ArticleScientific reports2016
Using contrast patterns between true complexes and random subgraphs in PPI networks to predict unknown protein complexes.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Heterogeneous network approaches to protein pathway prediction.Computational and structural biotechnology journal · 2024Review
- An Ensemble Learning Framework for Detecting Protein Complexes From PPI Networks.Frontiers in genetics · 2022Article
- Identification of Protein Complexes Based on Core-Attachment Structure and Combination of Centrality Measures and Biological Properties in PPI Weighted Networks.The protein journal · 2020Article
- A seed-extended algorithm for detecting protein complexes based on density and modularity with topological structure and GO annotations.BMC genomics · 2019Article
- Identifying protein complexes based on node embeddings obtained from protein-protein interaction networks.BMC bioinformatics · 2018Article
- A common neighbor based technique to detect protein complexes in PPI networks.Journal, genetic engineering & biotechnology · 2018Article
- MTGO: PPI Network Analysis Via Topological and Functional Module Identification.Scientific reports · 2018Article
- Predicting protein complexes using a supervised learning method combined with local structural information.PloS one · 2018Article
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3 authors.
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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.
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