INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93213, 2009, 'bmc', 'JANE: efficient mapping of prokaryotic ESTs and variable length sequence reads on related template genomes', 'Liang, Chunguang|Schmid, Alexander|López-Sánchez, María José|Moya, Andres|Gross, Roy|Bernhardt, Jörg|Dandekar, Thomas', 'Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, D-97074 Würzburg, Germany. liang@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, D-97074 Würzburg, Germany. liang@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, D-97074 Würzburg, Germany. liang@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, D-97074 Würzburg, Germany. liang@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, D-97074 Würzburg, Germany. liang@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, D-97074 Würzburg, Germany. liang@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, D-97074 Würzburg, Germany. liang@biozentrum.uni-wuerzburg.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Base Sequence|Computational Biology|Databases, Genetic|Expressed Sequence Tags|Genome|Internet|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-391', '19943962', 'ESTs or variable sequence reads can be available in prokaryotic studies well before a complete genome is known. Use cases include (i) transcriptome studies or (ii) single cell sequencing of bacteria. Without suitable software their further analysis and mapping would have to await finalization of the corresponding genome.'),
(93214, 2009, 'bmc', 'siDirect 20: updated software for designing functional siRNA with reduced seed-dependent off-target effect', 'Naito, Yuki|Yoshimura, Jun|Morishita, Shinichi|Ui-Tei, Kumiko', 'Department of Biophysics and Biochemistry, Graduate School of Science, University of Tokyo, Tokyo, Japan. y-naito@RNAi.jp|Department of Biophysics and Biochemistry, Graduate School of Science, University of Tokyo, Tokyo, Japan. y-naito@RNAi.jp|Department of Biophysics and Biochemistry, Graduate School of Science, University of Tokyo, Tokyo, Japan. y-naito@RNAi.jp|Department of Biophysics and Biochemistry, Graduate School of Science, University of Tokyo, Tokyo, Japan. y-naito@RNAi.jp', 'Japan|Japan|Japan|Japan', 89, 'Computational Biology|Gene Silencing|Genomics|RNA, Small Interfering|Software', 'Journal Article', '10.1186/1471-2105-10-392', '19948054', 'RNA interference (RNAi), mediated by 21-nucleotide (nt)-length small interfering RNAs (siRNAs), is a powerful tool not only for studying gene function but also for therapeutic applications. RNAi, requiring perfect complementarity between the siRNA guide strand and the target mRNA, was believed to be extremely specific. However, a recent growing body of evidence has suggested that siRNA could down-regulate unintended genes whose transcripts possess complementarity to the 7-nt siRNA seed region. This off-target gene silencing may often provide incongruous results obtained from knockdown experiments, leading to misinterpretation. Thus, an efficient algorithm for designing functional siRNAs with minimal off-target effect based on the mechanistic features is considered of value.'),
(93215, 2009, 'bmc', 'Phylogeny-guided interaction mapping in seven eukaryotes', 'Dutkowski, Janusz|Tiuryn, Jerzy', 'Institute of Informatics, University of Warsaw, Warsaw, Poland. januszd@mimuw.edu.pl|Institute of Informatics, University of Warsaw, Warsaw, Poland. januszd@mimuw.edu.pl', 'Poland|Poland', 0, 'Animals|Computational Biology|Databases, Protein|Eukaryota|Humans|Phylogeny|Proteins', 'Journal Article', '10.1186/1471-2105-10-393', '19948065', 'The assembly of reliable and complete protein-protein interaction (PPI) maps remains one of the significant challenges in systems biology. Computational methods which integrate and prioritize interaction data can greatly aid in approaching this goal.'),
(93216, 2009, 'bmc', 'Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior', 'Kim, Yohan|Sidney, John|Pinilla, Clemencia|Sette, Alessandro|Peters, Bjoern', 'Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, California, USA. ykim@liai.org|Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, California, USA. ykim@liai.org|Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, California, USA. ykim@liai.org|Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, California, USA. ykim@liai.org|Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, California, USA. ykim@liai.org', 'United States|United States|United States|United States|United States', 82, 'Amino Acid Sequence|Amino Acids|Bayes Theorem|Binding Sites|Computational Biology|Databases, Protein|Histocompatibility Antigens Class I|Peptides|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-394', '19948066', 'Experts in peptide:MHC binding studies are often able to estimate the impact of a single residue substitution based on a heuristic understanding of amino acid similarity in an experimental context. Our aim is to quantify this measure of similarity to improve peptide:MHC binding prediction methods. This should help compensate for holes and bias in the sequence space coverage of existing peptide binding datasets.'),
(93217, 2009, 'bmc', 'Inferring protein function by domain context similarities in protein-protein interaction networks', 'Zhang, Song|Chen, Hu|Liu, Ke|Sun, Zhirong', 'MOE Key Laboratory of Bioinformatics, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing, PR China. zhangsong04@mails.tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing, PR China. zhangsong04@mails.tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing, PR China. zhangsong04@mails.tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing, PR China. zhangsong04@mails.tsinghua.edu.cn', 'China|China|China|China', 0, 'Binding Sites|Computational Biology|Databases, Protein|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins|Proteome', 'Journal Article', '10.1186/1471-2105-10-395', '19954509', 'Genome sequencing projects generate massive amounts of sequence data but there are still many proteins whose functions remain unknown. The availability of large scale protein-protein interaction data sets makes it possible to develop new function prediction methods based on protein-protein interaction (PPI) networks. Although several existing methods combine multiple information resources, there is no study that integrates protein domain information and PPI networks to predict protein functions.'),
(93218, 2009, 'bmc', 'Optimizing substitution matrix choice and gap parameters for sequence alignment', 'Edgar, Robert C', 'bob@drive5.com', '-', 29, 'Algorithms|Computational Biology|Databases, Protein|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-396', '19954534', 'While substitution matrices can readily be computed from reference alignments, it is challenging to compute optimal or approximately optimal gap penalties. It is also not well understood which substitution matrices are the most effective when alignment accuracy is the goal rather than homolog recognition. Here a new parameter optimization procedure, POP, is described and applied to the problems of optimizing gap penalties and selecting substitution matrices for pair-wise global protein alignments.'),
(93219, 2009, 'bmc', 'Towards a career in bioinformatics', 'Ranganathan, Shoba', 'bob@drive5.com', '-', 11, 'Computational Biology|Databases, Factual|Singapore|Systems Biology|Teaching', 'Introductory Journal Article', '10.1186/1471-2105-10-S15-S1', '19958508', 'The 2009 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asias oldest bioinformatics organisation from 1998, was organized as the 8th International Conference on Bioinformatics (InCoB), Sept. 9-11, 2009 at Biopolis, Singapore. InCoB has actively engaged researchers from the area of life sciences, systems biology and clinicians, to facilitate greater synergy between these groups. To encourage bioinformatics students and new researchers, tutorials and student symposium, the Singapore Symposium on Computational Biology (SYMBIO) were organized, along with the Workshop on Education in Bioinformatics and Computational Biology (WEBCB) and the Clinical Bioinformatics (CBAS) Symposium. However, to many students and young researchers, pursuing a career in a multi-disciplinary area such as bioinformatics poses a Himalayan challenge. A collection to tips is presented here to provide signposts on the road to a career in bioinformatics. An overview of the application of bioinformatics to traditional and emerging areas, published in this supplement, is also presented to provide possible future avenues of bioinformatics investigation. A case study on the application of e-learning tools in undergraduate bioinformatics curriculum provides information on how to go impart targeted education, to sustain bioinformatics in the Asia-Pacific region. The next InCoB is scheduled to be held in Tokyo, Japan, Sept. 26-28, 2010.'),
(93220, 2009, 'bmc', 'Physiochemical property space distribution among human metabolites, drugs and toxins', 'Khanna, Varun|Ranganathan, Shoba', 'Department of Chemistry and Biomolecular Sciences & ARC Centre of Excellence in Bioinformatics, Macquarie University, Sydney, Australia. vkhanna@cbms.mq.edu.au|Department of Chemistry and Biomolecular Sciences & ARC Centre of Excellence in Bioinformatics, Macquarie University, Sydney, Australia. vkhanna@cbms.mq.edu.au', 'Australia|Australia', 0, 'Computational Biology|Databases, Factual|Drug Design|Humans|Metabolome|Pharmaceutical Preparations|Solubility|Structure-Activity Relationship|Tissue Distribution|Toxins, Biological', 'Journal Article', '10.1186/1471-2105-10-S15-S10', '19958509', 'The current approach to screen for drug-like molecules is to sieve for molecules with biochemical properties suitable for desirable pharmacokinetics and reduced toxicity, using predominantly biophysical properties of chemical compounds, based on empirical rules such as Lipinskis rule of five (Ro5). For over a decade, Ro5 has been applied to combinatorial compounds, drugs and ligands, in the search for suitable lead compounds. Unfortunately, till date, a clear distinction between drugs and non-drugs has not been achieved. The current trend is to seek out drugs which show metabolite-likeness. In identifying similar physicochemical characteristics, compounds have usually been clustered based on some characteristic, to reduce the search space presented by large molecular datasets. This paper examines the similarity of current drug molecules with human metabolites and toxins, using a range of computed molecular descriptors as well as the effect of comparison to clustered data compared to searches against complete datasets.'),
(93221, 2009, 'bmc', 'The Korean Bird Information System (KBIS) through open and public participation', 'Paik, In-Hwan|Lim, Jeongheui|Chun, Byung-Sun|Jin, Seon-Duck|Yu, Jae-Pyoung|Lee, Joon-Woo|Bhak, Jong|Paek, Woon-Kee', 'Division of Natural History, National Science Museum, Daejeon, 305-705, Korea. inhwan1111@hanmail.net|Division of Natural History, National Science Museum, Daejeon, 305-705, Korea. inhwan1111@hanmail.net|Division of Natural History, National Science Museum, Daejeon, 305-705, Korea. inhwan1111@hanmail.net|Division of Natural History, National Science Museum, Daejeon, 305-705, Korea. inhwan1111@hanmail.net|Division of Natural History, National Science Museum, Daejeon, 305-705, Korea. inhwan1111@hanmail.net|Division of Natural History, National Science Museum, Daejeon, 305-705, Korea. inhwan1111@hanmail.net|Division of Natural History, National Science Museum, Daejeon, 305-705, Korea. inhwan1111@hanmail.net|Division of Natural History, National Science Museum, Daejeon, 305-705, Korea. inhwan1111@hanmail.net', '-|-|-|-|-|-|-|-', 1, 'Animal Migration|Animals|Biodiversity|Birds|Humans|Korea|Software', 'Journal Article', '10.1186/1471-2105-10-S15-S11', '19958510', 'The importance of biodiversity conservation has been increasing steadily due to its benefits to human beings. Recently, producing and managing biodiversity databases have become much easier because of the information technology (IT) advancement. This made the general publics participation in biodiversity conservation much more practical than ever. For example, an open free web service can be devised for a wider spectrum of people to collaborate with each other for sharing biodiversity information. Bird migration is one such area of the collaboration. Korean migratory birds are usually traceable in the important routes of the East Asian-Australia Flyway (EAAF), and they play a key role as an environmental change indicator of the Earth. Therefore, the preservation of migratory birds requires an information system which involves a broader range of voluntary and interactive knowledge network to process bird information production, circulation, and dissemination.'),
(93222, 2009, 'bmc', 'The implementation of e-learning tools to enhance undergraduate bioinformatics teaching and learning: a case study in the National University of Singapore', 'Lim, Shen Jean|Khan, Asif Mohammad|De Silva, Mark|Lim, Kuan Siong|Hu, Yongli|Tan, Chay Hoon|Tan, Tin Wee', 'Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore. jean@bic.nus.edu.sg|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore. jean@bic.nus.edu.sg|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore. jean@bic.nus.edu.sg|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore. jean@bic.nus.edu.sg|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore. jean@bic.nus.edu.sg|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore. jean@bic.nus.edu.sg|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore. jean@bic.nus.edu.sg', 'Singapore|Singapore|Singapore|Singapore|Singapore|Singapore|Singapore', 0, 'Computational Biology|Computer-Assisted Instruction|Curriculum|Education, Graduate|Singapore|Universities', 'Journal Article', '10.1186/1471-2105-10-S15-S12', '19958511', 'The rapid advancement of computer and information technology in recent years has resulted in the rise of e-learning technologies to enhance and complement traditional classroom teaching in many fields, including bioinformatics. This paper records the experience of implementing e-learning technology to support problem-based learning (PBL) in the teaching of two undergraduate bioinformatics classes in the National University of Singapore.'),
(93223, 2009, 'bmc', 'A comprehensive assessment of N-terminal signal peptides prediction methods', 'Choo, Khar Heng|Tan, Tin Wee|Ranganathan, Shoba', 'Institute for Infocomm Research, 1 Fusionopolis Way, #21-01 Connexis, Singapore. khchoo@i2r.a-star.edu.sg|Institute for Infocomm Research, 1 Fusionopolis Way, #21-01 Connexis, Singapore. khchoo@i2r.a-star.edu.sg|Institute for Infocomm Research, 1 Fusionopolis Way, #21-01 Connexis, Singapore. khchoo@i2r.a-star.edu.sg', 'Singapore|Singapore|Singapore', 51, 'Computational Biology|Databases, Protein|Protein Sorting Signals|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-S15-S2', '19958512', 'Amino-terminal signal peptides (SPs) are short regions that guide the targeting of secretory proteins to the correct subcellular compartments in the cell. They are cleaved off upon the passenger protein reaching its destination. The explosive growth in sequencing technologies has led to the deposition of vast numbers of protein sequences necessitating rapid functional annotation techniques, with subcellular localization being a key feature. Of the myriad software prediction tools developed to automate the task of assigning the SP cleavage site of these new sequences, we review here, the performance and reliability of commonly used SP prediction tools.'),
(93224, 2009, 'bmc', 'Gevab: a prototype genome variation analysis browsing server', 'Kim, Woo-Yeon|Kim, Sang-Yoon|Kim, Tae-Hyung|Ahn, Sung-Min|Byun, Ha Na|Kim, Deokhoon|Kim, Dae-Soo|Lee, Yong Seok|Ghang, Ho|Park, Daeui|Kim, Byoung-Chul|Kim, Chulhong|Lee, Sunghoon|Kim, Seong-Jin|Bhak, Jong', 'Korean BioInformation Center (KOBIC), KRIBB, Daejeon, Korea. kimplove@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon, Korea. kimplove@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon, Korea. kimplove@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon, Korea. kimplove@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon, Korea. kimplove@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon, Korea. kimplove@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon, Korea. kimplove@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon, Korea. kimplove@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon, Korea. kimplove@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon, Korea. kimplove@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon, Korea. kimplove@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon, Korea. kimplove@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon, Korea. kimplove@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon, Korea. kimplove@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon, Korea. kimplove@kribb.re.kr', '-|-|-|-|-|-|-|-|-|-|-|-|-|-|-', 0, 'Computational Biology|Databases, Genetic|Genetic Variation|Genome|Genomics|Genotype|Phenotype|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-10-S15-S3', '19958513', 'The first Korean individual diploid genome sequence data (KOREF) was publicized in December 2008.'),
(93225, 2009, 'bmc', 'Sub-population analysis based on temporal features of high content images', 'Veronika, Merlin|Evans, James|Matsudaira, Paul|Welsch, Roy|Rajapakse, Jagath', 'Computation and Systems Biology, Singapore-MIT Alliance, Nanyang Technological University, Singapore. merlin@pmail.ntu.edu.sg|Computation and Systems Biology, Singapore-MIT Alliance, Nanyang Technological University, Singapore. merlin@pmail.ntu.edu.sg|Computation and Systems Biology, Singapore-MIT Alliance, Nanyang Technological University, Singapore. merlin@pmail.ntu.edu.sg|Computation and Systems Biology, Singapore-MIT Alliance, Nanyang Technological University, Singapore. merlin@pmail.ntu.edu.sg|Computation and Systems Biology, Singapore-MIT Alliance, Nanyang Technological University, Singapore. merlin@pmail.ntu.edu.sg', 'Singapore|Singapore|Singapore|Singapore|Singapore', 4, 'Animals|Cell Movement|Cells|Cells, Cultured|Computational Biology|Macrophages|Mice|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-10-S15-S4', '19958514', 'High content screening techniques are increasingly used to understand the regulation and progression of cell motility. The demand of new platforms, coupled with availability of terabytes of data has challenged the traditional technique of identifying cell populations by manual methods and resulted in development of high-dimensional analytical methods.'),
(93226, 2009, 'bmc', 'Protein comparison at the domain architecture level', 'Lee, Byungwook|Lee, Doheon', 'Korean BioInformation Center, KRIBB, Daejeon 305-806, Korea. bulee@kribb.re.kr|Korean BioInformation Center, KRIBB, Daejeon 305-806, Korea. bulee@kribb.re.kr', '-|-', 20, 'Computational Biology|Databases, Protein|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein', 'Comparative Study', '10.1186/1471-2105-10-S15-S5', '19958515', 'The general method used to determine the function of newly discovered proteins is to transfer annotations from well-characterized homologous proteins. The process of selecting homologous proteins can largely be classified into sequence-based and domain-based approaches. Domain-based methods have several advantages for identifying distant homology and homology among proteins with multiple domains, as compared to sequence-based methods. However, these methods are challenged by large families defined by promiscuous (or mobile) domains.'),
(93227, 2009, 'bmc', 'Modulation of p53 binding to MDM2: computational studies reveal important roles of Tyr100', 'Dastidar, Shubhra Ghosh|Lane, David P|Verma, Chandra S', 'Bioinformatics Institute (A-STAR), 30 Biopolis Street, #07-01 Matrix, Singapore. chandra@bii.a-star.edu.sg|Bioinformatics Institute (A-STAR), 30 Biopolis Street, #07-01 Matrix, Singapore. chandra@bii.a-star.edu.sg|Bioinformatics Institute (A-STAR), 30 Biopolis Street, #07-01 Matrix, Singapore. chandra@bii.a-star.edu.sg', 'Singapore|Singapore|Singapore', 34, 'Binding Sites|Computational Biology|Hydrogen Bonding|Ligands|Models, Molecular|Protein Conformation|Proto-Oncogene Proteins c-mdm2|Tumor Suppressor Protein p53|Tyrosine', 'Journal Article', '10.1186/1471-2105-10-S15-S6', '19958516', 'The tumor suppressor protein p53 is regulated by the ubiquitin ligase MDM2 which down-regulates p53. In tumours with overexpressed MDM2, the p53-MDM2 interaction can be interrupted by a peptide or small molecule to stabilize p53 as a therapeutic strategy. Structural and biochemical/mutagenesis data show that p53 has 3 hydrophobic residues F19, W23 and L26 that embed into the ligand binding pocket of MDM2 which is highly plastic in nature and can modulate its size to accommodate a variety of ligands. This binding pocket is primarily dependent on the orientation of a particular residue, Y100. We have studied the role of the dynamics of Y100 in p53 recognition.'),
(93228, 2009, 'bmc', 'BIOADI: a machine learning approach to identifying abbreviations and definitions in biological literature', 'Kuo, Cheng-Ju|Ling, Maurice H T|Lin, Kuan-Ting|Hsu, Chun-Nan', 'Institute of Information Science, Academia Sinica, Taipei 115, Taiwan, Republic of China. clarkkuo@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Taipei 115, Taiwan, Republic of China. clarkkuo@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Taipei 115, Taiwan, Republic of China. clarkkuo@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Taipei 115, Taiwan, Republic of China. clarkkuo@iis.sinica.edu.tw', 'China|China|China|China', 26, 'Algorithms|Artificial Intelligence|Computational Biology|Data Mining|Natural Language Processing|PubMed|Software', 'Journal Article', '10.1186/1471-2105-10-S15-S7', '19958517', 'To automatically process large quantities of biological literature for knowledge discovery and information curation, text mining tools are becoming essential. Abbreviation recognition is related to NER and can be considered as a pair recognition task of a terminology and its corresponding abbreviation from free text. The successful identification of abbreviation and its corresponding definition is not only a prerequisite to index terms of text databases to produce articles of related interests, but also a building block to improve existing gene mention tagging and gene normalization tools.'),
(93229, 2009, 'bmc', 'Protein subcellular localization prediction of eukaryotes using a knowledge-based approach', 'Lin, Hsin-Nan|Chen, Ching-Tai|Sung, Ting-Yi|Ho, Shinn-Ying|Hsu, Wen-Lian', 'Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan, Republic of China. arith@iis.sinica.edu.tw|Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan, Republic of China. arith@iis.sinica.edu.tw|Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan, Republic of China. arith@iis.sinica.edu.tw|Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan, Republic of China. arith@iis.sinica.edu.tw|Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan, Republic of China. arith@iis.sinica.edu.tw', 'China|China|China|China|China', 0, 'Computational Biology|Databases, Protein|Eukaryota|Knowledge Bases|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-10-S15-S8', '19958518', 'The study of protein subcellular localization (PSL) is important for elucidating protein functions involved in various cellular processes. However, determining the localization sites of a protein through wet-lab experiments can be time-consuming and labor-intensive. Thus, computational approaches become highly desirable. Most of the PSL prediction systems are established for single-localized proteins. However, a significant number of eukaryotic proteins are known to be localized into multiple subcellular organelles. Many studies have shown that proteins may simultaneously locate or move between different cellular compartments and be involved in different biological processes with different roles.'),
(93230, 2009, 'bmc', 'HypertenGene: extracting key hypertension genes from biomedical literature with position and automatically-generated template features', 'Tsai, Richard Tzong-Han|Lai, Po-Ting|Dai, Hong-Jie|Huang, Chi-Hsin|Bow, Yue-Yang|Chang, Yen-Ching|Pan, Wen-Harn|Hsu, Wen-Lian', 'Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan, Republic of China. thtsai@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan, Republic of China. thtsai@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan, Republic of China. thtsai@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan, Republic of China. thtsai@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan, Republic of China. thtsai@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan, Republic of China. thtsai@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan, Republic of China. thtsai@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan, Republic of China. thtsai@saturn.yzu.edu.tw', 'China|China|China|China|China|China|China|China', 14, 'Computational Biology|Data Mining|Databases, Factual|Entropy|Genes|Hypertension|PubMed|Software', 'Journal Article', '10.1186/1471-2105-10-S15-S9', '19958519', 'The genetic factors leading to hypertension have been extensively studied, and large numbers of research papers have been published on the subject. One of hypertension researchers primary research tasks is to locate key hypertension-related genes in abstracts. However, gathering such information with existing tools is not easy: (1) Searching for articles often returns far too many hits to browse through. (2) The search results do not highlight the hypertension-related genes discovered in the abstract. (3) Even though some text mining services mark up gene names in the abstract, the key genes investigated in a paper are still not distinguished from other genes. To facilitate the information gathering process for hypertension researchers, one solution would be to extract the key hypertension-related genes in each abstract. Three major tasks are involved in the construction of this system: (1) gene and hypertension named entity recognition, (2) section categorization, and (3) gene-hypertension relation extraction.'),
(93231, 2009, 'bmc', 'Bioclipse 2: a scriptable integration platform for the life sciences', 'Spjuth, Ola|Alvarsson, Jonathan|Berg, Arvid|Eklund, Martin|Kuhn, Stefan|Mäsak, Carl|Torrance, Gilleain|Wagener, Johannes|Willighagen, Egon L|Steinbeck, Christoph|Wikberg, Jarl E S', 'Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. ola.spjuth@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. ola.spjuth@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. ola.spjuth@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. ola.spjuth@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. ola.spjuth@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. ola.spjuth@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. ola.spjuth@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. ola.spjuth@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. ola.spjuth@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. ola.spjuth@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. ola.spjuth@farmbio.uu.se', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Algorithms|Biological Science Disciplines|Computational Biology|Databases, Factual|Software', 'Journal Article', '10.1186/1471-2105-10-397', '19958528', 'Contemporary biological research integrates neighboring scientific domains to answer complex questions in fields such as systems biology and drug discovery. This calls for tools that are intuitive to use, yet flexible to adapt to new tasks.'),
(93232, 2009, 'bmc', 'Sorting by reversals and block-interchanges with various weight assignments', 'Lin, Ying Chih|Lin, Chun-Yuan|Lin, Chunhung Richard', 'Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan. yclin@cse.nsysu.edu.tw|Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan. yclin@cse.nsysu.edu.tw|Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan. yclin@cse.nsysu.edu.tw', '-|-|-', 5, 'Algorithms|Computational Biology|Gene Rearrangement|Genome', 'Journal Article', '10.1186/1471-2105-10-398', '19958558', 'A classical problem in studying genome rearrangements is understanding the series of rearrangement events involved in transforming one genome into another in accordance with the parsimonious principle when two genomes with the same set of genes differ in gene order. The most studied event is the reversal, but an increasing number of reports have considered reversals along with other genome rearrangement events. Some recent studies have investigated the use of reversals and block-interchanges simultaneously with a weight proportion of 1:2. However, there has been less progress towards exploring additional combinations of weights.'),
(93233, 2009, 'bmc', 'Considering scores between unrelated proteins in the search database improves profile comparison', 'Sadreyev, Ruslan I|Wang, Yong|Grishin, Nick V', 'Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA. sadreyev@chop.swmed.edu|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA. sadreyev@chop.swmed.edu|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA. sadreyev@chop.swmed.edu', 'United States|United States|United States', 1, 'Computational Biology|Databases, Factual|Databases, Protein|Proteins|Sequence Analysis, Protein', 'Comparative Study', '10.1186/1471-2105-10-399', '19961610', 'Profile-based comparison of multiple sequence alignments is a powerful methodology for the detection remote protein sequence similarity, which is essential for the inference and analysis of protein structure, function, and evolution. Accurate estimation of statistical significance of detected profile similarities is essential for further development of this methodology. Here we analyze a novel approach to estimate the statistical significance of profile similarity: the explicit consideration of background score distributions for each database template (subject).'),
(93234, 2009, 'bmc', 'Uncovering transcriptional interactions via an adaptive fuzzy logic approach', 'Chuang, Cheng-Long|Hung, Kenneth|Chen, Chung-Ming|Shieh, Grace S', 'Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.|Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.|Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.|Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.', '-|-|-|-', 12, 'Algorithms|Computational Biology|Fuzzy Logic|Gene Expression Profiling|Gene Expression Regulation, Fungal|Gene Regulatory Networks|Promoter Regions, Genetic|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-10-400', '19961622', 'To date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM) performed poorly in inferring transcriptional interactions (TIs), which represent physical interactions between transcription factors (TF) and upstream sequences of target genes. Inferring a TI means that the promoter sequence of a target is inferred to match the consensus sequence motifs of a potential TF, and their interaction type such as AT or RT is also predicted. Thus, a robust PWM (rPWM) was developed to search for consensus sequence motifs. In addition to rPWM, one feature extracted from ChIP-chip data was incorporated to identify potential TIs under specific conditions. An interaction type classifier was assembled to predict activation/repression of potential TIs using microarray data. This approach, combining an adaptive (learning) fuzzy inference system and an interaction type classifier to predict transcriptional regulatory networks, was named AdaFuzzy.'),
(93235, 2009, 'bmc', 'A close examination of double filtering with fold change and T test in microarray analysis', 'Zhang, Song|Cao, Jing', 'Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA. song.zhang@utsouthwestern.edu|Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA. song.zhang@utsouthwestern.edu', 'United States|United States', 23, 'Bayes Theorem|Computational Biology|Gene Expression Profiling|Genetic Variation|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-402', '19995439', 'Many researchers use the double filtering procedure with fold change and t test to identify differentially expressed genes, in the hope that the double filtering will provide extra confidence in the results. Due to its simplicity, the double filtering procedure has been popular with applied researchers despite the development of more sophisticated methods.'),
(93236, 2009, 'bmc', 'Investigating heterogeneous protein annotations toward cross-corpora utilization', 'Wang, Yue|Kim, Jin-Dong|Saetre, Rune|Pyysalo, Sampo|Tsujii, Junichi', 'Department of Computer Science, School of Information Science and Technology, University of Tokyo, Tokyo, Japan. wangyue@is.s.u-tokyo.ac.jp|Department of Computer Science, School of Information Science and Technology, University of Tokyo, Tokyo, Japan. wangyue@is.s.u-tokyo.ac.jp|Department of Computer Science, School of Information Science and Technology, University of Tokyo, Tokyo, Japan. wangyue@is.s.u-tokyo.ac.jp|Department of Computer Science, School of Information Science and Technology, University of Tokyo, Tokyo, Japan. wangyue@is.s.u-tokyo.ac.jp|Department of Computer Science, School of Information Science and Technology, University of Tokyo, Tokyo, Japan. wangyue@is.s.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 11, 'Computational Biology|Databases, Factual|Genes|Pattern Recognition, Automated|Proteins', 'Journal Article', '10.1186/1471-2105-10-403', '19995463', 'The number of corpora, collections of structured texts, has been increasing, as a result of the growing interest in the application of natural language processing methods to biological texts. Many named entity recognition (NER) systems have been developed based on these corpora. However, in the biomedical community, there is yet no general consensus regarding named entity annotation, thus, the resources are largely incompatible, and it is difficult to compare the performance of systems developed on resources that were divergently annotated. On the other hand, from a practical application perspective, it is desirable to utilize as many existing annotated resources as possible, because annotation is costly. Thus, it becomes a task of interest to integrate the heterogeneous annotations in these resources.'),
(93237, 2009, 'bmc', 'Identifying differentially methylated genes using mixed effect and generalized least square models', 'Sun, Shuying|Yan, Pearlly S|Huang, Tim H M|Lin, Shili', 'Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio 44106, USA. shuying.sun@case.edu|Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio 44106, USA. shuying.sun@case.edu|Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio 44106, USA. shuying.sun@case.edu|Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio 44106, USA. shuying.sun@case.edu', 'United States|United States|United States|United States', 0, 'Computational Biology|CpG Islands|DNA Methylation|Databases, Genetic|Genome|Genomics|Least-Squares Analysis|Nucleic Acid Hybridization', 'Journal Article', '10.1186/1471-2105-10-404', '20003206', 'DNA methylation plays an important role in the process of tumorigenesis. Identifying differentially methylated genes or CpG islands (CGIs) associated with genes between two tumor subtypes is thus an important biological question. The methylation status of all CGIs in the whole genome can be assayed with differential methylation hybridization (DMH) microarrays. However, patient samples or cell lines are heterogeneous, so their methylation pattern may be very different. In addition, neighboring probes at each CGI are correlated. How these factors affect the analysis of DMH data is unknown.'),
(93238, 2009, 'bmc', 'Exploring biological network structure with clustered random networks', 'Bansal, Shweta|Khandelwal, Shashank|Meyers, Lauren Ancel', 'Center for Infectious Disease Dynamics, Penn State University, University Park, PA 16802, USA. shweta@sbansal.com|Center for Infectious Disease Dynamics, Penn State University, University Park, PA 16802, USA. shweta@sbansal.com|Center for Infectious Disease Dynamics, Penn State University, University Park, PA 16802, USA. shweta@sbansal.com', 'United States|United States|United States', 65, 'Algorithms|Cluster Analysis|Computational Biology|Markov Chains|Metabolic Networks and Pathways|Models, Biological|Proteome', 'Journal Article', '10.1186/1471-2105-10-405', '20003212', 'Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions) and the extent of clustering (the tendency for a set of three nodes to be interconnected) are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics.'),
(93239, 2009, 'bmc', 'Protein-protein docking using region-based 3D Zernike descriptors', 'Venkatraman, Vishwesh|Yang, Yifeng D|Sael, Lee|Kihara, Daisuke', 'Department of Biological Sciences, Purdue University, West Lafayette, Indiana 47907, USA. vishwesh.venkatraman@gmail.com|Department of Biological Sciences, Purdue University, West Lafayette, Indiana 47907, USA. vishwesh.venkatraman@gmail.com|Department of Biological Sciences, Purdue University, West Lafayette, Indiana 47907, USA. vishwesh.venkatraman@gmail.com|Department of Biological Sciences, Purdue University, West Lafayette, Indiana 47907, USA. vishwesh.venkatraman@gmail.com', 'India|India|India|India', 86, 'Binding Sites|Computational Biology|Models, Molecular|Protein Conformation|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/1471-2105-10-407', '20003235', 'Protein-protein interactions are a pivotal component of many biological processes and mediate a variety of functions. Knowing the tertiary structure of a protein complex is therefore essential for understanding the interaction mechanism. However, experimental techniques to solve the structure of the complex are often found to be difficult. To this end, computational protein-protein docking approaches can provide a useful alternative to address this issue. Prediction of docking conformations relies on methods that effectively capture shape features of the participating proteins while giving due consideration to conformational changes that may occur.'),
(93240, 2009, 'bmc', 'Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy', 'Liu, Bing|Li, Jiuyong|Tsykin, Anna|Liu, Lin|Gaur, Arti B|Goodall, Gregory J', 'School of Computer and Information Science, University of South Australia, Adelaide, SA 5095, Australia. Bing.Liu@unisa.edu.au|School of Computer and Information Science, University of South Australia, Adelaide, SA 5095, Australia. Bing.Liu@unisa.edu.au|School of Computer and Information Science, University of South Australia, Adelaide, SA 5095, Australia. Bing.Liu@unisa.edu.au|School of Computer and Information Science, University of South Australia, Adelaide, SA 5095, Australia. Bing.Liu@unisa.edu.au|School of Computer and Information Science, University of South Australia, Adelaide, SA 5095, Australia. Bing.Liu@unisa.edu.au|School of Computer and Information Science, University of South Australia, Adelaide, SA 5095, Australia. Bing.Liu@unisa.edu.au', 'Australia|Australia|Australia|Australia|Australia|Australia', 58, 'Bayes Theorem|Computational Biology|Databases, Genetic|Epithelial Cells|Gene Expression Profiling|Gene Regulatory Networks|Mesenchymal Stem Cells|MicroRNAs|RNA, Messenger', 'Journal Article', '10.1186/1471-2105-10-408', '20003267', 'microRNAs (miRNAs) regulate target gene expression by controlling their mRNAs post-transcriptionally. Increasing evidence demonstrates that miRNAs play important roles in various biological processes. However, the functions and precise regulatory mechanisms of most miRNAs remain elusive. Current research suggests that miRNA regulatory modules are complicated, including up-, down-, and mix-regulation for different physiological conditions. Previous computational approaches for discovering miRNA-mRNA interactions focus only on down-regulatory modules. In this work, we present a method to capture complex miRNA-mRNA interactions including all regulatory types between miRNAs and mRNAs.'),
(93241, 2009, 'bmc', 'An improved empirical bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian Estimation of Temporal Regulation)', 'Aryee, Martin J|Gutiérrez-Pabello, José A|Kramnik, Igor|Maiti, Tapabrata|Quackenbush, John', 'Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, Massachusetts 02115, USA. aryee@jhu.edu|Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, Massachusetts 02115, USA. aryee@jhu.edu|Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, Massachusetts 02115, USA. aryee@jhu.edu|Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, Massachusetts 02115, USA. aryee@jhu.edu|Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, Massachusetts 02115, USA. aryee@jhu.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Bayes Theorem|Computational Biology|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-409', '20003283', 'Microarray gene expression time-course experiments provide the opportunity to observe the evolution of transcriptional programs that cells use to respond to internal and external stimuli. Most commonly used methods for identifying differentially expressed genes treat each time point as independent and ignore important correlations, including those within samples and between sampling times. Therefore they do not make full use of the information intrinsic to the data, leading to a loss of power.'),
(93242, 2009, 'bmc', 'Refining gene signatures: a Bayesian approach', 'Djebbari, Amira|Labbe, Aurélie', 'Department of Epidemiology, Biostatistics and Occupational Health, Montréal, QC, Canada. amira.djebbari@nrc.cnrc.gc.ca|Department of Epidemiology, Biostatistics and Occupational Health, Montréal, QC, Canada. amira.djebbari@nrc.cnrc.gc.ca', 'Canada|Canada', 0, 'Bayes Theorem|Biomarkers, Tumor|Computational Biology|Databases, Genetic|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-410', '20003289', 'In high density arrays, the identification of relevant genes for disease classification is complicated by not only the curse of dimensionality but also the highly correlated nature of the array data. In this paper, we are interested in the question of how many and which genes should be selected for a disease class prediction. Our work consists of a Bayesian supervised statistical learning approach to refine gene signatures with a regularization which penalizes for the correlation between the variables selected.'),
(93243, 2009, 'bmc', 'An efficient algorithm for the stochastic simulation of the hybridization of DNA to microarrays', 'Arslan, Erdem|Laurenzi, Ian J', 'Department of Chemical Engineering, Lehigh University, Bethlehem, PA, USA. erdem@lehigh.edu|Department of Chemical Engineering, Lehigh University, Bethlehem, PA, USA. erdem@lehigh.edu', 'United States|United States', 0, 'Algorithms|Computational Biology|DNA|Gene Expression Profiling|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-411', '20003312', 'Although oligonucleotide microarray technology is ubiquitous in genomic research, reproducibility and standardization of expression measurements still concern many researchers. Cross-hybridization between microarray probes and non-target ssDNA has been implicated as a primary factor in sensitivity and selectivity loss. Since hybridization is a chemical process, it may be modeled at a population-level using a combination of material balance equations and thermodynamics. However, the hybridization reaction network may be exceptionally large for commercial arrays, which often possess at least one reporter per transcript. Quantification of the kinetics and equilibrium of exceptionally large chemical systems of this type is numerically infeasible with customary approaches.'),
(93244, 2009, 'bmc', 'The impact of measurement errors in the identification of regulatory networks', 'Fujita, André|Patriota, Alexandre G|Sato, João R|Miyano, Satoru', 'Computational Science Research Program, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan. andrefujita@riken.jp|Computational Science Research Program, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan. andrefujita@riken.jp|Computational Science Research Program, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan. andrefujita@riken.jp|Computational Science Research Program, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan. andrefujita@riken.jp', 'Japan|Japan|Japan|Japan', 0, 'Algorithms|Animals|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Humans|Mice|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-412', '20003382', 'There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93245, 2009, 'bmc', 'Survival prediction from clinico-genomic models--a comparative study', 'Bøvelstad, Hege M|Nygård, Ståle|Borgan, Ornulf', 'Department of Mathematics, University of Oslo, Blindern, NO 0316 Oslo, Norway. hegembo@math.uio.no|Department of Mathematics, University of Oslo, Blindern, NO 0316 Oslo, Norway. hegembo@math.uio.no|Department of Mathematics, University of Oslo, Blindern, NO 0316 Oslo, Norway. hegembo@math.uio.no', 'Norway|Norway|Norway', 43, 'Computational Biology|Gene Expression Profiling|Genomics|Survival Analysis', 'Comparative Study', '10.1186/1471-2105-10-413', '20003386', 'Survival prediction from high-dimensional genomic data is an active field in todays medical research. Most of the proposed prediction methods make use of genomic data alone without considering established clinical covariates that often are available and known to have predictive value. Recent studies suggest that combining clinical and genomic information may improve predictions, but there is a lack of systematic studies on the topic. Also, for the widely used Cox regression model, it is not obvious how to handle such combined models.'),
(93246, 2009, 'bmc', 'Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences', 'Mizianty, Marcin J|Kurgan, Lukasz', 'Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. mizianty@ualberta.ca|Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. mizianty@ualberta.ca', 'Canada|Canada', 0, 'Computational Biology|Databases, Protein|Protein Conformation|Protein Folding|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-414', '20003388', 'Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences.'),
(93247, 2009, 'bmc', 'Effects of normalization on quantitative traits in association test', 'Goh, Liang|Yap, Von Bing', 'Cancer & Stem Cell Biology Program, Duke-National University of Singapore Graduate Medical School, Singapore. liang.goh@duke-nus.edu.sg|Cancer & Stem Cell Biology Program, Duke-National University of Singapore Graduate Medical School, Singapore. liang.goh@duke-nus.edu.sg', 'Singapore|Singapore', 27, 'Computational Biology|Genetic Variation|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Sample Size', 'Journal Article', '10.1186/1471-2105-10-415', '20003414', 'Quantitative trait loci analysis assumes that the trait is normally distributed. In reality, this is often not observed and one strategy is to transform the trait. However, it is not clear how much normality is required and which transformation works best in association studies.'),
(93248, 2009, 'bmc', 'DescFold: a web server for protein fold recognition', 'Yan, Ren-Xiang|Si, Jing-Na|Wang, Chuan|Zhang, Ziding', 'State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China. simpleyrx@163.com|State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China. simpleyrx@163.com|State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China. simpleyrx@163.com|State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China. simpleyrx@163.com', 'China|China|China|China', 28, 'Artificial Intelligence|Computational Biology|Databases, Protein|Internet|Models, Molecular|Protein Folding|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-10-416', '20003426', 'Machine learning-based methods have been proven to be powerful in developing new fold recognition tools. In our previous work [Zhang, Kochhar and Grigorov (2005) Protein Science, 14: 431-444], a machine learning-based method called DescFold was established by using Support Vector Machines (SVMs) to combine the following four descriptors: a profile-sequence-alignment-based descriptor using Psi-blast e-values and bit scores, a sequence-profile-alignment-based descriptor using Rps-blast e-values and bit scores, a descriptor based on secondary structure element alignment (SSEA), and a descriptor based on the occurrence of PROSITE functional motifs. In this work, we focus on the improvement of DescFold by incorporating more powerful descriptors and setting up a user-friendly web server.'),
(93249, 2009, 'bmc', 'Graphle: Interactive exploration of large, dense graphs', 'Huttenhower, Curtis|Mehmood, Sajid O|Troyanskaya, Olga G', 'Department of Computer Science, Princeton University, Princeton, NJ 08540, USA. ogt@cs.princeton.edu.|Department of Computer Science, Princeton University, Princeton, NJ 08540, USA. ogt@cs.princeton.edu.|Department of Computer Science, Princeton University, Princeton, NJ 08540, USA. ogt@cs.princeton.edu.', 'United States|United States|United States', 0, 'Computational Biology|Computer Graphics|Databases, Protein|Internet|Metabolic Networks and Pathways|Protein Interaction Mapping|Software|Systems Integration', 'Journal Article', '10.1186/1471-2105-10-417', '20003429', 'A wide variety of biological data can be modeled as network structures, including experimental results (e.g. protein-protein interactions), computational predictions (e.g. functional interaction networks), or curated structures (e.g. the Gene Ontology). While several tools exist for visualizing large graphs at a global level or small graphs in detail, previous systems have generally not allowed interactive analysis of dense networks containing thousands of vertices at a level of detail useful for biologists. Investigators often wish to explore specific portions of such networks from a detailed, gene-specific perspective, and balancing this requirement with the networks large size, complex structure, and rich metadata is a substantial computational challenge.'),
(93250, 2009, 'bmc', 'TransportTP: a two-phase classification approach for membrane transporter prediction and characterization', 'Li, Haiquan|Benedito, Vagner A|Udvardi, Michael K|Zhao, Patrick Xuechun', 'Plant Biology Division, The Samuel Roberts Noble Foundation, Inc, Ardmore, OK 73401, USA. hqli@noble.org|Plant Biology Division, The Samuel Roberts Noble Foundation, Inc, Ardmore, OK 73401, USA. hqli@noble.org|Plant Biology Division, The Samuel Roberts Noble Foundation, Inc, Ardmore, OK 73401, USA. hqli@noble.org|Plant Biology Division, The Samuel Roberts Noble Foundation, Inc, Ardmore, OK 73401, USA. hqli@noble.org', 'United States|United States|United States|United States', 37, 'Artificial Intelligence|Computational Biology|Databases, Protein|Membrane Transport Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-10-418', '20003433', 'Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms. We developed a novel genome-scale transporter prediction and characterization system called TransportTP that combined homology-based and machine learning methods in a two-phase classification approach. First, traditional homology methods were employed to predict novel transporters based on sequence similarity to known classified proteins in the Transporter Classification Database (TCDB). Second, machine learning methods were used to integrate a variety of features to refine the initial predictions. A set of rules based on transporter features was developed by machine learning using well-curated proteomes as guides.'),
(93251, 2009, 'bmc', 'Critical assessment of sequence-based protein-protein interaction prediction methods that do not require homologous protein sequences', 'Park, Yungki', 'Institute of Cellular and Molecular Biology (MBB 3 210B), Center for Systems and Synthetic Biology, University of Texas at Austin, 2500 Speedway, Austin, Texas, USA. shinysyk@gmail.com', 'United States', 33, 'Amino Acid Sequence|Binding Sites|Computational Biology|Databases, Protein|Protein Interaction Mapping|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-419', '20003442', 'Protein-protein interactions underlie many important biological processes. Computational prediction methods can nicely complement experimental approaches for identifying protein-protein interactions. Recently, a unique category of sequence-based prediction methods has been put forward--unique in the sense that it does not require homologous protein sequences. This enables it to be universally applicable to all protein sequences unlike many of previous sequence-based prediction methods. If effective as claimed, these new sequence-based, universally applicable prediction methods would have far-reaching utilities in many areas of biology research.'),
(93252, 2009, 'bmc', 'Web-based interrogation of gene expression signatures using EXALT', 'Wu, Jun|Qiu, Qingchao|Xie, Lu|Fullerton, Joseph|Yu, Jian|Shyr, Yu|George, Alfred L|Yi, Yajun', 'Department of Medicine, Vanderbilt University, Nashville, TN 37232-0275, USA.|Department of Medicine, Vanderbilt University, Nashville, TN 37232-0275, USA.|Department of Medicine, Vanderbilt University, Nashville, TN 37232-0275, USA.|Department of Medicine, Vanderbilt University, Nashville, TN 37232-0275, USA.|Department of Medicine, Vanderbilt University, Nashville, TN 37232-0275, USA.|Department of Medicine, Vanderbilt University, Nashville, TN 37232-0275, USA.|Department of Medicine, Vanderbilt University, Nashville, TN 37232-0275, USA.|Department of Medicine, Vanderbilt University, Nashville, TN 37232-0275, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Databases, Genetic|Gene Expression|Gene Expression Profiling|Humans|Internet|Neoplasms|Software', 'Journal Article', '10.1186/1471-2105-10-420', '20003458', 'Widespread use of high-throughput techniques such as microarrays to monitor gene expression levels has resulted in an explosive growth of data sets in public domains. Integration and exploration of these complex and heterogeneous data have become a major challenge.'),
(93253, 2009, 'bmc', 'BLAST+: architecture and applications', 'Camacho, Christiam|Coulouris, George|Avagyan, Vahram|Ma, Ning|Papadopoulos, Jason|Bealer, Kevin|Madden, Thomas L', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA. camacho@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA. camacho@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA. camacho@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA. camacho@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA. camacho@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA. camacho@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA. camacho@ncbi.nlm.nih.gov', 'United States|United States|United States|United States|United States|United States|United States', 5450, 'Computational Biology|Databases, Genetic|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-10-421', '20003500', 'Sequence similarity searching is a very important bioinformatics task. While Basic Local Alignment Search Tool (BLAST) outperforms exact methods through its use of heuristics, the speed of the current BLAST software is suboptimal for very long queries or database sequences. There are also some shortcomings in the user-interface of the current command-line applications.'),
(93254, 2009, 'bmc', 'Quantitative comparison of microarray experiments with published leukemia related gene expression signatures', 'Klein, Hans-Ulrich|Ruckert, Christian|Kohlmann, Alexander|Bullinger, Lars|Thiede, Christian|Haferlach, Torsten|Dugas, Martin', 'Department of Medical Informatics and Biomathematics, University of Münster, Domagkstrasse 9, 48149 Münster, Germany. h.klein@uni-muenster.de|Department of Medical Informatics and Biomathematics, University of Münster, Domagkstrasse 9, 48149 Münster, Germany. h.klein@uni-muenster.de|Department of Medical Informatics and Biomathematics, University of Münster, Domagkstrasse 9, 48149 Münster, Germany. h.klein@uni-muenster.de|Department of Medical Informatics and Biomathematics, University of Münster, Domagkstrasse 9, 48149 Münster, Germany. h.klein@uni-muenster.de|Department of Medical Informatics and Biomathematics, University of Münster, Domagkstrasse 9, 48149 Münster, Germany. h.klein@uni-muenster.de|Department of Medical Informatics and Biomathematics, University of Münster, Domagkstrasse 9, 48149 Münster, Germany. h.klein@uni-muenster.de|Department of Medical Informatics and Biomathematics, University of Münster, Domagkstrasse 9, 48149 Münster, Germany. h.klein@uni-muenster.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 24, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Humans|Leukemia|Oligonucleotide Array Sequence Analysis|Software', 'Comparative Study', '10.1186/1471-2105-10-422', '20003504', 'Multiple gene expression signatures derived from microarray experiments have been published in the field of leukemia research. A comparison of these signatures with results from new experiments is useful for verification as well as for interpretation of the results obtained. Currently, the percentage of overlapping genes is frequently used to compare published gene signatures against a signature derived from a new experiment. However, it has been shown that the percentage of overlapping genes is of limited use for comparing two experiments due to the variability of gene signatures caused by different array platforms or assay-specific influencing parameters. Here, we present a robust approach for a systematic and quantitative comparison of published gene expression signatures with an exemplary query dataset.'),
(93255, 2009, 'bmc', 'Comparison of phylogenetic trees through alignment of embedded evolutionary distances', 'Choi, Kwangbom|Gomez, Shawn M', 'Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. kbchoi@cs.unc.edu|Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. kbchoi@cs.unc.edu', 'United States|United States', 12, 'Computational Biology|Evolution, Molecular|Gene Transfer, Horizontal|Phylogeny|Sequence Alignment', 'Comparative Study', '10.1186/1471-2105-10-423', '20003527', 'The understanding of evolutionary relationships is a fundamental aspect of modern biology, with the phylogenetic tree being a primary tool for describing these associations. However, comparison of trees for the purpose of assessing similarity and the quantification of various biological processes remains a significant challenge.'),
(93256, 2009, 'bmc', 'CHESS (CgHExpreSS): a comprehensive analysis tool for the analysis of genomic alterations and their effects on the expression profile of the genome', 'Lee, Mikyung|Kim, Yangseok', 'Department of Physiology, College of Oriental Medicine, KyungHee University, #1 Hoegi-Dong, Dongdaemun-gu, Seoul 130-701, South Korea. mklee77@khu.ac.kr|Department of Physiology, College of Oriental Medicine, KyungHee University, #1 Hoegi-Dong, Dongdaemun-gu, Seoul 130-701, South Korea. mklee77@khu.ac.kr', '-|-', 5, 'Gene Expression Profiling|Genome|Genomics|Software', 'Journal Article', '10.1186/1471-2105-10-424', '20003544', 'Genomic alterations frequently occur in many cancer patients and play important mechanistic roles in the pathogenesis of cancer. Furthermore, they can modify the expression level of genes due to altered copy number in the corresponding region of the chromosome. An accumulating body of evidence supports the possibility that strong genome-wide correlation exists between DNA content and gene expression. Therefore, more comprehensive analysis is needed to quantify the relationship between genomic alteration and gene expression. A well-designed bioinformatics tool is essential to perform this kind of integrative analysis. A few programs have already been introduced for integrative analysis. However, there are many limitations in their performance of comprehensive integrated analysis using published software because of limitations in implemented algorithms and visualization modules.'),
(93257, 2009, 'bmc', 'Crossfit analysis: a novel method to characterize the dynamics of induced plant responses', 'Jansen, Jeroen J|van Dam, Nicole M|Hoefsloot, Huub C J|Smilde, Age K', 'Biosystems Data Analysis, Swammerdam Institute for Life Sciences, Faculty of Sciences, Universiteit van Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands. j.j.jansen@uva.nl|Biosystems Data Analysis, Swammerdam Institute for Life Sciences, Faculty of Sciences, Universiteit van Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands. j.j.jansen@uva.nl|Biosystems Data Analysis, Swammerdam Institute for Life Sciences, Faculty of Sciences, Universiteit van Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands. j.j.jansen@uva.nl|Biosystems Data Analysis, Swammerdam Institute for Life Sciences, Faculty of Sciences, Universiteit van Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands. j.j.jansen@uva.nl', 'Netherlands|Netherlands|Netherlands|Netherlands', 12, 'Brassica|Computational Biology|Glucosinolates|Principal Component Analysis', 'Journal Article', '10.1186/1471-2105-10-425', '20015363', 'Many plant species show induced responses that protect them against exogenous attacks. These responses involve the production of many different bioactive compounds. Plant species belonging to the Brassicaceae family produce defensive glucosinolates, which may greatly influence their favorable nutritional properties for humans. Each responding compound may have its own dynamic profile and metabolic relationships with other compounds. The chemical background of the induced response is therefore highly complex and may therefore not reveal all the properties of the response in any single model.'),
(93258, 2009, 'bmc', 'Prediction of protein-protein interaction sites using an ensemble method', 'Deng, Lei|Guan, Jihong|Dong, Qiwen|Zhou, Shuigeng', 'Department of Computer Science and Technology, Tongji University, Shanghai 201804, China. 9sking@tongji.edu.cn|Department of Computer Science and Technology, Tongji University, Shanghai 201804, China. 9sking@tongji.edu.cn|Department of Computer Science and Technology, Tongji University, Shanghai 201804, China. 9sking@tongji.edu.cn|Department of Computer Science and Technology, Tongji University, Shanghai 201804, China. 9sking@tongji.edu.cn', 'China|China|China|China', 60, 'Binding Sites|Computational Biology|Databases, Protein|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|Software|Viral Nonstructural Proteins', 'Journal Article', '10.1186/1471-2105-10-426', '20015386', 'Prediction of protein-protein interaction sites is one of the most challenging and intriguing problems in the field of computational biology. Although much progress has been achieved by using various machine learning methods and a variety of available features, the problem is still far from being solved.'),
(93259, 2009, 'bmc', 'In silico method for systematic analysis of feature importance in microRNA-mRNA interactions', 'Xiao, Jiamin|Li, Yizhou|Wang, Kelong|Wen, Zhining|Li, Menglong|Zhang, Lifang|Guang, Xuanmin', 'College of Chemistry, Sichuan University, Chengdu 610064, PR China. xiaojiamin07@126.com|College of Chemistry, Sichuan University, Chengdu 610064, PR China. xiaojiamin07@126.com|College of Chemistry, Sichuan University, Chengdu 610064, PR China. xiaojiamin07@126.com|College of Chemistry, Sichuan University, Chengdu 610064, PR China. xiaojiamin07@126.com|College of Chemistry, Sichuan University, Chengdu 610064, PR China. xiaojiamin07@126.com|College of Chemistry, Sichuan University, Chengdu 610064, PR China. xiaojiamin07@126.com|College of Chemistry, Sichuan University, Chengdu 610064, PR China. xiaojiamin07@126.com', 'China|China|China|China|China|China|China', 11, 'Algorithms|Base Sequence|Binding Sites|Computational Biology|MicroRNAs|Molecular Sequence Data|RNA, Messenger', 'Journal Article', '10.1186/1471-2105-10-427', '20015389', 'MicroRNA (miRNA), which is short non-coding RNA, plays a pivotal role in the regulation of many biological processes and affects the stability and/or translation of mRNA. Recently, machine learning algorithms were developed to predict potential miRNA targets. Most of these methods are robust but are not sensitive to redundant or irrelevant features. Despite their good performance, the relative importance of each feature is still unclear. With increasing experimental data becoming available, research interest has shifted from higher prediction performance to uncovering the mechanism of microRNA-mRNA interactions.'),
(93260, 2009, 'bmc', 'TargetSearch--a Bioconductor package for the efficient preprocessing of GC-MS metabolite profiling data', 'Cuadros-Inostroza, Alvaro|Caldana, Camila|Redestig, Henning|Kusano, Miyako|Lisec, Jan|Peña-Cortés, Hugo|Willmitzer, Lothar|Hannah, Matthew A', 'Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany. inostroza@mpimp-golm.mpg.de|Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany. inostroza@mpimp-golm.mpg.de|Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany. inostroza@mpimp-golm.mpg.de|Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany. inostroza@mpimp-golm.mpg.de|Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany. inostroza@mpimp-golm.mpg.de|Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany. inostroza@mpimp-golm.mpg.de|Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany. inostroza@mpimp-golm.mpg.de|Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany. inostroza@mpimp-golm.mpg.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 125, 'Computational Biology|Databases, Factual|Gas Chromatography-Mass Spectrometry|Metabolome|Pattern Recognition, Automated|Proteome|Proteomics|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-428', '20015393', 'Metabolite profiling, the simultaneous quantification of multiple metabolites in an experiment, is becoming increasingly popular, particularly with the rise of systems-level biology. The workhorse in this field is gas-chromatography hyphenated with mass spectrometry (GC-MS). The high-throughput of this technology coupled with a demand for large experiments has led to data pre-processing, i.e. the quantification of metabolites across samples, becoming a major bottleneck. Existing software has several limitations, including restricted maximum sample size, systematic errors and low flexibility. However, the biggest limitation is that the resulting data usually require extensive hand-curation, which is subjective and can typically take several days to weeks.'),
(93261, 2009, 'bmc', 'A new permutation strategy of pathway-based approach for genome-wide association study', 'Guo, Yan-Fang|Li, Jian|Chen, Yuan|Zhang, Li-Shu|Deng, Hong-Wen', '1School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, PR China. guoyanfang@gmail.com|1School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, PR China. guoyanfang@gmail.com|1School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, PR China. guoyanfang@gmail.com|1School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, PR China. guoyanfang@gmail.com|1School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, PR China. guoyanfang@gmail.com', 'China|China|China|China|China', 25, 'Algorithms|Computational Biology|Databases, Genetic|Genetic Predisposition to Disease|Genetic Variation|Genome-Wide Association Study|Genotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-10-429', '20021635', 'Recently introduced pathway-based approach is promising and advantageous to improve the efficiency of analyzing genome-wide association scan (GWAS) data to identify disease variants by jointly considering variants of the genes that belong to the same biological pathway. However, the current available pathway-based approaches for analyzing GWAS have limited power and efficiency.'),
(93262, 2009, 'bmc', 'WebCARMA: a web application for the functional and taxonomic classification of unassembled metagenomic reads', 'Gerlach, Wolfgang|Jünemann, Sebastian|Tille, Felix|Goesmann, Alexander|Stoye, Jens', 'Faculty of Technology, Bielefeld University, Bielefeld, Germany. wolfgang.gerlach@cebitec.uni-bielefeld.de|Faculty of Technology, Bielefeld University, Bielefeld, Germany. wolfgang.gerlach@cebitec.uni-bielefeld.de|Faculty of Technology, Bielefeld University, Bielefeld, Germany. wolfgang.gerlach@cebitec.uni-bielefeld.de|Faculty of Technology, Bielefeld University, Bielefeld, Germany. wolfgang.gerlach@cebitec.uni-bielefeld.de|Faculty of Technology, Bielefeld University, Bielefeld, Germany. wolfgang.gerlach@cebitec.uni-bielefeld.de', 'Germany|Germany|Germany|Germany|Germany', 62, 'Computational Biology|Genome|Genomics|Internet|Metagenomics|Software', 'Journal Article', '10.1186/1471-2105-10-430', '20021646', 'Metagenomics is a new field of research on natural microbial communities. High-throughput sequencing techniques like 454 or Solexa-Illumina promise new possibilities as they are able to produce huge amounts of data in much shorter time and with less efforts and costs than the traditional Sanger technique. But the data produced comes in even shorter reads (35-100 basepairs with Illumina, 100-500 basepairs with 454-sequencing). CARMA is a new software pipeline for the characterisation of species composition and the genetic potential of microbial samples using short, unassembled reads.'),
(93263, 2009, 'bmc', 'Discovering collectively informative descriptors from high-throughput experiments', 'Jeffries, Clark D|Ward, William O|Perkins, Diana O|Wright, Fred A', 'Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, NC, USA. clark_jeffries@med.unc.edu|Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, NC, USA. clark_jeffries@med.unc.edu|Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, NC, USA. clark_jeffries@med.unc.edu|Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, NC, USA. clark_jeffries@med.unc.edu', 'United States|United States|United States|United States', 3, 'Algorithms|Computational Biology|Databases, Factual|Pattern Recognition, Automated|Proteins|RNA, Messenger', 'Journal Article', '10.1186/1471-2105-10-431', '20021653', 'Improvements in high-throughput technology and its increasing use have led to the generation of many highly complex datasets that often address similar biological questions. Combining information from these studies can increase the reliability and generalizability of results and also yield new insights that guide future research.'),
(93264, 2009, 'bmc', 'Algorithms for locating extremely conserved elements in multiple sequence alignments', 'Tseng, Huei-Hun E|Tompa, Martin', 'Department of Computer Science and Engineering, University of Washington, Box 352350, Seattle, WA 98195-2350, USA. lachesis@cs.washington.edu|Department of Computer Science and Engineering, University of Washington, Box 352350, Seattle, WA 98195-2350, USA. lachesis@cs.washington.edu', 'United States|United States', 0, 'Algorithms|Animals|Base Sequence|Computational Biology|Conserved Sequence|Genome|Humans|Mice|Rats|Sequence Alignment|Sequence Analysis, DNA|Vertebrates', 'Journal Article', '10.1186/1471-2105-10-432', '20021665', 'In 2004, Bejerano et al. announced the startling discovery of hundreds of ultraconserved elements, long genomic sequences perfectly conserved across human, mouse, and rat. Their announcement stimulated a flurry of subsequent research.'),
(93265, 2009, 'bmc', 'Using mechanistic Bayesian networks to identify downstream targets of the sonic hedgehog pathway', 'Shah, Abhik|Tenzen, Toyoaki|McMahon, Andrew P|Woolf, Peter J', 'Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. shahad@umich.edu|Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. shahad@umich.edu|Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. shahad@umich.edu|Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. shahad@umich.edu', 'United States|United States|United States|United States', 6, 'Bayes Theorem|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Hedgehog Proteins|Oligonucleotide Array Sequence Analysis|Signal Transduction', 'Journal Article', '10.1186/1471-2105-10-433', '20021670', 'The topology of a biological pathway provides clues as to how a pathway operates, but rationally using this topology information with observed gene expression data remains a challenge.'),
(93266, 2009, 'bmc', 'Identification of ATP binding residues of a protein from its primary sequence', 'Chauhan, Jagat S|Mishra, Nitish K|Raghava, Gajendra P S', 'Institute of Microbial Technology, Chandigarh, India. jagat@imtech.res.in|Institute of Microbial Technology, Chandigarh, India. jagat@imtech.res.in|Institute of Microbial Technology, Chandigarh, India. jagat@imtech.res.in', 'India|India|India', 0, 'Adenosine Triphosphate|Amino Acid Sequence|Binding Sites|Computational Biology|Databases, Protein|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-434', '20021687', 'One of the major challenges in post-genomic era is to provide functional annotations for large number of proteins arising from genome sequencing projects. The function of many proteins depends on their interaction with small molecules or ligands. ATP is one such important ligand that plays critical role as a coenzyme in the functionality of many proteins. There is a need to develop method for identifying ATP interacting residues in a ATP binding proteins (ABPs), in order to understand mechanism of protein-ligands interaction.'),
(93267, 2009, 'bmc', 'In silico evaluation of predicted regulatory interactions in Arabidopsis thaliana', 'Nero, Damion|Katari, Manpreet S|Kelfer, Jonathan|Tranchina, Daniel|Coruzzi, Gloria M', 'Department of Biology, New York University, Center for Genomics and Systems Biology, New York, NY 10003, USA. dcn208@nyu.edu|Department of Biology, New York University, Center for Genomics and Systems Biology, New York, NY 10003, USA. dcn208@nyu.edu|Department of Biology, New York University, Center for Genomics and Systems Biology, New York, NY 10003, USA. dcn208@nyu.edu|Department of Biology, New York University, Center for Genomics and Systems Biology, New York, NY 10003, USA. dcn208@nyu.edu|Department of Biology, New York University, Center for Genomics and Systems Biology, New York, NY 10003, USA. dcn208@nyu.edu', 'United States|United States|United States|United States|United States', 3, 'Arabidopsis|Arabidopsis Proteins|Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-435', '20025756', 'Prediction of transcriptional regulatory mechanisms in Arabidopsis has become increasingly critical with the explosion of genomic data now available for both gene expression and gene sequence composition. We have shown in previous work 1, that a combination of correlation measurements and cis-regulatory element (CRE) detection methods are effective in predicting targets for candidate transcription factors for specific case studies which were validated. However, to date there has been no quantitative assessment as to which correlation measures or CRE detection methods used alone or in combination are most effective in predicting TF-->target relationships on a genome-wide scale.'),
(93268, 2009, 'bmc', 'PreDisorder: ab initio sequence-based prediction of protein disordered regions', 'Deng, Xin|Eickholt, Jesse|Cheng, Jianlin', 'Department of Computer Science, University of Missouri-Columbia, Columbia, MO 65211, USA. xd9d3@mail.missouri.edu|Department of Computer Science, University of Missouri-Columbia, Columbia, MO 65211, USA. xd9d3@mail.missouri.edu|Department of Computer Science, University of Missouri-Columbia, Columbia, MO 65211, USA. xd9d3@mail.missouri.edu', 'United States|United States|United States', 66, 'Databases, Protein|Protein Conformation|Protein Folding|Protein Structure, Tertiary|Proteins|Proteomics|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-10-436', '20025768', 'Disordered regions are segments of the protein chain which do not adopt stable structures. Such segments are often of interest because they have a close relationship with protein expression and functionality. As such, protein disorder prediction is important for protein structure prediction, structure determination and function annotation.'),
(93269, 2009, 'bmc', 'Prediction of backbone dihedral angles and protein secondary structure using support vector machines', 'Kountouris, Petros|Hirst, Jonathan D', 'School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK. pcxpk@nottingham.ac.uk|School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK. pcxpk@nottingham.ac.uk', 'United Kingdom|United Kingdom', 0, 'Algorithms|Databases, Protein|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-437', '20025785', 'The prediction of the secondary structure of a protein is a critical step in the prediction of its tertiary structure and, potentially, its function. Moreover, the backbone dihedral angles, highly correlated with secondary structures, provide crucial information about the local three-dimensional structure.'),
(93270, 2009, 'bmc', 'A response to information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments', 'Peddada, Shyamal D|Umbach, David M|Harris, Shawn F', 'School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK. pcxpk@nottingham.ac.uk|School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK. pcxpk@nottingham.ac.uk|School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK. pcxpk@nottingham.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software', 'Comment', '10.1186/1471-2105-10-438', '20028515', 'For gene expression data obtained from a time-course microarray experiment, Liu et al. developed a new algorithm for clustering genes with similar expression profiles over time. Performance of their proposal was compared with three other methods including the order-restricted inference based methodology of Peddada et al. In this note we point out several inaccuracies in Liu et al. and conclude that the order-restricted inference based methodology of Peddada et al. (programmed in the software ORIOGEN) indeed operates at the desired nominal Type 1 error level, an important feature of a statistical decision rule, while being computationally substantially faster than indicated by Liu et al.'),
(93271, 2009, 'bmc', 'svmPRAT: SVM-based protein residue annotation toolkit', 'Rangwala, Huzefa|Kauffman, Christopher|Karypis, George', 'Computer Science Department, George Mason University, Fairfax, VA, USA. rangwala@cs.gmu.edu|Computer Science Department, George Mason University, Fairfax, VA, USA. rangwala@cs.gmu.edu|Computer Science Department, George Mason University, Fairfax, VA, USA. rangwala@cs.gmu.edu', 'United States|United States|United States', 21, 'Artificial Intelligence|Binding Sites|Databases, Protein|Pattern Recognition, Automated|Protein Folding|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-10-439', '20028521', 'Over the last decade several prediction methods have been developed for determining the structural and functional properties of individual protein residues using sequence and sequence-derived information. Most of these methods are based on support vector machines as they provide accurate and generalizable prediction models.'),
(93272, 2009, 'bmc', 'ReseqChip: automated integration of multiple local context probe data from the MitoChip array in mitochondrial DNA sequence assembly', 'Thieme, Marian|Lottaz, Claudio|Niederstätter, Harald|Parson, Walther|Spang, Rainer|Oefner, Peter J', 'Institute of Functional Genomics, University of Regensburg, Josef-Engert-Str, 9, D-93053 Regensburg, Germany. marian.thieme@klinik.uni-regensburg.de|Institute of Functional Genomics, University of Regensburg, Josef-Engert-Str, 9, D-93053 Regensburg, Germany. marian.thieme@klinik.uni-regensburg.de|Institute of Functional Genomics, University of Regensburg, Josef-Engert-Str, 9, D-93053 Regensburg, Germany. marian.thieme@klinik.uni-regensburg.de|Institute of Functional Genomics, University of Regensburg, Josef-Engert-Str, 9, D-93053 Regensburg, Germany. marian.thieme@klinik.uni-regensburg.de|Institute of Functional Genomics, University of Regensburg, Josef-Engert-Str, 9, D-93053 Regensburg, Germany. marian.thieme@klinik.uni-regensburg.de|Institute of Functional Genomics, University of Regensburg, Josef-Engert-Str, 9, D-93053 Regensburg, Germany. marian.thieme@klinik.uni-regensburg.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Base Sequence|Computational Biology|DNA, Mitochondrial|Genome, Mitochondrial|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-10-440', '20028526', 'The Affymetrix MitoChip v2.0 is an oligonucleotide tiling array for the resequencing of the human mitochondrial (mt) genome. For each of 16,569 nucleotide positions of the mt genome it holds two sets of four 25-mer probes each that match the heavy and the light strand of a reference mt genome and vary only at their central position to interrogate all four possible alleles. In addition, the MitoChip v2.0 carries alternative local context probes to account for known mtDNA variants. These probes have been neglected in most studies due to the lack of software for their automated analysis.'),
(93273, 2009, 'bmc', 'The Medicago truncatula gene expression atlas web server', 'He, Ji|Benedito, Vagner A|Wang, Mingyi|Murray, Jeremy D|Zhao, Patrick X|Tang, Yuhong|Udvardi, Michael K', 'Plant Biology Division, the Samuel Roberts Noble Foundation, Ardmore, OK 73401, USA. jhe@noble.org|Plant Biology Division, the Samuel Roberts Noble Foundation, Ardmore, OK 73401, USA. jhe@noble.org|Plant Biology Division, the Samuel Roberts Noble Foundation, Ardmore, OK 73401, USA. jhe@noble.org|Plant Biology Division, the Samuel Roberts Noble Foundation, Ardmore, OK 73401, USA. jhe@noble.org|Plant Biology Division, the Samuel Roberts Noble Foundation, Ardmore, OK 73401, USA. jhe@noble.org|Plant Biology Division, the Samuel Roberts Noble Foundation, Ardmore, OK 73401, USA. jhe@noble.org|Plant Biology Division, the Samuel Roberts Noble Foundation, Ardmore, OK 73401, USA. jhe@noble.org', 'United States|United States|United States|United States|United States|United States|United States', 121, 'Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation, Plant|Internet|Medicago truncatula|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-441', '20028527', 'Legumes (Leguminosae or Fabaceae) play a major role in agriculture. Transcriptomics studies in the model legume species, Medicago truncatula, are instrumental in helping to formulate hypotheses about the role of legume genes. With the rapid growth of publically available Affymetrix GeneChip Medicago Genome Array GeneChip data from a great range of tissues, cell types, growth conditions, and stress treatments, the legume research community desires an effective bioinformatics system to aid efforts to interpret the Medicago genome through functional genomics. We developed the Medicago truncatula Gene Expression Atlas (MtGEA) web server for this purpose.'),
(93274, 2009, 'bmc', 'G+C content dominates intrinsic nucleosome occupancy', 'Tillo, Desiree|Hughes, Timothy R', 'Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada. desiree.tillo@utoronto.ca|Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada. desiree.tillo@utoronto.ca', 'Canada|Canada', 185, 'Base Composition|Base Sequence|Computational Biology|Databases, Genetic|Genome|Nucleosomes', 'Journal Article', '10.1186/1471-2105-10-442', '20028554', 'The relative preference of nucleosomes to form on individual DNA sequences plays a major role in genome packaging. A wide variety of DNA sequence features are believed to influence nucleosome formation, including periodic dinucleotide signals, poly-A stretches and other short motifs, and sequence properties that influence DNA structure, including base content. It was recently shown by Kaplan et al. that a probabilistic model using composition of all 5-mers within a nucleosome-sized tiling window accurately predicts intrinsic nucleosome occupancy across an entire genome in vitro. However, the model is complicated, and it is not clear which specific DNA sequence properties are most important for intrinsic nucleosome-forming preferences.'),
(93275, 2009, 'bmc', 'MultiTest V12, a program to binomially combine independent tests and performance comparison with other related methods on proportional data', 'De Meeûs, Thierry|Guégan, Jean-François|Teriokhin, Anatoly T', 'IRD, UMR 177 IRD-CIRAD Trypanosomoses, Centre International de Recherche-Développement sur lElevage en zone Subhumide, 01 BP 454 Bobo-Dioulasso 01, Burkina-Faso. demeeus@mpl.ird.fr|IRD, UMR 177 IRD-CIRAD Trypanosomoses, Centre International de Recherche-Développement sur lElevage en zone Subhumide, 01 BP 454 Bobo-Dioulasso 01, Burkina-Faso. demeeus@mpl.ird.fr|IRD, UMR 177 IRD-CIRAD Trypanosomoses, Centre International de Recherche-Développement sur lElevage en zone Subhumide, 01 BP 454 Bobo-Dioulasso 01, Burkina-Faso. demeeus@mpl.ird.fr', '-|-|-', 0, 'Gene Expression Profiling|Genetics, Population|Models, Statistical|Software', 'Comparative Study', '10.1186/1471-2105-10-443', '20030807', 'Combining multiple independent tests, when all test the same hypothesis and in the same direction, has been the subject of several approaches. Besides the inappropriate (in this case) Bonferroni procedure, the Fishers method has been widely used, in particular in population genetics. This last method has nevertheless been challenged by the SGM (symmetry around the geometric mean) and Stouffers Z-transformed methods that are less sensitive to asymmetry and deviations from uniformity of the distribution of the partial P-values. Performances of these different procedures were never compared on proportional data such as those currently used in population genetics.'),
(93276, 2009, 'bmc', 'IRIS: a method for reverse engineering of regulatory relations in gene networks', 'Morganella, Sandro|Zoppoli, Pietro|Ceccarelli, Michele', 'Department of Biological and Environmental Sciences, University of Sannio, Benevento, Italy. morganellaalx@gmail.com|Department of Biological and Environmental Sciences, University of Sannio, Benevento, Italy. morganellaalx@gmail.com|Department of Biological and Environmental Sciences, University of Sannio, Benevento, Italy. morganellaalx@gmail.com', 'Italy|Italy|Italy', 0, 'Algorithms|B-Lymphocytes|Computational Biology|Gene Regulatory Networks|Humans|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Software|Systems Biology', 'Journal Article', '10.1186/1471-2105-10-444', '20030818', 'The ultimate aim of systems biology is to understand and describe how molecular components interact to manifest collective behaviour that is the sum of the single parts. Building a network of molecular interactions is the basic step in modelling a complex entity such as the cell. Even if gene-gene interactions only partially describe real networks because of post-transcriptional modifications and protein regulation, using microarray technology it is possible to combine measurements for thousands of genes into a single analysis step that provides a picture of the cells gene expression. Several databases provide information about known molecular interactions and various methods have been developed to infer gene networks from expression data. However, network topology alone is not enough to perform simulations and predictions of how a molecular system will respond to perturbations. Rules for interactions among the single parts are needed for a complete definition of the network behaviour. Another interesting question is how to integrate information carried by the network topology, which can be derived from the literature, with large-scale experimental data.'),
(93277, 2009, 'bmc', 'LipocalinPred: a SVM-based method for prediction of lipocalins', 'Ramana, Jayashree|Gupta, Dinesh', 'Bioinformatics Laboratory, Structural and Computational Biology Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi, India. ramanj@icgeb.res.in|Bioinformatics Laboratory, Structural and Computational Biology Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi, India. ramanj@icgeb.res.in', 'India|India', 14, 'Computational Biology|Databases, Protein|Lipocalins|Pattern Recognition, Automated|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-10-445', '20030857', 'Functional annotation of rapidly amassing nucleotide and protein sequences presents a challenging task for modern bioinformatics. This is particularly true for protein families sharing extremely low sequence identity, as for lipocalins, a family of proteins with varied functions and great diversity at the sequence level, yet conserved structures.'),
(93278, 2009, 'bmc', 'Data mining of enzymes using specific peptides', 'Weingart, Uri|Lavi, Yair|Horn, David', 'School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel. uriweing@tau.ac.il|School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel. uriweing@tau.ac.il|School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel. uriweing@tau.ac.il', 'Israel|Israel|Israel', 10, 'Amino Acid Sequence|Data Mining|Peptides|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-446', '20034383', 'Predicting the function of a protein from its sequence is a long-standing challenge of bioinformatic research, typically addressed using either sequence-similarity or sequence-motifs. We employ the novel motif method that consists of Specific Peptides (SPs) that are unique to specific branches of the Enzyme Commission (EC) functional classification. We devise the Data Mining of Enzymes (DME) methodology that allows for searching SPs on arbitrary proteins, determining from its sequence whether a protein is an enzyme and what the enzymes EC classification is.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93279, 2009, 'bmc', 'Next generation transcriptomes for next generation genomes using est2assembly', 'Papanicolaou, Alexie|Stierli, Remo|Ffrench-Constant, Richard H|Heckel, David G', 'Department of Entomology, Max Planck Institute for Chemical Ecology, Jena, Germany. alexie@butterflybase.org|Department of Entomology, Max Planck Institute for Chemical Ecology, Jena, Germany. alexie@butterflybase.org|Department of Entomology, Max Planck Institute for Chemical Ecology, Jena, Germany. alexie@butterflybase.org|Department of Entomology, Max Planck Institute for Chemical Ecology, Jena, Germany. alexie@butterflybase.org', 'Germany|Germany|Germany|Germany', 0, 'Animals|Databases, Genetic|Drosophila|Expressed Sequence Tags|Gene Expression Profiling|Genome|Genomics|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-10-447', '20034392', 'The decreasing costs of capillary-based Sanger sequencing and next generation technologies, such as 454 pyrosequencing, have prompted an explosion of transcriptome projects in non-model species, where even shallow sequencing of transcriptomes can now be used to examine a range of research questions. This rapid growth in data has outstripped the ability of researchers working on non-model species to analyze and mine transcriptome data efficiently.'),
(93280, 2009, 'bmc', 'Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling', 'Mazur, Johanna|Ritter, Daniel|Reinelt, Gerhard|Kaderali, Lars', 'Viroquant Research Group Modeling, University of Heidelberg, Bioquant BQ26, INF 267, D-69120 Heidelberg, Germany. johanna.mazur@bioquant.uni-heidelberg.de|Viroquant Research Group Modeling, University of Heidelberg, Bioquant BQ26, INF 267, D-69120 Heidelberg, Germany. johanna.mazur@bioquant.uni-heidelberg.de|Viroquant Research Group Modeling, University of Heidelberg, Bioquant BQ26, INF 267, D-69120 Heidelberg, Germany. johanna.mazur@bioquant.uni-heidelberg.de|Viroquant Research Group Modeling, University of Heidelberg, Bioquant BQ26, INF 267, D-69120 Heidelberg, Germany. johanna.mazur@bioquant.uni-heidelberg.de', 'Germany|Germany|Germany|Germany', 0, 'Computational Biology|Gene Expression Regulation|Gene Regulatory Networks|Nonlinear Dynamics', 'Journal Article', '10.1186/1471-2105-10-448', '20038296', 'The reconstruction of gene regulatory networks from time series gene expression data is one of the most difficult problems in systems biology. This is due to several reasons, among them the combinatorial explosion of possible network topologies, limited information content of the experimental data with high levels of noise, and the complexity of gene regulation at the transcriptional, translational and post-translational levels. At the same time, quantitative, dynamic models, ideally with probability distributions over model topologies and parameters, are highly desirable.'),
(93281, 2009, 'bmc', 'A white-box approach to microarray probe response characterization: the BaFL pipeline', 'Thompson, Kevin J|Deshmukh, Hrishikesh|Solka, Jeffrey L|Weller, Jennifer W', 'Computer Science Dept, University of North Carolina at Charlotte, Charlotte, NC 28223, USA. kthom110@uncc.edu|Computer Science Dept, University of North Carolina at Charlotte, Charlotte, NC 28223, USA. kthom110@uncc.edu|Computer Science Dept, University of North Carolina at Charlotte, Charlotte, NC 28223, USA. kthom110@uncc.edu|Computer Science Dept, University of North Carolina at Charlotte, Charlotte, NC 28223, USA. kthom110@uncc.edu', 'United States|United States|United States|United States', 4, 'Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-10-449', '20040098', 'Microarrays depend on appropriate probe design to deliver the promise of accurate genome-wide measurement. Probe design, ideally, produces a unique probe-target match with homogeneous duplex stability over the complete set of probes. Much of microarray pre-processing is concerned with adjusting for non-ideal probes that do not report target concentration accurately. Cross-hybridizing probes (non-unique), probe composition and structure, as well as platform effects such as instrument limitations, have been shown to affect the interpretation of signal. Data cleansing pipelines seldom filter specifically for these constraints, relying instead on general statistical tests to remove the most variable probes from the samples in a study. This adjusts probes contributing to ProbeSet (gene) values in a study-specific manner. We refer to the complete set of factors as biologically applied filter levels (BaFL) and have assembled an analysis pipeline for managing them consistently. The pipeline and associated experiments reported here examine the outcome of comprehensively excluding probes affected by known factors on inter-experiment target behavior consistency.'),
(93282, 2009, 'bmc', 'A service-oriented architecture for integrating the modeling and formal verification of genetic regulatory networks', 'Monteiro, Pedro T|Dumas, Estelle|Besson, Bruno|Mateescu, Radu|Page, Michel|Freitas, Ana T|de Jong, Hidde', 'INRIA Grenoble-Rhône-Alpes, 655 Avenue de lEurope, Montbonnot, 38334 St Ismier Cedex, France. Pedro.Monteiro@inria.fr|INRIA Grenoble-Rhône-Alpes, 655 Avenue de lEurope, Montbonnot, 38334 St Ismier Cedex, France. Pedro.Monteiro@inria.fr|INRIA Grenoble-Rhône-Alpes, 655 Avenue de lEurope, Montbonnot, 38334 St Ismier Cedex, France. Pedro.Monteiro@inria.fr|INRIA Grenoble-Rhône-Alpes, 655 Avenue de lEurope, Montbonnot, 38334 St Ismier Cedex, France. Pedro.Monteiro@inria.fr|INRIA Grenoble-Rhône-Alpes, 655 Avenue de lEurope, Montbonnot, 38334 St Ismier Cedex, France. Pedro.Monteiro@inria.fr|INRIA Grenoble-Rhône-Alpes, 655 Avenue de lEurope, Montbonnot, 38334 St Ismier Cedex, France. Pedro.Monteiro@inria.fr|INRIA Grenoble-Rhône-Alpes, 655 Avenue de lEurope, Montbonnot, 38334 St Ismier Cedex, France. Pedro.Monteiro@inria.fr', 'France|France|France|France|France|France|France', 11, 'Computational Biology|Databases, Genetic|Gene Regulatory Networks|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-450', '20042075', 'The study of biological networks has led to the development of increasingly large and detailed models. Computer tools are essential for the simulation of the dynamical behavior of the networks from the model. However, as the size of the models grows, it becomes infeasible to manually verify the predictions against experimental data or identify interesting features in a large number of simulation traces. Formal verification based on temporal logic and model checking provides promising methods to automate and scale the analysis of the models. However, a framework that tightly integrates modeling and simulation tools with model checkers is currently missing, on both the conceptual and the implementational level.'),
(93283, 2009, 'bmc', 'Predicting functional upstream open reading frames in Saccharomyces cerevisiae', '-', '-', '-', 0, '5 Untranslated Regions|Computational Biology|Gene Expression Regulation, Fungal|Open Reading Frames|Protein Biosynthesis|RNA, Messenger|Saccharomyces cerevisiae|Yeasts', 'Journal Article', '10.1186/1471-2105-10-451', '20042076', 'Some upstream open reading frames (uORFs) regulate gene expression (i.e., they are functional) and can play key roles in keeping organisms healthy. However, how uORFs are involved in gene regulation is not yet fully understood. In order to get a complete view of how uORFs are involved in gene regulation, it is expected that a large number of experimentally verified functional uORFs are needed. Unfortunately, wet-experiments to verify that uORFs are functional are expensive.'),
(93284, 2009, 'bmc', 'Skittle: a 2-dimensional genome visualization tool', 'Seaman, Josiah D|Sanford, John C', 'FMS Foundation, 7160 Stone Hill Rd,, Livonia, NY 14487, USA. Josiah.D.Seaman.1@ohio.edu|FMS Foundation, 7160 Stone Hill Rd,, Livonia, NY 14487, USA. Josiah.D.Seaman.1@ohio.edu', 'United States|United States', 0, 'Alu Elements|Centromere|Chromosomes, Human, Y|Computational Biology|Genetic Variation|Genome|Genomics|Humans|Internet|Nucleotides|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA|Software|Tandem Repeat Sequences|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-452', '20042093', 'It is increasingly evident that there are multiple and overlapping patterns within the genome, and that these patterns contain different types of information--regarding both genome function and genome history. In order to discover additional genomic patterns which may have biological significance, novel strategies are required. To partially address this need, we introduce a new data visualization tool entitled Skittle.'),
(93285, 2009, 'bmc', 'Classification across gene expression microarray studies', 'Buness, Andreas|Ruschhaupt, Markus|Kuner, Ruprecht|Tresch, Achim', 'German Cancer Research Center (DKFZ), Department of Molecular Genome Analysis, 69120 Heidelberg, Germany. a.buness@gmx.de|German Cancer Research Center (DKFZ), Department of Molecular Genome Analysis, 69120 Heidelberg, Germany. a.buness@gmx.de|German Cancer Research Center (DKFZ), Department of Molecular Genome Analysis, 69120 Heidelberg, Germany. a.buness@gmx.de|German Cancer Research Center (DKFZ), Department of Molecular Genome Analysis, 69120 Heidelberg, Germany. a.buness@gmx.de', 'Germany|Germany|Germany|Germany', 15, 'Breast Neoplasms|Computational Biology|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-453', '20042109', 'The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive) and histological grade (low/high) of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM), predictive analysis of microarrays (PAM), random forest (RF) and k-top scoring pairs (kTSP). Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV) aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing.'),
(93286, 2009, 'bmc', 'AtPIN: Arabidopsis thaliana protein interaction network', 'Brandão, Marcelo M|Dantas, Luiza L|Silva-Filho, Marcio C', 'Departamento de Genética, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo, 13400-970 Piracicaba, SP, Brazil. mbrandao@esalq.usp.br|Departamento de Genética, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo, 13400-970 Piracicaba, SP, Brazil. mbrandao@esalq.usp.br|Departamento de Genética, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo, 13400-970 Piracicaba, SP, Brazil. mbrandao@esalq.usp.br', 'Brazil|Brazil|Brazil', 0, 'Arabidopsis|Arabidopsis Proteins|Binding Sites|Databases, Protein|Protein Interaction Mapping|Software', 'Journal Article', '10.1186/1471-2105-10-454', '20043823', 'Protein-protein interactions (PPIs) constitute one of the most crucial conditions to sustain life in living organisms. To study PPI in Arabidopsis thaliana we have developed AtPIN, a database and web interface for searching and building interaction networks based on publicly available protein-protein interaction datasets.'),
(93287, 2009, 'bmc', 'Integrative approaches to the prediction of protein functions based on the feature selection', 'Ko, Seokha|Lee, Hyunju', 'Department of Information and Communications, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea. sukdo@gist.ac.kr|Department of Information and Communications, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea. sukdo@gist.ac.kr', '-|-', 6, 'Animals|Binding Sites|Databases, Genetic|Databases, Protein|Genomics|Mice|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1186/1471-2105-10-455', '20043848', 'Protein function prediction has been one of the most important issues in functional genomics. With the current availability of various genomic data sets, many researchers have attempted to develop integration models that combine all available genomic data for protein function prediction. These efforts have resulted in the improvement of prediction quality and the extension of prediction coverage. However, it has also been observed that integrating more data sources does not always increase the prediction quality. Therefore, selecting data sources that highly contribute to the protein function prediction has become an important issue.'),
(93288, 2010, 'bmc', 'Detection of gene pathways with predictive power for breast cancer prognosis', 'Ma, Shuangge|Kosorok, Michael R', 'School of Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu|School of Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu', 'United States|United States', 1, 'Biomarkers, Tumor|Breast Neoplasms|Computational Biology|Female|Gene Expression Profiling|Genetic Predisposition to Disease|Humans|Prognosis|Signal Transduction', 'Journal Article', '10.1186/1471-2105-11-1', '20043860', 'Prognosis is of critical interest in breast cancer research. Biomedical studies suggest that genomic measurements may have independent predictive power for prognosis. Gene profiling studies have been conducted to search for predictive genomic measurements. Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated functions. The goal of this study is to identify gene pathways with predictive power for breast cancer prognosis. Since our goal is fundamentally different from that of existing studies, a new pathway analysis method is proposed.'),
(93289, 2010, 'bmc', 'Predicting gene function using hierarchical multi-label decision tree ensembles', 'Schietgat, Leander|Vens, Celine|Struyf, Jan|Blockeel, Hendrik|Kocev, Dragi|Dzeroski, Saso', 'Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium. leander.schietgat@cs.kuleuven.be|Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium. leander.schietgat@cs.kuleuven.be|Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium. leander.schietgat@cs.kuleuven.be|Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium. leander.schietgat@cs.kuleuven.be|Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium. leander.schietgat@cs.kuleuven.be|Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium. leander.schietgat@cs.kuleuven.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 2, 'Algorithms|Artificial Intelligence|Computational Biology|Decision Trees|Gene Expression Profiling|Genes|Open Reading Frames', 'Journal Article', '10.1186/1471-2105-11-2', '20044933', 'S. cerevisiae, A. thaliana and M. musculus are well-studied organisms in biology and the sequencing of their genomes was completed many years ago. It is still a challenge, however, to develop methods that assign biological functions to the ORFs in these genomes automatically. Different machine learning methods have been proposed to this end, but it remains unclear which method is to be preferred in terms of predictive performance, efficiency and usability.'),
(93290, 2010, 'bmc', 'Amino acid little Big Bang: representing amino acid substitution matrices as dot products of Euclidian vectors', 'Zimmermann, Karel|Gibrat, Jean-François', 'Université Pierre et Marie Curie (Paris VI), France. Karel.Zimmermann@jouy.inra.fr|Université Pierre et Marie Curie (Paris VI), France. Karel.Zimmermann@jouy.inra.fr', 'France|France', 26, 'Amino Acid Sequence|Amino Acid Substitution|Amino Acids|Computational Biology|Databases, Protein|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-4', '20047649', 'Sequence comparisons make use of a one-letter representation for amino acids, the necessary quantitative information being supplied by the substitution matrices. This paper deals with the problem of finding a representation that provides a comprehensive description of amino acid intrinsic properties consistent with the substitution matrices.'),
(93291, 2010, 'bmc', 'The BridgeDb framework: standardized access to gene, protein and metabolite identifier mapping services', 'van Iersel, Martijn P|Pico, Alexander R|Kelder, Thomas|Gao, Jianjiong|Ho, Isaac|Hanspers, Kristina|Conklin, Bruce R|Evelo, Chris T', 'Department of Bioinformatics-BiGCaT, Maastricht University, Maastricht, the Netherlands. martijn.vaniersel@bigcat.unimaas.nl|Department of Bioinformatics-BiGCaT, Maastricht University, Maastricht, the Netherlands. martijn.vaniersel@bigcat.unimaas.nl|Department of Bioinformatics-BiGCaT, Maastricht University, Maastricht, the Netherlands. martijn.vaniersel@bigcat.unimaas.nl|Department of Bioinformatics-BiGCaT, Maastricht University, Maastricht, the Netherlands. martijn.vaniersel@bigcat.unimaas.nl|Department of Bioinformatics-BiGCaT, Maastricht University, Maastricht, the Netherlands. martijn.vaniersel@bigcat.unimaas.nl|Department of Bioinformatics-BiGCaT, Maastricht University, Maastricht, the Netherlands. martijn.vaniersel@bigcat.unimaas.nl|Department of Bioinformatics-BiGCaT, Maastricht University, Maastricht, the Netherlands. martijn.vaniersel@bigcat.unimaas.nl|Department of Bioinformatics-BiGCaT, Maastricht University, Maastricht, the Netherlands. martijn.vaniersel@bigcat.unimaas.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 57, 'Computational Biology|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-5', '20047655', 'Many complementary solutions are available for the identifier mapping problem. This creates an opportunity for bioinformatics tool developers. Tools can be made to flexibly support multiple mapping services or mapping services could be combined to get broader coverage. This approach requires an interface layer between tools and mapping services.'),
(93292, 2010, 'bmc', 'A computational screen for site selective A-to-I editing detects novel sites in neuron specific Hu proteins', 'Ensterö, Mats|Akerborg, Orjan|Lundin, Daniel|Wang, Bei|Furey, Terrence S|Ohman, Marie|Lagergren, Jens', 'Department of Molecular Biology and Functional Genomics, Stockholm University, Svante Arrheniusväg 20C, SE-10691 Stockholm, Sweden.|Department of Molecular Biology and Functional Genomics, Stockholm University, Svante Arrheniusväg 20C, SE-10691 Stockholm, Sweden.|Department of Molecular Biology and Functional Genomics, Stockholm University, Svante Arrheniusväg 20C, SE-10691 Stockholm, Sweden.|Department of Molecular Biology and Functional Genomics, Stockholm University, Svante Arrheniusväg 20C, SE-10691 Stockholm, Sweden.|Department of Molecular Biology and Functional Genomics, Stockholm University, Svante Arrheniusväg 20C, SE-10691 Stockholm, Sweden.|Department of Molecular Biology and Functional Genomics, Stockholm University, Svante Arrheniusväg 20C, SE-10691 Stockholm, Sweden.|Department of Molecular Biology and Functional Genomics, Stockholm University, Svante Arrheniusväg 20C, SE-10691 Stockholm, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 10, 'Adenosine|Alu Elements|Animals|Base Sequence|Computational Biology|Genome|Inosine|Mice|Molecular Sequence Data|Neurons|Phylogeny|RNA|RNA Editing|RNA-Binding Proteins|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-11-6', '20047656', 'Several bioinformatic approaches have previously been used to find novel sites of ADAR mediated A-to-I RNA editing in human. These studies have discovered thousands of genes that are hyper-edited in their non-coding intronic regions, especially in alu retrotransposable elements, but very few substrates that are site-selectively edited in coding regions. Known RNA edited substrates suggest, however, that site selective A-to-I editing is particularly important for normal brain development in mammals.'),
(93293, 2010, 'bmc', 'TreeGraph 2: combining and visualizing evidence from different phylogenetic analyses', 'Stöver, Ben C|Müller, Kai F', 'Institute for Evolution and Biodiversity, University of Münster, Hüfferstrasse 1, 48149 Münster, Germany. stoever@bioinfweb.info|Institute for Evolution and Biodiversity, University of Münster, Hüfferstrasse 1, 48149 Münster, Germany. stoever@bioinfweb.info', 'Germany|Germany', 20, 'Algorithms|Computational Biology|Databases, Genetic|Information Storage and Retrieval|Phylogeny|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-7', '20051126', 'Today it is common to apply multiple potentially conflicting data sources to a given phylogenetic problem. At the same time, several different inference techniques are routinely employed instead of relying on just one. In view of both trends it is becoming increasingly important to be able to efficiently compare different sets of statistical values supporting (or conflicting with) the nodes of a given tree topology, and merging this into a meaningful representation. A tree editor supporting this should also allow for flexible editing operations and be able to produce ready-to-publish figures.'),
(93294, 2010, 'bmc', 'A novel approach to simulate gene-environment interactions in complex diseases', 'Amato, Roberto|Pinelli, Michele|DAndrea, Daniel|Miele, Gennaro|Nicodemi, Mario|Raiconi, Giancarlo|Cocozza, Sergio', 'Gruppo Interdipartimentale di Bioinformatica e Biologia Computazionale, Università di Napoli Federico II-Università di Salerno, Italy. roamato@na.infn.it|Gruppo Interdipartimentale di Bioinformatica e Biologia Computazionale, Università di Napoli Federico II-Università di Salerno, Italy. roamato@na.infn.it|Gruppo Interdipartimentale di Bioinformatica e Biologia Computazionale, Università di Napoli Federico II-Università di Salerno, Italy. roamato@na.infn.it|Gruppo Interdipartimentale di Bioinformatica e Biologia Computazionale, Università di Napoli Federico II-Università di Salerno, Italy. roamato@na.infn.it|Gruppo Interdipartimentale di Bioinformatica e Biologia Computazionale, Università di Napoli Federico II-Università di Salerno, Italy. roamato@na.infn.it|Gruppo Interdipartimentale di Bioinformatica e Biologia Computazionale, Università di Napoli Federico II-Università di Salerno, Italy. roamato@na.infn.it|Gruppo Interdipartimentale di Bioinformatica e Biologia Computazionale, Università di Napoli Federico II-Università di Salerno, Italy. roamato@na.infn.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 55, 'Disease|Environment|Genetic Predisposition to Disease|Humans|Models, Statistical|Monte Carlo Method|Risk Factors', 'Journal Article', '10.1186/1471-2105-11-8', '20051127', 'Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones.'),
(93295, 2010, 'bmc', 'PyMix--the python mixture package--a tool for clustering of heterogeneous biological data', 'Georgi, Benjamin|Costa, Ivan Gesteira|Schliep, Alexander', 'Max Planck Institute for Molecular Genetics, Dept, of Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin. bgeorgi@mail.med.upenn.edu|Max Planck Institute for Molecular Genetics, Dept, of Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin. bgeorgi@mail.med.upenn.edu|Max Planck Institute for Molecular Genetics, Dept, of Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin. bgeorgi@mail.med.upenn.edu', '-|-|-', 14, 'Cluster Analysis|Computational Biology|Databases, Genetic|Gene Expression Profiling|Pattern Recognition, Automated|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-11-9', '20053276', 'Cluster analysis is an important technique for the exploratory analysis of biological data. Such data is often high-dimensional, inherently noisy and contains outliers. This makes clustering challenging. Mixtures are versatile and powerful statistical models which perform robustly for clustering in the presence of noise and have been successfully applied in a wide range of applications.'),
(93296, 2010, 'bmc', 'MSOAR 20: Incorporating tandem duplications into ortholog assignment based on genome rearrangement', 'Shi, Guanqun|Zhang, Liqing|Jiang, Tao', 'Department of Computer Science, University of California, Riverside, CA 92521, USA. gshi@cs.ucr.edu|Department of Computer Science, University of California, Riverside, CA 92521, USA. gshi@cs.ucr.edu|Department of Computer Science, University of California, Riverside, CA 92521, USA. gshi@cs.ucr.edu', 'United States|United States|United States', 18, 'Animals|Databases, Genetic|Databases, Protein|Gene Duplication|Gene Rearrangement|Genome|Genomics|Humans|Mice|Rats|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-11-10', '20053291', 'Ortholog assignment is a critical and fundamental problem in comparative genomics, since orthologs are considered to be functional counterparts in different species and can be used to infer molecular functions of one species from those of other species. MSOAR is a recently developed high-throughput system for assigning one-to-one orthologs between closely related species on a genome scale. It attempts to reconstruct the evolutionary history of input genomes in terms of genome rearrangement and gene duplication events. It assumes that a gene duplication event inserts a duplicated gene into the genome of interest at a random location (i.e., the random duplication model). However, in practice, biologists believe that genes are often duplicated by tandem duplications, where a duplicated gene is located next to the original copy (i.e., the tandem duplication model).'),
(93297, 2010, 'bmc', 'Statistical method on nonrandom clustering with application to somatic mutations in cancer', 'Ye, Jingjing|Pavlicek, Adam|Lunney, Elizabeth A|Rejto, Paul A|Teng, Chi-Hse', 'Global Pre-Clinical Statistics, Pfizer Global Research and Development, San Diego, CA 92121, USA. Jingjing.Ye@pfizer.com|Global Pre-Clinical Statistics, Pfizer Global Research and Development, San Diego, CA 92121, USA. Jingjing.Ye@pfizer.com|Global Pre-Clinical Statistics, Pfizer Global Research and Development, San Diego, CA 92121, USA. Jingjing.Ye@pfizer.com|Global Pre-Clinical Statistics, Pfizer Global Research and Development, San Diego, CA 92121, USA. Jingjing.Ye@pfizer.com|Global Pre-Clinical Statistics, Pfizer Global Research and Development, San Diego, CA 92121, USA. Jingjing.Ye@pfizer.com', 'United States|United States|United States|United States|United States', 0, 'Cluster Analysis|Genes, ras|Genome, Human|Humans|Models, Statistical|Mutation|Neoplasms|Proto-Oncogene Proteins B-raf|beta Catenin', 'Journal Article', '10.1186/1471-2105-11-11', '20053295', 'Human cancer is caused by the accumulation of tumor-specific mutations in oncogenes and tumor suppressors that confer a selective growth advantage to cells. As a consequence of genomic instability and high levels of proliferation, many passenger mutations that do not contribute to the cancer phenotype arise alongside mutations that drive oncogenesis. While several approaches have been developed to separate driver mutations from passengers, few approaches can specifically identify activating driver mutations in oncogenes, which are more amenable for pharmacological intervention.'),
(93298, 2010, 'bmc', 'SKPDB: a structural database of shikimate pathway enzymes', 'Arcuri, Helen A|Zafalon, Geraldo Fd|Marucci, Evandro A|Bonalumi, Carlos E|da Silveira, Nelson Jf|Machado, José M|de Azevedo, Walter F|Palma, Mário S', 'CEIS/Departamento de Biologia, Instituto de Biociências, UNESP, Rio Claro, São Paulo, Brasil.|CEIS/Departamento de Biologia, Instituto de Biociências, UNESP, Rio Claro, São Paulo, Brasil.|CEIS/Departamento de Biologia, Instituto de Biociências, UNESP, Rio Claro, São Paulo, Brasil.|CEIS/Departamento de Biologia, Instituto de Biociências, UNESP, Rio Claro, São Paulo, Brasil.|CEIS/Departamento de Biologia, Instituto de Biociências, UNESP, Rio Claro, São Paulo, Brasil.|CEIS/Departamento de Biologia, Instituto de Biociências, UNESP, Rio Claro, São Paulo, Brasil.|CEIS/Departamento de Biologia, Instituto de Biociências, UNESP, Rio Claro, São Paulo, Brasil.|CEIS/Departamento de Biologia, Instituto de Biociências, UNESP, Rio Claro, São Paulo, Brasil.', '-|-|-|-|-|-|-|-', 6, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Enzymes|Protein Conformation|Sequence Analysis, Protein|Shikimic Acid|Software', 'Journal Article', '10.1186/1471-2105-11-12', '20055992', 'The functional and structural characterisation of enzymes that belong to microbial metabolic pathways is very important for structure-based drug design. The main interest in studying shikimate pathway enzymes involves the fact that they are essential for bacteria but do not occur in humans, making them selective targets for design of drugs that do not directly impact humans.'),
(93299, 2010, 'bmc', 'Evaluation of fecal mRNA reproducibility via a marginal transformed mixture modeling approach', 'George, Nysia I|Lupton, Joanne R|Turner, Nancy D|Chapkin, Robert S|Davidson, Laurie A|Wang, Naisyin', 'Department of Statistics, University of Michigan, Ann Arbor, MI 48109-1107, USA.|Department of Statistics, University of Michigan, Ann Arbor, MI 48109-1107, USA.|Department of Statistics, University of Michigan, Ann Arbor, MI 48109-1107, USA.|Department of Statistics, University of Michigan, Ann Arbor, MI 48109-1107, USA.|Department of Statistics, University of Michigan, Ann Arbor, MI 48109-1107, USA.|Department of Statistics, University of Michigan, Ann Arbor, MI 48109-1107, USA.', 'United States|United States|United States|United States|United States|United States', 9, 'Algorithms|Databases, Genetic|Feces|Gene Expression Profiling|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|RNA, Messenger|Reproducibility of Results', 'Evaluation Studies', '10.1186/1471-2105-11-13', '20055994', 'Developing and evaluating new technology that enables researchers to recover gene-expression levels of colonic cells from fecal samples could be key to a non-invasive screening tool for early detection of colon cancer. The current study, to the best of our knowledge, is the first to investigate and report the reproducibility of fecal microarray data. Using the intraclass correlation coefficient (ICC) as a measure of reproducibility and the preliminary analysis of fecal and mucosal data, we assessed the reliability of mixture density estimation and the reproducibility of fecal microarray data. Using Monte Carlo-based methods, we explored whether ICC values should be modeled as a beta-mixture or transformed first and fitted with a normal-mixture. We used outcomes from bootstrapped goodness-of-fit tests to determine which approach is less sensitive toward potential violation of distributional assumptions.'),
(93300, 2010, 'bmc', 'Estimation and efficient computation of the true probability of recurrence of short linear protein sequence motifs in unrelated proteins', 'Davey, Norman E|Edwards, Richard J|Shields, Denis C', 'UCD Complex and Adaptive Systems Laboratory, University College Dublin, Dublin, Ireland. davey@embl.de|UCD Complex and Adaptive Systems Laboratory, University College Dublin, Dublin, Ireland. davey@embl.de|UCD Complex and Adaptive Systems Laboratory, University College Dublin, Dublin, Ireland. davey@embl.de', 'Ireland|Ireland|Ireland', 0, 'Amino Acid Motifs|Computational Biology|Databases, Protein|Probability|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-14', '20055997', 'Large datasets of protein interactions provide a rich resource for the discovery of Short Linear Motifs (SLiMs) that recur in unrelated proteins. However, existing methods for estimating the probability of motif recurrence may be biased by the size and composition of the search dataset, such that p-value estimates from different datasets, or from motifs containing different numbers of non-wildcard positions, are not strictly comparable. Here, we develop more exact methods and explore the potential biases of computationally efficient approximations.'),
(93301, 2010, 'bmc', 'Machine learning methods for metabolic pathway prediction', 'Dale, Joseph M|Popescu, Liviu|Karp, Peter D', 'Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025, USA.|Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025, USA.|Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025, USA.', 'United States|United States|United States', 4, 'Artificial Intelligence|Computational Biology|Databases, Factual|Genome|Metabolic Networks and Pathways|Software', 'Journal Article', '10.1186/1471-2105-11-15', '20064214', 'A key challenge in systems biology is the reconstruction of an organisms metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism.'),
(93302, 2010, 'bmc', 'A pairwise residue contact area-based mean force potential for discrimination of native protein structure', 'Arab, Shahriar|Sadeghi, Mehdi|Eslahchi, Changiz|Pezeshk, Hamid|Sheari, Armita', 'Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.|Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.|Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.|Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.|Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.', '-|-|-|-|-', 0, 'Binding Sites|Computational Biology|Databases, Protein|Protein Conformation|Protein Folding|Proteins', 'Journal Article', '10.1186/1471-2105-11-16', '20064218', 'Considering energy function to detect a correct protein fold from incorrect ones is very important for protein structure prediction and protein folding. Knowledge-based mean force potentials are certainly the most popular type of interaction function for protein threading. They are derived from statistical analyses of interacting groups in experimentally determined protein structures. These potentials are developed at the atom or the amino acid level. Based on orientation dependent contact area, a new type of knowledge-based mean force potential has been developed.'),
(93303, 2010, 'bmc', 'A benchmark for statistical microarray data analysis that preserves actual biological and technical variance', 'De Hertogh, Benoît|De Meulder, Bertrand|Berger, Fabrice|Pierre, Michael|Bareke, Eric|Gaigneaux, Anthoula|Depiereux, Eric', 'Unité de Recherche en Biologie Moléculaire, Facultés Universitaires Notre-Dame de la Paix (F.U.N.D.P.), Rue de Bruxelles 61, B-5000 Namur, Belgium.|Unité de Recherche en Biologie Moléculaire, Facultés Universitaires Notre-Dame de la Paix (F.U.N.D.P.), Rue de Bruxelles 61, B-5000 Namur, Belgium.|Unité de Recherche en Biologie Moléculaire, Facultés Universitaires Notre-Dame de la Paix (F.U.N.D.P.), Rue de Bruxelles 61, B-5000 Namur, Belgium.|Unité de Recherche en Biologie Moléculaire, Facultés Universitaires Notre-Dame de la Paix (F.U.N.D.P.), Rue de Bruxelles 61, B-5000 Namur, Belgium.|Unité de Recherche en Biologie Moléculaire, Facultés Universitaires Notre-Dame de la Paix (F.U.N.D.P.), Rue de Bruxelles 61, B-5000 Namur, Belgium.|Unité de Recherche en Biologie Moléculaire, Facultés Universitaires Notre-Dame de la Paix (F.U.N.D.P.), Rue de Bruxelles 61, B-5000 Namur, Belgium.|Unité de Recherche en Biologie Moléculaire, Facultés Universitaires Notre-Dame de la Paix (F.U.N.D.P.), Rue de Bruxelles 61, B-5000 Namur, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Computational Biology|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-17', '20064233', 'Recent reanalysis of spike-in datasets underscored the need for new and more accurate benchmark datasets for statistical microarray analysis. We present here a fresh method using biologically-relevant data to evaluate the performance of statistical methods.'),
(93304, 2010, 'bmc', 'Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests', 'Edwards, David|de Abreu, Gabriel C G|Labouriau, Rodrigo', 'Institute of Genetics and Biotechnology, Faculty of Agricultural Sciences, Aarhus University, Aarhus, Denmark. David.Edwards@agrsci.dk|Institute of Genetics and Biotechnology, Faculty of Agricultural Sciences, Aarhus University, Aarhus, Denmark. David.Edwards@agrsci.dk|Institute of Genetics and Biotechnology, Faculty of Agricultural Sciences, Aarhus University, Aarhus, Denmark. David.Edwards@agrsci.dk', 'Denmark|Denmark|Denmark', 2, 'Algorithms|Breast Neoplasms|Female|Gene Expression Profiling|Gene Regulatory Networks|Humans|Likelihood Functions|Models, Statistical|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-11-18', '20064242', 'Chow and Liu showed that the maximum likelihood tree for multivariate discrete distributions may be found using a maximum weight spanning tree algorithm, for example Kruskals algorithm. The efficiency of the algorithm makes it tractable for high-dimensional problems.'),
(93305, 2010, 'bmc', 'Modelling p-value distributions to improve theme-driven survival analysis of cancer transcriptome datasets', 'Czwan, Esteban|Brors, Benedikt|Kipling, David', 'School of Medicine, Cardiff University, Heath Park, Cardiff CF144XN, UK.|School of Medicine, Cardiff University, Heath Park, Cardiff CF144XN, UK.|School of Medicine, Cardiff University, Heath Park, Cardiff CF144XN, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Biomarkers, Tumor|Computational Biology|Gene Expression Profiling|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Statistical Distributions|Survival Analysis', 'Journal Article', '10.1186/1471-2105-11-19', '20064243', 'Theme-driven cancer survival studies address whether the expression signature of genes related to a biological process can predict patient survival time. Although this should ideally be achieved by testing two separate null hypotheses, current methods treat both hypotheses as one. The first test should assess whether a geneset, independent of its composition, is associated with prognosis (frequently done with a survival test). The second test then verifies whether the theme of the geneset is relevant (usually done with an empirical test that compares the geneset of interest with random genesets). Current methods do not test this second null hypothesis because it has been assumed that the distribution of p-values for random genesets (when tested against the first null hypothesis) is uniform. Here we demonstrate that such an assumption is generally incorrect and consequently, such methods may erroneously associate the biology of a particular geneset with cancer prognosis.'),
(93306, 2010, 'bmc', 'Fast multi-core based multimodal registration of 2D cross-sections and 3D datasets', 'Scharfe, Michael|Pielot, Rainer|Schreiber, Falk', 'Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.|Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.|Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.', 'Germany|Germany|Germany', 4, 'Algorithms|Image Interpretation, Computer-Assisted|Image Processing, Computer-Assisted|Imaging, Three-Dimensional', 'Journal Article', '10.1186/1471-2105-11-20', '20064262', 'Solving bioinformatics tasks often requires extensive computational power. Recent trends in processor architecture combine multiple cores into a single chip to improve overall performance. The Cell Broadband Engine (CBE), a heterogeneous multi-core processor, provides power-efficient and cost-effective high-performance computing. One application area is image analysis and visualisation, in particular registration of 2D cross-sections into 3D image datasets. Such techniques can be used to put different image modalities into spatial correspondence, for example, 2D images of histological cuts into morphological 3D frameworks.'),
(93307, 2010, 'bmc', 'Assembly complexity of prokaryotic genomes using short reads', 'Kingsford, Carl|Schatz, Michael C|Pop, Mihai', 'Department of Computer Science, Institute for Advanced Computer Studies, University of Maryland, College Park, MD, USA. carlk@cs.umd.edu|Department of Computer Science, Institute for Advanced Computer Studies, University of Maryland, College Park, MD, USA. carlk@cs.umd.edu|Department of Computer Science, Institute for Advanced Computer Studies, University of Maryland, College Park, MD, USA. carlk@cs.umd.edu', 'United States|United States|United States', 25, 'Computational Biology|DNA Transposable Elements|Databases, Genetic|Genome|Prokaryotic Cells|Prophages|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-21', '20064276', 'De Bruijn graphs are a theoretical framework underlying several modern genome assembly programs, especially those that deal with very short reads. We describe an application of de Bruijn graphs to analyze the global repeat structure of prokaryotic genomes.'),
(93308, 2010, 'bmc', 'A method of predicting changes in human gene splicing induced by genetic variants in context of cis-acting elements', 'Churbanov, Alexander|Vorechovský, Igor|Hicks, Chindo', 'New Mexico State University, Biology Dept., MSC 3AF, PO Box 30001, Las Cruces, NM 88003, USA. atchourb@nmsu.edu|New Mexico State University, Biology Dept., MSC 3AF, PO Box 30001, Las Cruces, NM 88003, USA. atchourb@nmsu.edu|New Mexico State University, Biology Dept., MSC 3AF, PO Box 30001, Las Cruces, NM 88003, USA. atchourb@nmsu.edu', 'Mexico|Mexico|Mexico', 0, 'Breast Neoplasms|Databases, Genetic|Gene Expression Profiling|Genetic Predisposition to Disease|Genetic Variation|Genome, Human|Humans|Protein Isoforms|RNA Splicing', 'Journal Article', '10.1186/1471-2105-11-22', '20067640', 'Polymorphic variants and mutations disrupting canonical splicing isoforms are among the leading causes of human hereditary disorders. While there is a substantial evidence of aberrant splicing causing Mendelian diseases, the implication of such events in multi-genic disorders is yet to be well understood. We have developed a new tool (SpliceScan II) for predicting the effects of genetic variants on splicing and cis-regulatory elements. The novel Bayesian non-canonical 5GC splice site (SS) sensor used in our tool allows inference on non-canonical exons.'),
(93309, 2010, 'bmc', 'JunctionViewer: customizable annotation software for repeat-rich genomic regions', 'Wolfgruber, Thomas K|Presting, Gernot G', 'Department of Molecular Biosciences and Bioengineering, University of Hawaii at Mânoa, Honolulu, HI 96822, USA.|Department of Molecular Biosciences and Bioengineering, University of Hawaii at Mânoa, Honolulu, HI 96822, USA.', 'United States|United States', 29, 'Centromere|Chromosomes, Artificial, Bacterial|DNA, Plant|Genome, Plant|Genomics|Repetitive Sequences, Nucleic Acid|Software|Zea mays', 'Journal Article', '10.1186/1471-2105-11-23', '20067643', 'Repeat-rich regions such as centromeres receive less attention than their gene-rich euchromatic counterparts because the former are difficult to assemble and analyze. Our objectives were to 1) map all ten centromeres onto the maize genetic map and 2) characterize the sequence features of maize centromeres, each of which spans several megabases of highly repetitive DNA. Repetitive sequences can be mapped using special molecular markers that are based on PCR with primers designed from two unique repeat junctions. Efficient screening of large amounts of maize genome sequence data for repeat junctions, as well as key centromere sequence features required the development of specific annotation software.'),
(93310, 2010, 'bmc', 'ETE: a python Environment for Tree Exploration', 'Huerta-Cepas, Jaime|Dopazo, Joaquín|Gabaldón, Toni', 'Comparative Genomics group, Bioinformatics and Genomics Programme, Centre for Genomic Regulation, Barcelona, Spain. jhuerta@crg.es|Comparative Genomics group, Bioinformatics and Genomics Programme, Centre for Genomic Regulation, Barcelona, Spain. jhuerta@crg.es|Comparative Genomics group, Bioinformatics and Genomics Programme, Centre for Genomic Regulation, Barcelona, Spain. jhuerta@crg.es', 'Spain|Spain|Spain', 0, 'Computational Biology|Databases, Genetic|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-11-24', '20070885', 'Many bioinformatics analyses, ranging from gene clustering to phylogenetics, produce hierarchical trees as their main result. These are used to represent the relationships among different biological entities, thus facilitating their analysis and interpretation. A number of standalone programs are available that focus on tree visualization or that perform specific analyses on them. However, such applications are rarely suitable for large-scale surveys, in which a higher level of automation is required. Currently, many genome-wide analyses rely on tree-like data representation and hence there is a growing need for scalable tools to handle tree structures at large scale.'),
(93311, 2010, 'bmc', 'Calibur: a tool for clustering large numbers of protein decoys', 'Li, Shuai Cheng|Ng, Yen Kaow', 'David R, Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada. scli@bioinformatics.uwaterloo.ca|David R, Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada. scli@bioinformatics.uwaterloo.ca', 'Canada|Canada', 0, 'Cluster Analysis|Computational Biology|Databases, Protein|Protein Conformation|Protein Folding|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-11-25', '20070892', 'Ab initio protein structure prediction methods generate numerous structural candidates, which are referred to as decoys. The decoy with the most number of neighbors of up to a threshold distance is typically identified as the most representative decoy. However, the clustering of decoys needed for this criterion involves computations with runtimes that are at best quadratic in the number of decoys. As a result currently there is no tool that is designed to exactly cluster very large numbers of decoys, thus creating a bottleneck in the analysis.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93312, 2010, 'bmc', 'Detecting disease associated modules and prioritizing active genes based on high throughput data', 'Qiu, Yu-Qing|Zhang, Shihua|Zhang, Xiang-Sun|Chen, Luonan', 'Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, PR China.|Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, PR China.|Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, PR China.|Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, PR China.', 'China|China|China|China', 15, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Phenotype', 'Journal Article', '10.1186/1471-2105-11-26', '20070902', 'The accumulation of high-throughput data greatly promotes computational investigation of gene function in the context of complex biological systems. However, a biological function is not simply controlled by an individual gene since genes function in a cooperative manner to achieve biological processes. In the study of human diseases, rather than to discover disease related genes, identifying disease associated pathways and modules becomes an essential problem in the field of systems biology.'),
(93313, 2010, 'bmc', 'Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach', 'Repsilber, Dirk|Kern, Sabine|Telaar, Anna|Walzl, Gerhard|Black, Gillian F|Selbig, Joachim|Parida, Shreemanta K|Kaufmann, Stefan H E|Jacobsen, Marc', 'Department of Genetics and Biometry, Research Institute for Biology of Farm Animals, Wilhelm-Stahl Allee 2, D 18196 Dummerstorf, Germany. repsilber@fbn-dummerstorf.de|Department of Genetics and Biometry, Research Institute for Biology of Farm Animals, Wilhelm-Stahl Allee 2, D 18196 Dummerstorf, Germany. repsilber@fbn-dummerstorf.de|Department of Genetics and Biometry, Research Institute for Biology of Farm Animals, Wilhelm-Stahl Allee 2, D 18196 Dummerstorf, Germany. repsilber@fbn-dummerstorf.de|Department of Genetics and Biometry, Research Institute for Biology of Farm Animals, Wilhelm-Stahl Allee 2, D 18196 Dummerstorf, Germany. repsilber@fbn-dummerstorf.de|Department of Genetics and Biometry, Research Institute for Biology of Farm Animals, Wilhelm-Stahl Allee 2, D 18196 Dummerstorf, Germany. repsilber@fbn-dummerstorf.de|Department of Genetics and Biometry, Research Institute for Biology of Farm Animals, Wilhelm-Stahl Allee 2, D 18196 Dummerstorf, Germany. repsilber@fbn-dummerstorf.de|Department of Genetics and Biometry, Research Institute for Biology of Farm Animals, Wilhelm-Stahl Allee 2, D 18196 Dummerstorf, Germany. repsilber@fbn-dummerstorf.de|Department of Genetics and Biometry, Research Institute for Biology of Farm Animals, Wilhelm-Stahl Allee 2, D 18196 Dummerstorf, Germany. repsilber@fbn-dummerstorf.de|Department of Genetics and Biometry, Research Institute for Biology of Farm Animals, Wilhelm-Stahl Allee 2, D 18196 Dummerstorf, Germany. repsilber@fbn-dummerstorf.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Biomarkers|Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-27', '20070912', 'For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues.'),
(93314, 2010, 'bmc', 'Gene prioritization and clustering by multi-view text mining', 'Yu, Shi|Tranchevent, Leon-Charles|De Moor, Bart|Moreau, Yves', 'Bioinformatics Group, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Heverlee B3001, Belgium. shi.yu@esat.kuleuven.be|Bioinformatics Group, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Heverlee B3001, Belgium. shi.yu@esat.kuleuven.be|Bioinformatics Group, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Heverlee B3001, Belgium. shi.yu@esat.kuleuven.be|Bioinformatics Group, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Heverlee B3001, Belgium. shi.yu@esat.kuleuven.be', 'Belgium|Belgium|Belgium|Belgium', 0, 'Cluster Analysis|Computational Biology|Data Mining|Databases, Factual|Disease|Genes|Information Storage and Retrieval|MEDLINE|United States', 'Journal Article', '10.1186/1471-2105-11-28', '20074336', 'Text mining has become a useful tool for biologists trying to understand the genetics of diseases. In particular, it can help identify the most interesting candidate genes for a disease for further experimental analysis. Many text mining approaches have been introduced, but the effect of disease-gene identification varies in different text mining models. Thus, the idea of incorporating more text mining models may be beneficial to obtain more refined and accurate knowledge. However, how to effectively combine these models still remains a challenging question in machine learning. In particular, it is a non-trivial issue to guarantee that the integrated model performs better than the best individual model.'),
(93315, 2010, 'bmc', 'miRMaid: a unified programming interface for microRNA data resources', 'Jacobsen, Anders|Krogh, Anders|Kauppinen, Sakari|Lindow, Morten', 'The Bioinformatics Centre, Department of biology, University of Copenhagen, 2200 Copenhagen N, Denmark. andersbj@binf.ku.dk|The Bioinformatics Centre, Department of biology, University of Copenhagen, 2200 Copenhagen N, Denmark. andersbj@binf.ku.dk|The Bioinformatics Centre, Department of biology, University of Copenhagen, 2200 Copenhagen N, Denmark. andersbj@binf.ku.dk|The Bioinformatics Centre, Department of biology, University of Copenhagen, 2200 Copenhagen N, Denmark. andersbj@binf.ku.dk', 'Denmark|Denmark|Denmark|Denmark', 7, 'Computational Biology|Databases, Genetic|MicroRNAs|Programming Languages|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-11-29', '20074352', 'MicroRNAs (miRNAs) are endogenous small RNAs that play a key role in post-transcriptional regulation of gene expression in animals and plants. The number of known miRNAs has increased rapidly over the years. The current release (version 14.0) of miRBase, the central online repository for miRNA annotation, comprises over 10.000 miRNA precursors from 115 different species. Furthermore, a large number of decentralized online resources are now available, each contributing with important miRNA annotation and information.'),
(93316, 2010, 'bmc', 'Enhanced CellClassifier: a multi-class classification tool for microscopy images', 'Misselwitz, Benjamin|Strittmatter, Gerhard|Periaswamy, Balamurugan|Schlumberger, Markus C|Rout, Samuel|Horvath, Peter|Kozak, Karol|Hardt, Wolf-Dietrich', 'Institute of Microbiology, ETH Zurich, Zürich, Switzerland. misselwitz@micro.biol.ethz.ch|Institute of Microbiology, ETH Zurich, Zürich, Switzerland. misselwitz@micro.biol.ethz.ch|Institute of Microbiology, ETH Zurich, Zürich, Switzerland. misselwitz@micro.biol.ethz.ch|Institute of Microbiology, ETH Zurich, Zürich, Switzerland. misselwitz@micro.biol.ethz.ch|Institute of Microbiology, ETH Zurich, Zürich, Switzerland. misselwitz@micro.biol.ethz.ch|Institute of Microbiology, ETH Zurich, Zürich, Switzerland. misselwitz@micro.biol.ethz.ch|Institute of Microbiology, ETH Zurich, Zürich, Switzerland. misselwitz@micro.biol.ethz.ch|Institute of Microbiology, ETH Zurich, Zürich, Switzerland. misselwitz@micro.biol.ethz.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 67, 'Databases, Factual|Image Processing, Computer-Assisted|Microscopy|Pattern Recognition, Automated|Software', 'Journal Article', '10.1186/1471-2105-11-30', '20074370', 'Light microscopy is of central importance in cell biology. The recent introduction of automated high content screening has expanded this technology towards automation of experiments and performing large scale perturbation assays. Nevertheless, evaluation of microscopy data continues to be a bottleneck in many projects. Currently, among open source software, CellProfiler and its extension Analyst are widely used in automated image processing. Even though revolutionizing image analysis in current biology, some routine and many advanced tasks are either not supported or require programming skills of the researcher. This represents a significant obstacle in many biology laboratories.'),
(93317, 2010, 'bmc', 'LucidDraw: efficiently visualizing complex biochemical networks within MATLAB', 'He, Sheng|Mei, Juan|Shi, Guiyang|Wang, Zhengxiang|Li, Weijiang', 'The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.|The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.|The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.|The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.|The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.', 'China|China|China|China|China', 0, 'Computational Biology|Databases, Factual|Models, Biological|Proteome|Software|Systems Biology', 'Journal Article', '10.1186/1471-2105-11-31', '20074382', 'Biochemical networks play an essential role in systems biology. Rapidly growing network data and versatile research activities call for convenient visualization tools to aid intuitively perceiving abstract structures of networks and gaining insights into the functional implications of networks. There are various kinds of network visualization software, but they are usually not adequate for visual analysis of complex biological networks mainly because of the two reasons: 1) most existing drawing methods suitable for biochemical networks have high computation loads and can hardly achieve near real-time visualization, 2) available network visualization tools are designed for working in certain network modeling platforms, so they are not convenient for general analyses due to lack of broader range of readily accessible numerical utilities.'),
(93318, 2010, 'bmc', 'The identification of informative genes from multiple datasets with increasing complexity', 'Anvar, S Yahya|t Hoen, Peter A C|Tucker, Allan', 'Center for Intelligent Data Analysis, School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK. s.y.anvar@lumc.nl|Center for Intelligent Data Analysis, School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK. s.y.anvar@lumc.nl|Center for Intelligent Data Analysis, School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK. s.y.anvar@lumc.nl', 'United Kingdom|United Kingdom|United Kingdom', 4, 'Cell Differentiation|Computational Biology|Databases, Genetic|Gene Expression|Gene Expression Profiling|Muscle Development|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-32', '20078860', 'In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness, (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets, (3) comparing the different gene selections and the influence of increasing the model complexity, (4) functional analysis of the informative genes.'),
(93319, 2010, 'bmc', 'Improving de novo sequence assembly using machine learning and comparative genomics for overlap correction', 'Palmer, Lance E|Dejori, Mathaeus|Bolanos, Randall|Fasulo, Daniel', 'Siemens Corporate Research, 755 College Road East, Princeton, NJ, USA. lance.palmer@siemens.com|Siemens Corporate Research, 755 College Road East, Princeton, NJ, USA. lance.palmer@siemens.com|Siemens Corporate Research, 755 College Road East, Princeton, NJ, USA. lance.palmer@siemens.com|Siemens Corporate Research, 755 College Road East, Princeton, NJ, USA. lance.palmer@siemens.com', 'United States|United States|United States|United States', 11, 'Artificial Intelligence|Base Sequence|Databases, Nucleic Acid|Genome, Bacterial|Genome, Fungal|Genomics|Sequence Analysis, DNA', 'Comparative Study', '10.1186/1471-2105-11-33', '20078885', 'With the rapid expansion of DNA sequencing databases, it is now feasible to identify relevant information from prior sequencing projects and completed genomes and apply it to de novo sequencing of new organisms. As an example, this paper demonstrates how such extra information can be used to improve de novo assemblies by augmenting the overlapping step. Finding all pairs of overlapping reads is a key task in many genome assemblers, and to this end, highly efficient algorithms have been developed to find alignments in large collections of sequences. It is well known that due to repeated sequences, many aligned pairs of reads nevertheless do not overlap. But no overlapping algorithm to date takes a rigorous approach to separating aligned but non-overlapping read pairs from true overlaps.'),
(93320, 2010, 'bmc', 'OLS dialog: an open-source front end to the ontology lookup service', 'Barsnes, Harald|Côté, Richard G|Eidhammer, Ingvar|Martens, Lennart', 'Department of Informatics, University of Bergen, Bergen, Norway. harald.barsnes@ii.uib.no|Department of Informatics, University of Bergen, Bergen, Norway. harald.barsnes@ii.uib.no|Department of Informatics, University of Bergen, Bergen, Norway. harald.barsnes@ii.uib.no|Department of Informatics, University of Bergen, Bergen, Norway. harald.barsnes@ii.uib.no', 'Norway|Norway|Norway|Norway', 10, 'Computational Biology|Databases, Factual|Information Storage and Retrieval|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-11-34', '20078892', 'With the growing amount of biomedical data available in public databases it has become increasingly important to annotate data in a consistent way in order to allow easy access to this rich source of information. Annotating the data using controlled vocabulary terms and ontologies makes it much easier to compare and analyze data from different sources. However, finding the correct controlled vocabulary terms can sometimes be a difficult task for the end user annotating these data.'),
(93321, 2010, 'bmc', 'Hybridization thermodynamics of NimbleGen microarrays', 'Mueckstein, Ulrike|Leparc, Germán G|Posekany, Alexandra|Hofacker, Ivo|Kreil, David P', 'WWTF Chair of Bioinformatics, Boku University Vienna, Muthgasse 18, 1190 Vienna, Austria. Ulrike.Mueckstein@boku.ac.at|WWTF Chair of Bioinformatics, Boku University Vienna, Muthgasse 18, 1190 Vienna, Austria. Ulrike.Mueckstein@boku.ac.at|WWTF Chair of Bioinformatics, Boku University Vienna, Muthgasse 18, 1190 Vienna, Austria. Ulrike.Mueckstein@boku.ac.at|WWTF Chair of Bioinformatics, Boku University Vienna, Muthgasse 18, 1190 Vienna, Austria. Ulrike.Mueckstein@boku.ac.at|WWTF Chair of Bioinformatics, Boku University Vienna, Muthgasse 18, 1190 Vienna, Austria. Ulrike.Mueckstein@boku.ac.at', 'Austria|Austria|Austria|Austria|Austria', 17, 'Binding Sites|Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Thermodynamics', 'Journal Article', '10.1186/1471-2105-11-35', '20085625', 'While microarrays are the predominant method for gene expression profiling, probe signal variation is still an area of active research. Probe signal is sequence dependent and affected by probe-target binding strength and the competing formation of probe-probe dimers and secondary structures in probes and targets.'),
(93322, 2010, 'bmc', 'Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53', 'Wang, Junbai|Tian, Tianhai', 'Division of Pathology, The Norwegian Radium Hospital, Rikshospitalet University Hospital, Montebello 0310 Oslo, Norway.|Division of Pathology, The Norwegian Radium Hospital, Rikshospitalet University Hospital, Montebello 0310 Oslo, Norway.', 'Norway|Norway', 20, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Tumor Suppressor Protein p53', 'Journal Article', '10.1186/1471-2105-11-36', '20085646', 'The availability of various omics datasets creates a prospect of performing the study of genome-wide genetic regulatory networks. However, one of the major challenges of using mathematical models to infer genetic regulation from microarray datasets is the lack of information for protein concentrations and activities. Most of the previous researches were based on an assumption that the mRNA levels of a gene are consistent with its protein activities, though it is not always the case. Therefore, a more sophisticated modelling framework together with the corresponding inference methods is needed to accurately estimate genetic regulation from omics datasets.'),
(93323, 2010, 'bmc', 'Predicting Bevirimat resistance of HIV-1 from genotype', 'Heider, Dominik|Verheyen, Jens|Hoffmann, Daniel', 'Department of Bioinformatics, Center of Medical Biotechnology, University of Duisburg-Essen, Universitaetsstr. 2, 45117 Essen, Germany. dominik.heider@uni-due.de|Department of Bioinformatics, Center of Medical Biotechnology, University of Duisburg-Essen, Universitaetsstr. 2, 45117 Essen, Germany. dominik.heider@uni-due.de|Department of Bioinformatics, Center of Medical Biotechnology, University of Duisburg-Essen, Universitaetsstr. 2, 45117 Essen, Germany. dominik.heider@uni-due.de', 'Germany|Germany|Germany', 28, 'Anti-HIV Agents|Computational Biology|Drug Resistance, Viral|Genotype|HIV-1|Mutation|Neural Networks (Computer)|Succinates|Triterpenes|Virus Replication', 'Journal Article', '10.1186/1471-2105-11-37', '20089140', 'Maturation inhibitors are a new class of antiretroviral drugs. Bevirimat (BVM) was the first substance in this class of inhibitors entering clinical trials. While the inhibitory function of BVM is well established, the molecular mechanisms of action and resistance are not well understood. It is known that mutations in the regions CS p24/p2 and p2 can cause phenotypic resistance to BVM. We have investigated a set of p24/p2 sequences of HIV-1 of known phenotypic resistance to BVM to test whether BVM resistance can be predicted from sequence, and to identify possible molecular mechanisms of BVM resistance in HIV-1.'),
(93324, 2010, 'bmc', 'SeqTrim: a high-throughput pipeline for pre-processing any type of sequence read', 'Falgueras, Juan|Lara, Antonio J|Fernández-Pozo, Noé|Cantón, Francisco R|Pérez-Trabado, Guillermo|Claros, M Gonzalo', 'Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Málaga, Spain.|Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Málaga, Spain.|Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Málaga, Spain.|Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Málaga, Spain.|Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Málaga, Spain.|Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Málaga, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain', 123, 'Base Sequence|Computational Biology|Databases, Genetic|Expressed Sequence Tags|Gene Library|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-11-38', '20089148', 'High-throughput automated sequencing has enabled an exponential growth rate of sequencing data. This requires increasing sequence quality and reliability in order to avoid database contamination with artefactual sequences. The arrival of pyrosequencing enhances this problem and necessitates customisable pre-processing algorithms.'),
(93325, 2010, 'bmc', 'An automatic method for identifying surface proteins in bacteria: SLEP', 'Giombini, Emanuela|Orsini, Massimiliano|Carrabino, Danilo|Tramontano, Anna', 'Department of Biochemical Sciences A, Rossi Fanelli, Sapienza University, 00185 Rome, Italy.|Department of Biochemical Sciences A, Rossi Fanelli, Sapienza University, 00185 Rome, Italy.|Department of Biochemical Sciences A, Rossi Fanelli, Sapienza University, 00185 Rome, Italy.|Department of Biochemical Sciences A, Rossi Fanelli, Sapienza University, 00185 Rome, Italy.', 'Italy|Italy|Italy|Italy', 15, 'Bacteria|Bacterial Proteins|Genome, Bacterial|Membrane Proteins|Proteomics', 'Journal Article', '10.1186/1471-2105-11-39', '20089159', 'Bacterial infections represent a global health challenge. The identification of novel antibacterial targets for both therapy and vaccination is needed on a constant basis because resistance continues to spread worldwide at an alarming rate. Even infections that were once easy to treat are becoming difficult or, in some cases, impossible to cure. Ideal targets for both therapy and vaccination are bacterial proteins exposed on the surface of the organism, which are often involved in host-pathogen interaction. Their identification can greatly benefit from technologies such as bioinformatics, proteomics and DNA microarrays.'),
(93326, 2010, 'bmc', 'Automatic symptom name normalization in clinical records of traditional Chinese medicine', 'Wang, Yaqiang|Yu, Zhonghua|Jiang, Yongguang|Xu, Kaikuo|Chen, Xia', 'Department of Computer Science, Sichuan University, Chengdu, Sichuan, PR China.|Department of Computer Science, Sichuan University, Chengdu, Sichuan, PR China.|Department of Computer Science, Sichuan University, Chengdu, Sichuan, PR China.|Department of Computer Science, Sichuan University, Chengdu, Sichuan, PR China.|Department of Computer Science, Sichuan University, Chengdu, Sichuan, PR China.', 'China|China|China|China|China', 13, 'Algorithms|China|Disease|Medical Records Systems, Computerized|Medicine, Chinese Traditional|Natural Language Processing|Pattern Recognition, Automated|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-11-40', '20089162', 'In recent years, Data Mining technology has been applied more than ever before in the field of traditional Chinese medicine (TCM) to discover regularities from the experience accumulated in the past thousands of years in China. Electronic medical records (or clinical records) of TCM, containing larger amount of information than well-structured data of prescriptions extracted manually from TCM literature such as information related to medical treatment process, could be an important source for discovering valuable regularities of TCM. However, they are collected by TCM doctors on a day to day basis without the support of authoritative editorial board, and owing to different experience and background of TCM doctors, the same concept might be described in several different terms. Therefore, clinical records of TCM cannot be used directly to Data Mining and Knowledge Discovery. This paper focuses its attention on the phenomena of one symptom with different names and investigates a series of metrics for automatically normalizing symptom names in clinical records of TCM.'),
(93327, 2010, 'bmc', 'Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model', 'Bordner, Andrew J|Mittelmann, Hans D', 'Mayo Clinic, Scottsdale, AZ 85259, USA. bordner.andrew@mayo.edu|Mayo Clinic, Scottsdale, AZ 85259, USA. bordner.andrew@mayo.edu', 'United States|United States', 23, 'Binding Sites|Computational Biology|Databases, Protein|Genes, MHC Class II|Peptides|Thermodynamics', 'Journal Article', '10.1186/1471-2105-11-41', '20089173', 'The binding of peptide fragments of extracellular peptides to class II MHC is a crucial event in the adaptive immune response. Each MHC allotype generally binds a distinct subset of peptides and the enormous number of possible peptide epitopes prevents their complete experimental characterization. Computational methods can utilize the limited experimental data to predict the binding affinities of peptides to class II MHC.'),
(93328, 2010, 'bmc', 'Applying unmixing to gene expression data for tumor phylogeny inference', 'Schwartz, Russell|Shackney, Stanley E', 'Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA USA. russells@andrew.cmu.edu|Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA USA. russells@andrew.cmu.edu', 'United States|United States', 35, 'Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Phylogeny', 'Journal Article', '10.1186/1471-2105-11-42', '20089185', 'While in principle a seemingly infinite variety of combinations of mutations could result in tumor development, in practice it appears that most human cancers fall into a relatively small number of sub-types, each characterized a roughly equivalent sequence of mutations by which it progresses in different patients. There is currently great interest in identifying the common sub-types and applying them to the development of diagnostics or therapeutics. Phylogenetic methods have shown great promise for inferring common patterns of tumor progression, but suffer from limits of the technologies available for assaying differences between and within tumors. One approach to tumor phylogenetics uses differences between single cells within tumors, gaining valuable information about intra-tumor heterogeneity but allowing only a few markers per cell. An alternative approach uses tissue-wide measures of whole tumors to provide a detailed picture of averaged tumor state but at the cost of losing information about intra-tumor heterogeneity.'),
(93329, 2010, 'bmc', 'Discovering local patterns of co-evolution: computational aspects and biological examples', 'Tuller, Tamir|Felder, Yifat|Kupiec, Martin', 'School of Computer Science, Tel Aviv University, Tel Aviv, Israel. tamirtul@post.tau.ac.il|School of Computer Science, Tel Aviv University, Tel Aviv, Israel. tamirtul@post.tau.ac.il|School of Computer Science, Tel Aviv University, Tel Aviv, Israel. tamirtul@post.tau.ac.il', 'Israel|Israel|Israel', 0, 'Algorithms|Computational Biology|Evolution, Molecular|Gene Expression Profiling|Phylogeny', 'Journal Article', '10.1186/1471-2105-11-43', '20096103', 'Co-evolution is the process in which two (or more) sets of orthologs exhibit a similar or correlative pattern of evolution. Co-evolution is a powerful way to learn about the functional interdependencies between sets of genes and cellular functions and to predict physical interactions. More generally, it can be used for answering fundamental questions about the evolution of biological systems.Orthologs that exhibit a strong signal of co-evolution in a certain part of the evolutionary tree may show a mild signal of co-evolution in other branches of the tree. The major reasons for this phenomenon are noise in the biological input, genes that gain or lose functions, and the fact that some measures of co-evolution relate to rare events such as positive selection. Previous publications in the field dealt with the problem of finding sets of genes that co-evolved along an entire underlying phylogenetic tree, without considering the fact that often co-evolution is local.'),
(93330, 2010, 'bmc', 'The curvHDR method for gating flow cytometry samples', 'Naumann, Ulrike|Luta, George|Wand, Matthew P', 'School of Mathematics and Applied Statistics, The University of New South Wales, Sydney, Australia.|School of Mathematics and Applied Statistics, The University of New South Wales, Sydney, Australia.|School of Mathematics and Applied Statistics, The University of New South Wales, Sydney, Australia.', 'Australia|Australia|Australia', 26, 'Databases, Factual|Flow Cytometry|Reproducibility of Results|Software', 'Journal Article', '10.1186/1471-2105-11-44', '20096119', 'High-throughput flow cytometry experiments produce hundreds of large multivariate samples of cellular characteristics. These samples require specialized processing to obtain clinically meaningful measurements. A major component of this processing is a form of cell subsetting known as gating. Manual gating is time-consuming and subjective. Good automatic and semi-automatic gating algorithms are very beneficial to high-throughput flow cytometry.'),
(93331, 2010, 'bmc', 'NeatMap--non-clustering heat map alternatives in R', 'Rajaram, Satwik|Oono, Yoshi', 'Department of Physics, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801-3080, USA. satwik.rajaram@utsouthwestern.edu|Department of Physics, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801-3080, USA. satwik.rajaram@utsouthwestern.edu', 'United States|United States', 0, 'Algorithms|Chromosome Mapping|Cluster Analysis|Computer Graphics|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-45', '20096121', 'The clustered heat map is the most popular means of visualizing genomic data. It compactly displays a large amount of data in an intuitive format that facilitates the detection of hidden structures and relations in the data. However, it is hampered by its use of cluster analysis which does not always respect the intrinsic relations in the data, often requiring non-standardized reordering of rows/columns to be performed post-clustering. This sometimes leads to uninformative and/or misleading conclusions. Often it is more informative to use dimension-reduction algorithms (such as Principal Component Analysis and Multi-Dimensional Scaling) which respect the topology inherent in the data. Yet, despite their proven utility in the analysis of biological data, they are not as widely used. This is at least partially due to the lack of user-friendly visualization methods with the visceral impact of the heat map.'),
(93332, 2010, 'bmc', 'Bayesian statistical modelling of human protein interaction network incorporating protein disorder information', 'Bulashevska, Svetlana|Bulashevska, Alla|Eils, Roland', 'Theoretical Bioinformatics Department, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. s.bulashevska@dkfz.de|Theoretical Bioinformatics Department, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. s.bulashevska@dkfz.de|Theoretical Bioinformatics Department, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. s.bulashevska@dkfz.de', 'Germany|Germany|Germany', 0, 'Bayes Theorem|Computational Biology|Databases, Protein|Humans|Models, Statistical|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/1471-2105-11-46', '20100321', 'We present a statistical method of analysis of biological networks based on the exponential random graph model, namely p2-model, as opposed to previous descriptive approaches. The model is capable to capture generic and structural properties of a network as emergent from local interdependencies and uses a limited number of parameters. Here, we consider one global parameter capturing the density of edges in the network, and local parameters representing each nodes contribution to the formation of edges in the network. The modelling suggests a novel definition of important nodes in the network, namely social, as revealed based on the local sociality parameters of the model. Moreover, the sociality parameters help to reveal organizational principles of the network. An inherent advantage of our approach is the possibility of hypotheses testing: a priori knowledge about biological properties of the nodes can be incorporated into the statistical model to investigate its influence on the structure of the network.'),
(93333, 2010, 'bmc', 'BSSF: a fingerprint based ultrafast binding site similarity search and function analysis server', 'Xiong, Bing|Wu, Jie|Burk, David L|Xue, Mengzhu|Jiang, Hualiang|Shen, Jingkang', 'State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Zhangjiang Hi-Tech Park, Pudong, Shanghai, 201203, PR China. bxiong@mail.shcnc.ac.cn|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Zhangjiang Hi-Tech Park, Pudong, Shanghai, 201203, PR China. bxiong@mail.shcnc.ac.cn|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Zhangjiang Hi-Tech Park, Pudong, Shanghai, 201203, PR China. bxiong@mail.shcnc.ac.cn|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Zhangjiang Hi-Tech Park, Pudong, Shanghai, 201203, PR China. bxiong@mail.shcnc.ac.cn|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Zhangjiang Hi-Tech Park, Pudong, Shanghai, 201203, PR China. bxiong@mail.shcnc.ac.cn|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Zhangjiang Hi-Tech Park, Pudong, Shanghai, 201203, PR China. bxiong@mail.shcnc.ac.cn', 'China|China|China|China|China|China', 18, 'Binding Sites|Computational Biology|Protein Structure, Secondary|Proteins|Software', 'Journal Article', '10.1186/1471-2105-11-47', '20100327', 'Genome sequencing and post-genomics projects such as structural genomics are extending the frontier of the study of sequence-structure-function relationship of genes and their products. Although many sequence/structure-based methods have been devised with the aim of deciphering this delicate relationship, there still remain large gaps in this fundamental problem, which continuously drives researchers to develop novel methods to extract relevant information from sequences and structures and to infer the functions of newly identified genes by genomics technology.'),
(93334, 2010, 'bmc', 'Power and sample size estimation in microarray studies', 'Lin, Wei-Jiun|Hsueh, Huey-Miin|Chen, James J', 'Division of Personalized Nutrition and Medicine, National Center for Toxicological Research, FDA, Jefferson, AR 72079, USA.|Division of Personalized Nutrition and Medicine, National Center for Toxicological Research, FDA, Jefferson, AR 72079, USA.|Division of Personalized Nutrition and Medicine, National Center for Toxicological Research, FDA, Jefferson, AR 72079, USA.', 'United States|United States|United States', 0, 'Computational Biology|False Positive Reactions|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Sample Size', 'Journal Article', '10.1186/1471-2105-11-48', '20100337', 'Before conducting a microarray experiment, one important issue that needs to be determined is the number of arrays required in order to have adequate power to identify differentially expressed genes. This paper discusses some crucial issues in the problem formulation, parameter specifications, and approaches that are commonly proposed for sample size estimation in microarray experiments. Common methods for sample size estimation are formulated as the minimum sample size necessary to achieve a specified sensitivity (proportion of detected truly differentially expressed genes) on average at a specified false discovery rate (FDR) level and specified expected proportion (pi1) of the true differentially expression genes in the array. Unfortunately, the probability of detecting the specified sensitivity in such a formulation can be low. We formulate the sample size problem as the number of arrays needed to achieve a specified sensitivity with 95% probability at the specified significance level. A permutation method using a small pilot dataset to estimate sample size is proposed. This method accounts for correlation and effect size heterogeneity among genes.'),
(93335, 2010, 'bmc', 'M3G: maximum margin microarray gridding', 'Bariamis, Dimitris|Iakovidis, Dimitris K|Maroulis, Dimitris', 'Department of Informatics and Telecommunications, University of Athens, Athens, Greece. d.bariamis@di.uoa.gr|Department of Informatics and Telecommunications, University of Athens, Athens, Greece. d.bariamis@di.uoa.gr|Department of Informatics and Telecommunications, University of Athens, Athens, Greece. d.bariamis@di.uoa.gr', 'Greece|Greece|Greece', 27, 'DNA, Complementary|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Journal Article', '10.1186/1471-2105-11-49', '20100338', 'Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding.'),
(93336, 2010, 'bmc', 'FiGS: a filter-based gene selection workbench for microarray data', 'Hwang, Taeho|Sun, Choong-Hyun|Yun, Taegyun|Yi, Gwan-Su', 'Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, South Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, South Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, South Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, South Korea.', '-|-|-|-', 0, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-11-50', '20100357', 'The selection of genes that discriminate disease classes from microarray data is widely used for the identification of diagnostic biomarkers. Although various gene selection methods are currently available and some of them have shown excellent performance, no single method can retain the best performance for all types of microarray datasets. It is desirable to use a comparative approach to find the best gene selection result after rigorous test of different methodological strategies for a given microarray dataset.'),
(93337, 2010, 'bmc', 'SAMPLEX: automatic mapping of perturbed and unperturbed regions of proteins and complexes', 'Krzeminski, Mickaël|Loth, Karine|Boelens, Rolf|Bonvin, Alexandre M J J', 'Bijvoet Center for Biomolecular Research, Science Faculty, Utrecht University, 3584 CH, Utrecht, The Netherlands.|Bijvoet Center for Biomolecular Research, Science Faculty, Utrecht University, 3584 CH, Utrecht, The Netherlands.|Bijvoet Center for Biomolecular Research, Science Faculty, Utrecht University, 3584 CH, Utrecht, The Netherlands.|Bijvoet Center for Biomolecular Research, Science Faculty, Utrecht University, 3584 CH, Utrecht, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 21, 'Ligands|Models, Molecular|Nuclear Magnetic Resonance, Biomolecular|Protein Conformation|Protein Folding|Proteins', 'Journal Article', '10.1186/1471-2105-11-51', '20102599', 'The activity of proteins within the cell is characterized by their motions, flexibility, interactions or even the particularly intriguing case of partially unfolded states. In the last two cases, a part of the protein is affected either by binding or unfolding and the detection of the respective perturbed and unperturbed region(s) is a fundamental part of the structural characterization of these states. This can be achieved by comparing experimental data of the same protein in two different states (bound/unbound, folded/unfolded). For instance, measurements of chemical shift perturbations (CSPs) from NMR 1H-15N HSQC experiments gives an excellent opportunity to discriminate both moieties.'),
(93338, 2010, 'bmc', 'Sites Inferred by Metabolic Background Assertion Labeling (SIMBAL): adapting the Partial Phylogenetic Profiling algorithm to scan sequences for signatures that predict protein function', 'Selengut, Jeremy D|Rusch, Douglas B|Haft, Daniel H', 'J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. selengut@jcvi.org|J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. selengut@jcvi.org|J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. selengut@jcvi.org', 'United States|United States|United States', 7, 'Algorithms|Amino Acid Sequence|Gene Expression Profiling|Molecular Sequence Data|Phylogeny|Proteins|Sequence Analysis, Protein|Structure-Activity Relationship', 'Evaluation Studies', '10.1186/1471-2105-11-52', '20102603', 'Comparative genomics methods such as phylogenetic profiling can mine powerful inferences from inherently noisy biological data sets. We introduce Sites Inferred by Metabolic Background Assertion Labeling (SIMBAL), a method that applies the Partial Phylogenetic Profiling (PPP) approach locally within a protein sequence to discover short sequence signatures associated with functional sites. The approach is based on the basic scoring mechanism employed by PPP, namely the use of binomial distribution statistics to optimize sequence similarity cutoffs during searches of partitioned training sets.'),
(93339, 2010, 'bmc', 'Global screening of potential Candida albicans biofilm-related transcription factors via network comparison', 'Wang, Yu-Chao|Lan, Chung-Yu|Hsieh, Wen-Ping|Murillo, Luis A|Agabian, Nina|Chen, Bor-Sen', 'Laboratory of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.|Laboratory of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.|Laboratory of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.|Laboratory of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.|Laboratory of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.|Laboratory of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.', '-|-|-|-|-|-', 0, 'Biofilms|Candida albicans|Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Genome, Fungal|Transcription Factors', 'Comparative Study', '10.1186/1471-2105-11-53', '20102611', 'Candida albicans is a commonly encountered fungal pathogen in humans. The formation of biofilm is a major virulence factor in C. albicans pathogenesis and is related to antidrug resistance of this organism. Although many factors affecting biofilm have been analyzed, molecular mechanisms that regulate biofilm formation still await to be elucidated.'),
(93340, 2010, 'bmc', 'Towards realistic benchmarks for multiple alignments of non-coding sequences', 'Kim, Jaebum|Sinha, Saurabh', 'Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.', 'United States|United States', 0, 'Animals|Benchmarking|Drosophila|Genomics|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-54', '20102627', 'With the continued development of new computational tools for multiple sequence alignment, it is necessary today to develop benchmarks that aid the selection of the most effective tools. Simulation-based benchmarks have been proposed to meet this necessity, especially for non-coding sequences. However, it is not clear if such benchmarks truly represent real sequence data from any given group of species, in terms of the difficulty of alignment tasks.'),
(93341, 2010, 'bmc', 'Semi-automated screening of biomedical citations for systematic reviews', 'Wallace, Byron C|Trikalinos, Thomas A|Lau, Joseph|Brodley, Carla|Schmid, Christopher H', 'Department of Computer Science, Tufts University, Medford, MA, USA. bwallace@tuftsmedicalcenter.org|Department of Computer Science, Tufts University, Medford, MA, USA. bwallace@tuftsmedicalcenter.org|Department of Computer Science, Tufts University, Medford, MA, USA. bwallace@tuftsmedicalcenter.org|Department of Computer Science, Tufts University, Medford, MA, USA. bwallace@tuftsmedicalcenter.org|Department of Computer Science, Tufts University, Medford, MA, USA. bwallace@tuftsmedicalcenter.org', 'United States|United States|United States|United States|United States', 137, 'Information Storage and Retrieval|Periodicals as Topic|Publications|Review Literature as Topic', 'Journal Article', '10.1186/1471-2105-11-55', '20102628', 'Systematic reviews address a specific clinical question by unbiasedly assessing and analyzing the pertinent literature. Citation screening is a time-consuming and critical step in systematic reviews. Typically, reviewers must evaluate thousands of citations to identify articles eligible for a given review. We explore the application of machine learning techniques to semi-automate citation screening, thereby reducing the reviewers workload.'),
(93342, 2010, 'bmc', 'Biana: a software framework for compiling biological interactions and analyzing networks', 'Garcia-Garcia, Javier|Guney, Emre|Aragues, Ramon|Planas-Iglesias, Joan|Oliva, Baldo', 'Structural Bioinformatics Lab, Universitat Pompeu Fabra-IMIM, Barcelona Research Park of Biomedicine, Barcelona, Catalonia, Spain.|Structural Bioinformatics Lab, Universitat Pompeu Fabra-IMIM, Barcelona Research Park of Biomedicine, Barcelona, Catalonia, Spain.|Structural Bioinformatics Lab, Universitat Pompeu Fabra-IMIM, Barcelona Research Park of Biomedicine, Barcelona, Catalonia, Spain.|Structural Bioinformatics Lab, Universitat Pompeu Fabra-IMIM, Barcelona Research Park of Biomedicine, Barcelona, Catalonia, Spain.|Structural Bioinformatics Lab, Universitat Pompeu Fabra-IMIM, Barcelona Research Park of Biomedicine, Barcelona, Catalonia, Spain.', 'Spain|Spain|Spain|Spain|Spain', 0, 'Algorithms|Computer Simulation|Database Management Systems|Databases, Protein|Gene Expression Profiling|Models, Biological|Protein Interaction Mapping|Proteome|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-11-56', '20105306', 'The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties.'),
(93343, 2010, 'bmc', 'Genome scale prediction of substrate specificity for acyl adenylate superfamily of enzymes based on active site residue profiles', 'Khurana, Pankaj|Gokhale, Rajesh S|Mohanty, Debasisa', 'National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi, India.|National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi, India.|National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi, India.', 'India|India|India', 20, 'Acetate-CoA Ligase|Algorithms|Amino Acid Sequence|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Structure-Activity Relationship|Substrate Specificity', 'Journal Article', '10.1186/1471-2105-11-57', '20105319', 'Enzymes belonging to acyl:CoA synthetase (ACS) superfamily activate wide variety of substrates and play major role in increasing the structural and functional diversity of various secondary metabolites in microbes and plants. However, due to the large sequence divergence within the superfamily, it is difficult to predict their substrate preference by annotation transfer from the closest homolog. Therefore, a large number of ACS sequences present in public databases lack any functional annotation at the level of substrate specificity. Recently, several examples have been reported where the enzymes showing high sequence similarity to luciferases or coumarate:CoA ligases have been surprisingly found to activate fatty acyl substrates in experimental studies. In this work, we have investigated the relationship between the substrate specificity of ACS and their sequence/structural features, and developed a novel computational protocol for in silico assignment of substrate preference.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93344, 2010, 'bmc', 'A variational Bayes algorithm for fast and accurate multiple locus genome-wide association analysis', 'Logsdon, Benjamin A|Hoffman, Gabriel E|Mezey, Jason G', 'Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA.|Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA.|Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA.', 'United States|United States|United States', 54, 'Algorithms|Bayes Theorem|Computational Biology|Databases, Genetic|Genetic Loci|Genome-Wide Association Study', 'Journal Article', '10.1186/1471-2105-11-58', '20105321', 'The success achieved by genome-wide association (GWA) studies in the identification of candidate loci for complex diseases has been accompanied by an inability to explain the bulk of heritability. Here, we describe the algorithm V-Bay, a variational Bayes algorithm for multiple locus GWA analysis, which is designed to identify weaker associations that may contribute to this missing heritability.'),
(93345, 2010, 'bmc', 'Comparison of evolutionary algorithms in gene regulatory network model inference', 'Sîrbu, Alina|Ruskin, Heather J|Crane, Martin', 'Centre for Scientific Computing and Complex Systems Modelling, Dublin City University, Dublin 9, Ireland. asirbu@computing.dcu.ie|Centre for Scientific Computing and Complex Systems Modelling, Dublin City University, Dublin 9, Ireland. asirbu@computing.dcu.ie|Centre for Scientific Computing and Complex Systems Modelling, Dublin City University, Dublin 9, Ireland. asirbu@computing.dcu.ie', 'Ireland|Ireland|Ireland', 0, 'Algorithms|Computational Biology|Evolution, Molecular|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis', 'Comparative Study', '10.1186/1471-2105-11-59', '20105328', 'The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient.'),
(93346, 2010, 'bmc', 'Testing for mean and correlation changes in microarray experiments: an application for pathway analysis', 'Alvo, Mayer|Liu, Zhongzhu|Williams, Andrew|Yauk, Carole', 'Environmental Health Science and Research Bureau, Environmental and Radiation Health Sciences Directorate, Health Canada, Ottawa, Ontario, Canada.|Environmental Health Science and Research Bureau, Environmental and Radiation Health Sciences Directorate, Health Canada, Ottawa, Ontario, Canada.|Environmental Health Science and Research Bureau, Environmental and Radiation Health Sciences Directorate, Health Canada, Ottawa, Ontario, Canada.|Environmental Health Science and Research Bureau, Environmental and Radiation Health Sciences Directorate, Health Canada, Ottawa, Ontario, Canada.', 'Canada|Canada|Canada|Canada', 12, 'Algorithms|Data Interpretation, Statistical|Gene Expression Profiling|Proteome|Regression Analysis|Signal Transduction|Statistics as Topic', 'Journal Article', '10.1186/1471-2105-11-60', '20109181', 'Microarray experiments examine the change in transcript levels of tens of thousands of genes simultaneously. To derive meaningful data, biologists investigate the response of genes within specific pathways. Pathways are comprised of genes that interact to carry out a particular biological function. Existing methods for analyzing pathways focus on detecting changes in the mean or over-representation of the number of differentially expressed genes relative to the total of genes within the pathway. The issue of how to incorporate the influence of correlation among the genes is not generally addressed.'),
(93347, 2010, 'bmc', 'Computational identification of rare codons of Escherichia coli based on codon pairs preference', 'Wu, Xianming|Wu, Songfeng|Li, Dong|Zhang, Jiyang|Hou, Lin|Ma, Jie|Liu, Wanlin|Ren, Daming|Zhu, Yunping|He, Fuchu', 'School of Biological Science and Technology, Shenyang Agricultural University, Shenyang, PR China.|School of Biological Science and Technology, Shenyang Agricultural University, Shenyang, PR China.|School of Biological Science and Technology, Shenyang Agricultural University, Shenyang, PR China.|School of Biological Science and Technology, Shenyang Agricultural University, Shenyang, PR China.|School of Biological Science and Technology, Shenyang Agricultural University, Shenyang, PR China.|School of Biological Science and Technology, Shenyang Agricultural University, Shenyang, PR China.|School of Biological Science and Technology, Shenyang Agricultural University, Shenyang, PR China.|School of Biological Science and Technology, Shenyang Agricultural University, Shenyang, PR China.|School of Biological Science and Technology, Shenyang Agricultural University, Shenyang, PR China.|School of Biological Science and Technology, Shenyang Agricultural University, Shenyang, PR China.', 'China|China|China|China|China|China|China|China|China|China', 9, 'Codon|Computational Biology|Databases, Genetic|Escherichia coli|Gene Expression Regulation, Bacterial', 'Journal Article', '10.1186/1471-2105-11-61', '20109184', 'Codon bias is believed to play an important role in the control of gene expression. In Escherichia coli, some rare codons, which can limit the expression level of exogenous protein, have been defined by gene engineering operations. Previous studies have confirmed the existence of codon pairs preference in many genomes, but the underlying cause of this bias has not been well established. Here we focus on the patterns of rarely-used synonymous codons. A novel method was introduced to identify the rare codons merely by codon pair bias in Escherichia coli.'),
(93348, 2010, 'bmc', 'A novel scoring function for discriminating hyperthermophilic and mesophilic proteins with application to predicting relative thermostability of protein mutants', 'Li, Yunqi|Middaugh, C Russell|Fang, Jianwen', 'Applied Bioinformatics Laboratory, the University of Kansas, Lawrence, KS 66047, USA.|Applied Bioinformatics Laboratory, the University of Kansas, Lawrence, KS 66047, USA.|Applied Bioinformatics Laboratory, the University of Kansas, Lawrence, KS 66047, USA.', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Bacterial Proteins|Computer Simulation|Discriminant Analysis|Models, Chemical|Molecular Sequence Data|Mutation|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-11-62', '20109199', 'The ability to design thermostable proteins is theoretically important and practically useful. Robust and accurate algorithms, however, remain elusive. One critical problem is the lack of reliable methods to estimate the relative thermostability of possible mutants.'),
(93349, 2010, 'bmc', 'Validation of differential gene expression algorithms: application comparing fold-change estimation to hypothesis testing', 'Yanofsky, Corey M|Bickel, David R', 'Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, Ottawa, Ontario, Canada.|Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, Ottawa, Ontario, Canada.', 'Canada|Canada', 14, 'Algorithms|Data Interpretation, Statistical|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Evaluation Studies', '10.1186/1471-2105-11-63', '20109217', 'Sustained research on the problem of determining which genes are differentially expressed on the basis of microarray data has yielded a plethora of statistical algorithms, each justified by theory, simulation, or ad hoc validation and yet differing in practical results from equally justified algorithms. Recently, a concordance method that measures agreement among gene lists have been introduced to assess various aspects of differential gene expression detection. This method has the advantage of basing its assessment solely on the results of real data analyses, but as it requires examining gene lists of given sizes, it may be unstable.'),
(93350, 2010, 'bmc', 'ICC-CLASS: isotopically-coded cleavable crosslinking analysis software suite', 'Petrotchenko, Evgeniy V|Borchers, Christoph H', 'University of Victoria Genome British Columbia Protein Centre, Department of Biochemistry & Microbiology, University of Victoria, #3101-4464 Markham Street, Vancouver Island Technology Park, Victoria, BC, Canada.|University of Victoria Genome British Columbia Protein Centre, Department of Biochemistry & Microbiology, University of Victoria, #3101-4464 Markham Street, Vancouver Island Technology Park, Victoria, BC, Canada.', 'Canada|Canada', 46, 'Cross-Linking Reagents|Databases, Protein|Isotope Labeling|Isotopes|Mass Spectrometry|Peptides|Proteins|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-11-64', '20109223', 'Successful application of crosslinking combined with mass spectrometry for studying proteins and protein complexes requires specifically-designed crosslinking reagents, experimental techniques, and data analysis software. Using isotopically-coded (heavy and light) versions of the crosslinker and cleavable crosslinking reagents is analytically advantageous for mass spectrometric applications and provides a handle that can be used to distinguish crosslinked peptides of different types, and to increase the confidence of the identification of the crosslinks.'),
(93351, 2010, 'bmc', 'Haplotype allelic classes for detecting ongoing positive selection', 'Hussin, Julie|Nadeau, Philippe|Lefebvre, Jean-François|Labuda, Damian', 'Bioinformatics Program, Department of Biochemistry, Université de Montréal, Montréal, Québec, Canada.|Bioinformatics Program, Department of Biochemistry, Université de Montréal, Montréal, Québec, Canada.|Bioinformatics Program, Department of Biochemistry, Université de Montréal, Montréal, Québec, Canada.|Bioinformatics Program, Department of Biochemistry, Université de Montréal, Montréal, Québec, Canada.', 'Canada|Canada|Canada|Canada', 12, 'Alleles|Genetics, Population|Genome, Human|Genotype|Haplotypes|Humans|Polymorphism, Genetic|Selection, Genetic', 'Journal Article', '10.1186/1471-2105-11-65', '20109229', 'Natural selection eliminates detrimental and favors advantageous phenotypes. This process leaves characteristic signatures in underlying genomic segments that can be recognized through deviations in allelic or haplotypic frequency spectra. To provide an identifiable signature of recent positive selection that can be detected by comparison with the background distribution, we introduced a new way of looking at genomic polymorphisms: haplotype allelic classes.'),
(93352, 2010, 'bmc', 'FastTagger: an efficient algorithm for genome-wide tag SNP selection using multi-marker linkage disequilibrium', 'Liu, Guimei|Wang, Yue|Wong, Limsoon', 'Department of Computer Science, National University of Singapore, Singapore. liugm@comp.nus.edu.sg|Department of Computer Science, National University of Singapore, Singapore. liugm@comp.nus.edu.sg|Department of Computer Science, National University of Singapore, Singapore. liugm@comp.nus.edu.sg', 'Singapore|Singapore|Singapore', 27, 'Algorithms|Chromosome Mapping|DNA Mutational Analysis|Genetic Markers|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-11-66', '20113476', 'Human genome contains millions of common single nucleotide polymorphisms (SNPs) and these SNPs play an important role in understanding the association between genetic variations and human diseases. Many SNPs show correlated genotypes, or linkage disequilibrium (LD), thus it is not necessary to genotype all SNPs for association study. Many algorithms have been developed to find a small subset of SNPs called tag SNPs that are sufficient to infer all the other SNPs. Algorithms based on the r2 LD statistic have gained popularity because r2 is directly related to statistical power to detect disease associations. Most of existing r2 based algorithms use pairwise LD. Recent studies show that multi-marker LD can help further reduce the number of tag SNPs. However, existing tag SNP selection algorithms based on multi-marker LD are both time-consuming and memory-consuming. They cannot work on chromosomes containing more than 100 k SNPs using length-3 tagging rules.'),
(93353, 2010, 'bmc', 'MLPAinter for MLPA interpretation: an integrated approach for the analysis, visualisation and data management of Multiplex Ligation-dependent Probe Amplification', 'van Eijk, Ronald|Eilers, Paul H C|Natté, Remco|Cleton-Jansen, Anne-Marie|Morreau, Hans|van Wezel, Tom|Oosting, Jan', 'Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.|Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.|Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.|Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.|Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.|Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.|Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 9, 'Algorithms|Amplified Fragment Length Polymorphism Analysis|Chromosome Aberrations|Chromosome Mapping|Chromosome Painting|Computer Graphics|Database Management Systems|Databases, Genetic|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-67', '20113482', 'Multiplex Ligation-Dependent Probe Amplification (MLPA) is an application that can be used for the detection of multiple chromosomal aberrations in a single experiment. In one reaction, up to 50 different genomic sequences can be analysed. For a reliable work-flow, tools are needed for administrative support, data management, normalisation, visualisation, reporting and interpretation.'),
(93354, 2010, 'bmc', 'A temporal precedence based clustering method for gene expression microarray data', 'Krishna, Ritesh|Li, Chang-Tsun|Buchanan-Wollaston, Vicky', 'Department of Computer Science, Warwick University, Coventry CV4 7AL, UK.|Department of Computer Science, Warwick University, Coventry CV4 7AL, UK.|Department of Computer Science, Warwick University, Coventry CV4 7AL, UK.', 'United Kingdom|United Kingdom|United Kingdom', 10, 'Arabidopsis|Cluster Analysis|Computational Biology|Databases, Genetic|Gene Expression|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-68', '20113513', 'Time-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in microarray data analysis where the data is grouped together according to certain characteristics. The majority of clustering techniques are based on distance or visual similarity measures which may not be suitable for clustering of temporal microarray data where the sequential nature of time is important. We present a Granger causality based technique to cluster temporal microarray gene expression data, which measures the interdependence between two time-series by statistically testing if one time-series can be used for forecasting the other time-series or not.'),
(93355, 2010, 'bmc', 'From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification', 'Slabbinck, Bram|Waegeman, Willem|Dawyndt, Peter|De Vos, Paul|De Baets, Bernard', 'Laboratory of Microbiology, Ghent University, Ghent, Belgium. Bram.Slabbinck@UGent.be|Laboratory of Microbiology, Ghent University, Ghent, Belgium. Bram.Slabbinck@UGent.be|Laboratory of Microbiology, Ghent University, Ghent, Belgium. Bram.Slabbinck@UGent.be|Laboratory of Microbiology, Ghent University, Ghent, Belgium. Bram.Slabbinck@UGent.be|Laboratory of Microbiology, Ghent University, Ghent, Belgium. Bram.Slabbinck@UGent.be', 'Belgium|Belgium|Belgium|Belgium|Belgium', 15, 'Artificial Intelligence|Bacteria|Databases, Factual|Fatty Acids|Phylogeny|RNA, Bacterial|RNA, Ribosomal, 16S', 'Journal Article', '10.1186/1471-2105-11-69', '20113515', 'Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification.'),
(93356, 2010, 'bmc', 'LAITOR--Literature Assistant for Identification of Terms co-Occurrences and Relationships', 'Barbosa-Silva, Adriano|Soldatos, Theodoros G|Magalhães, Ivan L F|Pavlopoulos, Georgios A|Fontaine, Jean-Fred|Andrade-Navarro, Miguel A|Schneider, Reinhard|Ortega, J Miguel', 'Max-Delbrück Center for Molecular Medicine, Berlin, Germany.|Max-Delbrück Center for Molecular Medicine, Berlin, Germany.|Max-Delbrück Center for Molecular Medicine, Berlin, Germany.|Max-Delbrück Center for Molecular Medicine, Berlin, Germany.|Max-Delbrück Center for Molecular Medicine, Berlin, Germany.|Max-Delbrück Center for Molecular Medicine, Berlin, Germany.|Max-Delbrück Center for Molecular Medicine, Berlin, Germany.|Max-Delbrück Center for Molecular Medicine, Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 23, 'Computational Biology|Data Mining|Information Storage and Retrieval|MEDLINE|Publications|Software|United States', 'Journal Article', '10.1186/1471-2105-11-70', '20122157', 'Biological knowledge is represented in scientific literature that often describes the function of genes/proteins (bioentities) in terms of their interactions (biointeractions). Such bioentities are often related to biological concepts of interest that are specific of a determined research field. Therefore, the study of the current literature about a selected topic deposited in public databases, facilitates the generation of novel hypotheses associating a set of bioentities to a common context.'),
(93357, 2010, 'bmc', 'APBC 2010 The Eighth Asia Pacific Bioinformatics Conference Bangalore, India, 18-21 January 2010', 'Parida, Laxmi|Myers, Gene', 'IBM T J Watson Research, Yorktown, USA. parida@us.ibm.com|IBM T J Watson Research, Yorktown, USA. parida@us.ibm.com', 'United States|United States', 0, 'Computational Biology', 'Introductory Journal Article', '10.1186/1471-2105-11-S1-I1', '20122179', '-'),
(93358, 2010, 'bmc', 'Nonnegative principal component analysis for mass spectral serum profiles and biomarker discovery', 'Han, Henry', 'Department of Mathematics and Bioinformatics, Eastern Michigan University, Ypsilanti, MI 48109, USA. henry.ahan@gmail.com', 'United States', 20, 'Algorithms|Artificial Intelligence|Biomarkers|Mass Spectrometry|Principal Component Analysis|Proteome|Proteomics', 'Journal Article', '10.1186/1471-2105-11-S1-S1', '20122180', 'As a novel cancer diagnostic paradigm, mass spectroscopic serum proteomic pattern diagnostics was reported superior to the conventional serologic cancer biomarkers. However, its clinical use is not fully validated yet. An important factor to prevent this young technology to become a mainstream cancer diagnostic paradigm is that robustly identifying cancer molecular patterns from high-dimensional protein expression data is still a challenge in machine learning and oncology research. As a well-established dimension reduction technique, PCA is widely integrated in pattern recognition analysis to discover cancer molecular patterns. However, its global feature selection mechanism prevents it from capturing local features. This may lead to difficulty in achieving high-performance proteomic pattern discovery, because only features interpreting global data behavior are used to train a learning machine.'),
(93359, 2010, 'bmc', 'PostMod: sequence based prediction of kinase-specific phosphorylation sites with indirect relationship', 'Jung, Inkyung|Matsuyama, Akihisa|Yoshida, Minoru|Kim, Dongsup', 'Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, S. Korea. snowdrop83@gmail.com|Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, S. Korea. snowdrop83@gmail.com|Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, S. Korea. snowdrop83@gmail.com|Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, S. Korea. snowdrop83@gmail.com', '-|-|-|-', 28, 'Algorithms|Phosphoproteins|Phosphorylation|Protein Kinases|Protein Processing, Post-Translational|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-S1-S10', '20122181', 'Post-translational modifications (PTMs) have a key role in regulating cell functions. Consequently, identification of PTM sites has a significant impact on understanding protein function and revealing cellular signal transductions. Especially, phosphorylation is a ubiquitous process with a large portion of proteins undergoing this modification. Experimental methods to identify phosphorylation sites are labor-intensive and of high-cost. With the exponentially growing protein sequence data, development of computational approaches to predict phosphorylation sites is highly desirable.'),
(93360, 2010, 'bmc', 'Fast motif recognition via application of statistical thresholds', 'Boucher, Christina|King, James', 'David R, Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada. cabouche@cs.uwaterloo.ca|David R, Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada. cabouche@cs.uwaterloo.ca', 'Canada|Canada', 6, 'Algorithms|Conserved Sequence|Models, Statistical|Pattern Recognition, Automated|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-S1-S11', '20122182', 'Improving the accuracy and efficiency of motif recognition is an important computational challenge that has application to detecting transcription factor binding sites in genomic data. Closely related to motif recognition is the CONSENSUS STRING decision problem that asks, given a parameter d and a set of l-length strings S = {s1, ..., sn}, whether there exists a consensus string that has Hamming distance at most d from any string in S. A set of strings S is pairwise bounded if the Hamming distance between any pair of strings in S is at most 2d. It is trivial to determine whether a set is pairwise bounded, and a set cannot have a consensus string unless it is pairwise bounded. We use CONSENSUS STRING to determine whether or not a pairwise bounded set has a consensus. Unfortunately, CONSENSUS STRING is NP-complete. The lack of an efficient method to solve the CONSENSUS STRING problem has caused it to become a computational bottleneck in MCL-WMR, a motif recognition program capable of solving difficult motif recognition problem instances.'),
(93361, 2010, 'bmc', 'Short clones or long clones? A simulation study on the use of paired reads in metagenomics', 'Mitra, Suparna|Schubach, Max|Huson, Daniel H', 'Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. mitra@informatik.uni-tuebingen.de|Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. mitra@informatik.uni-tuebingen.de|Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. mitra@informatik.uni-tuebingen.de', 'Germany|Germany|Germany', 13, 'Base Sequence|Databases, Genetic|Genome|Metagenomics|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-11-S1-S12', '20122183', 'Metagenomics is the study of environmental samples using sequencing. Rapid advances in sequencing technology are fueling a vast increase in the number and scope of metagenomics projects. Most metagenome sequencing projects so far have been based on Sanger or Roche-454 sequencing, as only these technologies provide long enough reads, while Illumina sequencing has not been considered suitable for metagenomic studies due to a short read length of only 35 bp. However, now that reads of length 75 bp can be sequenced in pairs, Illumina sequencing has become a viable option for metagenome studies.'),
(93362, 2010, 'bmc', 'In silico screening of herbal and nanoparticle lead compounds for effectivity against H5N1, H1N1 neuraminidase and telomerase', 'Ganguli, Sayak|Mazumder, Manjita|Basu, Protip|Roy, Paushali|Mitra, Sayani|Datta, Abhijit', 'DBT-Centre for Bioinformatics, Presidency College, Kolkata, India. sayakbif@yahoo.com|DBT-Centre for Bioinformatics, Presidency College, Kolkata, India. sayakbif@yahoo.com|DBT-Centre for Bioinformatics, Presidency College, Kolkata, India. sayakbif@yahoo.com|DBT-Centre for Bioinformatics, Presidency College, Kolkata, India. sayakbif@yahoo.com|DBT-Centre for Bioinformatics, Presidency College, Kolkata, India. sayakbif@yahoo.com|DBT-Centre for Bioinformatics, Presidency College, Kolkata, India. sayakbif@yahoo.com', 'India|India|India|India|India|India', 0, '-', 'Journal Article', '10.1186/1471-2105-11-S1-S13', '20122184', 'Traditionally, drugs were discovered by testing compounds synthesized in time consuming multi-step processes against a battery of in vivo biological screens. Promising compounds were then further studied in development, where their pharmacokinetic properties, metabolism and potential toxicity were investigated.'),
(93363, 2010, 'bmc', 'Prediction of novel miRNAs and associated target genes in Glycine max', 'Joshi, Trupti|Yan, Zhe|Libault, Marc|Jeong, Dong-Hoon|Park, Sunhee|Green, Pamela J|Sherrier, D Janine|Farmer, Andrew|May, Greg|Meyers, Blake C|Xu, Dong|Stacey, Gary', 'Digital Biology Laboratory, Computer Science Department and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. joshitr@missouri.edu|Digital Biology Laboratory, Computer Science Department and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. joshitr@missouri.edu|Digital Biology Laboratory, Computer Science Department and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. joshitr@missouri.edu|Digital Biology Laboratory, Computer Science Department and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. joshitr@missouri.edu|Digital Biology Laboratory, Computer Science Department and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. joshitr@missouri.edu|Digital Biology Laboratory, Computer Science Department and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. joshitr@missouri.edu|Digital Biology Laboratory, Computer Science Department and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. joshitr@missouri.edu|Digital Biology Laboratory, Computer Science Department and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. joshitr@missouri.edu|Digital Biology Laboratory, Computer Science Department and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. joshitr@missouri.edu|Digital Biology Laboratory, Computer Science Department and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. joshitr@missouri.edu|Digital Biology Laboratory, Computer Science Department and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. joshitr@missouri.edu|Digital Biology Laboratory, Computer Science Department and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. joshitr@missouri.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', 84, 'Computational Biology|DNA, Complementary|Databases, Genetic|Gene Expression Regulation, Plant|Genes, Plant|Genome, Plant|MicroRNAs|Sequence Analysis, RNA|Soybeans', 'Journal Article', '10.1186/1471-2105-11-S1-S14', '20122185', 'Small non-coding RNAs (21 to 24 nucleotides) regulate a number of developmental processes in plants and animals by silencing genes using multiple mechanisms. Among these, the most conserved classes are microRNAs (miRNAs) and small interfering RNAs (siRNAs), both of which are produced by RNase III-like enzymes called Dicers. Many plant miRNAs play critical roles in nutrient homeostasis, developmental processes, abiotic stress and pathogen responses. Currently, only 70 miRNA have been identified in soybean.'),
(93364, 2010, 'bmc', 'MrsRF: an efficient MapReduce algorithm for analyzing large collections of evolutionary trees', 'Matthews, Suzanne J|Williams, Tiffani L', 'Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA. sjm@cse.tamu.edu|Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA. sjm@cse.tamu.edu', 'United States|United States', 0, 'Algorithms|Databases, Genetic|Evolution, Molecular|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-11-S1-S15', '20122186', 'MapReduce is a parallel framework that has been used effectively to design large-scale parallel applications for large computing clusters. In this paper, we evaluate the viability of the MapReduce framework for designing phylogenetic applications. The problem of interest is generating the all-to-all Robinson-Foulds distance matrix, which has many applications for visualizing and clustering large collections of evolutionary trees. We introduce MrsRF (MapReduce Speeds up RF), a multi-core algorithm to generate a t x t Robinson-Foulds distance matrix between t trees using the MapReduce paradigm.'),
(93365, 2010, 'bmc', 'Classification of protein sequences by means of irredundant patterns', 'Comin, Matteo|Verzotto, Davide', 'Department of Information Engineering, University of Padova, Italy. ciompin@dei.unipd.it|Department of Information Engineering, University of Padova, Italy. ciompin@dei.unipd.it', 'Italy|Italy', 14, 'Algorithms|Proteins|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-11-S1-S16', '20122187', 'The classification of protein sequences using string algorithms provides valuable insights for protein function prediction. Several methods, based on a variety of different patterns, have been previously proposed. Almost all string-based approaches discover patterns that are not independent,  and therefore the associated scores overcount, a multiple number of times, the contribution of patterns that cover the same region of a sequence.'),
(93366, 2010, 'bmc', 'Amino acid classification based spectrum kernel fusion for protein subnuclear localization', 'Mei, Suyu|Fei, Wang', 'Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, PR China. meisuyureg@sohu.com|Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, PR China. meisuyureg@sohu.com', 'China|China', 0, 'Amino Acid Sequence|Amino Acids|Databases, Protein|Protein Conformation|Proteins|Proteomics|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-S1-S17', '20122188', 'Prediction of protein localization in subnuclear organelles is more challenging than general protein subcelluar localization. There are only three computational models for protein subnuclear localization thus far, to the best of our knowledge. Two models were based on protein primary sequence only. The first model assumed homogeneous amino acid substitution pattern across all protein sequence residue sites and used BLOSUM62 to encode k-mer of protein sequence. Ensemble of SVM based on different k-mers drew the final conclusion, achieving 50% overall accuracy. The simplified assumption did not exploit protein sequence profile and ignored the fact of heterogeneous amino acid substitution patterns across sites. The second model derived the PsePSSM feature representation from protein sequence by simply averaging the profile PSSM and combined the PseAA feature representation to construct a kNN ensemble classifier Nuc-PLoc, achieving 67.4% overall accuracy. The two models based on protein primary sequence only both achieved relatively poor predictive performance. The third model required that GO annotations be available, thus restricting the models applicability.'),
(93367, 2010, 'bmc', 'Identifying main effects and epistatic interactions from large-scale SNP data via adaptive group Lasso', 'Yang, Can|Wan, Xiang|Yang, Qiang|Xue, Hong|Yu, Weichuan', 'Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, PR China. eeyang@ust.hk|Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, PR China. eeyang@ust.hk|Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, PR China. eeyang@ust.hk|Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, PR China. eeyang@ust.hk|Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, PR China. eeyang@ust.hk', 'China|China|China|China|China', 32, 'Epistasis, Genetic|Genomics|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-11-S1-S18', '20122189', 'Single nucleotide polymorphism (SNP) based association studies aim at identifying SNPs associated with phenotypes, for example, complex diseases. The associated SNPs may influence the disease risk individually (main effects) or behave jointly (epistatic interactions). For the analysis of high throughput data, the main difficulty is that the number of SNPs far exceeds the number of samples. This difficulty is amplified when identifying interactions.'),
(93368, 2010, 'bmc', 'AntiBP2: improved version of antibacterial peptide prediction', 'Lata, Sneh|Mishra, Nitish K|Raghava, Gajendra P S', 'Institute of Microbial Technology, Sector 39A, Chandigarh, India. sheh@imtech.res.in|Institute of Microbial Technology, Sector 39A, Chandigarh, India. sheh@imtech.res.in|Institute of Microbial Technology, Sector 39A, Chandigarh, India. sheh@imtech.res.in', 'India|India|India', 113, 'Amino Acid Sequence|Antimicrobial Cationic Peptides|Computational Biology|Databases, Factual|Models, Molecular|Molecular Sequence Data|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-11-S1-S19', '20122190', 'Antibacterial peptides are one of the effecter molecules of innate immune system. Over the last few decades several antibacterial peptides have successfully approved as drug by FDA, which has prompted an interest in these antibacterial peptides. In our recent study we analyzed 999 antibacterial peptides, which were collected from Antibacterial Peptide Database (APD). We have also developed methods to predict and classify these antibacterial peptides using Support Vector Machine (SVM).'),
(93369, 2010, 'bmc', 'MTar: a computational microRNA target prediction architecture for human transcriptome', 'Chandra, Vinod|Girijadevi, Reshmi|Nair, Achuthsankar S|Pillai, Sreenadhan S|Pillai, Radhakrishna M', 'Centre for Bioinformatics, University of Kerala, Thiruvananthapuram, India. vinodchandrass@gmail.com|Centre for Bioinformatics, University of Kerala, Thiruvananthapuram, India. vinodchandrass@gmail.com|Centre for Bioinformatics, University of Kerala, Thiruvananthapuram, India. vinodchandrass@gmail.com|Centre for Bioinformatics, University of Kerala, Thiruvananthapuram, India. vinodchandrass@gmail.com|Centre for Bioinformatics, University of Kerala, Thiruvananthapuram, India. vinodchandrass@gmail.com', 'India|India|India|India|India', 0, 'Artificial Intelligence|Base Sequence|Computational Biology|Databases, Genetic|Gene Expression Profiling|Humans|MicroRNAs|Molecular Sequence Data|RNA, Messenger|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-11-S1-S2', '20122191', 'MicroRNAs (miRNAs) play an essential task in gene regulatory networks by inhibiting the expression of target mRNAs. As their mRNA targets are genes involved in important cell functions, there is a growing interest in identifying the relationship between miRNAs and their target mRNAs. So, there is now a imperative need to develop a computational method by which we can identify the target mRNAs of existing miRNAs. Here, we proposed an efficient machine learning model to unravel the relationship between miRNAs and their target mRNAs.'),
(93370, 2010, 'bmc', 'Integration of multiple data sources to prioritize candidate genes using discounted rating system', 'Li, Yongjin|Patra, Jagdish C', 'School of Computer Engineering, Nanyang Technological University, Singapore. yongjin.li@gmail.com|School of Computer Engineering, Nanyang Technological University, Singapore. yongjin.li@gmail.com', 'Singapore|Singapore', 42, 'Algorithms|Computational Biology|Data Collection|Databases, Genetic|Disease|Gene Expression Profiling|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-11-S1-S20', '20122192', 'Identifying disease gene from a list of candidate genes is an important task in bioinformatics. The main strategy is to prioritize candidate genes based on their similarity to known disease genes. Most of existing gene prioritization methods access only one genomic data source, which is noisy and incomplete. Thus, there is a need for the integration of multiple data sources containing different information.'),
(93371, 2010, 'bmc', 'Learning to predict expression efficacy of vectors in recombinant protein production', 'Chan, Wen-Ching|Liang, Po-Huang|Shih, Yan-Ping|Yang, Ueng-Cheng|Lin, Wen-chang|Hsu, Chun-Nan', 'Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan. wcchan@iis.sinica.edu.tw|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan. wcchan@iis.sinica.edu.tw|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan. wcchan@iis.sinica.edu.tw|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan. wcchan@iis.sinica.edu.tw|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan. wcchan@iis.sinica.edu.tw|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan. wcchan@iis.sinica.edu.tw', '-|-|-|-|-|-', 0, 'Artificial Intelligence|Databases, Protein|Escherichia coli|Genetic Vectors|Recombinant Proteins|Solubility', 'Journal Article', '10.1186/1471-2105-11-S1-S21', '20122193', 'Recombinant protein production is a useful biotechnology to produce a large quantity of highly soluble proteins. Currently, the most widely used production system is to fuse a target protein into different vectors in Escherichia coli (E. coli). However, the production efficacy of different vectors varies for different target proteins. Trial-and-error is still the common practice to find out the efficacy of a vector for a given target protein. Previous studies are limited in that they assumed that proteins would be over-expressed and focused only on the solubility of expressed proteins. In fact, many pairings of vectors and proteins result in no expression.'),
(93372, 2010, 'bmc', 'SFSSClass: an integrated approach for miRNA based tumor classification', 'Mitra, Ramkrishna|Bandyopadhyay, Sanghamitra|Maulik, Ujjwal|Zhang, Michael Q', 'Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India. rmitra_t@isical.ac.in|Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India. rmitra_t@isical.ac.in|Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India. rmitra_t@isical.ac.in|Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India. rmitra_t@isical.ac.in', 'India|India|India|India', 0, 'Computational Biology|Gene Expression Profiling|MicroRNAs|Neoplasms|Oligonucleotide Array Sequence Analysis|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-11-S1-S22', '20122194', 'MicroRNA (miRNA) expression profiling data has recently been found to be particularly important in cancer research and can be used as a diagnostic and prognostic tool. Current approaches of tumor classification using miRNA expression data do not integrate the experimental knowledge available in the literature. A judicious integration of such knowledge with effective miRNA and sample selection through a biclustering approach could be an important step in improving the accuracy of tumor classification.'),
(93373, 2010, 'bmc', 'An ontology-based search engine for protein-protein interactions', 'Park, Byungkyu|Han, Kyungsook', 'School of Computer Science and Engineering, Inha University, Incheon 402-751, South Korea. bpark@inhaian.net|School of Computer Science and Engineering, Inha University, Incheon 402-751, South Korea. bpark@inhaian.net', '-|-', 6, 'Databases, Factual|Protein Interaction Mapping|Proteins|Search Engine', 'Journal Article', '10.1186/1471-2105-11-S1-S23', '20122195', 'Keyword matching or ID matching is the most common searching method in a large database of protein-protein interactions. They are purely syntactic methods, and retrieve the records in the database that contain a keyword or ID specified in a query. Such syntactic search methods often retrieve too few search results or no results despite many potential matches present in the database.'),
(93374, 2010, 'bmc', 'HORI: a web server to compute Higher Order Residue Interactions in protein structures', 'Sundaramurthy, Pandurangan|Shameer, Khader|Sreenivasan, Raashi|Gakkhar, Sunita|Sowdhamini, Ramanathan', 'National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore, 560065, India. pksmurthy@ncbs.res.in|National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore, 560065, India. pksmurthy@ncbs.res.in|National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore, 560065, India. pksmurthy@ncbs.res.in|National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore, 560065, India. pksmurthy@ncbs.res.in|National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore, 560065, India. pksmurthy@ncbs.res.in', 'India|India|India|India|India', 0, 'Databases, Protein|Internet|Models, Molecular|Protein Conformation|Protein Folding|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-S1-S24', '20122196', 'Folding of a protein into its three dimensional structure is influenced by both local and global interactions within a protein. Higher order residue interactions, like pairwise, triplet and quadruplet ones, play a vital role in attaining the stable conformation of the protein structure. It is generally agreed that higher order interactions make significant contribution to the potential energy landscape of folded proteins and therefore it is important to identify them to estimate their contributions to overall stability of a protein structure.'),
(93375, 2010, 'bmc', 'A hub-attachment based method to detect functional modules from confidence-scored protein interactions and expression profiles', 'Chin, Chia-Hao|Chen, Shu-Hwa|Ho, Chin-Wen|Ko, Ming-Tat|Lin, Chung-Yen', 'Institute of Information Science, Academia Sinica, No, 128 Yan-Chiu-Yuan Rd, Sec, 2, Taipei 115, Taiwan. jovice@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, No, 128 Yan-Chiu-Yuan Rd, Sec, 2, Taipei 115, Taiwan. jovice@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, No, 128 Yan-Chiu-Yuan Rd, Sec, 2, Taipei 115, Taiwan. jovice@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, No, 128 Yan-Chiu-Yuan Rd, Sec, 2, Taipei 115, Taiwan. jovice@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, No, 128 Yan-Chiu-Yuan Rd, Sec, 2, Taipei 115, Taiwan. jovice@iis.sinica.edu.tw', '-|-|-|-|-', 35, 'Algorithms|Databases, Protein|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/1471-2105-11-S1-S25', '20122197', 'Many research results show that the biological systems are composed of functional modules. Members in the same module usually have common functions. This is useful information to understand how biological systems work. Therefore, detecting functional modules is an important research topic in the post-genome era. One of functional module detecting methods is to find dense regions in Protein-Protein Interaction (PPI) networks. Most of current methods neglect confidence-scores of interactions, and pay little attention on using gene expression data to improve their results.'),
(93376, 2010, 'bmc', 'Using genomic signatures for HIV-1 sub-typing', 'Pandit, Aridaman|Sinha, Somdatta', 'Centre for Cellular and Molecular Biology, Hyderabad, India. aridaman@ccmb.res.in|Centre for Cellular and Molecular Biology, Hyderabad, India. aridaman@ccmb.res.in', 'India|India', 21, 'Africa|Europe|Genetic Variation|Genome, Viral|HIV-1', 'Journal Article', '10.1186/1471-2105-11-S1-S26', '20122198', 'Human Immunodeficiency Virus type 1 (HIV-1), the causative agent of Acquired Immune Deficiency Syndrome (AIDS), exhibits very high genetic diversity with different variants or subtypes prevalent in different parts of the world. Proper classification of the HIV-1 subtypes, displaying differential infectivity, plays a major role in monitoring the epidemic and is also a critical component for effective treatment strategy. The existing methods to classify HIV-1 sequence subtypes, based on phylogenetic analysis focusing only on specific genes/regions, have shown inconsistencies as they lack the capability to analyse whole genome variations. Several isolates are left unclassified due to unresolved sub-typing. It is apparent that classification of subtypes based on complete genome sequences, rather than sub-genomic regions, is a more robust and comprehensive approach to address genome-wide heterogeneity. However, no simple methodology exists that directly computes HIV-1 subtype from the complete genome sequence.'),
(93377, 2010, 'bmc', 'Genome rearrangements with duplications', 'Bader, Martin', 'Institute of Theoretical Computer Science, Ulm University, 89069 Ulm, Germany. martin.bader@uni-ulm.de', 'Germany', 12, 'Algorithms|Evolution, Molecular|Gene Duplication|Gene Rearrangement|Genome|Genomics', 'Journal Article', '10.1186/1471-2105-11-S1-S27', '20122199', 'Finding sequences of evolutionary operations that transform one genome into another is a classical problem in comparative genomics. While most of the genome rearrangement algorithms assume that there is exactly one copy of each gene in both genomes, this does not reflect the biological reality very well - most of the studied genomes contain duplicated gene content, which has to be removed before applying those algorithms. However, dealing with unequal gene content is a very challenging task, and only few algorithms allow operations like duplications and deletions, especially if the duplicated or deleted segments are of arbitrary size.'),
(93378, 2010, 'bmc', 'Decoding HMMs using the k best paths: algorithms and applications', 'Brown, Daniel G|Golod, Daniil', 'Cheriton School of Computer Science, University of Waterloo, 200 University Avenue W, Waterloo, Ontario, Canada N2L 3G1. browndg@uwaterloo.ca|Cheriton School of Computer Science, University of Waterloo, 200 University Avenue W, Waterloo, Ontario, Canada N2L 3G1. browndg@uwaterloo.ca', 'Canada|Canada', 8, 'Algorithms|Databases, Protein|Markov Chains|Proteins', 'Journal Article', '10.1186/1471-2105-11-S1-S28', '20122200', 'Traditional algorithms for hidden Markov model decoding seek to maximize either the probability of a state path or the number of positions of a sequence assigned to the correct state. These algorithms provide only a single answer and in practice do not produce good results.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93379, 2010, 'bmc', 'Prediction of novel precursor miRNAs using a context-sensitive hidden Markov model (CSHMM)', 'Agarwal, Sumeet|Vaz, Candida|Bhattacharya, Alok|Srinivasan, Ashwin', 'Systems Biology Doctoral Training Centre and Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, UK. sumeet.agarwal@physics.ox.ac.uk|Systems Biology Doctoral Training Centre and Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, UK. sumeet.agarwal@physics.ox.ac.uk|Systems Biology Doctoral Training Centre and Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, UK. sumeet.agarwal@physics.ox.ac.uk|Systems Biology Doctoral Training Centre and Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, UK. sumeet.agarwal@physics.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 47, 'Genomics|Markov Chains|MicroRNAs|RNA', 'Journal Article', '10.1186/1471-2105-11-S1-S29', '20122201', 'It has been apparent in the last few years that small non coding RNAs (ncRNA) play a very significant role in biological regulation. Among these microRNAs (miRNAs), 22-23 nucleotide small regulatory RNAs, have been a major object of study as these have been found to be involved in some basic biological processes. So far about 706 miRNAs have been identified in humans alone. However, it is expected that there may be many more miRNAs encoded in the human genome. In this report, a context-sensitive Hidden Markov Model (CSHMM) to represent miRNA structures has been proposed and tested extensively. We also demonstrate how this model can be used in conjunction with filters as an ab initio method for miRNA identification.'),
(93380, 2010, 'bmc', 'Predicting the protein-protein interactions using primary structures with predicted protein surface', 'Chang, Darby Tien-Hao|Syu, Yu-Tang|Lin, Po-Chang', 'Department of Electrical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan. darby@ee.ncku.edu.tw|Department of Electrical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan. darby@ee.ncku.edu.tw|Department of Electrical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan. darby@ee.ncku.edu.tw', '-|-|-', 27, 'Amino Acid Sequence|Binding Sites|Databases, Protein|Models, Molecular|Protein Interaction Mapping|Proteins|Proteomics|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-11-S1-S3', '20122202', 'Many biological functions involve various protein-protein interactions (PPIs). Elucidating such interactions is crucial for understanding general principles of cellular systems. Previous studies have shown the potential of predicting PPIs based on only sequence information. Compared to approaches that require other auxiliary information, these sequence-based approaches can be applied to a broader range of applications.'),
(93381, 2010, 'bmc', 'Heuristics for the inversion median problem', 'Rajan, Vaibhav|Xu, Andrew Wei|Lin, Yu|Swenson, Krister M|Moret, Bernard M E', 'Laboratory for Computational Biology and Bioinformatics, EPFL, CH-1015 Lausanne, Switzerland. vaibhav.rajan@epfl.ch|Laboratory for Computational Biology and Bioinformatics, EPFL, CH-1015 Lausanne, Switzerland. vaibhav.rajan@epfl.ch|Laboratory for Computational Biology and Bioinformatics, EPFL, CH-1015 Lausanne, Switzerland. vaibhav.rajan@epfl.ch|Laboratory for Computational Biology and Bioinformatics, EPFL, CH-1015 Lausanne, Switzerland. vaibhav.rajan@epfl.ch|Laboratory for Computational Biology and Bioinformatics, EPFL, CH-1015 Lausanne, Switzerland. vaibhav.rajan@epfl.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 11, 'Algorithms|Evolution, Molecular|Gene Rearrangement|Genome|Genomics|Phylogeny', 'Journal Article', '10.1186/1471-2105-11-S1-S30', '20122203', 'The study of genome rearrangements has become a mainstay of phylogenetics and comparative genomics. Fundamental in such a study is the median problem: given three genomes find a fourth that minimizes the sum of the evolutionary distances between itself and the given three. Many exact algorithms and heuristics have been developed for the inversion median problem, of which the best known is MGR.'),
(93382, 2010, 'bmc', 'Reaction graph kernels predict EC numbers of unknown enzymatic reactions in plant secondary metabolism', 'Saigo, Hiroto|Hattori, Masahiro|Kashima, Hisashi|Tsuda, Koji', 'Max Planck Institute for Informatics, Campus E1 4, 66123 Saarbrucken, Germany. hiroto.saigo@mpi-inf.mpg.de|Max Planck Institute for Informatics, Campus E1 4, 66123 Saarbrucken, Germany. hiroto.saigo@mpi-inf.mpg.de|Max Planck Institute for Informatics, Campus E1 4, 66123 Saarbrucken, Germany. hiroto.saigo@mpi-inf.mpg.de|Max Planck Institute for Informatics, Campus E1 4, 66123 Saarbrucken, Germany. hiroto.saigo@mpi-inf.mpg.de', 'Germany|Germany|Germany|Germany', 7, 'Computational Biology|Databases, Factual|Enzymes|Plants', 'Journal Article', '10.1186/1471-2105-11-S1-S31', '20122204', 'Understanding of secondary metabolic pathway in plant is essential for finding druggable candidate enzymes. However, there are many enzymes whose functions are not yet discovered in organism-specific metabolic pathways. Towards identifying the functions of those enzymes, assignment of EC numbers to the enzymatic reactions they catalyze plays a key role, since EC numbers represent the categorization of enzymes on one hand, and the categorization of enzymatic reactions on the other hand.'),
(93383, 2010, 'bmc', 'Detection of characteristic sub pathway network for angiogenesis based on the comprehensive pathway network', 'Huang, Yezhou|Li, Shao', 'MOE Key Laboratory of Bioinformatics and Bioinformatics Div, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China. huangyezhou07@mails.tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics and Bioinformatics Div, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China. huangyezhou07@mails.tsinghua.edu.cn', 'China|China', 35, 'Humans|Interleukin-1|Neovascularization, Physiologic|Phenotype|Signal Transduction|Tumor Necrosis Factor-alpha', 'Journal Article', '10.1186/1471-2105-11-S1-S32', '20122205', 'Pathways in biological system often cooperate with each other to function. Changes of interactions among pathways tightly associate with alterations in the properties and functions of the cell and hence alterations in the phenotype. So, the pathway interactions and especially their changes over time corresponding to specific phenotype are critical to understanding cell functions and phenotypic plasticity.'),
(93384, 2010, 'bmc', 'A weighted q-gram method for glycan structure classification', 'Li, Limin|Ching, Wai-Ki|Yamaguchi, Takako|Aoki-Kinoshita, Kiyoko F', 'Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong. liminli@hkusua.hku.hk|Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong. liminli@hkusua.hku.hk|Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong. liminli@hkusua.hku.hk|Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong. liminli@hkusua.hku.hk', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong', 9, 'Algorithms|Carbohydrate Conformation|Glycomics|Polysaccharides', 'Journal Article', '10.1186/1471-2105-11-S1-S33', '20122206', 'Glycobiology pertains to the study of carbohydrate sugar chains, or glycans, in a particular cell or organism. Many computational approaches have been proposed for analyzing these complex glycan structures, which are chains of monosaccharides. The monosaccharides are linked to one another by glycosidic bonds, which can take on a variety of comformations, thus forming branches and resulting in complex tree structures. The q-gram method is one of these recent methods used to understand glycan function based on the classification of their tree structures. This q-gram method assumes that for a certain q, different q-grams share no similarity among themselves. That is, that if two structures have completely different components, then they are completely different. However, from a biological standpoint, this is not the case. In this paper, we propose a weighted q-gram method to measure the similarity among glycans by incorporating the similarity of the geometric structures, monosaccharides and glycosidic bonds among q-grams. In contrast to the traditional q-gram method, our weighted q-gram method admits similarity among q-grams for a certain q. Thus our new kernels for glycan structure were developed and then applied in SVMs to classify glycans.'),
(93385, 2010, 'bmc', 'Efficient protein alignment algorithm for protein search', 'Lu, Zaixin|Zhao, Zhiyu|Fu, Bin', 'Department of Computer Science, University of Texas-Pan American, Edinburg, TX 78539, USA. lzaixin@broncs.utpa.edu|Department of Computer Science, University of Texas-Pan American, Edinburg, TX 78539, USA. lzaixin@broncs.utpa.edu|Department of Computer Science, University of Texas-Pan American, Edinburg, TX 78539, USA. lzaixin@broncs.utpa.edu', 'United States|United States|United States', 2, 'Algorithms|Databases, Protein|Protein Conformation|Proteins|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-11-S1-S34', '20122207', 'Proteins show a great variety of 3D conformations, which can be used to infer their evolutionary relationship and to classify them into more general groups, therefore protein structure alignment algorithms are very helpful for protein biologists. However, an accurate alignment algorithm itself may be insufficient for effective discovering of structural relationships among tens of thousands of proteins. Due to the exponentially increasing amount of protein structural data, a fast and accurate structure alignment tool is necessary to access protein classification and protein similarity search, however, the complexity of current alignment algorithms are usually too high to make a fully alignment-based classification and search practical.'),
(93386, 2010, 'bmc', 'Functional characterization and topological modularity of molecular interaction networks', 'Pandey, Jayesh|Koyutürk, Mehmet|Grama, Ananth', 'Department of Computer Science, Purdue University, West Lafayette, IN, USA. jpandey@cs.purdue.edu|Department of Computer Science, Purdue University, West Lafayette, IN, USA. jpandey@cs.purdue.edu|Department of Computer Science, Purdue University, West Lafayette, IN, USA. jpandey@cs.purdue.edu', 'United States|United States|United States', 11, 'Protein Interaction Mapping|Proteins|Proteome|Proteomics', 'Journal Article', '10.1186/1471-2105-11-S1-S35', '20122208', 'Analyzing interaction networks for functional characterization poses significant challenges arising from the noisy, incomplete, and generic nature of both the interaction data as well as functional annotation of molecules. Network-based methods focus on interacting molecules (pairs or sets) occurring in close proximity to infer functional associations.'),
(93387, 2010, 'bmc', 'Towards high performance computing for molecular structure prediction using IBM Cell Broadband Engine--an implementation perspective', 'Krishnan, S P T|Liang, Sim Sze|Veeravalli, Bharadwaj', 'Institute for Infocomm Research, 1 Fusionopolis Way, #21-01, Connexis South Tower, Singapore 138632. krishnan@i2r.a-star.edu.sg|Institute for Infocomm Research, 1 Fusionopolis Way, #21-01, Connexis South Tower, Singapore 138632. krishnan@i2r.a-star.edu.sg|Institute for Infocomm Research, 1 Fusionopolis Way, #21-01, Connexis South Tower, Singapore 138632. krishnan@i2r.a-star.edu.sg', 'Singapore|Singapore|Singapore', 4, 'Computing Methodologies|Molecular Structure|Nucleic Acid Conformation|RNA', 'Journal Article', '10.1186/1471-2105-11-S1-S36', '20122209', 'RNA structure prediction problem is a computationally complex task, especially with pseudo-knots. The problem is well-studied in existing literature and predominantly uses highly coupled Dynamic Programming (DP) solutions. The problem scale and complexity become embarrassingly humungous to handle as sequence size increases. This makes the case for parallelization. Parallelization can be achieved by way of networked platforms (clusters, grids, etc) as well as using modern day multi-core chips.'),
(93388, 2010, 'bmc', 'Hit integration for identifying optimal spaced seeds', 'Chung, Won-Hyoung|Park, Seong-Bae', 'Department of Computer Engineering, Kyungpook National University, Daegu 702-701, South Korea. whchung@sejong.knu.ac.kr|Department of Computer Engineering, Kyungpook National University, Daegu 702-701, South Korea. whchung@sejong.knu.ac.kr', '-|-', 2, 'Algorithms|Models, Statistical|Pattern Recognition, Automated|Probability', 'Journal Article', '10.1186/1471-2105-11-S1-S37', '20122210', 'Introduction of spaced speeds opened a way of sensitivity improvement in homology search without loss of search speed. Since then, the efforts of finding optimal seed which maximizes the sensitivity have been continued today. The sensitivity of a seed is generally computed by its hit probability. However, the limitation of hit probability is that it computes the sensitivity only at a specific similarity level while homologous regions usually distributed in various similarity levels. As a result, the optimal seed found by hit probability is not actually optimal for various similarity levels. Therefore, a new measure of seed sensitivity is required to recommend seeds that are robust to various similarity levels.'),
(93389, 2010, 'bmc', 'Comparative classification of species and the study of pathway evolution based on the alignment of metabolic pathways', 'Mano, Adi|Tuller, Tamir|Béjà, Oded|Pinter, Ron Y', 'Dept, of Computer Science, Technion - Israel Institute of Technology, Haifa 32000, Israel. amano@cs.technion.ac.il|Dept, of Computer Science, Technion - Israel Institute of Technology, Haifa 32000, Israel. amano@cs.technion.ac.il|Dept, of Computer Science, Technion - Israel Institute of Technology, Haifa 32000, Israel. amano@cs.technion.ac.il|Dept, of Computer Science, Technion - Israel Institute of Technology, Haifa 32000, Israel. amano@cs.technion.ac.il', 'Israel|Israel|Israel|Israel', 0, 'Archaea|Evolution, Molecular|Genome|Metabolic Networks and Pathways|Phylogeny|Proteins|Species Specificity', 'Comparative Study', '10.1186/1471-2105-11-S1-S38', '20122211', 'Pathways provide topical descriptions of cellular circuitry. Comparing analogous pathways reveals intricate insights into individual functional differences among species. While previous works in the field performed genomic comparisons and evolutionary studies that were based on specific genes or proteins, whole genomic sequence, or even single pathways, none of them described a genomic system level comparative analysis of metabolic pathways. In order to properly implement such an analysis one should overcome two specific challenges: how to combine the effect of many pathways under a unified framework and how to appropriately analyze co-evolution of pathways. Here we present a computational approach for solving these two challenges. First, we describe a comprehensive, scalable, information theory based computational pipeline that calculates pathway alignment information and then compiles it in a novel manner that allows further analysis. This approach can be used for building phylogenies and for pointing out specific differences that can then be analyzed in depth. Second, we describe a new approach for comparing the evolution of metabolic pathways. This approach can be used for detecting co-evolutionary relationships between metabolic pathways.'),
(93390, 2010, 'bmc', 'A hybrid approach to protein folding problem integrating constraint programming with local search', 'Ullah, Abu Dayem|Steinhöfel, Kathleen', 'Kings College London, Department of Computer Science, London WC2R 2LS, UK. abu.dayem_ullah@kcl.ac.uk|Kings College London, Department of Computer Science, London WC2R 2LS, UK. abu.dayem_ullah@kcl.ac.uk', 'United Kingdom|United Kingdom', 0, 'Computational Biology|Models, Molecular|Protein Conformation|Protein Folding|Proteins|Thermodynamics', 'Journal Article', '10.1186/1471-2105-11-S1-S39', '20122212', 'The protein folding problem remains one of the most challenging open problems in computational biology. Simplified models in terms of lattice structure and energy function have been proposed to ease the computational hardness of this optimization problem. Heuristic search algorithms and constraint programming are two common techniques to approach this problem. The present study introduces a novel hybrid approach to simulate the protein folding problem using constraint programming technique integrated within local search.'),
(93391, 2010, 'bmc', 'Better score function for peptide identification with ETD MS/MS spectra', 'Liu, Xiaowen|Shan, Baozhen|Xin, Lei|Ma, Bin', 'David R, Cheriton School of Computer Science, University of Waterloo, Canada. x83liu@cs.uwaterloo.ca|David R, Cheriton School of Computer Science, University of Waterloo, Canada. x83liu@cs.uwaterloo.ca|David R, Cheriton School of Computer Science, University of Waterloo, Canada. x83liu@cs.uwaterloo.ca|David R, Cheriton School of Computer Science, University of Waterloo, Canada. x83liu@cs.uwaterloo.ca', 'Canada|Canada|Canada|Canada', 9, 'Peptides|Proteins|Proteomics|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-11-S1-S4', '20122213', 'Tandem mass spectrometry (MS/MS) has become the primary way for protein identification in proteomics. A good score function for measuring the match quality between a peptide and an MS/MS spectrum is instrumental for the protein identification. Traditionally the to-be-measured peptides are fragmented with the collision induced dissociation (CID) method. More recently, the electron transfer dissociation (ETD) method was introduced and has proven to produce better fragment ion ladders for larger and more basic peptides. However, the existing software programs that analyze ETD MS/MS data are not as advanced as they are for CID.'),
(93392, 2010, 'bmc', 'New decoding algorithms for Hidden Markov Models using distance measures on labellings', 'Brown, Daniel G|Truszkowski, Jakub', 'David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada. browndg@uwaterloo.ca|David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada. browndg@uwaterloo.ca', 'Canada|Canada', 0, 'Algorithms|Databases, Protein|Markov Chains|Membrane Proteins', 'Journal Article', '10.1186/1471-2105-11-S1-S40', '20122214', 'Existing hidden Markov model decoding algorithms do not focus on approximately identifying the sequence feature boundaries.'),
(93393, 2010, 'bmc', 'Analysis of interactions between ribosomal proteins and RNA structural motifs', 'Ciriello, Giovanni|Gallina, Claudio|Guerra, Concettina', 'Dept, of Information Engineering, University of Padova, Via Gradenigo 6a, 35131 Padova, Italy. gcunipd@gmail.com|Dept, of Information Engineering, University of Padova, Via Gradenigo 6a, 35131 Padova, Italy. gcunipd@gmail.com|Dept, of Information Engineering, University of Padova, Via Gradenigo 6a, 35131 Padova, Italy. gcunipd@gmail.com', 'Italy|Italy|Italy', 10, 'Binding Sites|Models, Molecular|Nucleic Acid Conformation|Protein Conformation|RNA|Ribosomal Proteins', 'Comparative Study', '10.1186/1471-2105-11-S1-S41', '20122215', 'One important goal of structural bioinformatics is to recognize and predict the interactions between protein binding sites and RNA. Recently, a comprehensive analysis of ribosomal proteins and their interactions with rRNA has been done. Interesting results emerged from the comparison of r-proteins within the small subunit in T. thermophilus and E. coli, supporting the idea of a core made by both RNA and proteins, conserved by evolution. Recent work showed also that ribosomal RNA is modularly composed. Motifs are generally single-stranded sequences of consecutive nucleotides (ssRNA) with characteristic folding. The role of these motifs in protein-RNA interactions has been so far only sparsely investigated.'),
(93394, 2010, 'bmc', 'Efficient genome-scale phylogenetic analysis under the duplication-loss and deep coalescence cost models', 'Bansal, Mukul S|Burleigh, J Gordon|Eulenstein, Oliver', 'School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel. bansal@tau.ac.il|School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel. bansal@tau.ac.il|School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel. bansal@tau.ac.il', 'Israel|Israel|Israel', 0, 'Algorithms|Evolution, Molecular|Gene Duplication|Genome|Phylogeny', 'Journal Article', '10.1186/1471-2105-11-S1-S42', '20122216', 'Genomic data provide a wealth of new information for phylogenetic analysis. Yet making use of this data requires phylogenetic methods that can efficiently analyze extremely large data sets and account for processes of gene evolution, such as gene duplication and loss, incomplete lineage sorting (deep coalescence), or horizontal gene transfer, that cause incongruence among gene trees. One such approach is gene tree parsimony, which, given a set of gene trees, seeks a species tree that requires the smallest number of evolutionary events to explain the incongruence of the gene trees. However, the only existing algorithms for gene tree parsimony under the duplication-loss or deep coalescence reconciliation cost are prohibitively slow for large datasets.'),
(93395, 2010, 'bmc', 'Stability analysis of the GAL regulatory network in Saccharomyces cerevisiae and Kluyveromyces lactis', 'Kulkarni, Vishwesh V|Kareenhalli, Venkatesh|Malakar, Pushkar|Pao, Lucy Y|Safonov, Michael G|Viswanathan, Ganesh A', 'Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India. vishwesh@ee.iitb.ac.in|Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India. vishwesh@ee.iitb.ac.in|Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India. vishwesh@ee.iitb.ac.in|Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India. vishwesh@ee.iitb.ac.in|Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India. vishwesh@ee.iitb.ac.in|Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India. vishwesh@ee.iitb.ac.in', 'India|India|India|India|India|India', 13, 'Computational Biology|Fungal Proteins|Galactose|Kluyveromyces|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction', 'Journal Article', '10.1186/1471-2105-11-S1-S43', '20122217', 'In the yeast Saccharomyces cerevisiae, interactions between galactose, Gal3p, Gal80p, and Gal4p determine the transcriptional status of the genes required for the galactose utilization. Increase in the cellular galactose concentration causes the galactose molecules to bind onto Gal3p which, via Gal80p, activates Gal4p, which induces the GAL3 and GAL80 gene transcription. Recently, a linear time-invariant multi-input multi-output (MIMO) model of this GAL regulatory network has been proposed, the inputs being galactose and Gal4p, and the outputs being the active Gal4p and galactose utilization. Unfortunately, this model assumes the cell culture to be homogeneous, although it is not so in practice. We overcome this drawback by including more biochemical reactions, and derive a quadratic ordinary differential equation (ODE) based model.'),
(93396, 2010, 'bmc', 'Structure prediction for the helical skeletons detected from the low resolution protein density map', 'Al Nasr, Kamal|Sun, Weitao|He, Jing', 'Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA. kal@cs.odu.edu|Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA. kal@cs.odu.edu|Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA. kal@cs.odu.edu', 'United States|United States|United States', 17, 'Cryoelectron Microscopy|Databases, Protein|Protein Folding|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1186/1471-2105-11-S1-S44', '20122218', 'The current advances in electron cryo-microscopy technique have made it possible to obtain protein density maps at about 6-10 A resolution. Although it is hard to derive the protein chain directly from such a low resolution map, the location of the secondary structures such as helices and strands can be computationally detected. It has been demonstrated that such low-resolution map can be used during the protein structure prediction process to enhance the structure prediction.'),
(93397, 2010, 'bmc', 'A novel approach for haplotype-based association analysis using family data', 'Chen, Yixuan|Li, Xin|Li, Jing', 'Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH 44106, USA. yixuan.chen@case.edu|Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH 44106, USA. yixuan.chen@case.edu|Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH 44106, USA. yixuan.chen@case.edu', 'United States|United States|United States', 7, 'Algorithms|Alleles|Computational Biology|Genome, Human|Haplotypes|Humans|Linkage Disequilibrium|Phylogeny', 'Journal Article', '10.1186/1471-2105-11-S1-S45', '20122219', 'Haplotype-based approaches have been extensively studied for case-control association mapping in recent years. It has been shown that haplotype methods can provide more consistent results comparing to single-locus based approaches, especially in cases where causal variants are not typed. Improved power has been observed by clustering similar or rare haplotypes into groups to reduce the degrees of freedom of association tests. For family-based association studies, one commonly used strategy is Transmission Disequilibrium Tests (TDT), which examine the imbalanced transmission of alleles/haplotypes to affected and normal children. Many extensions have been developed to deal with general pedigrees and continuous traits.'),
(93398, 2010, 'bmc', 'A fast indexing approach for protein structure comparison', 'Zhang, Lei|Bailey, James|Konagurthu, Arun S|Ramamohanarao, Kotagiri', 'National ICT Australia Victoria Research Laboratory, The University of Melbourne, Melbourne, Victoria, Australia. lzhan@csse.unimelb.edu.au|National ICT Australia Victoria Research Laboratory, The University of Melbourne, Melbourne, Victoria, Australia. lzhan@csse.unimelb.edu.au|National ICT Australia Victoria Research Laboratory, The University of Melbourne, Melbourne, Victoria, Australia. lzhan@csse.unimelb.edu.au|National ICT Australia Victoria Research Laboratory, The University of Melbourne, Melbourne, Victoria, Australia. lzhan@csse.unimelb.edu.au', 'Australia|Australia|Australia|Australia', 0, 'Algorithms|Databases, Protein|Models, Molecular|Protein Folding|Proteins', 'Comparative Study', '10.1186/1471-2105-11-S1-S46', '20122220', 'Protein structure comparison is a fundamental task in structural biology. While the number of known protein structures has grown rapidly over the last decade, searching a large database of protein structures is still relatively slow using existing methods. There is a need for new techniques which can rapidly compare protein structures, whilst maintaining high matching accuracy.'),
(93399, 2010, 'bmc', 'Computational prediction of type III secreted proteins from gram-negative bacteria', 'Yang, Yang|Zhao, Jiayuan|Morgan, Robyn L|Ma, Wenbo|Jiang, Tao', 'Department of Computer Science and Engineering, Information Engineering College, Shanghai Maritime University, 1550 Haigang Ave, Shanghai 201306, PR China. yangyang@shmtu.edu.cn|Department of Computer Science and Engineering, Information Engineering College, Shanghai Maritime University, 1550 Haigang Ave, Shanghai 201306, PR China. yangyang@shmtu.edu.cn|Department of Computer Science and Engineering, Information Engineering College, Shanghai Maritime University, 1550 Haigang Ave, Shanghai 201306, PR China. yangyang@shmtu.edu.cn|Department of Computer Science and Engineering, Information Engineering College, Shanghai Maritime University, 1550 Haigang Ave, Shanghai 201306, PR China. yangyang@shmtu.edu.cn|Department of Computer Science and Engineering, Information Engineering College, Shanghai Maritime University, 1550 Haigang Ave, Shanghai 201306, PR China. yangyang@shmtu.edu.cn', 'China|China|China|China|China', 53, 'Bacterial Proteins|Computational Biology|Genome, Bacterial|Gram-Negative Bacteria|Pseudomonas syringae|Rhizobium', 'Journal Article', '10.1186/1471-2105-11-S1-S47', '20122221', 'Type III secretion system (T3SS) is a specialized protein delivery system in gram-negative bacteria that injects proteins (called effectors) directly into the eukaryotic host cytosol and facilitates bacterial infection. For many plant and animal pathogens, T3SS is indispensable for disease development. Recently, T3SS has also been found in rhizobia and plays a crucial role in the nodulation process. Although a great deal of efforts have been done to understand type III secretion, the precise mechanism underlying the secretion and translocation process has not been fully understood. In particular, defined secretion and translocation signals enabling the secretion have not been identified from the type III secreted effectors (T3SEs), which makes the identification of these important virulence factors notoriously challenging. The availability of a large number of sequenced genomes for plant and animal-associated bacteria demands the development of efficient and effective prediction methods for the identification of T3SEs using bioinformatics approaches.'),
(93400, 2010, 'bmc', 'Prediction of FAD interacting residues in a protein from its primary sequence using evolutionary information', 'Mishra, Nitish K|Raghava, Gajendra P S', 'Institute of Microbial Technology, Sector 39A, Chandigarh, India. nitish@imtech.res.in|Institute of Microbial Technology, Sector 39A, Chandigarh, India. nitish@imtech.res.in', 'India|India', 0, 'Algorithms|Amino Acid Sequence|Binding Sites|Databases, Protein|Evolution, Molecular|Flavin-Adenine Dinucleotide|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-S1-S48', '20122222', 'Flavin binding proteins (FBP) plays a critical role in several biological functions such as electron transport system (ETS). These flavoproteins contain very tightly bound, sometimes covalently, flavin adenine dinucleotide (FAD) or flavin mono nucleotide (FMN). The interaction between flavin nucleotide and amino acids of flavoprotein is essential for their functionality. Thus identification of FAD interacting residues in a FBP is an important step for understanding their function and mechanism.'),
(93401, 2010, 'bmc', 'Comparative analysis of thermophilic and mesophilic proteins using Protein Energy Networks', 'Vijayabaskar, M S|Vishveshwara, Saraswathi', 'Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India. vijay@mbu.iisc.ernet.in|Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India. vijay@mbu.iisc.ernet.in', 'India|India', 17, 'Computational Biology|Models, Molecular|Protein Conformation|Proteins|Temperature|Thermodynamics', 'Comparative Study', '10.1186/1471-2105-11-S1-S49', '20122223', 'Thermophilic proteins sustain themselves and function at higher temperatures. Despite their structural and functional similarities with their mesophilic homologues, they show enhanced stability. Various comparative studies at genomic, protein sequence and structure levels, and experimental works highlight the different factors and dominant interacting forces contributing to this increased stability.'),
(93402, 2010, 'bmc', 'A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data', 'Yang, Pengyi|Zhou, Bing B|Zhang, Zili|Zomaya, Albert Y', 'School of Information Technologies (J12), The University of Sydney, NSW 2006, Australia. yangpy@it.usyd.edu.au|School of Information Technologies (J12), The University of Sydney, NSW 2006, Australia. yangpy@it.usyd.edu.au|School of Information Technologies (J12), The University of Sydney, NSW 2006, Australia. yangpy@it.usyd.edu.au|School of Information Technologies (J12), The University of Sydney, NSW 2006, Australia. yangpy@it.usyd.edu.au', 'Australia|Australia|Australia|Australia', 36, 'Algorithms|Gene Expression Profiling|Genes|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-S1-S5', '20122224', 'Feature selection techniques are critical to the analysis of high dimensional datasets. This is especially true in gene selection from microarray data which are commonly with extremely high feature-to-sample ratio. In addition to the essential objectives such as to reduce data noise, to reduce data redundancy, to improve sample classification accuracy, and to improve model generalization property, feature selection also helps biologists to focus on the selected genes to further validate their biological hypotheses.'),
(93403, 2010, 'bmc', 'Discrete wavelet transform de-noising in eukaryotic gene splicing', 'George, Tina P|Thomas, Tessamma', 'Department of Electronics and Instrumentation, College of Engineering, Kidangoor, Kottayam, Kerala, India. tinapgcusat@gmail.com|Department of Electronics and Instrumentation, College of Engineering, Kidangoor, Kottayam, Kerala, India. tinapgcusat@gmail.com', 'India|India', 28, 'Algorithms|Databases, Genetic|Eukaryota|Exons|RNA Splicing', 'Journal Article', '10.1186/1471-2105-11-S1-S50', '20122225', 'This paper compares the most common digital signal processing methods of exon prediction in eukaryotes, and also proposes a technique for noise suppression in exon prediction. The specimen used here which has relevance in medical research, has been taken from the public genomic database - GenBank.'),
(93404, 2010, 'bmc', 'Comparison of PGH2 binding site in prostaglandin synthases', 'Paragi-Vedanthi, Padmapriya|Doble, Mukesh', 'Department of Biotechnology, Indian Institute of Technology Madras, Chennai, 600036, India. padmapriya@smail.iitm.ac.in|Department of Biotechnology, Indian Institute of Technology Madras, Chennai, 600036, India. padmapriya@smail.iitm.ac.in', 'India|India', 3, 'Binding Sites|Computational Biology|Databases, Protein|Models, Molecular|Prostaglandin H2|Prostaglandin-Endoperoxide Synthases|Protein Conformation|Substrate Specificity', 'Comparative Study', '10.1186/1471-2105-11-S1-S51', '20122226', 'Prostaglandin H2 (PGH2) is a common precursor for the synthesis of five different Prostanoids via specific Prostanoid Synthases. The binding of this substrate with these Synthases is not properly understood. Moreover, currently no crystal structure of complexes bound with PGH2 has been reported. Hence, understanding the interactions of PGH2 and characterizing its binding sites in these synthases is crucial for developing novel therapeutics based on these proteins as targets.'),
(93405, 2010, 'bmc', 'Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm', 'Hsieh, Chih-Hung|Chang, Darby Tien-Hao|Hsueh, Cheng-Hao|Wu, Chi-Yeh|Oyang, Yen-Jen', 'Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan. hsiehch@gmail.com|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan. hsiehch@gmail.com|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan. hsiehch@gmail.com|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan. hsiehch@gmail.com|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan. hsiehch@gmail.com', '-|-|-|-|-', 15, 'Algorithms|Base Sequence|Genome|Genomics|Humans|MicroRNAs|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-11-S1-S52', '20122227', 'MicroRNAs (miRNAs) are short non-coding RNA molecules, which play an important role in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, ab initio approaches have attracted more attention because they do not depend on homology information and provide broader applications than comparative approaches. Kernel based classifiers such as support vector machine (SVM) are extensively adopted in these ab initio approaches due to the prediction performance they achieved. On the other hand, logic based classifiers such as decision tree, of which the constructed model is interpretable, have attracted less attention.'),
(93406, 2010, 'bmc', 'Virtual Screening of potential drug-like inhibitors against Lysine/DAP pathway of Mycobacterium tuberculosis', 'Garg, Aarti|Tewari, Rupinder|Raghava, Gajendra P S', 'Bioinformatics Centre, Institute of Microbial Technology, Sector-39A, Chandigarh, India. aarti@imtech.res.in|Bioinformatics Centre, Institute of Microbial Technology, Sector-39A, Chandigarh, India. aarti@imtech.res.in|Bioinformatics Centre, Institute of Microbial Technology, Sector-39A, Chandigarh, India. aarti@imtech.res.in', 'India|India|India', 17, 'Antitubercular Agents|Binding Sites|Catalytic Domain|Databases, Factual|Diaminopimelic Acid|Drug Design|Hydro-Lyases|Lysine|Models, Molecular|Mycobacterium tuberculosis|Pyruvic Acid|Structure-Activity Relationship|Tuberculosis, Multidrug-Resistant', 'Journal Article', '10.1186/1471-2105-11-S1-S53', '20122228', 'An explosive global spreading of multidrug resistant Mycobacterium tuberculosis (Mtb) is a catastrophe, which demands an urgent need to design or develop novel/potent antitubercular agents. The Lysine/DAP biosynthetic pathway is a promising target due its specific role in cell wall and amino acid biosynthesis. Here, we report identification of potential antitubercular candidates targeting Mtb dihydrodipicolinate synthase (DHDPS) enzyme of the pathway using virtual screening protocols.'),
(93407, 2010, 'bmc', 'Estimating true evolutionary distances under rearrangements, duplications, and losses', 'Lin, Yu|Rajan, Vaibhav|Swenson, Krister M|Moret, Bernard M E', 'Laboratory for Computational Biology and Bioinformatics, Swiss Federal Institute of Technology (EPFL), EPFL-IIS-LCBB, INJ 230, Station 14, CH-1015 Lausanne, Switzerland. yu.lin@epfl.ch|Laboratory for Computational Biology and Bioinformatics, Swiss Federal Institute of Technology (EPFL), EPFL-IIS-LCBB, INJ 230, Station 14, CH-1015 Lausanne, Switzerland. yu.lin@epfl.ch|Laboratory for Computational Biology and Bioinformatics, Swiss Federal Institute of Technology (EPFL), EPFL-IIS-LCBB, INJ 230, Station 14, CH-1015 Lausanne, Switzerland. yu.lin@epfl.ch|Laboratory for Computational Biology and Bioinformatics, Swiss Federal Institute of Technology (EPFL), EPFL-IIS-LCBB, INJ 230, Station 14, CH-1015 Lausanne, Switzerland. yu.lin@epfl.ch', 'Switzerland|Switzerland|Switzerland|Switzerland', 10, 'Evolution, Molecular|Gene Duplication|Gene Rearrangement|Genome|Genomics|Phylogeny', 'Journal Article', '10.1186/1471-2105-11-S1-S54', '20122229', 'The rapidly increasing availability of whole-genome sequences has enabled the study of whole-genome evolution. Evolutionary mechanisms based on genome rearrangements have attracted much attention and given rise to many models, somewhat independently, the mechanisms of gene duplication and loss have seen much work. However, the two are not independent and thus require a unified treatment, which remains missing to date. Moreover, existing rearrangement models do not fit the dichotomy between most prokaryotic genomes (one circular chromosome) and most eukaryotic genomes (multiple linear chromosomes).'),
(93408, 2010, 'bmc', 'An automated framework for understanding structural variations in the binding grooves of MHC class II molecules', 'Yeturu, Kalidas|Utriainen, Tapani|Kemp, Graham J L|Chandra, Nagasuma', 'Bioinformatics Centre, Indian Institute of Science, Bangalore, India. kalidas@rishi.serc.iisc.ernet.in|Bioinformatics Centre, Indian Institute of Science, Bangalore, India. kalidas@rishi.serc.iisc.ernet.in|Bioinformatics Centre, Indian Institute of Science, Bangalore, India. kalidas@rishi.serc.iisc.ernet.in|Bioinformatics Centre, Indian Institute of Science, Bangalore, India. kalidas@rishi.serc.iisc.ernet.in', 'India|India|India|India', 1, 'Alleles|Binding Sites|Epitopes|Histocompatibility Antigens Class II|Models, Molecular|Protein Conformation|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-11-S1-S55', '20122230', 'MHC/HLA class II molecules are important components of the immune system and play a critical role in processes such as phagocytosis. Understanding peptide recognition properties of the hundreds of MHC class II alleles is essential to appreciate determinants of antigenicity and ultimately to predict epitopes. While there are several methods for epitope prediction, each differing in their success rates, there are no reports so far in the literature to systematically characterize the binding sites at the structural level and infer recognition profiles from them.'),
(93409, 2010, 'bmc', 'Reverse engineering gene regulatory network from microarray data using linear time-variant model', 'Kabir, Mitra|Noman, Nasimul|Iba, Hitoshi', 'Department of Computer Science and Engineering, University of Dhaka, Dhaka, Bangladesh. mitrakabir@gmail.com|Department of Computer Science and Engineering, University of Dhaka, Dhaka, Bangladesh. mitrakabir@gmail.com|Department of Computer Science and Engineering, University of Dhaka, Dhaka, Bangladesh. mitrakabir@gmail.com', 'Bangladesh|Bangladesh|Bangladesh', 0, 'Cyclic AMP|Dictyostelium|Escherichia coli|Gene Expression Profiling|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-S1-S56', '20122231', 'Gene regulatory network is an abstract mapping of gene regulations in living cells that can help to predict the system behavior of living organisms. Such prediction capability can potentially lead to the development of improved diagnostic tests and therapeutics. DNA microarrays, which measure the expression level of thousands of genes in parallel, constitute the numeric seed for the inference of gene regulatory networks. In this paper, we have proposed a new approach for inferring gene regulatory networks from time-series gene expression data using linear time-variant model. Here, Self-Adaptive Differential Evolution, a versatile and robust Evolutionary Algorithm, is used as the learning paradigm.'),
(93410, 2010, 'bmc', 'Active learning for human protein-protein interaction prediction', 'Mohamed, Thahir P|Carbonell, Jaime G|Ganapathiraju, Madhavi K', 'Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. mop13+bmc@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. mop13+bmc@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. mop13+bmc@pitt.edu', 'United States|United States|United States', 0, 'Algorithms|Databases, Protein|Humans|Protein Conformation|Proteins|Proteomics', 'Journal Article', '10.1186/1471-2105-11-S1-S57', '20122232', 'Biological processes in cells are carried out by means of protein-protein interactions. Determining whether a pair of proteins interacts by wet-lab experiments is resource-intensive, only about 38,000 interactions, out of a few hundred thousand expected interactions, are known today. Active machine learning can guide the selection of pairs of proteins for future experimental characterization in order to accelerate accurate prediction of the human protein interactome.'),
(93411, 2010, 'bmc', 'Active machine learning for transmembrane helix prediction', 'Osmanbeyoglu, Hatice U|Wehner, Jessica A|Carbonell, Jaime G|Ganapathiraju, Madhavi K', 'Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. huo1+bmc@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. huo1+bmc@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. huo1+bmc@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. huo1+bmc@pitt.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Databases, Protein|Membrane Proteins|Protein Folding|Protein Structure, Secondary|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-S1-S58', '20122233', 'About 30% of genes code for membrane proteins, which are involved in a wide variety of crucial biological functions. Despite their importance, experimentally determined structures correspond to only about 1.7% of protein structures deposited in the Protein Data Bank due to the difficulty in crystallizing membrane proteins. Algorithms that can identify proteins whose high-resolution structure can aid in predicting the structure of many previously unresolved proteins are therefore of potentially high value. Active machine learning is a supervised machine learning approach which is suitable for this domain where there are a large number of sequences but only very few have known corresponding structures. In essence, active learning seeks to identify proteins whose structure, if revealed experimentally, is maximally predictive of others.'),
(93412, 2010, 'bmc', 'Translog, a web browser for studying the expression divergence of homologous genes', 'Dong, Xianjun|Akalin, Altuna|Sharma, Yogita|Lenhard, Boris', 'Computational Biology Unit, Bergen Center for Computational Science, University of Bergen, Thormøhlensgate 55, N-5008 Bergen, Norway. xianjun.dong@bccs.uib.no|Computational Biology Unit, Bergen Center for Computational Science, University of Bergen, Thormøhlensgate 55, N-5008 Bergen, Norway. xianjun.dong@bccs.uib.no|Computational Biology Unit, Bergen Center for Computational Science, University of Bergen, Thormøhlensgate 55, N-5008 Bergen, Norway. xianjun.dong@bccs.uib.no|Computational Biology Unit, Bergen Center for Computational Science, University of Bergen, Thormøhlensgate 55, N-5008 Bergen, Norway. xianjun.dong@bccs.uib.no', 'Norway|Norway|Norway|Norway', 0, 'Databases, Genetic|Gene Expression Profiling|Genome|Genomics|Internet|Regulatory Sequences, Nucleic Acid|Software', 'Journal Article', '10.1186/1471-2105-11-S1-S59', '20122234', 'Increasing amount of data from comparative genomics, and newly developed technologies producing accurate gene expression data facilitate the study of the expression divergence of homologous genes. Previous studies have individually highlighted factors that contribute to the expression divergence of duplicate genes, e.g. promoter changes, exon structure heterogeneity, asymmetric histone modifications and genomic neighborhood conservation. However, there is a lack of a tool to integrate multiple factors and visualize their variety among homologous genes in a straightforward way.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93413, 2010, 'bmc', 'Genome-wide sequence-based prediction of peripheral proteins using a novel semi-supervised learning technique', 'Bhardwaj, Nitin|Gerstein, Mark|Lu, Hui', 'Bioinformatics Program, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA. nitin.bhardwaj@yale.edu|Bioinformatics Program, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA. nitin.bhardwaj@yale.edu|Bioinformatics Program, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA. nitin.bhardwaj@yale.edu', 'United States|United States|United States', 12, 'Artificial Intelligence|Genome|Models, Molecular|Protein Conformation|Proteins|Proteomics|Sequence Analysis, Protein|Surface Properties', 'Journal Article', '10.1186/1471-2105-11-S1-S6', '20122235', 'In supervised learning, traditional approaches to building a classifier use two sets of examples with pre-defined classes along with a learning algorithm. The main limitation of this approach is that examples from both classes are required which might be infeasible in certain cases, especially those dealing with biological data. Such is the case for membrane-binding peripheral domains that play important roles in many biological processes, including cell signaling and membrane trafficking by reversibly binding to membranes. For these domains, a well-defined positive set is available with domains known to bind membrane along with a large unlabeled set of domains whose membrane binding affinities have not been measured. The aforementioned limitation can be addressed by a special class of semi-supervised machine learning called positive-unlabeled (PU) learning that uses a positive set with a large unlabeled set. METHODS In this study, we implement the first application of PU-learning to a protein function prediction problem: identification of peripheral domains. PU-learning starts by identifying reliable negative (RN) examples iteratively from the unlabeled set until convergence and builds a classifier using the positive and the final RN set. A data set of 232 positive cases and ~3750 unlabeled ones were used to construct and validate the protocol.'),
(93414, 2010, 'bmc', 'A parameter-adaptive dynamic programming approach for inferring cophylogenies', 'Merkle, Daniel|Middendorf, Martin|Wieseke, Nicolas', 'Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark. daniel@imada.sdu.dk|Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark. daniel@imada.sdu.dk|Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark. daniel@imada.sdu.dk', 'Denmark|Denmark|Denmark', 79, 'Algorithms|Evolution, Molecular|Genetic Variation|Genomics|Models, Genetic|Phylogeny', 'Journal Article', '10.1186/1471-2105-11-S1-S60', '20122236', 'Coevolutionary systems like hosts and their parasites are commonly used model systems for evolutionary studies. Inferring the coevolutionary history based on given phylogenies of both groups is often done by employing a set of possible types of events that happened during coevolution. Costs are assigned to the different types of events and a reconstruction of the common history with a minimal sum of event costs is sought.'),
(93415, 2010, 'bmc', 'NeMo: Network Module identification in Cytoscape', 'Rivera, Corban G|Vakil, Rachit|Bader, Joel S', 'Department of Biomedical Engineering and High-Throughput Biology Center, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA. cgrivera@jhu.edu|Department of Biomedical Engineering and High-Throughput Biology Center, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA. cgrivera@jhu.edu|Department of Biomedical Engineering and High-Throughput Biology Center, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA. cgrivera@jhu.edu', 'United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Databases, Protein|Protein Interaction Mapping|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-11-S1-S61', '20122237', 'As the size of the known human interactome grows, biologists increasingly rely on computational tools to identify patterns that represent protein complexes and pathways. Previous studies have shown that densely connected network components frequently correspond to community structure and functionally related modules. In this work, we present a novel method to identify densely connected and bipartite network modules based on a log odds score for shared neighbours.'),
(93416, 2010, 'bmc', 'An integrative modular approach to systematically predict gene-phenotype associations', 'Mehan, Michael R|Nunez-Iglesias, Juan|Dai, Chao|Waterman, Michael S|Zhou, Xianghong Jasmine', 'Program in Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles CA 90089, USA. rielmeha@usc.edu|Program in Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles CA 90089, USA. rielmeha@usc.edu|Program in Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles CA 90089, USA. rielmeha@usc.edu|Program in Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles CA 90089, USA. rielmeha@usc.edu|Program in Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles CA 90089, USA. rielmeha@usc.edu', 'United States|United States|United States|United States|United States', 10, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Genome|Phenotype', 'Journal Article', '10.1186/1471-2105-11-S1-S62', '20122238', 'Complex human diseases are often caused by multiple mutations, each of which contributes only a minor effect to the disease phenotype. To study the basis for these complex phenotypes, we developed a network-based approach to identify coexpression modules specifically activated in particular phenotypes. We integrated these modules, protein-protein interaction data, Gene Ontology annotations, and our database of gene-phenotype associations derived from literature to predict novel human gene-phenotype associations. Our systematic predictions provide us with the opportunity to perform a global analysis of human gene pleiotropy and its underlying regulatory mechanisms.'),
(93417, 2010, 'bmc', 'Bidirectional best hit r-window gene clusters', 'Zhang, Melvin|Leong, Hon Wai', 'School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore 117417, Republic of Singapore. melvin@comp.nus.edu.sg|School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore 117417, Republic of Singapore. melvin@comp.nus.edu.sg', 'Singapore|Singapore', 12, 'Bacillus subtilis|Escherichia coli|Genome, Bacterial|Genomics|Multigene Family', 'Journal Article', '10.1186/1471-2105-11-S1-S63', '20122239', 'Conserved gene clusters are groups of genes that are located close to one another in the genomes of several species. They tend to code for proteins that have a functional interaction. The identification of conserved gene clusters is an important step towards understanding genome evolution and predicting gene function.'),
(93418, 2010, 'bmc', 'Predicting protein functions by relaxation labelling protein interaction network', 'Hu, Pingzhao|Jiang, Hui|Emili, Andrew', 'Department of Computer Science and Engineering, York University, ON, Toronto, Canada. phu@cse.yorku.ca|Department of Computer Science and Engineering, York University, ON, Toronto, Canada. phu@cse.yorku.ca|Department of Computer Science and Engineering, York University, ON, Toronto, Canada. phu@cse.yorku.ca', 'Canada|Canada|Canada', 18, 'Databases, Protein|Escherichia coli|Protein Interaction Mapping|Proteins|Proteomics', 'Journal Article', '10.1186/1471-2105-11-S1-S64', '20122240', 'One of key issues in the post-genomic era is to assign functions to uncharacterized proteins. Since proteins seldom act alone, rather, they must interact with other biomolecular units to execute their functions. Thus, the functions of unknown proteins may be discovered through studying their interactions with proteins having known functions. Although many approaches have been developed for this purpose, one of main limitations in most of these methods is that the dependence among functional terms has not been taken into account.'),
(93419, 2010, 'bmc', 'Annotation of gene promoters by integrative data-mining of ChIP-seq Pol-II enrichment data', 'Gupta, Ravi|Wikramasinghe, Priyankara|Bhattacharyya, Anirban|Perez, Francisco A|Pal, Sharmistha|Davuluri, Ramana V', 'Center for Systems and Computational Biology, Molecular and Cellular Oncogenesis Program, The Wistar Institute, Philadelphia, PA, USA. rgupta@wistar.org|Center for Systems and Computational Biology, Molecular and Cellular Oncogenesis Program, The Wistar Institute, Philadelphia, PA, USA. rgupta@wistar.org|Center for Systems and Computational Biology, Molecular and Cellular Oncogenesis Program, The Wistar Institute, Philadelphia, PA, USA. rgupta@wistar.org|Center for Systems and Computational Biology, Molecular and Cellular Oncogenesis Program, The Wistar Institute, Philadelphia, PA, USA. rgupta@wistar.org|Center for Systems and Computational Biology, Molecular and Cellular Oncogenesis Program, The Wistar Institute, Philadelphia, PA, USA. rgupta@wistar.org|Center for Systems and Computational Biology, Molecular and Cellular Oncogenesis Program, The Wistar Institute, Philadelphia, PA, USA. rgupta@wistar.org', 'United States|United States|United States|United States|United States|United States', 22, 'Algorithms|Chromatin Immunoprecipitation|DNA Polymerase II|Data Mining|Genomics|Oligonucleotide Array Sequence Analysis|Promoter Regions, Genetic', 'Journal Article', '10.1186/1471-2105-11-S1-S65', '20122241', 'Use of alternative gene promoters that drive widespread cell-type, tissue-type or developmental gene regulation in mammalian genomes is a common phenomenon. Chromatin immunoprecipitation methods coupled with DNA microarray (ChIP-chip) or massive parallel sequencing (ChIP-seq) are enabling genome-wide identification of active promoters in different cellular conditions using antibodies against Pol-II. However, these methods produce enrichment not only near the gene promoters but also inside the genes and other genomic regions due to the non-specificity of the antibodies used in ChIP. Further, the use of these methods is limited by their high cost and strong dependence on cellular type and context.'),
(93420, 2010, 'bmc', 'BayesMotif: de novo protein sorting motif discovery from impure datasets', 'Hu, Jianjun|Zhang, Fan', 'Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA. jianjunh@cse.sc.edu|Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA. jianjunh@cse.sc.edu', 'United States|United States', 2, 'Algorithms|Amino Acid Motifs|Databases, Protein|Position-Specific Scoring Matrices|Proteins', 'Journal Article', '10.1186/1471-2105-11-S1-S66', '20122242', 'Protein sorting is the process that newly synthesized proteins are transported to their target locations within or outside of the cell. This process is precisely regulated by protein sorting signals in different forms. A major category of sorting signals are amino acid sub-sequences usually located at the N-terminals or C-terminals of protein sequences. Genome-wide experimental identification of protein sorting signals is extremely time-consuming and costly. Effective computational algorithms for de novo discovery of protein sorting signals is needed to improve the understanding of protein sorting mechanisms.'),
(93421, 2010, 'bmc', 'Formal reasoning on qualitative models of coinfection of HIV and Tuberculosis and HAART therapy', 'Sorathiya, Anil|Bracciali, Andrea|Liò, Pietro', 'Computer Laboratory, Cambridge University, William Gates Building,15 JJ Thomson Avenue, Cambridge CB3 0FD, UK. anilkumar.sorathiya@cl.cam.ac.uk|Computer Laboratory, Cambridge University, William Gates Building,15 JJ Thomson Avenue, Cambridge CB3 0FD, UK. anilkumar.sorathiya@cl.cam.ac.uk|Computer Laboratory, Cambridge University, William Gates Building,15 JJ Thomson Avenue, Cambridge CB3 0FD, UK. anilkumar.sorathiya@cl.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 6, 'Antiretroviral Therapy, Highly Active|Disease Progression|HIV Infections|Models, Statistical|Tuberculosis', 'Journal Article', '10.1186/1471-2105-11-S1-S67', '20122243', 'Several diseases, many of which nowadays pandemic, consist of multifactorial pathologies. Paradigmatic examples come from the immune response to pathogens, in which cases the effects of different infections combine together, yielding complex mutual feedback, often a positive one that boosts infection progression in a scenario that can easily become lethal. HIV is one such infection, which weakens the immune system favouring the insurgence of opportunistic infections, amongst which Tuberculosis (TB). The treatment with antiretroviral therapies has shown effective in reducing mortality. An in-depth understanding of complex systems, like the one consisting of HIV, TB and related therapies, is an open great challenge, on the boundaries of bioinformatics, computational and systems biology.'),
(93422, 2010, 'bmc', 'Molecular dynamics simulation studies and in vitro site directed mutagenesis of avian beta-defensin Apl_AvBD2', 'Soman, Soja Saghar|Sivakumar, Krishnankutty Chandrika|Sreekumar, Easwaran', 'Molecular Virology Laboratory, Department of Molecular Microbiology, Rajiv Gandhi Centre for Biotechnology, Thycaud PO, Thiruvananthapuram-695014, Kerala, India. soja.saghar@gmail.com|Molecular Virology Laboratory, Department of Molecular Microbiology, Rajiv Gandhi Centre for Biotechnology, Thycaud PO, Thiruvananthapuram-695014, Kerala, India. soja.saghar@gmail.com|Molecular Virology Laboratory, Department of Molecular Microbiology, Rajiv Gandhi Centre for Biotechnology, Thycaud PO, Thiruvananthapuram-695014, Kerala, India. soja.saghar@gmail.com', 'India|India|India', 12, 'Animals|Antimicrobial Cationic Peptides|Avian Proteins|Birds|Escherichia coli|Hydrogen Bonding|Hydrophobic and Hydrophilic Interactions|Models, Molecular|Molecular Dynamics Simulation|Mutagenesis, Site-Directed|Protein Structure, Secondary|Recombinant Proteins|beta-Defensins', 'Journal Article', '10.1186/1471-2105-11-S1-S7', '20122244', 'Defensins comprise a group of antimicrobial peptides, widely recognized as important elements of the innate immune system in both animals and plants. Cationicity, rather than the secondary structure, is believed to be the major factor defining the antimicrobial activity of defensins. To test this hypothesis and to improve the activity of the newly identified avian beta-defensin Apl_AvBD2 by enhancing the cationicity, we performed in silico site directed mutagenesis, keeping the predicted secondary structure intact. Molecular dynamics (MD) simulation studies were done to predict the activity. Mutant proteins were made by in vitro site directed mutagenesis and recombinant protein expression, and tested for antimicrobial activity to confirm the results obtained in MD simulation analysis.'),
(93423, 2010, 'bmc', 'Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships', 'Seok, Junhee|Kaushal, Amit|Davis, Ronald W|Xiao, Wenzhong', 'Stanford Genome Technology Center, 955 California Avenue, Palo Alto, California, 94305, USA. jseok@stanford.edu|Stanford Genome Technology Center, 955 California Avenue, Palo Alto, California, 94305, USA. jseok@stanford.edu|Stanford Genome Technology Center, 955 California Avenue, Palo Alto, California, 94305, USA. jseok@stanford.edu|Stanford Genome Technology Center, 955 California Avenue, Palo Alto, California, 94305, USA. jseok@stanford.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Databases, Protein|Gene Regulatory Networks|Knowledge Bases|Oligonucleotide Array Sequence Analysis|Proteome|Saccharomyces cerevisiae|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-11-S1-S8', '20122245', 'The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions.'),
(93424, 2010, 'bmc', 'Prediction of protein structural classes for low-homology sequences based on predicted secondary structure', 'Yang, Jian-Yi|Peng, Zhen-Ling|Chen, Xin', 'Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore. yang0241@ntu.edu.sg|Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore. yang0241@ntu.edu.sg|Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore. yang0241@ntu.edu.sg', 'Singapore|Singapore|Singapore', 90, 'Algorithms|Amino Acid Sequence|Entropy|Models, Molecular|Molecular Sequence Data|Protein Conformation|Protein Structure, Secondary|Proteins|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-11-S1-S9', '20122246', 'Prediction of protein structural classes (alpha, beta, alpha + beta and alpha/beta) from amino acid sequences is of great importance, as it is beneficial to study protein function, regulation and interactions. Many methods have been developed for high-homology protein sequences, and the prediction accuracies can achieve up to 90%. However, for low-homology sequences whose average pairwise sequence identity lies between 20% and 40%, they perform relatively poorly, yielding the prediction accuracy often below 60%.'),
(93425, 2010, 'bmc', 'Multiple structure alignment and consensus identification for proteins', 'Ilinkin, Ivaylo|Ye, Jieping|Janardan, Ravi', 'Department of Computer Science, Gettysburg College, Gettysburg, PA, USA. iilinkin@gettysburg.edu|Department of Computer Science, Gettysburg College, Gettysburg, PA, USA. iilinkin@gettysburg.edu|Department of Computer Science, Gettysburg College, Gettysburg, PA, USA. iilinkin@gettysburg.edu', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Databases, Protein|Protein Conformation|Proteins', 'Journal Article', '10.1186/1471-2105-11-71', '20122279', 'An algorithm is presented to compute a multiple structure alignment for a set of proteins and to generate a consensus (pseudo) protein which captures common substructures present in the given proteins. The algorithm represents each protein as a sequence of triples of coordinates of the alpha-carbon atoms along the backbone. It then computes iteratively a sequence of transformation matrices (i.e., translations and rotations) to align the proteins in space and generate the consensus. The algorithm is a heuristic in that it computes an approximation to the optimal alignment that minimizes the sum of the pairwise distances between the consensus and the transformed proteins.'),
(93426, 2010, 'bmc', 'Bias correction and Bayesian analysis of aggregate counts in SAGE libraries', 'Zaretzki, Russell L|Gilchrist, Michael A|Briggs, William M|Armagan, Artin', 'Department of Statistics, Operations, and Management Science, The University of Tennessee, 331 Stokely Management Center, Knoxville, TN 37996, USA. rzaretzk@utk.edu|Department of Statistics, Operations, and Management Science, The University of Tennessee, 331 Stokely Management Center, Knoxville, TN 37996, USA. rzaretzk@utk.edu|Department of Statistics, Operations, and Management Science, The University of Tennessee, 331 Stokely Management Center, Knoxville, TN 37996, USA. rzaretzk@utk.edu|Department of Statistics, Operations, and Management Science, The University of Tennessee, 331 Stokely Management Center, Knoxville, TN 37996, USA. rzaretzk@utk.edu', 'United States|United States|United States|United States', 0, 'Bayes Theorem|Bias|Gene Expression Profiling|RNA, Messenger', 'Journal Article', '10.1186/1471-2105-11-72', '20128916', 'Tag-based techniques, such as SAGE, are commonly used to sample the mRNA pool of an organisms transcriptome. Incomplete digestion during the tag formation process may allow for multiple tags to be generated from a given mRNA transcript. The probability of forming a tag varies with its relative location. As a result, the observed tag counts represent a biased sample of the actual transcript pool. In SAGE this bias can be avoided by ignoring all but the 3 most tag but will discard a large fraction of the observed data. Taking this bias into account should allow more of the available data to be used leading to increased statistical power.'),
(93427, 2010, 'bmc', 'BABAR: an R package to simplify the normalisation of common reference design microarray-based transcriptomic datasets', 'Alston, Mark J|Seers, John|Hinton, Jay C D|Lucchini, Sacha', 'Foodborne Bacterial Pathogens, Institute of Food Research, Norwich Research Park, Norwich, NR4 7UA, UK. mark.alston@bbsrc.ac.uk|Foodborne Bacterial Pathogens, Institute of Food Research, Norwich Research Park, Norwich, NR4 7UA, UK. mark.alston@bbsrc.ac.uk|Foodborne Bacterial Pathogens, Institute of Food Research, Norwich Research Park, Norwich, NR4 7UA, UK. mark.alston@bbsrc.ac.uk|Foodborne Bacterial Pathogens, Institute of Food Research, Norwich Research Park, Norwich, NR4 7UA, UK. mark.alston@bbsrc.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Reverse Transcriptase Polymerase Chain Reaction|Software', 'Journal Article', '10.1186/1471-2105-11-73', '20128918', 'The development of DNA microarrays has facilitated the generation of hundreds of thousands of transcriptomic datasets. The use of a common reference microarray design allows existing transcriptomic data to be readily compared and re-analysed in the light of new data, and the combination of this design with large datasets is ideal for systems-level analyses. One issue is that these datasets are typically collected over many years and may be heterogeneous in nature, containing different microarray file formats and gene array layouts, dye-swaps, and showing varying scales of log2- ratios of expression between microarrays. Excellent software exists for the normalisation and analysis of microarray data but many data have yet to be analysed as existing methods struggle with heterogeneous datasets, options include normalising microarrays on an individual or experimental group basis. Our solution was to develop the Batch Anti-Banana Algorithm in R (BABAR) algorithm and software package which uses cyclic loess to normalise across the complete dataset. We have already used BABAR to analyse the function of Salmonella genes involved in the process of infection of mammalian cells.'),
(93428, 2010, 'bmc', 'CNV Workshop: an integrated platform for high-throughput copy number variation discovery and clinical diagnostics', 'Gai, Xiaowu|Perin, Juan C|Murphy, Kevin|OHara, Ryan|Darcy, Monica|Wenocur, Adam|Xie, Hongbo M|Rappaport, Eric F|Shaikh, Tamim H|White, Peter S', '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', 0, 'Algorithms|Comparative Genomic Hybridization|Computational Biology|Databases, Genetic|Gene Dosage|Genetic Variation|Genome, Human|Humans|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-11-74', '20132550', 'Recent studies have shown that copy number variations (CNVs) are frequent in higher eukaryotes and associated with a substantial portion of inherited and acquired risk for various human diseases. The increasing availability of high-resolution genome surveillance platforms provides opportunity for rapidly assessing research and clinical samples for CNV content, as well as for determining the potential pathogenicity of identified variants. However, few informatics tools for accurate and efficient CNV detection and assessment currently exist.'),
(93429, 2010, 'bmc', 'Mining protein loops using a structural alphabet and statistical exceptionality', 'Regad, Leslie|Martin, Juliette|Nuel, Gregory|Camproux, Anne-Claude', 'MTi, Inserm UMR-S 973, Université Paris Diderot- Paris 7, Paris, F-75205 Cedex 13, France. leslie.regad@univ-paris-diderot.fr|MTi, Inserm UMR-S 973, Université Paris Diderot- Paris 7, Paris, F-75205 Cedex 13, France. leslie.regad@univ-paris-diderot.fr|MTi, Inserm UMR-S 973, Université Paris Diderot- Paris 7, Paris, F-75205 Cedex 13, France. leslie.regad@univ-paris-diderot.fr|MTi, Inserm UMR-S 973, Université Paris Diderot- Paris 7, Paris, F-75205 Cedex 13, France. leslie.regad@univ-paris-diderot.fr', 'France|France|France|France', 0, 'Amino Acid Motifs|Amino Acid Sequence|Data Mining|Databases, Protein|Models, Molecular|Molecular Sequence Data|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1186/1471-2105-11-75', '20132552', 'Protein loops encompass 50% of protein residues in available three-dimensional structures. These regions are often involved in protein functions, e.g. binding site, catalytic pocket... However, the description of protein loops with conventional tools is an uneasy task. Regular secondary structures, helices and strands, have been widely studied whereas loops, because they are highly variable in terms of sequence and structure, are difficult to analyze. Due to data sparsity, long loops have rarely been systematically studied.'),
(93430, 2010, 'bmc', 'Extracting consistent knowledge from highly inconsistent cancer gene data sources', 'Gong, Xue|Wu, Ruihong|Zhang, Yuannv|Zhao, Wenyuan|Cheng, Lixin|Gu, Yunyan|Zhang, Lin|Wang, Jing|Zhu, Jing|Guo, Zheng', 'College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.', 'China|China|China|China|China|China|China|China|China|China', 0, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Neoplasms', 'Journal Article', '10.1186/1471-2105-11-76', '20137077', 'Hundreds of genes that are causally implicated in oncogenesis have been found and collected in various databases. For efficient application of these abundant but diverse data sources, it is of fundamental importance to evaluate their consistency.'),
(93431, 2010, 'bmc', 'A random effect multiplicative heteroscedastic model for bacterial growth', 'Cao, Ricardo|Francisco-Fernández, Mario|Quinto, Emiliano J', 'Department of Mathematics, University of A Coruña, School of Computer Science, Campus de Elviña, s/n, 15071 A Coruña, Spain.|Department of Mathematics, University of A Coruña, School of Computer Science, Campus de Elviña, s/n, 15071 A Coruña, Spain.|Department of Mathematics, University of A Coruña, School of Computer Science, Campus de Elviña, s/n, 15071 A Coruña, Spain.', 'Spain|Spain|Spain', 9, 'Colony Count, Microbial|Hydrogen-Ion Concentration|Listeria monocytogenes|Models, Theoretical|Temperature', 'Journal Article', '10.1186/1471-2105-11-77', '20141635', 'Predictive microbiology develops mathematical models that can predict the growth rate of a microorganism population under a set of environmental conditions. Many primary growth models have been proposed. However, when primary models are applied to bacterial growth curves, the biological variability is reduced to a single curve defined by some kinetic parameters (lag time and growth rate), and sometimes the models give poor fits in some regions of the curve. The development of a prediction band (from a set of bacterial growth curves) using non-parametric and bootstrap methods permits to overcome that problem and include the biological variability of the microorganism into the modelling process.'),
(93432, 2010, 'bmc', 'Testing the additional predictive value of high-dimensional molecular data', 'Boulesteix, Anne-Laure|Hothorn, Torsten', 'Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr 15, D-81377 Munich, Germany. boulesteix@ibe.med.uni-muenchen.de|Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr 15, D-81377 Munich, Germany. boulesteix@ibe.med.uni-muenchen.de', 'Germany|Germany', 30, 'Computational Biology|Gene Expression Profiling|Humans|Logistic Models|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-78', '20144191', 'While high-dimensional molecular data such as microarray gene expression data have been used for disease outcome prediction or diagnosis purposes for about ten years in biomedical research, the question of the additional predictive value of such data given that classical predictors are already available has long been under-considered in the bioinformatics literature.'),
(93433, 2010, 'bmc', 'Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies', 'David, Maria Pamela C|Concepcion, Gisela P|Padlan, Eduardo A', 'Virtual Laboratory of Biomolecular Structures, Marine Science Institute, College of Science, University of the Philippines Diliman, Quezon City 1101, Philippines. maria.pamela.david@gmail.com|Virtual Laboratory of Biomolecular Structures, Marine Science Institute, College of Science, University of the Philippines Diliman, Quezon City 1101, Philippines. maria.pamela.david@gmail.com|Virtual Laboratory of Biomolecular Structures, Marine Science Institute, College of Science, University of the Philippines Diliman, Quezon City 1101, Philippines. maria.pamela.david@gmail.com', 'Philippines|Philippines|Philippines', 24, 'Algorithms|Amino Acid Sequence|Amyloid|Antibodies|Artificial Intelligence|Molecular Sequence Data|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-79', '20144194', 'All polypeptide backbones have the potential to form amyloid fibrils, which are associated with a number of degenerative disorders. However, the likelihood that amyloidosis would actually occur under physiological conditions depends largely on the amino acid composition of a protein. We explore using a naive Bayesian classifier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences.'),
(93434, 2010, 'bmc', 'Parameters for accurate genome alignment', 'Frith, Martin C|Hamada, Michiaki|Horton, Paul', 'Computational Biology Research Center, Institute for Advanced Industrial Science and Technology, Tokyo 135-0064, Japan. martin@cbrc.jp|Computational Biology Research Center, Institute for Advanced Industrial Science and Technology, Tokyo 135-0064, Japan. martin@cbrc.jp|Computational Biology Research Center, Institute for Advanced Industrial Science and Technology, Tokyo 135-0064, Japan. martin@cbrc.jp', 'Japan|Japan|Japan', 119, 'Base Sequence|Computational Biology|Genome|Molecular Sequence Data|Proteins|RNA|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-11-80', '20144198', 'Genome sequence alignments form the basis of much research. Genome alignment depends on various mundane but critical choices, such as how to mask repeats and which score parameters to use. Surprisingly, there has been no large-scale assessment of these choices using real genomic data. Moreover, rigorous procedures to control the rate of spurious alignment have not been employed.'),
(93435, 2010, 'bmc', 'An effective approach for identification of in vivo protein-DNA binding sites from paired-end ChIP-Seq data', 'Wang, Congmao|Xu, Jie|Zhang, Dasheng|Wilson, Zoe A|Zhang, Dabing', 'School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.|School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.|School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.|School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.|School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.', 'China|China|China|China|China', 18, 'Binding Sites|Chromatin Immunoprecipitation|Computational Biology|DNA|DNA-Binding Proteins|Genome', 'Journal Article', '10.1186/1471-2105-11-81', '20144209', 'ChIP-Seq, which combines chromatin immunoprecipitation (ChIP) with high-throughput massively parallel sequencing, is increasingly being used for identification of protein-DNA interactions in vivo in the genome. However, to maximize the effectiveness of data analysis of such sequences requires the development of new algorithms that are able to accurately predict DNA-protein binding sites.'),
(93436, 2010, 'bmc', 'Dynamic probe selection for studying microbial transcriptome with high-density genomic tiling microarrays', 'Høvik, Hedda|Chen, Tsute', 'Department of Oral Biology, Faculty of Dentistry, University of Oslo, Oslo, Norway.|Department of Oral Biology, Faculty of Dentistry, University of Oslo, Oslo, Norway.', 'Norway|Norway', 9, 'Computational Biology|DNA Probes|Gene Expression Profiling|Genome, Bacterial|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-82', '20144223', 'Current commercial high-density oligonucleotide microarrays can hold millions of probe spots on a single microscopic glass slide and are ideal for studying the transcriptome of microbial genomes using a tiling probe design. This paper describes a comprehensive computational pipeline implemented specifically for designing tiling probe sets to study microbial transcriptome profiles.'),
(93437, 2010, 'bmc', 'Simulation of a Petri net-based model of the terpenoid biosynthesis pathway', 'Hawari, Aliah Hazmah|Mohamed-Hussein, Zeti-Azura', 'School of Biosciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia 43600 UKM Bangi, Selangor, Malaysia.|School of Biosciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia 43600 UKM Bangi, Selangor, Malaysia.', 'Malaysia|Malaysia', 7, 'Computational Biology|Proteome|Signal Transduction|Terpenes', 'Journal Article', '10.1186/1471-2105-11-83', '20144236', 'The development and simulation of dynamic models of terpenoid biosynthesis has yielded a systems perspective that provides new insights into how the structure of this biochemical pathway affects compound synthesis. These insights may eventually help identify reactions that could be experimentally manipulated to amplify terpenoid production. In this study, a dynamic model of the terpenoid biosynthesis pathway was constructed based on the Hybrid Functional Petri Net (HFPN) technique. This technique is a fusion of three other extended Petri net techniques, namely Hybrid Petri Net (HPN), Dynamic Petri Net (HDN) and Functional Petri Net (FPN).'),
(93438, 2010, 'bmc', 'Using machine learning to speed up manual image annotation: application to a 3D imaging protocol for measuring single cell gene expression in the developing C elegans embryo', 'Aydin, Zafer|Murray, John I|Waterston, Robert H|Noble, William S', 'Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. zafer@u.washington.edu|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. zafer@u.washington.edu|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. zafer@u.washington.edu|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. zafer@u.washington.edu', 'United States|United States|United States|United States', 12, 'Animals|Artificial Intelligence|Caenorhabditis elegans|Caenorhabditis elegans Proteins|Embryo, Nonmammalian|Gene Expression Profiling|Image Interpretation, Computer-Assisted|Imaging, Three-Dimensional', 'Journal Article', '10.1186/1471-2105-11-84', '20146825', 'Image analysis is an essential component in many biological experiments that study gene expression, cell cycle progression, and protein localization. A protocol for tracking the expression of individual C. elegans genes was developed that collects image samples of a developing embryo by 3-D time lapse microscopy. In this protocol, a program called StarryNite performs the automatic recognition of fluorescently labeled cells and traces their lineage. However, due to the amount of noise present in the data and due to the challenges introduced by increasing number of cells in later stages of development, this program is not error free. In the current version, the error correction (i.e., editing) is performed manually using a graphical interface tool named AceTree, which is specifically developed for this task. For a single experiment, this manual annotation task takes several hours.'),
(93439, 2010, 'bmc', 'LINNAEUS: a species name identification system for biomedical literature', 'Gerner, Martin|Nenadic, Goran|Bergman, Casey M', 'Faculty of Life Sciences, University of Manchester, Manchester, M13 9PT, UK.|Faculty of Life Sciences, University of Manchester, Manchester, M13 9PT, UK.|Faculty of Life Sciences, University of Manchester, Manchester, M13 9PT, UK.', 'United Kingdom|United Kingdom|United Kingdom', 146, 'Computational Biology|Information Storage and Retrieval|PubMed|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-11-85', '20149233', 'The task of recognizing and identifying species names in biomedical literature has recently been regarded as critical for a number of applications in text and data mining, including gene name recognition, species-specific document retrieval, and semantic enrichment of biomedical articles.'),
(93440, 2009, 'bmc', 'Proceedings from Semantic Web Applications and Tools for Life Sciences, 2008  Edinburgh, UK 28 November 2008', '-', '-', '-', 0, 'Biological Science Disciplines|Internet|Medical Informatics|Semantics|Software', 'Congress', '10.1186/1471-2105-11-85', '20151483', '-'),
(93441, 2009, 'bmc', 'Proceedings from Sixth Annual MCBIOS Conference Transformational Bioinformatics: Delivering Value from Genomes Starkville, MS, USA 20-21 February 2009', '-', '-', '-', 146, 'Computational Biology|Genomics|Oligonucleotide Array Sequence Analysis|Systems Biology', 'Congress', '10.1186/1471-2105-11-85', '20151484', '-'),
(93442, 2010, 'bmc', 'Enrichment of homologs in insignificant BLAST hits by co-complex network alignment', 'Fokkens, Like|Botelho, Sandra M C|Boekhorst, Jos|Snel, Berend', 'Theoretical Biology and Bioinformatics group, Department of Biology, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, Utrecht, the Netherlands. l.fokkens@uu.nl|Theoretical Biology and Bioinformatics group, Department of Biology, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, Utrecht, the Netherlands. l.fokkens@uu.nl|Theoretical Biology and Bioinformatics group, Department of Biology, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, Utrecht, the Netherlands. l.fokkens@uu.nl|Theoretical Biology and Bioinformatics group, Department of Biology, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, Utrecht, the Netherlands. l.fokkens@uu.nl', 'Netherlands|Netherlands|Netherlands|Netherlands', 7, 'Algorithms|Databases, Factual|Genomics|Proteins|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-11-86', '20152020', 'Homology is a crucial concept in comparative genomics. The algorithm probably most widely used for homology detection in comparative genomics, is BLAST. Usually a stringent score cutoff is applied to distinguish putative homologs from possible false positive hits. As a consequence, some BLAST hits are discarded that are in fact homologous.'),
(93443, 2010, 'bmc', 'Lists2Networks: integrated analysis of gene/protein lists', 'Lachmann, Alexander|Maayan, Avi', 'Department of Pharmacology and Systems Therapeutics, Systems Biology Center New York, Mount Sinai School of Medicine, 1425 Madison Avenue, New York, NY 10029, USA.|Department of Pharmacology and Systems Therapeutics, Systems Biology Center New York, Mount Sinai School of Medicine, 1425 Madison Avenue, New York, NY 10029, USA.', 'United States|United States', 33, 'Computational Biology|Databases, Genetic|Databases, Protein|Gene Expression Profiling|Gene Regulatory Networks|Genes|Internet|Oligonucleotide Array Sequence Analysis|Proteins|Software|Systems Integration', 'Journal Article', '10.1186/1471-2105-11-87', '20152038', 'Systems biologists are faced with the difficulty of analyzing results from large-scale studies that profile the activity of many genes, RNAs and proteins, applied in different experiments, under different conditions, and reported in different publications. To address this challenge it is desirable to compare the results from different related studies such as mRNA expression microarrays, genome-wide ChIP-X, RNAi screens, proteomics and phosphoproteomics experiments in a coherent global framework. In addition, linking high-content multilayered experimental results with prior biological knowledge can be useful for identifying functional themes and form novel hypotheses.'),
(93444, 2010, 'bmc', 'HDAPD: a web tool for searching the disease-associated protein structures', 'Lin, Yi-Ruen|Wei, Hsin-Yuan|Tsai, Tsung-Lin|Lin, Thy-Hou', 'Institute of Molecular Medicine and Department of Life Science, National Tsing Hua University, HsinChu, 30013, Taiwan, Republic of China.|Institute of Molecular Medicine and Department of Life Science, National Tsing Hua University, HsinChu, 30013, Taiwan, Republic of China.|Institute of Molecular Medicine and Department of Life Science, National Tsing Hua University, HsinChu, 30013, Taiwan, Republic of China.|Institute of Molecular Medicine and Department of Life Science, National Tsing Hua University, HsinChu, 30013, Taiwan, Republic of China.', 'China|China|China|China', 1, 'Computational Biology|Databases, Protein|Internet|Protein Conformation|Proteins|Software', 'Journal Article', '10.1186/1471-2105-11-88', '20158919', 'The protein structures of the disease-associated proteins are important for proceeding with the structure-based drug design to against a particular disease. Up until now, proteins structures are usually searched through a PDB id or some sequence information. However, in the HDAPD database presented here the protein structure of a disease-associated protein can be directly searched through the associated disease name keyed in.'),
(93445, 2010, 'bmc', 'Detection of distant evolutionary relationships between protein families using theory of sequence profile-profile comparison', 'Margelevicius, Mindaugas|Venclovas, Ceslovas', 'Institute of Biotechnology, Graicixc5xabno 8, LT-02241 Vilnius, Lithuania.|Institute of Biotechnology, Graicixc5xabno 8, LT-02241 Vilnius, Lithuania.', 'Lithuania|Lithuania', 45, 'Amino Acid Sequence|Databases, Protein|Evolution, Molecular|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Comparative Study', '10.1186/1471-2105-11-89', '20158924', 'Detection of common evolutionary origin (homology) is a primary means of inferring protein structure and function. At present, comparison of protein families represented as sequence profiles is arguably the most effective homology detection strategy. However, finding the best way to represent evolutionary information of a protein sequence family in the profile, to compare profiles and to estimate the biological significance of such comparisons, remains an active area of research.'),
(93446, 2010, 'bmc', 'Predicting MHC class I epitopes in large datasets', 'Roomp, Kirsten|Antes, Iris|Lengauer, Thomas', 'Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, 66123 Saarbruecken, Germany.|Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, 66123 Saarbruecken, Germany.|Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, 66123 Saarbruecken, Germany.', 'Germany|Germany|Germany', 36, 'Alleles|Computational Biology|Databases, Protein|Epitopes|Genes, MHC Class I|Neural Networks (Computer)|Peptides', 'Journal Article', '10.1186/1471-2105-11-90', '20163709', 'Experimental screening of large sets of peptides with respect to their MHC binding capabilities is still very demanding due to the large number of possible peptide sequences and the extensive polymorphism of the MHC proteins. Therefore, there is significant interest in the development of computational methods for predicting the binding capability of peptides to MHC molecules, as a first step towards selecting peptides for actual screening.'),
(93447, 2010, 'bmc', 'BisoGenet: a new tool for gene network building, visualization and analysis', 'Martin, Alexander|Ochagavia, Maria E|Rabasa, Laya C|Miranda, Jamilet|Fernandez-de-Cossio, Jorge|Bringas, Ricardo', 'Center for Genetic Engineering and Biotechnology, Havana, Cuba.|Center for Genetic Engineering and Biotechnology, Havana, Cuba.|Center for Genetic Engineering and Biotechnology, Havana, Cuba.|Center for Genetic Engineering and Biotechnology, Havana, Cuba.|Center for Genetic Engineering and Biotechnology, Havana, Cuba.|Center for Genetic Engineering and Biotechnology, Havana, Cuba.', 'Cuba|Cuba|Cuba|Cuba|Cuba|Cuba', 173, 'Algorithms|Computer Graphics|Computer Simulation|Database Management Systems|Databases, Factual|Gene Expression Profiling|Models, Biological|Protein Interaction Mapping|Proteome|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-91', '20163717', 'The increasing availability and diversity of omics data in the post-genomic era offers new perspectives in most areas of biomedical research. Graph-based biological networks models capture the topology of the functional relationships between molecular entities such as gene, protein and small compounds and provide a suitable framework for integrating and analyzing omics-data. The development of software tools capable of integrating data from different sources and to provide flexible methods to reconstruct, represent and analyze topological networks is an active field of research in bioinformatics.'),
(93448, 2010, 'bmc', 'Potentials R Us web-server for protein energy estimations with coarse-grained knowledge-based potentials', 'Feng, Yaping|Kloczkowski, Andrzej|Jernigan, Robert L', 'Department of Biochemistry, Biophysics, and Molecular Biology, Iowa State University, Ames, IA 50011-0320, USA.|Department of Biochemistry, Biophysics, and Molecular Biology, Iowa State University, Ames, IA 50011-0320, USA.|Department of Biochemistry, Biophysics, and Molecular Biology, Iowa State University, Ames, IA 50011-0320, USA.', 'United States|United States|United States', 24, 'Amino Acid Sequence|Artificial Intelligence|Computer Simulation|Energy Transfer|Internet|Models, Chemical|Models, Molecular|Molecular Sequence Data|Programming Languages|Protein Conformation|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-11-92', '20163737', 'Knowledge-based potentials have been widely used in the last 20 years for fold recognition, protein structure prediction from amino acid sequence, ligand binding, protein design, and many other purposes. However generally these are not readily accessible online.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93449, 2010, 'bmc', 'Finding sRNA generative locales from high-throughput sequencing data with NiBLS', 'MacLean, Daniel|Moulton, Vincent|Studholme, David J', 'The Sainsbury Laboratory, John Innes Centre, Colney Lane, Norwich NR47UH, UK. dan.maclean@sainsbury-laboratory.ac.uk|The Sainsbury Laboratory, John Innes Centre, Colney Lane, Norwich NR47UH, UK. dan.maclean@sainsbury-laboratory.ac.uk|The Sainsbury Laboratory, John Innes Centre, Colney Lane, Norwich NR47UH, UK. dan.maclean@sainsbury-laboratory.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 9, 'Algorithms|Base Sequence|Computational Biology|MicroRNAs|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-11-93', '20167070', 'Next-generation sequencing technologies allow researchers to obtain millions of sequence reads in a single experiment. One important use of the technology is the sequencing of small non-coding regulatory RNAs and the identification of the genomic locales from which they originate. Currently, there is a paucity of methods for finding small RNA generative locales.'),
(93450, 2010, 'bmc', 'Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments', 'Bullard, James H|Purdom, Elizabeth|Hansen, Kasper D|Dudoit, Sandrine', 'Division of Biostatistics, University of California, Berkeley, Berkeley, CA, USA. bullard@berkeley.edu|Division of Biostatistics, University of California, Berkeley, Berkeley, CA, USA. bullard@berkeley.edu|Division of Biostatistics, University of California, Berkeley, Berkeley, CA, USA. bullard@berkeley.edu|Division of Biostatistics, University of California, Berkeley, Berkeley, CA, USA. bullard@berkeley.edu', 'United States|United States|United States|United States', 920, 'Computational Biology|Databases, Genetic|RNA, Messenger|Sequence Analysis, RNA', 'Evaluation Studies', '10.1186/1471-2105-11-94', '20167110', 'High-throughput sequencing technologies, such as the Illumina Genome Analyzer, are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key for drawing meaningful and accurate conclusions from the massive and complex datasets generated by the sequencers. We provide a detailed evaluation of statistical methods for normalization and differential expression (DE) analysis of Illumina transcriptome sequencing (mRNA-Seq) data.'),
(93451, 2010, 'bmc', 'A statistical framework for differential network analysis from microarray data', 'Gill, Ryan|Datta, Somnath|Datta, Susmita', 'Department of Mathematics, University of Louisville, Louisville, KY 40292, USA.|Department of Mathematics, University of Louisville, Louisville, KY 40292, USA.|Department of Mathematics, University of Louisville, Louisville, KY 40292, USA.', 'United States|United States|United States', 77, 'Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-95', '20170493', 'It has been long well known that genes do not act alone, rather groups of genes act in consort during a biological process. Consequently, the expression levels of genes are dependent on each other. Experimental techniques to detect such interacting pairs of genes have been in place for quite some time. With the advent of microarray technology, newer computational techniques to detect such interaction or association between gene expressions are being proposed which lead to an association network. While most microarray analyses look for genes that are differentially expressed, it is of potentially greater significance to identify how entire association network structures change between two or more biological settings, say normal versus diseased cell types.'),
(93452, 2010, 'bmc', 'SING: subgraph search in non-homogeneous graphs', 'Di Natale, Raffaele|Ferro, Alfredo|Giugno, Rosalba|Mongiovì, Misael|Pulvirenti, Alfredo|Shasha, Dennis', 'Dipartimento di Matematica ed Informatica, Università di Catania, Catania, Italy.|Dipartimento di Matematica ed Informatica, Università di Catania, Catania, Italy.|Dipartimento di Matematica ed Informatica, Università di Catania, Catania, Italy.|Dipartimento di Matematica ed Informatica, Università di Catania, Catania, Italy.|Dipartimento di Matematica ed Informatica, Università di Catania, Catania, Italy.|Dipartimento di Matematica ed Informatica, Università di Catania, Catania, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 40, 'Algorithms|Computational Biology|Computer Graphics|Databases, Factual|Databases, Protein|Information Storage and Retrieval|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-96', '20170516', 'Finding the subgraphs of a graph database that are isomorphic to a given query graph has practical applications in several fields, from cheminformatics to image understanding. Since subgraph isomorphism is a computationally hard problem, indexing techniques have been intensively exploited to speed up the process. Such systems filter out those graphs which cannot contain the query, and apply a subgraph isomorphism algorithm to each residual candidate graph. The applicability of such systems is limited to databases of small graphs, because their filtering power degrades on large graphs.'),
(93453, 2010, 'bmc', 'Structural alphabets derived from attractors in conformational space', 'Pandini, Alessandro|Fornili, Arianna|Kleinjung, Jens', 'Division of Mathematical Biology, MRC National Institute for Medical Research, London, UK.|Division of Mathematical Biology, MRC National Institute for Medical Research, London, UK.|Division of Mathematical Biology, MRC National Institute for Medical Research, London, UK.', 'United Kingdom|United Kingdom|United Kingdom', 37, 'Computational Biology|Databases, Protein|Models, Molecular|Protein Conformation|Protein Folding|Proteins|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-11-97', '20170534', 'The hierarchical and partially redundant nature of protein structures justifies the definition of frequently occurring conformations of short fragments as states. Collections of selected representatives for these states define Structural Alphabets, describing the most typical local conformations within protein structures. These alphabets form a bridge between the string-oriented methods of sequence analysis and the coordinate-oriented methods of protein structure analysis.'),
(93454, 2010, 'bmc', 'Unifying generative and discriminative learning principles', 'Keilwagen, Jens|Grau, Jan|Posch, Stefan|Strickert, Marc|Grosse, Ivo', 'Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. Jens.Keilwagen@ipk-gatersleben.de|Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. Jens.Keilwagen@ipk-gatersleben.de|Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. Jens.Keilwagen@ipk-gatersleben.de|Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. Jens.Keilwagen@ipk-gatersleben.de|Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. Jens.Keilwagen@ipk-gatersleben.de', 'Germany|Germany|Germany|Germany|Germany', 5, 'Algorithms|DNA|Discriminant Analysis|Genome|Genomics|Information Storage and Retrieval|Likelihood Functions|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-11-98', '20175896', 'The recognition of functional binding sites in genomic DNA remains one of the fundamental challenges of genome research. During the last decades, a plethora of different and well-adapted models has been developed, but only little attention has been payed to the development of different and similarly well-adapted learning principles. Only recently it was noticed that discriminative learning principles can be superior over generative ones in diverse bioinformatics applications, too.'),
(93455, 2010, 'bmc', 'A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: application to ligand prediction', 'Hoffmann, Brice|Zaslavskiy, Mikhail|Vert, Jean-Philippe|Stoven, Véronique', 'Mines ParisTech, Centre for Computational Biology, Fontainbleau, France.|Mines ParisTech, Centre for Computational Biology, Fontainbleau, France.|Mines ParisTech, Centre for Computational Biology, Fontainbleau, France.|Mines ParisTech, Centre for Computational Biology, Fontainbleau, France.', 'France|France|France|France', 62, 'Binding Sites|Computational Biology|Databases, Protein|Ligands|Models, Molecular|Protein Conformation|Proteins|Structure-Activity Relationship', 'Comparative Study', '10.1186/1471-2105-11-99', '20175916', 'Predicting which molecules can bind to a given binding site of a protein with known 3D structure is important to decipher the protein function, and useful in drug design. A classical assumption in structural biology is that proteins with similar 3D structures have related molecular functions, and therefore may bind similar ligands. However, proteins that do not display any overall sequence or structure similarity may also bind similar ligands if they contain similar binding sites. Quantitatively assessing the similarity between binding sites may therefore be useful to propose new ligands for a given pocket, based on those known for similar pockets.'),
(93456, 2010, 'bmc', 'DraGnET: software for storing, managing and analyzing annotated draft genome sequence data', 'Duncan, Stacy|Sirkanungo, Ruchita|Miller, Leslie|Phillips, Gregory J', 'Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, Iowa, USA. gregory@iastate.edu.|Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, Iowa, USA. gregory@iastate.edu.|Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, Iowa, USA. gregory@iastate.edu.|Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, Iowa, USA. gregory@iastate.edu.', 'United States|United States|United States|United States', 4, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Graphics|Database Management Systems|Databases, Genetic|Documentation|Molecular Sequence Data|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-100', '20175920', 'New next generation DNA sequencing technologies offer individual researchers the ability to rapidly generate large amounts of genome sequence data at dramatically reduced costs. As a result, a need has arisen for new software tools for storage, management and analysis of genome sequence data. Although bioinformatic tools are available for the analysis and management of genome sequences, limitations still remain. For example, restrictions on the submission of data and use of these tools may be imposed, thereby making them unsuitable for sequencing projects that need to remain in-house or proprietary during their initial stages. Furthermore, the availability and use of next generation sequencing in industrial, governmental and academic environments requires biologist to have access to computational support for the curation and analysis of the data generated, however, this type of support is not always immediately available.'),
(93457, 2010, 'bmc', 'Extracting causal relations on HIV drug resistance from literature', 'Bui, Quoc-Chinh|Nualláin, Breanndán O|Boucher, Charles A|Sloot, Peter M A', 'Computational Science, University of Amsterdam, Science Park 107, 1098 XG Amsterdam, The Netherlands. bqchinh@gmail.com|Computational Science, University of Amsterdam, Science Park 107, 1098 XG Amsterdam, The Netherlands. bqchinh@gmail.com|Computational Science, University of Amsterdam, Science Park 107, 1098 XG Amsterdam, The Netherlands. bqchinh@gmail.com|Computational Science, University of Amsterdam, Science Park 107, 1098 XG Amsterdam, The Netherlands. bqchinh@gmail.com', 'Netherlands|Netherlands|Netherlands|Netherlands', 24, 'Abstracting and Indexing as Topic|Anti-HIV Agents|Databases, Genetic|Drug Resistance, Viral|Genome, Viral|HIV Infections|Information Storage and Retrieval|Internet|Mutation|PubMed', 'Journal Article', '10.1186/1471-2105-11-101', '20178611', 'In HIV treatment it is critical to have up-to-date resistance data of applicable drugs since HIV has a very high rate of mutation. These data are made available through scientific publications and must be extracted manually by experts in order to be used by virologists and medical doctors. Therefore there is an urgent need for a tool that partially automates this process and is able to retrieve relations between drugs and virus mutations from literature.'),
(93458, 2010, 'bmc', 'Reconstructing genome trees of prokaryotes using overlapping genes', 'Cheng, Chih-Hsien|Yang, Chung-Han|Chiu, Hsien-Tai|Lu, Chin Lung', 'Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan.', '-|-|-|-', 9, 'Databases, Genetic|Genes, Overlapping|Genome, Bacterial|Genomics|Phylogeny|Proteobacteria', 'Journal Article', '10.1186/1471-2105-11-102', '20181237', 'Overlapping genes (OGs) are defined as adjacent genes whose coding sequences overlap partially or entirely. In fact, they are ubiquitous in microbial genomes and more conserved between species than non-overlapping genes. Based on this property, we have previously implemented a web server, named OGtree, that allows the user to reconstruct genome trees of some prokaryotes according to their pairwise OG distances. By analogy to the analyses of gene content and gene order, the OG distance between two genomes we defined was based on a measure of combining OG content (i.e., the normalized number of shared orthologous OG pairs) and OG order (i.e., the normalized OG breakpoint distance) in their whole genomes. A shortcoming of using the concept of breakpoints to define the OG distance is its inability to analyze the OG distance of multi-chromosomal genomes. In addition, the amount of overlapping coding sequences between some distantly related prokaryotic genomes may be limited so that it is hard to find enough OGs to properly evaluate their pairwise OG distances.'),
(93459, 2010, 'bmc', 'Word add-in for ontology recognition: semantic enrichment of scientific literature', 'Fink, J Lynn|Fernicola, Pablo|Chandran, Rahul|Parastatidis, Savas|Wade, Alex|Naim, Oscar|Quinn, Gregory B|Bourne, Philip E', 'Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA 92093-0444, USA. jlfink@ucsd.edu|Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA 92093-0444, USA. jlfink@ucsd.edu|Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA 92093-0444, USA. jlfink@ucsd.edu|Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA 92093-0444, USA. jlfink@ucsd.edu|Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA 92093-0444, USA. jlfink@ucsd.edu|Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA 92093-0444, USA. jlfink@ucsd.edu|Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA 92093-0444, USA. jlfink@ucsd.edu|Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA 92093-0444, USA. jlfink@ucsd.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Databases, Factual|Information Storage and Retrieval|Natural Language Processing|Programming Languages|Publications|Semantics|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-11-103', '20181245', 'In the current era of scientific research, efficient communication of information is paramount. As such, the nature of scholarly and scientific communication is changing, cyberinfrastructure is now absolutely necessary and new media are allowing information and knowledge to be more interactive and immediate. One approach to making knowledge more accessible is the addition of machine-readable semantic data to scholarly articles.'),
(93460, 2010, 'bmc', 'Probe set filtering increases correlation between Affymetrix GeneChip and qRT-PCR expression measurements', 'Mieczkowski, Jakub|Tyburczy, Magdalena E|Dabrowski, Michal|Pokarowski, Piotr', 'Laboratory of Transcription Regulation, Department of Cell Biology, The Nencki Institute of Experimental Biology, Pasteur 3, 02-093 Warsaw, Poland. j.mieczkowski@nencki.gov.pl|Laboratory of Transcription Regulation, Department of Cell Biology, The Nencki Institute of Experimental Biology, Pasteur 3, 02-093 Warsaw, Poland. j.mieczkowski@nencki.gov.pl|Laboratory of Transcription Regulation, Department of Cell Biology, The Nencki Institute of Experimental Biology, Pasteur 3, 02-093 Warsaw, Poland. j.mieczkowski@nencki.gov.pl|Laboratory of Transcription Regulation, Department of Cell Biology, The Nencki Institute of Experimental Biology, Pasteur 3, 02-093 Warsaw, Poland. j.mieczkowski@nencki.gov.pl', 'Poland|Poland|Poland|Poland', 0, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Reverse Transcriptase Polymerase Chain Reaction', 'Journal Article', '10.1186/1471-2105-11-104', '20181266', 'Affymetrix GeneChip microarrays are popular platforms for expression profiling in two types of studies: detection of differential expression computed by p-values of t-test and estimation of fold change between analyzed groups. There are many different preprocessing algorithms for summarizing Affymetrix data. The main goal of these methods is to remove effects of non-specific hybridization, and to optimally combine information from multiple probes annotated to the same transcript. The methods are benchmarked by comparison with reference methods, such as quantitative reverse-transcription PCR (qRT-PCR).'),
(93461, 2010, 'bmc', 'Free energy estimation of short DNA duplex hybridizations', 'Tulpan, Dan|Andronescu, Mirela|Leger, Serge', 'National Research Council of Canada, Institute of Information Technology, 100 des Aboiteaux Street, Suite 1100, Moncton, NB E1A7R1, Canada. dan.tulpan@nrc-cnrc.gc.ca|National Research Council of Canada, Institute of Information Technology, 100 des Aboiteaux Street, Suite 1100, Moncton, NB E1A7R1, Canada. dan.tulpan@nrc-cnrc.gc.ca|National Research Council of Canada, Institute of Information Technology, 100 des Aboiteaux Street, Suite 1100, Moncton, NB E1A7R1, Canada. dan.tulpan@nrc-cnrc.gc.ca', 'Canada|Canada|Canada', 14, 'Algorithms|Base Composition|Base Sequence|DNA|Models, Molecular|Nucleic Acid Conformation|Nucleic Acid Hybridization|Oligonucleotides|Thermodynamics', 'Journal Article', '10.1186/1471-2105-11-105', '20181279', 'Estimation of DNA duplex hybridization free energy is widely used for predicting cross-hybridizations in DNA computing and microarray experiments. A number of software programs based on different methods and parametrizations are available for the theoretical estimation of duplex free energies. However, significant differences in free energy values are sometimes observed among estimations obtained with various methods, thus being difficult to decide what value is the accurate one.'),
(93462, 2010, 'bmc', 'SpectraClassifier 10: a user friendly, automated MRS-based classifier-development system', 'Ortega-Martorell, Sandra|Olier, Iván|Julià-Sapé, Margarida|Arús, Carles', 'Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, UAB, Cerdanyola del Vallés (Barcelona), 08193, Spain.|Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, UAB, Cerdanyola del Vallés (Barcelona), 08193, Spain.|Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, UAB, Cerdanyola del Vallés (Barcelona), 08193, Spain.|Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, UAB, Cerdanyola del Vallés (Barcelona), 08193, Spain.', 'Spain|Spain|Spain|Spain', 0, 'Magnetic Resonance Spectroscopy|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Proteomics|ROC Curve|Software', 'Journal Article', '10.1186/1471-2105-11-106', '20181285', 'SpectraClassifier (SC) is a Java solution for designing and implementing Magnetic Resonance Spectroscopy (MRS)-based classifiers. The main goal of SC is to allow users with minimum background knowledge of multivariate statistics to perform a fully automated pattern recognition analysis. SC incorporates feature selection (greedy stepwise approach, either forward or backward), and feature extraction (PCA). Fisher Linear Discriminant Analysis is the method of choice for classification. Classifier evaluation is performed through various methods: display of the confusion matrix of the training and testing datasets, K-fold cross-validation, leave-one-out and bootstrapping as well as Receiver Operating Characteristic (ROC) curves.'),
(93463, 2010, 'bmc', 'Walk-weighted subsequence kernels for protein-protein interaction extraction', 'Kim, Seonho|Yoon, Juntae|Yang, Jihoon|Park, Seog', 'Department of Computer Science, Sogang University, Seoul, Korea. shkim@lex.yonsei.ac.kr|Department of Computer Science, Sogang University, Seoul, Korea. shkim@lex.yonsei.ac.kr|Department of Computer Science, Sogang University, Seoul, Korea. shkim@lex.yonsei.ac.kr|Department of Computer Science, Sogang University, Seoul, Korea. shkim@lex.yonsei.ac.kr', '-|-|-|-', 0, 'Computational Biology|Databases, Protein|Pattern Recognition, Automated|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/1471-2105-11-107', '20184736', 'The construction of interaction networks between proteins is central to understanding the underlying biological processes. However, since many useful relations are excluded in databases and remain hidden in raw text, a study on automatic interaction extraction from text is important in bioinformatics field.'),
(93464, 2010, 'bmc', 'SplicerAV: a tool for mining microarray expression data for changes in RNA processing', 'Robinson, Timothy J|Dinan, Michaela A|Dewhirst, Mark|Garcia-Blanco, Mariano A|Pearson, James L', 'Molecular Cancer Biology Program, Duke University Medical Center, Durham, USA.|Molecular Cancer Biology Program, Duke University Medical Center, Durham, USA.|Molecular Cancer Biology Program, Duke University Medical Center, Durham, USA.|Molecular Cancer Biology Program, Duke University Medical Center, Durham, USA.|Molecular Cancer Biology Program, Duke University Medical Center, Durham, USA.', 'United States|United States|United States|United States|United States', 13, 'Algorithms|Biomarkers, Tumor|Data Mining|Databases, Genetic|Gene Expression Profiling|Neoplasms|Oligonucleotide Array Sequence Analysis|Protein Isoforms|RNA, Messenger|Software', 'Journal Article', '10.1186/1471-2105-11-108', '20184770', 'Over the past two decades more than fifty thousand unique clinical and biological samples have been assayed using the Affymetrix HG-U133 and HG-U95 GeneChip microarray platforms. This substantial repository has been used extensively to characterize changes in gene expression between biological samples, but has not been previously mined en masse for changes in mRNA processing. We explored the possibility of using HG-U133 microarray data to identify changes in alternative mRNA processing in several available archival datasets.'),
(93465, 2010, 'bmc', 'Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data', 'Tang, Kai-Lin|Li, Tong-Hua|Xiong, Wen-Wei|Chen, Kai', 'Department of Chemistry, Tongji University, Shanghai, 200092, China.|Department of Chemistry, Tongji University, Shanghai, 200092, China.|Department of Chemistry, Tongji University, Shanghai, 200092, China.|Department of Chemistry, Tongji University, Shanghai, 200092, China.', 'China|China|China|China', 0, 'Biomarkers, Tumor|Female|Gene Expression Profiling|Humans|Ovarian Neoplasms|Proteomics|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1186/1471-2105-11-109', '20187963', 'Recent advances in proteomics technologies such as SELDI-TOF mass spectrometry has shown promise in the detection of early stage cancers. However, dimensionality reduction and classification are considerable challenges in statistical machine learning. We therefore propose a novel approach for dimensionality reduction and tested it using published high-resolution SELDI-TOF data for ovarian cancer.'),
(93466, 2010, 'bmc', 'The behaviour of random forest permutation-based variable importance measures under predictor correlation', 'Nicodemus, Kristin K|Malley, James D|Strobl, Carolin|Ziegler, Andreas', 'Statistical Genetics, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. kristin.nicodemus@well.ox.ac.uk|Statistical Genetics, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. kristin.nicodemus@well.ox.ac.uk|Statistical Genetics, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. kristin.nicodemus@well.ox.ac.uk|Statistical Genetics, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. kristin.nicodemus@well.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Genome|Genome-Wide Association Study|Genomics', 'Journal Article', '10.1186/1471-2105-11-110', '20187966', 'Random forests (RF) have been increasingly used in applications such as genome-wide association and microarray studies where predictor correlation is frequently observed. Recent works on permutation-based variable importance measures (VIMs) used in RF have come to apparently contradictory conclusions. We present an extended simulation study to synthesize results.'),
(93467, 2010, 'bmc', 'Modeling expression quantitative trait loci in data combining ethnic populations', 'Hsiao, Ching-Lin|Lian, Ie-Bin|Hsieh, Ai-Ru|Fann, Cathy Sj', 'Division of Biostatistics, Institute & Department of Public Health, National Yang-Ming University, Taipei 112, Taiwan.|Division of Biostatistics, Institute & Department of Public Health, National Yang-Ming University, Taipei 112, Taiwan.|Division of Biostatistics, Institute & Department of Public Health, National Yang-Ming University, Taipei 112, Taiwan.|Division of Biostatistics, Institute & Department of Public Health, National Yang-Ming University, Taipei 112, Taiwan.', '-|-|-|-', 0, 'Databases, Genetic|Gene Expression Profiling|Genetics, Population|Genomics|Humans|Models, Statistical|Quantitative Trait Loci', 'Journal Article', '10.1186/1471-2105-11-111', '20187971', 'Combining data from different ethnic populations in a study can increase efficacy of methods designed to identify expression quantitative trait loci (eQTL) compared to analyzing each population independently. In such studies, however, the genetic diversity of minor allele frequencies among populations has rarely been taken into account. Due to the fact that allele frequency diversity and population-level expression differences are present in populations, a consensus regarding the optimal statistical approach for analysis of eQTL in data combining different populations remains inconclusive.'),
(93468, 2010, 'bmc', 'The IronChip evaluation package: a package of perl modules for robust analysis of custom microarrays', 'Vainshtein, Yevhen|Sanchez, Mayka|Brazma, Alvis|Hentze, Matthias W|Dandekar, Thomas|Muckenthaler, Martina U', 'MMPU, Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Heidelberg, Germany. vainshte@embl.de|MMPU, Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Heidelberg, Germany. vainshte@embl.de|MMPU, Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Heidelberg, Germany. vainshte@embl.de|MMPU, Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Heidelberg, Germany. vainshte@embl.de|MMPU, Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Heidelberg, Germany. vainshte@embl.de|MMPU, Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Heidelberg, Germany. vainshte@embl.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 4, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Programming Languages|Software', 'Journal Article', '10.1186/1471-2105-11-112', '20193060', 'Gene expression studies greatly contribute to our understanding of complex relationships in gene regulatory networks. However, the complexity of array design, production and manipulations are limiting factors, affecting data quality. The use of customized DNA microarrays improves overall data quality in many situations, however, only if for these specifically designed microarrays analysis tools are available.'),
(93469, 2010, 'bmc', 'Mathematical model for empirically optimizing large scale production of soluble protein domains', 'Chikayama, Eisuke|Kurotani, Atsushi|Tanaka, Takanori|Yabuki, Takashi|Miyazaki, Satoshi|Yokoyama, Shigeyuki|Kuroda, Yutaka', 'Genomic Sciences Center RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan. chika@psc.riken.jp|Genomic Sciences Center RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan. chika@psc.riken.jp|Genomic Sciences Center RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan. chika@psc.riken.jp|Genomic Sciences Center RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan. chika@psc.riken.jp|Genomic Sciences Center RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan. chika@psc.riken.jp|Genomic Sciences Center RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan. chika@psc.riken.jp|Genomic Sciences Center RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan. chika@psc.riken.jp', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 7, 'Databases, Protein|Models, Theoretical|Protein Structure, Tertiary|Proteins|Proteome|Proteomics', 'Journal Article', '10.1186/1471-2105-11-113', '20193068', 'Efficient dissection of large proteins into their structural domains is critical for high throughput proteome analysis. So far, no study has focused on mathematically modeling a protein dissection protocol in terms of a production system. Here, we report a mathematical model for empirically optimizing the cost of large-scale domain production in proteomics research.'),
(93470, 2010, 'bmc', 'OrgConv: detection of gene conversion using consensus sequences and its application in plant mitochondrial and chloroplast homologs', 'Hao, Weilong', 'Department of Biology, Indiana University, Bloomington, IN 47405, USA. haoweilong@gmail.com', 'India', 0, 'Chloroplasts|Consensus Sequence|Databases, Genetic|Gene Conversion|Genes, Mitochondrial|Genome, Plant|Internet|Mitochondria|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-11-114', '20196863', 'The ancestry of mitochondria and chloroplasts traces back to separate endosymbioses of once free-living bacteria. The highly reduced genomes of these two organelles therefore contain very distant homologs that only recently have been shown to recombine inside the mitochondrial genome. Detection of gene conversion between mitochondrial and chloroplast homologs was previously impossible due to the lack of suitable computer programs. Recently, I developed a novel method and have, for the first time, discovered recurrent gene conversion between chloroplast mitochondrial genes. The method will further our understanding of plant organellar genome evolution and help identify and remove gene regions with incongruent phylogenetic signals for several genes widely used in plant systematics. Here, I implement such a method that is available in a user friendly web interface.'),
(93471, 2010, 'bmc', 'Automatic prediction of catalytic residues by modeling residue structural neighborhood', 'Cilia, Elisa|Passerini, Andrea', 'Information Engineering and Computer Science Department, via Sommarive 14 - I38100 (Povo) Trento, Italy. cilia@disi.unitn.it|Information Engineering and Computer Science Department, via Sommarive 14 - I38100 (Povo) Trento, Italy. cilia@disi.unitn.it', 'Italy|Italy', 0, 'Catalysis|Catalytic Domain|Databases, Protein|Models, Theoretical|Protein Conformation|Protein Folding|Proteins', 'Journal Article', '10.1186/1471-2105-11-115', '20199672', 'Prediction of catalytic residues is a major step in characterizing the function of enzymes. In its simpler formulation, the problem can be cast into a binary classification task at the residue level, by predicting whether the residue is directly involved in the catalytic process. The task is quite hard also when structural information is available, due to the rather wide range of roles a functional residue can play and to the large imbalance between the number of catalytic and non-catalytic residues.'),
(93472, 2010, 'bmc', 'ABCtoolbox: a versatile toolkit for approximate Bayesian computations', 'Wegmann, Daniel|Leuenberger, Christoph|Neuenschwander, Samuel|Excoffier, Laurent', 'Institute of Ecology and Evolution, University of Bern, 3012 Bern, Switzerland. daniel.wegmann@ucla.edu|Institute of Ecology and Evolution, University of Bern, 3012 Bern, Switzerland. daniel.wegmann@ucla.edu|Institute of Ecology and Evolution, University of Bern, 3012 Bern, Switzerland. daniel.wegmann@ucla.edu|Institute of Ecology and Evolution, University of Bern, 3012 Bern, Switzerland. daniel.wegmann@ucla.edu', 'Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Animals|Arvicolinae|Bayes Theorem|Evolution, Molecular|Female|Genetics, Population|Male|Microsatellite Repeats|Software', 'Journal Article', '10.1186/1471-2105-11-116', '20202215', 'The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractable for many realistic evolutionary models, and the use of Bayesian inference has therefore been limited to very simple models. The situation changed recently with the advent of Approximate Bayesian Computation (ABC) algorithms allowing one to obtain parameter posterior distributions based on simulations not requiring likelihood computations.'),
(93473, 2010, 'bmc', 'AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number', 'Newman, Aaron M|Cooper, James B', 'Biomolecular Science and Engineering Program, University of California, Santa Barbara, CA 93106, USA.|Biomolecular Science and Engineering Program, University of California, Santa Barbara, CA 93106, USA.', 'United States|United States', 79, 'Cluster Analysis|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-11-117', '20202218', 'Clustering the information content of large high-dimensional gene expression datasets has widespread application in omics biology. Unfortunately, the underlying structure of these natural datasets is often fuzzy, and the computational identification of data clusters generally requires knowledge about cluster number and geometry.'),
(93474, 2010, 'bmc', 'A high-throughput de novo sequencing approach for shotgun proteomics using high-resolution tandem mass spectrometry', 'Pan, Chongle|Park, Byung H|McDonald, William H|Carey, Patricia A|Banfield, Jillian F|VerBerkmoes, Nathan C|Hettich, Robert L|Samatova, Nagiza F', 'Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA. panc@ornl.gov|Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA. panc@ornl.gov|Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA. panc@ornl.gov|Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA. panc@ornl.gov|Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA. panc@ornl.gov|Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA. panc@ornl.gov|Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA. panc@ornl.gov|Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA. panc@ornl.gov', 'United States|United States|United States|United States|United States|United States|United States|United States', 48, 'Algorithms|Amino Acid Sequence|Databases, Protein|Peptides|Protein Processing, Post-Translational|Proteomics|Sequence Analysis, Protein|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-11-118', '20205730', 'High-resolution tandem mass spectra can now be readily acquired with hybrid instruments, such as LTQ-Orbitrap and LTQ-FT, in high-throughput shotgun proteomics workflows. The improved spectral quality enables more accurate de novo sequencing for identification of post-translational modifications and amino acid polymorphisms.'),
(93475, 2010, 'bmc', 'Prodigal: prokaryotic gene recognition and translation initiation site identification', 'Hyatt, Doug|Chen, Gwo-Liang|Locascio, Philip F|Land, Miriam L|Larimer, Frank W|Hauser, Loren J', 'Computational Biology and Bioinformatics Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. hyattpd@ornl.gov|Computational Biology and Bioinformatics Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. hyattpd@ornl.gov|Computational Biology and Bioinformatics Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. hyattpd@ornl.gov|Computational Biology and Bioinformatics Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. hyattpd@ornl.gov|Computational Biology and Bioinformatics Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. hyattpd@ornl.gov|Computational Biology and Bioinformatics Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. hyattpd@ornl.gov', 'United States|United States|United States|United States|United States|United States', 3005, 'Algorithms|Databases, Genetic|Genome, Bacterial|Peptide Chain Initiation, Translational|Prokaryotic Cells|Software', 'Journal Article', '10.1186/1471-2105-11-119', '20211023', 'The quality of automated gene prediction in microbial organisms has improved steadily over the past decade, but there is still room for improvement. Increasing the number of correct identifications, both of genes and of the translation initiation sites for each gene, and reducing the overall number of false positives, are all desirable goals.'),
(93476, 2010, 'bmc', 'SCPS: a fast implementation of a spectral method for detecting protein families on a genome-wide scale', 'Nepusz, Tamás|Sasidharan, Rajkumar|Paccanaro, Alberto', 'Centre for Systems and Synthetic Biology, Department of Computer Science, Royal Holloway, University of London, TW20 0EX, Egham, UK. tamas@cs.rhul.ac.uk|Centre for Systems and Synthetic Biology, Department of Computer Science, Royal Holloway, University of London, TW20 0EX, Egham, UK. tamas@cs.rhul.ac.uk|Centre for Systems and Synthetic Biology, Department of Computer Science, Royal Holloway, University of London, TW20 0EX, Egham, UK. tamas@cs.rhul.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 41, 'Amino Acid Sequence|Cluster Analysis|Databases, Protein|Genome, Fungal|Genomics|Pattern Recognition, Automated|Proteins|Saccharomyces cerevisiae|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-11-120', '20214776', 'An important problem in genomics is the automatic inference of groups of homologous proteins from pairwise sequence similarities. Several approaches have been proposed for this task which are local in the sense that they assign a protein to a cluster based only on the distances between that protein and the other proteins in the set. It was shown recently that global methods such as spectral clustering have better performance on a wide variety of datasets. However, currently available implementations of spectral clustering methods mostly consist of a few loosely coupled Matlab scripts that assume a fair amount of familiarity with Matlab programming and hence they are inaccessible for large parts of the research community.'),
(93477, 2010, 'bmc', 'Mayday--integrative analytics for expression data', 'Battke, Florian|Symons, Stephan|Nieselt, Kay', 'Center for Bioinformatics Tübingen, University of Tübingen, Sand 14, 72076 Tübingen, Germany.|Center for Bioinformatics Tübingen, University of Tübingen, Sand 14, 72076 Tübingen, Germany.|Center for Bioinformatics Tübingen, University of Tübingen, Sand 14, 72076 Tübingen, Germany.', 'Germany|Germany|Germany', 93, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Internet|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-11-121', '20214778', 'DNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics. The increasing complexity and inherent noisiness of the generated data makes visual data exploration ever more important. Fast deployment of new methods as well as a combination of predefined, easy to apply methods with programmers access to the data are important requirements for any analysis framework. Mayday is an open source platform with emphasis on visual data exploration and analysis. Many built-in methods for clustering, machine learning and classification are provided for dissecting complex datasets. Plugins can easily be written to extend Maydays functionality in a large number of ways. As Java program, Mayday is platform-independent and can be used as Java WebStart application without any installation. Mayday can import data from several file formats, database connectivity is included for efficient data organization. Numerous interactive visualization tools, including box plots, profile plots, principal component plots and a heatmap are available, can be enhanced with metadata and exported as publication quality vector files.'),
(93478, 2010, 'bmc', 'eCOMPAGT integrates mtDNA: import, validation and export of mitochondrial DNA profiles for population genetics, tumour dynamics and genotype-phenotype association studies', 'Weissensteiner, Hansi|Schönherr, Sebastian|Specht, Günther|Kronenberg, Florian|Brandstätter, Anita', 'Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria.', 'Austria|Austria|Austria|Austria|Austria', 13, 'Computational Biology|DNA, Mitochondrial|Databases, Genetic|Genetic Association Studies|Genetics, Population|Genotype|Humans|Neoplasms|Phenotype|Software', 'Journal Article', '10.1186/1471-2105-11-122', '20214782', 'Mitochondrial DNA (mtDNA) is widely being used for population genetics, forensic DNA fingerprinting and clinical disease association studies. The recent past has uncovered severe problems with mtDNA genotyping, not only due to the genotyping method itself, but mainly to the post-lab transcription, storage and report of mtDNA genotypes.'),
(93479, 2010, 'bmc', 'Enhancing metabolomic data analysis with Progressive Consensus Alignment of NMR Spectra (PCANS)', 'Staab, Jennifer M|OConnell, Thomas M|Gomez, Shawn M', 'Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.|Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.|Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.', 'United States|United States|United States', 20, 'Magnetic Resonance Spectroscopy|Metabolomics|Proteome|Software|Statistics as Topic', 'Journal Article', '10.1186/1471-2105-11-123', '20214818', 'Nuclear magnetic resonance spectroscopy is one of the primary tools in metabolomics analyses, where it is used to track and quantify changes in metabolite concentrations or profiles in response to perturbation through disease, toxicants or drugs. The spectra generated through such analyses are typically confounded by noise of various types, obscuring the signals and hindering downstream statistical analysis. Such issues are becoming increasingly significant as greater numbers of large-scale systems or longitudinal studies are being performed, in which many spectra from different conditions need to be compared simultaneously.'),
(93480, 2010, 'bmc', 'A fast and robust hepatocyte quantification algorithm including vein processing', 'Ivanovska, Tetyana|Schenk, Andrea|Homeyer, André|Deng, Meihong|Dahmen, Uta|Dirsch, Olaf|Hahn, Horst K|Linsen, Lars', 'Jacobs University, Bremen, Germany. tetyana.ivanovska@uni-greifswald.de|Jacobs University, Bremen, Germany. tetyana.ivanovska@uni-greifswald.de|Jacobs University, Bremen, Germany. tetyana.ivanovska@uni-greifswald.de|Jacobs University, Bremen, Germany. tetyana.ivanovska@uni-greifswald.de|Jacobs University, Bremen, Germany. tetyana.ivanovska@uni-greifswald.de|Jacobs University, Bremen, Germany. tetyana.ivanovska@uni-greifswald.de|Jacobs University, Bremen, Germany. tetyana.ivanovska@uni-greifswald.de|Jacobs University, Bremen, Germany. tetyana.ivanovska@uni-greifswald.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 2, 'Algorithms|Animals|Cell Count|Hepatocytes|Image Processing, Computer-Assisted|Rats|Veins', 'Journal Article', '10.1186/1471-2105-11-124', '20219107', 'Quantification of different types of cells is often needed for analysis of histological images. In our project, we compute the relative number of proliferating hepatocytes for the evaluation of the regeneration process after partial hepatectomy in normal rat livers.'),
(93481, 2010, 'bmc', 'KiDoQ: using docking based energy scores to develop ligand based model for predicting antibacterials', 'Garg, Aarti|Tewari, Rupinder|Raghava, Gajendra P S', 'Bioinformatics Centre, Institute of Microbial Technology, Sector-39A, Chandigarh, India.|Bioinformatics Centre, Institute of Microbial Technology, Sector-39A, Chandigarh, India.|Bioinformatics Centre, Institute of Microbial Technology, Sector-39A, Chandigarh, India.', 'India|India|India', 30, 'Anti-Bacterial Agents|Drug Design|Hydro-Lyases|Ligands|Models, Molecular|Quantitative Structure-Activity Relationship|Software|Thermodynamics', 'Journal Article', '10.1186/1471-2105-11-125', '20222969', 'Identification of novel drug targets and their inhibitors is a major challenge in the field of drug designing and development. Diaminopimelic acid (DAP) pathway is a unique lysine biosynthetic pathway present in bacteria, however absent in mammals. This pathway is vital for bacteria due to its critical role in cell wall biosynthesis. One of the essential enzymes of this pathway is dihydrodipicolinate synthase (DHDPS), considered to be crucial for the bacterial survival. In view of its importance, the development and prediction of potent inhibitors against DHDPS may be valuable to design effective drugs against bacteria, in general.'),
(93482, 2010, 'bmc', 'Mocapy++--a toolkit for inference and learning in dynamic Bayesian networks', 'Paluszewski, Martin|Hamelryck, Thomas', 'Bioinformatics Centre, University of Copenhagen, Denmark. palu@binf.ku.dk|Bioinformatics Centre, University of Copenhagen, Denmark. palu@binf.ku.dk', 'Denmark|Denmark', 13, 'Bayes Theorem|Models, Statistical|Protein Conformation|Proteins|RNA|Software', 'Journal Article', '10.1186/1471-2105-11-126', '20226024', 'Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distributions, including distributions from directional statistics (the statistics of angles, directions and orientations).');
