
Finding patterns in biomolecular data, particularly in DNA and RNA, is at the center of modern biological research. These data are complex and growing rapidly, so the search for patterns requires increasingly sophisticated computer methods. Pattern Discovery in Biomolecular Data provides a clear, up-to-date summary of the principal techniques. Each chapter is self-contained, and the techniques are drawn from many fields, including graph theory, information theory, statistics, genetic algorithms, computer visualization, and vision. Since pattern searches often benefit from multiple approaches, the book presents methods in their purest form so that readers can best choose the method or combination that fits their needs. The chapters focus on finding patterns in DNA, RNA, and protein sequences, finding patterns in 2D and 3D structures, and choosing system components. This volume will be invaluable for all workers in genomics and genetic analysis, and others whose research requires biocomputing.
This book investigates the computational methodologies required to identify and analyze complex patterns within rapidly expanding biomolecular datasets. The authors, Bruce A. Shapiro, Dennis Elliott Shasha, and Jason T. L. Wang, leverage their expertise in computer science and biological research to synthesize a framework for pattern discovery. They present a collection of techniques derived from diverse disciplines to address the inherent complexity of DNA, RNA, and protein sequence analysis.
What You Will Find
Scope Limits
Experts recognize this volume as a practical reference for researchers needing to integrate computational tools into genomic analysis. Readers frequently note the clarity of the self-contained chapters, which allow for targeted application of specific techniques to individual research problems.
Page Count:
271
Publication Date:
1999-01-01
Publisher:
Oxford University Press
ISBN-10:
0190283726
ISBN-13:
9780190283728
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