[Transcriptomics and the "Curse of Dimensionality": Monte Carlo Simulations of ML-Models as a Tool for Analyzing Multidimensional Data in Tasks of Searching Markers of Biological Processes].

High-throughput transcriptomic research methods provide the assessment of a vast number of factors valuable for researchers. At the same time, "curse of dimensionality" issues arise, which lead to increasing the requirements on data processing and analysis methods. In this study, we propose a new algorithm that combines Monte Carlo methods and machine learning. This algorithm will enable feature space reduction by highlighting genes most likely associated with the investigated diseases. Our approach allows one not only to generate a set of "interesting" genes but also to assign weight to each gene, indicating its "importance." This measure can be used in subsequent statistical analysis, visualization, and interpretation of results. Algorithm performance was demonstrated on open transcriptomic data of patients with HCM (GSE36961 and GSE1145). The analysis revealed genes MYH6, FCN3, RASD1, and SERPINA3, which is in good agreement with the available literature.