Peer-Reviewed Publication
medRxiv2026May 17, 2026Journal Article
A phenotype-to-mechanism framework links phenome-wide comorbidity architecture to molecular mechanisms and therapeutic discovery in complex diseases.
Wei-Ting Wang1,2, Manqi Zhou1,2,3, Jie Tong4, Meng-Ju Lin4, Alison Ke1,2,3, Meihan Wei1,2,3, Zhenxing Xu1,2, Hansen Tai5, Aarthi Parvathaneni5, Khyla T Hill5, Steven R Cohen5, Lynn Petukhova6, Ernest S Chiu4, Fei Wang1,2, Catherine P Lu4,7, Chang Su1,2
1Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
2Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA.
3Department of Computational Biology, Cornell University, Ithaca, NY, USA.
4The Hansjörg Wyss Department of Plastic Surgery, New York University Grossman School of Medicine, New York, NY, USA.
5Israel Englander Department of Dermatology, Weill Cornell Medicine, Cornell University, New York, NY, USA.
6Ronald O. Perelman Department of Dermatology, Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA.
7Department of Cell Biology, Regenerative Medicine Institute, New York University Grossman School of Medicine, New York, NY, USA.
Abstract
Complex human diseases exhibit substantial clinical heterogeneity driven by poorly understood molecular mechanisms, while many also lack sufficient molecular and omics data for mechanistic investigation, hindering therapeutic development. We introduce PiMInfer, a "phenotype-to-mechanism" framework that leveraged largely available real-world clinical data-based deep phenotypic characterizations wit…
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