Artificial neural networks for molecular sequence analysis
Computers & ChemistryPublished 1 January 1997
Cathy Wu
Citations108
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TL;DR
An overview of major neural network paradigms is provided, discusses design issues, and reviews current applications in DNA/RNA and protein sequence analysis.
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
Biochemistry, Genetics and Molecular Biology
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