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.

中文翻译：

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蛋白质-蛋白质相互作用网络的深度表示学习，以增强模式发现


蛋白质-蛋白质相互作用 （PPI） 网络，其中节点代表蛋白质，边缘描述它们之间的无数相互作用，是理解生物系统内部动力学的基础。尽管它们在现代生物学中发挥着关键作用，但从这些交织在一起的网络中可靠地辨别模式仍然是一项重大挑战。挑战的本质在于全面描述每个节点与网络中其他节点的关系，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，我们引入了一个称为判别网络嵌入 （DNE） 的自监督网络嵌入框架。与主要关注直接或有限阶节点邻近度的传统方法不同，DNE 通过利用来自相邻节点和远距离节点的表示之间的对比来在本地和全局表征节点。我们的实验结果表明，DNE 在各种关键网络分析（包括 PPI 推断和蛋白质功能模块的鉴定）中优于现有技术。DNE 成为 PPI 网络中节点表示的稳健策略，为各种生物医学应用提供了有前途的途径。