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检索条件"任意字段=Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004"

共 372 条记录，以下是141-150 订阅

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Functional modules from protein networks of kinome and cell cycle in Saccharomyces cerevisiae

Functional modules from protein networks of kinome and cell ...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： Hee Young Kang Hak Yong Kim Division of Life Sciences College of Natural Sciences Chungbuk National University South Korea

ISBN: (纸本)0769521940

We have constructed protein networks of kinome and cell cycle from MIPS database. The networks show character of scale free network, in which a few highly connected proteins play a central role in most cells. Protein-protein interaction database obtained from a yeast two hybrid screen or a composite data set includes random false positives. To purify the database, we employed cellular localization and DNA expression profile and then reconstructed kinome and cell cycle networks. The kinome and cell cycle networks constructed from the purified database show characters of scale free network and hierarchical network, in which preserves it's scale-free organization and displays inherent modularity of protein clusters. To find functional modules from the network, we propose a new technique that is based on multi-body correlations in kinome or cell cycle network. From the derived modules, we predicted and estimated tentative functions for unannotated proteins with high certainty.

关键词： Proteins Intelligent networks Databases Fungi DNA bioinformatics Genomics Cellular networks Educational institutions Displays

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Boosted PRIM with application to searching for oncogenic pathway of lung cancer

Boosted PRIM with application to searching for oncogenic pat...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： Pei Wang Young Kim J. Pollack R. Tibshirani Department of Statistics University of Stanford Stanford CA USA Department of Pathology University of Stanford Stanford CA USA Department of HRP and Statistics University of Stanford Stanford CA USA

Boosted PRIM (patient rule induction method) is a new algorithm developed for two-class classification problems. PRIM is a variation of those tree-based methods, seeking box-shaped regions in the feature space to separate different classes. Boosted PRIM is to implement PRIM-styled weak learners in Adaboost, one of the most popular boosting algorithms. In addition, we improve the performance of the algorithm by introducing a regularization to the boosting process, which supports the perspective of viewing boosting as a steepest-descent numerical optimization by Jerry Friedman. The motivation for boosted PRIM is to solve the problem of "searching for oncogenic pathways" based on array-CGH (comparative genomic hybridization) data, though the algorithm itself is suitable for general classification problems. We illustrate the performance of the method through some simulation studies as well as an application on a lung cancer array-CGH data set.

关键词： Lungs Cancer Neoplasms bioinformatics Genomics Genetics DNA Boosting Biological system modeling Statistics

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MinPD: distance-based phylogenetic analysis and recombination detection of serially-sampled HIV quasispecies

MinPD: distance-based phylogenetic analysis and recombinatio...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： P. Buendia G. Narasimhan Bioinformatics Research Group School of Computer Science Florida International University Miami FL USA

A new computational method to study within-host viral evolution is explored to better understand the evolution and pathogenesis of viruses. Traditional phylogenetic tree methods are better suited to study relationships between contemporaneous species, which appear as leaves of a phylogenetic tree. However, viral sequences are often sampled serially from a single host. Consequently, data may be available at the leaves as well as the internal nodes of a phylogenetic tree. Recombination may further complicate the analysis. Such relationships are not easily expressed by traditional phylogenetic methods. We propose a new algorithm, called MinPD, based on minimum pairwise distances. Our algorithm uses multiple distance matrices and correlation rules to output a MinPD tree or network. We test our algorithm using extensive simulations and apply it to a set of HIV sequence data isolated from one patient over a period often years. The proposed visualization of the phylogenetic tree/spl bsol/network further enhances the benefits of our methods.

关键词： Human immunodeficiency virus Phylogeny bioinformatics Pathogens Immune system Sampling methods Capacitive sensors Diseases Computer science Electronic mail

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On complexity measures for biological sequences

On complexity measures for biological sequences

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： Fei Nan D. Adjeroh Department of Computer Science and Electrical Engineering West Virginia University Morgantown WV USA

In this work, we perform an empirical study of different published measures of complexity for general sequences, to determine their effectiveness in dealing with biological sequences. By effectiveness, we refer to how closely the given complexity measure is able to identify known biologically relevant relationships, such as closeness on a phylogenic tree. In particular, we study three complexity measures, namely, the traditional Shanon's entropy, linguistic complexity, and T-complexity. For each complexity measure, we construct the complexity profile for each sequence in our test set, and based on the profiles we compare the sequences using different performance measures based on: (i) the information theoretic divergence measure of relative entropy; (ii) apparent periodicity in the complexity profile; and (iii) correct phylogeny. The preliminary results show that the T-complexity was the least effective in identifying previously established known associations between the sequences in our test set. Shannon's entropy and linguistic-complexity provided better results, with Shannon's entropy having an upper hand.

关键词： Sequences Entropy bioinformatics Genomics Organisms Biological information theory Testing DNA Particle measurements Computer science

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GeneTide - Terra Incognita Discovery Endeavor mining ESTs and expression data to elucidate known and de-novo GeneCards/spl reg/ genes

GeneTide - Terra Incognita Discovery Endeavor mining ESTs an...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： M. Shklar O. Shmueli L. Strichman-Almashanu M. Shmoish D. Lancet M. Safran Department of Molecular Genetics Weizmann Institute of Science Rehovot Israel Biological Services (Bioinformatics Unit) Weizmann Institute of Science Rehovot Israel

The construction of a complete EST-based gene index is an intricate task yet to be accomplished. GeneTide, the Gene Terra Incognita Discovery Endeavor (http://***/genetide/), which is the newest addition to the GeneCards suite of databases, comprehensively maps >4.5 of the /spl sim/5.5 million human ESTs currently available at dbEST with either known or newly defined putative human genes. The association is accomplished via data mining genomic resources, and integrating using a unified scoring scheme. Groups of unassociated transcripts serve as a basis for defining EST-based gene candidates (EGCs). These EGCs are annotated with various parameters, including expression data, to determine their validity as possible de-novo genes. An immediate application of GeneTide to microarray annotation has increased, in a specific example, the number of annotated Affymetrix HGU95A-E probe sets by 50% in comparison to previous attempts.

关键词： bioinformatics Probes Genomics Databases Humans Genetics Biology Biological information theory Data mining Throughput

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Multiple alignment of rearranged genomes

Multiple alignment of rearranged genomes

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： A. Darling M. Craven B. Mau N.T. Perna Department of Computer Science University of Wisconsin Madison Madison WI USA Department of Animal Health and Biomedical Sciences University of Wisconsin Madison Madison WI USA Department of Biostatistics and Medical Informatics University of Wisconsin Madison Madison WI USA

The nature of large-scale evolutionary processes that shape genomes over time fundamentally differs from the forces governing local evolution within individual genes. Large-scale events such as horizontal transfer, genome rearrangements, gene duplication, and gene loss obscure the notion of orthology and have demanded new models of evolution. Multiple genome alignment tools must cope with such large-scale changes in addition to local changes such as nucleotide substitution and indels. Using simulated genomes containing both large and small-scale evolutionary changes, we present an alignment quality comparison of Mauve, a multiple genome aligner that considers large-scale evolutionary events, to alignments generated by other state-of-the-art genome alignment systems. Our results indicate that in the presence of large-scale rearrangement events, Mauve has superior accuracy. Mauve is available from http: //gel. ***/mauve.

关键词： Genomics bioinformatics Large-scale systems Sequences Organisms Computer science Animals Biomedical informatics Shape Discrete event simulation

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FigSearch: using maximum entropy classifier to categorize biological figures

FigSearch: using maximum entropy classifier to categorize bi...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： Fang Liu T.-K. Jenssen V. Nygaard J. Sack E. Hovig Department of Tumor Biology Institute for Cancer Research Norwegian Radium Hospital Oslo Norway PubGene AS Oslo Norway University of Stanford Palo Alto CA USA

Figures in scientific papers represent an intuitive and concise way of knowledge presentation. With more attention being paid on full-text mining in bioinformatics, we initiated an effort of studying figures in full articles. FigSearch is a prototype figure legend indexing and classification system, using both text-mining and supervised machine learning. We defined schematic representations of protein interactions and signaling events as an interesting figure type. A maximum entropy classifier was used in categorizing each figure, by assigning an estimated likelihood, as being relevant/non-relevant according to our definition. One advantage of the maximum entropy principle is that it provides a probability of decision, instead of a binary assignment. In our pilot study, FigSearch showed satisfactory performance in a preliminary validation by domain experts. Such a system can be useful in applications such as for a publisher's website, in bio-picture gallery constructions, or as an aid for other complicated text-mining projects.

关键词： Entropy Proteins bioinformatics Machine learning Neoplasms Cancer Hospitals Milling machines Prototypes Indexing

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The application of grid technology in systems biology: Parameter estimation

The application of grid technology in systems biology: Param...

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proceedings - Seventh International conference on High Performance Computing and Grid in Asia Pacific Region, HPCAsia 2004

作者： Dhar, Pawan K. Bioinformatics Institute Singapore Singapore Dept. of Biochemistry National University of Singapore Singapore

ISBN: (纸本)076952138X

The systems biology is a recent scientific discipline that arose from the need to combine biology with mathematics, physics, chemistry and computer science. Partly driven by the availability of a morass of data and partly driven by the availability of computational resources, the field of systems biology was reborn few years ago. Recently, a number of computational methods have been developed to model cellular pathways and networks. One of the major issues in building mathematical models of cellular processes is the difficulty of estimating parameters. Due to a wide numerical range within which parameters operate, it takes a large number of iterations to find the biologically relevant values. In this paper I will describe how grid technology can be used to salvage this situation and help in building robust in-silico models.

关键词： Parameter estimation

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High-throughput computational and experimental biology strategy in identifying tumor expressing CAMs

High-throughput computational and experimental biology strat...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： A. Sadanandam M.L. Varney R.K. Singh Department of Pathology and Microbiology University of Nebraska Medical Center Omaha NE USA

Organ-specific homing of tumor cells includes receptor-ligand interactions of cell adhesion molecules (CAMs), which stimulate or inhibit the cellular growth. In this report we identified unique CAMs using sequence and pattern analysis with organ-specific peptides as query against mouse genome and proteome sequences. The peptides were seven amino acids in length, and were affinity selected against specific organs utilizing in vivo phage display peptide library in NOD-SCID mice. Various databases were used for the analyses including Local Mouse Cell Adhesion Molecule (LMCAM) database developed by keyword search. Thirty annotated CAMs corresponding to eleven different organ-specific peptides were identified using the bioinformatics analysis. One such identified protein, SEMA5A is reported for the first time to be expressed in human pancreatic cancer cell lines. This combined strategy of experimental and computation biology is an initial approach in identifying novel tumor-specific molecules, thereby paving the way for complete understanding of their role in various processes of tumor metastasis and making organ-specific targeting possible using these peptides.

关键词： Biology computing computational biology Neoplasms Cams Peptides Sequences Metastasis bioinformatics Databases Adhesives

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A new approach to clustering biological data using message passing

A new approach to clustering biological data using message p...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： Huimin Geng D. Bastola H. Ali Department of Computer Science University of Nebraska Omaha USA Department of Pathology and Microbiology University of Nebraska Medical Center USA

Clustering algorithms are widely used in bioinformatics to classify data, as in the analysis of gene expression and in the building of phylogenetic trees. Biological data often describe parallel and spontaneous processes. To capture these features, we propose a new clustering algorithm that employs the concept of message passing. Message passing clustering (MPC) allows data objects to communicate with each other and produces clusters in parallel, thereby making the clustering process intrinsic. We have proved that MPC shares similarity with hierarchical clustering (HC) but offers significantly improved performance because it takes into account both local and global structure. We analyzed 35 sets of simulated dynamic gene expression data, achieving a 95% hit rate in which 639 genes out of total 674 genes were correctly clustered. We have also applied MPC to a real data set to build a phylogenetic tree from aligned mycobacterium sequences. The results show higher classification accuracies as compared to traditional clustering methods such as HC.

关键词： Message passing Clustering algorithms Phylogeny Gene expression bioinformatics Algorithm design and analysis Clustering methods Couplings Computer science Pathology

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