Computational Intelligence and Pattern Analysis in Biology Informatics
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Authored by leading researchers in computational intelligence in biology informatics, this book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. This unique volume provides both theoretical and experimental results, deepening the reader's understanding of the ways in which CI principles can be used for analyzing biological data in an efficient manner. Program code and relevant data sets are included. It is geared for researchers and practitioners in industry/R&D laboratories as well as graduate students.
An invaluable tool in Bioinformatics, this unique volume provides both theoretical and experimental results, and describes basic principles of computational intelligence and pattern analysis while deepening the reader's understanding of the ways in which these principles can be used for analyzing biological data in an efficient manner. This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful information and insight from it. Biological data for analysis include sequence data, secondary and tertiary structure data, and microarray data. These data types are complex and advanced methods are required, including the use of domain-specific knowledge for reducing search space, dealing with uncertainty, partial truth and imprecision, efficient linear and/or sub-linear scalability, incremental approaches to knowledge discovery, and increased level and intelligence of interactivity with human experts and decision makers * Chapters authored by leading researchers in CI in biology informatics. * Covers highly relevant topics: rational drug design; analysis of microRNAs and their involvement in human diseases. * Supplementary material included: program code and relevant data sets correspond to chapters.
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