Feature Analysis And Recognition Method Research Of Escherichia Coli Promoter

Abstract This project is derived from Country Nature Science Foundation(CNSF)"Researching of some problems in bioinformatics in the sense of complex system (No.60234020) ".In this paper, the Escherichia Coli Promoter sequences are analyzed byusing intelligent information processing method, and the emphases is recognition ofE.coli promoter. The main contents of this paper as follows: 1. In this paper, two types of neural network architecture are used. One islength-changed type, the other is scanned with a hole in the input window. The E.colipromoter elements are studied and analyzed on the basis of biological theory andstatistical feature of E.coli genome. The experiment results show that thenon-canonical elements can affect the recognition except the two canonical elements. 2. A recognition model is established on the basis of data optimization and BPneural network. The positive samples used in this paper are aligned in their –10 region,and the negative samples are selected from E.coli coding region. The WMM model isused to optimize training samples. Experiments results show that model based on dataoptimization has high sensibility and good accuracy. 3. In this paper, Support Vector Machine is applied to predict E.coli promoters.Sequences with definite length are selected from database and they are divided into3:1 as training samples and testing samples. A SVM-based classifier is constructed.Experiments exhibit that comparing with neural network based approaches, the SVM- based approach has better prediction performance for the testing sets. These resultsshow that SVM has good application future in bioinformatics. E.coli promoter recognition is one of the most important subjects inbioinformatics. The research results in this paper can provide reference for promoterrecognition research.