FMvPCI: A Multiview Fusion Neural Network for Identifying Protein Complex via Fuzzy Clustering

Protein complexes play a crucial role in regulating various biological processes that govern cell activities. Numerous computational algorithms have been proposed to identify protein complexes from protein-protein interaction (PPI) networks. However, many of these algorithms face limitations in effectively leveraging multiview biological information of proteins, restricting their ability to capture the intricate characteristics of protein complexes in PPI networks. While deep learning-based algorithms have significantly advanced the identification of protein complexes, they often integrate graph representation learning techniques into traditional clustering algorithms without explicitly capturing the dependency between protein embeddings and resulting complexes. To address these issues, we present a multiview fusion neural network, named FMvPCI, for protein complex identification via fuzzy clustering. In FMvPCI, we introduce a novel multiview graph convolution encoder to effectively manipulate and fuse the biological information of proteins from different perspectives. Subsequently, the optimization of FMvPCI incorporates our expectations about protein complexes through the concept of fuzzy clustering. This approach unifies the embeddings of proteins and their cluster memberships within a coherent framework. Leveraging a heuristic search strategy, FMvPCI can discover overlapping protein complexes based on the cluster memberships of proteins. A series of experiments on five different PPI networks collected from two species have been conducted to evaluate the performance of FMvPCI by comparing it with state-of-the-art identification algorithms, and the results demonstrate the superior performance of FMvPCI by significantly improving the identification accuracy for protein complexes.