The Construction Of Eukaryotic Gene Promoter Location Model Based On Evolutionary Neural Network

With the development of HGP, lots of genome information are acquired. It is an important and necessary work to analyze the information for HGP study. As a part of which, the recognition of promoter field in eukaryote's gene is very hard. For getting exon from gene, it is necessary to having promoter, which provides signal series of biologic function. On the basis of analyzing relative conservative series of promoters in eukaryote's gene, the conservative property of TATA box,CAAT box and GC box in the promoters of eukaryote's gene and there statistical truth are used in this paper, combined with evolutionary NN to build recognition model of promoter in eukaryote's gene, emulate computer program was designed too. The main work and conclusions in this paper are as follow: (1) Because the conservative series in promoter field of eukaryote's gene display false positive, it can not be found the relations and correlative feature among series using old physical and chemical methods. Neural network system which can self study and self adapt can well solve the shortcoming, there still have the shortcoming of low training speed,apt to get into local minimal value and poor macrocosm searching ability for neural network .After studying genetic algorithms, it was found that GA can better mend the shortcoming. On the basis of evolutionary neural network, the recognition model of promoter in eukaryote's gene was built using BP algorithms and genetic algorithms.(2) The designed recognition model is used to program emulator, and trained the model with download data from EPD(eukaryote promoter database),then predict unknown data for model, the result indicate the building of recognition model is relatively successful. (3) Giving a common stencil-plate for intelligent ascertaining model of other component in non-coding region.