Michelle M. Li

Medea, an AI agent, performs therapeutic reasoning across biological contexts by preserving context, verifying steps, and reconciling evidence, outperforming existing models in multiple domains.

SHEPHERD uses few-shot learning and a knowledge graph to improve diagnosis of rare genetic diseases using simulated data and real-world patient cohorts.

This paper introduces a multimodal foundation model for predicting protein phenotypes using sequence, structure, and natural language, showing competitive performance and novel insights into disease-related proteins.

This paper introduces a geometric deep learning model for context-aware protein representations, improving therapeutic target nomination in immune-related diseases.

The paper introduces Therapeutics Commons (TDC-2), a resource integrating cell-type-specific biomarkers with machine learning tasks for drug discovery.

This paper simulates patients with novel genetic conditions to evaluate gene prioritization methods in diagnosing rare disorders.