Our machine learning methods are used:
- to characterize genome organization
- to decipher adaptive immunity
- for cancer biomarker discovery
- for integrative modeling of multiple modalities of biomedical datasets including genetic epigenetic, transcriptomic and imaging datasets.
Our current methodological focus is on incorporating causal modeling and informative priors in deep learning models and multiple instance learning.