Abstract
<jats:p>Understanding how molecular interactions give rise to disease phenotypes across cellular contexts remains a central challenge in biomedical research. Here, we introduce a Disease-Cell-Protein (DCP) paradigm for modeling multi-scale disease biology, which jointly represents disease states, cellular composition, and protein interaction networks within a unified graph architecture. We instantiate this paradigm in the liver as LiverDCP by integrating a large-scale liver single-cell atlas (LiverHomo) with proteome-wide predicted protein-protein interactions to construct over 280 context-specific interactomes across diverse liver disease and cellular conditions. LiverDCP employs a multi-context representation learning strategy that enables joint training across hundreds of disease-cell environments, capturing shared interaction principles while preserving context-specific variation. LiverDCP incorporates pretrained protein sequence-derived features through a geometry-aware two-phase training scheme that preserves embedding structure while improving predictive performance. The resulting protein embeddings encode context-specific functional states and reveal extensive rewiring of protein roles across diseases. They provide a context-resolved representation of protein function, enabling interpretation of GWAS risk genes and prioritization of therapeutic targets, including recovery of known targets and nomination of candidate repurposed and novel targets for MASH. Overall, this work establishes a generalizable framework for linking molecular interactions to disease phenotypes and enabling mechanistic understanding and target discovery across complex diseases.</jats:p>