PT  - JOURNAL ARTICLE
AU  - Zhang, Heming
AU  - Goedegebuure, S. Peter
AU  - Ding, Li
AU  - DeNardo, David
AU  - Fields, Ryan C.
AU  - Chen, Yixin
AU  - Payne, Philip
AU  - Li, Fuhai
TI  - M3NetFlow: A novel multi-scale multi-hop graph AI model for integrative multi-omic data analysis
AID  - 10.1101/2023.06.15.545130
DP  - 2024 Jan 01
TA  - bioRxiv
PG  - 2023.06.15.545130
4099  - http://biorxiv.org/content/early/2024/09/04/2023.06.15.545130.short
4100  - http://biorxiv.org/content/early/2024/09/04/2023.06.15.545130.full
AB  - Multi-omic data-driven studies, characterizing complex disease signaling system from multiple levels, are at the forefront of precision medicine and healthcare. The integration and interpretation of multi-omic data are essential for identifying molecular targets and deciphering core signaling pathways of complex diseases. However, it remains an open problem due the large number of biomarkers and complex interactions among them. In this study, we propose a novel Multi-scale Multi-hop Multi-omic graph model, M3NetFlow, to facilitate generic multi-omic data analysis to rank targets and infer core signaling flows/pathways. To evaluate M3NetFlow, we applied it in two independent multi-omic case studies: 1) uncovering mechanisms of synergistic drug combination response (defined as anchor-target guided learning), and 2) identifying biomarkers and pathways of Alzheimer’s disease (AD). The evaluation and comparison results showed M3NetFlow achieves the best prediction accuracy (accurate), and identifies a set of essential targets and core signaling pathways (interpretable). The model can be directly applied to other multi-omic data-driven studies. The code is publicly accessible at: https://github.com/FuhaiLiAiLab/M3NetFlowCompeting Interest StatementThe authors have declared no competing interest.