Using contrast patterns between true complexes and random subgraphs in PPI networks to predict unknown protein complexes

Quanzhong Liu, Jiangning Song, Jinyan Li

Research output: Contribution to journalArticleResearchpeer-review

9 Citations (Scopus)

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.
Original languageEnglish
Article number21223
Number of pages15
JournalScientific Reports
Volume6
DOIs
Publication statusPublished - 12 Feb 2016

Keywords

  • computer science
  • data mining

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