295 citations · 451 across the 11 of their papers we have counts for
12 papers
Agentic discovery of blood biomarker from distilled private health records
Seffi Cohen, Liat Antwarg Friedman, Amir Anisman +7
Routine complete blood counts (CBCs) could yield new biomarkers, but the private records needed to evaluate candidates cannot be shared with frontier language model agents that exc…
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, Joaquín 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…
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…
Current and future directions in network biology
Marinka Zitnik, Michelle M. Li, Aydin Wells +34
Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseas…
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…
Discrepancies in Epidemiological Modeling of Aggregated Heterogeneous Data
Anna L. Trella, Peniel N. Argaw, Michelle M. Li +1
Within epidemiological modeling, the majority of analyses assume a single epidemic process for generating ground-truth data. However, this assumed data generation process can be un…
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…