Artificial neural networks for molecular sequence analysis.
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.
Wu
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Department of Epidemiology/Biomathematics, University of Texas Health Center at Tyler 75710, USA. wu@uthct.edu
D000465:Algorithms; D000595:Amino Acid Sequence; D001483:Base Sequence; D004247:DNA / Q000737:chemistry; D004926:Escherichia coli / Q000235:genetics; D016571:Neural Networks, Computer; D010802:Phylogeny; D011401:Promoter Regions, Genetic; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry; D012313:RNA / Q000737:chemistry
D011506:Proteins; D012313:RNA; D004247:DNA