Network embedding for detecting protein complexes in attributed networks

Detecting protein complexes holds paramount importance in elucidating cellular organization and protein functionalities. Over the past decade, numerous approaches have centered their attention on the topological intricacies of protein-protein interaction (PPI) networks, yet these have often fallen short in harnessing the full spectrum of biological information inherent in proteins. To bridge this gap, we introduce a novel methodology, designated NE-DPC, which integrates both the topological landscape of PPI networks and the attribute profiles of proteins to facilitate the identification of protein complexes. Initially, we fuse the static PPI network with protein attributes through advanced network embedding techniques. Subsequently, we construct a cosine similarity matrix grounded on these embedded vectors, capturing the intricate relationships among proteins. Lastly, we employ a core-attachment strategy to pinpoint protein complexes. The results reveal that NE-DPC outperforms cutting-edge methods, demonstrating its effectiveness and potential in advancing protein complex detection.