Data Mining in Bioinformatics / edited by Xindong Wu, Lakhmi Jain, Jason T.L. Wang, Mohammed J. Zaki, Hannu T.T. Toivonen, Dennis Shasha.

Springer eBooks

Overview -- to Data Mining in Bioinformatics -- Survey of Biodata Analysis from a Data Mining Perspective -- Sequence and Structure Alignment -- AntiClustAl: Multiple Sequence Alignment by Antipole Clustering -- RNA Structure Comparison and Alignment -- Biological Data Mining -- Piecewise Constant Modeling of Sequential Data Using Reversible Jump Markov Chain Monte Carlo -- Gene Mapping by Pattern Discovery -- Predicting Protein Folding Pathways -- Data Mining Methods for a Systematics of Protein Subcellular Location -- Mining Chemical Compounds -- Biological Data Management -- Phyloinformatics: Toward a Phylogenetic Database -- Declarative and Efficient Querying on Protein Secondary Structures -- Scalable Index Structures for Biological Data.

The goal of this book is to help readers understand state-of-the-art techniques in biological data mining and data management and includes topics such as: - preprocessing tasks such as data cleaning and data integration as applied to biological data - classification and clustering techniques for microarrays - comparison of RNA structures based on string properties and energetics - discovery of the sequence characteristics of different parts of the genome - mining of haplotypes to find disease markers - sequencing of events leading to the folding of a protein - inference of the subcellular location of protein activity - classification of chemical compounds based on structure - special purpose metrics and index structures for phylogenetic applications - a new query language for protein searching based on the shape of proteins - very fast indexing schemes for sequences and pathways Aimed at computer scientists, necessary biology is explained.

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