Cover image for Computational methods in molecular biology
Computational methods in molecular biology
Title:
Computational methods in molecular biology
Author:
Salzberg, Steven L., 1960-
ISBN:
9780080860930

9780444828750
Publication Information:
Amsterdam ; New York : Elsevier, c1998.
Physical Description:
1 online resource (xxvi, 371 p.) : ill.
Series:
New comprehensive biochemistry ; v. 32

New comprehensive biochemistry ; v. 32.
Contents:
Grand challenges in computational biology / David B. Searls -- A tutorial introduction to computation for biologists / Steven L. Salzberg -- An introduction to biological sequence analysis / Kenneth H. Fasman, Steven L. Salzberg -- An introduction to hidden Markov models for biological sequences / Anders Krogh -- Case-based reasoning driven gene annotation / G. Christian Overton, Juergen Haas -- Classification-based molecular sequence analysis / David J. States, William C. Reisdorf Jr. -- Computational gene prediction using neural networks and similarity search / Ying Xu, Edward C. Uberbacher -- Modeling dependencies in pre-mRNA splicing signals / Christopher B. Burge -- Evolutionary approaches to computational biology / Rebecca J. Parsons -- Decision trees and Markov chains for gene finding / Steven L. Salzberg -- Statistical analysis of protein structures : using environmental features for multiple purposes / Liping Wei, Jeffrey T. Chang, Russ B. Altman -- Analysis and algorithms for protein sequence-structure alignment / Richard H. Lathrop, Robert G. Rogers Jr., Jadwiga Bienkowska, Barbara K.M. Bryant, Ljubomir J. Buturović, Chrysanthe Gaitatzes, Raman Nambudripad, James V. White, Temple F. Smith -- THREADER : protein sequence threading by double dynamic programming / David Jones -- From computer vision to protein structure and association / Haim J. Wolfson, Ruth Nussinov -- Modeling biological data and structure with probabilistic networks / Simon Kasif, Arthur L. Delcher.
Abstract:
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.
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