MosGraphFlow: a novel integrative graph AI model mining signaling targets from multi-omic data

Multi-omic dataset can better characterize complex cellular signaling pathways from multiple views compared to individual omic data. However, integrative multi-omic data analysis to rank key disease biomarkers and infer core signaling pathways remains an open problem. In this study, we developed a novel graph AI model, mosGraphFlow, for analyzing multi-omic signaling graphs (mosGraphs), 2) analyzed multi-omic mosGraph datasets of Alzheimers' Disease (AD), and 3) developed a visualization tool to facilitate the visualization of identified disease associated signaling biomarkers and network. The comparison results show that the proposed model not only achieves the best classification accuracy but also identifies important AD disease biomarkers and signaling interactions. In the visualization, the signaling sources are highlighted at specific omic levels to facilitate the understanding of disease pathogenesis. The proposed model can also be applied and expanded for other multi-omic data-driven studies. The code of the model is publicly accessible via GitHub: https://github.com/FuhaiLiAiLab/mosGraphFlow.

The online version contains supplementary material available at 10.1186/s44330-025-00041-8.