Elana P. Simon

The paper develops a pipeline to annotate protein functions using latent features from protein language models, identifying missing annotations and structurally matching proteins.

This paper introduces InterPLM, a framework using sparse autoencoders to extract interpretable features from protein language models, revealing both known and novel biological concepts.

This paper introduces an alignment-free deep generative model for protein design and variant effect prediction, showing improved performance over existing methods in challenging cases.