5 papers
How Post-Training Shapes Biological Reasoning Models
Lukas Fesser, Hanlin Zhang, Michelle M. Li +5
Scientific reasoning models for biology combine language models with foundation models trained on multimodal biological data, including DNA, RNA, and proteins. These models are bui…
Learning Normal Representations for Blood Biomarkers
Aashna P. Shah, Michelle M. Li, Yash Lal +9
Blood-based biomarkers underpin clinical diagnosis and management, yet their interpretation relies largely on fixed population reference intervals that ignore stable, intra-patient…
Graph AI generates neurological hypotheses validated in molecular, organoid, and clinical systems
Ayush Noori, JoaquÃn Polonuer, Katharina Meyer +26
Neurological diseases are the leading global cause of disability, yet most lack disease-modifying treatments. We present PROTON, a heterogeneous graph transformer that generates te…
One Patient, Many Contexts: Scaling Medical AI with Contextual Intelligence
Michelle M. Li, Ben Y. Reis, Adam Rodman +6
Medical AI, including clinical language models, vision-language models, and multimodal health record models, already summarizes notes, answers questions, and supports decisions. Th…
Multi Scale Graph Neural Network for Alzheimer's Disease
Anya Chauhan, Ayush Noori, Zhaozhi Li +4
Alzheimer's disease (AD) is a complex, progressive neurodegenerative disorder characterized by extracellular A\b{eta} plaques, neurofibrillary tau tangles, glial activation, and ne…