Associate Professor MIT, Massachusetts, United States
Massive data collection holds the promise of a better understanding of complex phenomena and ultimately, of better decisions. An exciting opportunity in this regard stems from the growing availability of perturbation / intervention data (drugs, knockouts, overexpression, etc.) in biology. In order to obtain mechanistic insights from such data, a major challenge is the integration of different data modalities (transcriptomic, proteomic, structural, etc.). I will first discuss our recent work on coupling autoencoders to integrate and translate between data of very different modalities such as sequencing and imaging. I will then present a framework for integrating observational and interventional data for causal structure discovery and characterize the causal relationships that are identifiable from such data. We end by demonstrating how these ideas can be applied for drug repurposing in the current SARS-CoV-2 crisis.
