Zhang Heming, Goedegebuure S Peter, Ding Li, DeNardo David, Fields Ryan C, Chen Yixin, Payne Philip, Li Fuhai
Institute for Informatics, Data Science and Biostatistics (I2DB), Washington University School of Medicine, St. Louis, MO, USA.
Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA.
bioRxiv. 2024 Sep 11:2023.06.15.545130. doi: 10.1101/2023.06.15.545130.
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, , 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 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/M3NetFlow.
多组学数据驱动的研究从多个层面刻画复杂疾病信号系统，处于精准医学和医疗保健的前沿。多组学数据的整合与解读对于识别复杂疾病的分子靶点和破译核心信号通路至关重要。然而，由于生物标志物数量众多且它们之间存在复杂的相互作用，这仍然是一个悬而未决的问题。在本研究中，我们提出了一种新颖的多尺度多跳多组学图模型，即M3NetFlow，以促进通用的多组学数据分析，对靶点进行排序并推断核心信号流/通路。为了评估M3NetFlow，我们将其应用于两个独立的多组学案例研究中：1）揭示协同药物联合反应的机制（定义为锚定靶点引导学习），以及2）识别阿尔茨海默病（AD）的生物标志物和通路。评估和比较结果表明，M3NetFlow实现了最佳的预测准确性（准确），并识别出一组关键靶点和核心信号通路（可解释）。该模型可直接应用于其他多组学数据驱动的研究。代码可在以下网址公开获取：https://github.com/FuhaiLiAiLab/M3NetFlow 。
