The computational methods of bioinformatics are being used more and more to process the large volume of current biological data. Promoting an understanding of the underlying biology that produces this data, Pattern Discovery in Bioinformatics: Theory and Algorithms provides the tools to study regularities in biological data.
Taking a systematic approach to pattern discovery, the book supplies sound mathematical definitions and efficient algorithms to explain vital information about biological data. It explores various data patterns, including strings, clusters, permutations, topology, partial orders, and boolean expressions. Each of these classes captures a different form of regularity in the data, providing possible answers to a wide range of questions. The book also reviews basic statistics, including probability, information theory, and the central limit theorem.
This self-contained book provides a solid foundation in computational methods, enabling the solution of difficult biological questions.
Table of Contents
Introduction. Basic Algorithms. Basic Statistics. What Are Patterns? Modeling the Stream of Life. String Pattern Specifications. Algorithms and Pattern Statistics. Motif Learning. The Subtle Motif. Permutation Patterns. Permutation Pattern Probabilities. Topological Motifs. Set-Theoretic Algorithmic Tools. Expression and Partial Order Motifs. References. Index.