Optimization of SVM parameters for recognition of regulatory DNA sequences

Abstract

Identification and recognition of specific functionally-important DNA sequence fragments such as regulatory sequences are considered 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 regulatory DNA sequences is important for successful gene prediction and gene expression studies. In this paper, Support Vector Machine (SVM) is used for classification of DNA sequences and recognition of the regulatory sequences. For optimal classification, various SVM learning and kernel parameters (hyperparameters) and their optimization methods are analyzed. In a case study, 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 regulatory DNA sequences. The results of promoter recognition for the drosophila sequence datasets are presented.