina A', 'Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Virginia Commonwealth University, 1112 East Clay Street, P.O. Box 980533, Richmond, VA 23298, USA. ejvandenoord@vcu.edu|Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Virginia Commonwealth University, 1112 East Clay Street, P.O. Box 980533, Richmond, VA 23298, USA. ejvandenoord@vcu.edu|Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Virginia Commonwealth University, 1112 East Clay Street, P.O. Box 980533, Richmond, VA 23298, USA. ejvandenoord@vcu.edu|Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Virginia Commonwealth University, 1112 East Clay Street, P.O. Box 980533, Richmond, VA 23298, USA. ejvandenoord@vcu.edu|Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Virginia Commonwealth University, 1112 East Clay Street, P.O. Box 980533, Richmond, VA 23298, USA. ejvandenoord@vcu.edu|Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Virginia Commonwealth University, 1112 East Clay Street, P.O. Box 980533, Richmond, VA 23298, USA. ejvandenoord@vcu.edu|Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Virginia Commonwealth University, 1112 East Clay Street, P.O. Box 980533, Richmond, VA 23298, USA. ejvandenoord@vcu.edu', 'United States|United States|United States|United States|United States|United States|United States', 19, 'Animals|CpG Islands|DNA Methylation|High-Throughput Nucleotide Sequencing|Male|Mice|Mice, Inbred C57BL|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-14-50', '23398781', 'Methylation studies are a promising complement to genetic studies of DNA sequence. However, detailed prior biological knowledge is typically lacking, so methylome-wide association studies (MWAS) will be critical to detect disease relevant sites. A cost-effective approach involves the next-generation sequencing (NGS) of single-end libraries created from samples that are enriched for methylated DNA fragments. A limitation of single-end libraries is that the fragment size distribution is not observed. This hampers several aspects of the data analysis such as the calculation of enrichment measures that are based on the number of fragments covering the CpGs.'),
(95443, 2013, 'bmc', 'Discovery of novel biomarkers and phenotypes by semantic technologies', 'Trugenberger, Carlo A|Wälti, Christoph|Peregrim, David|Sharp, Mark E|Bureeva, Svetlana', 'InfoCodex AG, Semantic Technologies, Buchs (SG), Switzerland.|InfoCodex AG, Semantic Technologies, Buchs (SG), Switzerland.|InfoCodex AG, Semantic Technologies, Buchs (SG), Switzerland.|InfoCodex AG, Semantic Technologies, Buchs (SG), Switzerland.|InfoCodex AG, Semantic Technologies, Buchs (SG), Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 8, 'Biomarkers|Data Mining|Diabetes Insipidus|Diabetes Mellitus|Humans|Obesity|Phenotype|PubMed|Semantics|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-51', '23402646', 'Biomarkers and target-specific phenotypes are important to targeted drug design and individualized medicine, thus constituting an important aspect of modern pharmaceutical research and development. More and more, the discovery of relevant biomarkers is aided by in silico techniques based on applying data mining and computational chemistry on large molecular databases. However, there is an even larger source of valuable information available that can potentially be tapped for such discoveries: repositories constituted by research documents.'),
(95444, 2013, 'bmc', 'PREDIVAC: CD4+ T-cell epitope prediction for vaccine design that covers 95% of HLA class II DR protein diversity', 'Oyarzún, Patricio|Ellis, Jonathan J|Bodén, Mikael|Kobe, Boštjan', 'School of Chemistry and Molecular Biosciences, Institute for Molecular Bioscience and Australian Infectious Diseases Research Centre, University of Queensland, Brisbane, QLD 4072, Australia. patricio.oyarzun@uss.cl|School of Chemistry and Molecular Biosciences, Institute for Molecular Bioscience and Australian Infectious Diseases Research Centre, University of Queensland, Brisbane, QLD 4072, Australia. patricio.oyarzun@uss.cl|School of Chemistry and Molecular Biosciences, Institute for Molecular Bioscience and Australian Infectious Diseases Research Centre, University of Queensland, Brisbane, QLD 4072, Australia. patricio.oyarzun@uss.cl|School of Chemistry and Molecular Biosciences, Institute for Molecular Bioscience and Australian Infectious Diseases Research Centre, University of Queensland, Brisbane, QLD 4072, Australia. patricio.oyarzun@uss.cl', 'Australia|Australia|Australia|Australia', 0, 'Alleles|CD4-Positive T-Lymphocytes|Epitopes, T-Lymphocyte|HLA-DR Antigens|Humans|Immunodominant Epitopes|Peptides|Software|Vaccines', 'Journal Article', '10.1186/1471-2105-14-52', '23409948', 'CD4+ T-cell epitopes play a crucial role in eliciting vigorous protective immune responses during peptide (epitope)-based vaccination. The prediction of these epitopes focuses on the peptide binding process by MHC class II proteins. The ability to account for MHC class II polymorphism is critical for epitope-based vaccine design tools, as different allelic variants can have different peptide repertoires. In addition, the specificity of CD4+ T-cells is often directed to a very limited set of immunodominant peptides in pathogen proteins. The ability to predict what epitopes are most likely to dominate an immune response remains a challenge.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95445, 2013, 'bmc', 'STOP using just GO: a multi-ontology hypothesis generation tool for high throughput experimentation', 'Wittkop, Tobias|TerAvest, Emily|Evani, Uday S|Fleisch, K Mathew|Berman, Ari E|Powell, Corey|Shah, Nigam H|Mooney, Sean D', 'Buck Institute for Research on Aging, Novato, CA, USA.|Buck Institute for Research on Aging, Novato, CA, USA.|Buck Institute for Research on Aging, Novato, CA, USA.|Buck Institute for Research on Aging, Novato, CA, USA.|Buck Institute for Research on Aging, Novato, CA, USA.|Buck Institute for Research on Aging, Novato, CA, USA.|Buck Institute for Research on Aging, Novato, CA, USA.|Buck Institute for Research on Aging, Novato, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 11, 'Genes|Humans|Huntington Disease|Internet|Molecular Sequence Annotation|Parkinson Disease|Protein Interaction Mapping|Proteins|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-53', '23409969', 'Gene Ontology (GO) enrichment analysis remains one of the most common methods for hypothesis generation from high throughput datasets. However, we believe that researchers strive to test other hypotheses that fall outside of GO. Here, we developed and evaluated a tool for hypothesis generation from gene or protein lists using ontological concepts present in manually curated text that describes those genes and proteins.'),
(95446, 2013, 'bmc', 'Gimli: open source and high-performance biomedical name recognition', 'Campos, David|Matos, Sérgio|Oliveira, José Luís', 'IEETA/DETI, University of Aveiro, Campus Universitário de Santiago, Aveiro, Portugal. david.campos@ua.pt|IEETA/DETI, University of Aveiro, Campus Universitário de Santiago, Aveiro, Portugal. david.campos@ua.pt|IEETA/DETI, University of Aveiro, Campus Universitário de Santiago, Aveiro, Portugal. david.campos@ua.pt', 'Portugal|Portugal|Portugal', 61, 'Cell Line|DNA|Data Mining|Genes|Humans|Proteins|RNA|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-54', '23413997', 'Automatic recognition of biomedical names is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. In recent years, various solutions have been implemented to tackle this problem. However, limitations regarding system characteristics, customization and usability still hinder their wider application outside text mining research.'),
(95447, 2013, 'bmc', 'Optimal precursor ion selection for LC-MALDI MS/MS', 'Zerck, Alexandra|Nordhoff, Eckhard|Lehrach, Hans|Reinert, Knut', 'Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany. zerck@molgen.mpg.de|Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany. zerck@molgen.mpg.de|Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany. zerck@molgen.mpg.de|Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany. zerck@molgen.mpg.de', 'Germany|Germany|Germany|Germany', 3, 'Algorithms|Chromatography, Liquid|Humans|Ions|Peptides|Proteins|Proteomics|Sequence Analysis, Protein|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-14-56', '23418672', 'Liquid chromatography mass spectrometry (LC-MS) maps in shotgun proteomics are often too complex to select every detected peptide signal for fragmentation by tandem mass spectrometry (MS/MS). Standard methods for precursor ion selection, commonly based on data dependent acquisition, select highly abundant peptide signals in each spectrum. However, these approaches produce redundant information and are biased towards high-abundance proteins.'),
(95448, 2013, 'bmc', 'A computational method for detecting copy number variations using scale-space filtering', 'Lee, Jongkeun|Lee, Unjoo|Kim, Baeksop|Yoon, Jeehee', 'Cancer Genomics Branch and Research Institute and Hospital, National Cancer Center, Goyang, Korea.|Cancer Genomics Branch and Research Institute and Hospital, National Cancer Center, Goyang, Korea.|Cancer Genomics Branch and Research Institute and Hospital, National Cancer Center, Goyang, Korea.|Cancer Genomics Branch and Research Institute and Hospital, National Cancer Center, Goyang, Korea.', '-|-|-|-', 6, 'Computational Biology|DNA Copy Number Variations|Genome|HapMap Project|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-14-57', '23418726', 'As next-generation sequencing technology made rapid and cost-effective sequencing available, the importance of computational approaches in finding and analyzing copy number variations (CNVs) has been amplified. Furthermore, most genome projects need to accurately analyze sequences with fairly low-coverage read data. It is urgently needed to develop a method to detect the exact types and locations of CNVs from low coverage read data.'),
(95449, 2013, 'bmc', 'Prediction of a time-to-event trait using genome wide SNP data', 'Kim, Jinseog|Sohn, Insuk|Son, Dae-Soon|Kim, Dong Hwan|Ahn, Taejin|Jung, Sin-Ho', 'Department of Statistics and Information Science, Dongguk University, Gyeongju 780-714, Korea.|Department of Statistics and Information Science, Dongguk University, Gyeongju 780-714, Korea.|Department of Statistics and Information Science, Dongguk University, Gyeongju 780-714, Korea.|Department of Statistics and Information Science, Dongguk University, Gyeongju 780-714, Korea.|Department of Statistics and Information Science, Dongguk University, Gyeongju 780-714, Korea.|Department of Statistics and Information Science, Dongguk University, Gyeongju 780-714, Korea.', '-|-|-|-|-|-', 3, 'Algorithms|Genome-Wide Association Study|Genomics|Humans|Leukemia, Myeloid, Acute|Models, Genetic|Polymorphism, Single Nucleotide|Proportional Hazards Models', 'Evaluation Studies', '10.1186/1471-2105-14-58', '23418752', 'A popular objective of many high-throughput genome projects is to discover various genomic markers associated with traits and develop statistical models to predict traits of future patients based on marker values.'),
(95450, 2013, 'bmc', 'Predictive modeling of anti-malarial molecules inhibiting apicoplast formation', 'Jamal, Salma|Periwal, Vinita|Open Source Drug Discovery Consortium|Scaria, Vinod', 'CSIR Open Source Drug Discovery Unit, Anusandhan Bhavan, Delhi 110001, India.|CSIR Open Source Drug Discovery Unit, Anusandhan Bhavan, Delhi 110001, India.|CSIR Open Source Drug Discovery Unit, Anusandhan Bhavan, Delhi 110001, India.|CSIR Open Source Drug Discovery Unit, Anusandhan Bhavan, Delhi 110001, India.', 'India|India|India|India', 24, 'Algorithms|Antimalarials|Artificial Intelligence|Computer Simulation|Drug Discovery|High-Throughput Screening Assays|Plasmodium falciparum', 'Journal Article', '10.1186/1471-2105-14-55', '23419172', 'Malaria is a major healthcare problem worldwide resulting in an estimated 0.65 million deaths every year. It is caused by the members of the parasite genus Plasmodium. The current therapeutic options for malaria are limited to a few classes of molecules, and are fast shrinking due to the emergence of widespread resistance to drugs in the pathogen. The recent availability of high-throughput phenotypic screen datasets for antimalarial activity offers a possibility to create computational models for bioactivity based on chemical descriptors of molecules with potential to accelerate drug discovery for malaria.'),
(95451, 2013, 'bmc', 'Active subnetwork recovery with a mechanism-dependent scoring function, with application to angiogenesis and organogenesis studies', 'Lichtenstein, Ilana|Charleston, Michael A|Caetano, Tiberio S|Gamble, Jennifer R|Vadas, Mathew A', 'School of Information Technologies, University of Sydney, Sydney, NSW 2006, Australia. Ilana.Lichtenstein@nicta.com.au|School of Information Technologies, University of Sydney, Sydney, NSW 2006, Australia. Ilana.Lichtenstein@nicta.com.au|School of Information Technologies, University of Sydney, Sydney, NSW 2006, Australia. Ilana.Lichtenstein@nicta.com.au|School of Information Technologies, University of Sydney, Sydney, NSW 2006, Australia. Ilana.Lichtenstein@nicta.com.au|School of Information Technologies, University of Sydney, Sydney, NSW 2006, Australia. Ilana.Lichtenstein@nicta.com.au', 'Australia|Australia|Australia|Australia|Australia', 3, 'Algorithms|Animals|Gene Regulatory Networks|Mice|Models, Statistical|Neovascularization, Physiologic|Organogenesis|Protein Interaction Mapping|Signal Transduction', 'Journal Article', '10.1186/1471-2105-14-59', '23432934', 'The learning active subnetworks problem involves finding subnetworks of a bio-molecular network that are active in a particular condition. Many approaches integrate observation data (e.g., gene expression) with the network topology to find candidate subnetworks. Increasingly, pathway databases contain additional annotation information that can be mined to improve prediction accuracy, e.g., interaction mechanism (e.g., transcription, microRNA, cleavage) annotations. We introduce a mechanism-based approach to active subnetwork recovery which exploits such annotations. We suggest that neighboring interactions in a network tend to be co-activated in a way that depends on the correlation of their mechanism annotations. e.g., neighboring phosphorylation and de-phosphorylation interactions may be more likely to be co-activated than neighboring phosphorylation and covalent bonding interactions.'),
(95452, 2013, 'bmc', 'Genome sequence-based species delimitation with confidence intervals and improved distance functions', 'Meier-Kolthoff, Jan P|Auch, Alexander F|Klenk, Hans-Peter|Göker, Markus', 'Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany.|Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany.|Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany.|Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany.', 'Germany|Germany|Germany|Germany', 2036, 'Archaea|Bacteria|Confidence Intervals|DNA|Genomics|Models, Statistical|Nucleic Acid Hybridization|Phylogeny|Regression Analysis|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-14-60', '23432962', 'For the last 25 years species delimitation in prokaryotes (Archaea and Bacteria) was to a large extent based on DNA-DNA hybridization (DDH), a tedious lab procedure designed in the early 1970s that served its purpose astonishingly well in the absence of deciphered genome sequences. With the rapid progress in genome sequencing time has come to directly use the now available and easy to generate genome sequences for delimitation of species. GBDP (Genome Blast Distance Phylogeny) infers genome-to-genome distances between pairs of entirely or partially sequenced genomes, a digital, highly reliable estimator for the relatedness of genomes. Its application as an in-silico replacement for DDH was recently introduced. The main challenge in the implementation of such an application is to produce digital DDH values that must mimic the wet-lab DDH values as close as possible to ensure consistency in the Prokaryotic species concept.'),
(95453, 2013, 'bmc', 'ETHNOPRED: a novel machine learning method for accurate continental and sub-continental ancestry identification and population stratification correction', 'Hajiloo, Mohsen|Sapkota, Yadav|Mackey, John R|Robson, Paula|Greiner, Russell|Damaraju, Sambasivarao', 'Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada.|Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada.|Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada.|Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada.|Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada.|Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 6, 'African Continental Ancestry Group|Artificial Intelligence|Asian Continental Ancestry Group|Continental Population Groups|Decision Trees|Ethnic Groups|European Continental Ancestry Group|Gene Frequency|Genome-Wide Association Study|Genotype|HapMap Project|Humans|Phenotype|Polymorphism, Single Nucleotide|Principal Component Analysis', 'Journal Article', '10.1186/1471-2105-14-61', '23432980', 'Population stratification is a systematic difference in allele frequencies between subpopulations. This can lead to spurious association findings in the case-control genome wide association studies (GWASs) used to identify single nucleotide polymorphisms (SNPs) associated with disease-linked phenotypes. Methods such as self-declared ancestry, ancestry informative markers, genomic control, structured association, and principal component analysis are used to assess and correct population stratification but each has limitations. We provide an alternative technique to address population stratification.'),
(95454, 2013, 'bmc', 'ClusCo: clustering and comparison of protein models', 'Jamroz, Michal|Kolinski, Andrzej', 'Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland.|Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland.', 'Poland|Poland', 23, 'Algorithms|Cluster Analysis|Models, Molecular|Molecular Dynamics Simulation|Protein Conformation|Proteins|Software', 'Journal Article', '10.1186/1471-2105-14-62', '23433004', 'The development, optimization and validation of protein modeling methods require efficient tools for structural comparison. Frequently, a large number of models need to be compared with the target native structure. The main reason for the development of Clusco software was to create a high-throughput tool for all-versus-all comparison, because calculating similarity matrix is the one of the bottlenecks in the protein modeling pipeline.'),
(95455, 2013, 'bmc', 'Protein structure based prediction of catalytic residues', 'Fajardo, J Eduardo|Fiser, Andras', 'Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.|Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.', 'United States|United States', 20, 'Amino Acid Sequence|Amino Acids|Biocatalysis|Catalytic Domain|Computational Biology|Conserved Sequence|Databases, Protein|Enzymes|Models, Molecular|Neural Networks (Computer)', 'Journal Article', '10.1186/1471-2105-14-63', '23433045', 'Worldwide structural genomics projects continue to release new protein structures at an unprecedented pace, so far nearly 6000, but only about 60% of these proteins have any sort of functional annotation.'),
(95456, 2013, 'bmc', 'Improved shrunken centroid classifiers for high-dimensional class-imbalanced data', '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. rok.blagus@mf.uni-lj.si', 'Slovenia|Slovenia', 16, 'Algorithms|Breast Neoplasms|Classification|Female|Gene Expression Profiling|Humans', 'Journal Article', '10.1186/1471-2105-14-64', '23433084', 'PAM, a nearest shrunken centroid method (NSC), is a popular classification method for high-dimensional data. ALP and AHP are NSC algorithms that were proposed to improve upon PAM. The NSC methods base their classification rules on shrunken centroids, in practice the amount of shrinkage is estimated minimizing the overall cross-validated (CV) error rate.'),
(95457, 2013, 'bmc', 'FAVR (Filtering and Annotation of Variants that are Rare): methods to facilitate the analysis of rare germline genetic variants from massively parallel sequencing datasets', 'Pope, Bernard J|Nguyen-Dumont, Tú|Odefrey, Fabrice|Hammet, Fleur|Bell, Russell|Tao, Kayoko|Tavtigian, Sean V|Goldgar, David E|Lonie, Andrew|Southey, Melissa C|Park, Daniel J', 'Victorian Life Sciences Computation Initiative, The University of Melbourne, 187 Grattan Street Carlton, Melbourne, Victoria 3010, Australia.|Victorian Life Sciences Computation Initiative, The University of Melbourne, 187 Grattan Street Carlton, Melbourne, Victoria 3010, Australia.|Victorian Life Sciences Computation Initiative, The University of Melbourne, 187 Grattan Street Carlton, Melbourne, Victoria 3010, Australia.|Victorian Life Sciences Computation Initiative, The University of Melbourne, 187 Grattan Street Carlton, Melbourne, Victoria 3010, Australia.|Victorian Life Sciences Computation Initiative, The University of Melbourne, 187 Grattan Street Carlton, Melbourne, Victoria 3010, Australia.|Victorian Life Sciences Computation Initiative, The University of Melbourne, 187 Grattan Street Carlton, Melbourne, Victoria 3010, Australia.|Victorian Life Sciences Computation Initiative, The University of Melbourne, 187 Grattan Street Carlton, Melbourne, Victoria 3010, Australia.|Victorian Life Sciences Computation Initiative, The University of Melbourne, 187 Grattan Street Carlton, Melbourne, Victoria 3010, Australia.|Victorian Life Sciences Computation Initiative, The University of Melbourne, 187 Grattan Street Carlton, Melbourne, Victoria 3010, Australia.|Victorian Life Sciences Computation Initiative, The University of Melbourne, 187 Grattan Street Carlton, Melbourne, Victoria 3010, Australia.|Victorian Life Sciences Computation Initiative, The University of Melbourne, 187 Grattan Street Carlton, Melbourne, Victoria 3010, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|', 0, 'Breast Neoplasms|Exome|Female|Genetic Variation|High-Throughput Nucleotide Sequencing|Humans|Molecular Sequence Annotation|Phenotype|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-14-65', '23441864', 'Characterising genetic diversity through the analysis of massively parallel sequencing (MPS) data offers enormous potential to significantly improve our understanding of the genetic basis for observed phenotypes, including predisposition to and progression of complex human disease. Great challenges remain in resolving genetic variants that are genuine from the millions of artefactual signals.'),
(95458, 2013, 'bmc', 'Local search for the generalized tree alignment problem', 'Varón, Andrés|Wheeler, Ward C', 'Division of Invertebrate Zoology, American Museum of Natural History, New York, NY 10024, USA.|Division of Invertebrate Zoology, American Museum of Natural History, New York, NY 10024, USA.', 'United States|United States', 0, 'Algorithms|Phylogeny|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-14-66', '23441880', 'A phylogeny postulates shared ancestry relationships among organisms in the form of a binary tree. Phylogenies attempt to answer an important question posed in biology: what are the ancestor-descendent relationships between organisms? At the core of every biological problem lies a phylogenetic component. The patterns that can be observed in nature are the product of complex interactions, constrained by the template that our ancestors provide. The problem of simultaneous tree and alignment estimation under Maximum Parsimony is known in combinatorial optimization as the Generalized Tree Alignment Problem (GTAP). The GTAP is the Steiner Tree Problem for the sequence edit distance. Like many biologically interesting problems, the GTAP is NP-Hard. Typically the Steiner Tree is presented under the Manhattan or the Hamming distances.'),
(95459, 2013, 'bmc', 'A hybrid short read mapping accelerator', 'Chen, Yupeng|Schmidt, Bertil|Maskell, Douglas L', 'School of Computer Engineering, Nanyang Technological University, Singapore, Singapore. chen0511@e.ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore, Singapore. chen0511@e.ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore, Singapore. chen0511@e.ntu.edu.sg', 'Singapore|Singapore|Singapore', 0, 'Algorithms|Chromosome Mapping|Genome|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-14-67', '23441908', 'The rapid growth of short read datasets poses a new challenge to the short read mapping problem in terms of sensitivity and execution speed. Existing methods often use a restrictive error model for computing the alignments to improve speed, whereas more flexible error models are generally too slow for large-scale applications. A number of short read mapping software tools have been proposed. However, designs based on hardware are relatively rare. Field programmable gate arrays (FPGAs) have been successfully used in a number of specific application areas, such as the DSP and communications domains due to their outstanding parallel data processing capabilities, making them a competitive platform to solve problems that are inherently parallel.'),
(95460, 2013, 'bmc', 'Predictability of gene ontology slim-terms from primary structure information in Embryophyta plant proteins', 'Jaramillo-Garzón, Jorge Alberto|Gallardo-Chacón, Joan Josep|Castellanos-Domínguez, César Germán|Perera-Lluna, Alexandre', 'Departamento de Ingeniería Eléctrica, Electrónica y Computación, Universidad Nacional de Colombia sede Manizales, Campus La Nubia, Km 7 Vía al Magdalena, Manizales-Caldas, Colombia. jajaramillog@unal.edu.co|Departamento de Ingeniería Eléctrica, Electrónica y Computación, Universidad Nacional de Colombia sede Manizales, Campus La Nubia, Km 7 Vía al Magdalena, Manizales-Caldas, Colombia. jajaramillog@unal.edu.co|Departamento de Ingeniería Eléctrica, Electrónica y Computación, Universidad Nacional de Colombia sede Manizales, Campus La Nubia, Km 7 Vía al Magdalena, Manizales-Caldas, Colombia. jajaramillog@unal.edu.co|Departamento de Ingeniería Eléctrica, Electrónica y Computación, Universidad Nacional de Colombia sede Manizales, Campus La Nubia, Km 7 Vía al Magdalena, Manizales-Caldas, Colombia. jajaramillog@unal.edu.co', 'Colombia|Colombia|Colombia|Colombia', 9, 'Amino Acid Sequence|Databases, Protein|Embryophyta|Genes, Plant|Molecular Sequence Annotation|Plant Proteins|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-68', '23441934', 'Proteins are the key elements on the path from genetic information to the development of life. The roles played by the different proteins are difficult to uncover experimentally as this process involves complex procedures such as genetic modifications, injection of fluorescent proteins, gene knock-out methods and others. The knowledge learned from each protein is usually annotated in databases through different methods such as the proposed by The Gene Ontology (GO) consortium. Different methods have been proposed in order to predict GO terms from primary structure information, but very few are available for large-scale functional annotation of plants, and reported success rates are much less than the reported by other non-plant predictors. This paper explores the predictability of GO annotations on proteins belonging to the Embryophyta group from a set of features extracted solely from their primary amino acid sequence.'),
(95461, 2013, 'bmc', 'BOND: Basic OligoNucleotide Design', 'Ilie, Lucian|Mohamadi, Hamid|Golding, Geoffrey Brian|Smyth, William F', 'Department of Computer Science, University of Western Ontario, London, ON, Canada. ilie@csd.uwo.ca|Department of Computer Science, University of Western Ontario, London, ON, Canada. ilie@csd.uwo.ca|Department of Computer Science, University of Western Ontario, London, ON, Canada. ilie@csd.uwo.ca|Department of Computer Science, University of Western Ontario, London, ON, Canada. ilie@csd.uwo.ca', 'Canada|Canada|Canada|Canada', 0, 'Algorithms|Genes|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes|Oligonucleotides|Software', 'Journal Article', '10.1186/1471-2105-14-69', '23444904', 'DNA microarrays have become ubiquitous in biological and medical research. The most difficult problem that needs to be solved is the design of DNA oligonucleotides that (i) are highly specific, that is, bind only to the intended target, (ii) cover the highest possible number of genes, that is, all genes that allow such unique regions, and (iii) are computed fast. None of the existing programs meet all these criteria.'),
(95462, 2013, 'bmc', 'A novel method for cross-species gene expression analysis', 'Kristiansson, Erik|Österlund, Tobias|Gunnarsson, Lina|Arne, Gabriella|Larsson, D G Joakim|Nerman, Olle', 'Department of Mathematical Statistics, Chalmers University of Technology/University of Gothenburg, Gothenburg, Sweden. erik.kristiansson@chalmers.se|Department of Mathematical Statistics, Chalmers University of Technology/University of Gothenburg, Gothenburg, Sweden. erik.kristiansson@chalmers.se|Department of Mathematical Statistics, Chalmers University of Technology/University of Gothenburg, Gothenburg, Sweden. erik.kristiansson@chalmers.se|Department of Mathematical Statistics, Chalmers University of Technology/University of Gothenburg, Gothenburg, Sweden. erik.kristiansson@chalmers.se|Department of Mathematical Statistics, Chalmers University of Technology/University of Gothenburg, Gothenburg, Sweden. erik.kristiansson@chalmers.se|Department of Mathematical Statistics, Chalmers University of Technology/University of Gothenburg, Gothenburg, Sweden. erik.kristiansson@chalmers.se', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Animals|Data Interpretation, Statistical|Estrogens|Evolution, Molecular|Fishes|Gene Expression Profiling|Heat-Shock Response|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-14-70', '23444967', 'Analysis of gene expression from different species is a powerful way to identify evolutionarily conserved transcriptional responses. However, due to evolutionary events such as gene duplication, there is no one-to-one correspondence between genes from different species which makes comparison of their expression profiles complex.'),
(95463, 2013, 'bmc', 'Evaluating the use of different positional strategies for sentence selection in biomedical literature summarization', 'Plaza, Laura|Carrillo-de-Albornoz, Jorge', 'Universidad Autxc3xb3noma de Madrid, C/Francisco Tomxc3xa1s y Valiente 11, 28049 Madrid, Spain. laura.plaza@uam.es|Universidad Autxc3xb3noma de Madrid, C/Francisco Tomxc3xa1s y Valiente 11, 28049 Madrid, Spain. laura.plaza@uam.es', 'Spain|Spain', 12, 'Abstracting and Indexing as Topic', 'Evaluation Studies', '10.1186/1471-2105-14-71', '23445074', 'The position of a sentence in a document has been traditionally considered an indicator of the relevance of the sentence, and therefore it is frequently used by automatic summarization systems as an attribute for sentence selection. Sentences close to the beginning of the document are supposed to deal with the main topic and thus are selected for the summary. This criterion has shown to be very effective when summarizing some types of documents, such as news items. However, this property is not likely to be found in other types of documents, such as scientific articles, where other positional criteria may be preferred. The purpose of the present work is to study the utility of different positional strategies for biomedical literature summarization.'),
(95464, 2013, 'bmc', 'Identification of germ cell-specific genes in mammalian meiotic prophase', 'Li, Yunfei|Ray, Debjit|Ye, Ping', 'School of Molecular Biosciences, Washington State University, PO Box 647520, Pullman, WA 99164, USA.|School of Molecular Biosciences, Washington State University, PO Box 647520, Pullman, WA 99164, USA.|School of Molecular Biosciences, Washington State University, PO Box 647520, Pullman, WA 99164, USA.', 'United States|United States|United States', 0, 'Animals|Artificial Intelligence|Female|Gene Expression Profiling|Gene Expression Regulation|Germ Cells|Male|Meiotic Prophase I|Mice|MicroRNAs|Oocytes|Spermatocytes|Support Vector Machine|Transcription Factors', 'Journal Article', '10.1186/1471-2105-14-72', '23445120', 'Mammalian germ cells undergo meiosis to produce sperm or eggs, haploid cells that are primed to meet and propagate life. Meiosis is initiated by retinoic acid and meiotic prophase is the first and most complex stage of meiosis when homologous chromosomes pair to exchange genetic information. Errors in meiosis can lead to infertility and birth defects. However, despite the importance of this process, germ cell-specific gene expression patterns during meiosis remain undefined due to difficulty in obtaining pure germ cell samples, especially in females, where prophase occurs in the embryonic ovary. Indeed, mixed signals from both germ cells and somatic cells complicate gonadal transcriptome studies.'),
(95465, 2013, 'bmc', 'search GenBank: interactive orchestration and ad-hoc choreography of Web services in the exploration of the biomedical resources of the National Center For Biotechnology Information', 'Mrozek, Dariusz|Maxc5x82ysiak-Mrozek, Boxc5xbcena|Sixc4x85xc5xbcnik, Artur', 'Institute of Informatics, Silesian University of Technology, Akademicka 16, Gliwice, 44-100, Poland. dariusz.mrozek@polsl.pl|Institute of Informatics, Silesian University of Technology, Akademicka 16, Gliwice, 44-100, Poland. dariusz.mrozek@polsl.pl|Institute of Informatics, Silesian University of Technology, Akademicka 16, Gliwice, 44-100, Poland. dariusz.mrozek@polsl.pl', 'Poland|Poland|Poland', 0, 'Data Mining|Databases, Nucleic Acid|Internet|National Library of Medicine (U.S.)|Search Engine|Software|United States', 'Journal Article', '10.1186/1471-2105-14-73', '23452691', 'Due to the growing number of biomedical entries in data repositories of the National Center for Biotechnology Information (NCBI), it is difficult to collect, manage and process all of these entries in one place by third-party software developers without significant investment in hardware and software infrastructure, its maintenance and administration. Web services allow development of software applications that integrate in one place the functionality and processing logic of distributed software components, without integrating the components themselves and without integrating the resources to which they have access. This is achieved by appropriate orchestration or choreography of available Web services and their shared functions. After the successful application of Web services in the business sector, this technology can now be used to build composite software tools that are oriented towards biomedical data processing.'),
(95466, 2013, 'bmc', 'MethylPCA: a toolkit to control for confounders in methylome-wide association studies', 'Chen, Wenan|Gao, Guimin|Nerella, Srilaxmi|Hultman, Christina M|Magnusson, Patrik K E|Sullivan, Patrick F|Aberg, Karolina A|van den Oord, Edwin J C G', 'Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.|Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.|Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.|Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.|Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.|Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.|Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.|Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 14, 'Algorithms|DNA Methylation|Genetic Association Studies|Genome, Human|Humans|Principal Component Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-14-74', '23452721', 'In methylome-wide association studies (MWAS) there are many possible differences between cases and controls (e.g. related to life style, diet, and medication use) that may affect the methylome and produce false positive findings. An effective approach to control for these confounders is to first capture the major sources of variation in the methylation data and then regress out these components in the association analyses. This approach is, however, computationally very challenging due to the extremely large number of methylation sites in the human genome.'),
(95467, 2013, 'bmc', 'virtualArray: a R/bioconductor package to merge raw data from different microarray platforms', 'Heider, Andreas|Alt, Rüdiger', 'Translational Centre for Regenerative Medicine Leipzig, University of Leipzig, Semmelweisstr. 14, Leipzig 04103, Germany. aheider@trm.uni-leipzig.de|Translational Centre for Regenerative Medicine Leipzig, University of Leipzig, Semmelweisstr. 14, Leipzig 04103, Germany. aheider@trm.uni-leipzig.de', 'Germany|Germany', 0, 'Databases, Genetic|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-14-75', '23452776', 'Microarrays have become a routine tool to address diverse biological questions. Therefore, different types and generations of microarrays have been produced by several manufacturers over time. Likewise, the diversity of raw data deposited in public databases such as NCBI GEO or EBI ArrayExpress has grown enormously.This has resulted in databases currently containing several hundred thousand microarray samples clustered by different species, manufacturers and chip generations. While one of the original goals of these databases was to make the data available to other researchers for independent analysis and, where appropriate, integration with their own data, current software implementations could not provide that feature.Only those data sets generated on the same chip platform can be readily combined and even here there are batch effects to be taken care of. A straightforward approach to deal with multiple chip types and batch effects has been missing.The software presented here was designed to solve both of these problems in a convenient and user friendly way.'),
(95468, 2013, 'bmc', 'BioTile, a Perl based tool for the identification of differentially enriched regions in tiling microarray data', 'Guintivano, Jerry|Arad, Michal|Tamashiro, Kellie L K|Gould, Todd D|Kaminsky, Zachary A', 'Mood Disorders Center, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.|Mood Disorders Center, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.|Mood Disorders Center, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.|Mood Disorders Center, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.|Mood Disorders Center, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.', 'United States|United States|United States|United States|United States', 3, 'Algorithms|DNA Methylation|Genome|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-14-76', '23452827', 'Genome-wide tiling array experiments are increasingly used for the analysis of DNA methylation. Because DNA methylation patterns are tissue and cell type specific, the detection of differentially methylated regions (DMRs) with small effect size is a necessary feature of tiling microarray peak finding algorithms, as cellular heterogeneity within a studied tissue may lead to a dilution of the phenotypically relevant effects. Additionally, the ability to detect short length DMRs is necessary as biologically relevant signal may occur in focused regions throughout the genome.'),
(95469, 2013, 'bmc', 'Prediction of B-cell epitopes using evolutionary information and propensity scales', 'Lin, Scott Yi-Heng|Cheng, Cheng-Wei|Su, Emily Chia-Yu', 'School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.|School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.|School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.', '-|-|-', 3, 'Algorithms|Amino Acids|Area Under Curve|Computational Biology|Epitopes, B-Lymphocyte|Evolution, Molecular|Position-Specific Scoring Matrices|Propensity Score|Sensitivity and Specificity|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-14-76', '23484214', 'Development of computational tools that can accurately predict presence and location of B-cell epitopes on pathogenic proteins has a valuable application to the field of vaccinology. Because of the highly variable yet enigmatic nature of B-cell epitopes, their prediction presents a great challenge to computational immunologists.'),
(95470, 2013, 'bmc', 'An inferential framework for biological network hypothesis tests', 'Yates, Phillip D|Mukhopadhyay, Nitai D', 'Pfizer Global Research and Development, Groton, CT, USA. pyatesgene@gmail.com|Pfizer Global Research and Development, Groton, CT, USA. pyatesgene@gmail.com', 'United States|United States', 0, 'Algorithms|Diabetes Mellitus, Type 2|Female|Gene Regulatory Networks|Humans|Models, Biological|Ovarian Neoplasms|Systems Biology|Transcriptome', 'Journal Article', '10.1186/1471-2105-14-94', '23496778', 'Networks are ubiquitous in modern cell biology and physiology. A large literature exists for inferring/proposing biological pathways/networks using statistical or machine learning algorithms. Despite these advances a formal testing procedure for analyzing network-level observations is in need of further development. Comparing the behaviour of a pharmacologically altered pathway to its canonical form is an example of a salient one-sample comparison. Locating which pathways differentiate disease from no-disease phenotype may be recast as a two-sample network inference problem.'),
(95471, 2013, 'bmc', 'CGAP: a new comprehensive platform for the comparative analysis of chloroplast genomes', 'Cheng, Jinkui|Zeng, Xu|Ren, Guomin|Liu, Zhihua', 'Department of Computational Biology and Bioinformatics, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100193, China.|Department of Computational Biology and Bioinformatics, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100193, China.|Department of Computational Biology and Bioinformatics, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100193, China.|Department of Computational Biology and Bioinformatics, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100193, China.', 'China|China|China|China', 21, 'Genome, Chloroplast|Internet|Molecular Sequence Annotation|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-14-95', '23496817', 'Chloroplast is an essential organelle in plants which contains independent genome. Chloroplast genomes have been widely used for plant phylogenetic inference recently. The number of complete chloroplast genomes increases rapidly with the development of various genome sequencing projects. However, no comprehensive platform or tool has been developed for the comparative and phylogenetic analysis of chloroplast genomes. Thus, we constructed a comprehensive platform for the comparative and phylogenetic analysis of complete chloroplast genomes which was named as chloroplast genome analysis platform (CGAP).'),
(95472, 2013, 'bmc', 'Mining for class-specific motifs in protein sequence classification', 'Srinivasan, Satish M|Vural, Suleyman|King, Brian R|Guda, Chittibabu', 'Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198-5145, USA.|Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198-5145, USA.|Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198-5145, USA.|Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198-5145, USA.', 'United States|United States|United States|United States', 14, 'Amino Acid Motifs|Amino Acid Substitution|Data Mining|Enzymes|Proteins|Sequence Analysis, Protein', 'Evaluation Studies', '10.1186/1471-2105-14-96', '23496846', 'In protein sequence classification, identification of the sequence motifs or n-grams that can precisely discriminate between classes is a more interesting scientific question than the classification itself. A number of classification methods aim at accurate classification but fail to explain which sequence features indeed contribute to the accuracy. We hypothesize that sequences in lower denominations (n-grams) can be used to explore the sequence landscape and to identify class-specific motifs that discriminate between classes during classification. Discriminative n-grams are short peptide sequences that are highly frequent in one class but are either minimally present or absent in other classes. In this study, we present a new substitution-based scoring function for identifying discriminative n-grams that are highly specific to a class.'),
(95473, 2013, 'bmc', 'Estimate hidden dynamic profiles of siRNA effect on apoptosis', 'Ueda, Takanori|Tominaga, Daisuke|Araki, Noriko|Yoshikawa, Tomohiro', 'Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198-5145, USA.|Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198-5145, USA.|Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198-5145, USA.|Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198-5145, USA.', 'United States|United States|United States|United States', 2, 'Algorithms|Apoptosis|HeLa Cells|Humans|Models, Biological|RNA Interference|RNA, Small Interfering', 'Journal Article', '10.1186/1471-2105-14-97', '23496896', 'For the representation of RNA interference (RNAi) dynamics, several mathematical models based on systems of ordinary differential equations (ODEs) have been proposed. These models consist of equations for each molecule that are involved in RNAi phenomena. Therefore, many real-value parameters must be optimized to identify the models. They also have many hidden variables, which cannot be observed directly through experimentation. Calculation of the values of the hidden variables is generally very difficult, if not impossible in some special cases. Identification of the ODE models is also quite difficult.'),
(95474, 2013, 'bmc', 'NightShift: NMR shift inference by general hybrid model training--a framework for NMR chemical shift prediction', 'Dehof, Anna Katharina|Loew, Simon|Lenhof, Hans-Peter|Hildebrandt, Andreas', 'Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany.|Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany.|Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany.|Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany.', 'Germany|Germany|Germany|Germany', 1, 'Algorithms|Databases, Protein|Models, Statistical|Nuclear Magnetic Resonance, Biomolecular|Proteins|Software', 'Journal Article', '10.1186/1471-2105-14-98', '23496927', 'NMR chemical shift prediction plays an important role in various applications in computational biology. Among others, structure determination, structure optimization, and the scoring of docking results can profit from efficient and accurate chemical shift estimation from a three-dimensional model.A variety of NMR chemical shift prediction approaches have been presented in the past, but nearly all of these rely on laborious manual data set preparation and the training itself is not automatized, making retraining the model, e.g., if new data is made available, or testing new models a time-consuming manual chore.'),
(95475, 2013, 'bmc', 'GenePainter: a fast tool for aligning gene structures of eukaryotic protein families, visualizing the alignments and mapping gene structures onto protein structures', 'Hammesfahr, Björn|Odronitz, Florian|Mühlhausen, Stefanie|Waack, Stephan|Kollmar, Martin', 'Department of NMR-based Structural Biology, Max-Planck-Institute for Biophysical Chemistry, Am Fassberg 11, Göttingen, 37077, Germany.|Department of NMR-based Structural Biology, Max-Planck-Institute for Biophysical Chemistry, Am Fassberg 11, Göttingen, 37077, Germany.|Department of NMR-based Structural Biology, Max-Planck-Institute for Biophysical Chemistry, Am Fassberg 11, Göttingen, 37077, Germany.|Department of NMR-based Structural Biology, Max-Planck-Institute for Biophysical Chemistry, Am Fassberg 11, Göttingen, 37077, Germany.|Department of NMR-based Structural Biology, Max-Planck-Institute for Biophysical Chemistry, Am Fassberg 11, Göttingen, 37077, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Chromosome Mapping|Computer Graphics|Eukaryota|Evolution, Molecular|Exons|Introns|Models, Molecular|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-14-77', '23496949', 'All sequenced eukaryotic genomes have been shown to possess at least a few introns. This includes those unicellular organisms, which were previously suspected to be intron-less. Therefore, gene splicing must have been present at least in the last common ancestor of the eukaryotes. To explain the evolution of introns, basically two mutually exclusive concepts have been developed. The introns-early hypothesis says that already the very first protein-coding genes contained introns while the introns-late concept asserts that eukaryotic genes gained introns only after the emergence of the eukaryotic lineage. A very important aspect in this respect is the conservation of intron positions within homologous genes of different taxa.'),
(95476, 2013, 'bmc', 'AUREA: an open-source software system for accurate and user-friendly identification of relative expression molecular signatures', 'Earls, John C|Eddy, James A|Funk, Cory C|Ko, Younhee|Magis, Andrew T|Price, Nathan D', 'Institute for Systems Biology, Seattle, WA, USA.|Institute for Systems Biology, Seattle, WA, USA.|Institute for Systems Biology, Seattle, WA, USA.|Institute for Systems Biology, Seattle, WA, USA.|Institute for Systems Biology, Seattle, WA, USA.|Institute for Systems Biology, Seattle, WA, USA.', 'United States|United States|United States|United States|United States|United States', 6, 'Algorithms|Databases, Genetic|Software|Transcriptome|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-14-78', '23496976', 'Public databases such as the NCBI Gene Expression Omnibus contain extensive and exponentially increasing amounts of high-throughput data that can be applied to molecular phenotype characterization. Collectively, these data can be analyzed for such purposes as disease diagnosis or phenotype classification. One family of algorithms that has proven useful for disease classification is based on relative expression analysis and includes the Top-Scoring Pair (TSP), k-Top-Scoring Pairs (k-TSP), Top-Scoring Triplet (TST) and Differential Rank Conservation (DIRAC) algorithms. These relative expression analysis algorithms hold significant advantages for identifying interpretable molecular signatures for disease classification, and have been implemented previously on a variety of computational platforms with varying degrees of usability. To increase the user-base and maximize the utility of these methods, we developed the program AUREA (Adaptive Unified Relative Expression Analyzer)-a cross-platform tool that has a consistent application programming interface (API), an easy-to-use graphical user interface (GUI), fast running times and automated parameter discovery.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95477, 2013, 'bmc', 'Comparative analysis of microbiome measurement platforms using latent variable structural equation modeling', 'Wu, Xiao|Berkow, Kathryn|Frank, Daniel N|Li, Ellen|Gulati, Ajay S|Zhu, Wei', 'Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA. xiaowu1@ic.sunysb.edu|Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA. xiaowu1@ic.sunysb.edu|Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA. xiaowu1@ic.sunysb.edu|Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA. xiaowu1@ic.sunysb.edu|Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA. xiaowu1@ic.sunysb.edu|Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA. xiaowu1@ic.sunysb.edu', 'United States|United States|United States|United States|United States|United States', 8, 'Analysis of Variance|Bacteria|Humans|Inflammatory Bowel Diseases|Metagenome|Models, Statistical|Phylogeny|RNA, Ribosomal, 16S|Sequence Analysis, DNA|Statistics, Nonparametric', 'Journal Article', '10.1186/1471-2105-14-79', '23497007', 'Culture-independent phylogenetic analysis of 16S ribosomal RNA (rRNA) gene sequences has emerged as an incisive method of profiling bacteria present in a specimen. Currently, multiple techniques are available to enumerate the abundance of bacterial taxa in specimens, including the Sanger sequencing, the next generation pyrosequencing, microarrays, quantitative PCR, and the rapidly emerging, third generation sequencing, and fourth generation sequencing methods. An efficient statistical tool is in urgent need for the followings tasks: (1) to compare the agreement between these measurement platforms, (2) to select the most reliable platform(s), and (3) to combine different platforms of complementary strengths, for a unified analysis.'),
(95478, 2013, 'bmc', 'Prediction of heterogeneous differential genes by detecting outliers to a Gaussian tight cluster', 'Yang, Zihua|Yang, Zhengrong', 'Wolfson Institute for Preventive Medicine, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. z.h.yang@qmul.ac.uk|Wolfson Institute for Preventive Medicine, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. z.h.yang@qmul.ac.uk', 'United Kingdom|United Kingdom', 3, 'Algorithms|Breast Neoplasms|Female|Gene Expression|Humans|Normal Distribution', 'Journal Article', '10.1186/1471-2105-14-81', '23497043', 'Heterogeneously and differentially expressed genes (hDEG) are a common phenomenon due to bio-logical diversity. A hDEG is often observed in gene expression experiments (with two experimental conditions) where it is highly expressed in a few experimental samples, or in drug trial experiments for cancer studies with drug resistance heterogeneity among the disease group. These highly expressed samples are called outliers. Accurate detection of outliers among hDEGs is then desirable for dis- ease diagnosis and effective drug design. The standard approach for detecting hDEGs is to choose the appropriate subset of outliers to represent the experimental group. However, existing methods typically overlook hDEGs with very few outliers.'),
(95479, 2013, 'bmc', 'Learning a peptide-protein binding affinity predictor with kernel ridge regression', 'Giguxc3xa8re, Sxc3xa9bastien|Marchand, Mario|Laviolette, Franxc3xa7ois|Drouin, Alexandre|Corbeil, Jacques', 'Department of Computer Science and Software Engineering, Universitxc3xa9 Laval, Quxc3xa9bec, Canada. sebastien.giguere.8@ulaval.ca|Department of Computer Science and Software Engineering, Universitxc3xa9 Laval, Quxc3xa9bec, Canada. sebastien.giguere.8@ulaval.ca|Department of Computer Science and Software Engineering, Universitxc3xa9 Laval, Quxc3xa9bec, Canada. sebastien.giguere.8@ulaval.ca|Department of Computer Science and Software Engineering, Universitxc3xa9 Laval, Quxc3xa9bec, Canada. sebastien.giguere.8@ulaval.ca|Department of Computer Science and Software Engineering, Universitxc3xa9 Laval, Quxc3xa9bec, Canada. sebastien.giguere.8@ulaval.ca', 'Canada|Canada|Canada|Canada|Canada', 27, 'Algorithms|Alleles|Artificial Intelligence|Binding Sites|Computer Simulation|Histocompatibility Antigens Class II|Peptides|Protein Interaction Domains and Motifs|Protein Interaction Mapping', 'Journal Article', '10.1186/1471-2105-14-82', '23497081', 'The cellular function of a vast majority of proteins is performed through physical interactions with other biomolecules, which, most of the time, are other proteins. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interaction. They are thus an interesting class of therapeutics since they also display strong activity, high selectivity, low toxicity and few drug-drug interactions. Furthermore, predicting peptides that would bind to a specific MHC alleles would be of tremendous benefit to improve vaccine based therapy and possibly generate antibodies with greater affinity. Modern computational methods have the potential to accelerate and lower the cost of drug and vaccine discovery by selecting potential compounds for testing in silico prior to biological validation.'),
(95480, 2013, 'bmc', 'HuntMi: an efficient and taxon-specific approach in pre-miRNA identification', 'Gudyxc5x9b, Adam|Szczexc5x9bniak, Michaxc5x82 Wojciech|Sikora, Marek|Makaxc5x82owska, Izabela', 'Institute of Informatics, Faculty Of Automatic Control, Electronics And Computer Science, Silesian University of Technology, Gliwice, Poland. adam.gudys@polsl.pl|Institute of Informatics, Faculty Of Automatic Control, Electronics And Computer Science, Silesian University of Technology, Gliwice, Poland. adam.gudys@polsl.pl|Institute of Informatics, Faculty Of Automatic Control, Electronics And Computer Science, Silesian University of Technology, Gliwice, Poland. adam.gudys@polsl.pl|Institute of Informatics, Faculty Of Automatic Control, Electronics And Computer Science, Silesian University of Technology, Gliwice, Poland. adam.gudys@polsl.pl', 'Poland|Poland|Poland|Poland', 0, 'Algorithms|Artificial Intelligence|MicroRNAs|RNA Precursors|Software', 'Journal Article', '10.1186/1471-2105-14-83', '23497112', 'Machine learning techniques are known to be a powerful way of distinguishing microRNA hairpins from pseudo hairpins and have been applied in a number of recognised miRNA search tools. However, many current methods based on machine learning suffer from some drawbacks, including not addressing the class imbalance problem properly. It may lead to overlearning the majority class and/or incorrect assessment of classification performance. Moreover, those tools are effective for a narrow range of species, usually the model ones. This study aims at improving performance of miRNA classification procedure, extending its usability and reducing computational time.'),
(95481, 2013, 'bmc', 'Bivariate segmentation of SNP-array data for allele-specific copy number analysis in tumour samples', 'Mosén-Ansorena, David|Aransay, Ana María', 'Genome Analysis Platform, CIC bioGUNE & CIBERehd, Technologic Park of Bizkaia, Building 502, 48160 Derio, Spain. dmosen.gn@cicbiogune.es|Genome Analysis Platform, CIC bioGUNE & CIBERehd, Technologic Park of Bizkaia, Building 502, 48160 Derio, Spain. dmosen.gn@cicbiogune.es', 'Spain|Spain', 2, 'Alleles|Gene Dosage|Genes, Neoplasm|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-14-84', '23497144', 'SNP arrays output two signals that reflect the total genomic copy number (LRR) and the allelic ratio (BAF), which in combination allow the characterisation of allele-specific copy numbers (ASCNs). While methods based on hidden Markov models (HMMs) have been extended from array comparative genomic hybridisation (aCGH) to jointly handle the two signals, only one method based on change-point detection, ASCAT, performs bivariate segmentation.'),
(95482, 2013, 'bmc', 'Make the most of your samples: Bayes factor estimators for high-dimensional models of sequence evolution', 'Baele, Guy|Lemey, Philippe|Vansteelandt, Stijn', 'Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium. guy.baele@rega.kuleuven.be|Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium. guy.baele@rega.kuleuven.be|Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium. guy.baele@rega.kuleuven.be', 'Belgium|Belgium|Belgium', 0, 'Algorithms|Animals|Bayes Theorem|Evolution, Molecular|Genes, rRNA|Humans|Likelihood Functions|Models, Genetic|Phylogeny|Pseudogenes|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-14-85', '23497171', 'Accurate model comparison requires extensive computation times, especially for parameter-rich models of sequence evolution. In the Bayesian framework, model selection is typically performed through the evaluation of a Bayes factor, the ratio of two marginal likelihoods (one for each model). Recently introduced techniques to estimate (log) marginal likelihoods, such as path sampling and stepping-stone sampling, offer increased accuracy over the traditional harmonic mean estimator at an increased computational cost. Most often, each models marginal likelihood will be estimated individually, which leads the resulting Bayes factor to suffer from errors associated with each of these independent estimation processes.'),
(95483, 2013, 'bmc', 'Age-adjusted nonparametric detection of differential DNA methylation with case-control designs', 'Huang, Hanwen|Chen, Zhongxue|Huang, Xudong', 'Department of Epidemiology and Biostatistics, University of Georgia, Athens, GA 30605, USA.|Department of Epidemiology and Biostatistics, University of Georgia, Athens, GA 30605, USA.|Department of Epidemiology and Biostatistics, University of Georgia, Athens, GA 30605, USA.', 'Georgia|Georgia|Georgia', 16, 'Age Factors|Aged|Case-Control Studies|DNA Methylation|Female|Humans|Middle Aged|Ovarian Neoplasms|Statistics, Nonparametric', 'Journal Article', '10.1186/1471-2105-14-86', '23497201', 'DNA methylation profiles differ among disease types and, therefore, can be used in disease diagnosis. In addition, large-scale whole genome DNA methylation data offer tremendous potential in understanding the role of DNA methylation in normal development and function. However, due to the unique feature of the methylation data, powerful and robust statistical methods are very limited in this area.'),
(95484, 2013, 'bmc', 'Empirical Bayes estimation of posterior probabilities of enrichment: a comparative study of five estimators of the local false discovery rate', 'Yang, Zhenyu|Li, Zuojing|Bickel, David R', 'Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology, and Immunology, Department of Mathematics and Statistics, University of Ottawa, 451 Smyth Road, Ottawa, Ontario, K1H 8M5 Canada.|Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology, and Immunology, Department of Mathematics and Statistics, University of Ottawa, 451 Smyth Road, Ottawa, Ontario, K1H 8M5 Canada.|Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology, and Immunology, Department of Mathematics and Statistics, University of Ottawa, 451 Smyth Road, Ottawa, Ontario, K1H 8M5 Canada.', 'Canada|Canada|Canada', 11, 'Bayes Theorem|Breast Neoplasms|Female|Gene Expression Profiling|Genes|Humans|Likelihood Functions|Oligonucleotide Array Sequence Analysis|Probability|Vocabulary, Controlled', 'Comparative Study', '10.1186/1471-2105-14-87', '23497228', 'In investigating differentially expressed genes or other selected features, researchers conduct hypothesis tests to determine which biological categories, such as those of the Gene Ontology (GO), are enriched for the selected features. Multiple comparison procedures (MCPs) are commonly used to prevent excessive false positive rates. Traditional MCPs, e.g., the Bonferroni method, go to the opposite extreme: strictly controlling a family-wise error rate, resulting in excessive false negative rates. Researchers generally prefer the more balanced approach of instead controlling the false discovery rate (FDR). However, the q-values that methods of FDR control assign to biological categories tend to be too low to reliably estimate the probability that a biological category is not enriched for the preselected features. Thus, we study an application of the other estimators of that probability, which is called the local FDR (LFDR).'),
(95485, 2013, 'bmc', 'DNdisorder: predicting protein disorder using boosting and deep networks', 'Eickholt, Jesse|Cheng, Jianlin', 'Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.', 'United States|United States', 0, 'Protein Conformation|Proteins|Sequence Analysis, Protein|Software', 'Evaluation Studies', '10.1186/1471-2105-14-88', '23497251', 'A number of proteins contain regions which do not adopt a stable tertiary structure in their native state. Such regions known as disordered regions have been shown to participate in many vital cell functions and are increasingly being examined as drug targets.'),
(95486, 2013, 'bmc', 'Digital sorting of complex tissues for cell type-specific gene expression profiles', 'Zhong, Yi|Wan, Ying-Wooi|Pang, Kaifang|Chow, Lionel M L|Liu, Zhandong', 'Department of Pediatrics, Neurological Research Institute, Baylor College of Medicine, Houston, TX, USA.|Department of Pediatrics, Neurological Research Institute, Baylor College of Medicine, Houston, TX, USA.|Department of Pediatrics, Neurological Research Institute, Baylor College of Medicine, Houston, TX, USA.|Department of Pediatrics, Neurological Research Institute, Baylor College of Medicine, Houston, TX, USA.|Department of Pediatrics, Neurological Research Institute, Baylor College of Medicine, Houston, TX, USA.', 'United States|United States|United States|United States|United States', 76, 'Algorithms|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-14-89', '23497278', 'Cellular heterogeneity is present in almost all gene expression profiles. However, transcriptome analysis of tissue specimens often ignores the cellular heterogeneity present in these samples. Standard deconvolution algorithms require prior knowledge of the cell type frequencies within a tissue or their in vitro expression profiles. Furthermore, these algorithms tend to report biased estimations.'),
(95487, 2013, 'bmc', 'An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis', 'Zou, Chuanxin|Gong, Jiayu|Li, Honglin', 'Shanghai Key Laboratory of New Drug Design, State Key Laboratory of Bioreactor Engineering, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.|Shanghai Key Laboratory of New Drug Design, State Key Laboratory of Bioreactor Engineering, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.|Shanghai Key Laboratory of New Drug Design, State Key Laboratory of Bioreactor Engineering, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.', 'China|China|China', 48, 'Amino Acids|DNA-Binding Proteins|Protein Conformation|Sequence Analysis, Protein|Support Vector Machine', 'Evaluation Studies', '10.1186/1471-2105-14-90', '23497329', 'DNA-binding proteins (DNA-BPs) play a pivotal role in both eukaryotic and prokaryotic proteomes. There have been several computational methods proposed in the literature to deal with the DNA-BPs, many informative features and properties were used and proved to have significant impact on this problem. However the ultimate goal of Bioinformatics is to be able to predict the DNA-BPs directly from primary sequence.'),
(95488, 2013, 'bmc', 'A comparison of methods for differential expression analysis of RNA-seq data', 'Soneson, Charlotte|Delorenzi, Mauro', 'Bioinformatics Core Facility, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland. Charlotte.Soneson@isb-sib.ch|Bioinformatics Core Facility, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland. Charlotte.Soneson@isb-sib.ch', 'Switzerland|Switzerland', 0, 'Animals|DNA, Complementary|Gene Expression Profiling|Genome|Genomics|Mice|Mice, Inbred C57BL|Mice, Inbred DBA|RNA, Messenger|Sequence Analysis, RNA|Software', 'Comparative Study', '10.1186/1471-2105-14-91', '23497356', 'Finding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-seq for differential expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential expression analysis of RNA-seq data.'),
(95489, 2013, 'bmc', 'Inferring microRNA and transcription factor regulatory networks in heterogeneous data', 'Le, Thuc D|Liu, Lin|Liu, Bing|Tsykin, Anna|Goodall, Gregory J|Satou, Kenji|Li, Jiuyong', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA 5095, Australia. leyty017@mymail.unisa.edu.au|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA 5095, Australia. leyty017@mymail.unisa.edu.au|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA 5095, Australia. leyty017@mymail.unisa.edu.au|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA 5095, Australia. leyty017@mymail.unisa.edu.au|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA 5095, Australia. leyty017@mymail.unisa.edu.au|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA 5095, Australia. leyty017@mymail.unisa.edu.au|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA 5095, Australia. leyty017@mymail.unisa.edu.au', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Algorithms|Bayes Theorem|Cell Line|Epithelial-Mesenchymal Transition|Gene Expression Profiling|Gene Regulatory Networks|Humans|MicroRNAs|RNA, Messenger|Transcription Factors', 'Journal Article', '10.1186/1471-2105-14-92', '23497388', 'Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA.'),
(95490, 2013, 'bmc', 'A distance-field based automatic neuron tracing method', 'Yang, Jinzhu|Gonzalez-Bellido, Paloma T|Peng, Hanchuan', 'Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.', 'United States|United States|United States', 0, 'Algorithms|Animals|Anisotropy|Imaging, Three-Dimensional|Microscopy, Confocal|Neurites|Neurons|Odonata', 'Journal Article', '10.1186/1471-2105-14-93', '23497429', 'Automatic 3D digital reconstruction (tracing) of neurons embedded in noisy microscopic images is challenging, especially when the cell morphology is complex.'),
(95491, 2013, 'bmc', 'Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees', 'Chou, Hsiu-Ling|Yao, Chung-Tay|Su, Sui-Lun|Lee, Chia-Yi|Hu, Kuang-Yu|Terng, Harn-Jing|Shih, Yun-Wen|Chang, Yu-Tien|Lu, Yu-Fen|Chang, Chi-Wen|Wahlqvist, Mark L|Wetter, Thomas|Chu, Chi-Ming', 'Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Breast Neoplasms|DNA, Complementary|Databases, Genetic|Decision Trees|Female|Gene Expression Profiling|Humans|Logistic Models|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Recurrence|Sample Size|Survival Analysis', 'Journal Article', '10.1186/1471-2105-14-100', '23506640', 'Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann-Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression.'),
(95492, 2013, 'bmc', 'Unsupervised Bayesian linear unmixing of gene expression microarrays', 'Bazot, Cécile|Dobigeon, Nicolas|Tourneret, Jean-Yves|Zaas, Aimee K|Ginsburg, Geoffrey S|Hero, Alfred O', 'University of Toulouse, IRIT/INP-ENSEEIHT, 2 rue Camichel, BP 7122, 31071 Toulouse cedex 7, France. cecile.bazot@enseeiht.fr|University of Toulouse, IRIT/INP-ENSEEIHT, 2 rue Camichel, BP 7122, 31071 Toulouse cedex 7, France. cecile.bazot@enseeiht.fr|University of Toulouse, IRIT/INP-ENSEEIHT, 2 rue Camichel, BP 7122, 31071 Toulouse cedex 7, France. cecile.bazot@enseeiht.fr|University of Toulouse, IRIT/INP-ENSEEIHT, 2 rue Camichel, BP 7122, 31071 Toulouse cedex 7, France. cecile.bazot@enseeiht.fr|University of Toulouse, IRIT/INP-ENSEEIHT, 2 rue Camichel, BP 7122, 31071 Toulouse cedex 7, France. cecile.bazot@enseeiht.fr|University of Toulouse, IRIT/INP-ENSEEIHT, 2 rue Camichel, BP 7122, 31071 Toulouse cedex 7, France. cecile.bazot@enseeiht.fr', 'France|France|France|France|France|France', 13, 'Algorithms|Bayes Theorem|Gene Expression Profiling|Humans|Influenza A Virus, H3N2 Subtype|Influenza, Human|Male|Microarray Analysis', 'Journal Article', '10.1186/1471-2105-14-99', '23506672', 'This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters.'),
(95493, 2013, 'bmc', 'Adaptive filtering of microarray gene expression data based on Gaussian mixture decomposition', 'Marczyk, Michal|Jaksik, Roman|Polanski, Andrzej|Polanska, Joanna', 'Institute of Automatic Control, Silesian University of Technology, Gliwice 44-100, Poland. Michal.Marczyk@polsl.pl|Institute of Automatic Control, Silesian University of Technology, Gliwice 44-100, Poland. Michal.Marczyk@polsl.pl|Institute of Automatic Control, Silesian University of Technology, Gliwice 44-100, Poland. Michal.Marczyk@polsl.pl|Institute of Automatic Control, Silesian University of Technology, Gliwice 44-100, Poland. Michal.Marczyk@polsl.pl', 'Poland|Poland|Poland|Poland', 35, 'Algorithms|Animals|Gene Expression Profiling|Humans|Normal Distribution|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-14-101', '23510016', 'DNA microarrays are used for discovery of genes expressed differentially between various biological conditions. In microarray experiments the number of analyzed samples is often much lower than the number of genes (probe sets) which leads to many false discoveries. Multiple testing correction methods control the number of false discoveries but decrease the sensitivity of discovering differentially expressed genes. Concerning this problem, filtering methods for improving the power of detection of differentially expressed genes were proposed in earlier papers. These techniques are two-step procedures, where in the first step some pool of non-informative genes is removed and in the second step only the pool of the retained genes is used for searching for differentially expressed genes.'),
(95494, 2013, 'bmc', 'The Enzyme Portal: a case study in applying user-centred design methods in bioinformatics', 'de Matos, Paula|Cham, Jennifer A|Cao, Hong|Alcántara, Rafael|Rowland, Francis|Lopez, Rodrigo|Steinbeck, Christoph', 'EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 13, 'Communication|Computational Biology|Databases, Protein|Enzymes|Internet|Software Design|User-Computer Interface|Workflow', 'Letter', '10.1186/1471-2105-14-103', '23514033', 'User-centred design (UCD) is a type of user interface design in which the needs and desires of users are taken into account at each stage of the design process for a service or product, often for software applications and websites. Its goal is to facilitate the design of software that is both useful and easy to use. To achieve this, you must characterise users requirements, design suitable interactions to meet their needs, and test your designs using prototypes and real life scenarios.For bioinformatics, there is little practical information available regarding how to carry out UCD in practice. To address this we describe a complete, multi-stage UCD process used for creating a new bioinformatics resource for integrating enzyme information, called the Enzyme Portal (http://www.ebi.ac.uk/enzymeportal). This freely-available service mines and displays data about proteins with enzymatic activity from public repositories via a single search, and includes biochemical reactions, biological pathways, small molecule chemistry, disease information, 3D protein structures and relevant scientific literature.We employed several UCD techniques, including: persona development, interviews, canvas sort card sorting, user workflows, usability testing and others. Our hope is that this case study will motivate the reader to apply similar UCD approaches to their own software design for bioinformatics. Indeed, we found the benefits included more effective decision-making for design ideas and technologies, enhanced team-working and communication, cost effectiveness, and ultimately a service that more closely meets the needs of our target audience.'),
(95495, 2013, 'bmc', 'Computational vaccinology and the ICoVax 2012 workshop', 'He, Yongqun|Cao, Zhiwei|De Groot, Anne S|Brusic, Vladimir|Schönbach, Christian|Petrovsky, Nikolai', 'Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, and Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA. yongqunh@umich.edu|Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, and Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA. yongqunh@umich.edu|Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, and Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA. yongqunh@umich.edu|Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, and Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA. yongqunh@umich.edu|Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, and Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA. yongqunh@umich.edu|Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, and Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA. yongqunh@umich.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Epitopes|Epitopes, T-Lymphocyte|Humans|Vaccines', 'Editorial', '10.1186/1471-2105-14-S4-I1', '23514034', 'Computational vaccinology or vaccine informatics is an interdisciplinary field that addresses scientific and clinical questions in vaccinology using computational and informatics approaches. Computational vaccinology overlaps with many other fields such as immunoinformatics, reverse vaccinology, postlicensure vaccine research, vaccinomics, literature mining, and systems vaccinology. The second ISV Pre-conference Computational Vaccinology Workshop (ICoVax 2012) was held on October 13, 2013 in Shanghai, China. A number of topics were presented in the workshop, including allergen predictions, prediction of linear T cell epitopes and functional conformational epitopes, prediction of protein-ligand binding regions, vaccine design using reverse vaccinology, and case studies in computational vaccinology. Although a significant progress has been made to date, a number of challenges still exist in the field. This Editorial provides a list of major challenges for the future of computational vaccinology and identifies developing themes that will expand and evolve over the next few years.'),
(95496, 2013, 'bmc', 'Evaluation and integration of existing methods for computational prediction of allergens', 'Wang, Jing|Yu, Yabin|Zhao, Yunan|Zhang, Dabing|Li, Jing', 'Bor Luh Food Safety Center, National Center for Molecular Characterization of Genetically Modified Organisms, State Key Laboratory of Hybrid Rice, School of Life Science and Biotechnology, Shanghai Jiao Tong University, China.|Bor Luh Food Safety Center, National Center for Molecular Characterization of Genetically Modified Organisms, State Key Laboratory of Hybrid Rice, School of Life Science and Biotechnology, Shanghai Jiao Tong University, China.|Bor Luh Food Safety Center, National Center for Molecular Characterization of Genetically Modified Organisms, State Key Laboratory of Hybrid Rice, School of Life Science and Biotechnology, Shanghai Jiao Tong University, China.|Bor Luh Food Safety Center, National Center for Molecular Characterization of Genetically Modified Organisms, State Key Laboratory of Hybrid Rice, School of Life Science and Biotechnology, Shanghai Jiao Tong University, China.|Bor Luh Food Safety Center, National Center for Molecular Characterization of Genetically Modified Organisms, State Key Laboratory of Hybrid Rice, School of Life Science and Biotechnology, Shanghai Jiao Tong University, China.', 'China|China|China|China|China', 0, 'Algorithms|Allergens|Amino Acid Motifs|Computational Biology|Food Hypersensitivity|Humans|Protein Structure, Tertiary|Proteins|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-14-S4-S1', '23514097', 'Allergy involves a series of complex reactions and factors that contribute to the development of the disease and triggering of the symptoms, including rhinitis, asthma, atopic eczema, skin sensitivity, even acute and fatal anaphylactic shock. Prediction and evaluation of the potential allergenicity is of importance for safety evaluation of foods and other environment factors. Although several computational approaches for assessing the potential allergenicity of proteins have been developed, their performance and relative merits and shortcomings have not been compared systematically.'),
(95497, 2013, 'bmc', 'Protein function prediction by massive integration of evolutionary analyses and multiple data sources', 'Cozzetto, Domenico|Buchan, Daniel W A|Bryson, Kevin|Jones, David T', 'Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.|Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.|Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.|Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 56, 'Computational Biology|Databases, Protein|Evolution, Molecular|Gene Expression|Molecular Sequence Annotation|Protein Interaction Mapping|Proteins|Sequence Analysis', 'Journal Article', '10.1186/1471-2105-14-S3-S1', '23514099', 'Accurate protein function annotation is a severe bottleneck when utilizing the deluge of high-throughput, next generation sequencing data. Keeping database annotations up-to-date has become a major scientific challenge that requires the development of reliable automatic predictors of protein function. The CAFA experiment provided a unique opportunity to undertake comprehensive blind testing of many diverse approaches for automated function prediction. We report on the methodology we used for this challenge and on the lessons we learnt.'),
(95498, 2013, 'bmc', 'Combining heterogeneous data sources for accurate functional annotation of proteins', 'Sokolov, Artem|Funk, Christopher|Graim, Kiley|Verspoor, Karin|Ben-Hur, Asa', 'Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California 95064, USA. sokolov@soe.ucsc.edu|Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California 95064, USA. sokolov@soe.ucsc.edu|Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California 95064, USA. sokolov@soe.ucsc.edu|Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California 95064, USA. sokolov@soe.ucsc.edu|Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California 95064, USA. sokolov@soe.ucsc.edu', 'United States|United States|United States|United States|United States', 38, 'Algorithms|Animals|Computational Biology|Gene Expression|Mice|Molecular Sequence Annotation|Protein Interaction Mapping|Proteins|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-S3-S10', '23514123', 'Combining heterogeneous sources of data is essential for accurate prediction of protein function. The task is complicated by the fact that while sequence-based features can be readily compared across species, most other data are species-specific. In this paper, we present a multi-view extension to GOstruct, a structured-output framework for function annotation of proteins. The extended framework can learn from disparate data sources, with each data source provided to the framework in the form of a kernel. Our empirical results demonstrate that the multi-view framework is able to utilize all available information, yielding better performance than sequence-based models trained across species and models trained from collections of data within a given species. This version of GOstruct participated in the recent Critical Assessment of Functional Annotations (CAFA) challenge, since then we have significantly improved the natural language processing component of the method, which now provides performance that is on par with that provided by sequence information. The GOstruct framework is available for download at http://strut.sourceforge.net.'),
(95499, 2013, 'bmc', 'Genome-wide prediction of vaccine targets for human herpes simplex viruses using Vaxign reverse vaccinology', 'Xiang, Zuoshuang|He, Yongqun', 'Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA.|Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA.', 'United States|United States', 33, 'Capsid Proteins|Computational Biology|Epitopes|Genome, Viral|Herpes Simplex Virus Vaccines|Humans|Simplexvirus', 'Journal Article', '10.1186/1471-2105-14-S4-S2', '23514126', 'Herpes simplex virus (HSV) types 1 and 2 (HSV-1 and HSV-2) are the most common infectious agents of humans. No safe and effective HSV vaccines have been licensed. Reverse vaccinology is an emerging and revolutionary vaccine development strategy that starts with the prediction of vaccine targets by informatics analysis of genome sequences. Vaxign (http://www.violinet.org/vaxign) is the first web-based vaccine design program based on reverse vaccinology. In this study, we used Vaxign to analyze 52 herpesvirus genomes, including 3 HSV-1 genomes, one HSV-2 genome, 8 other human herpesvirus genomes, and 40 non-human herpesvirus genomes. The HSV-1 strain 17 genome that contains 77 proteins was used as the seed genome. These 77 proteins are conserved in two other HSV-1 strains (strain F and strain H129). Two envelope glycoproteins gJ and gG do not have orthologs in HSV-2 or 8 other human herpesviruses. Seven HSV-1 proteins (including gJ and gG) do not have orthologs in all 40 non-human herpesviruses. Nineteen proteins are conserved in all human herpesviruses, including capsid scaffold protein UL26.5 (NP_044628.1). As the only HSV-1 protein predicted to be an adhesin, UL26.5 is a promising vaccine target. The MHC Class I and II epitopes were predicted by the Vaxign Vaxitop prediction program and IEDB prediction programs recently installed and incorporated in Vaxign. Our comparative analysis found that the two programs identified largely the same top epitopes but also some positive results predicted from one program might not be positive from another program. Overall, our Vaxign computational prediction provides many promising candidates for rational HSV vaccine development. The method is generic and can also be used to predict other viral vaccine targets.'),
(95500, 2013, 'bmc', 'Functional inference by ProtoNet family tree: the uncharacterized proteome of Daphnia pulex', 'Rappoport, Nadav|Linial, Michal', 'School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel.|School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel.', 'Israel|Israel', 1, 'Animals|Arthropod Proteins|Classification|Cluster Analysis|Daphnia|Genes, Duplicate|Ion Channels|Mice|Molecular Sequence Annotation|Proteome|Receptors, Cell Surface|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-14-S3-S11', '23514195', 'Daphnia pulex (Water flea) is the first fully sequenced crustacean genome. The crustaceans and insects have diverged from a common ancestor. It is a model organism for studying the molecular makeup for coping with the environmental challenges. In the complete proteome, there are 30,550 putative proteins. However, about 10,000 of them have no known homologues. Currently, the UniProtoKB reports on 95% of the Daphnias proteins as putative and uncharacterized proteins.'),
(95501, 2013, 'bmc', 'Prediction of conformational epitopes with the use of a knowledge-based energy function and geometrically related neighboring residue characteristics', 'Lo, Ying-Tsang|Pai, Tun-Wen|Wu, Wei-Kuo|Chang, Hao-Teng', 'Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan, ROC.|Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan, ROC.|Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan, ROC.|Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan, ROC.', '-|-|-|-', 14, 'Algorithms|Animals|Antigens|Computational Biology|Databases, Protein|Epitopes, B-Lymphocyte|Knowledge Bases|Mice|Models, Molecular|Potassium Channels|Thermodynamics', 'Journal Article', '10.1186/1471-2105-14-S4-S3', '23514199', 'A conformational epitope (CE) in an antigentic protein is composed of amino acid residues that are spatially near each other on the antigens surface but are separated in sequence, CEs bind their complementary paratopes in B-cell receptors and/or antibodies. CE predication is used during vaccine design and in immuno-biological experiments. Here, we develop a novel system, CE-KEG, which predicts CEs based on knowledge-based energy and geometrical neighboring residue contents. The workflow applied grid-based mathematical morphological algorithms to efficiently detect the surface atoms of the antigens. After extracting surface residues, we ranked CE candidate residues first according to their local average energy distributions. Then, the frequencies at which geometrically related neighboring residue combinations in the potential CEs occurred were incorporated into our workflow, and the weighted combinations of the average energies and neighboring residue frequencies were used to assess the sensitivity, accuracy, and efficiency of our prediction workflow.'),
(95502, 2013, 'bmc', 'Concomitant prediction of function and fold at the domain level with GO-based profiles', 'Lopez, Daniel|Pazos, Florencio', 'Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), C/ Darwin 3, 28049 Madrid, Spain.|Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), C/ Darwin 3, 28049 Madrid, Spain.', 'Spain|Spain', 0, 'Computational Biology|Molecular Sequence Annotation|Position-Specific Scoring Matrices|Protein Folding|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-14-S3-S12', '23514233', 'Predicting the function of newly sequenced proteins is crucial due to the pace at which these raw sequences are being obtained. Almost all resources for predicting protein function assign functional terms to whole chains, and do not distinguish which particular domain is responsible for the allocated function. This is not a limitation of the methodologies themselves but it is due to the fact that in the databases of functional annotations these methods use for transferring functional terms to new proteins, these annotations are done on a whole-chain basis. Nevertheless, domains are the basic evolutionary and often functional units of proteins. In many cases, the domains of a protein chain have distinct molecular functions, independent from each other. For that reason resources with functional annotations at the domain level, as well as methodologies for predicting function for individual domains adapted to these resources are required.We present a methodology for predicting the molecular function of individual domains, based on a previously developed database of functional annotations at the domain level. The approach, which we show outperforms a standard method based on sequence searches in assigning function, concomitantly predicts the structural fold of the domains and can give hints on the functionally important residues associated to the predicted function.'),
(95503, 2013, 'bmc', 'Protein-ligand binding region prediction (PLB-SAVE) based on geometric features and CUDA acceleration', 'Lo, Ying-Tsang|Wang, Hsin-Wei|Pai, Tun-Wen|Tzou, Wen-Shoung|Hsu, Hui-Huang|Chang, Hao-Teng', 'Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan, ROC.|Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan, ROC.|Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan, ROC.|Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan, ROC.|Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan, ROC.|Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan, ROC.', '-|-|-|-|-|-', 7, 'Computational Biology|Computer Simulation|Databases, Protein|Drug Design|Humans|Ligands|Models, Molecular|Protein Binding|Protein Structure, Tertiary|Proteins|Software|Vaccines', 'Journal Article', '10.1186/1471-2105-14-S4-S4', '23514235', 'Protein-ligand interactions are key processes in triggering and controlling biological functions within cells. Prediction of protein binding regions on the protein surface assists in understanding the mechanisms and principles of molecular recognition. In silico geometrical shape analysis plays a primary step in analyzing the spatial characteristics of protein binding regions and facilitates applications of bioinformatics in drug discovery and design. Here, we describe the novel software, PLB-SAVE, which uses parallel processing technology and is ideally suited to extract the geometrical construct of solid angles from surface atoms. Representative clusters and corresponding anchors were identified from all surface elements and were assigned according to the ranking of their solid angles. In addition, cavity depth indicators were obtained by proportional transformation of solid angles and cavity volumes were calculated by scanning multiple directional vectors within each selected cavity. Both depth and volume characteristics were combined with various weighting coefficients to rank predicted potential binding regions.');
