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International Joint Conference on Neural Networks (IJCNN)

作者： Xiaoxu Han Department of Mathematics and Bioinformatics Program Eastern Michigan University Ypsilanti MI USA

Although entropy and relative entropy (K-L distance) are widely applied in many bioinformatics areas, there is no method given to compute the multispecies gene entropy yet. This paper presents the first multispecies gene entropy estimation method from the data mining point of view. In this study, a self-organizing map (SOM) is employed to mine a multispecies gene set to obtain the probability distribution of each gene in the feature space, which is the approximation of its corresponding probability distribution in the original sequence space. The multispecies gene entropy is computed by the probability distribution of a gene in the feature space. The phylogenetic applications of the multispecies gene entropy are investigated in an example of resolving incongruence between gene trees and species trees. It is found that genes with nearest K-L distances to the minimum entropy gene are more likely to be phylogenetically informative. A K-L distance based gene concatenation approach under gene clustering is proposed to resolve the gene tree and species tree problem. Under the same testing dataset, the K-L distance based approach not only avoids the ad-hoc mechanism of the original gene concatenation method but also is easy to extend to other dataset and free from prohibitive phylogenetic computing from large number of taxa.

关键词： Entropy Sequences DNA Probability distribution bioinformatics Phylogeny Data mining Distributed computing Testing Information analysis

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Organization of brain complexity - Synapse proteome form and function

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Briefings in Functional Genomics and Proteomics 2006年第1期5卷 66-73页

作者： Pocklington, Andrew J. Armstrong, J. Douglas Grant, Seth G.N. Institute for Adaptive and Neural Computation School of Informatics University of Edinburgh Edinburgh United Kingdom Edinburgh Centre for Bioinformatics University of Edinburgh Edinburgh United Kingdom Cognition Program Wellcome Trust Sanger Institute Hinxton CB10 1SA United Kingdom

Proteomic study of the synapse has generated an extensive list of molecular components, revealing one of the most complex functional systems currently known to cell biology. While fundamental to neural information processing, behaviour and disease, the molecular organisation of the synapse and its relation to higher-level function has yet to be clearly understood. Neurotransmitter receptor complexes, such as the N-methyl-D-aspartate receptor complex (NRC/MASC), are major components of the synaptic proteome. We have recently completed a detailed study of MASC, its functional organisation and involvement in behaviour and disease. This pointed to simple design principles underlying synaptic organisation. Drawing together the results of proteomic and analytical study, we sketch out a model for synaptic functional organisation. © 2006 Oxford University Press.

关键词： LTP Networks NMDA receptor Protein complex Synaptic plasticity

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Breaking the high-throughput bottleneck

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Scientific Computing 2006年第4期23卷 22-26页

作者： Waters, Katrina M. Singhal, Mudita Webb-Robertson, Bobbie-Jo M. Stephan, Eric G. Gephart, Julie M. Computational Biology and Bioinformatics Groups United States Lab's Systems Biology Program Biological Sciences Division United States PNNL United States

The development of high-throughput (HTP) technologies such as transcriptomics (microarrays) and proteomics has made a revolution in the medical industry. PNNL is developing a systems biology computational framework using a problem-solving environment called bioinformatics Resource Manager (BRM) that automates routine data-merging and data-mining methods, and provides seamless linkage to data visualization tools. The BRM is designed to be operated by biologists looking for greater insights into their experimental results. BRM uses Remote Method Invocation (RMI) to launch several external data analysis tools, including NCBI's PubMed, the Conserved Domain Architectural Retrieval Tool (CDART) and the Basic Local Alignment Search Tool (BLAST). PQuad is a proteomics visualization tool that combines DNA or RNA sequence and proteomics data at multiple resolutions. It enables visualization of the data at the chromosome, gene and sequence levels.

关键词： Data visualization

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Comparing Kernels for Predicting Protein Binding Sites from Amino Acid Sequence

Comparing Kernels for Predicting Protein Binding Sites from ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Feihong Wu B. Olson D. Dobbs V. Honavar Bioinformatics and Computational Biology Graduate Program Center for Computational Intelligence Learning and Discovery Iowa State University Ames IA USA

The ability to identify protein binding sites and to detect specific amino acid residues that contribute to the specificity and affinity of protein interactions has important implications for problems ranging from rational drug design to analysis of metabolic and signal transduction networks. Support vector machines (SVM) and related kernel methods offer an attractive approach to predicting protein binding sites. An appropriate choice of the kernel function is critical to the performance of SVM. Kernel functions offer a way to incorporate domain-specific knowledge into the classifier. We compare the performance of three types of kernels functions: identity kernel, sequence-alignment kernel, and amino acid substitution matrix kernel in the case of SVM classifiers for predicting protein-protein, protein-DNA and protein-RNA binding sites. The results show that the identity kernel is quite effective in on all three tasks. The substitution kernel based on amino acid substitution matrices that take into account structural or evolutionary conservation or physicochemical properties of amino acids yields modest improvement.

关键词： Kernel Proteins Amino acids Support vector machines Support vector machine classification Matrices Drugs Signal design Signal analysis Signal processing

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High torsional energy disulfides: Relationship between cross-strand disulfides and right-handed staples

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Journal of bioinformatics and Computational Biology 2006年第1期4卷 155-168页

作者： Haworth, Naomi L. Feng, Lina L. Wouters, Merridee A. Computational Biology and Bioinformatics Program Victor Chang Cardiac Research Institute Darlinghurst NSW 2010 384 Victoria Rd. Australia School of Chemistry University of Sydney Sydney NSW 2006 Australia School of Biotechnology and Biomolecular Sciences University of New South Wales Sydney Sydney NSW 2052 Australia

Redox-active disulfides are capable of being oxidized and reduced under physiological conditions. The enzymatic role of redox-active disulfides in thiol-disulfide reductases is well-known, but redox-active disulfides are also present in non-enzymatic protein structures where they may act as switches of protein function. Here, we examine disulfides linking adjacent β-strands (cross-strand disulfides), which have been reported to be redox-active. Our previous work has established that these cross-strand disulfides have high torsional energies, a quantity likely to be related to the ease with which the disulfide is reduced. We examine the relationship between conformations of disulfides and their location in protein secondary structures. By identifying the overlap between cross-strand disulfides and various conformations, we wish to address whether the high torsional energy of a cross-strand disulfide is sufficient to confer redox activity or whether other factors, such as the presence of the cross-strand disulfide in a strained β-sheet, are required. © Imperial College Press.

关键词： Cross-strand disulfide Disulfide Redox signaling Right-handed staple

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Biologically Supervised Hierarchical Clustering Algorithms for Gene Expression Data

Biologically Supervised Hierarchical Clustering Algorithms f...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Grzegorz M Boratyn Susmita Datta Somnath Datta Kidney Disease Program and Clinical Proteomics Center University of Louisville Louisville KY Faculty of Depertment of Bioinformatics and Biostatistics University of Louisville Louisville KY

Cluster analysis has become a standard part of gene expression analysis. In this paper, we propose a novel semi-supervised approach that offers the same flexibility as that of a hierarchical clustering. Yet it utilizes, along with the experimental gene expression data, common biological information about different genes that is being complied at various public, Web accessible databases. We argue that such an approach is inherently superior than the standard unsupervised approach of grouping genes based on expression data alone. It is shown that our biologically supervised methods produce better clustering results than the corresponding unsupervised methods as judged by the distance from the model temporal profiles. R-codes of the clustering algorithm are available from the authors upon request

关键词： Clustering algorithms Gene expression Databases Algorithm design and analysis Biological system modeling Biology computing Cities and towns Data analysis Measurement standards Diseases

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Bayesian hierarchical model for estimating gene association network from microarray data

Bayesian hierarchical model for estimating gene association ...

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IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

作者： Dongxiao Zhu Alfred O. Hero Bioinformatics Program University of Michigan Ann Arbor MI USA Departments of EECS Biomedical Engineering and Statistics University of Michigan Ann Arbor MI USA

Estimating gene association networks from gene microar- ray data is the key to decipher complicated Web of functional relationship between genes. However, the process remains to be challenging due to the relatively few independent samples and the large amount of correlation parameters. In a gene association network, vertices represent genes, and edges represent biological association between genes. The network edges are declared to be present if the corresponding correlation parameters are significantly different from a non-zero threshold. The approach has been very useful in inferring gene association networks, and facilitating network based discovery. However, as a Frequentist approach, it often suffers from the "overfitting" problem especially for analyzing small sample size data. Approaches that are able to globally estimate the correlation parameters with variance regularization followed by the seamless correlation thresholding are highly desirable.

关键词： Bayesian methods Gaussian distribution Gene expression Blindness bioinformatics Biomedical engineering Statistical distributions Computational modeling Computational complexity Parameter estimation

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GetItFull - A tool for downloading and pre-processing full-text journal articles 1

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PAKDD 2006 International Workshop on Knowledge Discovery in Life Science Literature, KDLL 2006

作者： Natarajan, Jeyakumar Haines, Cliff Berglund, Brian DeSesa, Catherine Hack, Catherine J. Dubitzky, Werner Bremer, Eric G. Bioinformatics Research Group University of Ulster United Kingdom CTH Technologies Inc. Oak Brook Terrace IL United States PSS Inc. Chicago IL United States Brain Tumor Research Program Children's Memorial Hospital Northwestern University Chicago IL United States

ISBN: (数字)9783540328100

ISBN: (纸本)3540328092

Automated analysis of full-text life science research articles and technical documents is becoming increasingly important, In contrast to abstracts, accessing and processing full-text is considerably more complex. GetltFull is a tool for downloading and pre-processing full-text journal articles. GetltFull automatically connects to a journal's Web site, downloads the journal content and performs various commonly used pre-processing steps, The output comprises a structured XML, document for each article with tags identifying the various sections and journal information. The output may then be used as the basis for text mining applications or exported to a database for further processing. © springer-verlag Berlin Heidelberg 2006.

关键词： Online searching

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A direct comparison of protein interaction confidence assignment schemes

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BMC bioinformatics 2006年第1期7卷 360-360页

作者： Suthram, Silpa Shlomi, Tomer Ruppin, Eytan Sharan, Roded Ideker, Trey Department of Bioengineering University of California San Diego CA 92093 USA Program in Bioinformatics University of California San Diego CA 92093 USA School of Computer Science Tel-Aviv University Tel Aviv 69978 Israel

Background: Recent technological advances have enabled high-throughput measurements of protein-protein interactions in the cell, producing large protein interaction networks for various species at an ever-growing pace. However, common technologies like yeast two-hybrid may experience high rates of false positive detection. To combat false positive discoveries, a number of different methods have been recently developed that associate confidence scores with protein interactions. Here, we perform a rigorous comparative analysis and performance assessment among these different methods. Results: We measure the extent to which each set of confidence scores correlates with similarity of the interacting proteins in terms of function, expression, pattern of sequence conservation, and homology to interacting proteins in other species. We also employ a new metric, the Signal-to-Noise Ratio of protein complexes embedded in each network, to assess the power of the different methods. Seven confidence assignment schemes, including those of Bader et al., Deane et al., Deng et al., Sharan et al., and Qi et al., are compared in this work. Conclusion: Although the performance of each assignment scheme varies depending on the particular metric used for assessment, we observe that Deng et al. yields the best performance overall ( in three out of four viable measures). Importantly, we also find that utilizing any of the probability assignment schemes is always more beneficial than assuming all observed interactions to be true or equally likely.

关键词： Gene Ontology Protein Interaction Network Assignment Scheme Probability Scheme Yeast Interaction

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SPLITS: A new program for predicting split and intron-containing tRNA genes at the genome level

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In Silico Biology 2006年第5期6卷 411-418页

作者： Sugahara, Junichi Yachie, Nozomu Sekine, Yasuhiko Soma, Akiko Matsui, Motomu Tomita, Masaru Kanai, Akio Institute for Advanced Biosciences Keio University Tsuruoka 997-0017 Japan Department of Environmental Information Keio University Fujisawa 252-8520 Japan Bioinformatics Program Graduate School of Media and Governance Keio University Fujisawa 252-8520 Japan Department of Life Science College of Science Rikkyo University Toshima-ku Tokyo 171-8501 3-34-1 Nishiikebukuro Japan

In the archaea, some tRNA precursors contain intron(s) not only in the anticodon loop region but also in diverse sites of the gene (intron-containing tRNA or cis-spliced tRNA). The parasite Nanoarchaeum equitans, a member of the Nanoarchaeota kingdom, creates functional tRNA from separate genes, one encoding the 5'-half and the other the 3'-half (split tRNA or trans-spliced tRNA). Although recent genome projects have revealed a huge amount of nucleotide sequence data in the archaea, a comprehensive methodology for intron-containing and split tRNA searching is yet to be established. We therefore developed SPLITS, which is aimed at searching for any type of tRNA gene and is especially focused on intron-containing tRNAs or split tRNAs at the genome level. SPLITS initially predicts the bulge-helix-bulge splicing motif (a well-known, required structure in archaeal pre-tRNA introns) to determine and remove the intronic regions of tRNA genes. The intron-removed DNA sequences are automatically queried to tRNAscan-SE. SPLITS can predict known tRNAs with single introns located at unconventional sites on the genes (100%, tRNAs with double introns (85.7%), and known split tRNAs (100%). Our program will be very useful for identifying novel tRNA genes after completion of genome projects. The SPLITS source code is freely downloadable at http://***/. © 2006 IOS Press. All rights reserved.

关键词： Bulge-helix-bulge splicing motif Genome analysis Intron-containing tRNA Software Split tRNA tRNAscan-SE

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