M3NetFlow: A multi-scale multi-hop graph AI model for integrative multi-omic data analysis

Multi-omic data-driven studies are at the forefront of precision medicine by characterizing complex disease signaling systems across multiple views and levels. The integration and interpretation of multi-omic data are critical for identifying disease targets and deciphering disease signaling pathways. However, it remains an open problem due to the complex signaling interactions among many proteins. Herein, we propose a multi-scale multi-hop multi-omic network flow model, M3NetFlow, to facilitate both hypothesis-guided and generic multi-omic data analysis tasks. We evaluated M3NetFlow using two independent case studies: (1) uncovering mechanisms of synergy of drug combinations (hypothesis/anchor-target guided multi-omic analysis) and (2) identifying biomarkers of Alzheimer's disease (generic multi-omic analysis). The evaluation and comparison results showed that M3NetFlow achieved the best prediction accuracy and identified a set of drug combination synergy- and disease-associated targets. The model can be directly applied to other multi-omic data-driven studies.