Artificial Intelligence and Molecular Biology

by Hunter, Lawrence

• ISBN13: 9780262581158
• ISBN10: 0262581159
• Format: Paperback
• Copyright: 1993-03-18
• Publisher: Aaai Pr

Summary

The enormous amount of data generated by the Human Genome Project and other large-scale biological research has created a rich and challenging domain for research in artificial intelligence. These original contributions provide a current sampling of AI approaches to problems of biological significance; they are the first to treat the computational needs of the biology community hand-in-hand with appropriate advances in artificial intelligence. Focusing on novel technologies and approaches, rather than on proven applications, they cover genetic sequence analysis, protein structure representation and prediction, automated data analysis aids, and simulation of biological systems. A brief introductory primer on molecular biology and Al gives computer scientists sufficient background to understand much of the biology discussed in the book. Lawrence Hunter is Director of the Machine Learning Project at the National Library of Medicine, National Institutes of Health.

Table of Contents

Foreword	p. ix
Molecular Biology for Computer Scientists	p. 1
The Computational Linguistics of Biological Sequences	p. 47
Neural Networks, Adaptive Optimization, and RNA Secondary Structure Prediction	p. 121
Predicting Protein Structural Features with Artificial Neural Networks	p. 161
Developing Hierarchical Representations for Protein Structures: An Incremental Approach	p. 195
Integrating AI with Sequence Analysis	p. 210
Planning to Learn about Protein Structure	p. 259
A Qualitative Biochemistry and Its Application to the Regulation of the Tryptophan Operon	p. 289
Identification of Qualitatively Feasible Metabolic Pathways	p. 325
Knowledge-Based Simulation of DNA Metabolism: Prediction of Action and Envisionment of Pathways	p. 365
An AI Approach to the Interpretation of the NMR Spectra of Proteins	p. 396
Molecular Scene Analysis: Crystal Structure Determination through Imagery	p. 433
Afterword: The Anti-Expert System -- Thirteen Hypotheses an AI Program Should Have Seen Through	p. 459
Index	p. 464
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