Michelle M. Li

The paper introduces SEMPER, a multimodal model that aligns protein language models with brain cell-type-specific interactomes to predict protein functions and drug targets.

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.