Simulation Modelling for Machine Learning Identification of Single Nucleotide Polymorphisms in Human Genomes

Abstract

An approach for simulation modelling of Single Nucleotide Polymorphisms (SNPs) in DNA sequences is proposed, which implements the generation of random events according to the beta or normal distributions, the parameters of which are estimated from the available experimental data. This approach improves the accuracy of determining SNPs in DNA molecules. The verification of the developed model and analysis methods was carried out on a set of reference data provided by the GIAB consortium. The best results were obtained for the machine learning model of Conditional Inference Trees – the accuracy of the SNP identification by the score F1 is 82,8 %, which is higher than those obtained by traditional SNP identification methods, such as binomial distribution, entropy-based and Fisher's exact tests