Training, Evaluating, and Understanding Evolutionary Models for Protein Sequences

Описание: PhD Dissertation Talk. Covers work on using methods from natural language processing, specifically large scale language models, for learning representations of protein sequences.

Papers covered:
Evaluating Protein Transfer Learning with TAPE (https://www.biorxiv.org/content/10.11...)
Transformer protein language models are unsupervised structure learners (https://www.biorxiv.org/content/10.11...)
MSA Transformer (https://www.biorxiv.org/content/10.11...)
Language models enable zero-shot prediction of the effects of mutations on protein function (https://www.biorxiv.org/content/10.11...)

Timestamps:
0:00 - Intro
0:56 - Evolutionary Models
11:06 - Neural Evolutionary Models
13:25 - Evaluating Protein Transfer Learning with TAPE
15:58 - Transformer protein language models are unsupervised structure learners
26:18 - MSA Transformer
33:48 - Language models enable zero-shot prediction of the effects of mutations on protein function
42:30 - Future Work
46:42 - Conclusion
47:15 - Thank yous
50:21 - Q&A

Thank you very much to my advisors, John Canny and Pieter Abbeel, and to everyone who helped along the way!