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.

Written especially for computer scientists, all necessary biology is explained.

Presents new techniques on gene expression data mining, gene mapping for disease detection, and phylogenetic knowledge discovery.

This book constitutes the refereed proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery, PKDD 2002, held in Helsinki, Finland in August 2002.The 39 revised full papers presented together with 4 invited contributions were carefully reviewed and selected from numerous submissions. Among the topics covered are kernel methods, probabilistic methods, association rule mining, rough sets, sampling algorithms, pattern discovery, web text mining, meta data clustering, rule induction, information extraction, dependency detection, rare class prediction, classifier systems, text classification, temporal sequence analysis, unsupervised learning, time series analysis, medical data mining, etc.

This book constitutes the refereed preceedings of the 13th European Conference on Machine Learning, ECML 2002, held in Helsinki, Finland in August 2002.The 41 revised full papers presented together with 4 invited contributions were carefully reviewed and selected from numerous submissions. Among the topics covered are computational discovery, search strategies, Classification, support vector machines, kernel methods, rule induction, linear learning, decision tree learning, boosting, collaborative learning, statistical learning, clustering, instance-based learning, reinforcement learning, multiagent learning, multirelational learning, Markov decision processes, active learning, etc.