Tymor Hamamsy

The paper introduces a framework using protein language models to learn sequence-structure-function representations and develops a retrieval system for large-scale protein annotation and function prediction.

TM-Vec predicts structural similarity from sequence pairs to detect remote homologs, outperforming sequence alignment methods and matching structure-based alignment performance.

The paper introduces Inferelator 3.0, a tool for inferring gene regulatory networks from single-cell data, showing improved performance on yeast and mouse brain datasets.

This paper quantifies adverse drug reaction severity using social media data and presents scores correlated with real-world outcomes.