The proceedings contain 127 papers. The topics discussed include: sequential classification for microarray and clinical data;efficient image texture analysis and classification for prostate ultrasound diagnosis;diagno...
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ISBN:
(纸本)0769524427
The proceedings contain 127 papers. The topics discussed include: sequential classification for microarray and clinical data;efficient image texture analysis and classification for prostate ultrasound diagnosis;diagnosis and biomarker identification on SELDI proteomics data by ADTboost;inverse design of large molecules using linear Diophantine equations;reducing gene regulatory networks by decomposition;a diffusion model to estimate the inter-arrival time of charged molecules in stochastic event based modeling of complex biological networks;operon prediction in cyanobacteria using comparative genomics;patterns of gene deletion following genome duplication in yeast;classification methods for HIV-1 medicated neuronal damage;key features of the UCSC genome site;data integration in the Mouse Genome Informatics (MGI) database;HIV structural and biothermodynamics databases: A resource for the pharmaceutical and biotechnology industry;a new clustering strategy with stochastic merging and removing based on kernel functions;head and neck cancer metastasis prediction via artificial neural networks;and problem solving environment approach to integrating diverse biological data sources.
The reverse engineering paradigm is given increasing attention in computational molecular biology lately. One of the goals is to understand how gene regulatory networks (complex systems of genes, proteins and other mo...
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The reverse engineering paradigm is given increasing attention in computational molecular biology lately. One of the goals is to understand how gene regulatory networks (complex systems of genes, proteins and other molecules) function and interact to carry out specific cell functions. We present an approach for inferring the complex causal relationships among genes from microarray experimental data based on a recurrent neuro-fuzzy method. The method derives information on the gene interactions in a highly interpretable form (fuzzy rules) and takes into account dynamical aspects of genes regulation through its recurrent structure. We tested our approach on a set of genes known to be highly regulated during the yeast cell-cycle. The retrieved gene interactions correspond to the ones validated by previous biological studies, while our method surpasses previous computational techniques that attempted gene networks reconstruction, being able to retrieve significantly more biologically valid relationships among genes. At the same time, our method is able to predict time series for the expression of the genes based on the information extracted from a training subset of the data. The results prove highly accurate prediction capability
The Cancer Biomedical Informatics Grid (caBIGtrade) is a new project initiated by the National Cancer Institute to create a computational network connecting scientists and institutions to enable the sharing of data an...
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The Cancer Biomedical Informatics Grid (caBIGtrade) is a new project initiated by the National Cancer Institute to create a computational network connecting scientists and institutions to enable the sharing of data and the use of common analytical tools. The emergence of genomics and proteomics high-throughput technologies are creating a paradigm shift in biomedical research from small independent labs to large teams of researchers exploring entire genomes and proteomes and how they relate to disease. caBIGtrade is developing new software and modifying existing software within Clinical Trials Management systems, Tissue Banks and Pathology Tools and Integrated Cancer Research tools to manage the huge volume of data being generated and to facilitate collaboration across the broad spectrum of cancer research
An important goal of functional genomics is to develop methods for determining ways in which individual actions of genes are integrated in the cell. One way of gaining insight into a gene's role in cellular activi...
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An important goal of functional genomics is to develop methods for determining ways in which individual actions of genes are integrated in the cell. One way of gaining insight into a gene's role in cellular activity is to study its expression pattern in a variety of circumstances and contexts, as it responds to its environment and to the action of other genes. Microarrays provide large-scale surveys of gene expression in which transcript levels can be determined for thousands of genes simultaneously. The coefficient of determination (CoD) has been proposed for the analysis of gene interaction via multivariate expression arrays. Parallel computing is essential to the application of the CoD to a large set of genes because of the large number of expression-based functions that must be statistically designed and compared. The results of the calculation of the CoD for a large set of genes with multiple superscalar processors are presented. A proposal for calculating the CoD with multiple vector processors is described. Multiple vector processor systems offer the potential to greatly reduce the time to calculate the CoD for a large set of genes
Cutting-edge biological and bioinformatics research seeks a systems perspective through the analysis of multiple types of high-throughput and other experimental data for the same sample. systems-level analysis require...
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ISBN:
(纸本)0769523447
Cutting-edge biological and bioinformatics research seeks a systems perspective through the analysis of multiple types of high-throughput and other experimental data for the same sample. systems-level analysis requires the integration and fusion of such data, typically through advanced statistics and mathematics. Visualization is a complementary computational approach that supports integration and analysis of complex data or its derivatives. We present a bioinformatics visualization prototype, Juxter, which depicts categorical information derived from or assigned to these diverse data for the purpose of comparing patterns across categorizations. The visualization allows users to easily, discern correlated and anomalous patterns in the data. These patterns, which might not be detected automatically by, algorithms, may, reveal valuable information leading to insight and discovery. We describe the visualization and interaction capabilities and demonstrate its utility in a new field, metagenomics, which combines molecular biology and genetics to identify and characterize genetic material from multi-species microbial samples.
Functionally related genes co-evolve, probably due to the strong selection pressure in evolution. Thus we expect that they are present in multiple genomes. Physical proximity among genes, known as gene team, is a very...
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