Target identification from multi-omics data using systems biology and machine-learning approaches

The aim of this project is to develop robust explainable AI methodologies to mine large multi-omics perturbation datasets for novel mechanistic insights. In particular, we are interested in developing supervised learning models that predict the effect of chemical perturbation on individual genes and proteins. Using machine-learning approaches, you will capture omics information from chemical perturbations resources (e.g., Connectivity Map) and disease-induced perturbations to understand fundamental biological mechanisms.