Our scientific team develops omics data analysis methods to identify potential targets for therapeutic intervention against age-related diseases and aging.

Since modern omics data is high dimensional, i.e. the number of features in it is much higher than the number of measurements, the use of traditional machine learning methods is impossible due to the emerging problem of overfitting. Therefore, it is necessary to develop new mathematical methods to analyze this type of data.

In our work we use the models of statistical physics to analyze gene networks stability and predict their dynamics over time. The proposed models allow us to link gene network stability with mortality. The models we developed were validated on omics data of different types, such as transcriptome, proteome and metabolome measured in different tissues of various organisms.

Our techniques open new opportunities to identify targets to develop new therapy candidates against aging.