Identification of Single Nucleotide Genetic Polymorphism Sites Using Machine Learning Methods

Abstract

The paper presents an algorithm for simulation modelling of nucleotide variations in the genomic DNA molecule. To identify single nucleotide genetic polymorphisms, it is proposed to use machine learning methods trained on simulated data. A comparative analysis of the most effective classical and machine learning algorithms for identifying single nucleotide polymorphisms was performed on simulated data. The most optimal method for identifying single nucleotide genetic polymorphisms in DNA molecules at various experimental noise levels is the machine learning algorithm CART.