Evolutionary Computation in Bioinformatics

Gary B. Fogel (Edited by), David W. Corne (Edited by)

9781558607972, Elsevier Science

Hardback, published 27 September 2002

393 pages
23.4 x 18.6 x 2.6 cm, 0.91 kg

"This is a fine book that clearly discusses the applications of evolutionary computation techniques to a variety of different areas. It covers most topics a bioinformatician will find interesting." --Santosh Mishra, Eli Lilly

Bioinformatics has never been as popular as it is today. The genomics revolution is generating so much data in such rapid succession that it has become difficult for biologists to decipher. In particular, there are many problems in biology that are too large to solve with standard methods. Researchers in evolutionary computation (EC) have turned their attention to these problems. They understand the power of EC to rapidly search very large and complex spaces and return reasonable solutions. While these researchers are increasingly interested in problems from the biological sciences, EC and its problem-solving capabilities are generally not yet understood or applied in the biology community.

This book offers a definitive resource to bridge the computer science and biology communities. Gary Fogel and David Corne, well-known representatives of these fields, introduce biology and bioinformatics to computer scientists, and evolutionary computation to biologists and computer scientists unfamiliar with these techniques. The fourteen chapters that follow are written by leading computer scientists and biologists who examine successful applications of evolutionary computation to various problems in the biological sciences.

PART I - Introduction to the Concepts of Bioinformatics and Evolutionary Computation

Chapter 1. An Introduction to Bioinformatics for Computer Scientists
By David W. Corne and Gary B. Fogel
Chapter 2. An Introduction to Evolutionary Computation for Biologists
By Gary B. Fogel and David W. Corne

PART II - Sequence and Structure Alignment
Chapter 3. Determining Genome Sequences from Experimental Data Using Evolutionary Computation
By Jacek Blazewic and Marta Kasprzak
Chapter 4. Protein Structure Alignment Using Evolutionary Computation
By Joseph D. Szustakowski and Zhipeng Weng

Chapter 5. Using Genetic Algorithms for Pairwise and Multiple Sequence Alignments
By Cédric Notredame

PART III - Protein Folding
Chapter 6. On the Evolutionary Search for Solutions to the Protein Folding Problem
By Garrison W. Greenwood and Jae-Min Shin
Chapter 7. Toward Effective Polypeptide Structure Prediction with Parallel Fast Messy Genetic Algorithms
By Gary B. Lamont and Laurence D. Merkle

Chapter 8. Application of Evolutionary Computation to Protein Folding with Specialized Operators
By Steffen Schulze-Kremer

PART IV - Machine Learning and Inference
Chapter 9. Identification of Coding Regions in DNA Sequences Using Evolved Neural Networks
By Gary B. Fogel, Kumar Chellapilla, and David B. Fogel
Chapter 10. Clustering Microarray Data with Evolutionary Algorithms
By Emanuel Falkenauer and Arnaud Marchand

Chapter 11. Evolutionary Computation and Fractal Visualization of Sequence Data
By Dan Ashlock and Jim Golden

Chapter 12. Identifying Metabolic Pathways and Gene Regulation Networks with Evolutionary Algorithms
By Junji Kitagawa and Hitoshi Iba

Chapter 13. Evolutionary Computational Support for the Characterization of Biological Systems
By Bogdan Filipic and Janez Strancar

PART V - Feature Selection
Chapter 14. Discovery of Genetic and Environmental Interactions in Disease Data Using Evolutionary Computation
By Laetitia Jourdan, Clarisse Dhaenens[AQ2], and El-Ghazali Talbi
Chapter 15. Feature Selection Methods Based on Genetic Algorithms for in Silico Drug Design
By Mark J. Embrechts, Muhsin Ozdemir, Larry Lockwood, Curt Breneman, Kristin Bennet, Dirk Devogelaere, and Marcel Rijkaert

Chapter 16. Interpreting Analytical Spectra with Evolutionary Computation
By Jem J. Rowland

Appendix: Internet Resources for Bioinformatics Data and Tools

Subject Areas: Artificial intelligence [UYQ], Algorithms & data structures [UMB]