Laboratory of Experimental and Cellular Medicine

The development of biomedical research and technologies with high throughput and intensive use of data has caused researchers to develop strategies for analyzing, integrating and interpreting the huge amounts of data they generate. Although many statistical methods have been developed to work with "big data", the experience of using artificial intelligence methods shows that the latter can be especially suitable for medical purposes. The results of process modeling and data analysis using machine learning reveal a large heterogeneity of pathophysiological factors and processes contributing to the disease, which indicates the need to adapt or "personalize" medicines, taking into account the nuances and often unique features that patients possess. Experimental in situ study of individual patients' pathologies in combination with computer modeling and functional analysis makes it possible to develop new methods of patient treatment and decision-making assistance systems for doctors. This approach can be called full-fledged personalized medicine. Given how important data-intensive analyses are to identify appropriate intervention goals and treatment strategies for a person with a disease, computer modeling based on medical data about a patient can, in particular, predict the manifestations of ischemia and postoperative pathologies for the heart and other organs. In this way, our laboratory develops 3 main areas:

1) Development of new personalized computational digital technologies and machine learning approaches for the prediction and treatment of organ socially significant diseases

2) Development of new methods of biological tissue repair and enhancement of their regenerative potential

3) Development of methods for early diagnosis and selection of treatment of socially significant diseases based on studies of patient pathologies