Artificial intelligence and molecular biology
Author(s)
Bibliographic Information
Artificial intelligence and molecular biology
AAAI Press/the MIT Press, c1993
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
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.
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
- 1. Molecular Biology for Computer Scientists, Lawrence Hunter. 2. The Computational Linguistics of Biological Sequences, David B. Searls. 3. Neural Networks, Adaptive Optimisation, and RNA Secondary Structure Prediction, Evan W Steeg. 4. Predicting Protein Structural Features With Artificial Neural Networks, Stephen R. Holbrook, et al
- 5. Developing Hierarchical Representations for Protein Structures: An Incremental Approach, Xiru Zhang and David Waltz. 6. Integrating AI with Sequence Analysis, Richard H. Lathrop, et al. 7. Planning to Learn about Protein Structure, Lawrence Hunter. 8. A Qualitative Biochemistry and its Application to the Regulation of the Tryptophan Operon, Peter D. Karp. 9. Identification of Qualitatively Feasible Metabolic Pathways, Michael L Mavrovouniotis. 10. Knowledge-Based Simulation of DNA Metabolism: Prediction of Action and Envisionment of Pathways, Adam R. Galper, et al. 11. An AI Approach to the Interpretation of the NMR Spectra of Proteins, Peter Edwards, et al. 12. Molecular Scene Analysis: Crystal Structure Determination Through Imagery, Janice I. Glasgow, et al. Afterword: The Anti-Expert System-Thirteen Hypotheses an AI Program Should Have Seen Through, Joshua Lederberg.
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