The current revolution in machine learning is transforming research in biology and medicine. I develop machine learning (ML) algorithms and perform experiments to improve our understanding of human gene regulation and drug mechanisms of action at a genome-wide scale. This systems biology approach is expected to enable the discovery of novel therapeutics against diseases. In my current research program, I focus on algorithm development for target identification in lung cancer. To inform and validate my mathematical models, I perform nascent RNA sequencing and other experiments.
With my broad expertise, I am looking to transition into a group leader position in academia, pharma, or to found a start up on machine learning for drug discovery.
During my postdoc, I have developed GeneWalk, an ML tool that identifies the most important genes and their relevant functions from any input list of experimental gene hits. GeneWalk is available as an open source software package for the biomedical research community, with a tutorial to get started.
In a collaboration between researchers from Novartis and academic institutions, we used ML to identify associations between drug target genes and adverse drug reactions. Our models can be used to predict at the preclinical stage whether therapeutic candidates are likely to cause adverse events. Detailed info in our EBioMedicine publication.