Improving Variant Effect Predictions using Language Models and Cross-Protein Transfer Learning

PRBB Computational Genomics Seminars

by Yun S. Song, Computer Science Division and Department of Statistics. University of California, Berkeley

Abstract

Predicting the effects of mutations is a major challenge in genomics with important applications in disease diagnosis, protein design, and understanding gene regulation. In this talk, I will describe my lab’s work on improving variant effect predictions, for both coding and non-coding regions, by leveraging recent advances in unsupervised learning, especially self-supervised learning in natural language processing. For coding variants, I will also present an approach to transfer models between unrelated proteins and demonstrate how it is able to achieve state-of-the-art performance on clinical disease variant prediction.