Accepted_test

Application of Machine Learning Methods for Analyzing Genetic Polymorphism in Populations

by Maryanovskaya T.A. | Shcherbakov D.Yu. | Novosibirsk National Research State University, Novosibirsk, Russia | 1 Novosibirsk National Research State University, Novosibirsk, Russia 2 Limnological Institute of the Siberian Branch of the Russian Academy of Sciences, Irkutsk, Russia

Abstract ID: 509

Event: BGRS-abstracts

Sections: [Sym 2] Section “Computational analysis and modeling of population, ecological and genetic systems and processes”

The application of deep learning is increasingly prevalent across various scientific disciplines. Its relevance in biology, particularly in population genetics, has surged due to the vast genetic datasets available. Additionally, neural network models have been sparingly utilized in this field. This study introduces a model among the first to employ deep learning techniques in population genetics tasks. The developed model signifies a noteworthy advancement in the integration of deep learning in genetic research, where traditional approaches have predominated. It highlights the potential for leveraging deep learning methodologies to address challenges in population genetics and opens doors for innovative research avenues in this domain.