by Prokofiev V. | Shevchenko A. | Konenkov V. | Research Institute of Clinical and Experimental
Lymрhology – Branch of the Institute of Cytology and Ge-netics SB RAS, Novosibirsk, Russia of Cytology
and Genetics, SB RAS, Novosibirsk, Russia | Research Institute of Clinical and Experimental Lymрhology
– Branch of the Institute of Cytology and Ge-netics SB RAS, Novosibirsk, Russia of Cytology and Genetics,
SB RAS, Novosibirsk, Russia | Research Institute of Clinical and Experimental Lymрhology – Branch of
the Institute of Cytology and Ge-netics SB RAS, Novosibirsk, Russia of Cytology and Genetics, SB RAS,
Novosibirsk, Russia
It is impossible to imagine modern medical genetics of polygenic human diseases without
the widespread use of intelligent analysis and machine learning methods, in particular, such
as simulation of stochastic processes, MDR analysis, bioinformatic analysis of complex
biological networks based on graph theory, which allow replacing the original, largely
stochastic, object with its image – a mathematical model with further study of this model
using computer implemented computational logic algorithms or heuristic. In connection
with the above, it is very relevant to conduct comparative clinical and genetic studies with
the analysis of combined genotypes in combination with various variants of mathematical
modeling, the results of which can become the basis for the development of fundamentally
new ways to predict the development of socially significant human diseases, their early
diagnosis and prevention.