Abstract: Predicting the impact of genetic variants is a significant challenge in computational biology, with crucial applications in disease diagnosis, gene regulation modeling, and protein engineering. In this talk, I will describe my lab's recent work on improving variant effect prediction for both coding and non-coding regions by leveraging advances in unsupervised learning, particularly self-supervised learning from natural language processing. For coding variants, I will introduce a robust learning framework to transfer properties between unrelated proteins and discuss how this approach fares in comparison with the recently published PrimateAI-3D and AlphaMissense methods. Regarding non-coding variants, I will present our work on DNA language models, highlighting their efficacy in genome-wide variant effect prediction.
Zoom link: https://mit.zoom.us/j/93513735220
Location: 32 G-575
Refreshments will be available