Deep representation learning of protein-protein interaction networks for enhanced pattern discovery.

ABSTRACT: 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 har

SUBMITTER: Yan R 

PROVIDER: S-EPMC11654695 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature