title: Using contrast patterns between true complexes and random subgraphs in PPI networks to predict unknown protein complexes.
abstract: Most protein complex detection methods utilize unsupervised techniques to cluster densely connected nodes in a protein-protein interaction (PPI) network, in spite of the fact that many true complexes are not dense subgraphs. Supervised methods have been proposed recently, but they do not answer why a group of proteins are predicted as a complex, and they have not investigated how to detect new complexes of one species by training the model on the PPI data of another species. We propose a novel supervised method to address these issues. The key idea is to discover emerging patterns (EPs), a type of contrast pattern, which can clearly distinguish true complexes from random subgraphs in a PPI network. An integrative score of EPs is defined to measure how likely a subgraph of proteins can form a complex. New complexes thus can grow from our seed proteins by iteratively updating this score. The performance of our method is tested on eight benchmark PPI datasets and compared with seven unsupervised methods, two supervised and one semi-supervised methods under five standards to assess the quality of the predicted complexes. The results show that in most cases our method achieved a better performance, sometimes significantly.
authors: College of Information Engineering, Northwest A&F University, Yangling 712100, China.
authors: Quanzhong
authors: Q
authors: Liu
authors: Monash Bioinformatics Platform and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.|Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia.|National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
authors: Jiangning
authors: J
authors: Song
authors: Advanced Analytics Institute and Centre for Health Technologies, Faculty of Engineering and IT, University of Technology Sydney, 81 Broadway, NSW 2007, Australia.
authors: Jinyan
authors: J
authors: Li
mesh_terms: D008956:Models, Chemical; D046912:Multiprotein Complexes / Q000737:chemistry* / Q000378:metabolism; D012984:Software
chemical_list: D046912:Multiprotein Complexes