Evolutionary Computation in BioinformaticsEdited by
- Gary Fogel
- David Corne
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.
Researchers and graduate students in computer science and biology
Hardbound, 393 Pages
Published: September 2002
Imprint: Morgan Kaufmann
"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
- 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. CornePART 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 CÃ©dric NotredamePART 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-KremerPART 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 StrancarPART 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. RowlandAppendix: Internet Resources for Bioinformatics Data and Tools