295 citations · 451 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
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…
Graph AI in Medicine
Ruth Johnson, Michelle M. Li, Ayush Noori +2
In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks (GNNs), stands out for its capability to capture intricate relationshi…
Deep Contextual Learners for Protein Networks
Michelle M. Li, Marinka Zitnik
Spatial context is central to understanding health and disease. Yet reference protein interaction networks lack such contextualization, thereby limiting the study of where protein…
Graph Representation Learning in Biomedicine
Michelle M. Li, Kexin Huang, Marinka Zitnik
Biomedical networks (or graphs) are universal descriptors for systems of interacting elements, from molecular interactions and disease co-morbidity to healthcare systems and scient…
Subgraph Neural Networks
Emily Alsentzer, Samuel G. Finlayson, Michelle M. Li +1
Deep learning methods for graphs achieve remarkable performance on many node-level and graph-level prediction tasks. However, despite the proliferation of the methods and their suc…