Identification of single nucleotide polymorphism using support vector machine on imbalanced data

Abstract

The advance of DNA sequencing technology presents a significant bioinformatic challenges in a downstream analysis such as identification of single nucleotide polymorphism (SNP). SNP is the most abundant form of genetic marker and have been one of the most crucial researches in bioinformatics. SNP has been applied in wide area, but analysis of SNP in plants is very limited, as in cultivated soybean (Glycine max L.). This paper discusses the identification of SNP in cultivated soybean using Support Vector Machine (SVM). SVM is trained using positive and negative SNP. Previously, we performed a balancing positive and negative SNP with undersampling and oversampling to obtain training data. As a result, the model which is trained with balanced data has better performance than that with imbalanced data.