Yan Rui, Islam Md Tauhidul, Xing Lei
Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA.
Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.
Sci Adv. 2024 Dec 20;10(51):eadq4324. doi: 10.1126/sciadv.adq4324. Epub 2024 Dec 18.
Protein-protein interaction (PPI) networks, where nodes represent proteins and edges depict myriad interactions among them, are fundamental to understanding the dynamics within biological systems. Despite their pivotal role in modern biology, reliably discerning patterns from these intertwined networks remains a substantial challenge. The essence of the challenge lies in holistically characterizing the relationships of each node with others in the network and effectively using this information for accurate pattern discovery. In this work, we introduce a self-supervised network embedding framework termed discriminative network embedding (DNE). Unlike conventional methods that primarily focus on direct or limited-order node proximity, DNE characterizes a node both locally and globally by harnessing the contrast between representations from neighboring and distant nodes. Our experimental results demonstrate DNE's superior performance over existing techniques across various critical network analyses, including PPI inference and the identification of protein functional modules. DNE emerges as a robust strategy for node representation in PPI networks, offering promising avenues for diverse biomedical applications.
蛋白质-蛋白质相互作用（PPI）网络中，节点代表蛋白质，边描绘了它们之间的众多相互作用，对于理解生物系统内的动态过程至关重要。尽管它们在现代生物学中起着关键作用，但从这些相互交织的网络中可靠地识别模式仍然是一项重大挑战。挑战的本质在于全面表征网络中每个节点与其他节点的关系，并有效地利用这些信息进行准确的模式发现。在这项工作中，我们引入了一种称为判别式网络嵌入（DNE）的自监督网络嵌入框架。与主要关注直接或有限阶节点接近度的传统方法不同，DNE通过利用相邻节点和遥远节点表示之间的对比，从局部和全局两个方面对节点进行表征。我们的实验结果表明，在包括PPI推理和蛋白质功能模块识别在内的各种关键网络分析中，DNE比现有技术具有更优越的性能。DNE成为PPI网络中节点表示的一种强大策略，为各种生物医学应用提供了有前景的途径。
