Biological structure and function emerge from unsupervised learning

This lecture explores how biological structure and function can be understood by scaling unsupervised learning to analyze protein sequences. It covers topics such as the exponential growth of sequence data, metagenomics, protein language models, self-supervision, and the relationship between language modeling objectives and structure learning. The lecture also delves into the use of transformer models, self-attention mechanisms, and linear projections for tasks like structure prediction, remote homology detection, and contact precision evaluation.