Russian Federation
from 01.01.2015 to 01.01.2020
Rostov-na-Donu, Rostov-on-Don, Russian Federation
The intensive development of machine learning methods and big data analysis makes it possible to significantly expand the possibilities of studying complex biological systems, in particular, the mechanisms of gene expression regulation and the interaction of protein molecules in cellular processes. Approaches to modeling the dynamics of genetic networks using graph neural networks and Bayesian models are considered, which provide high accuracy in predicting phenotypic manifestations under various mutations. The results of integrating multi-omics data, including transcriptomics, proteomics, and metabolomics, for constructing comprehensive maps of metabolic pathways are analyzed. Particular attention is paid to clustering algorithms and dimensionality reduction techniques, such as t-SNE and UMAP, applied for the visualization of multidimensional biological datasets. Issues of model robustness to noise in experimental data and transfer learning methods for adapting models trained on model organisms to human data are discussed. Comparative characteristics of various architectures are presented, demonstrating the advantages of hybrid models combining deep learning with mechanistic models of biochemical reactions. Such studies contribute to accelerating the discovery of new disease biomarkers and the development of personalized medicine, opening pathways for targeted therapy at the molecular level. In addition, the ethical and methodological aspects of using artificial intelligence in biology are considered, including ensuring the reproducibility of results and the interpretability of predictions. Overall, the integration of computational methods with experimental biology represents an important step toward understanding the fundamental principles of life and developing innovative medical technologies, determining the relevance of the materials for specialists in related disciplines and the advisability of referring to the full text for a detailed examination of the mathematical foundations and practical conclusions.
economic efficiency, higher education, digitalization, labor market, professional competencies.
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