ArticlePloS one2014
Detecting overlapping protein complexes by rough-fuzzy clustering in protein-protein interaction networks.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 59 citations in OpenAlex.
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- Integrating Protein-Protein Interaction Networks and Somatic Mutation Data to Detect Driver Modules in Pan-Cancer.Interdisciplinary sciences, computational life sciences · 2022Article
- Overcoming Drug Resistance in Advanced Prostate Cancer by Drug Repurposing.Medical sciences (Basel, Switzerland) · 2022Review
- A systems biology analysis of protein-protein interaction of digestive disorders and Covid-19 virus based on comprehensive gene information.Gastroenterology and hepatology from bed to bench · 2022Article
- Exploring the new horizons of drug repurposing: A vital tool for turning hard work into smart work.European journal of medicinal chemistry · 2019Review
- Detection of Protein Complexes Based on Penalized Matrix Decomposition in a Sparse Protein⁻Protein Interaction Network.Molecules (Basel, Switzerland) · 2018Article
- Overlapping functional modules detection in PPI network with pair-wise constrained non-negative matrix tri-factorisation.IET systems biology · 2018Article
- Review of Drug Repositioning Approaches and Resources.International journal of biological sciences · 2018Review
- A density-based approach for detecting complexes in weighted PPI networks by semantic similarity.PloS one · 2017Article
- A community detection algorithm using network topologies and rule-based hierarchical arc-merging strategies.PloS one · 2017Article
- A systems biology analysis protein-protein interaction of NASH and IBD based on comprehensive gene information.Gastroenterology and hepatology from bed to bench · 2017Article
- Identifying robust communities and multi-community nodes by combining top-down and bottom-up approaches to clustering.Scientific reports · 2015Article
- Conserved abundance and topological features in chromatin-remodeling protein interaction networks.EMBO reports · 2015Article
- Identifying overlapping mutated driver pathways by constructing gene networks in cancer.BMC bioinformatics · 2015Article
- Machine learning in postgenomic biology and personalized medicine.Wiley interdisciplinary reviews. Data mining and knowledge discoveryArticle
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we present a novel rough-fuzzy clustering (RFC) method to detect overlapping protein complexes in protein-protein interaction (PPI) networks. RFC focuses on fuzzy relation model rather than graph model by integrating fuzzy sets and rough sets, employs the upper and lower approximations of rough sets to deal with overlapping complexes, and calculates the number of complexes automatically. Fuzzy relation between proteins is established and then transformed into fuzzy equivalence relation. Non-overlapping complexes correspond to equivalence classes satisfying certain equivalence relation. To obtain overlapping complexes, we calculate the similarity between one protein and each complex, and then determine whether the protein belongs to one or multiple complexes by computing the ratio of each similarity to maximum similarity. To validate RFC quantitatively, we test it in Gavin, Collins, Krogan and BioGRID datasets. Experiment results show that there is a good correspondence to reference complexes in MIPS and SGD databases. Then we compare RFC with several previous methods, including ClusterONE, CMC, MCL, GCE, OSLOM and CFinder. Results show the precision, sensitivity and separation are 32.4%, 42.9% and 81.9% higher than mean of the five methods in four weighted networks, and are 0.5%, 11.2% and 66.1% higher than mean of the six methods in five unweighted networks. Our method RFC works well for protein complexes detection and provides a new insight of network division, and it can also be applied to identify overlapping community structure in social networks and LFR benchmark networks.
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