Using Artificial Intelligence to Optimize Genetic Research

Abstract

The purpose of the article is to analyse the main achievements and prospects for the introduction of artificial intelligence in genetics, to improve the efficiency of research and the reliability of the results obtained. The article analyses publications that reveal the areas of integration of neural networks into DNA fingerprinting, in particular PCR-PCR analysis, sequencing, FISH diagnostics, etc., increasing the sensitivity of these technologies and reducing the cost of research. The author also discusses technologies for processing huge data sets of Big Data and their effectiveness on the example of international projects in theoretical and applied genetics. Attention is paid to artificial intelligence in genomics, proteomics and genetic engineering, in particular, optimisation of GED (genome editing) methods based on CRISPR (short palindromic repeats regularly interspaced in groups) to achieve better accuracy of genome editing. Neural networks are important in creating guide RNAs (gRNAs) for CRISPR-Cas systems, which determine the direction of endonuclease function. In addition to specialised applications and resources, artificial intelligence generally improves research work by optimising many processes that used to take a lot of time and effort. Examples of neural network-based applications that are widely used by the scientific community are presented.