A machine learning approach to predict cancer-related mutations

One of the most ambitious challenges in molecular biology consists in the comprehension of the effects of Single Nucleotide Polymorphisms (SNPs) both at genomic and protein level. SNPs account for about 90% of genetic variations in human population. Recent investigations are focused on non-synonymous coding SNPs (nscSNPs) that are responsible of protein single point mutation, since mutations occurring in coding regions may have a large effect on gene functionality. In this work we present a machine learning-based method to predict the effect of a given mutation on human health. In particular the relationships between SNP and the insurgence of cancer pathologies has been studied using a support vector machine (SVM). The SVM predictor here proposed reaches the overall accuracy of 75% and a correlation coefficient of 0.50 on a set of 2187 mutations.