title: Neural network models for promoter recognition.
abstract: The problem of recognition of promoter sites in the DNA sequence has been treated with models of learning neural networks. The maximum network capacity admissible for this problem has been estimated on the basis of the total of experimental data available on the determined promoter sequences. The model of a block neural network has been constructed to satisfy this estimate and rules have been elaborated for its learning and testing. The learning process involves a small (of the order of 10%) part of the total set of promoter sequences. During this procedure the neural network develops a system of distinctive features (key words) to be used as a reference in identifying promoters against the background of random sequences. The learning quality is then tested with the whole set. The efficiency of promoter recognition has been found to amount to 94 to 99%. The probability of an arbitrary sequence being identified as a promoter is 2 to 6%.
authors.affiliation: Institute of Molecular Genetics, USSR Academy of Sciences, Moscow.
authors.forename: A V
authors.initials: AV
authors.lastname: Lukashin
authors.forename: V V
authors.initials: VV
authors.lastname: Anshelevich
authors.forename: B R
authors.initials: BR
authors.lastname: Amirikyan
authors.forename: A I
authors.initials: AI
authors.lastname: Gragerov
authors.forename: M D
authors.initials: MD
authors.lastname: Frank-Kamenetskii
mesh_terms: D001483:Base Sequence; D003198:Computer Simulation; D004247:DNA; D008958:Models, Molecular; D008959:Models, Neurological; D011401:Promoter Regions, Genetic
chemical_list: D004247:DNA