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biblioteka Srbije za objedinjenu nabavku

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Computers & Chemistry

Volume
21, Issue 4, 1997, Pages 237–256

Open Problems of Computational Molecular
Biology

Artificial neural networks for molecular sequence
analysis

Cathy H. Wu1,

Department of Epidemiology/Biomathematics, The University of Texas
Health Center at Tyler, Tyler, TX 75710, USA

Received 25 July 1996

Accepted 2 December 1996

Available online 13 April 1998

http://dx.doi.org.proxy.kobson.nb.rs:2048/10.1016/S0097-8485(96)00038-1,
How to Cite or Link Using DOI

Abstract

Artificial neural networks provide a unique computing architecture whose
potential has attracted interest from researchers across different disciplines.
As a technique for computational analysis, neural network technology is very
well suited for the analysis of molecular sequence data. It has been applied
successfully to a variety of problems, ranging from gene identification, to
protein structure prediction and sequence classification. This article provides
an overview of major neural network paradigms, discusses design issues, and
reviews current applications in DNA/RNA and protein sequence analysis.

Keywords

artificial neural networks;

back-propagation;

gene identification;

protein structure prediction;

sequence analysis;

sequence classification

There are no figures or tables for this document.

1

Tel.: (903) 877-7962; Fax: (903)
877-5914.

Copyright © 1997 Published by Elsevier Ltd.

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