Evolutionary computation in bioinformatics
- Gary B. Fogel, David W. Corne.
- San Francisco, Calif. : Morgan Kaufmann ; Oxford : Elsevier Science, 2003.
- Physical description
- xxi, 393 p. : ill. ; 24 cm.
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- Includes bibliographical references and index.
- 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 Cedric 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.
- (source: Nielsen Book Data)
- Publisher's summary
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. It describes applications of EC to bioinformatics in a wide variety of areas including DNA sequencing, protein folding, gene and protein classification, drug targeting, drug design, data mining of biological databases, and biodata visualization. It offers industrial and academic researchers in computer science, biology, and bioinformatics an important resource for applying evolutionary computation. It includes a detailed appendix of biological data resources.
(source: Nielsen Book Data)
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