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PRBB Computational Genomics Seminars Yun S. Song

PRBB Computational Genomics Seminars Yun S. SongPRBB Computational Genomics Seminars Yun S. Song

22/11/2022
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PRBB Computational Genomics Seminars Yun S. Song

R_473.10_AULA

22/11/202211:00R_473.10_AULAPRBB Computational Genomics SeminarsYun S. SongComputer Science Division and Department of Statistics. University of California, BerkeleyImproving Variant Effect Predictions using Language Models and Cross-Protein Transfer LearningHost: Guigó Serra, RodericAbstract:link hybrid seminar - https://us02web.zoom.us/j/89225816012?pwd=SUhkQ0k4djNRdHZQTU1GV0c2OXJTQT09

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.