title: Artificial neural network method for discriminating coding regions of eukaryotic genes.
abstract: 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 results 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.
authors.affiliation: Shanghai Research Center of Biotechnology, Chinese Academy of Sciences, China.
authors.forename: Y
authors.initials: Y
authors.lastname: Cai
authors.forename: C
authors.initials: C
authors.lastname: Chen
mesh_terms: D000818:Animals; D002417:Cattle; D003062:Codon / Q000235:genetics*; D003201:Computers; D004247:DNA / Q000235:genetics; D016208:Databases, Factual; D005057:Eukaryotic Cells; D005091:Exons; D006736:Horses; D006801:Humans; D007438:Introns; D051379:Mice; D016571:Neural Networks, Computer; D011401:Promoter Regions, Genetic; D011506:Proteins / Q000235:genetics; D011817:Rabbits; D051381:Rats; D012756:Sheep; D013025:Glycine max
chemical_list: D003062:Codon; D011506:Proteins; D004247:DNA