A novel approach to estimation of E. coli promoter gene sequences: Combining feature selection and least square support vector machine (FS_LSSVM)

Abstract

In this paper, we have investigated the real-world task of recognizing biological concepts in DNA sequences. Recognizing promoters in strings that represent nucleotides (one of A, G, T, or C) has been performed using a novel approach based on combining feature selection (FS) and least square support vector machine (LSSVM). Dimensionality of Escherichia coli promoter gene sequences dataset has 57 attributes and 106 samples including 53 promoters and 53 non-promoters. The proposed system consists of two parts. Firstly, we have used the FS process to reduce the dimensionality of E. coli promoter gene sequences dataset that has 57 attributes. So the dimensionality of this dataset has been reduced to 4 attributes by means of FS process. Secondly, LSSVM classifier algorithm has been run to estimation the E. coli promoter gene sequences. In order to show the performance of the proposed system, we have used the success rate, sensitivity and specificity analysis, 10-fold cross validation, and confusion matrix. Whilst only LSSVM classifier has been obtained 80% success rate using 10-fold cross validation, the proposed system has been obtained 100% success rate for same condition. These obtained results indicate that the proposed approach improve the success rate in recognizing promoters in strings that represent nucleotides.