Machine Learning for Life Sciences

In this talk, I will give examples of methods developed in our group at the intersection of machine learning and computational biology. The main body of the talk will focus on our drug synergy prediction efforts. Combination drug therapies are effective treatments for cancer. However, the genetic heterogeneity of the patients and exponentially large space of drug pairings pose significant challenges for finding the right combination for a specific patient. Current in silico prediction methods promise to reduce the vast number of candidate drug combinations for further screening. However, existing powerful methods are trained with cancer cell line gene expression data, which limits their applicability in clinical settings. While synergy measurements on cell lines models are available at large scale, patient-derived samples are too few to train a complex model. On the other hand, patient-specific single-drug response data are relatively more available. In this talk, I will first present our method trained on cell line gene expression data and further describe training strategies for customizing patient drug synergy predictions using single drug response data.