ARTIFICIAL NEURAL-NETWORK METHOD FOR DISCRIMINATING CODING REGIONS OFEUKARYOTIC GENES

This paper describes the application of artificial neural networks to discriminating the coding system of eukaryotic genes. We choose >300 g enes from eight eukaryotic organisms: human, mouse, rat, horse, ox, sh eep, soybean and rabbit, from which we build up different discriminati on 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 regi on can be chosen from the large number of possible solutions discrimin ated by neural networks.