An Imbalance SVM for MicroRNA Target Genes Prediction

Abstract

Imbalance miRNA target sample data bring about the lower prediction accuracy of SVM(Support Vector Machine). This paper proposes an SVM algorithm to predict the target genes based on biased discriminant idea. This paper selects an optimal feature sets as input data, and constructs a kernel optimization objective function based on the biased discriminant analysis criteria in the empirical feature space. The conformal transformation of a kernel is utilized to gradually optimize the kernel matrix. Through the comparative analysis of the experimental results of human, mouse and rat, the imbalance SVM with biased discriminant has higher specificity, sensitivity and prediction accuracy, which proves that it has stronger generalization ability and better robustness.