AdaPPI: identification of novel protein functional modules via adaptive graph convolution networks in a protein–protein interaction network. Issue 1 (16th December 2022)

• Record Type: Journal Article
• Authors: Chen, Hongwei; Cai, Yunpeng; Ji, Chaojie; Selvaraj, Gurudeeban; Wei, Dongqing; Wu, Hongyan
• Is Part Of: Briefings in bioinformatics. Volume 24:Issue 1(2023)
• Publication Date: 2022-12-16
• Subjects: protein functional module identification -- adaptive graph convolution networks -- protein complex -- pathway; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac523
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Identifying unknown protein functional modules, such as protein complexes and biological pathways, from protein–protein interaction (PPI) networks, provides biologists with an opportunity to efficiently understand cellular function and organization. Finding complex nonlinear relationships in underlying functional modules may involve a long-chain of PPI and pose great challenges in a PPI network with an unevenly sparse and dense node distribution. To overcome these challenges, we propose AdaPPI, an adaptive convolution graph network in PPI networks to predict protein functional modules. We first suggest an attributed graph node presentation algorithm. It can effectively integrate protein gene ontology attributes and network topology, and adaptively aggregates low- or high-order graph structural information according to the node distribution by considering graph node smoothness. Based on the obtained node representations, core cliques and expansion algorithms are applied to find functional modules in PPI networks. Comprehensive performance evaluations and case studies indicate that the framework significantly outperforms state-of-the-art methods. We also presented potential functional modules based on their confidence.