High Performance Computational Methods for Biological Sequence Analysis

Yap Tieng K. Martino Robert L. Frieder Ophir

ISBN 10: 1461286026 / ISBN 13: 9781461286028

Published by Springer, 2012

About this Item

Print on Demand pp. 236 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.

Seller Inventory # 44784328

Bibliographic Details

• Title: High Performance Computational Methods for Biological Sequence Analysis
• Publisher: Springer
• Publication Date: 2012
• Language: English
• ISBN 10: 1461286026
• ISBN 13: 9781461286028
• Binding: Soft cover
• Condition: New

About this title

Synopsis

High Performance Computational Methods for Biological Sequence Analysis presents biological sequence analysis using an interdisciplinary approach that integrates biological, mathematical and computational concepts. These concepts are presented so that computer scientists and biomedical scientists can obtain the necessary background for developing better algorithms and applying parallel computational methods. This book will enable both groups to develop the depth of knowledge needed to work in this interdisciplinary field.
This work focuses on high performance computational approaches that are used to perform computationally intensive biological sequence analysis tasks: pairwise sequence comparison, multiple sequence alignment, and sequence similarity searching in large databases. These computational methods are becoming increasingly important to the molecular biology community allowing researchers to explore the increasingly large amounts of sequence data generated by the Human Genome Project and other related biological projects. The approaches presented by the authors are state-of-the-art and show how to reduce analysis times significantly, sometimes from days to minutes.
High Performance Computational Methods for Biological Sequence Analysis is tremendously important to biomedical science students and researchers who are interested in applying sequence analyses to their studies, and to computational science students and researchers who are interested in applying new computational approaches to biological sequence analyses.