INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(94493, 2011, 'bmc', 'Predicting receptor-ligand pairs through kernel learning', 'Iacucci, Ernesto|Ojeda, Fabian|De Moor, Bart|Moreau, Yves', 'SCD-ESAT, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Leuven 3001, Belgium.|SCD-ESAT, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Leuven 3001, Belgium.|SCD-ESAT, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Leuven 3001, Belgium.|SCD-ESAT, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Leuven 3001, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Animals|Artificial Intelligence|Databases, Protein|Humans|Ligands|Phylogeny|Protein Binding|Protein Interaction Mapping|Receptors, Cell Surface|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-12-336', '21834994', 'Regulation of cellular events is, often, initiated via extracellular signaling. Extracellular signaling occurs when a circulating ligand interacts with one or more membrane-bound receptors. Identification of receptor-ligand pairs is thus an important and specific form of PPI prediction.'),
(94494, 2011, 'bmc', 'Constructing gene regulatory networks for long term photosynthetic light acclimation in Arabidopsis thaliana', 'Yao, Cheng-Wei|Hsu, Ban-Dar|Chen, Bor-Sen', 'Lab of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsin-Chu, 300, Taiwan.|Lab of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsin-Chu, 300, Taiwan.|Lab of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsin-Chu, 300, Taiwan.', '-|-|-', 15, 'Acclimatization|Arabidopsis|Arabidopsis Proteins|DNA, Plant|Gene Regulatory Networks|Light|Oligonucleotide Array Sequence Analysis|Photosynthesis|Photosystem I Protein Complex|Photosystem II Protein Complex', 'Journal Article', '10.1186/1471-2105-12-335', '21834997', 'Photosynthetic light acclimation is an important process that allows plants to optimize the efficiency of photosynthesis, which is the core technology for green energy. However, currently little is known about the molecular mechanisms behind the regulation of the photosynthetic light acclimation response. In this study, a systematic method is proposed to investigate this mechanism by constructing gene regulatory networks from microarray data of Arabidopsis thaliana.'),
(94495, 2011, 'bmc', 'IQMNMR: Open source software using time-domain NMR data for automated identification and quantification of metabolites in batches', 'Song, Xu|Zhang, Bo-Li|Liu, Hong-Min|Yu, Bo-Yang|Gao, Xiu-Mei|Kang, Li-Yuan', 'Department of Chinese Medicinal Prescription, China Pharmaceutical University, Nanjing, China.|Department of Chinese Medicinal Prescription, China Pharmaceutical University, Nanjing, China.|Department of Chinese Medicinal Prescription, China Pharmaceutical University, Nanjing, China.|Department of Chinese Medicinal Prescription, China Pharmaceutical University, Nanjing, China.|Department of Chinese Medicinal Prescription, China Pharmaceutical University, Nanjing, China.|Department of Chinese Medicinal Prescription, China Pharmaceutical University, Nanjing, China.', 'China|China|China|China|China|China', 4, 'Algorithms|Automation|Humans|Magnetic Resonance Imaging|Magnetic Resonance Spectroscopy|Metabolomics|Protons|Software|Workflow', 'Journal Article', '10.1186/1471-2105-12-337', '21838867', 'One of the most promising aspects of metabolomics is metabolic modeling and simulation. Central to such applications is automated high-throughput identification and quantification of metabolites. NMR spectroscopy is a reproducible, nondestructive, and nonselective method that has served as the foundation of metabolomics studies. However, the automated high-throughput identification and quantification of metabolites in NMR spectroscopy is limited by severe spectral overlap. Although numerous software programs have been developed for resolving overlapping resonances, as well as for identifying and quantifying metabolites, most of these programs are frequency-domain methods, considerably influenced by phase shifts and baseline distortions, and effective only in small-scale studies. Almost all these programs require multiple spectra for each application, and do not automatically identify and quantify metabolites in batches.'),
(94496, 2011, 'bmc', 'PChopper: high throughput peptide prediction for MRM/SRM transition design', 'Afzal, Vackar|Huang, Jeffrey T-J|Atrih, Abdel|Crowther, Daniel J', 'Translational Medicine Research Collaboration, Dundee, DD1 9SY, UK.|Translational Medicine Research Collaboration, Dundee, DD1 9SY, UK.|Translational Medicine Research Collaboration, Dundee, DD1 9SY, UK.|Translational Medicine Research Collaboration, Dundee, DD1 9SY, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Chromatography, Liquid|Humans|Internet|Peptides|Phosphorylation|Protein Processing, Post-Translational|Proteins|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-12-338', '21838934', 'The use of selective reaction monitoring (SRM) based LC-MS/MS analysis for the quantification of phosphorylation stoichiometry has been rapidly increasing. At the same time, the number of sites that can be monitored in a single LC-MS/MS experiment is also increasing. The manual processes associated with running these experiments have highlighted the need for computational assistance to quickly design MRM/SRM candidates.'),
(94497, 2011, 'bmc', 'Visual gene developer: a fully programmable bioinformatics software for synthetic gene optimization', 'Jung, Sang-Kyu|McDonald, Karen', 'Department of Chemical Engineering and Materials Science, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA.|Department of Chemical Engineering and Materials Science, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA.', 'United States|United States', 47, 'Algorithms|Computational Biology|Computers|Gene Expression|Genes, Synthetic|Neural Networks (Computer)|Programming Languages|RNA, Messenger|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-12-340', '21846353', 'Direct gene synthesis is becoming more popular owing to decreases in gene synthesis pricing. Compared with using natural genes, gene synthesis provides a good opportunity to optimize gene sequence for specific applications. In order to facilitate gene optimization, we have developed a stand-alone software called Visual Gene Developer.'),
(94498, 2011, 'bmc', 'Prediction of conformational B-cell epitopes from 3D structures by random forests with a distance-based feature', 'Zhang, Wen|Xiong, Yi|Zhao, Meng|Zou, Hua|Ye, Xinghuo|Liu, Juan', 'School of Computer, Wuhan University, Wuhan 430072, China. zhangwen@whu.edu.cn|School of Computer, Wuhan University, Wuhan 430072, China. zhangwen@whu.edu.cn|School of Computer, Wuhan University, Wuhan 430072, China. zhangwen@whu.edu.cn|School of Computer, Wuhan University, Wuhan 430072, China. zhangwen@whu.edu.cn|School of Computer, Wuhan University, Wuhan 430072, China. zhangwen@whu.edu.cn|School of Computer, Wuhan University, Wuhan 430072, China. zhangwen@whu.edu.cn', 'China|China|China|China|China|China', 75, 'Algorithms|Animals|Antigens|Binding Sites|Computational Biology|Epitopes, B-Lymphocyte|Humans|Protein Conformation', 'Journal Article', '10.1186/1471-2105-12-341', '21846404', 'Antigen-antibody interactions are key events in immune system, which provide important clues to the immune processes and responses. In Antigen-antibody interactions, the specific sites on the antigens that are directly bound by the B-cell produced antibodies are well known as B-cell epitopes. The identification of epitopes is a hot topic in bioinformatics because of their potential use in the epitope-based drug design. Although most B-cell epitopes are discontinuous (or conformational), insufficient effort has been put into the conformational epitope prediction, and the performance of existing methods is far from satisfaction.'),
(94499, 2011, 'bmc', 'Dimension reduction with gene expression data using targeted variable importance measurement', 'Wang, Hui|van der Laan, Mark J', 'Department of Pediatrics, Stanford University, MSOB X111, Stanford, CA 94305, USA. hwangui@stanford.edu|Department of Pediatrics, Stanford University, MSOB X111, Stanford, CA 94305, USA. hwangui@stanford.edu', 'United States|United States', 11, 'Algorithms|Artificial Intelligence|Breast Neoplasms|Computer Simulation|Female|Gene Expression Profiling|Humans|Regression Analysis', 'Journal Article', '10.1186/1471-2105-12-312', '21849016', 'When a large number of candidate variables are present, a dimension reduction procedure is usually conducted to reduce the variable space before the subsequent analysis is carried out. The goal of dimension reduction is to find a list of candidate genes with a more operable length ideally including all the relevant genes. Leaving many uninformative genes in the analysis can lead to biased estimates and reduced power. Therefore, dimension reduction is often considered a necessary predecessor of the analysis because it can not only reduce the cost of handling numerous variables, but also has the potential to improve the performance of the downstream analysis algorithms.'),
(94500, 2011, 'bmc', 'A comparison of the functional modules identified from time course and static PPI network data', 'Tang, Xiwei|Wang, Jianxin|Liu, Binbin|Li, Min|Chen, Gang|Pan, Yi', 'School of Information Science and Engineering, Central South University, Changsha, 410083, China.|School of Information Science and Engineering, Central South University, Changsha, 410083, China.|School of Information Science and Engineering, Central South University, Changsha, 410083, China.|School of Information Science and Engineering, Central South University, Changsha, 410083, China.|School of Information Science and Engineering, Central South University, Changsha, 410083, China.|School of Information Science and Engineering, Central South University, Changsha, 410083, China.', 'China|China|China|China|China|China', 86, 'Algorithms|Cluster Analysis|Databases, Genetic|Protein Interaction Maps|Proteins|Saccharomyces cerevisiae|Signal Transduction|Time Factors', 'Comparative Study', '10.1186/1471-2105-12-339', '21849017', 'Cellular systems are highly dynamic and responsive to cues from the environment. Cellular function and response patterns to external stimuli are regulated by biological networks. A protein-protein interaction (PPI) network with static connectivity is dynamic in the sense that the nodes implement so-called functional activities that evolve in time. The shift from static to dynamic network analysis is essential for further understanding of molecular systems.'),
(94501, 2011, 'bmc', 'Extracting quantitative genetic interaction phenotypes from matrix combinatorial RNAi', 'Axelsson, Elin|Sandmann, Thomas|Horn, Thomas|Boutros, Michael|Huber, Wolfgang|Fischer, Bernd', 'EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. elin@embl.de|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. elin@embl.de|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. elin@embl.de|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. elin@embl.de|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. elin@embl.de|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. elin@embl.de', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 13, 'Animals|Cells, Cultured|Databases, Genetic|Drosophila|Epistasis, Genetic|Genotype|Humans|Phenotype|RNA Interference', 'Journal Article', '10.1186/1471-2105-12-342', '21849035', 'Systematic measurement of genetic interactions by combinatorial RNAi (co-RNAi) is a powerful tool for mapping functional modules and discovering components. It also provides insights into the role of epistasis on the way from genotype to phenotype. The interpretation of co-RNAi data requires computational and statistical analysis in order to detect interactions reliably and sensitively.'),
(94502, 2011, 'bmc', 'A systems approach to identifying correlated gene targets for the loss of colour pigmentation in plants', 'Clark, Sangaalofa T|Verwoerd, Wynand S', 'Centre for Advanced Computational Solutions (CfACS), Agriculture and Life Sciences Division Lincoln University, Canterbury, New Zealand. sangaa@xtra.co.nz|Centre for Advanced Computational Solutions (CfACS), Agriculture and Life Sciences Division Lincoln University, Canterbury, New Zealand. sangaa@xtra.co.nz', 'New Zealand|New Zealand', 24, 'Anthocyanins|Aquilegia|Arabidopsis|Biosynthetic Pathways|Flavonoids|Genes, Regulator|Metabolic Networks and Pathways|Pigmentation|Systems Biology', 'Journal Article', '10.1186/1471-2105-12-343', '21849042', 'The numerous diverse metabolic pathways by which plant compounds can be produced make it difficult to predict how colour pigmentation is lost for different tissues and plants. This study employs mathematical and in silico methods to identify correlated gene targets for the loss of colour pigmentation in plants from a whole cell perspective based on the full metabolic network of Arabidopsis. This involves extracting a self-contained flavonoid subnetwork from the AraCyc database and calculating feasible metabolic routes or elementary modes (EMs) for it. Those EMs leading to anthocyanin compounds are taken to constitute the anthocyanin biosynthetic pathway (ABP) and their interplay with the rest of the EMs is used to study the minimal cut sets (MCSs), which are different combinations of reactions to block for eliminating colour pigmentation. By relating the reactions to their corresponding genes, the MCSs are used to explore the phenotypic roles of the ABP genes, their relevance to the ABP and the impact their eliminations would have on other processes in the cell.'),
(94503, 2011, 'bmc', 'A novel method to compare protein structures using local descriptors', 'Daniluk, Pawexc5x82|Lesyng, Bogdan', 'Faculty of Physics, Department of Biophysics and CoE BioExploratorium, University of Warsaw, xc5xbbwirki i Wigury 93, Warsaw, Poland.|Faculty of Physics, Department of Biophysics and CoE BioExploratorium, University of Warsaw, xc5xbbwirki i Wigury 93, Warsaw, Poland.', 'Poland|Poland', 17, 'Algorithms|Animals|Bacterial Proteins|Carrier Proteins|Computational Biology|GTP Phosphohydrolases|Humans|Models, Molecular|Proteins|Saposins|Structural Homology, Protein', 'Journal Article', '10.1186/1471-2105-12-344', '21849047', 'Protein structure comparison is one of the most widely performed tasks in bioinformatics. However, currently used methods have problems with the so-called difficult similarities, including considerable shifts and distortions of structure, sequential swaps and circular permutations. There is a demand for efficient and automated systems capable of overcoming these difficulties, which may lead to the discovery of previously unknown structural relationships.'),
(94504, 2011, 'bmc', 'BLProt: prediction of bioluminescent proteins based on support vector machine and relieff feature selection', 'Kandaswamy, Krishna Kumar|Pugalenthi, Ganesan|Hazrati, Mehrnaz Khodam|Kalies, Kai-Uwe|Martinetz, Thomas', 'Institute for Neuro- and Bioinformatics, University of Lübeck, 23538 Lübeck, Germany. krishna@inb.uni-luebeck.de|Institute for Neuro- and Bioinformatics, University of Lübeck, 23538 Lübeck, Germany. krishna@inb.uni-luebeck.de|Institute for Neuro- and Bioinformatics, University of Lübeck, 23538 Lübeck, Germany. krishna@inb.uni-luebeck.de|Institute for Neuro- and Bioinformatics, University of Lübeck, 23538 Lübeck, Germany. krishna@inb.uni-luebeck.de|Institute for Neuro- and Bioinformatics, University of Lübeck, 23538 Lübeck, Germany. krishna@inb.uni-luebeck.de', 'Germany|Germany|Germany|Germany|Germany', 21, 'Animals|Humans|Luminescent Proteins|Markov Chains|Software|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-12-345', '21849049', 'Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.'),
(94505, 2011, 'bmc', 'ProbPS: a new model for peak selection based on quantifying the dependence of the existence of derivative peaks on primary ion intensity', 'Zhang, Shenghui|Wang, Yaojun|Bu, Dongbo|Zhang, Hong|Sun, Shiwei', 'Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.', 'China|China|China|China|China', 5, 'Algorithms|Mass Spectrometry|Models, Statistical|Peptides|Proteins|Tandem Mass Spectrometry|Trypsin', 'Journal Article', '10.1186/1471-2105-12-346', '21849060', 'The analysis of mass spectra suggests that the existence of derivative peaks is strongly dependent on the intensity of the primary peaks. Peak selection from tandem mass spectrum is used to filter out noise and contaminant peaks. It is widely accepted that a valid primary peak tends to have high intensity and is accompanied by derivative peaks, including isotopic peaks, neutral loss peaks, and complementary peaks. Existing models for peak selection ignore the dependence between the existence of the derivative peaks and the intensity of the primary peaks. Simple models for peak selection assume that these two attributes are independent, however, this assumption is contrary to real data and prone to error.'),
(94506, 2011, 'bmc', 'Development and application of a modified dynamic time warping algorithm (DTW-S) to analyses of primate brain expression time series', 'Yuan, Yuan|Chen, Yi-Ping Phoebe|Ni, Shengyu|Xu, Augix Guohua|Tang, Lin|Vingron, Martin|Somel, Mehmet|Khaitovich, Philipp', 'Key Laboratory for Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China. y7yuan@students.latrobe.edu.au|Key Laboratory for Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China. y7yuan@students.latrobe.edu.au|Key Laboratory for Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China. y7yuan@students.latrobe.edu.au|Key Laboratory for Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China. y7yuan@students.latrobe.edu.au|Key Laboratory for Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China. y7yuan@students.latrobe.edu.au|Key Laboratory for Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China. y7yuan@students.latrobe.edu.au|Key Laboratory for Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China. y7yuan@students.latrobe.edu.au|Key Laboratory for Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China. y7yuan@students.latrobe.edu.au', 'China|China|China|China|China|China|China|China', 0, 'Adolescent|Adult|Aged|Aged, 80 and over|Algorithms|Animals|Cerebellum|Child|Child, Preschool|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation, Developmental|Humans|Infant|Macaca mulatta|Middle Aged|Pan troglodytes|Prefrontal Cortex|Primates|Time|Young Adult', 'Journal Article', '10.1186/1471-2105-12-347', '21851598', 'Comparing biological time series data across different conditions, or different specimens, is a common but still challenging task. Algorithms aligning two time series represent a valuable tool for such comparisons. While many powerful computation tools for time series alignment have been developed, they do not provide significance estimates for time shift measurements.'),
(94507, 2011, 'bmc', 'DARS-RNP and QUASI-RNP: new statistical potentials for protein-RNA docking', 'Tuszynska, Irina|Bujnicki, Janusz M', 'Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Ul, Ks. Trojdena 4, PL-02-109 Warsaw, Poland.|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Ul, Ks. Trojdena 4, PL-02-109 Warsaw, Poland.', 'Poland|Poland', 62, 'Animals|Computational Biology|Humans|Knowledge Bases|Models, Molecular|Models, Statistical|RNA|RNA-Binding Proteins|Ribonucleoproteins', 'Journal Article', '10.1186/1471-2105-12-348', '21851628', 'Protein-RNA interactions play fundamental roles in many biological processes. Understanding the molecular mechanism of protein-RNA recognition and formation of protein-RNA complexes is a major challenge in structural biology. Unfortunately, the experimental determination of protein-RNA complexes is tedious and difficult, both by X-ray crystallography and NMR. For many interacting proteins and RNAs the individual structures are available, enabling computational prediction of complex structures by computational docking. However, methods for protein-RNA docking remain scarce, in particular in comparison to the numerous methods for protein-protein docking.'),
(94508, 2011, 'bmc', 'Statistical methods for analyzing immunosignatures', 'Brown, Justin R|Stafford, Phillip|Johnston, Stephen A|Dinu, Valentin', 'Department of Biomedical Informatics, Arizona State University, Mayo Clinic - Samuel C, Johnson Research Bldg, 13212 East Shea Boulevard, Scottsdale, AZ 85259, USA. jrbrown2@asu.edu|Department of Biomedical Informatics, Arizona State University, Mayo Clinic - Samuel C, Johnson Research Bldg, 13212 East Shea Boulevard, Scottsdale, AZ 85259, USA. jrbrown2@asu.edu|Department of Biomedical Informatics, Arizona State University, Mayo Clinic - Samuel C, Johnson Research Bldg, 13212 East Shea Boulevard, Scottsdale, AZ 85259, USA. jrbrown2@asu.edu|Department of Biomedical Informatics, Arizona State University, Mayo Clinic - Samuel C, Johnson Research Bldg, 13212 East Shea Boulevard, Scottsdale, AZ 85259, USA. jrbrown2@asu.edu', 'United States|United States|United States|United States', 0, 'Antibodies|Humans|Models, Statistical|Neoplasms|Peptide Library|Protein Array Analysis', 'Journal Article', '10.1186/1471-2105-12-349', '21854615', 'Immunosignaturing is a new peptide microarray based technology for profiling of humoral immune responses. Despite new challenges, immunosignaturing gives us the opportunity to explore new and fundamentally different research questions. In addition to classifying samples based on disease status, the complex patterns and latent factors underlying immunosignatures, which we attempt to model, may have a diverse range of applications.'),
(94509, 2011, 'bmc', 'Haplo2Ped: a tool using haplotypes as markers for linkage analysis', 'Cheng, Feng|Zhang, Xianglong|Zhang, Yinan|Li, Chaohua|Zeng, Changqing', 'Laboratory of Disease Genomics and Individualized Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, No.7 Beitucheng West Road, Beijing 100029, PR China.|Laboratory of Disease Genomics and Individualized Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, No.7 Beitucheng West Road, Beijing 100029, PR China.|Laboratory of Disease Genomics and Individualized Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, No.7 Beitucheng West Road, Beijing 100029, PR China.|Laboratory of Disease Genomics and Individualized Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, No.7 Beitucheng West Road, Beijing 100029, PR China.|Laboratory of Disease Genomics and Individualized Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, No.7 Beitucheng West Road, Beijing 100029, PR China.', 'China|China|China|China|China', 2, 'Disease|Genetic Linkage|Genome-Wide Association Study|Haplotypes|Humans|Pedigree|Polymorphism, Single Nucleotide|Retinitis Pigmentosa|Software', 'Journal Article', '10.1186/1471-2105-12-350', '21854652', 'Generally, SNPs are abundant in the genome, however, they display low power in linkage analysis because of their limited heterozygosity. Haplotype markers, on the other hand, which are composed of many SNPs, greatly increase heterozygosity and have superiority in linkage statistics.'),
(94510, 2011, 'bmc', 'Knowledge engineering tools for reasoning with scientific observations and interpretations: a neural connectivity use case', 'Russ, Thomas A|Ramakrishnan, Cartic|Hovy, Eduard H|Bota, Mihail|Burns, Gully A P C', 'Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA.|Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA.|Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA.|Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA.|Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA.', 'United States|United States|United States|United States|United States', 0, 'Animals|Brain Mapping|Computational Biology|Humans|Internet|Knowledge Bases|Rats|Software', 'Journal Article', '10.1186/1471-2105-12-351', '21859449', 'We address the goal of curating observations from published experiments in a generalizable form, reasoning over these observations to generate interpretations and then querying this interpreted knowledge to supply the supporting evidence. We present web-application software as part of the BioScholar project (R01-GM083871) that fully instantiates this process for a well-defined domain: using tract-tracing experiments to study the neural connectivity of the rat brain.'),
(94511, 2011, 'bmc', 'Improving the prediction of protein binding sites by combining heterogeneous data and Voronoi diagrams', 'Segura, Joan|Jones, Pamela F|Fernandez-Fuentes, Narcis', 'Leeds Institute of Molecular Medicine, Section of Experimental Therapeutics, University of Leeds, Leeds, LS9 7TF, UK.|Leeds Institute of Molecular Medicine, Section of Experimental Therapeutics, University of Leeds, Leeds, LS9 7TF, UK.|Leeds Institute of Molecular Medicine, Section of Experimental Therapeutics, University of Leeds, Leeds, LS9 7TF, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Binding Sites|Crystallography, X-Ray|Databases, Protein|Humans|Protein Binding|Protein Interaction Maps|Proteins|Software', 'Journal Article', '10.1186/1471-2105-12-352', '21861881', 'Protein binding site prediction by computational means can yield valuable information that complements and guides experimental approaches to determine the structure of protein complexes. Predictions become even more relevant and timely given the current resolution of protein interaction maps, where there is a very large and still expanding gap between the available information on: (i) which proteins interact and (ii) how proteins interact. Proteins interact through exposed residues that present differential physicochemical properties, and these can be exploited to identify protein interfaces.'),
(94512, 2011, 'bmc', 'Vertical decomposition with Genetic Algorithm for Multiple Sequence Alignment', 'Naznin, Farhana|Sarker, Ruhul|Essam, Daryl', 'School of Engineering and Information Technology, University of New South Wales at Australian Defence Force Academy, Canberra, Australia.|School of Engineering and Information Technology, University of New South Wales at Australian Defence Force Academy, Canberra, Australia.|School of Engineering and Information Technology, University of New South Wales at Australian Defence Force Academy, Canberra, Australia.', 'Australia|Australia|Australia', 27, 'Algorithms|Base Sequence|Child|Computational Biology|Evolution, Molecular|Humans|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-12-353', '21867510', 'Many Bioinformatics studies begin with a multiple sequence alignment as the foundation for their research. This is because multiple sequence alignment can be a useful technique for studying molecular evolution and analyzing sequence structure relationships.'),
(94513, 2011, 'bmc', 'Parallelized short read assembly of large genomes using de Bruijn graphs', 'Liu, Yongchao|Schmidt, Bertil|Maskell, Douglas L', 'School of Computer Engineering, Nanyang Technological University, Singapore. liuy0039@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. liuy0039@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. liuy0039@ntu.edu.sg', 'Singapore|Singapore|Singapore', 0, 'Bacteria|Computational Biology|Genome|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Software', 'Journal Article', '10.1186/1471-2105-12-354', '21867511', 'Next-generation sequencing technologies have given rise to the explosive increase in DNA sequencing throughput, and have promoted the recent development of de novo short read assemblers. However, existing assemblers require high execution times and a large amount of compute resources to assemble large genomes from quantities of short reads.'),
(94514, 2011, 'bmc', 'Studying the correlation between different word sense disambiguation methods and summarization effectiveness in biomedical texts', 'Plaza, Laura|Jimeno-Yepes, Antonio J|Díaz, Alberto|Aronson, Alan R', 'Universidad Complutense de Madrid, Calle Profesor José García Santesmases s/n, 28040 Madrid, Spain. lplazam@fdi.ucm.es|Universidad Complutense de Madrid, Calle Profesor José García Santesmases s/n, 28040 Madrid, Spain. lplazam@fdi.ucm.es|Universidad Complutense de Madrid, Calle Profesor José García Santesmases s/n, 28040 Madrid, Spain. lplazam@fdi.ucm.es|Universidad Complutense de Madrid, Calle Profesor José García Santesmases s/n, 28040 Madrid, Spain. lplazam@fdi.ucm.es', 'Spain|Spain|Spain|Spain', 13, 'Algorithms|Data Mining|Humans|Knowledge Bases|Natural Language Processing|Unified Medical Language System', 'Journal Article', '10.1186/1471-2105-12-355', '21871110', 'Word sense disambiguation (WSD) attempts to solve lexical ambiguities by identifying the correct meaning of a word based on its context. WSD has been demonstrated to be an important step in knowledge-based approaches to automatic summarization. However, the correlation between the accuracy of the WSD methods and the summarization performance has never been studied.'),
(94515, 2011, 'bmc', 'CloVR: a virtual machine for automated and portable sequence analysis from the desktop using cloud computing', 'Angiuoli, Samuel V|Matalka, Malcolm|Gussman, Aaron|Galens, Kevin|Vangala, Mahesh|Riley, David R|Arze, Cesar|White, James R|White, Owen|Fricke, W Florian', 'Institute for Genome Sciences (IGS), University of Maryland School of Medicine, Baltimore, Maryland, USA. angiuoli@umiacs.umd.edu|Institute for Genome Sciences (IGS), University of Maryland School of Medicine, Baltimore, Maryland, USA. angiuoli@umiacs.umd.edu|Institute for Genome Sciences (IGS), University of Maryland School of Medicine, Baltimore, Maryland, USA. angiuoli@umiacs.umd.edu|Institute for Genome Sciences (IGS), University of Maryland School of Medicine, Baltimore, Maryland, USA. angiuoli@umiacs.umd.edu|Institute for Genome Sciences (IGS), University of Maryland School of Medicine, Baltimore, Maryland, USA. angiuoli@umiacs.umd.edu|Institute for Genome Sciences (IGS), University of Maryland School of Medicine, Baltimore, Maryland, USA. angiuoli@umiacs.umd.edu|Institute for Genome Sciences (IGS), University of Maryland School of Medicine, Baltimore, Maryland, USA. angiuoli@umiacs.umd.edu|Institute for Genome Sciences (IGS), University of Maryland School of Medicine, Baltimore, Maryland, USA. angiuoli@umiacs.umd.edu|Institute for Genome Sciences (IGS), University of Maryland School of Medicine, Baltimore, Maryland, USA. angiuoli@umiacs.umd.edu|Institute for Genome Sciences (IGS), University of Maryland School of Medicine, Baltimore, Maryland, USA. angiuoli@umiacs.umd.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 206, 'Computational Biology|Computers|Genomics|High-Throughput Nucleotide Sequencing|Internet|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-12-356', '21878105', 'Next-generation sequencing technologies have decentralized sequence acquisition, increasing the demand for new bioinformatics tools that are easy to use, portable across multiple platforms, and scalable for high-throughput applications. Cloud computing platforms provide on-demand access to computing infrastructure over the Internet and can be used in combination with custom built virtual machines to distribute pre-packaged with pre-configured software.'),
(94516, 2011, 'bmc', 'An integrative approach to ortholog prediction for disease-focused and other functional studies', 'Hu, Yanhui|Flockhart, Ian|Vinayagam, Arunachalam|Bergwitz, Clemens|Berger, Bonnie|Perrimon, Norbert|Mohr, Stephanie E', 'Drosophila RNAi Screening Center, Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.|Drosophila RNAi Screening Center, Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.|Drosophila RNAi Screening Center, Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.|Drosophila RNAi Screening Center, Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.|Drosophila RNAi Screening Center, Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.|Drosophila RNAi Screening Center, Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.|Drosophila RNAi Screening Center, Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Animals|Databases, Genetic|Disease|Disease Models, Animal|Evolution, Molecular|Genetic Predisposition to Disease|Genome-Wide Association Study|Humans', 'Journal Article', '10.1186/1471-2105-12-357', '21880147', 'Mapping of orthologous genes among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one species based on what is known about the functions of orthologs in other species. Several tools for predicting orthologous gene relationships are available. However, these tools can give different results and identification of predicted orthologs is not always straightforward.'),
(94517, 2011, 'bmc', 'Clustering with position-specific constraints on variance: applying redescending M-estimators to label-free LC-MS data analysis', 'Frühwirth, Rudolf|Mani, D R|Pyne, Saumyadipta', 'Institute of High Energy Physics, Austrian Academy of Sciences, Vienna, Austria.|Institute of High Energy Physics, Austrian Academy of Sciences, Vienna, Austria.|Institute of High Energy Physics, Austrian Academy of Sciences, Vienna, Austria.', 'Austria|Austria|Austria', 0, 'Algorithms|Biomarkers|Chromatography, Liquid|Cluster Analysis|Humans|Mass Spectrometry|Proteomics', 'Journal Article', '10.1186/1471-2105-12-358', '21884583', 'Clustering is a widely applicable pattern recognition method for discovering groups of similar observations in data. While there are a large variety of clustering algorithms, very few of these can enforce constraints on the variation of attributes for data points included in a given cluster. In particular, a clustering algorithm that can limit variation within a cluster according to that clusters position (centroid location) can produce effective and optimal results in many important applications ranging from clustering of silicon pixels or calorimeter cells in high-energy physics to label-free liquid chromatography based mass spectrometry (LC-MS) data analysis in proteomics and metabolomics.'),
(94518, 2011, 'bmc', 'Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data', 'Gao, Shouguo|Wang, Xujing', 'Department of Physics, University of Alabama, Birmingham, AL 35294, USA.|Department of Physics, University of Alabama, Birmingham, AL 35294, USA.', 'United States|United States', 0, 'Algorithms|Animals|Bayes Theorem|Computational Biology|Gene Expression Profiling|Markov Chains|Mice|Models, Genetic|Pancreas|Probability|PubMed|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/1471-2105-12-359', '21884587', 'Bayesian Network (BN) is a powerful approach to reconstructing genetic regulatory networks from gene expression data. However, expression data by itself suffers from high noise and lack of power. Incorporating prior biological knowledge can improve the performance. As each type of prior knowledge on its own may be incomplete or limited by quality issues, integrating multiple sources of prior knowledge to utilize their consensus is desirable.'),
(94519, 2011, 'bmc', 'GO-based functional dissimilarity of gene sets', 'Díaz-Díaz, Norberto|Aguilar-Ruiz, Jesús S', 'School of Engineering, Pablo de Olavide University, Seville, Spain. ndiaz@upo.es|School of Engineering, Pablo de Olavide University, Seville, Spain. ndiaz@upo.es', 'Spain|Spain', 0, 'Algorithms|Databases, Genetic|Genes|Humans|Proteins|ROC Curve|Saccharomyces cerevisiae|Semantics|Unified Medical Language System|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-12-360', '21884611', 'The Gene Ontology (GO) provides a controlled vocabulary for describing the functions of genes and can be used to evaluate the functional coherence of gene sets. Many functional coherence measures consider each pair of gene functions in a set and produce an output based on all pairwise distances. A single gene can encode multiple proteins that may differ in function. For each functionality, other proteins that exhibit the same activity may also participate. Therefore, an identification of the most common function for all of the genes involved in a biological process is important in evaluating the functional similarity of groups of genes and a quantification of functional coherence can helps to clarify the role of a group of genes working together.'),
(94520, 2011, 'bmc', 'Implementing a genomic data management system using iRODS in the Wellcome Trust Sanger Institute', 'Chiang, Gen-Tao|Clapham, Peter|Qi, Guoying|Sale, Kevin|Coates, Guy', 'Wellcome Trust Sanger Institute, Informatics System Group, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK. gtc@sanger.ac.uk|Wellcome Trust Sanger Institute, Informatics System Group, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK. gtc@sanger.ac.uk|Wellcome Trust Sanger Institute, Informatics System Group, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK. gtc@sanger.ac.uk|Wellcome Trust Sanger Institute, Informatics System Group, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK. gtc@sanger.ac.uk|Wellcome Trust Sanger Institute, Informatics System Group, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK. gtc@sanger.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 16, 'Academies and Institutes|Computer Systems|Database Management Systems|Databases, Genetic|Genome|Genomics|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-12-361', '21906284', 'Increasingly large amounts of DNA sequencing data are being generated within the Wellcome Trust Sanger Institute (WTSI). The traditional file system struggles to handle these increasing amounts of sequence data. A good data management system therefore needs to be implemented and integrated into the current WTSI infrastructure. Such a system enables good management of the IT infrastructure of the sequencing pipeline and allows biologists to track their data.'),
(94521, 2011, 'bmc', 'Integrating GPCR-specific information with full text articles', 'Vroling, Bas|Thorne, David|McDermott, Philip|Attwood, Teresa K|Vriend, Gert|Pettifer, Steve', 'CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Geert Grooteplein 26-28, Nijmegen, 6525 GA, The Netherlands.|CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Geert Grooteplein 26-28, Nijmegen, 6525 GA, The Netherlands.|CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Geert Grooteplein 26-28, Nijmegen, 6525 GA, The Netherlands.|CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Geert Grooteplein 26-28, Nijmegen, 6525 GA, The Netherlands.|CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Geert Grooteplein 26-28, Nijmegen, 6525 GA, The Netherlands.|CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Geert Grooteplein 26-28, Nijmegen, 6525 GA, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Animals|Data Mining|Databases, Protein|Humans|Information Systems|Periodicals as Topic|Receptors, G-Protein-Coupled', 'Journal Article', '10.1186/1471-2105-12-362', '21910883', 'With the continued growth in the volume both of experimental G protein-coupled receptor (GPCR) data and of the related peer-reviewed literature, the ability of GPCR researchers to keep up-to-date is becoming increasingly curtailed.'),
(94522, 2011, 'bmc', 'Selection of organisms for the co-evolution-based study of protein interactions', 'Herman, Dorota|Ochoa, David|Juan, David|Lopez, Daniel|Valencia, Alfonso|Pazos, Florencio', 'Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), Cantoblanco, 28049 Madrid, Spain.|Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), Cantoblanco, 28049 Madrid, Spain.|Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), Cantoblanco, 28049 Madrid, Spain.|Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), Cantoblanco, 28049 Madrid, Spain.|Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), Cantoblanco, 28049 Madrid, Spain.|Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), Cantoblanco, 28049 Madrid, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain', 10, 'Algorithms|Archaea|Bacteria|Evolution, Molecular|Phylogeny|Protein Interaction Domains and Motifs|Proteins', 'Journal Article', '10.1186/1471-2105-12-363', '21910884', 'The prediction and study of protein interactions and functional relationships based on similarity of phylogenetic trees, exemplified by the mirrortree and related methodologies, is being widely used. Although dependence between the performance of these methods and the set of organisms used to build the trees was suspected, so far nobody assessed it in an exhaustive way, and, in general, previous works used as many organisms as possible. In this work we asses the effect of using different sets of organism (chosen according with various phylogenetic criteria) on the performance of this methodology in detecting protein interactions of different nature.'),
(94523, 2011, 'bmc', 'Characterizing genetic interactions in human disease association studies using statistical epistasis networks', 'Hu, Ting|Sinnott-Armstrong, Nicholas A|Kiralis, Jeff W|Andrew, Angeline S|Karagas, Margaret R|Moore, Jason H', 'Department of Genetics, Dartmouth Medical School, Dartmouth College, Lebanon, NH, USA.|Department of Genetics, Dartmouth Medical School, Dartmouth College, Lebanon, NH, USA.|Department of Genetics, Dartmouth Medical School, Dartmouth College, Lebanon, NH, USA.|Department of Genetics, Dartmouth Medical School, Dartmouth College, Lebanon, NH, USA.|Department of Genetics, Dartmouth Medical School, Dartmouth College, Lebanon, NH, USA.|Department of Genetics, Dartmouth Medical School, Dartmouth College, Lebanon, NH, USA.', 'Lebanon|Lebanon|Lebanon|Lebanon|Lebanon|Lebanon', 0, 'Adult|Aged|Epistasis, Genetic|Genotype|Humans|Middle Aged|New Hampshire|Polymorphism, Single Nucleotide|Urinary Bladder Neoplasms', 'Journal Article', '10.1186/1471-2105-12-364', '21910885', 'Epistasis is recognized ubiquitous in the genetic architecture of complex traits such as disease susceptibility. Experimental studies in model organisms have revealed extensive evidence of biological interactions among genes. Meanwhile, statistical and computational studies in human populations have suggested non-additive effects of genetic variation on complex traits. Although these studies form a baseline for understanding the genetic architecture of complex traits, to date they have only considered interactions among a small number of genetic variants. Our goal here is to use network science to determine the extent to which non-additive interactions exist beyond small subsets of genetic variants. We infer statistical epistasis networks to characterize the global space of pairwise interactions among approximately 1500 Single Nucleotide Polymorphisms (SNPs) spanning nearly 500 cancer susceptibility genes in a large population-based study of bladder cancer.'),
(94524, 2011, 'bmc', 'Simple Shared Motifs (SSM) in conserved region of promoters: a new approach to identify co-regulation patterns', 'Gruel, Jérémy|LeBorgne, Michel|LeMeur, Nolwenn|Théret, Nathalie', 'EA 4427 SeRAIC IFR140, Université de Rennes 1, 2 avenue du Pr, Léon Bernard, Rennes 35043, France. gruelj@gmail.com|EA 4427 SeRAIC IFR140, Université de Rennes 1, 2 avenue du Pr, Léon Bernard, Rennes 35043, France. gruelj@gmail.com|EA 4427 SeRAIC IFR140, Université de Rennes 1, 2 avenue du Pr, Léon Bernard, Rennes 35043, France. gruelj@gmail.com|EA 4427 SeRAIC IFR140, Université de Rennes 1, 2 avenue du Pr, Léon Bernard, Rennes 35043, France. gruelj@gmail.com', 'France|France|France|France', 1, 'Algorithms|Animals|Base Sequence|Conserved Sequence|Gene Expression Regulation|Genomics|Humans|Phylogeny|Promoter Regions, Genetic|Software', 'Journal Article', '10.1186/1471-2105-12-365', '21910886', 'Regulation of gene expression plays a pivotal role in cellular functions. However, understanding the dynamics of transcription remains a challenging task. A host of computational approaches have been developed to identify regulatory motifs, mainly based on the recognition of DNA sequences for transcription factor binding sites. Recent integration of additional data from genomic analyses or phylogenetic footprinting has significantly improved these methods.'),
(94525, 2011, 'bmc', 'MetaboLab--advanced NMR data processing and analysis for metabolomics', 'Ludwig, Christian|Günther, Ulrich L', 'The Henry Wellcome Building for Biomolecular NMR Spectroscopy, School of Cancer Sciences, University of Birmingham,Edgbaston, Birmingham B152TT, UK. C.Ludwig@bham.ac.uk|The Henry Wellcome Building for Biomolecular NMR Spectroscopy, School of Cancer Sciences, University of Birmingham,Edgbaston, Birmingham B152TT, UK. C.Ludwig@bham.ac.uk', 'United Kingdom|United Kingdom', 0, 'Algorithms|Humans|Magnetic Resonance Spectroscopy|Metabolomics|Software', 'Journal Article', '10.1186/1471-2105-12-366', '21914187', 'Despite wide-spread use of Nuclear Magnetic Resonance (NMR) in metabolomics for the analysis of biological samples there is a lack of graphically driven, publicly available software to process large one and two-dimensional NMR data sets for statistical analysis.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(94526, 2011, 'bmc', 'Methodology and software to detect viral integration site hot-spots', 'Presson, Angela P|Kim, Namshin|Xiaofei, Yan|Chen, Irvin Sy|Kim, Sanggu', 'Department of Biostatistics, University of California Los Angeles, School of Public Health, USA. apresson@ucla.edu|Department of Biostatistics, University of California Los Angeles, School of Public Health, USA. apresson@ucla.edu|Department of Biostatistics, University of California Los Angeles, School of Public Health, USA. apresson@ucla.edu|Department of Biostatistics, University of California Los Angeles, School of Public Health, USA. apresson@ucla.edu|Department of Biostatistics, University of California Los Angeles, School of Public Health, USA. apresson@ucla.edu', 'United States|United States|United States|United States|United States', 8, 'Adrenoleukodystrophy|Aged|Bayes Theorem|Clinical Trials as Topic|Cluster Analysis|Female|Genetic Therapy|Genetic Vectors|Genome, Human|Granulomatous Disease, Chronic|Humans|Lentivirus|Male|Middle Aged|Patient Safety|Software|X-Linked Combined Immunodeficiency Diseases', 'Journal Article', '10.1186/1471-2105-12-367', '21914224', 'Modern gene therapy methods have limited control over where a therapeutic viral vector inserts into the host genome. Vector integration can activate local gene expression, which can cause cancer if the vector inserts near an oncogene. Viral integration hot-spots or common insertion sites (CIS) are scrutinized to evaluate and predict patient safety. CIS are typically defined by a minimum density of insertions (such as 2-4 within a 30-100 kb region), which unfortunately depends on the total number of observed VIS. This is problematic for comparing hot-spot distributions across data sets and patients, where the VIS numbers may vary.'),
(94527, 2011, 'bmc', 'LoCo: a novel main chain scoring function for protein structure prediction based on local coordinates', 'Moughon, Stewart E|Samudrala, Ram', 'Department of Microbiology, University of Washington, Box 357735, Seattle, Washington 98195-7242, USA. wtec@u.washington.edu|Department of Microbiology, University of Washington, Box 357735, Seattle, Washington 98195-7242, USA. wtec@u.washington.edu', 'United States|United States', 7, 'Amino Acids|Knowledge Bases|Models, Chemical|Protein Conformation|Proteins', 'Journal Article', '10.1186/1471-2105-12-368', '21920038', 'Successful protein structure prediction requires accurate low-resolution scoring functions so that protein main chain conformations that are close to the native can be identified. Once that is accomplished, a more detailed and time-consuming treatment to produce all-atom models can be undertaken. The earliest low-resolution scoring used simple distance-based contact potentials, but more recently, the relative orientations of interacting amino acids have been taken into account to improve performance.'),
(94528, 2011, 'bmc', 'Thawing Frozen Robust Multi-array Analysis (fRMA)', 'McCall, Matthew N|Irizarry, Rafael A', 'Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA. mccallm@gmail.com|Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA. mccallm@gmail.com', 'United States|United States', 27, 'Algorithms|Animals|Databases, Factual|Freezing|Humans|Oligonucleotide Array Sequence Analysis|Preservation, Biological|Software', 'Journal Article', '10.1186/1471-2105-12-369', '21923903', 'A novel method of microarray preprocessing--Frozen Robust Multi-array Analysis (fRMA)--has recently been developed. This algorithm allows the user to preprocess arrays individually while retaining the advantages of multi-array preprocessing methods. The frozen parameter estimates required by this algorithm are generated using a large database of publicly available arrays. Curation of such a database and creation of the frozen parameter estimates is time-consuming, therefore, fRMA has only been implemented on the most widely used Affymetrix platforms.'),
(94529, 2011, 'bmc', 'Toponomics method for the automated quantification of membrane protein translocation', 'Domanova, Olga|Borbe, Stefan|Mühlfeld, Stefanie|Becker, Martin|Kubitz, Ralf|Häussinger, Dieter|Berlage, Thomas', 'Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, Sankt Augustin, Germany. olga.domanova@gmail.com|Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, Sankt Augustin, Germany. olga.domanova@gmail.com|Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, Sankt Augustin, Germany. olga.domanova@gmail.com|Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, Sankt Augustin, Germany. olga.domanova@gmail.com|Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, Sankt Augustin, Germany. olga.domanova@gmail.com|Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, Sankt Augustin, Germany. olga.domanova@gmail.com|Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, Sankt Augustin, Germany. olga.domanova@gmail.com', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Animals|Automation|Densitometry|Humans|Membrane Proteins|Protein Transport|Rats|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-12-370', '21929784', 'Intra-cellular and inter-cellular protein translocation can be observed by microscopic imaging of tissue sections prepared immunohistochemically. A manual densitometric analysis is time-consuming, subjective and error-prone. An automated quantification is faster, more reproducible, and should yield results comparable to manual evaluation. The automated method presented here was developed on rat liver tissue sections to study the translocation of bile salt transport proteins in hepatocytes. For validation, the cholestatic liver state was compared to the normal biological state.'),
(94530, 2011, 'bmc', 'The representation of protein complexes in the Protein Ontology (PRO)', 'Bult, Carol J|Drabkin, Harold J|Evsikov, Alexei|Natale, Darren|Arighi, Cecilia|Roberts, Natalia|Ruttenberg, Alan|DEustachio, Peter|Smith, Barry|Blake, Judith A|Wu, Cathy', 'The Jackson Laboratory, Bar Harbor, ME 04609, USA. carol.bult@jax.org|The Jackson Laboratory, Bar Harbor, ME 04609, USA. carol.bult@jax.org|The Jackson Laboratory, Bar Harbor, ME 04609, USA. carol.bult@jax.org|The Jackson Laboratory, Bar Harbor, ME 04609, USA. carol.bult@jax.org|The Jackson Laboratory, Bar Harbor, ME 04609, USA. carol.bult@jax.org|The Jackson Laboratory, Bar Harbor, ME 04609, USA. carol.bult@jax.org|The Jackson Laboratory, Bar Harbor, ME 04609, USA. carol.bult@jax.org|The Jackson Laboratory, Bar Harbor, ME 04609, USA. carol.bult@jax.org|The Jackson Laboratory, Bar Harbor, ME 04609, USA. carol.bult@jax.org|The Jackson Laboratory, Bar Harbor, ME 04609, USA. carol.bult@jax.org|The Jackson Laboratory, Bar Harbor, ME 04609, USA. carol.bult@jax.org', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Animals|Computational Biology|Databases, Protein|Humans|Internet|Multienzyme Complexes|Multiprotein Complexes|Proteins', 'Journal Article', '10.1186/1471-2105-12-371', '21929785', 'Representing species-specific proteins and protein complexes in ontologies that are both human- and machine-readable facilitates the retrieval, analysis, and interpretation of genome-scale data sets. Although existing protin-centric informatics resources provide the biomedical research community with well-curated compendia of protein sequence and structure, these resources lack formal ontological representations of the relationships among the proteins themselves. The Protein Ontology (PRO) Consortium is filling this informatics resource gap by developing ontological representations and relationships among proteins and their variants and modified forms. Because proteins are often functional only as members of stable protein complexes, the PRO Consortium, in collaboration with existing protein and pathway databases, has launched a new initiative to implement logical and consistent representation of protein complexes.'),
(94531, 2011, 'bmc', 'Significance testing in ridge regression for genetic data', 'Cule, Erika|Vineis, Paolo|De Iorio, Maria', 'Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK. erika.cule05@imperial.ac.uk|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK. erika.cule05@imperial.ac.uk|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK. erika.cule05@imperial.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 38, 'Case-Control Studies|Chromosome Mapping|Disease|Genetic Association Studies|Genetic Markers|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Prospective Studies|Regression Analysis', 'Journal Article', '10.1186/1471-2105-12-372', '21929786', 'Technological developments have increased the feasibility of large scale genetic association studies. Densely typed genetic markers are obtained using SNP arrays, next-generation sequencing technologies and imputation. However, SNPs typed using these methods can be highly correlated due to linkage disequilibrium among them, and standard multiple regression techniques fail with these data sets due to their high dimensionality and correlation structure. There has been increasing interest in using penalised regression in the analysis of high dimensional data. Ridge regression is one such penalised regression technique which does not perform variable selection, instead estimating a regression coefficient for each predictor variable. It is therefore desirable to obtain an estimate of the significance of each ridge regression coefficient.'),
(94532, 2011, 'bmc', 'Quantification of protein group coherence and pathway assignment using functional association', 'Chitale, Meghana|Palakodety, Shriphani|Kihara, Daisuke', 'Department of Computer Science, Purdue University, 305 N, University Street, West Lafayette, Indiana 47907, USA.|Department of Computer Science, Purdue University, 305 N, University Street, West Lafayette, Indiana 47907, USA.|Department of Computer Science, Purdue University, 305 N, University Street, West Lafayette, Indiana 47907, USA.', 'India|India|India', 6, 'Computational Biology|Genomics|Probability|Proteins|Proteomics|PubMed|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-12-373', '21929787', 'Genomics and proteomics experiments produce a large amount of data that are awaiting functional elucidation. An important step in analyzing such data is to identify functional units, which consist of proteins that play coherent roles to carry out the function. Importantly, functional coherence is not identical with functional similarity. For example, proteins in the same pathway may not share the same Gene Ontology (GO) terms, but they work in a coordinated fashion so that the aimed function can be performed. Thus, simply applying existing functional similarity measures might not be the best solution to identify functional units in omics data.'),
(94533, 2011, 'bmc', 'Hierarchical parallelization of gene differential association analysis', 'Needham, Mark|Hu, Rui|Dwarkadas, Sandhya|Qiu, Xing', 'Department of Computer Science, University of Rochester, New York 14627, USA.|Department of Computer Science, University of Rochester, New York 14627, USA.|Department of Computer Science, University of Rochester, New York 14627, USA.|Department of Computer Science, University of Rochester, New York 14627, USA.', 'United States|United States|United States|United States', 1, 'Algorithms|Cluster Analysis|Disease|Genetic Association Studies|Humans|Oligonucleotide Array Sequence Analysis|Programming Languages|Software', 'Journal Article', '10.1186/1471-2105-12-374', '21936916', 'Microarray gene differential expression analysis is a widely used technique that deals with high dimensional data and is computationally intensive for permutation-based procedures. Microarray gene differential association analysis is even more computationally demanding and must take advantage of multicore computing technology, which is the driving force behind increasing compute power in recent years. In this paper, we present a two-layer hierarchical parallel implementation of gene differential association analysis. It takes advantage of both fine- and coarse-grain (with granularity defined by the frequency of communication) parallelism in order to effectively leverage the non-uniform nature of parallel processing available in the cutting-edge systems of today.'),
(94534, 2011, 'bmc', 'Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction', 'Shi, Ping|Ray, Surajit|Zhu, Qifu|Kon, Mark A', 'Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, MA 02215, USA. pscshi@gmail.com|Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, MA 02215, USA. pscshi@gmail.com|Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, MA 02215, USA. pscshi@gmail.com|Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, MA 02215, USA. pscshi@gmail.com', 'United States|United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Humans|Neoplasms|Prognosis|Software|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-12-375', '21939564', 'The widely used k top scoring pair (k-TSP) algorithm is a simple yet powerful parameter-free classifier. It owes its success in many cancer microarray datasets to an effective feature selection algorithm that is based on relative expression ordering of gene pairs. However, its general robustness does not extend to some difficult datasets, such as those involving cancer outcome prediction, which may be due to the relatively simple voting scheme used by the classifier. We believe that the performance can be enhanced by separating its effective feature selection component and combining it with a powerful classifier such as the support vector machine (SVM). More generally the top scoring pairs generated by the k-TSP ranking algorithm can be used as a dimensionally reduced subspace for other machine learning classifiers.'),
(94535, 2011, 'bmc', 'EnzymeDetector: an integrated enzyme function prediction tool and database', 'Quester, Susanne|Schomburg, Dietmar', 'Institute of Bioinformatics and Biochemistry, Technische Universität Braunschweig, Braunschweig, Germany. S.Quester@tu-bs.de|Institute of Bioinformatics and Biochemistry, Technische Universität Braunschweig, Braunschweig, Germany. S.Quester@tu-bs.de', 'Germany|Germany', 36, 'Bacteria|Databases, Protein|Enzymes|Genome|Internet|Molecular Sequence Annotation|Prokaryotic Cells|Software', 'Journal Article', '10.1186/1471-2105-12-376', '21943292', 'The ability to accurately predict enzymatic functions is an essential prerequisite for the interpretation of cellular functions, and the reconstruction and analysis of metabolic models. Several biological databases exist that provide such information. However, in many cases these databases provide partly different and inconsistent genome annotations.'),
(94536, 2011, 'bmc', 'A comparative study on gene-set analysis methods for assessing differential expression associated with the survival phenotype', 'Lee, Seungyeoun|Kim, Jinheum|Lee, Sunho', 'Department of Mathematics and Statistics, Sejong University, Seoul, 143-747, Korea. leesy@sejong.ac.kr|Department of Mathematics and Statistics, Sejong University, Seoul, 143-747, Korea. leesy@sejong.ac.kr|Department of Mathematics and Statistics, Sejong University, Seoul, 143-747, Korea. leesy@sejong.ac.kr', '-|-|-', 0, 'Computer Simulation|Female|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Ovarian Neoplasms|Phenotype|Research Design|Survival Analysis', 'Journal Article', '10.1186/1471-2105-12-377', '21943316', 'Many gene-set analysis methods have been previously proposed and compared through simulation studies and analysis of real datasets for binary phenotypes. We focused on the survival phenotype and compared the performances of Gene Set Enrichment Analysis (GSEA), Global Test (GT), Wald-type Test (WT) and Global Boost Test (GBST) methods in a simulation study and on two ovarian cancer data sets. We considered two versions of GSEA by allowing different weights: GSEA1 uses equal weights, yielding results similar to the Kolmogorov-Smirnov test, while GSEA2s weights are based on the correlation between genes and the phenotype.'),
(94537, 2011, 'bmc', 'Prediction of protein-binding areas by small-world residue networks and application to docking', 'Pons, Carles|Glaser, Fabian|Fernandez-Recio, Juan', 'Joint BSC-IRB research programme in Computational Biology, Barcelona Supercomputing Center, Barcelona 08034, Spain.|Joint BSC-IRB research programme in Computational Biology, Barcelona Supercomputing Center, Barcelona 08034, Spain.|Joint BSC-IRB research programme in Computational Biology, Barcelona Supercomputing Center, Barcelona 08034, Spain.', 'Spain|Spain|Spain', 43, 'Algorithms|Humans|Models, Molecular|Protein Binding|Proteins|Software|Thermodynamics', 'Journal Article', '10.1186/1471-2105-12-378', '21943333', 'Protein-protein interactions are involved in most cellular processes, and their detailed physico-chemical and structural characterization is needed in order to understand their function at the molecular level. In-silico docking tools can complement experimental techniques, providing three-dimensional structural models of such interactions at atomic resolution. In several recent studies, protein structures have been modeled as networks (or graphs), where the nodes represent residues and the connecting edges their interactions. From such networks, it is possible to calculate different topology-based values for each of the nodes, and to identify protein regions with high centrality scores, which are known to positively correlate with key functional residues, hot spots, and protein-protein interfaces.'),
(94538, 2011, 'bmc', 'GOmotif: A web server for investigating the biological role of protein sequence motifs', 'Bristow, Franklin|He, Runtao|Van Domselaar, Gary', 'National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, R3E 3R2, Canada.|National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, R3E 3R2, Canada.|National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, R3E 3R2, Canada.', 'Canada|Canada|Canada', 0, 'Amino Acid Motifs|Amino Acid Sequence|Animals|Computational Biology|Conserved Sequence|Databases, Protein|Humans|Internet|Protein Structure, Tertiary|Proteins|Software', 'Journal Article', '10.1186/1471-2105-12-379', '21943350', 'Many proteins contain conserved sequence patterns (motifs) that contribute to their functionality. The process of experimentally identifying and validating novel protein motifs can be difficult, expensive, and time consuming. A means for helping to identify in advance the possible function of a novel motif is important to test hypotheses concerning the biological relevance of these motifs, thus reducing experimental trial-and-error.'),
(94539, 2011, 'bmc', 'iCTNet: a Cytoscape plugin to produce and analyze integrative complex traits networks', 'Wang, Lili|Khankhanian, Pouya|Baranzini, Sergio E|Mousavi, Parvin', 'School of Computing, Queens University, 25 Union Street, Goodwin Hall, Kingston, Ontario K7L 3N6, Canada.|School of Computing, Queens University, 25 Union Street, Goodwin Hall, Kingston, Ontario K7L 3N6, Canada.|School of Computing, Queens University, 25 Union Street, Goodwin Hall, Kingston, Ontario K7L 3N6, Canada.|School of Computing, Queens University, 25 Union Street, Goodwin Hall, Kingston, Ontario K7L 3N6, Canada.', 'Canada|Canada|Canada|Canada', 24, 'Databases, Factual|Databases, Genetic|Disease|Genome-Wide Association Study|Humans|Phenotype|Polymorphism, Single Nucleotide|Proteins|Software|Systems Integration', 'Journal Article', '10.1186/1471-2105-12-380', '21943367', 'The speed at which biological datasets are being accumulated stands in contrast to our ability to integrate them meaningfully. Large-scale biological databases containing datasets of genes, proteins, cells, organs, and diseases are being created but they are not connected. Integration of these vast but heterogeneous sources of information will allow the systematic and comprehensive analysis of molecular and clinical datasets, spanning hundreds of dimensions and thousands of individuals. This integration is essential to capitalize on the value of current and future molecular- and cellular-level data on humans to gain novel insights about health and disease.'),
(94540, 2011, 'bmc', 'Discovering biological connections between experimental conditions based on common patterns of differential gene expression', 'Gower, Adam C|Spira, Avrum|Lenburg, Marc E', 'Bioinformatics Program, Boston University, Boston, Massachusetts 02215, USA.|Bioinformatics Program, Boston University, Boston, Massachusetts 02215, USA.|Bioinformatics Program, Boston University, Boston, Massachusetts 02215, USA.', 'United States|United States|United States', 0, 'Algorithms|Breast Neoplasms|Cell Line, Tumor|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Membrane Proteins|Oligonucleotide Array Sequence Analysis|Receptors, Estrogen', 'Journal Article', '10.1186/1471-2105-12-381', '21951600', 'Identifying similarities between patterns of differential gene expression provides an opportunity to identify similarities between the experimental and biological conditions that give rise to these gene expression alterations. The growing volume of gene expression data in open data repositories such as the NCBI Gene Expression Omnibus (GEO) presents an opportunity to identify these gene expression similarities on a large scale across a diverse collection of datasets. We have developed a fast, pattern-based computational approach, named openSESAME (Search of Expression Signatures Across Many Experiments), that identifies datasets enriched in samples that display coordinate differential expression of a query signature. Importantly, openSESAME performs this search without prior knowledge of the phenotypic or experimental groups in the datasets being searched. This allows openSESAME to identify perturbations of gene expression that are due to phenotypic attributes that may not have been described in the sample annotation included in the repository. : To demonstrate the utility of openSESAME, we used gene expression signatures of two biological perturbations to query a set of 75,164 human expression profiles that were generated using Affymetrix microarrays and deposited in GEO. The first query, using a signature of estradiol treatment, identified experiments in which estrogen signaling was perturbed and also identified differences in estrogen signaling between estrogen receptor-positive and -negative breast cancers. The second query, which used a signature of silencing of the transcription factor p63 (a key regulator of epidermal differentiation), identified datasets related to stratified squamous epithelia or epidermal diseases such as melanoma.'),
(94541, 2011, 'bmc', 'Mulcom: a multiple comparison statistical test for microarray data in Bioconductor', 'Isella, Claudio|Renzulli, Tommaso|Corà, Davide|Medico, Enzo', 'Department of Oncological Sciences, University of Torino, Candiolo, Italy. claudio.isella@ircc.it|Department of Oncological Sciences, University of Torino, Candiolo, Italy. claudio.isella@ircc.it|Department of Oncological Sciences, University of Torino, Candiolo, Italy. claudio.isella@ircc.it|Department of Oncological Sciences, University of Torino, Candiolo, Italy. claudio.isella@ircc.it', 'Italy|Italy|Italy|Italy', 0, 'Algorithms|Gene Expression Profiling|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Regression Analysis', 'Journal Article', '10.1186/1471-2105-12-382', '21955789', 'Many microarray experiments search for genes with differential expression between a common reference group and multiple test groups. In such cases currently employed statistical approaches based on t-tests or close derivatives have limited efficacy, mainly because estimation of the standard error is done on only two groups at a time. Alternative approaches based on ANOVA correctly capture within-group variance from all the groups, but then do not confront single test groups with the reference. Ideally, a t-test better suited for this type of data would compare each test group with the reference, but use within-group variance calculated from all the groups.'),
(94542, 2011, 'bmc', 'caCORRECT2: Improving the accuracy and reliability of microarray data in the presence of artifacts', 'Moffitt, Richard A|Yin-Goen, Qiqin|Stokes, Todd H|Parry, R Mitchell|Torrance, James H|Phan, John H|Young, Andrew N|Wang, May D', 'The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA.|The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA.|The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA.|The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA.|The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA.|The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA.|The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA.|The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA.', 'Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia', 0, 'Artifacts|Carcinoma, Renal Cell|Follow-Up Studies|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Quality Control|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-12-383', '21957981', 'In previous work, we reported the development of caCORRECT, a novel microarray quality control system built to identify and correct spatial artifacts commonly found on Affymetrix arrays. We have made recent improvements to caCORRECT, including the development of a model-based data-replacement strategy and integration with typical microarray workflows via caCORRECTs web portal and caBIG grid services. In this report, we demonstrate that caCORRECT improves the reproducibility and reliability of experimental results across several common Affymetrix microarray platforms. caCORRECT represents an advance over state-of-art quality control methods such as Harshlighting, and acts to improve gene expression calculation techniques such as PLIER, RMA and MAS5.0, because it incorporates spatial information into outlier detection as well as outlier information into probe normalization. The ability of caCORRECT to recover accurate gene expressions from low quality probe intensity data is assessed using a combination of real and synthetic artifacts with PCR follow-up confirmation and the affycomp spike in data. The caCORRECT tool can be accessed at the website: http://cacorrect.bme.gatech.edu.'),
(94543, 2011, 'bmc', 'Multilocus association mapping using generalized ridge logistic regression', 'Liu, Zhe|Shen, Yuanyuan|Ott, Jurg', 'Department of Statistics, University of Chicago, IL 60637, USA.|Department of Statistics, University of Chicago, IL 60637, USA.|Department of Statistics, University of Chicago, IL 60637, USA.', 'United States|United States|United States', 11, 'Case-Control Studies|Chromosome Mapping|Disease|Genetic Predisposition to Disease|Genome-Wide Association Study|Humans|Linkage Disequilibrium|Logistic Models|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-12-384', '21958005', 'In genome-wide association studies, it is widely accepted that multilocus methods are more powerful than testing single-nucleotide polymorphisms (SNPs) one at a time. Among statistical approaches considering many predictors simultaneously, scan statistics are an effective tool for detecting susceptibility genomic regions and mapping disease genes. In this study, inspired by the idea of scan statistics, we propose a novel sliding window-based method for identifying a parsimonious subset of contiguous SNPs that best predict disease status.'),
(94544, 2011, 'bmc', 'Interactive metagenomic visualization in a Web browser', 'Ondov, Brian D|Bergman, Nicholas H|Phillippy, Adam M', 'National Biodefense Analysis and Countermeasures Center, 110 Thomas Johnson Drive, Frederick, MD 21702, USA. ondovb@nbacc.net|National Biodefense Analysis and Countermeasures Center, 110 Thomas Johnson Drive, Frederick, MD 21702, USA. ondovb@nbacc.net|National Biodefense Analysis and Countermeasures Center, 110 Thomas Johnson Drive, Frederick, MD 21702, USA. ondovb@nbacc.net', 'United States|United States|United States', 0, 'Computational Biology|Gastrointestinal Tract|Humans|Internet|Metagenomics|Software', 'Journal Article', '10.1186/1471-2105-12-385', '21961884', 'A critical output of metagenomic studies is the estimation of abundances of taxonomical or functional groups. The inherent uncertainty in assignments to these groups makes it important to consider both their hierarchical contexts and their prediction confidence. The current tools for visualizing metagenomic data, however, omit or distort quantitative hierarchical relationships and lack the facility for displaying secondary variables.'),
(94545, 2011, 'bmc', 'Cross-validated stepwise regression for identification of novel non-nucleoside reverse transcriptase inhibitor resistance associated mutations', 'Van der Borght, Koen|Van Craenenbroeck, Elke|Lecocq, Pierre|Van Houtte, Margriet|Van Kerckhove, Barbara|Bacheler, Lee|Verbeke, Geert|van Vlijmen, Herman', 'Tibotec-Virco, Beerse, Belgium. kvdborgh@its.jnj.com|Tibotec-Virco, Beerse, Belgium. kvdborgh@its.jnj.com|Tibotec-Virco, Beerse, Belgium. kvdborgh@its.jnj.com|Tibotec-Virco, Beerse, Belgium. kvdborgh@its.jnj.com|Tibotec-Virco, Beerse, Belgium. kvdborgh@its.jnj.com|Tibotec-Virco, Beerse, Belgium. kvdborgh@its.jnj.com|Tibotec-Virco, Beerse, Belgium. kvdborgh@its.jnj.com|Tibotec-Virco, Beerse, Belgium. kvdborgh@its.jnj.com', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Anti-HIV Agents|Genotype|HIV Infections|HIV-1|Humans|Linear Models|Mutation|Pyridazines|Reverse Transcriptase Inhibitors', 'Journal Article', '10.1186/1471-2105-12-386', '21966893', 'Linear regression models are used to quantitatively predict drug resistance, the phenotype, from the HIV-1 viral genotype. As new antiretroviral drugs become available, new resistance pathways emerge and the number of resistance associated mutations continues to increase. To accurately identify which drug options are left, the main goal of the modeling has been to maximize predictivity and not interpretability. However, we originally selected linear regression as the preferred method for its transparency as opposed to other techniques such as neural networks. Here, we apply a method to lower the complexity of these phenotype prediction models using a 3-fold cross-validated selection of mutations.'),
(94546, 2011, 'bmc', 'Biblio-MetReS: a bibliometric network reconstruction application and server', 'Usié, Anabel|Karathia, Hiren|Teixidó, Ivan|Valls, Joan|Faus, Xavier|Alves, Rui|Solsona, Francesc', 'Department dInformàtica i Enginyeria Industrial, Universitat de Lleida, Av, Jaume II n°69, 25001 Lleida, Spain.|Department dInformàtica i Enginyeria Industrial, Universitat de Lleida, Av, Jaume II n°69, 25001 Lleida, Spain.|Department dInformàtica i Enginyeria Industrial, Universitat de Lleida, Av, Jaume II n°69, 25001 Lleida, Spain.|Department dInformàtica i Enginyeria Industrial, Universitat de Lleida, Av, Jaume II n°69, 25001 Lleida, Spain.|Department dInformàtica i Enginyeria Industrial, Universitat de Lleida, Av, Jaume II n°69, 25001 Lleida, Spain.|Department dInformàtica i Enginyeria Industrial, Universitat de Lleida, Av, Jaume II n°69, 25001 Lleida, Spain.|Department dInformàtica i Enginyeria Industrial, Universitat de Lleida, Av, Jaume II n°69, 25001 Lleida, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 12, 'Animals|Bibliometrics|Data Mining|Gene Regulatory Networks|Internet|Publications|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-12-387', '21975133', 'Reconstruction of genes and/or protein networks from automated analysis of the literature is one of the current targets of text mining in biomedical research. Some user-friendly tools already perform this analysis on precompiled databases of abstracts of scientific papers. Other tools allow expert users to elaborate and analyze the full content of a corpus of scientific documents. However, to our knowledge, no user friendly tool that simultaneously analyzes the latest set of scientific documents available on line and reconstructs the set of genes referenced in those documents is available.'),
(94547, 2011, 'bmc', 'iRefScape A Cytoscape plug-in for visualization and data mining of protein interaction data from iRefIndex', 'Razick, Sabry|Mora, Antonio|Michalickova, Katerina|Boddie, Paul|Donaldson, Ian M', 'The Biotechnology Centre of Oslo, University of Oslo, P,O, Box 1125 Blindern, 0317 Oslo, Norway.|The Biotechnology Centre of Oslo, University of Oslo, P,O, Box 1125 Blindern, 0317 Oslo, Norway.|The Biotechnology Centre of Oslo, University of Oslo, P,O, Box 1125 Blindern, 0317 Oslo, Norway.|The Biotechnology Centre of Oslo, University of Oslo, P,O, Box 1125 Blindern, 0317 Oslo, Norway.|The Biotechnology Centre of Oslo, University of Oslo, P,O, Box 1125 Blindern, 0317 Oslo, Norway.', 'Norway|Norway|Norway|Norway|Norway', 12, 'Data Mining|Database Management Systems|Databases, Genetic|Databases, Protein|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1186/1471-2105-12-388', '21975162', 'The iRefIndex consolidates protein interaction data from ten databases in a rigorous manner using sequence-based hash keys. Working with consolidated interaction data comes with distinct challenges: data are redundant, overlapping, highly interconnected and may be collected and represented using different curation practices. These phenomena were quantified in our previous studies.'),
(94548, 2011, 'bmc', 'ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples', 'Mordelet, Fantine|Vert, Jean-Philippe', 'Centre for Computational Biology, Mines ParisTech, Fontainebleau, F-77300 France.|Centre for Computational Biology, Mines ParisTech, Fontainebleau, F-77300 France.', 'France|France', 0, 'Algorithms|Artificial Intelligence|Computational Biology|Databases, Genetic|Disease|Genetic Predisposition to Disease|Genome-Wide Association Study|Humans', 'Journal Article', '10.1186/1471-2105-12-389', '21977986', 'Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene candidates, the identification of disease genes among the candidates remains time-consuming and expensive. Efficient computational methods are therefore needed to prioritize genes within the list of candidates, by exploiting the wealth of information available about the genes in various databases.'),
(94549, 2011, 'bmc', 'Classification of microarrays, synergistic effects between normalization, gene selection and machine learning', 'Önskog, Jenny|Freyhult, Eva|Landfors, Mattias|Rydén, Patrik|Hvidsten, Torgeir R', 'Umeå Plant Science Center, Department of Plant Physiology, Umeå University, 901 87 Umeå, Sweden.|Umeå Plant Science Center, Department of Plant Physiology, Umeå University, 901 87 Umeå, Sweden.|Umeå Plant Science Center, Department of Plant Physiology, Umeå University, 901 87 Umeå, Sweden.|Umeå Plant Science Center, Department of Plant Physiology, Umeå University, 901 87 Umeå, Sweden.|Umeå Plant Science Center, Department of Plant Physiology, Umeå University, 901 87 Umeå, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Algorithms|Artificial Intelligence|Gene Expression Profiling|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Software|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-12-390', '21982277', 'Machine learning is a powerful approach for describing and predicting classes in microarray data. Although several comparative studies have investigated the relative performance of various machine learning methods, these often do not account for the fact that performance (e.g. error rate) is a result of a series of analysis steps of which the most important are data normalization, gene selection and machine learning.'),
(94550, 2011, 'bmc', 'Microarray-based cancer prediction using single genes', 'Wang, Xiaosheng|Simon, Richard', 'Biometric Research Branch, National Cancer Institute, National Institutes of Health, Rockville, MD 20852, USA.|Biometric Research Branch, National Cancer Institute, National Institutes of Health, Rockville, MD 20852, USA.', 'United States|United States', 0, 'Algorithms|Cluster Analysis|Gene Expression Profiling|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-12-391', '21982331', 'Although numerous methods of using microarray data analysis for cancer classification have been proposed, most utilize many genes to achieve accurate classification. This can hamper interpretability of the models and ease of translation to other assay platforms. We explored the use of single genes to construct classification models. We first identified the genes with the most powerful univariate class discrimination ability and then constructed simple classification rules for class prediction using the single genes.'),
(94551, 2011, 'bmc', 'An empirical Bayes model using a competition score for metabolite identification in gas chromatography mass spectrometry', 'Jeong, Jaesik|Shi, Xue|Zhang, Xiang|Kim, Seongho|Shen, Changyu', 'Department of Biostatistics, Indiana University, 410 West 10th Street, Indianapolis, IN 46202, USA.|Department of Biostatistics, Indiana University, 410 West 10th Street, Indianapolis, IN 46202, USA.|Department of Biostatistics, Indiana University, 410 West 10th Street, Indianapolis, IN 46202, USA.|Department of Biostatistics, Indiana University, 410 West 10th Street, Indianapolis, IN 46202, USA.|Department of Biostatistics, Indiana University, 410 West 10th Street, Indianapolis, IN 46202, USA.', 'India|India|India|India|India', 0, 'Animals|Bayes Theorem|Fatty Acids|Gas Chromatography-Mass Spectrometry|Plasma|Rats', 'Journal Article', '10.1186/1471-2105-12-392', '21985394', 'Mass spectrometry (MS) based metabolite profiling has been increasingly popular for scientific and biomedical studies, primarily due to recent technological development such as comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC/TOF-MS). Nevertheless, the identifications of metabolites from complex samples are subject to errors. Statistical/computational approaches to improve the accuracy of the identifications and false positive estimate are in great need. We propose an empirical Bayes model which accounts for a competing score in addition to the similarity score to tackle this problem. The competition score characterizes the propensity of a candidate metabolite of being matched to some spectrum based on the metabolites similarity score with other spectra in the library searched against. The competition score allows the model to properly assess the evidence on the presence/absence status of a metabolite based on whether or not the metabolite is matched to some sample spectrum.'),
(94552, 2011, 'bmc', 'Enriching a biomedical event corpus with meta-knowledge annotation', 'Thompson, Paul|Nawaz, Raheel|McNaught, John|Ananiadou, Sophia', 'National Centre for Text Mining, Manchester Interdisciplinary Biocentre, School of Computer Science, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. paul.thompson@manchester.ac.uk|National Centre for Text Mining, Manchester Interdisciplinary Biocentre, School of Computer Science, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. paul.thompson@manchester.ac.uk|National Centre for Text Mining, Manchester Interdisciplinary Biocentre, School of Computer Science, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. paul.thompson@manchester.ac.uk|National Centre for Text Mining, Manchester Interdisciplinary Biocentre, School of Computer Science, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. paul.thompson@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Data Mining|Database Management Systems|Humans|Information Storage and Retrieval|Information Systems|Knowledge|Semantics', 'Journal Article', '10.1186/1471-2105-12-393', '21985429', 'Biomedical papers contain rich information about entities, facts and events of biological relevance. To discover these automatically, we use text mining techniques, which rely on annotated corpora for training. In order to extract protein-protein interactions, genotype-phenotype/gene-disease associations, etc., we rely on event corpora that are annotated with classified, structured representations of important facts and findings contained within text. These provide an important resource for the training of domain-specific information extraction (IE) systems, to facilitate semantic-based searching of documents. Correct interpretation of these events is not possible without additional information, e.g., does an event describe a fact, a hypothesis, an experimental result or an analysis of results? How confident is the author about the validity of her analyses? These and other types of information, which we collectively term meta-knowledge, can be derived from the context of the event.'),
(94553, 2011, 'bmc', 'Improving probe set selection for microbial community analysis by leveraging taxonomic information of training sequences', 'Ruegger, Paul M|Della Vedova, Gianluca|Jiang, Tao|Borneman, James', 'Department of Plant Pathology and Microbiology, University of California, Riverside, CA 92521, USA.|Department of Plant Pathology and Microbiology, University of California, Riverside, CA 92521, USA.|Department of Plant Pathology and Microbiology, University of California, Riverside, CA 92521, USA.|Department of Plant Pathology and Microbiology, University of California, Riverside, CA 92521, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Bacteria|Bacterial Typing Techniques|Cluster Analysis|DNA Probes|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-12-394', '21985453', 'Population levels of microbial phylotypes can be examined using a hybridization-based method that utilizes a small set of computationally-designed DNA probes targeted to a gene common to all. Our previous algorithm attempts to select a set of probes such that each training sequence manifests a unique theoretical hybridization pattern (a binary fingerprint) to a probe set. It does so without taking into account similarity between training gene sequences or their putative taxonomic classifications, however. We present an improved algorithm for probe set selection that utilizes the available taxonomic information of training gene sequences and attempts to choose probes such that the resultant binary fingerprints cluster into real taxonomic groups.'),
(94554, 2011, 'bmc', 'The BioAssay network and its implications to future therapeutic discovery', 'Zhang, Jintao|Lushington, Gerald H|Huan, Jun', 'Center for Bioinformatics, University of Kansas, Lawrence, KS 66045, USA.|Center for Bioinformatics, University of Kansas, Lawrence, KS 66045, USA.|Center for Bioinformatics, University of Kansas, Lawrence, KS 66045, USA.', 'United States|United States|United States', 21, 'Animals|Biological Assay|Databases, Factual|Drug Discovery|Drug Therapy|Humans|Internet|Mice|Mice, Knockout|Proteins', 'Journal Article', '10.1186/1471-2105-12-S5-S1', '21988927', 'Despite intense investment growth and technology development, there is an observed bottleneck in drug discovery and development over the past decade. NIH started the Molecular Libraries Initiative (MLI) in 2003 to enlarge the pool for potential drug targets, especially from the undruggable part of human genome, and potential drug candidates from much broader types of drug-like small molecules. All results are being made publicly available in a web portal called PubChem.'),
(94555, 2011, 'bmc', 'Detection of splicing events and multiread locations from RNA-seq data based on a geometric-tail (GT) distribution of intron length', 'Lou, Shao-Ke|Li, Jing-Woei|Qin, Hao|Yim, Aldrin Kay-Yuen|Lo, Leung-Yau|Ni, Bing|Leung, Kwong-Sak|Tsui, Stephen Kwok-Wing|Chan, Ting-Fung', 'School of Life Sciences, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong', 0, 'Animals|Gene Expression|Genome|Humans|Introns|Likelihood Functions|RNA Splicing|Sequence Analysis, RNA|Software|Statistical Distributions', 'Journal Article', '10.1186/1471-2105-12-S5-S2', '21988959', 'RNA sequencing (RNA-seq) measures gene expression levels and permits splicing analysis. Many existing aligners are capable of mapping millions of sequencing reads onto a reference genome. For reads that can be mapped to multiple positions along the reference genome (multireads), these aligners may either randomly assign them to a location, or discard them altogether. Either way could bias downstream analyses. Meanwhile, challenges remain in the alignment of reads spanning across splice junctions. Existing splicing-aware aligners that rely on the read-count method in identifying junction sites are inevitably affected by sequencing depths.'),
(94556, 2011, 'bmc', 'Integrated analysis of the heterogeneous microarray data', 'Yi, Sung Gon|Park, Taesung', 'Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA.|Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA.', 'United States|United States', 0, 'Analysis of Variance|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Linear Models|Liver Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-12-S5-S3', '21989042', 'As the magnitude of the experiment increases, it is common to combine various types of microarrays such as paired and non-paired microarrays from different laboratories or hospitals. Thus, it is important to analyze microarray data together to derive a combined conclusion after accounting for heterogeneity among data sets. One of the main objectives of the microarray experiment is to identify differentially expressed genes among the different experimental groups. We propose the linear mixed effect model for the integrated analysis of the heterogeneous microarray data sets.'),
(94557, 2011, 'bmc', 'SNP and gene networks construction and analysis from classification of copy number variations data', 'Liu, Yang|Lee, Yiu Fai|Ng, Michael K', 'Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong.|Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong.|Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong.', 'Hong Kong|Hong Kong|Hong Kong', 0, 'Case-Control Studies|DNA Copy Number Variations|Diabetes Mellitus, Type 1|Gene Regulatory Networks|Genome-Wide Association Study|Hirschsprung Disease|Humans|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-12-S5-S4', '21989070', 'Detection of genomic DNA copy number variations (CNVs) can provide a complete and more comprehensive view of human disease. It is interesting to identify and represent relevant CNVs from a genome-wide data due to high data volume and the complexity of interactions.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(94558, 2011, 'bmc', 'Unsupervised fuzzy pattern discovery in gene expression data', 'Wu, Gene P K|Chan, Keith C C|Wong, Andrew K C', 'Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong. cspkwu@comp.polyu.edu.hk|Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong. cspkwu@comp.polyu.edu.hk|Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong. cspkwu@comp.polyu.edu.hk', 'Hong Kong|Hong Kong|Hong Kong', 1, 'Algorithms|Cluster Analysis|Colonic Neoplasms|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-12-S5-S5', '21989090', 'Discovering patterns from gene expression levels is regarded as a classification problem when tissue classes of the samples are given and solved as a discrete-data problem by discretizing the expression levels of each gene into intervals maximizing the interdependence between that gene and the class labels. However, when class information is unavailable, discovering gene expression patterns becomes difficult.'),
(94559, 2011, 'bmc', 'A robust approach to optimizing multi-source information for enhancing genomics retrieval performance', 'Hu, Qinmin|Huang, Jimmy Xiangji|Miao, Jun', 'Information Retrieval and Knowledge Management Research Lab, York University, Toronto, ON, M3J1P3, Canada.|Information Retrieval and Knowledge Management Research Lab, York University, Toronto, ON, M3J1P3, Canada.|Information Retrieval and Knowledge Management Research Lab, York University, Toronto, ON, M3J1P3, Canada.', 'Canada|Canada|Canada', 0, 'Gene Expression Profiling|Genomics|Humans|Information Storage and Retrieval', 'Journal Article', '10.1186/1471-2105-12-S5-S6', '21989123', 'The users desire to be provided short, specific answers to questions and put them in context by linking original sources from the biomedical literature. Through the use of information retrieval technologies, information systems retrieve information to index data based on all kinds of pre-defined searching techniques/functions such that various ranking strategies are designed depending on different sources. In this paper, we propose a robust approach to optimizing multi-source information for improving genomics retrieval performance.'),
(94560, 2011, 'bmc', 'Exploring matrix factorization techniques for significant genes identification of Alzheimers disease microarray gene expression data', 'Kong, Wei|Mou, Xiaoyang|Hu, Xiaohua', 'Information Engineering College, Shanghai Maritime University, Haigang Ave., Shanghai, 201306, P R China. weikong@shmtu.edu.cn|Information Engineering College, Shanghai Maritime University, Haigang Ave., Shanghai, 201306, P R China. weikong@shmtu.edu.cn|Information Engineering College, Shanghai Maritime University, Haigang Ave., Shanghai, 201306, P R China. weikong@shmtu.edu.cn', 'China|China|China', 19, 'Algorithms|Alzheimer Disease|Artificial Intelligence|Cluster Analysis|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Hippocampus|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-12-S5-S7', '21989140', 'The wide use of high-throughput DNA microarray technology provide an increasingly detailed view of human transcriptome from hundreds to thousands of genes. Although biomedical researchers typically design microarray experiments to explore specific biological contexts, the relationships between genes are hard to identified because they are complex and noisy high-dimensional data and are often hindered by low statistical power. The main challenge now is to extract valuable biological information from the colossal amount of data to gain insight into biological processes and the mechanisms of human disease. To overcome the challenge requires mathematical and computational methods that are versatile enough to capture the underlying biological features and simple enough to be applied efficiently to large datasets.'),
(94561, 2011, 'bmc', 'Promoting ranking diversity for genomics search with relevance-novelty combined model', 'Yin, Xiaoshi|Li, Zhoujun|Huang, Jimmy Xiangji|Hu, Xiaohua', 'State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China.|State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China.|State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China.|State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China.', 'China|China|China|China', 0, 'Algorithms|Genomics|Information Storage and Retrieval|Semantics|Unified Medical Language System|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-12-S5-S8', '21989180', 'In the biomedical domain, the desired information of a question (query) asked by biologists usually is a list of a certain type of entities covering different aspects that are related to the question, such as genes, proteins, diseases, mutations, etc. Hence it is important for a biomedical information retrieval system to be able to provide comprehensive and diverse answers to fulfill biologists information needs. However, traditional retrieval models assume that the relevance of a document is independent of the relevance of other documents. This assumption may result in high redundancy and low diversity in the retrieval ranked lists.'),
(94562, 2011, 'bmc', 'Inferring viral quasispecies spectra from 454 pyrosequencing reads', 'Astrovskaya, Irina|Tork, Bassam|Mangul, Serghei|Westbrooks, Kelly|Mxc4x83ndoiu, Ion|Balfe, Peter|Zelikovsky, Alex', 'Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA. iraa@cs.gsu.edu|Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA. iraa@cs.gsu.edu|Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA. iraa@cs.gsu.edu|Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA. iraa@cs.gsu.edu|Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA. iraa@cs.gsu.edu|Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA. iraa@cs.gsu.edu|Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA. iraa@cs.gsu.edu', 'Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia', 0, 'Algorithms|Genome, Viral|HIV|Hepacivirus|Humans|Microbial Sensitivity Tests|Phylogeny|RNA, Viral|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-12-S6-S1', '21989211', 'RNA viruses infecting a host usually exist as a set of closely related sequences, referred to as quasispecies. The genomic diversity of viral quasispecies is a subject of great interest, particularly for chronic infections, since it can lead to resistance to existing therapies. High-throughput sequencing is a promising approach to characterizing viral diversity, but unfortunately standard assembly software was originally designed for single genome assembly and cannot be used to simultaneously assemble and estimate the abundance of multiple closely related quasispecies sequences.'),
(94563, 2011, 'bmc', 'Genotyping common and rare variation using overlapping pool sequencing', 'He, Dan|Zaitlen, Noah|Pasaniuc, Bogdan|Eskin, Eleazar|Halperin, Eran', 'Department of Computer Science, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science, University of California, Los Angeles, CA 90095, USA.', 'United States|United States|United States|United States|United States', 4, 'Algorithms|Base Sequence|Gene Frequency|Gene Fusion|Genotype|Humans|Likelihood Functions|Neoplasms|Polymorphism, Single Nucleotide|Probability|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-12-S6-S2', '21989232', 'Recent advances in sequencing technologies set the stage for large, population based studies, in which the ANA or RNA of thousands of individuals will be sequenced. Currently, however, such studies are still infeasible using a straightforward sequencing approach, as a result, recently a few multiplexing schemes have been suggested, in which a small number of ANA pools are sequenced, and the results are then deconvoluted using compressed sensing or similar approaches. These methods, however, are limited to the detection of rare variants.'),
(94564, 2011, 'bmc', 'Assembly of non-unique insertion content using next-generation sequencing', 'Parrish, Nathaniel|Hormozdiari, Farhad|Eskin, Eleazar', 'Department of Computer Science, University of California Los Angeles, CA, USA.|Department of Computer Science, University of California Los Angeles, CA, USA.|Department of Computer Science, University of California Los Angeles, CA, USA.', 'United States|United States|United States', 7, 'Animals|Base Sequence|Genome, Human|Genomics|Humans|Mice|Mutagenesis, Insertional|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-12-S6-S3', '21989261', 'Recent studies in genomics have highlighted the significance of sequence insertions in determining individual variation. Efforts to discover the content of these sequence insertions have been limited to short insertions and long unique insertions. Much of the inserted sequence in the typical human genome, however, is a mixture of repeated and unique sequence. Current methods are designed to assemble only unique sequence insertions, using reads that do not map to the reference. These methods are not able to assemble repeated sequence insertions, as the reads will map to the reference in a different locus.In this paper, we present a computational method for discovering the content of sequence insertions that are unique, repeated, or a combination of the two. Our method analyzes the read mappings and depth of coverage of paired-end reads to identify reads that originated from inserted sequence. We demonstrate the process of assembling these reads to characterize the insertion content. Our method is based on the idea of segment extension, which progressively extends segments of known content using paired-end reads. We apply our method in simulation to discover the content of inserted sequences in a modified mouse chromosome and show that our method produces reliable results at 40x coverage.'),
(94565, 2011, 'bmc', 'A hidden Markov model for copy number variant prediction from whole genome resequencing data', 'Shen, Yufeng|Gu, Yiwei|Peer, Itsik', 'Department of Computer Science, Columbia University, New York, NY 10027, USA. yshen@c2b2.columbia.edu|Department of Computer Science, Columbia University, New York, NY 10027, USA. yshen@c2b2.columbia.edu|Department of Computer Science, Columbia University, New York, NY 10027, USA. yshen@c2b2.columbia.edu', 'United States|United States|United States', 0, 'Algorithms|Base Sequence|Chromosome Mapping|DNA Copy Number Variations|Genome|Genome, Human|Genome-Wide Association Study|Humans|Markov Chains', 'Journal Article', '10.1186/1471-2105-12-S6-S4', '21989326', 'Copy Number Variants (CNVs) are important genetic factors for studying human diseases. While high-throughput whole genome re-sequencing provides multiple lines of evidence for detecting CNVs, computational algorithms need to be tailored for different type or size of CNVs under different experimental designs.'),
(94566, 2011, 'bmc', 'Phamerator: a bioinformatic tool for comparative bacteriophage genomics', 'Cresawn, Steven G|Bogel, Matt|Day, Nathan|Jacobs-Sera, Deborah|Hendrix, Roger W|Hatfull, Graham F', 'Department of Biology, James Madison University, 820 Madison Dr, MSC 7801, Harrisonburg, VA 22807 USA. cresawsg@jmu.edu|Department of Biology, James Madison University, 820 Madison Dr, MSC 7801, Harrisonburg, VA 22807 USA. cresawsg@jmu.edu|Department of Biology, James Madison University, 820 Madison Dr, MSC 7801, Harrisonburg, VA 22807 USA. cresawsg@jmu.edu|Department of Biology, James Madison University, 820 Madison Dr, MSC 7801, Harrisonburg, VA 22807 USA. cresawsg@jmu.edu|Department of Biology, James Madison University, 820 Madison Dr, MSC 7801, Harrisonburg, VA 22807 USA. cresawsg@jmu.edu|Department of Biology, James Madison University, 820 Madison Dr, MSC 7801, Harrisonburg, VA 22807 USA. cresawsg@jmu.edu', 'United States|United States|United States|United States|United States|United States', 156, 'Bacillus Phages|Bacteriophages|Genome, Viral|Genomics|Open Reading Frames|Phylogeny|Software|Streptomyces', 'Journal Article', '10.1186/1471-2105-12-395', '21991981', 'Bacteriophage genomes have mosaic architectures and are replete with small open reading frames of unknown function, presenting challenges in their annotation, comparative analysis, and representation.'),
(94567, 2011, 'bmc', 'Techniques for analysing pattern formation in populations of stem cells and their progeny', 'Fozard, John A|Kirkham, Glen R|Buttery, Lee Dk|King, John R|Jensen, Oliver E|Byrne, Helen M', 'Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK.|Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK.|Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK.|Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK.|Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK.|Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, 'Animals|Cell Culture Techniques|Cell Differentiation|Cell Lineage|Mice|Models, Biological|Signal Transduction|Stem Cells', 'Journal Article', '10.1186/1471-2105-12-396', '21991994', 'To investigate how patterns of cell differentiation are related to underlying intra- and inter-cellular signalling pathways, we use a stochastic individual-based model to simulate pattern formation when stem cells and their progeny are cultured as a monolayer. We assume that the fate of an individual cell is regulated by the signals it receives from neighbouring cells via either diffusive or juxtacrine signalling. We analyse simulated patterns using two different spatial statistical measures that are suited to planar multicellular systems: pair correlation functions (PCFs) and quadrat histograms (QHs).'),
(94568, 2011, 'bmc', 'The BioLexicon: a large-scale terminological resource for biomedical text mining', 'Thompson, Paul|McNaught, John|Montemagni, Simonetta|Calzolari, Nicoletta|del Gratta, Riccardo|Lee, Vivian|Marchi, Simone|Monachini, Monica|Pezik, Piotr|Quochi, Valeria|Rupp, C J|Sasaki, Yutaka|Venturi, Giulia|Rebholz-Schuhmann, Dietrich|Ananiadou, Sophia', 'School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. paul.thompson@manchester.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. paul.thompson@manchester.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. paul.thompson@manchester.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. paul.thompson@manchester.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. paul.thompson@manchester.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. paul.thompson@manchester.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. paul.thompson@manchester.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. paul.thompson@manchester.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. paul.thompson@manchester.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. paul.thompson@manchester.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. paul.thompson@manchester.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. paul.thompson@manchester.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. paul.thompson@manchester.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. paul.thompson@manchester.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. paul.thompson@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Data Mining|Databases, Factual|Humans|Semantics|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-12-397', '21992002', 'Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events.'),
(94569, 2011, 'bmc', 'SCOWLP update: 3D classification of protein-protein, -peptide, -saccharide and -nucleic acid interactions, and structure-based binding inferences across folds', 'Teyra, Joan|Samsonov, Sergey A|Schreiber, Sven|Pisabarro, M Teresa', 'Structural Bioinformatics BIOTEC TU Dresden, Tatzberg 47-51 01037 Dresden, Germany. jt@biotec.tu-dresden.de|Structural Bioinformatics BIOTEC TU Dresden, Tatzberg 47-51 01037 Dresden, Germany. jt@biotec.tu-dresden.de|Structural Bioinformatics BIOTEC TU Dresden, Tatzberg 47-51 01037 Dresden, Germany. jt@biotec.tu-dresden.de|Structural Bioinformatics BIOTEC TU Dresden, Tatzberg 47-51 01037 Dresden, Germany. jt@biotec.tu-dresden.de', 'Germany|Germany|Germany|Germany', 0, 'Animals|Carbohydrate Metabolism|Carbohydrates|Databases, Protein|Humans|Internet|Ligands|Nucleic Acids|Peptides|Protein Binding|Protein Interaction Maps|Protein Structure, Tertiary|Proteins|Software', 'Journal Article', '10.1186/1471-2105-12-398', '21992011', 'Protein interactions are essential for coordinating cellular functions. Proteomic studies have already elucidated a huge amount of protein-protein interactions that require detailed functional analysis. Understanding the structural basis of each individual interaction through their structural determination is necessary, yet an unfeasible task. Therefore, computational tools able to predict protein binding regions and recognition modes are required to rationalize putative molecular functions for proteins. With this aim, we previously created SCOWLP, a structural classification of protein binding regions at protein family level, based on the information obtained from high-resolution 3D protein-protein and protein-peptide complexes.'),
(94570, 2011, 'bmc', 'Characterization of pathogenic germline mutations in human protein kinases', 'Izarzugaza, Jose M G|Hopcroft, Lisa E M|Baresic, Anja|Orengo, Christine A|Martin, Andrew C R|Valencia, Alfonso', 'Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernandez Almagro 3, E28029 Madrid, Spain.|Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernandez Almagro 3, E28029 Madrid, Spain.|Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernandez Almagro 3, E28029 Madrid, Spain.|Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernandez Almagro 3, E28029 Madrid, Spain.|Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernandez Almagro 3, E28029 Madrid, Spain.|Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernandez Almagro 3, E28029 Madrid, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain', 9, 'Germ-Line Mutation|Humans|Models, Molecular|Neoplasms|Nuclear Receptor Subfamily 4, Group A, Member 2|Point Mutation|Protein Binding|Protein Kinases|Protein Structure, Tertiary|Signal Transduction', 'Journal Article', '10.1186/1471-2105-12-S4-S1', '21992016', 'Protein Kinases are a superfamily of proteins involved in crucial cellular processes such as cell cycle regulation and signal transduction. Accordingly, they play an important role in cancer biology. To contribute to the study of the relation between kinases and disease we compared pathogenic mutations to neutral mutations as an extension to our previous analysis of cancer somatic mutations. First, we analyzed native and mutant proteins in terms of amino acid composition. Secondly, mutations were characterized according to their potential structural effects and finally, we assessed the location of the different classes of polymorphisms with respect to kinase-relevant positions in terms of subfamily specificity, conservation, accessibility and functional sites.'),
(94571, 2011, 'bmc', 'Genome-wide prediction of splice-modifying SNPs in human genes using a new analysis pipeline called AASsites', 'Faber, Kirsten|Glatting, Karl-Heinz|Mueller, Phillip J|Risch, Angela|Hotz-Wagenblatt, Agnes', 'Bioinformatics (HUSAR), Core Facility Genomics and Proteomics, German Cancer Research Center, D-69120 Heidelberg, Germany.|Bioinformatics (HUSAR), Core Facility Genomics and Proteomics, German Cancer Research Center, D-69120 Heidelberg, Germany.|Bioinformatics (HUSAR), Core Facility Genomics and Proteomics, German Cancer Research Center, D-69120 Heidelberg, Germany.|Bioinformatics (HUSAR), Core Facility Genomics and Proteomics, German Cancer Research Center, D-69120 Heidelberg, Germany.|Bioinformatics (HUSAR), Core Facility Genomics and Proteomics, German Cancer Research Center, D-69120 Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Disease|Genome, Human|Genome-Wide Association Study|Humans|Internet|Open Reading Frames|Polymorphism, Single Nucleotide|RNA Splice Sites|RNA Splicing|Software', 'Journal Article', '10.1186/1471-2105-12-S4-S2', '21992029', 'Some single nucleotide polymorphisms (SNPs) are known to modify the risk of developing certain diseases or the reaction to drugs. Due to next generation sequencing methods the number of known human SNPs has grown. Not all SNPs lead to a modified protein, which may be the origin of a disease. Therefore, the recognition of functional SNPs is needed. Because most SNP annotation tools look for SNPs which lead to an amino acid exchange or a premature stop, we designed a new tool called AASsites which searches for SNPs which modify splicing.'),
(94572, 2011, 'bmc', 'Improving the prediction of disease-related variants using protein three-dimensional structure', 'Capriotti, Emidio|Altman, Russ B', 'Department of Bioengineering, Stanford University, Stanford, CA, USA. emidio@stanford.edu|Department of Bioengineering, Stanford University, Stanford, CA, USA. emidio@stanford.edu', 'United States|United States', 61, 'Amino Acid Sequence|Disease|Humans|Models, Molecular|Mutation|Polymorphism, Single Nucleotide|Proteins|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-12-S4-S3', '21992054', 'Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability. Non-synonymous SNPs occurring in coding regions result in single amino acid polymorphisms (SAPs) that may affect protein function and lead to pathology. Several methods attempt to estimate the impact of SAPs using different sources of information. Although sequence-based predictors have shown good performance, the quality of these predictions can be further improved by introducing new features derived from three-dimensional protein structures.'),
(94573, 2011, 'bmc', 'Challenges in the association of human single nucleotide polymorphism mentions with unique database identifiers', 'Thomas, Philippe E|Klinger, Roman|Furlong, Laura I|Hofmann-Apitius, Martin|Friedrich, Christoph M', 'Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Department of Bioinformatics, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. thomas@informatik.hu-berlin.de|Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Department of Bioinformatics, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. thomas@informatik.hu-berlin.de|Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Department of Bioinformatics, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. thomas@informatik.hu-berlin.de|Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Department of Bioinformatics, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. thomas@informatik.hu-berlin.de|Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Department of Bioinformatics, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. thomas@informatik.hu-berlin.de', 'Germany|Germany|Germany|Germany|Germany', 29, 'Databases, Genetic|Disease|Genetic Variation|Genomics|Humans|Information Storage and Retrieval|Mutation|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-12-S4-S4', '21992066', 'Most information on genomic variations and their associations with phenotypes are covered exclusively in scientific publications rather than in structured databases. These texts commonly describe variations using natural language, database identifiers are seldom mentioned. This complicates the retrieval of variations, associated articles, as well as information extraction, e. g. the search for biological implications. To overcome these challenges, procedures to map textual mentions of variations to database identifiers need to be developed.'),
(94574, 2011, 'bmc', 'A formalized description of the standard human variant nomenclature in Extended Backus-Naur Form', 'Laros, Jeroen F J|Blavier, André|den Dunnen, Johan T|Taschner, Peter E M', 'Department of Human Genetics, Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands.|Department of Human Genetics, Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands.|Department of Human Genetics, Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands.|Department of Human Genetics, Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Data Mining|Genetic Variation|Genome, Human|Humans|Mutation|Proteins|Sequence Analysis, DNA|Software|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-12-S4-S5', '21992071', 'The use of a standard human sequence variant nomenclature is advocated by the Human Genome Variation Society in order to unambiguously describe genetic variants in databases and literature. There is a clear need for tools that allow the mining of data about human sequence variants and their functional consequences from databases and literature. Existing text mining focuses on the recognition of protein variants and their effects. The recognition of variants at the DNA and RNA levels is essential for dissemination of variant data for diagnostic purposes. Development of new tools is hampered by the complexity of the current nomenclature, which requires processing at the character level to recognize the specific syntactic constructs used in variant descriptions.'),
(94575, 2011, 'bmc', 'Deploying mutation impact text-mining software with the SADI Semantic Web Services framework', 'Riazanov, Alexandre|Laurila, Jonas Bergman|Baker, Christopher J O', 'Department of Computer Science & Applied Statistics, University of New Brunswick, Saint John, New Brunswick, E2L 4L5, Canada.|Department of Computer Science & Applied Statistics, University of New Brunswick, Saint John, New Brunswick, E2L 4L5, Canada.|Department of Computer Science & Applied Statistics, University of New Brunswick, Saint John, New Brunswick, E2L 4L5, Canada.', 'Canada|Canada|Canada', 14, 'Animals|Computational Biology|Data Mining|Humans|Mutation|Proteins|Semantics|Software', 'Journal Article', '10.1186/1471-2105-12-S4-S6', '21992079', 'Mutation impact extraction is an important task designed to harvest relevant annotations from scientific documents for reuse in multiple contexts. Our previous work on text mining for mutation impacts resulted in (i) the development of a GATE-based pipeline that mines texts for information about impacts of mutations on proteins, (ii) the population of this information into our OWL DL mutation impact ontology, and (iii) establishing an experimental semantic database for storing the results of text mining.'),
(94576, 2011, 'bmc', 'Bayesian hierarchical clustering for microarray time series data with replicates and outlier measurements', 'Cooke, Emma J|Savage, Richard S|Kirk, Paul D W|Darkins, Robert|Wild, David L', 'Systems Biology Centre, University of Warwick, Coventry, UK.|Systems Biology Centre, University of Warwick, Coventry, UK.|Systems Biology Centre, University of Warwick, Coventry, UK.|Systems Biology Centre, University of Warwick, Coventry, UK.|Systems Biology Centre, University of Warwick, Coventry, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 36, 'Algorithms|Bayes Theorem|Cluster Analysis|Gene Expression Profiling|Humans|Models, Biological|Normal Distribution|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-12-399', '21995452', 'Post-genomic molecular biology has resulted in an explosion of data, providing measurements for large numbers of genes, proteins and metabolites. Time series experiments have become increasingly common, necessitating the development of novel analysis tools that capture the resulting data structure. Outlier measurements at one or more time points present a significant challenge, while potentially valuable replicate information is often ignored by existing techniques.'),
(94577, 2011, 'bmc', 'MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures', 'Tulpan, Dan|Lxc3xa9ger, Serge|Belliveau, Luc|Culf, Adrian|Cuperlovixc4x87-Culf, Miroslava', 'Institute for Information Technology, National Research Council of Canada, Moncton, New Brunswick, E1A 7R1, Canada. dan.tulpan@nrc-cnrc.gc.ca|Institute for Information Technology, National Research Council of Canada, Moncton, New Brunswick, E1A 7R1, Canada. dan.tulpan@nrc-cnrc.gc.ca|Institute for Information Technology, National Research Council of Canada, Moncton, New Brunswick, E1A 7R1, Canada. dan.tulpan@nrc-cnrc.gc.ca|Institute for Information Technology, National Research Council of Canada, Moncton, New Brunswick, E1A 7R1, Canada. dan.tulpan@nrc-cnrc.gc.ca|Institute for Information Technology, National Research Council of Canada, Moncton, New Brunswick, E1A 7R1, Canada. dan.tulpan@nrc-cnrc.gc.ca', 'Canada|Canada|Canada|Canada|Canada', 0, 'Internet|Magnetic Resonance Imaging|Magnetic Resonance Spectroscopy|Metabolomics|Software', 'Journal Article', '10.1186/1471-2105-12-400', '21999117', 'One-dimensional 1H-NMR spectroscopy is widely used for high-throughput characterization of metabolites in complex biological mixtures. However, the accurate identification of individual compounds is still a challenging task, particularly in spectral regions with higher peak densities. The need for automatic tools to facilitate and further improve the accuracy of such tasks, while using increasingly larger reference spectral libraries becomes a priority of current metabolomics research.'),
(94578, 2011, 'bmc', 'Comparison of lists of genes based on functional profiles', 'Salicrú, Miquel|Ocaña, Jordi|Sánchez-Pla, Alex', 'Statistics Department, University of Barcelona, Barcelona, Spain.|Statistics Department, University of Barcelona, Barcelona, Spain.|Statistics Department, University of Barcelona, Barcelona, Spain.', 'Spain|Spain|Spain', 7, 'Algorithms|Databases, Genetic|Disease|Gene Expression Profiling|Genes, Dominant|Genes, Recessive|Genomics|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-12-401', '21999355', 'How to compare studies on the basis of their biological significance is a problem of central importance in high-throughput genomics. Many methods for performing such comparisons are based on the information in databases of functional annotation, such as those that form the Gene Ontology (GO). Typically, they consist of analyzing gene annotation frequencies in some pre-specified GO classes, in a class-by-class way, followed by p-value adjustment for multiple testing. Enrichment analysis, where a list of genes is compared against a wider universe of genes, is the most common example.'),
(94579, 2011, 'bmc', 'Proceedings of the Tenth Annual UT-ORNL-KBRIN Bioinformatics Summit 2011', 'Rouchka, Eric C|Flight, Robert M|Homayouni, Ramin', 'Department of Computer Engineering and Computer Science, University of Louisville, Duthie Center for Engineering, Louisville, KY 40292, USA. eric.rouchka@louisville.edu|Department of Computer Engineering and Computer Science, University of Louisville, Duthie Center for Engineering, Louisville, KY 40292, USA. eric.rouchka@louisville.edu|Department of Computer Engineering and Computer Science, University of Louisville, Duthie Center for Engineering, Louisville, KY 40292, USA. eric.rouchka@louisville.edu', 'United States|United States|United States', 7, 'Computational Biology|Genomics|Systems Biology', 'Congress', '10.1186/1471-2105-12-401', '21999392', '-'),
(94580, 2011, 'bmc', 'Mitochondrial genome sequence analysis: a custom bioinformatics pipeline substantially improves Affymetrix MitoChip v20 call rate and accuracy', 'Xie, Hongbo M|Perin, Juan C|Schurr, Theodore G|Dulik, Matthew C|Zhadanov, Sergey I|Baur, Joseph A|King, Michael P|Place, Emily|Clarke, Colleen|Grauer, Michael|Schug, Jonathan|Santani, Avni|Albano, Anthony|Kim, Cecilia|Procaccio, Vincent|Hakonarson, Hakon|Gai, Xiaowu|Falk, Marni J', 'Center for Biomedical Informatics, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Center for Biomedical Informatics, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Center for Biomedical Informatics, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Center for Biomedical Informatics, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Center for Biomedical Informatics, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Center for Biomedical Informatics, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Center for Biomedical Informatics, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Center for Biomedical Informatics, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Center for Biomedical Informatics, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Center for Biomedical Informatics, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Center for Biomedical Informatics, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Center for Biomedical Informatics, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Center for Biomedical Informatics, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Center for Biomedical Informatics, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Center for Biomedical Informatics, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Center for Biomedical Informatics, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Center for Biomedical Informatics, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, USA.|Center for Biomedical Informatics, The Childrens Hospital of Philadelphia, Philadelphia, PA 19104, 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|United States|United States|United States', 0, 'Computational Biology|Genome, Human|Genome, Mitochondrial|Humans|Mitochondria|Mutation|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-12-402', '22011106', 'Mitochondrial genome sequence analysis is critical to the diagnostic evaluation of mitochondrial disease. Existing methodologies differ widely in throughput, complexity, cost efficiency, and sensitivity of heteroplasmy detection. Affymetrix MitoChip v2.0, which uses a sequencing-by-genotyping technology, allows potentially accurate and high-throughput sequencing of the entire human mitochondrial genome to be completed in a cost-effective fashion. However, the relatively low call rate achieved using existing software tools has limited the wide adoption of this platform for either clinical or research applications. Here, we report the design and development of a custom bioinformatics software pipeline that achieves a much improved call rate and accuracy for the Affymetrix MitoChip v2.0 platform. We used this custom pipeline to analyze MitoChip v2.0 data from 24 DNA samples representing a broad range of tissue types (18 whole blood, 3 skeletal muscle, 3 cell lines), mutations (a 5.8 kilobase pair deletion and 6 known heteroplasmic mutations), and haplogroup origins. All results were compared to those obtained by at least one other mitochondrial DNA sequence analysis method, including Sanger sequencing, denaturing HPLC-based heteroduplex analysis, and/or the Illumina Genome Analyzer II next generation sequencing platform.'),
(94581, 2011, 'bmc', 'DART: Denoising Algorithm based on Relevance network Topology improves molecular pathway activity inference', 'Jiao, Yan|Lawler, Katherine|Patel, Gargi S|Purushotham, Arnie|Jones, Annette F|Grigoriadis, Anita|Tutt, Andrew|Ng, Tony|Teschendorff, Andrew E', 'Statistical Genomics Group, Paul OGorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK.|Statistical Genomics Group, Paul OGorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK.|Statistical Genomics Group, Paul OGorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK.|Statistical Genomics Group, Paul OGorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK.|Statistical Genomics Group, Paul OGorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK.|Statistical Genomics Group, Paul OGorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK.|Statistical Genomics Group, Paul OGorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK.|Statistical Genomics Group, Paul OGorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK.|Statistical Genomics Group, Paul OGorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Breast Neoplasms|Computer Simulation|Female|Gene Expression Profiling|Humans|Lung Neoplasms|Mammography|Neoplasms|Protein Interaction Maps|Signal Transduction', 'Journal Article', '10.1186/1471-2105-12-403', '22011170', 'Inferring molecular pathway activity is an important step towards reducing the complexity of genomic data, understanding the heterogeneity in clinical outcome, and obtaining molecular correlates of cancer imaging traits. Increasingly, approaches towards pathway activity inference combine molecular profiles (e.g gene or protein expression) with independent and highly curated structural interaction data (e.g protein interaction networks) or more generally with prior knowledge pathway databases. However, it is unclear how best to use the pathway knowledge information in the context of molecular profiles of any given study.'),
(94582, 2011, 'bmc', 'pcrEfficiency: a Web tool for PCR amplification efficiency prediction', 'Mallona, Izaskun|Weiss, Julia|Egea-Cortines, Marcos', 'Genetics, Institute of Plant Biotechnology (IBV), Technical University of Cartagena (UPCT), Campus Muralla del Mar, 30202 Cartagena, Spain. izaskun.mallona@upct.es|Genetics, Institute of Plant Biotechnology (IBV), Technical University of Cartagena (UPCT), Campus Muralla del Mar, 30202 Cartagena, Spain. izaskun.mallona@upct.es|Genetics, Institute of Plant Biotechnology (IBV), Technical University of Cartagena (UPCT), Campus Muralla del Mar, 30202 Cartagena, Spain. izaskun.mallona@upct.es', 'Spain|Spain|Spain', 0, 'Bacteria|DNA Primers|Humans|Internet|Models, Statistical|Plants|Polymerase Chain Reaction|Real-Time Polymerase Chain Reaction|Software', 'Journal Article', '10.1186/1471-2105-12-404', '22014212', 'Relative calculation of differential gene expression in quantitative PCR reactions requires comparison between amplification experiments that include reference genes and genes under study. Ignoring the differences between their efficiencies may lead to miscalculation of gene expression even with the same starting amount of template. Although there are several tools performing PCR primer design, there is no tool available that predicts PCR efficiency for a given amplicon and primer pair.'),
(94583, 2011, 'bmc', 'An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data', 'Vu, Trung N|Valkenborg, Dirk|Smets, Koen|Verwaest, Kim A|Dommisse, Roger|Lemière, Filip|Verschoren, Alain|Goethals, Bart|Laukens, Kris', 'Department of Mathematics and Computer Science, University of Antwerp, Belgium. trungnghia.vu@ua.ac.be|Department of Mathematics and Computer Science, University of Antwerp, Belgium. trungnghia.vu@ua.ac.be|Department of Mathematics and Computer Science, University of Antwerp, Belgium. trungnghia.vu@ua.ac.be|Department of Mathematics and Computer Science, University of Antwerp, Belgium. trungnghia.vu@ua.ac.be|Department of Mathematics and Computer Science, University of Antwerp, Belgium. trungnghia.vu@ua.ac.be|Department of Mathematics and Computer Science, University of Antwerp, Belgium. trungnghia.vu@ua.ac.be|Department of Mathematics and Computer Science, University of Antwerp, Belgium. trungnghia.vu@ua.ac.be|Department of Mathematics and Computer Science, University of Antwerp, Belgium. trungnghia.vu@ua.ac.be|Department of Mathematics and Computer Science, University of Antwerp, Belgium. trungnghia.vu@ua.ac.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Analysis of Variance|Cluster Analysis|Magnetic Resonance Imaging|Magnetic Resonance Spectroscopy|Metabolomics|Software|Workflow', 'Journal Article', '10.1186/1471-2105-12-405', '22014236', 'Nuclear magnetic resonance spectroscopy (NMR) is a powerful technique to reveal and compare quantitative metabolic profiles of biological tissues. However, chemical and physical sample variations make the analysis of the data challenging, and typically require the application of a number of preprocessing steps prior to data interpretation. For example, noise reduction, normalization, baseline correction, peak picking, spectrum alignment and statistical analysis are indispensable components in any NMR analysis pipeline.'),
(94584, 2011, 'bmc', 'Selective phenotyping, entropy reduction, and the mastermind game', 'Gagneur, Julien|Elze, Markus C|Tresch, Achim', 'European Molecular Biology Lab, 69122 Heidelberg, Germany. julien.gagneur@embl.de|European Molecular Biology Lab, 69122 Heidelberg, Germany. julien.gagneur@embl.de|European Molecular Biology Lab, 69122 Heidelberg, Germany. julien.gagneur@embl.de', 'Germany|Germany|Germany', 15, 'Bayes Theorem|Entropy|Genotype|Humans|Phenotype|Quantitative Trait Loci|Yeasts', 'Journal Article', '10.1186/1471-2105-12-406', '22014271', 'With the advance of genome sequencing technologies, phenotyping, rather than genotyping, is becoming the most expensive task when mapping genetic traits. The need for efficient selective phenotyping strategies, i.e. methods to select a subset of genotyped individuals for phenotyping, therefore increases. Current methods have focused either on improving the detection of causative genetic variants or their precise genomic location separately.'),
(94585, 2011, 'bmc', 'Dual channel rank-based intensity weighting for quantitative co-localization of microscopy images', 'Singan, Vasanth R|Jones, Thouis R|Curran, Kathleen M|Simpson, Jeremy C', '1School of Medicine and Medical Science, University College Dublin, Belfield, Dublin 4, Ireland.|1School of Medicine and Medical Science, University College Dublin, Belfield, Dublin 4, Ireland.|1School of Medicine and Medical Science, University College Dublin, Belfield, Dublin 4, Ireland.|1School of Medicine and Medical Science, University College Dublin, Belfield, Dublin 4, Ireland.', 'Ireland|Ireland|Ireland|Ireland', 0, 'Algorithms|Chaperonin 60|HeLa Cells|Humans|Microscopy|Mitochondria|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-12-407', '22017789', 'Accurate quantitative co-localization is a key parameter in the context of understanding the spatial co-ordination of molecules and therefore their function in cells. Existing co-localization algorithms consider either the presence of co-occurring pixels or correlations of intensity in regions of interest. Depending on the image source, and the algorithm selected, the co-localization coefficients determined can be highly variable, and often inaccurate. Furthermore, this choice of whether co-occurrence or correlation is the best approach for quantifying co-localization remains controversial.'),
(94586, 2011, 'bmc', 'A motif-independent metric for DNA sequence specificity', 'Pinello, Luca|Lo Bosco, Giosuè|Hanlon, Bret|Yuan, Guo-Cheng', 'Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA.|Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA.|Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA.|Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Cell Line|DNA|DNA-Binding Proteins|Genome, Human|Genome-Wide Association Study|Humans|Protein Binding|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-12-408', '22017798', 'Genome-wide mapping of protein-DNA interactions has been widely used to investigate biological functions of the genome. An important question is to what extent such interactions are regulated at the DNA sequence level. However, current investigation is hampered by the lack of computational methods for systematic evaluating sequence specificity.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(94587, 2011, 'bmc', 'Improving accuracy of protein-protein interaction prediction by considering the converse problem for sequence representation', 'Ren, Xianwen|Wang, Yong-Cui|Wang, Yong|Zhang, Xiang-Sun|Deng, Nai-Yang', 'State Key Laboratory for Molecular Virology and Genetic Engineering, Institute of Pathogen Biology, Chinese Academy Medical Sciences & Peking Union Medical College, Beijing, 100730, China.|State Key Laboratory for Molecular Virology and Genetic Engineering, Institute of Pathogen Biology, Chinese Academy Medical Sciences & Peking Union Medical College, Beijing, 100730, China.|State Key Laboratory for Molecular Virology and Genetic Engineering, Institute of Pathogen Biology, Chinese Academy Medical Sciences & Peking Union Medical College, Beijing, 100730, China.|State Key Laboratory for Molecular Virology and Genetic Engineering, Institute of Pathogen Biology, Chinese Academy Medical Sciences & Peking Union Medical College, Beijing, 100730, China.|State Key Laboratory for Molecular Virology and Genetic Engineering, Institute of Pathogen Biology, Chinese Academy Medical Sciences & Peking Union Medical College, Beijing, 100730, China.', 'China|China|China|China|China', 0, 'Amino Acid Sequence|Escherichia coli|Protein Interaction Mapping|Proteins|Saccharomyces cerevisiae|Sensitivity and Specificity|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-12-409', '22024143', 'With the development of genome-sequencing technologies, protein sequences are readily obtained by translating the measured mRNAs. Therefore predicting protein-protein interactions from the sequences is of great demand. The reason lies in the fact that identifying protein-protein interactions is becoming a bottleneck for eventually understanding the functions of proteins, especially for those organisms barely characterized. Although a few methods have been proposed, the converse problem, if the features used extract sufficient and unbiased information from protein sequences, is almost untouched.'),
(94588, 2011, 'bmc', 'PMS5: an efficient exact algorithm for the (xe2x84x93, d)-motif finding problem', 'Dinh, Hieu|Rajasekaran, Sanguthevar|Kundeti, Vamsi K', 'Department of CSE, University of Connecticut, Storrs, CT 06269, USA.|Department of CSE, University of Connecticut, Storrs, CT 06269, USA.|Department of CSE, University of Connecticut, Storrs, CT 06269, USA.', 'United States|United States|United States', 45, 'Algorithms|Computational Biology|DNA|DNA-Binding Proteins|Humans|Protein Binding|Regulatory Elements, Transcriptional', 'Journal Article', '10.1186/1471-2105-12-410', '22024209', 'Motifs are patterns found in biological sequences that are vital for understanding gene function, human disease, drug design, etc. They are helpful in finding transcriptional regulatory elements, transcription factor binding sites, and so on. As a result, the problem of identifying motifs is very crucial in biology.'),
(94589, 2011, 'bmc', 'EPEPT: a web service for enhanced P-value estimation in permutation tests', 'Knijnenburg, Theo A|Lin, Jake|Rovira, Hector|Boyle, John|Shmulevich, Ilya', 'Institute for Systems Biology, Seattle, WA, USA. tknijnenburg@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. tknijnenburg@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. tknijnenburg@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. tknijnenburg@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. tknijnenburg@systemsbiology.org', 'United States|United States|United States|United States|United States', 5, 'Computational Biology|Humans|Internet|Programming Languages|Regression Analysis|Software', 'Journal Article', '10.1186/1471-2105-12-411', '22024252', 'In computational biology, permutation tests have become a widely used tool to assess the statistical significance of an event under investigation. However, the common way of computing the P-value, which expresses the statistical significance, requires a very large number of permutations when small (and thus interesting) P-values are to be accurately estimated. This is computationally expensive and often infeasible. Recently, we proposed an alternative estimator, which requires far fewer permutations compared to the standard empirical approach while still reliably estimating small P-values.'),
(94590, 2011, 'bmc', 'Three-dimensional modeling of chromatin structure from interaction frequency data using Markov chain Monte Carlo sampling', 'Rousseau, Mathieu|Fraser, James|Ferraiuolo, Maria A|Dostie, Josée|Blanchette, Mathieu', 'McGill Centre for Bioinformatics, Bellini Building, Life Sciences Complex, 3649 Promenade Sir William Osler, Montréal, Québec, H3G 0B1, Canada.|McGill Centre for Bioinformatics, Bellini Building, Life Sciences Complex, 3649 Promenade Sir William Osler, Montréal, Québec, H3G 0B1, Canada.|McGill Centre for Bioinformatics, Bellini Building, Life Sciences Complex, 3649 Promenade Sir William Osler, Montréal, Québec, H3G 0B1, Canada.|McGill Centre for Bioinformatics, Bellini Building, Life Sciences Complex, 3649 Promenade Sir William Osler, Montréal, Québec, H3G 0B1, Canada.|McGill Centre for Bioinformatics, Bellini Building, Life Sciences Complex, 3649 Promenade Sir William Osler, Montréal, Québec, H3G 0B1, Canada.', 'Canada|Canada|Canada|Canada|Canada', 0, 'Cell Line, Tumor|Chromatin|Chromosomes, Human, Pair 14|Computer Simulation|Genome, Human|Homeodomain Proteins|Humans|Markov Chains|Models, Biological|Monte Carlo Method|Regulatory Sequences, Nucleic Acid', 'Journal Article', '10.1186/1471-2105-12-414', '22026390', 'Long-range interactions between regulatory DNA elements such as enhancers, insulators and promoters play an important role in regulating transcription. As chromatin contacts have been found throughout the human genome and in different cell types, spatial transcriptional control is now viewed as a general mechanism of gene expression regulation. Chromosome Conformation Capture Carbon Copy (5C) and its variant Hi-C are techniques used to measure the interaction frequency (IF) between specific regions of the genome. Our goal is to use the IF data generated by these experiments to computationally model and analyze three-dimensional chromatin organization.'),
(94591, 2011, 'bmc', 'Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of features', 'Demir-Kavuk, Ozgur|Kamada, Mayumi|Akutsu, Tatsuya|Knapp, Ernst-Walter', 'Institute of Chemistry and Biochemistry, Freie Universität Berlin, Fabeckstrasse 36A, 14195 Berlin, Germany.|Institute of Chemistry and Biochemistry, Freie Universität Berlin, Fabeckstrasse 36A, 14195 Berlin, Germany.|Institute of Chemistry and Biochemistry, Freie Universität Berlin, Fabeckstrasse 36A, 14195 Berlin, Germany.|Institute of Chemistry and Biochemistry, Freie Universität Berlin, Fabeckstrasse 36A, 14195 Berlin, Germany.', 'Germany|Germany|Germany|Germany', 17, 'Animals|Artificial Intelligence|Computational Biology|Databases, Genetic|Humans|Internet|Principal Component Analysis|Regression Analysis', 'Journal Article', '10.1186/1471-2105-12-412', '22026913', 'Machine learning methods are nowadays used for many biological prediction problems involving drugs, ligands or polypeptide segments of a protein. In order to build a prediction model a so called training data set of molecules with measured target properties is needed. For many such problems the size of the training data set is limited as measurements have to be performed in a wet lab. Furthermore, the considered problems are often complex, such that it is not clear which molecular descriptors (features) may be suitable to establish a strong correlation with the target property. In many applications all available descriptors are used. This can lead to difficult machine learning problems, when thousands of descriptors are considered and only few (e.g. below hundred) molecules are available for training.'),
(94592, 2011, 'bmc', 'Impact of the spotted microarray preprocessing method on fold-change compression and variance stability', 'Ambroise, Jérôme|Bearzatto, Bertrand|Robert, Annie|Govaerts, Bernadette|Macq, Benoît|Gala, Jean-Luc', 'ICTEAM institute, ELEN department, Université Catholique de Louvain, Place du Levant 2, 1348 Louvain-la-Neuve, Belgium. jerome.ambroise@uclouvain.be|ICTEAM institute, ELEN department, Université Catholique de Louvain, Place du Levant 2, 1348 Louvain-la-Neuve, Belgium. jerome.ambroise@uclouvain.be|ICTEAM institute, ELEN department, Université Catholique de Louvain, Place du Levant 2, 1348 Louvain-la-Neuve, Belgium. jerome.ambroise@uclouvain.be|ICTEAM institute, ELEN department, Université Catholique de Louvain, Place du Levant 2, 1348 Louvain-la-Neuve, Belgium. jerome.ambroise@uclouvain.be|ICTEAM institute, ELEN department, Université Catholique de Louvain, Place du Levant 2, 1348 Louvain-la-Neuve, Belgium. jerome.ambroise@uclouvain.be|ICTEAM institute, ELEN department, Université Catholique de Louvain, Place du Levant 2, 1348 Louvain-la-Neuve, Belgium. jerome.ambroise@uclouvain.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 16, 'Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Quality Control', 'Journal Article', '10.1186/1471-2105-12-413', '22026942', 'The standard approach for preprocessing spotted microarray data is to subtract the local background intensity from the spot foreground intensity, to perform a log2 transformation and to normalize the data with a global median or a lowess normalization. Although well motivated, standard approaches for background correction and for transformation have been widely criticized because they produce high variance at low intensities. Whereas various alternatives to the standard background correction methods and to log2 transformation were proposed, impacts of both successive preprocessing steps were not compared in an objective way.'),
(94593, 2011, 'bmc', 'Exploratory analysis of genomic segmentations with Segtools', 'Buske, Orion J|Hoffman, Michael M|Ponts, Nadia|Le Roch, Karine G|Noble, William Stafford', 'Department of Genome Sciences, University of Washington, PO Box 355065, Seattle, WA 98195-5065, USA.|Department of Genome Sciences, University of Washington, PO Box 355065, Seattle, WA 98195-5065, USA.|Department of Genome Sciences, University of Washington, PO Box 355065, Seattle, WA 98195-5065, USA.|Department of Genome Sciences, University of Washington, PO Box 355065, Seattle, WA 98195-5065, USA.|Department of Genome Sciences, University of Washington, PO Box 355065, Seattle, WA 98195-5065, USA.', 'United States|United States|United States|United States|United States', 0, 'Gene Expression|Genome, Human|Genomics|Histone Code|Histones|Humans|Plasmodium falciparum|Software', 'Journal Article', '10.1186/1471-2105-12-415', '22029426', 'As genome-wide experiments and annotations become more prevalent, researchers increasingly require tools to help interpret data at this scale. Many functional genomics experiments involve partitioning the genome into labeled segments, such that segments sharing the same label exhibit one or more biochemical or functional traits. For example, a collection of ChlP-seq experiments yields a compendium of peaks, each labeled with one or more associated DNA-binding proteins. Similarly, manually or automatically generated annotations of functional genomic elements, including cis-regulatory modules and protein-coding or RNA genes, can also be summarized as genomic segmentations.'),
(94594, 2011, 'bmc', 'SegMine workflows for semantic microarray data analysis in Orange4WS', 'Podpexc4x8dan, Vid|Lavraxc4x8d, Nada|Mozetixc4x8d, Igor|Novak, Petra Kralj|Trajkovski, Igor|Langohr, Laura|Kulovesi, Kimmo|Toivonen, Hannu|Petek, Marko|Motaln, Helena|Gruden, Kristina', 'Joxc5xbdef Stefan Institute, Ljubljana, Slovenia. vid.podpecan@ijs.si|Joxc5xbdef Stefan Institute, Ljubljana, Slovenia. vid.podpecan@ijs.si|Joxc5xbdef Stefan Institute, Ljubljana, Slovenia. vid.podpecan@ijs.si|Joxc5xbdef Stefan Institute, Ljubljana, Slovenia. vid.podpecan@ijs.si|Joxc5xbdef Stefan Institute, Ljubljana, Slovenia. vid.podpecan@ijs.si|Joxc5xbdef Stefan Institute, Ljubljana, Slovenia. vid.podpecan@ijs.si|Joxc5xbdef Stefan Institute, Ljubljana, Slovenia. vid.podpecan@ijs.si|Joxc5xbdef Stefan Institute, Ljubljana, Slovenia. vid.podpecan@ijs.si|Joxc5xbdef Stefan Institute, Ljubljana, Slovenia. vid.podpecan@ijs.si|Joxc5xbdef Stefan Institute, Ljubljana, Slovenia. vid.podpecan@ijs.si|Joxc5xbdef Stefan Institute, Ljubljana, Slovenia. vid.podpecan@ijs.si', 'Slovenia|Slovenia|Slovenia|Slovenia|Slovenia|Slovenia|Slovenia|Slovenia|Slovenia|Slovenia|Slovenia', 24, 'Adipose Tissue|Algorithms|Autophagy|Cellular Senescence|Gene Expression Profiling|Humans|Mesenchymal Stem Cells|Oligonucleotide Array Sequence Analysis|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Software|Stem Cells|Workflow', 'Journal Article', '10.1186/1471-2105-12-416', '22029475', 'In experimental data analysis, bioinformatics researchers increasingly rely on tools that enable the composition and reuse of scientific workflows. The utility of current bioinformatics workflow environments can be significantly increased by offering advanced data mining services as workflow components. Such services can support, for instance, knowledge discovery from diverse distributed data and knowledge sources (such as GO, KEGG, PubMed, and experimental databases). Specifically, cutting-edge data analysis approaches, such as semantic data mining, link discovery, and visualization, have not yet been made available to researchers investigating complex biological datasets.'),
(94595, 2011, 'bmc', 'BioNxc3x98T: a searchable database of biomedical negated sentences', 'Agarwal, Shashank|Yu, Hong|Kohane, Issac', 'Medical Informatics, College of Engineering and Applied Sciences, University of Wisconsin-Milwaukee, 3200 N, Cramer St, Milwaukee, WI 53201-0784, USA.|Medical Informatics, College of Engineering and Applied Sciences, University of Wisconsin-Milwaukee, 3200 N, Cramer St, Milwaukee, WI 53201-0784, USA.|Medical Informatics, College of Engineering and Applied Sciences, University of Wisconsin-Milwaukee, 3200 N, Cramer St, Milwaukee, WI 53201-0784, USA.', 'United States|United States|United States', 13, 'Algorithms|Alzheimer Disease|Autistic Disorder|Data Mining|Databases, Factual|Humans|Internet|Parkinson Disease|PubMed', 'Journal Article', '10.1186/1471-2105-12-420', '22032181', 'Negated biomedical events are often ignored by text-mining applications, however, such events carry scientific significance. We report on the development of BioNxc3x98T, a database of negated sentences that can be used to extract such negated events.'),
(94596, 2011, 'bmc', 'Automated NMR relaxation dispersion data analysis using NESSY', 'Bieri, Michael|Gooley, Paul R', 'Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, Vic 3010, Australia.|Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, Vic 3010, Australia.', 'Australia|Australia', 44, 'Magnetic Resonance Imaging|Molecular Dynamics Simulation|Nuclear Magnetic Resonance, Biomolecular|Programming Languages|Protein Conformation|Proteins|Software', 'Journal Article', '10.1186/1471-2105-12-421', '22032230', 'Proteins are dynamic molecules with motions ranging from picoseconds to longer than seconds. Many protein functions, however, appear to occur on the micro to millisecond timescale and therefore there has been intense research of the importance of these motions in catalysis and molecular interactions. Nuclear Magnetic Resonance (NMR) relaxation dispersion experiments are used to measure motion of discrete nuclei within the micro to millisecond timescale. Information about conformational/chemical exchange, populations of exchanging states and chemical shift differences are extracted from these experiments. To ensure these parameters are correctly extracted, accurate and careful analysis of these experiments is necessary.'),
(94597, 2011, 'bmc', 'Predicting protein ligand binding motions with the conformation explorer', 'Flores, Samuel C|Gerstein, Mark B', 'Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden. samuel.flores@icm.uu.se|Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden. samuel.flores@icm.uu.se', 'Sweden|Sweden', 0, 'Carrier Proteins|Drug Design|Humans|Ligands|Molecular Dynamics Simulation|Motion|Protein Binding|Protein Conformation|Proteins', 'Journal Article', '10.1186/1471-2105-12-417', '22032721', 'Knowledge of the structure of proteins bound to known or potential ligands is crucial for biological understanding and drug design. Often the 3D structure of the protein is available in some conformation, but binding the ligand of interest may involve a large scale conformational change which is difficult to predict with existing methods.'),
(94598, 2011, 'bmc', 'Improving ontologies by automatic reasoning and evaluation of logical definitions', 'Köhler, Sebastian|Bauer, Sebastian|Mungall, Chris J|Carletti, Gabriele|Smith, Cynthia L|Schofield, Paul|Gkoutos, Georgios V|Robinson, Peter N', 'Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. sebastian.koehler@charite.de|Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. sebastian.koehler@charite.de|Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. sebastian.koehler@charite.de|Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. sebastian.koehler@charite.de|Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. sebastian.koehler@charite.de|Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. sebastian.koehler@charite.de|Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. sebastian.koehler@charite.de|Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. sebastian.koehler@charite.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 22, 'Animals|Humans|Knowledge|Logic|Phenotype|Semantics|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-12-418', '22032770', 'Ontologies are widely used to represent knowledge in biomedicine. Systematic approaches for detecting errors and disagreements are needed for large ontologies with hundreds or thousands of terms and semantic relationships. A recent approach of defining terms using logical definitions is now increasingly being adopted as a method for quality control as well as for facilitating interoperability and data integration.'),
(94599, 2011, 'bmc', 'MAPI: towards the integrated exploitation of bioinformatics Web Services', 'Ramirez, Sergio|Karlsson, Johan|Trelles, Oswaldo', 'Computer Architecture Department, University of Malaga, Málaga, Spain.|Computer Architecture Department, University of Malaga, Málaga, Spain.|Computer Architecture Department, University of Malaga, Málaga, Spain.', 'Spain|Spain|Spain', 2, 'Computational Biology|Internet|Libraries|Software|Workflow', 'Journal Article', '10.1186/1471-2105-12-419', '22032807', 'Bioinformatics is commonly featured as a well assorted list of available web resources. Although diversity of services is positive in general, the proliferation of tools, their dispersion and heterogeneity complicate the integrated exploitation of such data processing capacity.'),
(94600, 2011, 'bmc', 'Stepwise classification of cancer samples using clinical and molecular data', 'Obulkasim, Askar|Meijer, Gerrit A|van de Wiel, Mark A', 'Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. askar.wubulikasimu@vumc.nl|Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. askar.wubulikasimu@vumc.nl|Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. askar.wubulikasimu@vumc.nl', 'Netherlands|Netherlands|Netherlands', 11, 'Algorithms|Breast Neoplasms|Computational Biology|Female|Humans|Protein Structure, Secondary', 'Journal Article', '10.1186/1471-2105-12-422', '22034839', 'Combining clinical and molecular data types may potentially improve prediction accuracy of a classifier. However, currently there is a shortage of effective and efficient statistical and bioinformatic tools for true integrative data analysis. Existing integrative classifiers have two main disadvantages: First, coarse combination may lead to subtle contributions of one data type to be overshadowed by more obvious contributions of the other. Second, the need to measure both data types for all patients may be both unpractical and (cost) inefficient.'),
(94601, 2011, 'bmc', 'Enhanced peptide quantification using spectral count clustering and cluster abundance', 'Lee, Seungmook|Kwon, Min-Seok|Lee, Hyoung-Joo|Paik, Young-Ki|Tang, Haixu|Lee, Jae K|Park, Taesung', 'Department of Statistics, Seoul National University, Korea.|Department of Statistics, Seoul National University, Korea.|Department of Statistics, Seoul National University, Korea.|Department of Statistics, Seoul National University, Korea.|Department of Statistics, Seoul National University, Korea.|Department of Statistics, Seoul National University, Korea.|Department of Statistics, Seoul National University, Korea.', '-|-|-|-|-|-|-', 11, 'Algorithms|Carcinoma, Hepatocellular|Chromatography, Liquid|Cluster Analysis|Humans|Liver Neoplasms|Peptides|Proteomics|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-12-423', '22034872', 'Quantification of protein expression by means of mass spectrometry (MS) has been introduced in various proteomics studies. In particular, two label-free quantification methods, such as spectral counting and spectra feature analysis have been extensively investigated in a wide variety of proteomic studies. The cornerstone of both methods is peptide identification based on a proteomic database search and subsequent estimation of peptide retention time. However, they often suffer from restrictive database search and inaccurate estimation of the liquid chromatography (LC) retention time. Furthermore, conventional peptide identification methods based on the spectral library search algorithms such as SEQUEST or SpectraST have been found to provide neither the best match nor high-scored matches. Lastly, these methods are limited in the sense that target peptides cannot be identified unless they have been previously generated and stored into the database or spectral libraries.To overcome these limitations, we propose a novel method, namely Quantification method based on Finding the Identical Spectral set for a Homogenous peptide (Q-FISH) to estimate the peptides abundance from its tandem mass spectrometry (MS/MS) spectra through the direct comparison of experimental spectra. Intuitively, our Q-FISH method compares all possible pairs of experimental spectra in order to identify both known and novel proteins, significantly enhancing identification accuracy by grouping replicated spectra from the same peptide targets.'),
(94602, 2011, 'bmc', 'An active learning based classification strategy for the minority class problem: application to histopathology annotation', 'Doyle, Scott|Monaco, James|Feldman, Michael|Tomaszewski, John|Madabhushi, Anant', 'Biomedical Engineering Department, Rutgers University, Taylor Road, New Jersey, USA. scottdo@eden.rutgers.edu.|Biomedical Engineering Department, Rutgers University, Taylor Road, New Jersey, USA. scottdo@eden.rutgers.edu.|Biomedical Engineering Department, Rutgers University, Taylor Road, New Jersey, USA. scottdo@eden.rutgers.edu.|Biomedical Engineering Department, Rutgers University, Taylor Road, New Jersey, USA. scottdo@eden.rutgers.edu.|Biomedical Engineering Department, Rutgers University, Taylor Road, New Jersey, USA. scottdo@eden.rutgers.edu.', 'Jersey|Jersey|Jersey|Jersey|Jersey', 42, 'Area Under Curve|Artificial Intelligence|Humans|Male|Models, Theoretical|Prostatic Neoplasms|ROC Curve', 'Journal Article', '10.1186/1471-2105-12-424', '22034914', 'Supervised classifiers for digital pathology can improve the ability of physicians to detect and diagnose diseases such as cancer. Generating training data for classifiers is problematic, since only domain experts (e.g. pathologists) can correctly label ground truth data. Additionally, digital pathology datasets suffer from the minority class problem, an issue where the number of exemplars from the non-target class outnumber target class exemplars which can bias the classifier and reduce accuracy. In this paper, we develop a training strategy combining active learning (AL) with class-balancing. AL identifies unlabeled samples that are informative (i.e. likely to increase classifier performance) for annotation, avoiding non-informative samples. This yields high accuracy with a smaller training set size compared with random learning (RL). Previous AL methods have not explicitly accounted for the minority class problem in biomedical images. Pre-specifying a target class ratio mitigates the problem of training bias. Finally, we develop a mathematical model to predict the number of annotations (cost) required to achieve balanced training classes. In addition to predicting training cost, the model reveals the theoretical properties of AL in the context of the minority class problem.'),
(94603, 2011, 'bmc', 'MolabIS--an integrated information system for storing and managing molecular genetics data', 'Truong, Cong V C|Groeneveld, Linn F|Morgenstern, Burkhard|Groeneveld, Eildert', 'Department of Breeding and Genetic Resources, Institute of Farm Animal Genetics, Neustadt, Germany. cong.chi@fli.bund.de|Department of Breeding and Genetic Resources, Institute of Farm Animal Genetics, Neustadt, Germany. cong.chi@fli.bund.de|Department of Breeding and Genetic Resources, Institute of Farm Animal Genetics, Neustadt, Germany. cong.chi@fli.bund.de|Department of Breeding and Genetic Resources, Institute of Farm Animal Genetics, Neustadt, Germany. cong.chi@fli.bund.de', 'Germany|Germany|Germany|Germany', 8, 'Animals|Database Management Systems|Databases, Genetic|Genotype|Information Management|Internet|Management Information Systems|Microsatellite Repeats', 'Journal Article', '10.1186/1471-2105-12-425', '22040322', 'Long-term sample storage, tracing of data flow and data export for subsequent analyses are of great importance in genetics studies. Therefore, molecular labs do need a proper information system to handle an increasing amount of data from different projects.'),
(94604, 2011, 'bmc', 'A Bayesian model for gene family evolution', 'Liu, Liang|Yu, Lili|Kalavacharla, Venugopal|Liu, Zhanji', 'Department of Statistics, University of Georgia, Athens, GA 30602, USA. lliu@uga.edu|Department of Statistics, University of Georgia, Athens, GA 30602, USA. lliu@uga.edu|Department of Statistics, University of Georgia, Athens, GA 30602, USA. lliu@uga.edu|Department of Statistics, University of Georgia, Athens, GA 30602, USA. lliu@uga.edu', 'Georgia|Georgia|Georgia|Georgia', 11, 'Animals|Bayes Theorem|Evolution, Molecular|Humans|Likelihood Functions|Models, Genetic|Phylogeny|Yeasts', 'Journal Article', '10.1186/1471-2105-12-426', '22044581', 'A birth and death process is frequently used for modeling the size of a gene family that may vary along the branches of a phylogenetic tree. Under the birth and death model, maximum likelihood methods have been developed to estimate the birth and death rate and the sizes of ancient gene families (numbers of gene copies at the internodes of the phylogenetic tree). This paper aims to provide a Bayesian approach for estimating parameters in the birth and death model.'),
(94605, 2011, 'bmc', 'Generalized shrinkage F-like statistics for testing an interaction term in gene expression analysis in the presence of heteroscedasticity', 'Yang, Jie|Casella, George|McIntyre, Lauren M', 'Department of Preventive Medicine, Stony Brook University, Stony Brook, NY 11794, USA. jie.yang@stonybrook.edu|Department of Preventive Medicine, Stony Brook University, Stony Brook, NY 11794, USA. jie.yang@stonybrook.edu|Department of Preventive Medicine, Stony Brook University, Stony Brook, NY 11794, USA. jie.yang@stonybrook.edu', 'United States|United States|United States', 0, 'Animals|Drosophila|Drosophila melanogaster|Gene Expression Profiling|Male|Models, Genetic|Models, Statistical', 'Journal Article', '10.1186/1471-2105-12-427', '22044602', 'Many analyses of gene expression data involve hypothesis tests of an interaction term between two fixed effects, typically tested using a residual variance. In expression studies, the issue of variance heteroscedasticity has received much attention, and previous work has focused on either between-gene or within-gene heteroscedasticity. However, in a single experiment, heteroscedasticity may exist both within and between genes. Here we develop flexible shrinkage error estimators considering both between-gene and within-gene heteroscedasticity and use them to construct F-like test statistics for testing interactions, with cutoff values obtained by permutation. These permutation tests are complicated, and several permutation tests are investigated here.'),
(94606, 2011, 'bmc', 'Fast MCMC sampling for hidden Markov Models to determine copy number variations', 'Mahmud, Md Pavel|Schliep, Alexander', 'Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, Piscataway, NJ 08854, USA. pavelm@cs.rutgers.edu|Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, Piscataway, NJ 08854, USA. pavelm@cs.rutgers.edu', 'United States|United States', 7, 'Algorithms|Base Sequence|Bayes Theorem|Comparative Genomic Hybridization|DNA Copy Number Variations|Humans|Lymphoma, Mantle-Cell|Markov Chains|Models, Genetic|Monte Carlo Method|Probability', 'Journal Article', '10.1186/1471-2105-12-428', '22047014', 'Hidden Markov Models (HMM) are often used for analyzing Comparative Genomic Hybridization (CGH) data to identify chromosomal aberrations or copy number variations by segmenting observation sequences. For efficiency reasons the parameters of a HMM are often estimated with maximum likelihood and a segmentation is obtained with the Viterbi algorithm. This introduces considerable uncertainty in the segmentation, which can be avoided with Bayesian approaches integrating out parameters using Markov Chain Monte Carlo (MCMC) sampling. While the advantages of Bayesian approaches have been clearly demonstrated, the likelihood based approaches are still preferred in practice for their lower running times, datasets coming from high-density arrays and next generation sequencing amplify these problems.'),
(94607, 2011, 'bmc', 'Identification of exonic regions in DNA sequences using cross-correlation and noise suppression by discrete wavelet transform', 'Abbasi, Omid|Rostami, Ali|Karimian, Ghader', 'School of Engineering-Emerging Technologies, University of Tabriz, Tabriz 5166614761, Iran.|School of Engineering-Emerging Technologies, University of Tabriz, Tabriz 5166614761, Iran.|School of Engineering-Emerging Technologies, University of Tabriz, Tabriz 5166614761, Iran.', '-|-|-', 23, 'Algorithms|Animals|Exons|Humans|Open Reading Frames|Sequence Analysis, DNA|Signal Processing, Computer-Assisted|Wavelet Analysis', 'Journal Article', '10.1186/1471-2105-12-430', '22050630', 'The identification of protein coding regions (exons) in DNA sequences using signal processing techniques is an important component of bioinformatics and biological signal processing. In this paper, a new method is presented for the identification of exonic regions in DNA sequences. This method is based on the cross-correlation technique that can identify periodic regions in DNA sequences.'),
(94608, 2011, 'bmc', 'Lost in folding space? Comparing four variants of the thermodynamic model for RNA secondary structure prediction', 'Janssen, Stefan|Schudoma, Christian|Steger, Gerhard|Giegerich, Robert', 'Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany.|Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany.|Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany.|Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany.', 'Germany|Germany|Germany|Germany', 21, 'Algorithms|Base Sequence|Computational Biology|Probability|RNA|RNA Folding|Sequence Analysis, RNA|Thermodynamics', 'Journal Article', '10.1186/1471-2105-12-429', '22051375', 'Many bioinformatics tools for RNA secondary structure analysis are based on a thermodynamic model of RNA folding. They predict a single, optimal structure by free energy minimization, they enumerate near-optimal structures, they compute base pair probabilities and dot plots, representative structures of different abstract shapes, or Boltzmann probabilities of structures and shapes. Although all programs refer to the same physical model, they implement it with considerable variation for different tasks, and little is known about the effects of heuristic assumptions and model simplifications used by the programs on the outcome of the analysis.'),
(94609, 2011, 'bmc', 'AIGO: towards a unified framework for the analysis and the inter-comparison of GO functional annotations', 'Defoin-Platel, Michael|Hindle, Matthew M|Lysenko, Artem|Powers, Stephen J|Habash, Dimah Z|Rawlings, Christopher J|Saqi, Mansoor', 'Department of Biomathematics and Bioinformatics, Rothamsted Research, Harpenden, UK. michael.defoin-platel@rothamsted.ac.uk|Department of Biomathematics and Bioinformatics, Rothamsted Research, Harpenden, UK. michael.defoin-platel@rothamsted.ac.uk|Department of Biomathematics and Bioinformatics, Rothamsted Research, Harpenden, UK. michael.defoin-platel@rothamsted.ac.uk|Department of Biomathematics and Bioinformatics, Rothamsted Research, Harpenden, UK. michael.defoin-platel@rothamsted.ac.uk|Department of Biomathematics and Bioinformatics, Rothamsted Research, Harpenden, UK. michael.defoin-platel@rothamsted.ac.uk|Department of Biomathematics and Bioinformatics, Rothamsted Research, Harpenden, UK. michael.defoin-platel@rothamsted.ac.uk|Department of Biomathematics and Bioinformatics, Rothamsted Research, Harpenden, UK. michael.defoin-platel@rothamsted.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Algorithms|Animals|Cattle|Chromosome Mapping|Databases, Genetic|Genome|Genomics|Humans|Molecular Sequence Annotation|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-12-431', '22054122', 'In response to the rapid growth of available genome sequences, efforts have been made to develop automatic inference methods to functionally characterize them. Pipelines that infer functional annotation are now routinely used to produce new annotations at a genome scale and for a broad variety of species. These pipelines differ widely in their inference algorithms, confidence thresholds and data sources for reasoning. This heterogeneity makes a comparison of the relative merits of each approach extremely complex. The evaluation of the quality of the resultant annotations is also challenging given there is often no existing gold-standard against which to evaluate precision and recall.'),
(94610, 2011, 'bmc', 'A parallel method for enumerating amino acid compositions and masses of all theoretical peptides', 'Nefedov, Alexey V|Sadygov, Rovshan G', 'Department of Biochemistry and Molecular Biology, Sealy Center for Molecular Medicine, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, USA.|Department of Biochemistry and Molecular Biology, Sealy Center for Molecular Medicine, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, USA.', 'United States|United States', 9, 'Amino Acids|Molecular Weight|Peptide Mapping|Peptides|Software', 'Journal Article', '10.1186/1471-2105-12-432', '22059886', 'Enumeration of all theoretically possible amino acid compositions is an important problem in several proteomics workflows, including peptide mass fingerprinting, mass defect labeling, mass defect filtering, and de novo peptide sequencing. Because of the high computational complexity of this task, reported methods for peptide enumeration were restricted to cover limited mass ranges (below 2 kDa). In addition, implementation details of these methods as well as their computational performance have not been provided. The increasing availability of parallel (multi-core) computers in all fields of research makes the development of parallel methods for peptide enumeration a timely topic.'),
(94611, 2011, 'bmc', 'Analyzing 2D gel images using a two-component empirical Bayes model', 'Li, Feng|Seillier-Moiseiwitsch, Françoise', 'Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, MD, USA. feng.li@fda.hhs.gov|Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, MD, USA. feng.li@fda.hhs.gov', 'United States|United States', 1, 'Algorithms|Animals|Bayes Theorem|Computer Simulation|Electrophoresis, Gel, Two-Dimensional|Female|Male|Nicotine|Proteome|Proteomics|Rats|Spleen', 'Journal Article', '10.1186/1471-2105-12-433', '22067142', 'Two-dimensional polyacrylomide gel electrophoresis (2D gel, 2D PAGE, 2-DE) is a powerful tool for analyzing the proteome of a organism. Differential analysis of 2D gel images aims at finding proteins that change under different conditions, which leads to large-scale hypothesis testing as in microarray data analysis. Two-component empirical Bayes (EB) models have been widely discussed for large-scale hypothesis testing and applied in the context of genomic data. They have not been implemented for the differential analysis of 2D gel data. In the literature, the mixture and null densities of the test statistics are estimated separately. The estimation of the mixture density does not take into account assumptions about the null density. Thus, there is no guarantee that the estimated null component will be no greater than the mixture density as it should be.'),
(94612, 2011, 'bmc', 'ProPhylo: partial phylogenetic profiling to guide protein family construction and assignment of biological process', 'Basu, Malay K|Selengut, Jeremy D|Haft, Daniel H', 'J. Craig Venter Institute, Rockville, MD 20850, USA. mbasu@jcvi.org|J. Craig Venter Institute, Rockville, MD 20850, USA. mbasu@jcvi.org|J. Craig Venter Institute, Rockville, MD 20850, USA. mbasu@jcvi.org', 'United States|United States|United States', 10, 'Algorithms|Archaea|Archaeal Proteins|DNA|Methane|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-12-434', '22070167', 'Phylogenetic profiling is a technique of scoring co-occurrence between a protein family and some other trait, usually another protein family, across a set of taxonomic groups. In spite of several refinements in recent years, the technique still invites significant improvement. To be its most effective, a phylogenetic profiling algorithm must be able to examine co-occurrences among protein families whose boundaries are uncertain within large homologous protein superfamilies.'),
(94613, 2011, 'bmc', 'PESCADOR, a web-based tool to assist text-mining of biointeractions extracted from PubMed queries', 'Barbosa-Silva, Adriano|Fontaine, Jean-Fred|Donnard, Elisa R|Stussi, Fernanda|Ortega, J Miguel|Andrade-Navarro, Miguel A', 'Computational Biology and Data Mining Group, Max Delbrück Center for Molecular Medicine, Robert-Rössle-Strasse, 10, D-13125, Berlin, Germany. adriano.barbosa@mdc-berlin.de|Computational Biology and Data Mining Group, Max Delbrück Center for Molecular Medicine, Robert-Rössle-Strasse, 10, D-13125, Berlin, Germany. adriano.barbosa@mdc-berlin.de|Computational Biology and Data Mining Group, Max Delbrück Center for Molecular Medicine, Robert-Rössle-Strasse, 10, D-13125, Berlin, Germany. adriano.barbosa@mdc-berlin.de|Computational Biology and Data Mining Group, Max Delbrück Center for Molecular Medicine, Robert-Rössle-Strasse, 10, D-13125, Berlin, Germany. adriano.barbosa@mdc-berlin.de|Computational Biology and Data Mining Group, Max Delbrück Center for Molecular Medicine, Robert-Rössle-Strasse, 10, D-13125, Berlin, Germany. adriano.barbosa@mdc-berlin.de|Computational Biology and Data Mining Group, Max Delbrück Center for Molecular Medicine, Robert-Rössle-Strasse, 10, D-13125, Berlin, Germany. adriano.barbosa@mdc-berlin.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 38, 'Colorectal Neoplasms|Data Mining|Humans|Internet|Neurodegenerative Diseases|Proteins|PubMed|Software', 'Journal Article', '10.1186/1471-2105-12-435', '22070195', 'Biological function is greatly dependent on the interactions of proteins with other proteins and genes. Abstracts from the biomedical literature stored in the NCBIs PubMed database can be used for the derivation of interactions between genes and proteins by identifying the co-occurrences of their terms. Often, the amount of interactions obtained through such an approach is large and may mix processes occurring in different contexts. Current tools do not allow studying these data with a focus on concepts of relevance to a user, for example, interactions related to a disease or to a biological mechanism such as protein aggregation.'),
(94614, 2011, 'bmc', 'clusterMaker: a multi-algorithm clustering plugin for Cytoscape', 'Morris, John H|Apeltsin, Leonard|Newman, Aaron M|Baumbach, Jan|Wittkop, Tobias|Su, Gang|Bader, Gary D|Ferrin, Thomas E', 'Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA. scooter@cgl.ucsf.edu|Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA. scooter@cgl.ucsf.edu|Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA. scooter@cgl.ucsf.edu|Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA. scooter@cgl.ucsf.edu|Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA. scooter@cgl.ucsf.edu|Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA. scooter@cgl.ucsf.edu|Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA. scooter@cgl.ucsf.edu|Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA. scooter@cgl.ucsf.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 238, 'Algorithms|Animals|Cluster Analysis|Genomics|Mice|Protein Interaction Maps|Racemases and Epimerases|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1186/1471-2105-12-436', '22070249', 'In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL.'),
(94615, 2011, 'bmc', 'An iterative approach of protein function prediction', 'Chi, Xiaoxiao|Hou, Jingyu', 'School of Information Technology, Deakin University, Melbourne, Australia.|School of Information Technology, Deakin University, Melbourne, Australia.', 'Australia|Australia', 32, 'Algorithms|Genomics|Humans|Molecular Sequence Annotation|Protein Interaction Maps|Proteins', 'Journal Article', '10.1186/1471-2105-12-437', '22074332', 'Current approaches of predicting protein functions from a protein-protein interaction (PPI) dataset are based on an assumption that the available functions of the proteins (a.k.a. annotated proteins) will determine the functions of the proteins whose functions are unknown yet at the moment (a.k.a. un-annotated proteins). Therefore, the protein function prediction is a mono-directed and one-off procedure, i.e. from annotated proteins to un-annotated proteins. However, the interactions between proteins are mutual rather than static and mono-directed, although functions of some proteins are unknown for some reasons at present. That means when we use the similarity-based approach to predict functions of un-annotated proteins, the un-annotated proteins, once their functions are predicted, will affect the similarities between proteins, which in turn will affect the prediction results. In other words, the function prediction is a dynamic and mutual procedure. This dynamic feature of protein interactions, however, was not considered in the existing prediction algorithms.'),
(94616, 2011, 'bmc', 'Consistent Differential Expression Pattern (CDEP) on microarray to identify genes related to metastatic behavior', 'Tsoi, Lam C|Qin, Tingting|Slate, Elizabeth H|Zheng, W Jim', 'Department of Biochemistry and Molecular Biology, Medical University of South Carolina, 135 Cannon St, Charleston, SC 29425, USA.|Department of Biochemistry and Molecular Biology, Medical University of South Carolina, 135 Cannon St, Charleston, SC 29425, USA.|Department of Biochemistry and Molecular Biology, Medical University of South Carolina, 135 Cannon St, Charleston, SC 29425, USA.|Department of Biochemistry and Molecular Biology, Medical University of South Carolina, 135 Cannon St, Charleston, SC 29425, USA.', 'United States|United States|United States|United States', 8, 'Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Meta-Analysis as Topic|Neoplasm Metastasis|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-12-438', '22078224', 'To utilize the large volume of gene expression information generated from different microarray experiments, several meta-analysis techniques have been developed. Despite these efforts, there remain significant challenges to effectively increasing the statistical power and decreasing the Type I error rate while pooling the heterogeneous datasets from public resources. The objective of this study is to develop a novel meta-analysis approach, Consistent Differential Expression Pattern (CDEP), to identify genes with common differential expression patterns across different datasets.'),
(94617, 2011, 'bmc', 'SCFIA: a statistical corresponding feature identification algorithm for LC/MS', 'Cui, Jian|Ma, Xuepo|Chen, Long|Zhang, Jianqiu', 'Department of Electrical and Computer Engineering, the University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA.|Department of Electrical and Computer Engineering, the University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA.|Department of Electrical and Computer Engineering, the University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA.|Department of Electrical and Computer Engineering, the University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Chromatography, Liquid|Mass Spectrometry|Models, Statistical|Peptides|Proteins', 'Journal Article', '10.1186/1471-2105-12-439', '22078262', 'Identifying corresponding features (LC peaks registered by identical peptides) in multiple Liquid Chromatography/Mass Spectrometry (LC-MS) datasets plays a crucial role in the analysis of complex peptide or protein mixtures. Warping functions are commonly used to correct the mean of elution time shifts among LC-MS datasets, which cannot resolve the ambiguity of corresponding feature identification since elution time shifts are random. We propose a Statistical Corresponding Feature Identification Algorithm(SCFIA) based on both elution time shifts and peak shape correlations between corresponding features. SCFIA first trains a set of statistical models, and then, all candidate corresponding features are scored by the statistical models to find the maximum likelihood solution.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(94618, 2011, 'bmc', 'Efficient xcexb1, xcexb2-motif finder for identification of phenotype-related functional modules', 'Schmidt, Matthew C|Rocha, Andrea M|Padmanabhan, Kanchana|Chen, Zhengzhang|Scott, Kathleen|Mihelcic, James R|Samatova, Nagiza F', 'Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA.|Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA.|Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA.|Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA.|Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA.|Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA.|Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA.', 'United States|United States|United States|United States|United States|United States|United States', 4, 'Acids|Algorithms|Bacteria|Citric Acid Cycle|Computing Methodologies|Hydrogen|Phenotype|Proteobacteria', 'Journal Article', '10.1186/1471-2105-12-440', '22078292', 'Microbial communities in their natural environments exhibit phenotypes that can directly cause particular diseases, convert biomass or wastewater to energy, or degrade various environmental contaminants. Understanding how these communities realize specific phenotypic traits (e.g., carbon fixation, hydrogen production) is critical for addressing health, bioremediation, or bioenergy problems.'),
(94619, 2011, 'bmc', 'Exact score distribution computation for ontological similarity searches', 'Schulz, Marcel H|Köhler, Sebastian|Bauer, Sebastian|Robinson, Peter N', 'Max Planck Institute for Molecular Genetics, Berlin, Germany. maschulz@andrew.cmu.edu|Max Planck Institute for Molecular Genetics, Berlin, Germany. maschulz@andrew.cmu.edu|Max Planck Institute for Molecular Genetics, Berlin, Germany. maschulz@andrew.cmu.edu|Max Planck Institute for Molecular Genetics, Berlin, Germany. maschulz@andrew.cmu.edu', 'Germany|Germany|Germany|Germany', 9, 'Algorithms|Databases, Genetic|Humans|Knowledge Bases|Phenotype|Proteins|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-12-441', '22078312', 'Semantic similarity searches in ontologies are an important component of many bioinformatic algorithms, e.g., finding functionally related proteins with the Gene Ontology or phenotypically similar diseases with the Human Phenotype Ontology (HPO). We have recently shown that the performance of semantic similarity searches can be improved by ranking results according to the probability of obtaining a given score at random rather than by the scores themselves. However, to date, there are no algorithms for computing the exact distribution of semantic similarity scores, which is necessary for computing the exact P-value of a given score.'),
(94620, 2011, 'bmc', 'Meta-analytic approach to the accurate prediction of secreted virulence effectors in gram-negative bacteria', 'Sato, Yoshiharu|Takaya, Akiko|Yamamoto, Tomoko', 'Graduate School of Pharmaceutical Sciences, Chiba University, Japan. y-sato@faculty.chiba-u.jp|Graduate School of Pharmaceutical Sciences, Chiba University, Japan. y-sato@faculty.chiba-u.jp|Graduate School of Pharmaceutical Sciences, Chiba University, Japan. y-sato@faculty.chiba-u.jp', 'Japan|Japan|Japan', 0, 'Bacterial Proteins|Bacterial Secretion Systems|Gram-Negative Bacteria|Meta-Analysis as Topic|Oligonucleotide Array Sequence Analysis|Protein Transport|Software|Support Vector Machine|Virulence|Virulence Factors', 'Journal Article', '10.1186/1471-2105-12-442', '22078363', 'Many pathogens use a type III secretion system to translocate virulence proteins (called effectors) in order to adapt to the host environment. To date, many prediction tools for effector identification have been developed. However, these tools are insufficiently accurate for producing a list of putative effectors that can be applied directly for labor-intensive experimental verification. This also suggests that important features of effectors have yet to be fully characterized.'),
(94621, 2011, 'bmc', 'SIGNATURE: a workbench for gene expression signature analysis', 'Chang, Jeffrey T|Gatza, Michael L|Lucas, Joseph E|Barry, William T|Vaughn, Peyton|Nevins, Joseph R', 'Department of Integrative Biology and Pharmacology, University of Texas Health Science Center at Houston, Houston, TX, USA. jeffrey.t.chang@uth.tmc.edu|Department of Integrative Biology and Pharmacology, University of Texas Health Science Center at Houston, Houston, TX, USA. jeffrey.t.chang@uth.tmc.edu|Department of Integrative Biology and Pharmacology, University of Texas Health Science Center at Houston, Houston, TX, USA. jeffrey.t.chang@uth.tmc.edu|Department of Integrative Biology and Pharmacology, University of Texas Health Science Center at Houston, Houston, TX, USA. jeffrey.t.chang@uth.tmc.edu|Department of Integrative Biology and Pharmacology, University of Texas Health Science Center at Houston, Houston, TX, USA. jeffrey.t.chang@uth.tmc.edu|Department of Integrative Biology and Pharmacology, University of Texas Health Science Center at Houston, Houston, TX, USA. jeffrey.t.chang@uth.tmc.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Bayes Theorem|Databases, Genetic|Gene Expression Profiling|Internet|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-12-443', '22078435', 'The biological phenotype of a cell, such as a characteristic visual image or behavior, reflects activities derived from the expression of collections of genes. As such, an ability to measure the expression of these genes provides an opportunity to develop more precise and varied sets of phenotypes. However, to use this approach requires computational methods that are difficult to implement and apply, and thus there is a critical need for intelligent software tools that can reduce the technical burden of the analysis. Tools for gene expression analyses are unusually difficult to implement in a user-friendly way because their application requires a combination of biological data curation, statistical computational methods, and database expertise.'),
(94622, 2011, 'bmc', 'Correlated mutations via regularized multinomial regression', 'Sreekumar, Janardanan|ter Braak, Cajo J F|van Ham, Roeland C H J|van Dijk, Aalt D J', 'Central Tuber Crops Research Institute, Thiruvananthapuram-695017, Kerala, India.|Central Tuber Crops Research Institute, Thiruvananthapuram-695017, Kerala, India.|Central Tuber Crops Research Institute, Thiruvananthapuram-695017, Kerala, India.|Central Tuber Crops Research Institute, Thiruvananthapuram-695017, Kerala, India.', 'India|India|India|India', 8, 'Algorithms|Amino Acid Sequence|Arabidopsis|Arabidopsis Proteins|Conserved Sequence|MADS Domain Proteins|Models, Molecular|Mutation|Protein Interaction Maps|Regression Analysis|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-12-444', '22082126', 'In addition to sequence conservation, protein multiple sequence alignments contain evolutionary signal in the form of correlated variation among amino acid positions. This signal indicates positions in the sequence that influence each other, and can be applied for the prediction of intra- or intermolecular contacts. Although various approaches exist for the detection of such correlated mutations, in general these methods utilize only pairwise correlations. Hence, they tend to conflate direct and indirect dependencies.'),
(94623, 2011, 'bmc', 'Validating clustering of molecular dynamics simulations using polymer models', 'Phillips, Joshua L|Colvin, Michael E|Newsam, Shawn', 'Center for Computational Biology, School of Natural Sciences, University of California, Merced, California, USA. jphillips7@ucmerced.edu|Center for Computational Biology, School of Natural Sciences, University of California, Merced, California, USA. jphillips7@ucmerced.edu|Center for Computational Biology, School of Natural Sciences, University of California, Merced, California, USA. jphillips7@ucmerced.edu', 'United States|United States|United States', 0, 'Algorithms|Biopolymers|Cluster Analysis|Computer Simulation|Models, Molecular|Molecular Conformation|Molecular Dynamics Simulation|Proteins', 'Journal Article', '10.1186/1471-2105-12-445', '22082218', 'Molecular dynamics (MD) simulation is a powerful technique for sampling the meta-stable and transitional conformations of proteins and other biomolecules. Computational data clustering has emerged as a useful, automated technique for extracting conformational states from MD simulation data. Despite extensive application, relatively little work has been done to determine if the clustering algorithms are actually extracting useful information. A primary goal of this paper therefore is to provide such an understanding through a detailed analysis of data clustering applied to a series of increasingly complex biopolymer models.'),
(94624, 2011, 'bmc', 'POPISK: T-cell reactivity prediction using support vector machines and string kernels', 'Tung, Chun-Wei|Ziehm, Matthias|Kämper, Andreas|Kohlbacher, Oliver|Ho, Shinn-Ying', 'School of Pharmacy, Kaohsiung Medical University, Kaohsiung 807, Taiwan.|School of Pharmacy, Kaohsiung Medical University, Kaohsiung 807, Taiwan.|School of Pharmacy, Kaohsiung Medical University, Kaohsiung 807, Taiwan.|School of Pharmacy, Kaohsiung Medical University, Kaohsiung 807, Taiwan.|School of Pharmacy, Kaohsiung Medical University, Kaohsiung 807, Taiwan.', '-|-|-|-|-', 46, 'Amino Acid Sequence|Databases, Genetic|Epitopes, T-Lymphocyte|HLA-A2 Antigen|Humans|Peptides|Receptors, Antigen, T-Cell|Software|Support Vector Machine|T-Lymphocytes', 'Journal Article', '10.1186/1471-2105-12-446', '22085524', 'Accurate prediction of peptide immunogenicity and characterization of relation between peptide sequences and peptide immunogenicity will be greatly helpful for vaccine designs and understanding of the immune system. In contrast to the prediction of antigen processing and presentation pathway, the prediction of subsequent T-cell reactivity is a much harder topic. Previous studies of identifying T-cell receptor (TCR) recognition positions were based on small-scale analyses using only a few peptides and concluded different recognition positions such as positions 4, 6 and 8 of peptides with length 9. Large-scale analyses are necessary to better characterize the effect of peptide sequence variations on T-cell reactivity and design predictors of a peptides T-cell reactivity (and thus immunogenicity). The identification and characterization of important positions influencing T-cell reactivity will provide insights into the underlying mechanism of immunogenicity.'),
(94625, 2011, 'bmc', 'OmniMapFree: a unified tool to visualise and explore sequenced genomes', 'Antoniw, John|Beacham, Andrew M|Baldwin, Thomas K|Urban, Martin|Rudd, Jason J|Hammond-Kosack, Kim E', 'Centre for Sustainable Pest and Disease Management, Rothamsted Research, Harpenden, Herts, AL5 2JQ, UK. john.antoniw@rothamsted.ac.uk|Centre for Sustainable Pest and Disease Management, Rothamsted Research, Harpenden, Herts, AL5 2JQ, UK. john.antoniw@rothamsted.ac.uk|Centre for Sustainable Pest and Disease Management, Rothamsted Research, Harpenden, Herts, AL5 2JQ, UK. john.antoniw@rothamsted.ac.uk|Centre for Sustainable Pest and Disease Management, Rothamsted Research, Harpenden, Herts, AL5 2JQ, UK. john.antoniw@rothamsted.ac.uk|Centre for Sustainable Pest and Disease Management, Rothamsted Research, Harpenden, Herts, AL5 2JQ, UK. john.antoniw@rothamsted.ac.uk|Centre for Sustainable Pest and Disease Management, Rothamsted Research, Harpenden, Herts, AL5 2JQ, UK. john.antoniw@rothamsted.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Expressed Sequence Tags|Follow-Up Studies|Fusarium|Genomics|Molecular Sequence Annotation|Reverse Genetics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-12-447', '22085540', '-'),
(94626, 2011, 'bmc', 'A flexible framework for sparse simultaneous component based data integration', 'Van Deun, Katrijn|Wilderjans, Tom F|van den Berg, Robert A|Antoniadis, Anestis|Van Mechelen, Iven', 'Center for Computational Systems Biology SymBioSys, Katholieke Universiteit Leuven, 3000 Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|Center for Computational Systems Biology SymBioSys, Katholieke Universiteit Leuven, 3000 Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|Center for Computational Systems Biology SymBioSys, Katholieke Universiteit Leuven, 3000 Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|Center for Computational Systems Biology SymBioSys, Katholieke Universiteit Leuven, 3000 Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|Center for Computational Systems Biology SymBioSys, Katholieke Universiteit Leuven, 3000 Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be', 'Belgium|Belgium|Belgium|Belgium|Belgium', 38, 'Algorithms|Escherichia coli|Metabolomics|Principal Component Analysis|Proteomics|Regression Analysis', 'Journal Article', '10.1186/1471-2105-12-448', '22085701', 'High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account.'),
(94627, 2011, 'bmc', 'ReCount: a multi-experiment resource of analysis-ready RNA-seq gene count datasets', 'Frazee, Alyssa C|Langmead, Ben|Leek, Jeffrey T', 'Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA.|Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA.|Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA.', 'United States|United States|United States', 99, 'Animals|Gene Expression Profiling|Humans|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-12-449', '22087737', '-'),
(94628, 2011, 'bmc', 'Random KNN feature selection - a fast and stable alternative to Random Forests', 'Li, Shengqiao|Harner, E James|Adjeroh, Donald A', 'The Department of Statistics, West Virginia University, Morgantown, WV 26506, USA.|The Department of Statistics, West Virginia University, Morgantown, WV 26506, USA.|The Department of Statistics, West Virginia University, Morgantown, WV 26506, USA.', 'United States|United States|United States', 0, 'Cluster Analysis|Gene Expression Profiling|Humans|Models, Genetic|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-12-450', '22093447', 'Successfully modeling high-dimensional data involving thousands of variables is challenging. This is especially true for gene expression profiling experiments, given the large number of genes involved and the small number of samples available. Random Forests (RF) is a popular and widely used approach to feature selection for such small n, large p problems. However, Random Forests suffers from instability, especially in the presence of noisy and/or unbalanced inputs.'),
(94629, 2011, 'bmc', 'Identification and correction of systematic error in high-throughput sequence data', 'Meacham, Frazer|Boffelli, Dario|Dhahbi, Joseph|Martin, David I K|Singer, Meromit|Pachter, Lior', 'Department of Mathematics, University of California, Berkeley, 970 Evans Hall #3840, Berkeley, CA 94720, USA.|Department of Mathematics, University of California, Berkeley, 970 Evans Hall #3840, Berkeley, CA 94720, USA.|Department of Mathematics, University of California, Berkeley, 970 Evans Hall #3840, Berkeley, CA 94720, USA.|Department of Mathematics, University of California, Berkeley, 970 Evans Hall #3840, Berkeley, CA 94720, USA.|Department of Mathematics, University of California, Berkeley, 970 Evans Hall #3840, Berkeley, CA 94720, USA.|Department of Mathematics, University of California, Berkeley, 970 Evans Hall #3840, Berkeley, CA 94720, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Genetic Variation|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Nucleotide Motifs|Polymorphism, Single Nucleotide|Research Design|Sequence Analysis, DNA|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1186/1471-2105-12-451', '22099972', 'A feature common to all DNA sequencing technologies is the presence of base-call errors in the sequenced reads. The implications of such errors are application specific, ranging from minor informatics nuisances to major problems affecting biological inferences. Recently developed next-gen sequencing technologies have greatly reduced the cost of sequencing, but have been shown to be more error prone than previous technologies. Both position specific (depending on the location in the read) and sequence specific (depending on the sequence in the read) errors have been identified in Illumina and Life Technology sequencing platforms. We describe a new type of systematic error that manifests as statistically unlikely accumulations of errors at specific genome (or transcriptome) locations.'),
(94630, 2011, 'bmc', 'eXframe: reusable framework for storage, analysis and visualization of genomics experiments', 'Sinha, Amit U|Merrill, Emily|Armstrong, Scott A|Clark, Tim W|Das, Sudeshna', 'Department of Pediatric Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02115, USA.|Department of Pediatric Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02115, USA.|Department of Pediatric Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02115, USA.|Department of Pediatric Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02115, USA.|Department of Pediatric Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02115, USA.', 'United States|United States|United States|United States|United States', 0, 'Aged|Aged, 80 and over|Female|Gene Expression Profiling|Genomics|Hematopoietic Stem Cells|Humans|Internet|Middle Aged|Parkinson Disease|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-12-452', '22103807', 'Genome-wide experiments are routinely conducted to measure gene expression, DNA-protein interactions and epigenetic status. Structured metadata for these experiments is imperative for a complete understanding of experimental conditions, to enable consistent data processing and to allow retrieval, comparison, and integration of experimental results. Even though several repositories have been developed for genomics data, only a few provide annotation of samples and assays using controlled vocabularies. Moreover, many of them are tailored for a single type of technology or measurement and do not support the integration of multiple data types.'),
(94631, 2011, 'bmc', 'iAssembler: a package for de novo assembly of Roche-454/Sanger transcriptome sequences', 'Zheng, Yi|Zhao, Liangjun|Gao, Junping|Fei, Zhangjun', 'Department of Ornamental Horticulture, China Agricultural University, Beijing 100094, China.|Department of Ornamental Horticulture, China Agricultural University, Beijing 100094, China.|Department of Ornamental Horticulture, China Agricultural University, Beijing 100094, China.|Department of Ornamental Horticulture, China Agricultural University, Beijing 100094, China.', 'China|China|China|China', 101, 'Expressed Sequence Tags|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software|Transcriptome', 'Journal Article', '10.1186/1471-2105-12-453', '22111509', 'Expressed Sequence Tags (ESTs) have played significant roles in gene discovery and gene functional analysis, especially for non-model organisms. For organisms with no full genome sequences available, ESTs are normally assembled into longer consensus sequences for further downstream analysis. However current de novo EST assembly programs often generate large number of assembly errors that will negatively affect the downstream analysis. In order to generate more accurate consensus sequences from ESTs, tools are needed to reduce or eliminate errors from de novo assemblies.'),
(94632, 2011, 'bmc', 'miREE: miRNA recognition elements ensemble', 'Reyes-Herrera, Paula H|Ficarra, Elisa|Acquaviva, Andrea|Macii, Enrico', 'Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 TO, Italy. phreyes@gmail.com|Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 TO, Italy. phreyes@gmail.com|Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 TO, Italy. phreyes@gmail.com|Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 TO, Italy. phreyes@gmail.com', 'Italy|Italy|Italy|Italy', 0, 'Animals|Artificial Intelligence|Gene Expression Regulation|Humans|MicroRNAs|RNA, Messenger|Regulatory Sequences, Ribonucleic Acid|Sensitivity and Specificity|Software|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-12-454', '22115078', 'Computational methods for microRNA target prediction are a fundamental step to understand the miRNA role in gene regulation, a key process in molecular biology. In this paper we present miREE, a novel microRNA target prediction tool. miREE is an ensemble of two parts entailing complementary but integrated roles in the prediction. The Ab-Initio module leverages upon a genetic algorithmic approach to generate a set of candidate sites on the basis of their microRNA-mRNA duplex stability properties. Then, a Support Vector Machine (SVM) learning module evaluates the impact of microRNA recognition elements on the target gene. As a result the prediction takes into account information regarding both miRNA-target structural stability and accessibility.'),
(94633, 2011, 'bmc', 'iRefR: an R package to manipulate the iRefIndex consolidated protein interaction database', 'Mora, Antonio|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.', 'Norway|Norway', 22, 'Animals|Databases, Protein|Humans|Mice|Protein Interaction Maps|Proteins|Rats|Reproducibility of Results|Software', 'Journal Article', '10.1186/1471-2105-12-455', '22115179', 'The iRefIndex addresses the need to consolidate protein interaction data into a single uniform data resource. iRefR provides the user with access to this data source from an R environment.'),
(94634, 2011, 'bmc', 'Unintended consequences of existential quantifications in biomedical ontologies', 'Boeker, Martin|Tudose, Ilinca|Hastings, Janna|Schober, Daniel|Schulz, Stefan', 'Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Freiburg, Germany. martin.boeker@uniklinik-freiburg.de|Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Freiburg, Germany. martin.boeker@uniklinik-freiburg.de|Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Freiburg, Germany. martin.boeker@uniklinik-freiburg.de|Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Freiburg, Germany. martin.boeker@uniklinik-freiburg.de|Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Freiburg, Germany. martin.boeker@uniklinik-freiburg.de', 'Germany|Germany|Germany|Germany|Germany', 9, 'Anatomy|Animals|Databases, Factual|Humans|Information Storage and Retrieval|Internet|Logic|Mice|Semantics|Vocabulary, Controlled|Xenopus|Zebrafish', 'Journal Article', '10.1186/1471-2105-12-456', '22115278', 'The Open Biomedical Ontologies (OBO) Foundry is a collection of freely available ontologically structured controlled vocabularies in the biomedical domain. Most of them are disseminated via both the OBO Flatfile Format and the semantic web format Web Ontology Language (OWL), which draws upon formal logic. Based on the interpretations underlying OWL description logics (OWL-DL) semantics, we scrutinize the OWL-DL releases of OBO ontologies to assess whether their logical axioms correspond to the meaning intended by their authors.'),
(94635, 2011, 'bmc', 'A novel substitution matrix fitted to the compositional bias in Mollicutes improves the prediction of homologous relationships', 'Lemaitre, Claire|Barré, Aurélien|Citti, Christine|Tardy, Florence|Thiaucourt, François|Sirand-Pugnet, Pascal|Thébault, Patricia', 'Université de Bordeaux, Centre de Bioinformatique et Génomique Fonctionnelle Bordeaux, F-33000 Bordeaux, France. claire.lemaitre@inria.fr|Université de Bordeaux, Centre de Bioinformatique et Génomique Fonctionnelle Bordeaux, F-33000 Bordeaux, France. claire.lemaitre@inria.fr|Université de Bordeaux, Centre de Bioinformatique et Génomique Fonctionnelle Bordeaux, F-33000 Bordeaux, France. claire.lemaitre@inria.fr|Université de Bordeaux, Centre de Bioinformatique et Génomique Fonctionnelle Bordeaux, F-33000 Bordeaux, France. claire.lemaitre@inria.fr|Université de Bordeaux, Centre de Bioinformatique et Génomique Fonctionnelle Bordeaux, F-33000 Bordeaux, France. claire.lemaitre@inria.fr|Université de Bordeaux, Centre de Bioinformatique et Génomique Fonctionnelle Bordeaux, F-33000 Bordeaux, France. claire.lemaitre@inria.fr|Université de Bordeaux, Centre de Bioinformatique et Génomique Fonctionnelle Bordeaux, F-33000 Bordeaux, France. claire.lemaitre@inria.fr', 'France|France|France|France|France|France|France', 6, 'Bacterial Proteins|Genomics|Sequence Homology, Amino Acid|Software|Tenericutes', 'Journal Article', '10.1186/1471-2105-12-457', '22115330', 'Substitution matrices are key parameters for the alignment of two protein sequences, and consequently for most comparative genomics studies. The composition of biological sequences can vary importantly between species and groups of species, and classical matrices such as those in the BLOSUM series fail to accurately estimate alignment scores and statistical significance with sequences sharing marked compositional biases.'),
(94636, 2011, 'bmc', 'Multiple-input multiple-output causal strategies for gene selection', 'Bontempi, Gianluca|Haibe-Kains, Benjamin|Desmedt, Christine|Sotiriou, Christos|Quackenbush, John', 'Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Belgium. gbonte@ulb.ac.be|Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Belgium. gbonte@ulb.ac.be|Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Belgium. gbonte@ulb.ac.be|Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Belgium. gbonte@ulb.ac.be|Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Belgium. gbonte@ulb.ac.be', 'Belgium|Belgium|Belgium|Belgium|Belgium', 3, 'Algorithms|Bayes Theorem|Breast Neoplasms|Female|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-12-458', '22118187', 'Traditional strategies for selecting variables in high dimensional classification problems aim to find sets of maximally relevant variables able to explain the target variations. If these techniques may be effective in generalization accuracy they often do not reveal direct causes. The latter is essentially related to the fact that high correlation (or relevance) does not imply causation. In this study, we show how to efficiently incorporate causal information into gene selection by moving from a single-input single-output to a multiple-input multiple-output setting.'),
(94637, 2011, 'bmc', 'Integrative set enrichment testing for multiple omics platforms', 'Poisson, Laila M|Taylor, Jeremy M|Ghosh, Debashis', 'Department of Public Health Sciences, Henry Ford Hospital, 1 Ford Place, Detroit, MI 48202, USA.|Department of Public Health Sciences, Henry Ford Hospital, 1 Ford Place, Detroit, MI 48202, USA.|Department of Public Health Sciences, Henry Ford Hospital, 1 Ford Place, Detroit, MI 48202, USA.', 'United States|United States|United States', 4, 'Computer Simulation|Gene Expression Profiling|Genomics|Humans|Logistic Models|Metabolomics|Oligonucleotide Array Sequence Analysis|Phenotype', 'Journal Article', '10.1186/1471-2105-12-459', '22118224', 'Enrichment testing assesses the overall evidence of differential expression behavior of the elements within a defined set. When we have measured many molecular aspects, e.g. gene expression, metabolites, proteins, it is desirable to assess their differential tendencies jointly across platforms using an integrated set enrichment test. In this work we explore the properties of several methods for performing a combined enrichment test using gene expression and metabolomics as the motivating platforms.'),
(94638, 2011, 'bmc', 'Semantic annotation of biological concepts interplaying microbial cellular responses', 'Carreira, Rafael|Carneiro, Sónia|Pereira, Rui|Rocha, Miguel|Rocha, Isabel|Ferreira, Eugénio C|Lourenço, Anália', 'Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.|Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.|Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.|Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.|Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.|Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.|Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.', 'Portugal|Portugal|Portugal|Portugal|Portugal|Portugal|Portugal', 6, 'Bacterial Proteins|Data Mining|Escherichia coli|Humans|Natural Language Processing|Semantics|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-12-460', '22122862', 'Automated extraction systems have become a time saving necessity in Systems Biology. Considerable human effort is needed to model, analyse and simulate biological networks. Thus, one of the challenges posed to Biomedical Text Mining tools is that of learning to recognise a wide variety of biological concepts with different functional roles to assist in these processes.'),
(94639, 2011, 'bmc', 'A unified framework for managing provenance information in translational research', 'Sahoo, Satya S|Nguyen, Vinh|Bodenreider, Olivier|Parikh, Priti|Minning, Todd|Sheth, Amit P', 'Department of Computer Science and Engineering, Wright State University, Dayton, OH, USA. satyasahoo@gmail.com|Department of Computer Science and Engineering, Wright State University, Dayton, OH, USA. satyasahoo@gmail.com|Department of Computer Science and Engineering, Wright State University, Dayton, OH, USA. satyasahoo@gmail.com|Department of Computer Science and Engineering, Wright State University, Dayton, OH, USA. satyasahoo@gmail.com|Department of Computer Science and Engineering, Wright State University, Dayton, OH, USA. satyasahoo@gmail.com|Department of Computer Science and Engineering, Wright State University, Dayton, OH, USA. satyasahoo@gmail.com', 'United States|United States|United States|United States|United States|United States', 8, 'Chagas Disease|Databases, Factual|Humans|Information Storage and Retrieval|Knowledge Bases|Periodicals as Topic|Semantics|Translational Medical Research|Trypanosoma cruzi', 'Journal Article', '10.1186/1471-2105-12-461', '22126369', 'A critical aspect of the NIH Translational Research roadmap, which seeks to accelerate the delivery of bench-side discoveries to patients bedside, is the management of the provenance metadata that keeps track of the origin and history of data resources as they traverse the path from the bench to the bedside and back. A comprehensive provenance framework is essential for researchers to verify the quality of data, reproduce scientific results published in peer-reviewed literature, validate scientific process, and associate trust value with data and results. Traditional approaches to provenance management have focused on only partial sections of the translational research life cycle and they do not incorporate domain semantics, which is essential to support domain-specific querying and analysis by scientists.'),
(94640, 2011, 'bmc', 'Segmentation and intensity estimation for microarray images with saturated pixels', 'Yang, Yan|Stafford, Phillip|Kim, YoonJoo', 'School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287, USA. Yan.Yang@asu.edu|School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287, USA. Yan.Yang@asu.edu|School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287, USA. Yan.Yang@asu.edu', 'United States|United States|United States', 10, 'Animals|Coccidioidomycosis|Dog Diseases|Dogs|Humans|Image Processing, Computer-Assisted|Lymphoma, B-Cell|Lymphoma, T-Cell|Microarray Analysis|Software', 'Journal Article', '10.1186/1471-2105-12-462', '22129216', 'Microarray image analysis processes scanned digital images of hybridized arrays to produce the input spot-level data for downstream analysis, so it can have a potentially large impact on those and subsequent analysis. Signal saturation is an optical effect that occurs when some pixel values for highly expressed genes or peptides exceed the upper detection threshold of the scanner software (2(16) - 1 = 65, 535 for 16-bit images). In practice, spots with a sizable number of saturated pixels are often flagged and discarded. Alternatively, the saturated values are used without adjustments for estimating spot intensities. The resulting expression data tend to be biased downwards and can distort high-level analysis that relies on these data. Hence, it is crucial to effectively correct for signal saturation.'),
(94641, 2011, 'bmc', 'Lack of sufficiently strong informative features limits the potential of gene expression analysis as predictive tool for many clinical classification problems', 'Hess, Kenneth R|Wei, Caimiao|Qi, Yuan|Iwamoto, Takayuki|Symmans, W Fraser|Pusztai, Lajos', 'Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, USA.|Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, USA.|Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, USA.|Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, USA.|Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, USA.|Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, USA.', 'United States|United States|United States|United States|United States|United States', 10, 'Breast Neoplasms|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Models, Genetic|Oligonucleotide Array Sequence Analysis|Receptors, Estrogen|Software', 'Journal Article', '10.1186/1471-2105-12-463', '22132775', 'Our goal was to examine how various aspects of a gene signature influence the success of developing multi-gene prediction models. We inserted gene signatures into three real data sets by altering the expression level of existing probe sets. We varied the number of probe sets perturbed (signature size), the fold increase of mean probe set expression in perturbed compared to unperturbed data (signature strength) and the number of samples perturbed. Prediction models were trained to identify which cases had been perturbed. Performance was estimated using Monte-Carlo cross validation.'),
(94642, 2011, 'bmc', 'From hybridization theory to microarray data analysis: performance evaluation', 'Berger, Fabrice|Carlon, Enrico', 'Institute for Theoretical Physics, KULeuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. fabrice.berger@gmail.com|Institute for Theoretical Physics, KULeuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. fabrice.berger@gmail.com', 'Belgium|Belgium', 3, 'Algorithms|Brain|Gene Expression Profiling|Humans|Myocardium|Oligonucleotide Array Sequence Analysis|Organ Specificity', 'Journal Article', '10.1186/1471-2105-12-464', '22136743', 'Several preprocessing methods are available for the analysis of Affymetrix Genechips arrays. The most popular algorithms analyze the measured fluorescence intensities with statistical methods. Here we focus on a novel algorithm, AffyILM, available from Bioconductor, which relies on inputs from hybridization thermodynamics and uses an extended Langmuir isotherm model to compute transcript concentrations. These concentrations are then employed in the statistical analysis. We compared the performance of AffyILM and other traditional methods both in the old and in the newest generation of GeneChips.'),
(94643, 2011, 'bmc', 'Comparison of methods for calculating conditional expectations of sufficient statistics for continuous time Markov chains', 'Tataru, Paula|Hobolth, Asger', 'Bioinformatics Research Center, Aarhus University, Aarhus, Denmark. paula@birc.au.dk|Bioinformatics Research Center, Aarhus University, Aarhus, Denmark. paula@birc.au.dk', 'Denmark|Denmark', 14, 'Algorithms|Codon|Evolution, Molecular|Markov Chains|Models, Genetic|Mutation', 'Comparative Study', '10.1186/1471-2105-12-465', '22142146', 'Continuous time Markov chains (CTMCs) is a widely used model for describing the evolution of DNA sequences on the nucleotide, amino acid or codon level. The sufficient statistics for CTMCs are the time spent in a state and the number of changes between any two states. In applications past evolutionary events (exact times and types of changes) are unaccessible and the past must be inferred from DNA sequence data observed in the present.'),
(94644, 2011, 'bmc', 'A method for computing an inventory of metazoan mitochondrial gene order rearrangements', 'Bernt, Matthias|Middendorf, Martin', 'Parallel Computing and Complex Systems Group, Institute of Computer Science, University Leipzig, Leipzig, 04103, Germany. bernt@informatik.uni-leipzig.de|Parallel Computing and Complex Systems Group, Institute of Computer Science, University Leipzig, Leipzig, 04103, Germany. bernt@informatik.uni-leipzig.de', 'Germany|Germany', 21, 'Animals|Arthropods|Evolution, Molecular|Gene Order|Gene Rearrangement|Genes, Mitochondrial|Mollusca|Phylogeny', 'Journal Article', '10.1186/1471-2105-12-S9-S6', '22151086', 'Changes in the order of mitochondrial genes are a good source of information for phylogenetic investigations. Phylogenetic hypotheses are often supported by parsimonious mitochondrial gene order rearrangement scenarios. CREx is a heuristic for computing short pairwise rearrangement scenarios for metazoan mitochondrial gene orders. Different from other methods, CREx considers four types of rearrangement operations: inversions, transpositions, inverse transpositions, and tandem duplication random loss operations.'),
(94645, 2011, 'bmc', 'Multi-stage gene normalization for full-text articles with context-based species filtering for dynamic dictionary entry selection', 'Tsai, Richard Tzong-Han|Lai, Po-Ting', 'Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan, ROC. thtsai@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan, ROC. thtsai@saturn.yzu.edu.tw', '-|-', 3, 'Algorithms|Data Mining|Genes|Periodicals as Topic|Reproducibility of Results|Software', 'Journal Article', '10.1186/1471-2105-12-S8-S7', '22151087', 'Gene normalization (GN) is the task of identifying the unique database IDs of genes and proteins in literature. The best-known public competition of GN systems is the GN task of the BioCreative challenge, which has been held four times since 2003. The last two BioCreatives, II.5 & III, had two significant differences from earlier tasks: firstly, they provided full-length articles in addition to abstracts, and secondly, they included multiple species without providing species ID information. Full papers introduce more complex targets for GN processing, while the inclusion of multiple species vastly increases the potential size of dictionaries needed for GN. BioCreative III GN uses Threshold Average Precision at a median of k errors per query (TAP-k), a new measure closely related to the well-known average precision, but also reflecting the reliability of the score provided by each GN system.'),
(94646, 2011, 'bmc', 'Structural properties of the reconciliation space and their applications in enumerating nearly-optimal reconciliations between a gene tree and a species tree', 'Wu, Taoyang|Zhang, Louxin', 'Department of Mathematics, National University of Singapore, Singapore 119076. taoyang.wu@gmail.com|Department of Mathematics, National University of Singapore, Singapore 119076. taoyang.wu@gmail.com', 'Singapore|Singapore', 0, 'Algorithms|Evolution, Molecular|Gene Deletion|Gene Duplication|Genes|Phylogeny', 'Journal Article', '10.1186/1471-2105-12-S9-S7', '22151151', 'A gene tree for a gene family is often discordant with the containing species tree because of its complex evolutionary course during which gene duplication, gene loss and incomplete lineage sorting events might occur. Hence, it is of great challenge to infer the containing species tree from a set of gene trees. One common approach to this inference problem is through gene tree and species tree reconciliation.'),
(94647, 2011, 'bmc', 'Benchmarking of the 2010 BioCreative Challenge III text-mining competition by the BioGRID and MINT interaction databases', 'Chatr-Aryamontri, Andrew|Winter, Andrew|Perfetto, Livia|Briganti, Leonardo|Licata, Luana|Iannuccelli, Marta|Castagnoli, Luisa|Cesareni, Gianni|Tyers, Mike', 'School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK. a.aryamontri@ed.ac.uk|School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK. a.aryamontri@ed.ac.uk|School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK. a.aryamontri@ed.ac.uk|School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK. a.aryamontri@ed.ac.uk|School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK. a.aryamontri@ed.ac.uk|School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK. a.aryamontri@ed.ac.uk|School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK. a.aryamontri@ed.ac.uk|School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK. a.aryamontri@ed.ac.uk|School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK. a.aryamontri@ed.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Data Mining|Databases, Protein|Genes|Humans|Natural Language Processing', 'Journal Article', '10.1186/1471-2105-12-S8-S8', '22151178', 'The vast amount of data published in the primary biomedical literature represents a challenge for the automated extraction and codification of individual data elements. Biological databases that rely solely on manual extraction by expert curators are unable to comprehensively annotate the information dispersed across the entire biomedical literature. The development of efficient tools based on natural language processing (NLP) systems is essential for the selection of relevant publications, identification of data attributes and partially automated annotation. One of the tasks of the Biocreative 2010 Challenge III was devoted to the evaluation of NLP systems developed to identify articles for curation and extraction of protein-protein interaction (PPI) data.'),
(94648, 2011, 'bmc', 'Genomic distance under gene substitutions', 'Braga, Marília D V|Machado, Raphael|Ribeiro, Leonardo C|Stoye, Jens', 'Instituto Nacional de Metrologia, Qualidade e Tecnologia, Duque de Caxias, 25250-020, Brazil. mdbraga@inmetro.gov.br|Instituto Nacional de Metrologia, Qualidade e Tecnologia, Duque de Caxias, 25250-020, Brazil. mdbraga@inmetro.gov.br|Instituto Nacional de Metrologia, Qualidade e Tecnologia, Duque de Caxias, 25250-020, Brazil. mdbraga@inmetro.gov.br|Instituto Nacional de Metrologia, Qualidade e Tecnologia, Duque de Caxias, 25250-020, Brazil. mdbraga@inmetro.gov.br', 'Brazil|Brazil|Brazil|Brazil', 9, 'Algorithms|Genetic Markers|Genome|Genomics|Humans|INDEL Mutation|Models, Genetic', 'Journal Article', '10.1186/1471-2105-12-S9-S8', '22151231', 'The distance between two genomes is often computed by comparing only the common markers between them. Some approaches are also able to deal with non-common markers, allowing the insertion or the deletion of such markers. In these models, a deletion and a subsequent insertion that occur at the same position of the genome count for two sorting steps.'),
(94649, 2011, 'bmc', 'Classifying protein-protein interaction articles using word and syntactic features', 'Kim, Sun|Wilbur, W John', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.', 'United States|United States', 0, 'Algorithms|Area Under Curve|Artificial Intelligence|Data Mining|Databases, Protein|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/1471-2105-12-S8-S9', '22151252', 'Identifying protein-protein interactions (PPIs) from literature is an important step in mining the function of individual proteins as well as their biological network. Since it is known that PPIs have distinctive patterns in text, machine learning approaches have been successfully applied to mine these patterns. However, the complex nature of PPI description makes the extraction process difficult.'),
(94650, 2011, 'bmc', 'Detecting genomic regions associated with a disease using variability functions and Adjusted Rand Index', 'Badescu, Dunarel|Boc, Alix|Diallo, Abdoulaye Baniré|Makarenkov, Vladimir', 'Département dlnformatique, Université du Quebec a Montreal, CP 8888, Succursale Centre-Ville, Montreal (Quebec), H3C 3P8, Canada.|Département dlnformatique, Université du Quebec a Montreal, CP 8888, Succursale Centre-Ville, Montreal (Quebec), H3C 3P8, Canada.|Département dlnformatique, Université du Quebec a Montreal, CP 8888, Succursale Centre-Ville, Montreal (Quebec), H3C 3P8, Canada.|Département dlnformatique, Université du Quebec a Montreal, CP 8888, Succursale Centre-Ville, Montreal (Quebec), H3C 3P8, Canada.', 'Canada|Canada|Canada|Canada', 3, 'Algorithms|Bacterial Infections|Bacterial Outer Membrane Proteins|Genome, Bacterial|Genome, Viral|Genomics|Humans|Neisseria meningitidis|Papillomaviridae|Sequence Alignment|Virus Diseases', 'Journal Article', '10.1186/1471-2105-12-S9-S9', '22151279', 'The identification of functional regions contained in a given multiple sequence alignment constitutes one of the major challenges of comparative genomics. Several studies have focused on the identification of conserved regions and motifs. However, most of existing methods ignore the relationship between the functional genomic regions and the external evidence associated with the considered group of species (e.g., carcinogenicity of Human Papilloma Virus). In the past, we have proposed a method that takes into account the prior knowledge on an external evidence (e.g., carcinogenicity or invasivity of the considered organisms) and identifies genomic regions related to a specific disease.'),
(94651, 2011, 'bmc', 'The rise and fall of breakpoint reuse depending on genome resolution', 'Attie, Oliver|Darling, Aaron E|Yancopoulos, Sophia', 'Department of Infectious Diseases, Mount Sinai School of Medicine, NY, NY 10029, USA.|Department of Infectious Diseases, Mount Sinai School of Medicine, NY, NY 10029, USA.|Department of Infectious Diseases, Mount Sinai School of Medicine, NY, NY 10029, USA.', 'United States|United States|United States', 11, 'Animals|Chromosome Breakage|Chromosome Fragile Sites|Chromosome Inversion|Chromosomes|Evolution, Molecular|Genome|Genome, Human|Humans|Mice|Rats|Synteny', 'Journal Article', '10.1186/1471-2105-12-S9-S1', '22151330', 'During evolution, large-scale genome rearrangements of chromosomes shuffle the order of homologous genome sequences (synteny blocks) across species. Some years ago, a controversy erupted in genome rearrangement studies over whether rearrangements recur, causing breakpoints to be reused.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(94652, 2011, 'bmc', 'Accelerated large-scale multiple sequence alignment', 'Lloyd, Scott|Snell, Quinn O', 'Computer Science Department, Brigham Young University, Provo, UT 84602, USA. gscott@byu.edu|Computer Science Department, Brigham Young University, Provo, UT 84602, USA. gscott@byu.edu', 'United States|United States', 15, 'Algorithms|DNA|Genomics|Nucleotide Motifs|Proteins|RNA|Sequence Alignment|Sequence Analysis', 'Journal Article', '10.1186/1471-2105-12-466', '22151470', 'Multiple sequence alignment (MSA) is a fundamental analysis method used in bioinformatics and many comparative genomic applications. Prior MSA acceleration attempts with reconfigurable computing have only addressed the first stage of progressive alignment and consequently exhibit performance limitations according to Amdahls Law. This work is the first known to accelerate the third stage of progressive alignment on reconfigurable hardware.'),
(94653, 2011, 'bmc', 'Empirical comparison of cross-platform normalization methods for gene expression data', 'Rudy, Jason|Valafar, Faramarz', 'Biomedical Informatics Research Center, San Diego State University, 5500 Campanile Dr, San Diego, CA, USA.|Biomedical Informatics Research Center, San Diego State University, 5500 Campanile Dr, San Diego, CA, USA.', 'United States|United States', 0, 'Algorithms|Gene Expression Profiling|Genome|Humans|Male|Oligonucleotide Array Sequence Analysis|Software|Spermatozoa', 'Comparative Study', '10.1186/1471-2105-12-467', '22151536', 'Simultaneous measurement of gene expression on a genomic scale can be accomplished using microarray technology or by sequencing based methods. Researchers who perform high throughput gene expression assays often deposit their data in public databases, but heterogeneity of measurement platforms leads to challenges for the combination and comparison of data sets. Researchers wishing to perform cross platform normalization face two major obstacles. First, a choice must be made about which method or methods to employ. Nine are currently available, and no rigorous comparison exists. Second, software for the selected method must be obtained and incorporated into a data analysis workflow.'),
(94654, 2011, 'bmc', 'openBIS: a flexible framework for managing and analyzing complex data in biology research', 'Bauch, Angela|Adamczyk, Izabela|Buczek, Piotr|Elmer, Franz-Josef|Enimanev, Kaloyan|Glyzewski, Pawel|Kohler, Manuel|Pylak, Tomasz|Quandt, Andreas|Ramakrishnan, Chandrasekhar|Beisel, Christian|Malmström, Lars|Aebersold, Ruedi|Rinn, Bernd', 'Department of Biosystems Science and Engineering, Center for Information Sciences and Databases, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.|Department of Biosystems Science and Engineering, Center for Information Sciences and Databases, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.|Department of Biosystems Science and Engineering, Center for Information Sciences and Databases, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.|Department of Biosystems Science and Engineering, Center for Information Sciences and Databases, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.|Department of Biosystems Science and Engineering, Center for Information Sciences and Databases, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.|Department of Biosystems Science and Engineering, Center for Information Sciences and Databases, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.|Department of Biosystems Science and Engineering, Center for Information Sciences and Databases, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.|Department of Biosystems Science and Engineering, Center for Information Sciences and Databases, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.|Department of Biosystems Science and Engineering, Center for Information Sciences and Databases, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.|Department of Biosystems Science and Engineering, Center for Information Sciences and Databases, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.|Department of Biosystems Science and Engineering, Center for Information Sciences and Databases, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.|Department of Biosystems Science and Engineering, Center for Information Sciences and Databases, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.|Department of Biosystems Science and Engineering, Center for Information Sciences and Databases, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.|Department of Biosystems Science and Engineering, Center for Information Sciences and Databases, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Swit', 0, 'Genomics|Information Management|Mass Spectrometry|Metabolomics|Software|Statistics as Topic|Systems Biology', 'Journal Article', '10.1186/1471-2105-12-468', '22151573', 'Modern data generation techniques used in distributed systems biology research projects often create datasets of enormous size and diversity. We argue that in order to overcome the challenge of managing those large quantitative datasets and maximise the biological information extracted from them, a sound information system is required. Ease of integration with data analysis pipelines and other computational tools is a key requirement for it.'),
(94655, 2011, 'bmc', 'The evolution of the tape measure protein: units, duplications and losses', 'Belcaid, Mahdi|Bergeron, Anne|Poisson, Guylaine', 'Information and Computer Sciences, University of Hawaii, Honolulu, Hawaii, USA.|Information and Computer Sciences, University of Hawaii, Honolulu, Hawaii, USA.|Information and Computer Sciences, University of Hawaii, Honolulu, Hawaii, USA.', 'United States|United States|United States', 16, 'Algorithms|Bacteriophages|Evolution, Molecular|Gene Duplication|Genomics|Repetitive Sequences, Amino Acid|Sequence Deletion|Viral Proteins', 'Journal Article', '10.1186/1471-2105-12-S9-S10', '22151602', 'A large family of viruses that infect bacteria, called phages, is characterized by long tails used to inject DNA into their victims cells. The tape measure protein got its name because the length of the corresponding gene is proportional to the length of the phages tail: a fact shown by actually copying or splicing out parts of DNA in exemplar species. A natural question is whether there exist units for these tape measures, and if different tape measures have different units and lengths. Such units would allow us to retrace the evolution of tape measure proteins using their duplication/loss history. The vast number of sequenced phages genomes allows us to attack this problem with a comparative genomics approach.'),
(94656, 2011, 'bmc', 'SNPInterForest: a new method for detecting epistatic interactions', 'Yoshida, Makiko|Koike, Asako', 'Central Research Laboratory, Hitachi, Ltd,, Higashi-Koigakubo, Kokubunji-shi, Tokyo, Japan. makiko.yoshida.yj@hitachi.com|Central Research Laboratory, Hitachi, Ltd,, Higashi-Koigakubo, Kokubunji-shi, Tokyo, Japan. makiko.yoshida.yj@hitachi.com', 'Japan|Japan', 35, 'Arthritis, Rheumatoid|Case-Control Studies|Computer Simulation|Epistasis, Genetic|Genetic Predisposition to Disease|Genome-Wide Association Study|Genotype|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-12-469', '22151604', 'Multiple genetic factors and their interactive effects are speculated to contribute to complex diseases. Detecting such genetic interactive effects, i.e., epistatic interactions, however, remains a significant challenge in large-scale association studies.'),
(94657, 2011, 'bmc', 'Constraints on genes shape long-term conservation of macro-synteny in metazoan genomes', 'Lv, Jie|Havlak, Paul|Putnam, Nicholas H', 'Department of Ecology and Evolutionary Biology, Rice University, Houston, TX 77098, USA.|Department of Ecology and Evolutionary Biology, Rice University, Houston, TX 77098, USA.|Department of Ecology and Evolutionary Biology, Rice University, Houston, TX 77098, USA.', 'United States|United States|United States', 0, 'Animals|Chromosomes|Evolution, Molecular|Genetic Linkage|Genome|Humans|Invertebrates|Models, Genetic|Software|Synteny', 'Journal Article', '10.1186/1471-2105-12-S9-S11', '22151646', 'Many metazoan genomes conserve chromosome-scale gene linkage relationships (macro-synteny) from the common ancestor of multicellular animal life 1234, but the biological explanation for this conservation is still unknown. Double cut and join (DCJ) is a simple, well-studied model of neutral genome evolution amenable to both simulation and mathematical analysis 5, but as we show here, it is not sufficent to explain long-term macro-synteny conservation.'),
(94658, 2011, 'bmc', 'Overview of the BioCreative III Workshop', 'Arighi, Cecilia N|Lu, Zhiyong|Krallinger, Martin|Cohen, Kevin B|Wilbur, W John|Valencia, Alfonso|Hirschman, Lynette|Wu, Cathy H', 'Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 60, 'Animals|Computational Biology|Data Mining|Genes|Humans|Periodicals as Topic|Proteins|Software', 'Introductory Journal Article', '10.1186/1471-2105-12-S8-S1', '22151647', 'The overall goal of the BioCreative Workshops is to promote the development of text mining and text processing tools which are useful to the communities of researchers and database curators in the biological sciences. To this end BioCreative I was held in 2004, BioCreative II in 2007, and BioCreative II.5 in 2009. Each of these workshops involved humanly annotated test data for several basic tasks in text mining applied to the biomedical literature. Participants in the workshops were invited to compete in the tasks by constructing software systems to perform the tasks automatically and were given scores based on their performance. The results of these workshops have benefited the community in several ways. They have 1) provided evidence for the most effective methods currently available to solve specific problems, 2) revealed the current state of the art for performance on those problems, 3) and provided gold standard data and results on that data by which future advances can be gauged. This special issue contains overview papers for the three tasks of BioCreative III.'),
(94659, 2011, 'bmc', 'Simple and efficient machine learning frameworks for identifying protein-protein interaction relevant articles and experimental methods used to study the interactions', 'Agarwal, Shashank|Liu, Feifan|Yu, Hong', 'Medical Informatics, College of Engineering and Applied Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, USA. agarwal@uwm.edu|Medical Informatics, College of Engineering and Applied Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, USA. agarwal@uwm.edu|Medical Informatics, College of Engineering and Applied Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, USA. agarwal@uwm.edu', 'United States|United States|United States', 10, 'Artificial Intelligence|Data Mining|Humans|Protein Interaction Mapping|Proteins|Proteomics', 'Journal Article', '10.1186/1471-2105-12-S8-S10', '22151701', 'Protein-protein interaction (PPI) is an important biomedical phenomenon. Automatically detecting PPI-relevant articles and identifying methods that are used to study PPI are important text mining tasks. In this study, we have explored domain independent features to develop two open source machine learning frameworks. One performs binary classification to determine whether the given article is PPI relevant or not, named Simple Classifier, and the other one maps the PPI relevant articles with corresponding interaction method nodes in a standardized PSI-MI (Proteomics Standards Initiative-Molecular Interactions) ontology, named OntoNorm.'),
(94660, 2011, 'bmc', 'Efficient algorithms for reconstructing gene content by co-evolution', 'Birin, Hadas|Tuller, Tamir', 'School of Computer Science, Tel Aviv University, Israel.|School of Computer Science, Tel Aviv University, Israel.', 'Israel|Israel', 2, 'Algorithms|Evolution, Molecular|Genes|Genome|Genomics|Humans|Programming, Linear', 'Journal Article', '10.1186/1471-2105-12-S9-S12', '22151715', 'In a previous study we demonstrated that co-evolutionary information can be utilized for improving the accuracy of ancestral gene content reconstruction. To this end, we defined a new computational problem, the Ancestral Co-Evolutionary (ACE) problem, and developed algorithms for solving it.'),
(94661, 2011, 'bmc', 'Detecting experimental techniques and selecting relevant documents for protein-protein interactions from biomedical literature', 'Wang, Xinglong|Rak, Rafal|Restificar, Angelo|Nobata, Chikashi|Rupp, C J|Batista-Navarro, Riza Theresa B|Nawaz, Raheel|Ananiadou, Sophia', 'National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester, UK. xinglong.wang@manchester.ac.uk|National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester, UK. xinglong.wang@manchester.ac.uk|National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester, UK. xinglong.wang@manchester.ac.uk|National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester, UK. xinglong.wang@manchester.ac.uk|National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester, UK. xinglong.wang@manchester.ac.uk|National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester, UK. xinglong.wang@manchester.ac.uk|National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester, UK. xinglong.wang@manchester.ac.uk|National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester, UK. xinglong.wang@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 20, 'Data Mining|Humans|Periodicals as Topic|Protein Interaction Mapping|Proteins|Proteomics', 'Journal Article', '10.1186/1471-2105-12-S8-S11', '22151769', 'The selection of relevant articles for curation, and linking those articles to experimental techniques confirming the findings became one of the primary subjects of the recent BioCreative III contest. The contests Protein-Protein Interaction (PPI) task consisted of two sub-tasks: Article Classification Task (ACT) and Interaction Method Task (IMT). ACT aimed to automatically select relevant documents for PPI curation, whereas the goal of IMT was to recognise the methods used in experiments for identifying the interactions in full-text articles.'),
(94662, 2011, 'bmc', 'On the weight of indels in genomic distances', 'Braga, Marília D V|Machado, Raphael|Ribeiro, Leonardo C|Stoye, Jens', 'Instituto Nacional de Metrologia, Qualidade e Tecnologia, Duque de Caxias, 25250-020, Brazil. mdbraga@inmetro.gov.br|Instituto Nacional de Metrologia, Qualidade e Tecnologia, Duque de Caxias, 25250-020, Brazil. mdbraga@inmetro.gov.br|Instituto Nacional de Metrologia, Qualidade e Tecnologia, Duque de Caxias, 25250-020, Brazil. mdbraga@inmetro.gov.br|Instituto Nacional de Metrologia, Qualidade e Tecnologia, Duque de Caxias, 25250-020, Brazil. mdbraga@inmetro.gov.br', 'Brazil|Brazil|Brazil|Brazil', 10, 'Algorithms|Genome|Genomics|INDEL Mutation|Models, Genetic', 'Journal Article', '10.1186/1471-2105-12-S9-S13', '22151784', 'Classical approaches to compute the genomic distance are usually limited to genomes with the same content, without duplicated markers. However, differences in the gene content are frequently observed and can reflect important evolutionary aspects. A few polynomial time algorithms that include genome rearrangements, insertions and deletions (or substitutions) were already proposed. These methods often allow a block of contiguous markers to be inserted, deleted or substituted at once but result in distance functions that do not respect the triangular inequality and hence do not constitute metrics.'),
(94663, 2011, 'bmc', 'A linear classifier based on entity recognition tools and a statistical approach to method extraction in the protein-protein interaction literature', 'Lourenço, Anália|Conover, Michael|Wong, Andrew|Nematzadeh, Azadeh|Pan, Fengxia|Shatkay, Hagit|Rocha, Luis M', 'Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Braga, Portugal.|Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Braga, Portugal.|Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Braga, Portugal.|Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Braga, Portugal.|Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Braga, Portugal.|Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Braga, Portugal.|Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Braga, Portugal.', 'Portugal|Portugal|Portugal|Portugal|Portugal|Portugal|Portugal', 8, 'Data Mining|Humans|Natural Language Processing|Periodicals as Topic|Proteins', 'Journal Article', '10.1186/1471-2105-12-S8-S12', '22151823', 'We participated, as Team 81, in the Article Classification and the Interaction Method subtasks (ACT and IMT, respectively) of the Protein-Protein Interaction task of the BioCreative III Challenge. For the ACT, we pursued an extensive testing of available Named Entity Recognition and dictionary tools, and used the most promising ones to extend our Variable Trigonometric Threshold linear classifier. Our main goal was to exploit the power of available named entity recognition and dictionary tools to aid in the classification of documents relevant to Protein-Protein Interaction (PPI). For the IMT, we focused on obtaining evidence in support of the interaction methods used, rather than on tagging the document with the method identifiers. We experimented with a primarily statistical approach, as opposed to employing a deeper natural language processing strategy. In a nutshell, we exploited classifiers, simple pattern matching for potential PPI methods within sentences, and ranking of candidate matches using statistical considerations. Finally, we also studied the benefits of integrating the method extraction approach that we have used for the IMT into the ACT pipeline.'),
(94664, 2011, 'bmc', 'Origin and evolution of gene families in Bacteria and Archaea', 'Collins, R Eric|Merz, Hugh|Higgs, Paul G', 'Origins Institute, McMaster University, Hamilton, ON L8S 4L8, Canada. rec3141@mcmaster.ca|Origins Institute, McMaster University, Hamilton, ON L8S 4L8, Canada. rec3141@mcmaster.ca|Origins Institute, McMaster University, Hamilton, ON L8S 4L8, Canada. rec3141@mcmaster.ca', 'Canada|Canada|Canada', 7, 'Cluster Analysis|Evolution, Molecular|Gene Duplication|Genes, Archaeal|Genes, Bacterial|Genome Size|Genome, Archaeal|Genome, Bacterial|Genomics|Multigene Family', 'Journal Article', '10.1186/1471-2105-12-S9-S14', '22151831', 'Comparison of complete genomes of Bacteria and Archaea shows that gene content varies considerably and that genomes evolve quite rapidly via gene duplication and deletion and horizontal gene transfer. We analyze a diverse set of 92 Bacteria and 79 Archaea in order to investigate the processes governing the origin and evolution of families of related genes within genomes.'),
(94665, 2011, 'bmc', 'Detection of interaction articles and experimental methods in biomedical literature', 'Schneider, Gerold|Clematide, Simon|Rinaldi, Fabio', 'Institute of Computational Linguistics, University of Zurich, 8050 Zurich, Switzerland.|Institute of Computational Linguistics, University of Zurich, 8050 Zurich, Switzerland.|Institute of Computational Linguistics, University of Zurich, 8050 Zurich, Switzerland.', 'Switzerland|Switzerland|Switzerland', 16, 'Computational Biology|Data Mining|Natural Language Processing|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/1471-2105-12-S8-S13', '22151872', 'This article describes the approaches taken by the OntoGene group at the University of Zurich in dealing with two tasks of the BioCreative III competition: classification of articles which contain curatable protein-protein interactions (PPI-ACT) and extraction of experimental methods (PPI-IMT).'),
(94666, 2011, 'bmc', 'STELLAR: fast and exact local alignments', 'Kehr, Birte|Weese, David|Reinert, Knut', 'Department of Computer Science, Free University Berlin, Takustr. 9, 14195 Berlin, Germany. birte.kehr@fu-berlin.de|Department of Computer Science, Free University Berlin, Takustr. 9, 14195 Berlin, Germany. birte.kehr@fu-berlin.de|Department of Computer Science, Free University Berlin, Takustr. 9, 14195 Berlin, Germany. birte.kehr@fu-berlin.de', 'Germany|Germany|Germany', 18, 'Algorithms|Animals|Drosophila|Genomics|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-12-S9-S15', '22151882', 'Large-scale comparison of genomic sequences requires reliable tools for the search of local alignments. Practical local aligners are in general fast, but heuristic, and hence sometimes miss significant matches.'),
(94667, 2011, 'bmc', 'The gene normalization task in BioCreative III', 'Lu, Zhiyong|Kao, Hung-Yu|Wei, Chih-Hsuan|Huang, Minlie|Liu, Jingchen|Kuo, Cheng-Ju|Hsu, Chun-Nan|Tsai, Richard Tzong-Han|Dai, Hong-Jie|Okazaki, Naoaki|Cho, Han-Cheol|Gerner, Martin|Solt, Illes|Agarwal, Shashank|Liu, Feifan|Vishnyakova, Dina|Ruch, Patrick|Romacker, Martin|Rinaldi, Fabio|Bhattacharya, Sanmitra|Srinivasan, Padmini|Liu, Hongfang|Torii, Manabu|Matos, Sergio|Campos, David|Verspoor, Karin|Livingston, Kevin M|Wilbur, W John', 'National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov|National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. zhiyong.lu@nih.gov', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|Uni', 86, 'Algorithms|Animals|Data Mining|Genes|Humans|National Library of Medicine (U.S.)|Periodicals as Topic|United States', 'Journal Article', '10.1186/1471-2105-12-S8-S2', '22151901', 'We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an Expectation Maximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k).'),
(94668, 2011, 'bmc', 'Evidence for intron length conservation in a set of mammalian genes associated with embryonic development', 'Seoighe, Cathal|Korir, Paul K', 'National University of Ireland, Galway, University Road, Galway, Republic of Ireland. cathal.seoighe@nuigalway.ie|National University of Ireland, Galway, University Road, Galway, Republic of Ireland. cathal.seoighe@nuigalway.ie', 'Ireland|Ireland', 9, 'Animals|Base Sequence|Basic Helix-Loop-Helix Transcription Factors|Conserved Sequence|Embryonic Development|Evolution, Molecular|Gene Expression|Gene Expression Regulation, Developmental|Humans|Introns|Mice|Mutation|Sequence Deletion', 'Journal Article', '10.1186/1471-2105-12-S9-S16', '22151910', 'We carried out an analysis of intron length conservation across a diverse group of nineteen mammalian species. Motivated by recent research suggesting a role for time delays associated with intron transcription in gene expression oscillations required for early embryonic patterning, we searched for examples of genes that showed the most extreme conservation of total intron content in mammals.'),
(94669, 2011, 'bmc', 'The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text', 'Krallinger, Martin|Vazquez, Miguel|Leitner, Florian|Salgado, David|Chatr-Aryamontri, Andrew|Winter, Andrew|Perfetto, Livia|Briganti, Leonardo|Licata, Luana|Iannuccelli, Marta|Castagnoli, Luisa|Cesareni, Gianni|Tyers, Mike|Schneider, Gerold|Rinaldi, Fabio|Leaman, Robert|Gonzalez, Graciela|Matos, Sergio|Kim, Sun|Wilbur, W John|Rocha, Luis|Shatkay, Hagit|Tendulkar, Ashish V|Agarwal, Shashank|Liu, Feifan|Wang, Xinglong|Rak, Rafal|Noto, Keith|Elkan, Charles|Lu, Zhiyong|Dogan, Rezarta Islamaj|Fontaine, Jean-Fred|Andrade-Navarro, Miguel A|Valencia, Alfonso', 'Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es|Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. mkrallinger@cnio.es', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spai', 93, 'Algorithms|Animals|Data Mining|Databases, Protein|Humans|Periodicals as Topic|Proteins|PubMed', 'Journal Article', '10.1186/1471-2105-12-S8-S3', '22151929', 'Determining usefulness of biomedical text mining systems requires realistic task definition and data selection criteria without artificial constraints, measuring performance aspects that go beyond traditional metrics. The BioCreative III Protein-Protein Interaction (PPI) tasks were motivated by such considerations, trying to address aspects including how the end user would oversee the generated output, for instance by providing ranked results, textual evidence for human interpretation or measuring time savings by using automated systems. Detecting articles describing complex biological events like PPIs was addressed in the Article Classification Task (ACT), where participants were asked to implement tools for detecting PPI-describing abstracts. Therefore the BCIII-ACT corpus was provided, which includes a training, development and test set of over 12,000 PPI relevant and non-relevant PubMed abstracts labeled manually by domain experts and recording also the human classification times. The Interaction Method Task (IMT) went beyond abstracts and required mining for associations between more than 3,500 full text articles and interaction detection method ontology concepts that had been applied to detect the PPIs reported in them.'),
(94670, 2011, 'bmc', 'Approximating the double-cut-and-join distance between unsigned genomes', 'Chen, Xin|Sun, Ruimin|Yu, Jiadong', 'Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore. chenxin@ntu.edu.sg|Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore. chenxin@ntu.edu.sg|Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore. chenxin@ntu.edu.sg', 'Singapore|Singapore|Singapore', 6, 'Algorithms|Chromosomes|Genome|Genomics|Models, Genetic', 'Journal Article', '10.1186/1471-2105-12-S9-S17', '22151948', 'In this paper we study the problem of sorting unsigned genomes by double-cut-and-join operations, where genomes allow a mix of linear and circular chromosomes to be present. First, we formulate an equivalent optimization problem, called maximum cycle/path decomposition, which is aimed at finding a largest collection of edge-disjoint cycles/AA-paths/AB-paths in a breakpoint graph. Then, we show that the problem of finding a largest collection of edge-disjoint cycles/AA-paths/AB-paths of length no more than l can be reduced to the well-known degree-bounded k-set packing problem with k = 2l. Finally, a polynomial-time approximation algorithm for the problem of sorting unsigned genomes by double-cut-and-join operations is devised, which achieves the approximation ratio 13/9 + xcexb5 xe2x89x88 1.4444 + xcexb5, for any positive xcexb5. For the restricted variation where each genome contains only one linear chromosome, the approximation ratio can be further improved to 69/49 + xcexb5 xe2x89x88 1.4082 + xcexb5.'),
(94671, 2011, 'bmc', 'BioCreative III interactive task: an overview', 'Arighi, Cecilia N|Roberts, Phoebe M|Agarwal, Shashank|Bhattacharya, Sanmitra|Cesareni, Gianni|Chatr-Aryamontri, Andrew|Clematide, Simon|Gaudet, Pascale|Giglio, Michelle Gwinn|Harrow, Ian|Huala, Eva|Krallinger, Martin|Leser, Ulf|Li, Donghui|Liu, Feifan|Lu, Zhiyong|Maltais, Lois J|Okazaki, Naoaki|Perfetto, Livia|Rinaldi, Fabio|Sætre, Rune|Salgado, David|Srinivasan, Padmini|Thomas, Philippe E|Toldo, Luca|Hirschman, Lynette|Wu, Cathy H', 'Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|Uni', 63, 'Animals|Computational Biology|Data Mining|Genes|Periodicals as Topic|Plants', 'Journal Article', '10.1186/1471-2105-12-S8-S4', '22151968', 'The BioCreative challenge evaluation is a community-wide effort for evaluating text mining and information extraction systems applied to the biological domain. The biocurator community, as an active user of biomedical literature, provides a diverse and engaged end user group for text mining tools. Earlier BioCreative challenges involved many text mining teams in developing basic capabilities relevant to biological curation, but they did not address the issues of system usage, insertion into the workflow and adoption by curators. Thus in BioCreative III (BC-III), the InterActive Task (IAT) was introduced to address the utility and usability of text mining tools for real-life biocuration tasks. To support the aims of the IAT in BC-III, involvement of both developers and end users was solicited, and the development of a user interface to address the tasks interactively was requested.'),
(94672, 2011, 'bmc', 'Identification of conserved gene clusters in multiple genomes based on synteny and homology', 'Sarkar, Anasua|Soueidan, Hayssam|Nikolski, Macha', 'LaBRI, CNRS/Université Bordeaux 1, 351 crs Libération, 33405 Talence, France. sarkar@LaBRI.U-Bordeaux.FR|LaBRI, CNRS/Université Bordeaux 1, 351 crs Libération, 33405 Talence, France. sarkar@LaBRI.U-Bordeaux.FR|LaBRI, CNRS/Université Bordeaux 1, 351 crs Libération, 33405 Talence, France. sarkar@LaBRI.U-Bordeaux.FR', 'France|France|France', 4, 'ATP-Binding Cassette Transporters|Algorithms|Chromosomes|Genome, Fungal|Genomics|Multigene Family|Sequence Homology, Nucleic Acid|Synteny|Yeasts', 'Evaluation Studies', '10.1186/1471-2105-12-S9-S18', '22151970', 'Uncovering the relationship between the conserved chromosomal segments and the functional relatedness of elements within these segments is an important question in computational genomics. We build upon the series of works on gene teams and homology teams.'),
(94673, 2011, 'bmc', 'Cross-species gene normalization by species inference', 'Wei, Chih-Hsuan|Kao, Hung-Yu', 'Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, ROC.|Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, ROC.', '-|-', 41, 'Data Mining|Genes|Periodicals as Topic|Software|Species Specificity', 'Journal Article', '10.1186/1471-2105-12-S8-S5', '22151999', 'To access and utilize the rich information contained in the biomedical literature, the ability to recognize and normalize gene mentions referenced in the literature is crucial. In this paper, we focus on improvements to the accuracy of gene normalization in cases where species information is not provided. Gene names are often ambiguous, in that they can refer to the genes of many species. Therefore, gene normalization is a difficult challenge.'),
(94674, 2011, 'bmc', 'Changes in transcriptional orientation are associated with increases in evolutionary rates of enterobacterial genes', 'Lin, Chieh-Hua|Lian, Chun-Yi|Hsiung, Chao Agnes|Chen, Feng-Chi', 'Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, 35 Keyen Road, Zhunan Town, Miaoli County, Taiwan, Republic of China.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, 35 Keyen Road, Zhunan Town, Miaoli County, Taiwan, Republic of China.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, 35 Keyen Road, Zhunan Town, Miaoli County, Taiwan, Republic of China.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, 35 Keyen Road, Zhunan Town, Miaoli County, Taiwan, Republic of China.', 'China|China|China|China', 3, 'Enterobacteriaceae|Escherichia coli|Evolution, Molecular|Genes, Bacterial|Genome, Bacterial|Genomics|Klebsiella|Mutation Rate|Salmonella typhimurium|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-12-S9-S19', '22152004', 'Changes in transcriptional orientation (CTOs) occur frequently in prokaryotic genomes. Such changes usually result from genomic inversions, which may cause a conflict between the directions of replication and transcription and an increase in mutation rate. However, CTOs do not always lead to the replication-transcription confrontation. Furthermore, CTOs may cause deleterious disruptions of operon structure and/or gene regulations. The currently existing CTOs may indicate relaxation of selection pressure. Therefore, it is of interest to investigate whether CTOs have an independent effect on the evolutionary rates of the affected genes, and whether these genes are subject to any type of selection pressure in prokaryotes.'),
(94675, 2011, 'bmc', 'Soft tagging of overlapping high confidence gene mention variants for cross-species full-text gene normalization', 'Kuo, Cheng-Ju|Ling, Maurice H T|Hsu, Chun-Nan', 'Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.|Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.|Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.', '-|-|-', 0, 'Data Mining|Genes|Natural Language Processing|Periodicals as Topic|Software|Species Specificity|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-12-S8-S6', '22152021', 'Previously, gene normalization (GN) systems are mostly focused on disambiguation using contextual information. An effective gene mention tagger is deemed unnecessary because the subsequent steps will filter out false positives and high recall is sufficient. However, unlike similar tasks in the past BioCreative challenges, the BioCreative III GN task is particularly challenging because it is not species-specific. Required to process full-length articles, an ineffective gene mention tagger may produce a huge number of ambiguous false positives that overwhelm subsequent filtering steps while still missing many true positives.'),
(94676, 2011, 'bmc', 'Evolution of orthologous tandemly arrayed gene clusters', 'Tremblay Savard, Olivier|Bertrand, Denis|El-Mabrouk, Nadia', 'Department of Computer Science (DIRO), University of Montreal, Montreal, Quebec, Canada. olivier.tremblay-savard@umontreal.ca|Department of Computer Science (DIRO), University of Montreal, Montreal, Quebec, Canada. olivier.tremblay-savard@umontreal.ca|Department of Computer Science (DIRO), University of Montreal, Montreal, Quebec, Canada. olivier.tremblay-savard@umontreal.ca', 'Canada|Canada|Canada', 10, 'Algorithms|Animals|Evolution, Molecular|Gene Deletion|Gene Duplication|Humans|Mice|Multigene Family|Phylogeny|Rats', 'Journal Article', '10.1186/1471-2105-12-S9-S2', '22152029', 'Tandemly Arrayed Gene (TAG) clusters are groups of paralogous genes that are found adjacent on a chromosome. TAGs represent an important repertoire of genes in eukaryotes. In addition to tandem duplication events, TAG clusters are affected during their evolution by other mechanisms, such as inversion and deletion events, that affect the order and orientation of genes. The DILTAG algorithm developed in 1 makes it possible to infer a set of optimal evolutionary histories explaining the evolution of a single TAG cluster, from an ancestral single gene, through tandem duplications (simple or multiple, direct or inverted), deletions and inversion events.'),
(94677, 2011, 'bmc', 'Genome dedoubling by DCJ and reversal', 'Thomas, Antoine|Varré, Jean-Stéphane|Ouangraoua, Aïda', 'LIFL, UMR 8022 CNRS, Université Lille 1, INRIA Lille Nord Europe, Villeneuve dAscq, France.|LIFL, UMR 8022 CNRS, Université Lille 1, INRIA Lille Nord Europe, Villeneuve dAscq, France.|LIFL, UMR 8022 CNRS, Université Lille 1, INRIA Lille Nord Europe, Villeneuve dAscq, France.', 'France|France|France', 0, 'Algorithms|Animals|Drosophila|Evolution, Molecular|Genomics|Models, Genetic|Segmental Duplications, Genomic', 'Journal Article', '10.1186/1471-2105-12-S9-S20', '22152053', 'Segmental duplications in genomes have been studied for many years. Recently, several studies have highlighted a biological phenomenon called breakpoint-duplication that apparently associates a significant proportion of segmental duplications in Mammals, and the Drosophila species group, to breakpoints in rearrangement events.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(94678, 2011, 'bmc', 'Consistency-based detection of potential tumor-specific deletions in matched normal/tumor genomes', 'Wittler, Roland|Chauve, Cedric', 'Department of Mathematics, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada. roland@CeBiTec.Uni-Bielefeld.DE|Department of Mathematics, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada. roland@CeBiTec.Uni-Bielefeld.DE', 'Canada|Canada', 0, 'Genetic Variation|Genome, Human|Genomics|Humans|Neoplasms|Sequence Deletion', 'Journal Article', '10.1186/1471-2105-12-S9-S21', '22152084', 'Structural variations in human genomes, such as insertions, deletion, or rearrangements, play an important role in cancer development. Next-Generation Sequencing technologies have been central in providing ways to detect such variations. Most existing methods however are limited to the analysis of a single genome, and it is only recently that the comparison of closely related genomes has been considered. In particular, a few recent works considered the analysis of data sets obtained by sequencing both tumor and healthy tissues of the same cancer patient. In that context, the goal is to detect variations that are specific to exactly one of the genomes, for example to differentiate between patient-specific and tumor-specific variations. This is a difficult task, especially when facing the additional challenge of the possible contamination of healthy tissues by tumor cells and conversely.'),
(94679, 2011, 'bmc', 'The genomic features that affect the lengths of 5 untranslated regions in multicellular eukaryotes', 'Chen, Chun-Hsi|Lin, Hsuan-Yu|Pan, Chia-Lin|Chen, Feng-Chi', 'Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, 350 Taiwan, Republic of China.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, 350 Taiwan, Republic of China.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, 350 Taiwan, Republic of China.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, 350 Taiwan, Republic of China.', 'China|China|China|China', 9, '5 Untranslated Regions|Animals|Base Composition|Codon, Terminator|Genetic Variation|Genome|Genomics|Humans|Mice|Open Reading Frames|Rats', 'Journal Article', '10.1186/1471-2105-12-S9-S3', '22152105', 'The lengths of 5UTRs of multicellular eukaryotes have been suggested to be subject to stochastic changes, with upstream start codons (uAUGs) as the major constraint to suppress 5UTR elongation. However, this stochastic model cannot fully explain the variations in 5UTR length. We hypothesize that the selection pressure on a combination of genomic features is also important for 5UTR evolution. The ignorance of these features may have limited the explanatory power of the stochastic model. Furthermore, different selective constraints between vertebrates and invertebrates may lead to differences in the determinants of 5UTR length, which have not been systematically analyzed.'),
(94680, 2011, 'bmc', 'Fast and accurate methods for phylogenomic analyses', 'Yang, Jimmy|Warnow, Tandy', 'Department of Computer Science, University of Texas at Austin, Austin TX 78712, USA.|Department of Computer Science, University of Texas at Austin, Austin TX 78712, USA.', 'United States|United States', 0, 'Algorithms|Genes|Genomics|Phylogeny', 'Journal Article', '10.1186/1471-2105-12-S9-S4', '22152123', 'Species phylogenies are not estimated directly, but rather through phylogenetic analyses of different gene datasets. However, true gene trees can differ from the true species tree (and hence from one another) due to biological processes such as horizontal gene transfer, incomplete lineage sorting, and gene duplication and loss, so that no single gene tree is a reliable estimate of the species tree. Several methods have been developed to estimate species trees from estimated gene trees, differing according to the specific algorithmic technique used and the biological model used to explain differences between species and gene trees. Relatively little is known about the relative performance of these methods.'),
(94681, 2011, 'bmc', 'Fractionation statistics', 'Wang, Baoyong|Zheng, Chunfang|Sankoff, David', 'Department of Mathematics and Statistics, University of Ottawa Ottawa, Canada K1N 6N5.|Department of Mathematics and Statistics, University of Ottawa Ottawa, Canada K1N 6N5.|Department of Mathematics and Statistics, University of Ottawa Ottawa, Canada K1N 6N5.', 'Canada|Canada|Canada', 6, 'Chromosomes|Data Interpretation, Statistical|Evolution, Molecular|Gene Deletion|Gene Duplication|Genes, Duplicate|Genomics|Models, Genetic', 'Journal Article', '10.1186/1471-2105-12-S9-S5', '22152148', 'Paralog reduction, the loss of duplicate genes after whole genome duplication (WGD) is a pervasive process. Whether this loss proceeds gene by gene or through deletion of multi-gene DNA segments is controversial, as is the question of fractionation bias, namely whether one homeologous chromosome is more vulnerable to gene deletion than the other.'),
(94682, 2011, 'bmc', 'HOMER: a human organ-specific molecular electronic repository', 'Zhang, Fan|Chen, Jake Y', 'School of Informatics, Indiana University, Indianapolis, IN 46202, USA.|School of Informatics, Indiana University, Indianapolis, IN 46202, USA.', 'India|India', 14, 'Databases, Genetic|Disease|Expressed Sequence Tags|Gene Expression Profiling|Humans|Internet|Organ Specificity|Software', 'Journal Article', '10.1186/1471-2105-12-S10-S4', '22165817', 'Each organ has a specific function in the body. Organ-specificity refers to differential expressions of the same gene across different organs. An organ-specific gene/protein is defined as a gene/protein whose expression is significantly elevated in a specific human organ. An organ-specific marker is defined as an organ-specific gene/protein that is also implicated in human diseases related to the organ. Previous studies have shown that identifying specificity for the organ in which a gene or protein is significantly differentially expressed, can lead to discovery of its function. Most currently available resources for organ-specific genes/proteins either allow users to access tissue-specific expression over a limited range of organs, or do not contain disease information such as disease-organ relationship and disease-gene relationship.'),
(94683, 2011, 'bmc', 'An efficient and extensible approach for compressing phylogenetic trees', 'Matthews, Suzanne J|Williams, Tiffani L', 'Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, USA. sjm@cse.tamu.edu|Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, USA. sjm@cse.tamu.edu', 'United States|United States', 0, 'Algorithms|Animals|Classification|Humans|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-12-S10-S16', '22165819', 'Biologists require new algorithms to efficiently compress and store their large collections of phylogenetic trees. Our previous work showed that TreeZip is a promising approach for compressing phylogenetic trees. In this paper, we extend our TreeZip algorithm by handling trees with weighted branches. Furthermore, by using the compressed TreeZip file as input, we have designed an extensible decompressor that can extract subcollections of trees, compute majority and strict consensus trees, and merge tree collections using set operations such as union, intersection, and set difference.'),
(94684, 2011, 'bmc', 'Evaluation of the coverage and depth of transcriptome by RNA-Seq in chickens', 'Wang, Ying|Ghaffari, Noushin|Johnson, Charles D|Braga-Neto, Ulisses M|Wang, Hui|Chen, Rui|Zhou, Huaijun', 'Department of Poultry Science, Texas A &M University College Station, TX 77843-2472, USA. hjzhou@poultry.tamu.edu.|Department of Poultry Science, Texas A &M University College Station, TX 77843-2472, USA. hjzhou@poultry.tamu.edu.|Department of Poultry Science, Texas A &M University College Station, TX 77843-2472, USA. hjzhou@poultry.tamu.edu.|Department of Poultry Science, Texas A &M University College Station, TX 77843-2472, USA. hjzhou@poultry.tamu.edu.|Department of Poultry Science, Texas A &M University College Station, TX 77843-2472, USA. hjzhou@poultry.tamu.edu.|Department of Poultry Science, Texas A &M University College Station, TX 77843-2472, USA. hjzhou@poultry.tamu.edu.|Department of Poultry Science, Texas A &M University College Station, TX 77843-2472, USA. hjzhou@poultry.tamu.edu.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Animals|Chickens|Gene Expression Profiling|Gene Library|High-Throughput Nucleotide Sequencing|Lung|Molecular Sequence Annotation|RNA, Messenger|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-12-S10-S5', '22165852', 'RNA-Seq is the recently developed high-throughput sequencing technology for profiling the entire transcriptome in any organism. It has several major advantages over current hybridization-based approach such as microarrays. However, the cost per sample by RNA-Seq is still prohibitive for most laboratories. With continued improvement in sequence output, it would be cost-effective if multiple samples are multiplexed and sequenced in a single lane with sufficient transcriptome coverage. The objective of this analysis is to evaluate what sequencing depth might be sufficient to interrogate gene expression profiling in the chicken by RNA-Seq.'),
(94685, 2011, 'bmc', 'Scenario driven data modelling: a method for integrating diverse sources of data and data streams', 'Griffith, Shelton D|Quest, Daniel J|Brettin, Thomas S|Cottingham, Robert W', 'Biosciences Division, Oak Ridge National Laboratory, Building 1059, PO Box 2008, MS 6420, Oak Ridge, TN 37831-6420, USA.|Biosciences Division, Oak Ridge National Laboratory, Building 1059, PO Box 2008, MS 6420, Oak Ridge, TN 37831-6420, USA.|Biosciences Division, Oak Ridge National Laboratory, Building 1059, PO Box 2008, MS 6420, Oak Ridge, TN 37831-6420, USA.|Biosciences Division, Oak Ridge National Laboratory, Building 1059, PO Box 2008, MS 6420, Oak Ridge, TN 37831-6420, USA.', 'United States|United States|United States|United States', 2, 'Bacterial Infections|Drug Resistance, Bacterial|Humans|Internet|Models, Biological|Population Surveillance|Software', 'Journal Article', '10.1186/1471-2105-12-S10-S17', '22165854', 'Biology is rapidly becoming a data intensive, data-driven science. It is essential that data is represented and connected in ways that best represent its full conceptual content and allows both automated integration and data driven decision-making. Recent advancements in distributed multi-relational directed graphs, implemented in the form of the Semantic Web make it possible to deal with complicated heterogeneous data in new and interesting ways.'),
(94686, 2011, 'bmc', 'Algorithms, data structures, and numerics for likelihood-based phylogenetic inference of huge trees', 'Izquierdo-Carrasco, Fernando|Smith, Stephen A|Stamatakis, Alexandros', 'The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, D-69118 Heidelberg, Germany. Fernando.Izquierdo@h-its.org|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, D-69118 Heidelberg, Germany. Fernando.Izquierdo@h-its.org|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, D-69118 Heidelberg, Germany. Fernando.Izquierdo@h-its.org', 'Germany|Germany|Germany', 42, 'Algorithms|Bayes Theorem|Likelihood Functions|Models, Genetic|Molecular Sequence Data|Phylogeny|Probability', 'Journal Article', '10.1186/1471-2105-12-470', '22165866', 'The rapid accumulation of molecular sequence data, driven by novel wet-lab sequencing technologies, poses new challenges for large-scale maximum likelihood-based phylogenetic analyses on trees with more than 30,000 taxa and several genes. The three main computational challenges are: numerical stability, the scalability of search algorithms, and the high memory requirements for computing the likelihood.'),
(94687, 2011, 'bmc', 'Enhancing the accuracy of HMM-based conserved pathway prediction using global correspondence scores', 'Qian, Xiaoning|Sahraeian, Sayed Mohammad Ebrahim|Yoon, Byung-Jun', 'Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA. xqian@cse.usf.edu|Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA. xqian@cse.usf.edu|Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA. xqian@cse.usf.edu', 'United States|United States|United States', 0, 'Algorithms|Bacteria|Bacterial Proteins|Markov Chains|Models, Biological|Protein Interaction Maps|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-12-S10-S6', '22165903', 'Comparative network analysis aims to identify common subnetworks in biological networks. It can facilitate the prediction of conserved functional modules across different species and provide deep insights into their underlying regulatory mechanisms. Recently, it has been shown that hidden Markov models (HMMs) can provide a flexible and computationally efficient framework for modeling and comparing biological networks.'),
(94688, 2011, 'bmc', 'First-passage time analysis of a one-dimensional diffusion-reaction model: application to protein transport along DNA', 'Mayo, Michael L|Perkins, Edward J|Ghosh, Preetam', 'Environemental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS 39180, USA. Michael.L.Mayo@usace.army.mil|Environemental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS 39180, USA. Michael.L.Mayo@usace.army.mil|Environemental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS 39180, USA. Michael.L.Mayo@usace.army.mil', 'United States|United States|United States', 3, 'Bacterial Proteins|DNA, Bacterial|DNA-Binding Proteins|Diffusion|Kinetics|Models, Biological|Monte Carlo Method|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-12-S10-S18', '22165905', 'Proteins search along the DNA for targets, such as transcription initiation sequences, according to one-dimensional diffusion, which is interrupted by micro- and macro-hopping events and intersegmental transfers that occur under close packing conditions.'),
(94689, 2011, 'bmc', 'Proceedings of the 2011 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) conference Introduction', 'Wren, Jonathan D|Kupfer, Doris M|Perkins, Edward J|Bridges, Susan|Winters-Hilt, Stephen|Dozmorov, Mikhail G|Braga-Neto, Ulisses', 'Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation 825 NE 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation 825 NE 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation 825 NE 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation 825 NE 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation 825 NE 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation 825 NE 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation 825 NE 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Genomics|Oligonucleotide Array Sequence Analysis|Systems Biology', 'Introductory Journal Article', '10.1186/1471-2105-12-S10-S1', '22165918', '-'),
(94690, 2011, 'bmc', 'Constructing a robust protein-protein interaction network by integrating multiple public databases', 'Martha, Venkata-Swamy|Liu, Zhichao|Guo, Li|Su, Zhenqiang|Ye, Yanbin|Fang, Hong|Ding, Don|Tong, Weida|Xu, Xiaowei', 'Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.|Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.|Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.|Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.|Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.|Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.|Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.|Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.|Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Databases, Protein|Humans|Models, Biological|Protein Interaction Mapping|Protein Interaction Maps|Proteins', 'Journal Article', '10.1186/1471-2105-12-S10-S7', '22165958', 'Protein-protein interactions (PPIs) are a critical component for many underlying biological processes. A PPI network can provide insight into the mechanisms of these processes, as well as the relationships among different proteins and toxicants that are potentially involved in the processes. There are many PPI databases publicly available, each with a specific focus. The challenge is how to effectively combine their contents to generate a robust and biologically relevant PPI network.'),
(94691, 2011, 'bmc', 'Latent Semantic Indexing of PubMed abstracts for identification of transcription factor candidates from microarray derived gene sets', 'Roy, Sujoy|Heinrich, Kevin|Phan, Vinhthuy|Berry, Michael W|Homayouni, Ramin', 'Bioinformatics Program, University of Memphis, Memphis, TN 38152, USA.|Bioinformatics Program, University of Memphis, Memphis, TN 38152, USA.|Bioinformatics Program, University of Memphis, Memphis, TN 38152, USA.|Bioinformatics Program, University of Memphis, Memphis, TN 38152, USA.|Bioinformatics Program, University of Memphis, Memphis, TN 38152, USA.', 'United States|United States|United States|United States|United States', 15, 'Algorithms|Amino Acid Motifs|Animals|Data Mining|Gene Regulatory Networks|Humans|Mice|Oligonucleotide Array Sequence Analysis|PubMed|Systems Biology|Transcription Factors', 'Journal Article', '10.1186/1471-2105-12-S10-S19', '22165960', 'Identification of transcription factors (TFs) responsible for modulation of differentially expressed genes is a key step in deducing gene regulatory pathways. Most current methods identify TFs by searching for presence of DNA binding motifs in the promoter regions of co-regulated genes. However, this strategy may not always be useful as presence of a motif does not necessarily imply a regulatory role. Conversely, motif presence may not be required for a TF to regulate a set of genes. Therefore, it is imperative to include functional (biochemical and molecular) associations, such as those found in the biomedical literature, into algorithms for identification of putative regulatory TFs that might be explicitly or implicitly linked to the genes under investigation.'),
(94692, 2011, 'bmc', 'A CoD-based stationary control policy for intervening in large gene regulatory networks', 'Ghaffari, Noushin|Ivanov, Ivan|Qian, Xiaoning|Dougherty, Edward R', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843 USA. nghaffari@tamu.edu|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843 USA. nghaffari@tamu.edu|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843 USA. nghaffari@tamu.edu|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843 USA. nghaffari@tamu.edu', 'United States|United States|United States|United States', 0, 'Computer Simulation|Gastrointestinal Neoplasms|Gene Regulatory Networks|Humans|Models, Genetic|Probability', 'Journal Article', '10.1186/1471-2105-12-S10-S10', '22165980', 'One of the most important goals of the mathematical modeling of gene regulatory networks is to alter their behavior toward desirable phenotypes. Therapeutic techniques are derived for intervention in terms of stationary control policies. In large networks, it becomes computationally burdensome to derive an optimal control policy. To overcome this problem, greedy intervention approaches based on the concept of the Mean First Passage Time or the steady-state probability mass of the network states were previously proposed. Another possible approach is to use reduction mappings to compress the network and develop control policies on its reduced version. However, such mappings lead to loss of information and require an induction step when designing the control policy for the original network.'),
(94693, 2011, 'bmc', 'Analysis of cancer metabolism with high-throughput technologies', 'Markovets, Aleksandra A|Herman, Damir', 'Department of Internal Medicine, Division of Hematology and Oncology, Winthrop P, Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.|Department of Internal Medicine, Division of Hematology and Oncology, Winthrop P, Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.', 'United States|United States', 9, 'Adenosine Triphosphate|Citric Acid Cycle|Energy Metabolism|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genomics|Glycolysis|Humans|Neoplasms|Organ Specificity', 'Journal Article', '10.1186/1471-2105-12-S10-S8', '22166000', 'Recent advances in genomics and proteomics have allowed us to study the nuances of the Warburg effect--a long-standing puzzle in cancer energy metabolism--at an unprecedented level of detail. While modern next-generation sequencing technologies are extremely powerful, the lack of appropriate data analysis tools makes this study difficult. To meet this challenge, we developed a novel application for comparative analysis of gene expression and visualization of RNA-Seq data.'),
(94694, 2011, 'bmc', 'High-throughput processing and normalization of one-color microarrays for transcriptional meta-analyses', 'Dozmorov, Mikhail G|Wren, Jonathan D', 'Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation 825 NE 13th Street, Oklahoma City, Oklahoma 73104-5005, USA. Mikhail-Dozmorov@omrf.org|Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation 825 NE 13th Street, Oklahoma City, Oklahoma 73104-5005, USA. Mikhail-Dozmorov@omrf.org', 'United States|United States', 0, 'Databases, Genetic|Gene Expression Profiling|Humans|Meta-Analysis as Topic|Oligonucleotide Array Sequence Analysis|Quality Control|Reproducibility of Results|Sample Size|Software', 'Journal Article', '10.1186/1471-2105-12-S10-S2', '22166002', 'Microarray experiments are becoming increasingly common in biomedical research, as is their deposition in publicly accessible repositories, such as Gene Expression Omnibus (GEO). As such, there has been a surge in interest to use this microarray data for meta-analytic approaches, whether to increase sample size for a more powerful analysis of a specific disease (e.g. lung cancer) or to re-examine experiments for reasons different than those examined in the initial, publishing study that generated them. For the average biomedical researcher, there are a number of practical barriers to conducting such meta-analyses such as manually aggregating, filtering and formatting the data. Methods to automatically process large repositories of microarray data into a standardized, directly comparable format will enable easier and more reliable access to microarray data to conduct meta-analyses.'),
(94695, 2011, 'bmc', 'Mining FDA drug labels using an unsupervised learning technique--topic modeling', 'Bisgin, Halil|Liu, Zhichao|Fang, Hong|Xu, Xiaowei|Tong, Weida', 'Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.|Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.|Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.|Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.|Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.', 'United States|United States|United States|United States|United States', 61, 'Data Mining|Drug Labeling|Drug-Related Side Effects and Adverse Reactions|Food Labeling|Humans|Semantics|United States|United States Food and Drug Administration', 'Journal Article', '10.1186/1471-2105-12-S10-S11', '22166012', 'The Food and Drug Administration (FDA) approved drug labels contain a broad array of information, ranging from adverse drug reactions (ADRs) to drug efficacy, risk-benefit consideration, and more. However, the labeling language used to describe these information is free text often containing ambiguous semantic descriptions, which poses a great challenge in retrieving useful information from the labeling text in a consistent and accurate fashion for comparative analysis across drugs. Consequently, this task has largely relied on the manual reading of the full text by experts, which is time consuming and labor intensive.'),
(94696, 2011, 'bmc', 'Probabilistic reconstruction of the tumor progression process in gene regulatory networks in the presence of uncertainty', 'Esfahani, Mohammad Shahrokh|Yoon, Byung-Jun|Dougherty, Edward R', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA. m.shahrokh@tamu.edu|Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA. m.shahrokh@tamu.edu|Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA. m.shahrokh@tamu.edu', 'United States|United States|United States', 10, 'Algorithms|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Image Processing, Computer-Assisted|Models, Statistical|Mutation|Neoplasms|Proto-Oncogene Proteins c-mdm2|Signal Transduction|Tumor Suppressor Protein p53|Uncertainty', 'Journal Article', '10.1186/1471-2105-12-S10-S9', '22166046', 'Accumulation of gene mutations in cells is known to be responsible for tumor progression, driving it from benign states to malignant states. However, previous studies have shown that the detailed sequence of gene mutations, or the steps in tumor progression, may vary from tumor to tumor, making it difficult to infer the exact path that a given type of tumor may have taken.'),
(94697, 2011, 'bmc', 'RefNetBuilder: a platform for construction of integrated reference gene regulatory networks from expressed sequence tags', 'Li, Ying|Gong, Ping|Perkins, Edward J|Zhang, Chaoyang|Wang, Nan', 'School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406, USA.|School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406, USA.|School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406, USA.|School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406, USA.|School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406, USA.', 'United States|United States|United States|United States|United States', 5, 'Algorithms|Animals|Caenorhabditis elegans|Computational Biology|Expressed Sequence Tags|Gene Regulatory Networks|Oligochaeta|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1186/1471-2105-12-S10-S20', '22166047', 'Gene Regulatory Networks (GRNs) provide integrated views of gene interactions that control biological processes. Many public databases contain biological interactions extracted from experimentally validated literature reports, but most furnish only information for a few genetic model organisms. In order to provide a bioinformatic tool for researchers who work with non-model organisms, we developed RefNetBuilder, a new platform that allows construction of putative reference pathways or GRNs from expressed sequence tags (ESTs).'),
(94698, 2011, 'bmc', 'An improved border detection in dermoscopy images for density based clustering', 'Suer, Sait|Kockara, Sinan|Mete, Mutlu', 'University of Central Arkansas, 201 Donaghey Ave, Conway, 72035 AR, USA.|University of Central Arkansas, 201 Donaghey Ave, Conway, 72035 AR, USA.|University of Central Arkansas, 201 Donaghey Ave, Conway, 72035 AR, USA.', 'United States|United States|United States', 24, 'Algorithms|Cluster Analysis|Dermoscopy|Humans|Image Interpretation, Computer-Assisted|Melanoma|Observer Variation|Pattern Recognition, Automated|Skin Neoplasms', 'Journal Article', '10.1186/1471-2105-12-S10-S12', '22166058', 'Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. In current practice, dermatologists determine lesion area by manually drawing lesion borders. Therefore, automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. One of the most important steps in dermoscopy image analysis is automated detection of lesion borders. To our knowledge, in our 2010 study we achieved one of the highest accuracy rates in the automated lesion border detection field by using modified density based clustering algorithm. In the previous study, we proposed a novel method which removes redundant computations in well-known spatial density based clustering algorithm, DBSCAN, thus, in turn it speeds up clustering process considerably.'),
(94699, 2011, 'bmc', 'Abstracts of the Seventh International Society for Computational Biology (ISCB) Student Council Symposium 2011 Vienna, Austria July 15, 2011', '-', '-', '-', 0, 'Computational Biology', 'Congress', '10.1186/1471-2105-12-S10-S12', '22166071', '-'),
(94700, 2011, 'bmc', 'The NTD Nanoscope: potential applications and implementations', 'Winters-Hilt, Stephen|Horton-Chao, Evenie|Morales, Eric', 'Dept of Computer Science, University of New Orleans, 2000 Lakeshore Drive, New Orleans, LA 70148, USA. winters@cs.uno.edu|Dept of Computer Science, University of New Orleans, 2000 Lakeshore Drive, New Orleans, LA 70148, USA. winters@cs.uno.edu|Dept of Computer Science, University of New Orleans, 2000 Lakeshore Drive, New Orleans, LA 70148, USA. winters@cs.uno.edu', 'United States|United States|United States', 2, 'Base Sequence|Molecular Sequence Data|Nanopores|Nanotechnology|Oligonucleotides|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Transducers', 'Journal Article', '10.1186/1471-2105-12-S10-S21', '22166072', 'Nanopore transduction detection (NTD) offers prospects for a number of highly sensitive and discriminative applications, including: (i) single nucleotide polymorphism (SNP) detection, (ii) targeted DNA re-sequencing, (iii) protein isoform assaying, and (iv) biosensing via antibody or aptamer coupled molecules. Nanopore event transduction involves single-molecule biophysics, engineered information flows, and nanopore cheminformatics. The NTD Nanoscope has seen limited use in the scientific community, however, due to lack of information about potential applications, and lack of availability for the device itself. Meta Logos Inc. is developing both pre-packaged device platforms and component-level (unassembled) kit platforms (the latter described here). In both cases a lipid bi-layer workstation is first established, then augmentations and operational protocols are provided to have a nanopore transduction detector. In this paper we provide an overview of the NTD Nanoscope applications and implementations. The NTD Nanoscope Kit, in particular, is a component-level reproduction of the standard NTD device used in previous research papers.'),
(94701, 2011, 'bmc', 'A modified Stokes-Einstein equation for Axcexb2 aggregation', 'Achuthan, Srisairam|Chung, Bong Jae|Ghosh, Preetam|Rangachari, Vijayaraghavan|Vaidya, Ashwin', 'Department of Mathematical Sciences, Montclair State University, Montclair, NJ 07043, USA.|Department of Mathematical Sciences, Montclair State University, Montclair, NJ 07043, USA.|Department of Mathematical Sciences, Montclair State University, Montclair, NJ 07043, USA.|Department of Mathematical Sciences, Montclair State University, Montclair, NJ 07043, USA.|Department of Mathematical Sciences, Montclair State University, Montclair, NJ 07043, USA.', 'United States|United States|United States|United States|United States', 8, 'Amyloid beta-Peptides|Diffusion|Humans|Models, Biological|Neurodegenerative Diseases|Peptide Fragments|Plaque, Amyloid|Solvents|Viscosity', 'Journal Article', '10.1186/1471-2105-12-S10-S13', '22166081', 'In all amyloid diseases, protein aggregates have been implicated fully or partly, in the etiology of the disease. Due to their significance in human pathologies, there have been unprecedented efforts towards physiochemical understanding of aggregation and amyloid formation over the last two decades. An important relation from which hydrodynamic radii of the aggregate is routinely measured is the classic Stokes-Einstein equation. Here, we report a modification in the classical Stokes-Einstein equation using a mixture theory approach, in order to accommodate the changes in viscosity of the solvent due to the changes in solute size and shape, to implement a more realistic model for Axcexb2 aggregation involved in Alzheimers disease. Specifically, we have focused on validating this model in protofibrill lateral association reactions along the aggregation pathway, which has been experimentally well characterized.'),
(94702, 2011, 'bmc', 'Leveraging domain information to restructure biological prediction', 'Nan, Xiaofei|Fu, Gang|Zhao, Zhengdong|Liu, Sheng|Patel, Ronak Y|Liu, Haining|Daga, Pankaj R|Doerksen, Robert J|Dang, Xin|Chen, Yixin|Wilkins, Dawn', 'Department of Computer and Information Science, University of Mississippi, USA.|Department of Computer and Information Science, University of Mississippi, USA.|Department of Computer and Information Science, University of Mississippi, USA.|Department of Computer and Information Science, University of Mississippi, USA.|Department of Computer and Information Science, University of Mississippi, USA.|Department of Computer and Information Science, University of Mississippi, USA.|Department of Computer and Information Science, University of Mississippi, USA.|Department of Computer and Information Science, University of Mississippi, USA.|Department of Computer and Information Science, University of Mississippi, USA.|Department of Computer and Information Science, University of Mississippi, USA.|Department of Computer and Information Science, University of Mississippi, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Entropy|Glycogen Synthase Kinase 3|Glycogen Synthase Kinase 3 beta|Humans|Models, Biological|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Prognosis|Receptor, Cannabinoid, CB1|Receptor, Cannabinoid, CB2', 'Journal Article', '10.1186/1471-2105-12-S10-S22', '22166097', 'It is commonly believed that including domain knowledge in a prediction model is desirable. However, representing and incorporating domain information in the learning process is, in general, a challenging problem. In this research, we consider domain information encoded by discrete or categorical attributes. A discrete or categorical attribute provides a natural partition of the problem domain, and hence divides the original problem into several non-overlapping sub-problems. In this sense, the domain information is useful if the partition simplifies the learning task. The goal of this research is to develop an algorithm to identify discrete or categorical attributes that maximally simplify the learning task.'),
(94703, 2011, 'bmc', 'Predicting gene ontology from a global meta-analysis of 1-color microarray experiments', 'Dozmorov, Mikhail G|Giles, Cory B|Wren, Jonathan D', 'Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation 825 NE 13th Street, Oklahoma City, Oklahoma 73104-5005, USA.|Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation 825 NE 13th Street, Oklahoma City, Oklahoma 73104-5005, USA.|Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation 825 NE 13th Street, Oklahoma City, Oklahoma 73104-5005, USA.', 'United States|United States|United States', 20, 'Color|Databases, Genetic|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-12-S10-S14', '22166114', 'Global meta-analysis (GMA) of microarray data to identify genes with highly similar co-expression profiles is emerging as an accurate method to predict gene function and phenotype, even in the absence of published data on the gene(s) being analyzed. With a third of human genes still uncharacterized, this approach is a promising way to direct experiments and rapidly understand the biological roles of genes. To predict function for genes of interest, GMA relies on a guilt-by-association approach to identify sets of genes with known functions that are consistently co-expressed with it across different experimental conditions, suggesting coordinated regulation for a specific biological purpose. Our goal here is to define how sample, dataset size and ranking parameters affect prediction performance.'),
(94704, 2011, 'bmc', 'Selecting a single model or combining multiple models for microarray-based classifier development?--a comparative analysis based on large and diverse datasets generated from the MAQC-II project', 'Chen, Minjun|Shi, Leming|Kelly, Reagan|Perkins, Roger|Fang, Hong|Tong, Weida', 'Center for Bioinformatics, Division of Systems Biology, National Center for Toxicological Research, US Food & Drug Administration, 3900 NCTR Rd, Jefferson, Arkansas, USA.|Center for Bioinformatics, Division of Systems Biology, National Center for Toxicological Research, US Food & Drug Administration, 3900 NCTR Rd, Jefferson, Arkansas, USA.|Center for Bioinformatics, Division of Systems Biology, National Center for Toxicological Research, US Food & Drug Administration, 3900 NCTR Rd, Jefferson, Arkansas, USA.|Center for Bioinformatics, Division of Systems Biology, National Center for Toxicological Research, US Food & Drug Administration, 3900 NCTR Rd, Jefferson, Arkansas, USA.|Center for Bioinformatics, Division of Systems Biology, National Center for Toxicological Research, US Food & Drug Administration, 3900 NCTR Rd, Jefferson, Arkansas, USA.|Center for Bioinformatics, Division of Systems Biology, National Center for Toxicological Research, US Food & Drug Administration, 3900 NCTR Rd, Jefferson, Arkansas, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Gene Expression Profiling|Humans|Meta-Analysis as Topic|Models, Genetic|Neoplasms|Oligonucleotide Array Sequence Analysis|Quality Control|Toxicogenetics', 'Journal Article', '10.1186/1471-2105-12-S10-S3', '22166133', 'Genomic biomarkers play an increasing role in both preclinical and clinical application. Development of genomic biomarkers with microarrays is an area of intensive investigation. However, despite sustained and continuing effort, developing microarray-based predictive models (i.e., genomics biomarkers) capable of reliable prediction for an observed or measured outcome (i.e., endpoint) of unknown samples in preclinical and clinical practice remains a considerable challenge. No straightforward guidelines exist for selecting a single model that will perform best when presented with unknown samples. In the second phase of the MicroArray Quality Control (MAQC-II) project, 36 analysis teams produced a large number of models for 13 preclinical and clinical endpoints. Before external validation was performed, each team nominated one model per endpoint (referred to here as nominated models) from which MAQC-II experts selected 13 candidate models to represent the best model for each endpoint. Both the nominated and candidate models from MAQC-II provide benchmarks to assess other methodologies for developing microarray-based predictive models.'),
(94705, 2011, 'bmc', 'Validation of an arterial tortuosity measure with application to hypertension collection of clinical hypertensive patients', 'Diedrich, Karl T|Roberts, John A|Schmidt, Richard H|Kang, Chang-Ki|Cho, Zang-Hee|Parker, Dennis L', 'Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, 729 Arapeen Drive, Salt Lake City, UT 84108, USA. Karl.Diedrich@utah.edu|Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, 729 Arapeen Drive, Salt Lake City, UT 84108, USA. Karl.Diedrich@utah.edu|Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, 729 Arapeen Drive, Salt Lake City, UT 84108, USA. Karl.Diedrich@utah.edu|Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, 729 Arapeen Drive, Salt Lake City, UT 84108, USA. Karl.Diedrich@utah.edu|Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, 729 Arapeen Drive, Salt Lake City, UT 84108, USA. Karl.Diedrich@utah.edu|Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, 729 Arapeen Drive, Salt Lake City, UT 84108, USA. Karl.Diedrich@utah.edu', 'United States|United States|United States|United States|United States|United States', 9, 'Algorithms|Arteries|Brain|Female|Humans|Hypertension|Magnetic Resonance Angiography|Male|Middle Aged|Retrospective Studies', 'Journal Article', '10.1186/1471-2105-12-S10-S15', '22166145', 'Hypertension may increase tortuosity or twistedness of arteries. We applied a centerline extraction algorithm and tortuosity metric to magnetic resonance angiography (MRA) brain images to quantitatively measure the tortuosity of arterial vessel centerlines. The most commonly used arterial tortuosity measure is the distance factor metric (DFM). This study tested a DFM based measurements ability to detect increases in arterial tortuosity of hypertensives using existing images. Existing images presented challenges such as different resolutions which may affect the tortuosity measurement, different depths of the area imaged, and different artifacts of imaging that require filtering.'),
(94706, 2011, 'bmc', 'MeInfoText 20: gene methylation and cancer relation extraction from biomedical literature', 'Fang, Yu-Ching|Lai, Po-Ting|Dai, Hong-Jie|Hsu, Wen-Lian', 'Institute of Molecular and Cellular Biology, National Taiwan University, Taipei, Taiwan.|Institute of Molecular and Cellular Biology, National Taiwan University, Taipei, Taiwan.|Institute of Molecular and Cellular Biology, National Taiwan University, Taipei, Taiwan.|Institute of Molecular and Cellular Biology, National Taiwan University, Taipei, Taiwan.', '-|-|-|-', 18, 'Artificial Intelligence|CpG Islands|DNA Methylation|Data Mining|Epigenesis, Genetic|Humans|Neoplasms|Software', 'Journal Article', '10.1186/1471-2105-12-471', '22168213', 'DNA methylation is regarded as a potential biomarker in the diagnosis and treatment of cancer. The relations between aberrant gene methylation and cancer development have been identified by a number of recent scientific studies. In a previous work, we used co-occurrences to mine those associations and compiled the MeInfoText 1.0 database. To reduce the amount of manual curation and improve the accuracy of relation extraction, we have now developed MeInfoText 2.0, which uses a machine learning-based approach to extract gene methylation-cancer relations.'),
(94707, 2011, 'bmc', 'MSACompro: protein multiple sequence alignment using predicted secondary structure, solvent accessibility, and residue-residue contacts', 'Deng, Xin|Cheng, Jianlin', 'Department of Computer Science, University of Missouri-Columbia, Columbia, MO 65211, USA.|Department of Computer Science, University of Missouri-Columbia, Columbia, MO 65211, USA.', 'United States|United States', 0, 'Algorithms|Nucleotide Motifs|Phylogeny|Probability|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-12-472', '22168237', 'Multiple Sequence Alignment (MSA) is a basic tool for bioinformatics research and analysis. It has been used essentially in almost all bioinformatics tasks such as protein structure modeling, gene and protein function prediction, DNA motif recognition, and phylogenetic analysis. Therefore, improving the accuracy of multiple sequence alignment is important for advancing many bioinformatics fields.'),
(94708, 2011, 'bmc', 'Estimation of bacterial diversity using next generation sequencing of 16S rDNA: a comparison of different workflows', 'Barriuso, Jorge|Valverde, Jose R|Mellado, Rafael P', 'Centro Nacional de Biotecnología, c/Darwin 3, 28049 Madrid, Spain.|Centro Nacional de Biotecnología, c/Darwin 3, 28049 Madrid, Spain.|Centro Nacional de Biotecnología, c/Darwin 3, 28049 Madrid, Spain.', 'Spain|Spain|Spain', 0, 'Bacteria|Biodiversity|DNA, Ribosomal|High-Throughput Nucleotide Sequencing|Humans|RNA, Ribosomal, 16S|Regression Analysis|Sequence Analysis, DNA|Soil Microbiology|Workflow', 'Journal Article', '10.1186/1471-2105-12-473', '22168258', 'Next generation sequencing (NGS) enables a more comprehensive analysis of bacterial diversity from complex environmental samples. NGS data can be analysed using a variety of workflows. We test several simple and complex workflows, including frequently used as well as recently published tools, and report on their respective accuracy and efficiency under various conditions covering different sequence lengths, number of sequences and real world experimental data from rhizobacterial populations of glyphosate-tolerant maize treated or untreated with two different herbicides representative of differential diversity studies.'),
(94709, 2011, 'bmc', 'Discovery of error-tolerant biclusters from noisy gene expression data', 'Gupta, Rohit|Rao, Navneet|Kumar, Vipin', 'Department of Computer Science, University of Minnesota - Twin Cities, Minneapolis, MN 55455, USA. rohit@cs.umn.edu|Department of Computer Science, University of Minnesota - Twin Cities, Minneapolis, MN 55455, USA. rohit@cs.umn.edu|Department of Computer Science, University of Minnesota - Twin Cities, Minneapolis, MN 55455, USA. rohit@cs.umn.edu', 'United States|United States|United States', 0, 'Algorithms|Breast Neoplasms|Cluster Analysis|Female|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-12-S12-S1', '22168285', 'An important analysis performed on microarray gene-expression data is to discover biclusters, which denote groups of genes that are coherently expressed for a subset of conditions. Various biclustering algorithms have been proposed to find different types of biclusters from these real-valued gene-expression data sets. However, these algorithms suffer from several limitations such as inability to explicitly handle errors/noise in the data, difficulty in discovering small bicliusters due to their top-down approach, inability of some of the approaches to find overlapping biclusters, which is crucial as many genes participate in multiple biological processes. Association pattern mining also produce biclusters as their result and can naturally address some of these limitations. However, traditional association mining only finds exact biclusters, which limits its applicability in real-life data sets where the biclusters may be fragmented due to random noise/errors. Moreover, as they only work with binary or boolean attributes, their application on gene-expression data require transforming real-valued attributes to binary attributes, which often results in loss of information. Many past approaches have tried to address the issue of noise and handling real-valued attributes independently but there is no systematic approach that addresses both of these issues together.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(94710, 2011, 'bmc', 'Systematic identification of functional modules and cis-regulatory elements in Arabidopsis thaliana', 'Ruan, Jianhua|Perez, Joseph|Hernandez, Brian|Lei, Chengwei|Sunter, Garry|Sponsel, Valerie M', 'Department of Computer Science, The University of Texas at San Antonio, One UTSA Circle, San Antonio, Texas 78249, USA. jruan@cs.utsa.edu|Department of Computer Science, The University of Texas at San Antonio, One UTSA Circle, San Antonio, Texas 78249, USA. jruan@cs.utsa.edu|Department of Computer Science, The University of Texas at San Antonio, One UTSA Circle, San Antonio, Texas 78249, USA. jruan@cs.utsa.edu|Department of Computer Science, The University of Texas at San Antonio, One UTSA Circle, San Antonio, Texas 78249, USA. jruan@cs.utsa.edu|Department of Computer Science, The University of Texas at San Antonio, One UTSA Circle, San Antonio, Texas 78249, USA. jruan@cs.utsa.edu|Department of Computer Science, The University of Texas at San Antonio, One UTSA Circle, San Antonio, Texas 78249, USA. jruan@cs.utsa.edu', 'United States|United States|United States|United States|United States|United States', 8, 'Algorithms|Arabidopsis|Arabidopsis Proteins|Cluster Analysis|Gene Expression Profiling|Gene Expression Regulation, Plant|Gene Regulatory Networks|Gibberellins|Oligonucleotide Array Sequence Analysis|Promoter Regions, Genetic|Signal Transduction', 'Journal Article', '10.1186/1471-2105-12-S12-S2', '22168340', 'Several large-scale gene co-expression networks have been constructed successfully for predicting gene functional modules and cis-regulatory elements in Arabidopsis (Arabidopsis thaliana). However, these networks are usually constructed and analyzed in an ad hoc manner. In this study, we propose a completely parameter-free and systematic method for constructing gene co-expression networks and predicting functional modules as well as cis-regulatory elements.'),
(94711, 2011, 'bmc', 'FEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approach', 'Han, Bing|Chen, Xue-Wen|Talebizadeh, Zohreh', 'Bioinformatics and Computational Life Sciences Laboratory, Information and Telecommunication Technology Center, Department of Electrical Engineering and Computer Science, The University of Kansas, 1520 West 15th Street, Lawrence, KS 66045, USA.|Bioinformatics and Computational Life Sciences Laboratory, Information and Telecommunication Technology Center, Department of Electrical Engineering and Computer Science, The University of Kansas, 1520 West 15th Street, Lawrence, KS 66045, USA.|Bioinformatics and Computational Life Sciences Laboratory, Information and Telecommunication Technology Center, Department of Electrical Engineering and Computer Science, The University of Kansas, 1520 West 15th Street, Lawrence, KS 66045, USA.', 'United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Disease|Epistasis, Genetic|Genome-Wide Association Study|Humans|Markov Chains|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-12-S12-S3', '22168374', 'The interactions among genetic factors related to diseases are called epistasis. With the availability of genotyped data from genome-wide association studies, it is now possible to computationally unravel epistasis related to the susceptibility to common complex human diseases such as asthma, diabetes, and hypertension. However, the difficulties of detecting epistatic interaction arose from the large number of genetic factors and the enormous size of possible combinations of genetic factors. Most computational methods to detect epistatic interactions are predictor-based methods and can not find true causal factor elements. Moreover, they are both time-consuming and sample-consuming.'),
(94712, 2011, 'bmc', 'Combining active learning and semi-supervised learning techniques to extract protein interaction sentences', 'Song, Min|Yu, Hwanjo|Han, Wook-Shin', 'Information Systems Department, New Jersey Institute of Technology, University Heights, Newark, New Jersey, USA. min.song@njit.edu|Information Systems Department, New Jersey Institute of Technology, University Heights, Newark, New Jersey, USA. min.song@njit.edu|Information Systems Department, New Jersey Institute of Technology, University Heights, Newark, New Jersey, USA. min.song@njit.edu', 'Jersey|Jersey|Jersey', 19, 'Artificial Intelligence|Humans|MEDLINE|Natural Language Processing|Protein Interaction Maps|Proteins|Semantics|United States', 'Journal Article', '10.1186/1471-2105-12-S12-S4', '22168401', 'Protein-protein interaction (PPI) extraction has been a focal point of many biomedical research and database curation tools. Both Active Learning and Semi-supervised SVMs have recently been applied to extract PPI automatically. In this paper, we explore combining the AL with the SSL to improve the performance of the PPI task.'),
(94713, 2011, 'bmc', 'Planning combinatorial disulfide cross-links for protein fold determination', 'Xiong, Fei|Friedman, Alan M|Bailey-Kellogg, Chris', 'Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.|Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.|Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.', 'United States|United States|United States', 0, 'Algorithms|Bayes Theorem|Disulfides|Humans|Probability|Protein Folding|Protein Structure, Secondary|Proteins|Software', 'Journal Article', '10.1186/1471-2105-12-S12-S5', '22168447', 'Fold recognition techniques take advantage of the limited number of overall structural organizations, and have become increasingly effective at identifying the fold of a given target sequence. However, in the absence of sufficient sequence identity, it remains difficult for fold recognition methods to always select the correct model. While a native-like model is often among a pool of highly ranked models, it is not necessarily the highest-ranked one, and the model rankings depend sensitively on the scoring function used. Structure elucidation methods can then be employed to decide among the models based on relatively rapid biochemical/biophysical experiments.'),
(94714, 2011, 'bmc', 'A new method for identifying bivariate differential expression in high dimensional microarray data using quadratic discriminant analysis', 'Arevalillo, Jorge M|Navarro, Hilario', 'Department of Statistics and Operational Research, UNED, Paseo Senda del Rey 9, 28040 Madrid, Spain. jmartin@ccia.uned.es|Department of Statistics and Operational Research, UNED, Paseo Senda del Rey 9, 28040 Madrid, Spain. jmartin@ccia.uned.es', 'Spain|Spain', 6, 'Algorithms|Colonic Neoplasms|Discriminant Analysis|Gene Expression Profiling|Humans', 'Journal Article', '10.1186/1471-2105-12-S12-S6', '22168481', 'One of the drawbacks we face up when analyzing gene to phenotype associations in genomic data is the ugly performance of the designed classifier due to the small sample-high dimensional data structures (n xe2x89xaa p) at hand. This is known as the peaking phenomenon, a common situation in the analysis of gene expression data. Highly predictive bivariate gene interactions whose marginals are useless for discrimination are also affected by such phenomenon, so they are commonly discarded by state of the art sequential search algorithms. Such patterns are known as weak/marginal strong bivariate interactions. This paper addresses the problem of uncovering them in high dimensional settings.'),
(94715, 2011, 'bmc', 'New threats to health data privacy', 'Li, Fengjun|Zou, Xukai|Liu, Peng|Chen, Jake Y', 'Department of EECS, The University of Kansas, Lawrence, Kansas, USA. fli@ittc.ku.edu|Department of EECS, The University of Kansas, Lawrence, Kansas, USA. fli@ittc.ku.edu|Department of EECS, The University of Kansas, Lawrence, Kansas, USA. fli@ittc.ku.edu|Department of EECS, The University of Kansas, Lawrence, Kansas, USA. fli@ittc.ku.edu', 'United States|United States|United States|United States', 23, 'Computer Security|Confidentiality|Data Collection|Delivery of Health Care|Electronic Health Records|Female|Humans|Internet|Male|Privacy', 'Journal Article', '10.1186/1471-2105-12-S12-S7', '22168526', 'Along with the rapid digitalization of health data (e.g. Electronic Health Records), there is an increasing concern on maintaining data privacy while garnering the benefits, especially when the data are required to be published for secondary use. Most of the current research on protecting health data privacy is centered around data de-identification and data anonymization, which removes the identifiable information from the published health data to prevent an adversary from reasoning about the privacy of the patients. However, published health data is not the only source that the adversaries can count on: with a large amount of information that people voluntarily share on the Web, sophisticated attacks that join disparate information pieces from multiple sources against health data privacy become practical. Limited efforts have been devoted to studying these attacks yet.'),
(94716, 2011, 'bmc', 'Jetset: selecting the optimal microarray probe set to represent a gene', 'Li, Qiyuan|Birkbak, Nicolai J|Gyorffy, Balazs|Szallasi, Zoltan|Eklund, Aron C', 'Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Lyngby, Denmark.|Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Lyngby, Denmark.|Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Lyngby, Denmark.|Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Lyngby, Denmark.|Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Lyngby, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark', 0, 'Breast Neoplasms|Exons|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Oligonucleotide Array Sequence Analysis|Receptor, ErbB-2|Receptors, Estrogen|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-12-474', '22172014', 'Interpretation of gene expression microarrays requires a mapping from probe set to gene. On many Affymetrix gene expression microarrays, a given gene may be detected by multiple probe sets, which may deliver inconsistent or even contradictory measurements. Therefore, obtaining an unambiguous expression estimate of a pre-specified gene can be a nontrivial but essential task.'),
(94717, 2011, 'bmc', 'Performance analysis of novel methods for detecting epistasis', 'Shang, Junliang|Zhang, Junying|Sun, Yan|Liu, Dan|Ye, Daojun|Yin, Yaling', 'School of Computer Science & Technology, Xidian University, Xian 710071, China. jlshang@mail.xidian.edu.cn|School of Computer Science & Technology, Xidian University, Xian 710071, China. jlshang@mail.xidian.edu.cn|School of Computer Science & Technology, Xidian University, Xian 710071, China. jlshang@mail.xidian.edu.cn|School of Computer Science & Technology, Xidian University, Xian 710071, China. jlshang@mail.xidian.edu.cn|School of Computer Science & Technology, Xidian University, Xian 710071, China. jlshang@mail.xidian.edu.cn|School of Computer Science & Technology, Xidian University, Xian 710071, China. jlshang@mail.xidian.edu.cn', 'China|China|China|China|China|China', 45, 'Disease|Epistasis, Genetic|Genotype|Humans|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-12-475', '22172045', 'Epistasis is recognized fundamentally important for understanding the mechanism of disease-causing genetic variation. Though many novel methods for detecting epistasis have been proposed, few studies focus on their comparison. Undertaking a comprehensive comparison study is an urgent task and a pathway of the methods to real applications.'),
(94718, 2011, 'bmc', 'Phylogenetically informed logic relationships improve detection of biological network organization', 'Cui, Jike|DeLuca, Todd F|Jung, Jae-Yoon|Wall, Dennis P', 'Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.', 'United States|United States|United States|United States', 0, 'Databases, Genetic|Evolution, Molecular|Gene Regulatory Networks|Humans|Logic|Phylogeny|Proteins', 'Journal Article', '10.1186/1471-2105-12-476', '22172058', 'A phylogenetic profile refers to the presence or absence of a gene across a set of organisms, and it has been proven valuable for understanding gene functional relationships and network organization. Despite this success, few studies have attempted to search beyond just pairwise relationships among genes. Here we search for logic relationships involving three genes, and explore its potential application in gene network analyses.'),
(94719, 2011, 'bmc', 'A multifaceted analysis of HIV-1 protease multidrug resistance phenotypes', 'Doherty, Kathleen M|Nakka, Priyanka|King, Bracken M|Rhee, Soo-Yon|Holmes, Susan P|Shafer, Robert W|Radhakrishnan, Mala L', 'Department of Chemistry, Wellesley College, MA 02481, USA.|Department of Chemistry, Wellesley College, MA 02481, USA.|Department of Chemistry, Wellesley College, MA 02481, USA.|Department of Chemistry, Wellesley College, MA 02481, USA.|Department of Chemistry, Wellesley College, MA 02481, USA.|Department of Chemistry, Wellesley College, MA 02481, USA.|Department of Chemistry, Wellesley College, MA 02481, USA.', 'United States|United States|United States|United States|United States|United States|United States', 16, 'Anti-HIV Agents|Cluster Analysis|Drug Resistance, Multiple|Drug Resistance, Viral|HIV Infections|HIV Protease|HIV Protease Inhibitors|HIV-1|Humans|Mutation', 'Journal Article', '10.1186/1471-2105-12-477', '22172090', 'Great strides have been made in the effective treatment of HIV-1 with the development of second-generation protease inhibitors (PIs) that are effective against historically multi-PI-resistant HIV-1 variants. Nevertheless, mutation patterns that confer decreasing susceptibility to available PIs continue to arise within the population. Understanding the phenotypic and genotypic patterns responsible for multi-PI resistance is necessary for developing PIs that are active against clinically-relevant PI-resistant HIV-1 variants.'),
(94720, 2011, 'bmc', 'Survival models with preclustered gene groups as covariates', 'Kammers, Kai|Lang, Michel|Hengstler, Jan G|Schmidt, Marcus|Rahnenführer, Jörg', 'Department of Statistics, TU Dortmund University, Dortmund, Germany. kammers@statistik.tu-dortmund.de|Department of Statistics, TU Dortmund University, Dortmund, Germany. kammers@statistik.tu-dortmund.de|Department of Statistics, TU Dortmund University, Dortmund, Germany. kammers@statistik.tu-dortmund.de|Department of Statistics, TU Dortmund University, Dortmund, Germany. kammers@statistik.tu-dortmund.de|Department of Statistics, TU Dortmund University, Dortmund, Germany. kammers@statistik.tu-dortmund.de', 'Germany|Germany|Germany|Germany|Germany', 18, 'Breast Neoplasms|Databases, Genetic|Female|Gene Expression Profiling|Genomics|Humans|Proportional Hazards Models|Survival Analysis|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-12-478', '22177110', 'An important application of high dimensional gene expression measurements is the risk prediction and the interpretation of the variables in the resulting survival models. A major problem in this context is the typically large number of genes compared to the number of observations (individuals). Feature selection procedures can generate predictive models with high prediction accuracy and at the same time low model complexity. However, interpretability of the resulting models is still limited due to little knowledge on many of the remaining selected genes. Thus, we summarize genes as gene groups defined by the hierarchically structured Gene Ontology (GO) and include these gene groups as covariates in the hazard regression models. Since expression profiles within GO groups are often heterogeneous, we present a new method to obtain subgroups with coherent patterns. We apply preclustering to genes within GO groups according to the correlation of their gene expression measurements.'),
(94721, 2011, 'bmc', 'NanoStriDE: normalization and differential expression analysis of NanoString nCounter data', 'Brumbaugh, Christopher D|Kim, Hyunsung J|Giovacchini, Mario|Pourmand, Nader', 'Department of Biomolecular Engineering, University of California at Santa Cruz, Santa Cruz, CA 95064, USA.|Department of Biomolecular Engineering, University of California at Santa Cruz, Santa Cruz, CA 95064, USA.|Department of Biomolecular Engineering, University of California at Santa Cruz, Santa Cruz, CA 95064, USA.|Department of Biomolecular Engineering, University of California at Santa Cruz, Santa Cruz, CA 95064, USA.', 'United States|United States|United States|United States', 0, 'Gene Expression Profiling|Humans|Internet|Nanotechnology|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-12-479', '22177214', 'The nCounter analysis system (NanoString Technologies, Seattle, WA) is a technology that enables the digital quantification of multiplexed target RNA molecules using color-coded molecular barcodes and single-molecule imaging. This system gives discrete counts of RNA transcripts and is capable of providing a high level of precision and sensitivity at less than one transcript copy per cell.'),
(94722, 2011, 'bmc', 'GC-content normalization for RNA-Seq data', 'Risso, Davide|Schwartz, Katja|Sherlock, Gavin|Dudoit, Sandrine', 'Division of Biostatistics and Department of Statistics, University of California, Berkeley, USA.|Division of Biostatistics and Department of Statistics, University of California, Berkeley, USA.|Division of Biostatistics and Department of Statistics, University of California, Berkeley, USA.|Division of Biostatistics and Department of Statistics, University of California, Berkeley, USA.', 'United States|United States|United States|United States', 297, 'Base Composition|Gene Expression Profiling|Saccharomyces cerevisiae|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1186/1471-2105-12-480', '22177264', 'Transcriptome sequencing (RNA-Seq) has become the assay of choice for high-throughput studies of gene expression. However, as is the case with microarrays, major technology-related artifacts and biases affect the resulting expression measures. Normalization is therefore essential to ensure accurate inference of expression levels and subsequent analyses thereof.'),
(94723, 2011, 'bmc', 'U-Compare bio-event meta-service: compatible BioNLP event extraction services', 'Kano, Yoshinobu|Björne, Jari|Ginter, Filip|Salakoski, Tapio|Buyko, Ekaterina|Hahn, Udo|Cohen, K Bretonnel|Verspoor, Karin|Roeder, Christophe|Hunter, Lawrence E|Kilicoglu, Halil|Bergler, Sabine|Van Landeghem, Sofie|Van Parys, Thomas|Van de Peer, Yves|Miwa, Makoto|Ananiadou, Sophia|Neves, Mariana|Pascual-Montano, Alberto|Özgür, Arzucan|Radev, Dragomir R|Riedel, Sebastian|Sætre, Rune|Chun, Hong-Woo|Kim, Jin-Dong|Pyysalo, Sampo|Ohta, Tomoko|Tsujii, Junichi', 'Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp|Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. kano@nii.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japa', 7, 'Computer Systems|Data Mining|Periodicals as Topic|Software', 'Journal Article', '10.1186/1471-2105-12-481', '22177292', 'Bio-molecular event extraction from literature is recognized as an important task of bio text mining and, as such, many relevant systems have been developed and made available during the last decade. While such systems provide useful services individually, there is a need for a meta-service to enable comparison and ensemble of such services, offering optimal solutions for various purposes.'),
(94724, 2011, 'bmc', 'Cost sensitive hierarchical document classification to triage PubMed abstracts for manual curation', 'Seymour, Emily|Damle, Rohini|Sette, Alessandro|Peters, Bjoern', 'The La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA.|The La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA.|The La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA.|The La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Bayes Theorem|Databases, Factual|Epitopes|Humans|Information Systems|PubMed|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-12-482', '22182279', 'The Immune Epitope Database (IEDB) project manually curates information from published journal articles that describe immune epitopes derived from a wide variety of organisms and associated with different diseases. In the past, abstracts of scientific articles were retrieved by broad keyword queries of PubMed, and were classified as relevant (curatable) or irrelevant (not curatable) to the scope of the database by a Naïve Bayes classifier. The curatable abstracts were subsequently manually classified into categories corresponding to different disease domains. Over the past four years, we have examined how to further improve this approach in order to enhance classification performance and to reduce the need for manual intervention.'),
(94725, 2011, 'bmc', 'Supervised regularized canonical correlation analysis: integrating histologic and proteomic measurements for predicting biochemical recurrence following prostate surgery', 'Golugula, Abhishek|Lee, George|Master, Stephen R|Feldman, Michael D|Tomaszewski, John E|Speicher, David W|Madabhushi, Anant', 'Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey, USA.|Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey, USA.|Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey, USA.|Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey, USA.|Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey, USA.|Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey, USA.|Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey, USA.', 'Jersey|Jersey|Jersey|Jersey|Jersey|Jersey|Jersey', 0, 'Aged|Cohort Studies|Diagnostic Imaging|Humans|Male|Middle Aged|Multivariate Analysis|Principal Component Analysis|Prognosis|Prostate-Specific Antigen|Prostatectomy|Prostatic Neoplasms|Proteomics|Recurrence', 'Journal Article', '10.1186/1471-2105-12-483', '22182303', 'Multimodal data, especially imaging and non-imaging data, is being routinely acquired in the context of disease diagnostics, however, computational challenges have limited the ability to quantitatively integrate imaging and non-imaging data channels with different dimensionalities and scales. To the best of our knowledge relatively few attempts have been made to quantitatively fuse such data to construct classifiers and none have attempted to quantitatively combine histology (imaging) and proteomic (non-imaging) measurements for making diagnostic and prognostic predictions. The objective of this work is to create a common subspace to simultaneously accommodate both the imaging and non-imaging data (and hence data corresponding to different scales and dimensionalities), called a metaspace. This metaspace can be used to build a meta-classifier that produces better classification results than a classifier that is based on a single modality alone. Canonical Correlation Analysis (CCA) and Regularized CCA (RCCA) are statistical techniques that extract correlations between two modes of data to construct a homogeneous, uniform representation of heterogeneous data channels. In this paper, we present a novel modification to CCA and RCCA, Supervised Regularized Canonical Correlation Analysis (SRCCA), that (1) enables the quantitative integration of data from multiple modalities using a feature selection scheme, (2) is regularized, and (3) is computationally cheap. We leverage this SRCCA framework towards the fusion of proteomic and histologic image signatures for identifying prostate cancer patients at the risk of 5 year biochemical recurrence following radical prostatectomy.'),
(94726, 2011, 'bmc', 'Normalizing for individual cell population context in the analysis of high-content cellular screens', 'Knapp, Bettina|Rebhan, Ilka|Kumar, Anil|Matula, Petr|Kiani, Narsis A|Binder, Marco|Erfle, Holger|Rohr, Karl|Eils, Roland|Bartenschlager, Ralf|Kaderali, Lars', 'Heidelberg University, ViroQuant Research Group Modeling, BioQuant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.|Heidelberg University, ViroQuant Research Group Modeling, BioQuant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.|Heidelberg University, ViroQuant Research Group Modeling, BioQuant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.|Heidelberg University, ViroQuant Research Group Modeling, BioQuant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.|Heidelberg University, ViroQuant Research Group Modeling, BioQuant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.|Heidelberg University, ViroQuant Research Group Modeling, BioQuant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.|Heidelberg University, ViroQuant Research Group Modeling, BioQuant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.|Heidelberg University, ViroQuant Research Group Modeling, BioQuant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.|Heidelberg University, ViroQuant Research Group Modeling, BioQuant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.|Heidelberg University, ViroQuant Research Group Modeling, BioQuant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.|Heidelberg University, ViroQuant Research Group Modeling, BioQuant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 18, 'Cell Line|Cell Size|Dengue|Dengue Virus|Gene Knockdown Techniques|Hepacivirus|Hepatitis C|Humans|Phosphotransferases|RNA Interference|Reproducibility of Results|Sensitivity and Specificity|Single-Cell Analysis', 'Journal Article', '10.1186/1471-2105-12-485', '22185194', 'High-content, high-throughput RNA interference (RNAi) offers unprecedented possibilities to elucidate gene function and involvement in biological processes. Microscopy based screening allows phenotypic observations at the level of individual cells. It was recently shown that a cells population context significantly influences results. However, standard analysis methods for cellular screens do not currently take individual cell data into account unless this is important for the phenotype of interest, i.e. when studying cell morphology.'),
(94727, 2011, 'bmc', 'Constructing a semantic predication gold standard from the biomedical literature', 'Kilicoglu, Halil|Rosemblat, Graciela|Fiszman, Marcelo|Rindflesch, Thomas C', 'Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD, USA. kilicogluh@mail.nih.gov|Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD, USA. kilicogluh@mail.nih.gov|Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD, USA. kilicogluh@mail.nih.gov|Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD, USA. kilicogluh@mail.nih.gov', 'United States|United States|United States|United States', 0, 'Data Mining|Humans|MEDLINE|Semantics|Unified Medical Language System|United States|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-12-486', '22185221', 'Semantic relations increasingly underpin biomedical text mining and knowledge discovery applications. The success of such practical applications crucially depends on the quality of extracted relations, which can be assessed against a gold standard reference. Most such references in biomedical text mining focus on narrow subdomains and adopt different semantic representations, rendering them difficult to use for benchmarking independently developed relation extraction systems. In this article, we present a multi-phase gold standard annotation study, in which we annotated 500 sentences randomly selected from MEDLINE abstracts on a wide range of biomedical topics with 1371 semantic predications. The UMLS Metathesaurus served as the main source for conceptual information and the UMLS Semantic Network for relational information. We measured interannotator agreement and analyzed the annotations closely to identify some of the challenges in annotating biomedical text with relations based on an ontology or a terminology.'),
(94728, 2011, 'bmc', 'Morphometic analysis of TCGA glioblastoma multiforme', 'Chang, Hang|Fontenay, Gerald V|Han, Ju|Cong, Ge|Baehner, Frederick L|Gray, Joe W|Spellman, Paul T|Parvin, Bahram', 'Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Carcinoma, Renal Cell|Gene Expression Profiling|Glioblastoma|Humans|Signal Transduction', 'Journal Article', '10.1186/1471-2105-12-484', '22185703', 'Our goals are to develop a computational histopathology pipeline for characterizing tumor types that are being generated by The Cancer Genome Atlas (TCGA) for genomic association. TCGA is a national collaborative program where different tumor types are being collected, and each tumor is being characterized using a variety of genome-wide platforms. Here, we have developed a tumor-centric analytical pipeline to process tissue sections stained with hematoxylin and eosin (H&E) for visualization and cell-by-cell quantitative analysis. Thus far, analysis is limited to Glioblastoma Multiforme (GBM) and kidney renal clear cell carcinoma tissue sections. The final results are being distributed for subtyping and linking the histology sections to the genomic data.'),
(94729, 2011, 'bmc', 'New concepts for building vocabulary for cell image ontologies', 'Plant, Anne L|Elliott, John T|Bhat, Talapady N', 'Biochemical Science Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. Anne.plant@nist.gov|Biochemical Science Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. Anne.plant@nist.gov|Biochemical Science Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA. Anne.plant@nist.gov', 'United States|United States|United States', 7, 'Animals|Cell Line|Databases, Factual|Fibroblasts|Internet|Mice|Myocytes, Smooth Muscle|NIH 3T3 Cells|Rats|Semantics|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-12-487', '22188658', 'There are significant challenges associated with the building of ontologies for cell biology experiments including the large numbers of terms and their synonyms. These challenges make it difficult to simultaneously query data from multiple experiments or ontologies. If vocabulary terms were consistently used and reused across and within ontologies, queries would be possible through shared terms. One approach to achieving this is to strictly control the terms used in ontologies in the form of a pre-defined schema, but this approach limits the individual researchers ability to create new terms when needed to describe new experiments.'),
(94730, 2011, 'bmc', 'Graph based fusion of miRNA and mRNA expression data improves clinical outcome prediction in prostate cancer', 'Gade, Stephan|Porzelius, Christine|Fälth, Maria|Brase, Jan C|Wuttig, Daniela|Kuner, Ruprecht|Binder, Harald|Sültmann, Holger|Beissbarth, Tim', 'German Cancer Research Center, Cancer Genome Research, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany. s.gade@dkfz-heidelberg.de|German Cancer Research Center, Cancer Genome Research, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany. s.gade@dkfz-heidelberg.de|German Cancer Research Center, Cancer Genome Research, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany. s.gade@dkfz-heidelberg.de|German Cancer Research Center, Cancer Genome Research, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany. s.gade@dkfz-heidelberg.de|German Cancer Research Center, Cancer Genome Research, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany. s.gade@dkfz-heidelberg.de|German Cancer Research Center, Cancer Genome Research, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany. s.gade@dkfz-heidelberg.de|German Cancer Research Center, Cancer Genome Research, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany. s.gade@dkfz-heidelberg.de|German Cancer Research Center, Cancer Genome Research, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany. s.gade@dkfz-heidelberg.de|German Cancer Research Center, Cancer Genome Research, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany. s.gade@dkfz-heidelberg.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Humans|Male|MicroRNAs|Models, Biological|Prognosis|Prostatic Neoplasms|RNA, Messenger|Risk Assessment|Transcriptome', 'Journal Article', '10.1186/1471-2105-12-488', '22188670', 'One of the main goals in cancer studies including high-throughput microRNA (miRNA) and mRNA data is to find and assess prognostic signatures capable of predicting clinical outcome. Both mRNA and miRNA expression changes in cancer diseases are described to reflect clinical characteristics like staging and prognosis. Furthermore, miRNA abundance can directly affect target transcripts and translation in tumor cells. Prediction models are trained to identify either mRNA or miRNA signatures for patient stratification. With the increasing number of microarray studies collecting mRNA and miRNA from the same patient cohort there is a need for statistical methods to integrate or fuse both kinds of data into one prediction model in order to find a combined signature that improves the prediction.'),
(94731, 2011, 'bmc', 'Predicting RNA-protein interactions using only sequence information', 'Muppirala, Usha K|Honavar, Vasant G|Dobbs, Drena', 'Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, USA. usha@iastate.edu|Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, USA. usha@iastate.edu|Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, USA. usha@iastate.edu', 'United States|United States|United States', 182, 'Algorithms|Animals|Databases, Protein|Drosophila melanogaster|Escherichia coli|Humans|Mice|Protein Interaction Maps|Proteins|RNA|RNA Stability|RNA-Binding Proteins|Saccharomyces cerevisiae|Sequence Analysis, RNA|Software|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-12-489', '22192482', 'RNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions.'),
(94732, 2011, 'bmc', 'Antelope: a hybrid-logic model checker for branching-time Boolean GRN analysis', 'Arellano, Gustavo|Argil, Julián|Azpeitia, Eugenio|Benítez, Mariana|Carrillo, Miguel|Góngora, Pedro|Rosenblueth, David A|Alvarez-Buylla, Elena R', 'Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, 01000 México D.F., México.|Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, 01000 México D.F., México.|Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, 01000 México D.F., México.|Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, 01000 México D.F., México.|Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, 01000 México D.F., México.|Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, 01000 México D.F., México.|Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, 01000 México D.F., México.|Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, 01000 México D.F., México.', '-|-|-|-|-|-|-|-', 0, 'Animals|Arabidopsis|Gene Regulatory Networks|Humans|Logic|Models, Genetic|Software', 'Journal Article', '10.1186/1471-2105-12-490', '22192526', 'In Thomas formalism for modeling gene regulatory networks (GRNs), branching time, where a state can have more than one possible future, plays a prominent role. By representing a certain degree of unpredictability, branching time can model several important phenomena, such as (a) asynchrony, (b) incompletely specified behavior, and (c) interaction with the environment. Introducing more than one possible future for a state, however, creates a difficulty for ordinary simulators, because infinitely many paths may appear, limiting ordinary simulators to statistical conclusions. Model checkers for branching time, by contrast, are able to prove properties in the presence of infinitely many paths.'),
(94733, 2011, 'bmc', 'MAKER2: an annotation pipeline and genome-database management tool for second-generation genome projects', 'Holt, Carson|Yandell, Mark', 'Eccles Institute of Human Genetics, University of Utah, Salt Lake City, Utah 84112, USA.|Eccles Institute of Human Genetics, University of Utah, Salt Lake City, Utah 84112, USA.', 'United States|United States', 603, 'Animals|Databases, Genetic|Genome|High-Throughput Nucleotide Sequencing|Humans|Molecular Sequence Annotation|Plants|Software', 'Journal Article', '10.1186/1471-2105-12-491', '22192575', 'Second-generation sequencing technologies are precipitating major shifts with regards to what kinds of genomes are being sequenced and how they are annotated. While the first generation of genome projects focused on well-studied model organisms, many of todays projects involve exotic organisms whose genomes are largely terra incognita. This complicates their annotation, because unlike first-generation projects, there are no pre-existing gold-standard gene-models with which to train gene-finders. Improvements in genome assembly and the wide availability of mRNA-seq data are also creating opportunities to update and re-annotate previously published genome annotations. Todays genome projects are thus in need of new genome annotation tools that can meet the challenges and opportunities presented by second-generation sequencing technologies.'),
(94734, 2011, 'bmc', 'Structural measures for network biology using QuACN', 'Mueller, Laurin A J|Kugler, Karl G|Graber, Armin|Emmert-Streib, Frank|Dehmer, Matthias', 'Institute for Bioinformatics and Translational Research, Department of Biomedical Sciences and Engineering, University for Health Sciences, Medical Informatics and Technology (UMIT), EWZ 1, Hall in Tirol, Austria.|Institute for Bioinformatics and Translational Research, Department of Biomedical Sciences and Engineering, University for Health Sciences, Medical Informatics and Technology (UMIT), EWZ 1, Hall in Tirol, Austria.|Institute for Bioinformatics and Translational Research, Department of Biomedical Sciences and Engineering, University for Health Sciences, Medical Informatics and Technology (UMIT), EWZ 1, Hall in Tirol, Austria.|Institute for Bioinformatics and Translational Research, Department of Biomedical Sciences and Engineering, University for Health Sciences, Medical Informatics and Technology (UMIT), EWZ 1, Hall in Tirol, Austria.|Institute for Bioinformatics and Translational Research, Department of Biomedical Sciences and Engineering, University for Health Sciences, Medical Informatics and Technology (UMIT), EWZ 1, Hall in Tirol, Austria.', 'Austria|Austria|Austria|Austria|Austria', 18, 'Entropy|Gene Regulatory Networks|Protein Interaction Maps|Software', 'Journal Article', '10.1186/1471-2105-12-492', '22195644', 'Structural measures for networks have been extensively developed, but many of them have not yet demonstrated their sustainably. That means, it remains often unclear whether a particular measure is useful and feasible to solve a particular problem in network biology. Exemplarily, the classification of complex biological networks can be named, for which structural measures are used leading to a minimal classification error. Hence, there is a strong need to provide freely available software packages to calculate and demonstrate the appropriate usage of structural graph measures in network biology.'),
(94735, 2011, 'bmc', 'A new strategy for better genome assembly from very short reads', 'Ji, Yan|Shi, Yixiang|Ding, Guohui|Li, Yixue', 'Bioinformatics Center, Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China.|Bioinformatics Center, Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China.|Bioinformatics Center, Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China.|Bioinformatics Center, Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China.', 'China|China|China|China', 0, 'Algorithms|Bacteria|Contig Mapping|Fungi|Genome|Genomics|Metagenome|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-12-493', '22208765', 'With the rapid development of the next generation sequencing (NGS) technology, large quantities of genome sequencing data have been generated. Because of repetitive regions of genomes and some other factors, assembly of very short reads is still a challenging issue.'),
(94736, 2011, 'bmc', 'Identifying elemental genomic track types and representing them uniformly', 'Gundersen, Sveinung|Kalaš, Matúš|Abul, Osman|Frigessi, Arnoldo|Hovig, Eivind|Sandve, Geir Kjetil', 'Department of Tumor Biology, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0310 Oslo, Norway.|Department of Tumor Biology, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0310 Oslo, Norway.|Department of Tumor Biology, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0310 Oslo, Norway.|Department of Tumor Biology, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0310 Oslo, Norway.|Department of Tumor Biology, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0310 Oslo, Norway.|Department of Tumor Biology, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0310 Oslo, Norway.', 'Norway|Norway|Norway|Norway|Norway|Norway', 0, 'Genome|Genome, Human|Genomics|Humans|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-12-494', '22208806', 'With the recent advances and availability of various high-throughput sequencing technologies, data on many molecular aspects, such as gene regulation, chromatin dynamics, and the three-dimensional organization of DNA, are rapidly being generated in an increasing number of laboratories. The variation in biological context, and the increasingly dispersed mode of data generation, imply a need for precise, interoperable and flexible representations of genomic features through formats that are easy to parse. A host of alternative formats are currently available and in use, complicating analysis and tool development. The issue of whether and how the multitude of formats reflects varying underlying characteristics of data has to our knowledge not previously been systematically treated.');
