Optimization of Svm Parameters for Promoter Recognition in Dna Sequences

Recognition of specific functionally-important DNA sequence fragments is considered one of the most important problems in bioinformatics. One type of such fragments are promoters, i.e., short regulatory DNA sequences located upstream of a gene. Detection of promoters in DNA sequences is important for successful gene prediction. In this paper, Support Vector Machine (SVM) is used for classification of DNA sequences and promoter recognition. For optimal classification, various SVM learning and kernel parameters (hyperparameters) and their optimization methods are analyzed. In a case study, the optimization of the SVM hyperparameters for linear, polynomial and power series kernels is performed using a modification of the Nelder-Mead (downhill simplex) algorithm. The method allows for improving the precision of identification of the promoter sequences. The results of classification for a drosophila sequence dataset are presented.