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© Oxford University Press

Artificial neural network method for discriminating coding regions of eukaryotic genes

Yudong Cai 1 and Changqing Chen

Shanghai Research Center of Biotechnology, Chinese Academy of Sciences Shanghai, 200233 China

1To whom correspondence should be addressed at: Department of Mathematics, Stanford University, Stanford, CA 94305, USA

This paper describes the application of artificial neural networks to discriminating the coding system of eukaryotic genes. We choose >300 genes from eight eukaryotic organisms: human, mouse, rat, horse, ox, sheep, soybean and rabbit, from which we build up different discrimination models relevant to their promoter regions, poly(A) signals, splice site locations of introns and noose structures. The result shows that as long as the coding length is definite, the only correct coding region can be chosen from the large number of possible solutions discriminated by neural networks.

Received on December 9, 1994; revised on June 30, 1995; accepted on June 30, 1995

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