Computational methods in molecular biology
著者
書誌事項
Computational methods in molecular biology
（New comprehensive biochemistry / general editors, A. Neuberger, L.L.M. van Deenen, v. 32）
Elsevier, 1998
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
Computational biology is a rapidly expanding field, and the number and variety of computational methods used for DNA and protein sequence analysis is growing every day. These algorithms are extremely valuable to biotechnology companies and to researchers and teachers in universities who don't have time to find the best algorhythms. This book explains the latest computer technology for analyzing DNA, RNA, and protein sequences. Clear and easy to follow, designed specifically for the non-computer scientist, it should help biologists make better choices on which algorithm to use. New techniques and demonstrations are elucidated, as are state-of-the-art problems, and more advanced material on the latest algorithms. The primary audience for this volume are molecular biologists working either in biotechnology companies or academic research environments, individual researchers and the institutions they work for, and students.
目次
1. Grand challenges in computational biology (D.B. Searls). 2. A tutorial introduction to computation for biologists (S. Salzberg). 3. An introduction to biological sequence analysis (K.H. Fasman, S.L. Salzberg). 4. An introduction to hidden Markov models for biological sequences (A. Krogh). 5. Case-based reasoning driven gene annotation (G.C. Overton, J. Haas). 6. Classification based molecular sequence analysis (D.J. States, W.C. Reisdorf, Jr.) 7. Computational gene prediction using neural networks and similarity search (Y. Xu, E.C. Uberbacher). 8. Modeling dependencies in pre-mRNA splicing signals (C. Burge). 9. Evolutionary approaches to computational biology (R.J. Parsons). 10. Decision trees and Markov chains for gene finding (S. Salzberg). 11. Statistical analysis of protein structures: using environmental features for multiple purposes (L. Wei, J.T. Chang, R.B. Altman). 12. Analysis and algorithms for protein sequence-structure alignment (R.H. Lathrop et al). 13. Threader: protein sequence threading by double dynamic programming (D. Jones). 14. From computer vision to protein structure and association (H.J. Wolfson, R. Nussinov). 15. Modeling biological data and structure with probabilistic networks (S. Kasif, A.L. Delcher). Appendix A. Software and databases for computational biology on the internet. Appendix B: Suggestions for further reading in computational biology.
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