Zhang Heming, Goedegebuure S Peter, Ding Li, DeNardo David, Fields Ryan C, Province Michael, Chen Yixin, Payne Philip, Li Fuhai
Institute for Informatics, Data Science and Biostatistics (I2DB), Washington University in St. Louis, St. Louis, MO, USA.
Department of Surgery, Washington University in St. Louis, St. Louis, MO, USA.
iScience. 2025 Feb 6;28(3):111920. doi: 10.1016/j.isci.2025.111920. eCollection 2025 Mar 21.
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.
多组学数据驱动的研究通过跨多个视角和层面表征复杂疾病信号系统，处于精准医学的前沿。多组学数据的整合与解读对于识别疾病靶点和破译疾病信号通路至关重要。然而，由于许多蛋白质之间复杂的信号相互作用，这仍然是一个悬而未决的问题。在此，我们提出一种多尺度多跳多组学网络流模型M3NetFlow，以促进假设导向和通用的多组学数据分析任务。我们使用两个独立的案例研究对M3NetFlow进行了评估：（1）揭示药物组合的协同作用机制（假设/锚定靶点导向的多组学分析）和（2）识别阿尔茨海默病的生物标志物（通用多组学分析）。评估和比较结果表明，M3NetFlow实现了最佳预测准确性，并识别出一组与药物组合协同作用和疾病相关的靶点。该模型可直接应用于其他多组学数据驱动的研究。
