RT Journal Article
SR Electronic
T1 M3NetFlow: A novel multi-scale multi-hop graph AI model for integrative multi-omic data analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.06.15.545130
DO 10.1101/2023.06.15.545130
A1 Zhang, Heming
A1 Goedegebuure, S. Peter
A1 Ding, Li
A1 DeNardo, David
A1 Fields, Ryan C.
A1 Chen, Yixin
A1 Payne, Philip
A1 Li, Fuhai
YR 2024
UL http://biorxiv.org/content/early/2024/09/04/2023.06.15.545130.abstract
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.