Detecting Overlapping Protein Complexes by Rough-Fuzzy Clustering in Protein-Protein Interaction Networks
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A novel rough-fuzzy clustering method to detect overlapping protein complexes in protein-protein interaction (PPI) networks 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.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.read more
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References
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Book
Fuzzy Set Theory - and Its Applications
TL;DR: The book updates the research agenda with chapters on possibility theory, fuzzy logic and approximate reasoning, expert systems, fuzzy control, fuzzy data analysis, decision making and fuzzy set models in operations research.
Journal ArticleDOI
An automated method for finding molecular complexes in large protein interaction networks.
TL;DR: A novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes is described.
Journal ArticleDOI
Fuzzy Set Theory and Its Applications
TL;DR: In this paper, a new book about fuzzy set theory and its applications is presented, which can be used to explore the knowledge of the knowledge in a new way, even for only few minutes to read a book.
Journal ArticleDOI
BioGRID: a general repository for interaction datasets
Chris Stark,Bobby-Joe Breitkreutz,Teresa Reguly,Lorrie Boucher,Ashton Breitkreutz,Mike Tyers +5 more
TL;DR: BioGRID is a freely accessible database of physical and genetic interactions that includes >116 000 interactions from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens.
Journal ArticleDOI
An Information Flow Model for Conflict and Fission in Small Groups
TL;DR: In this paper, the authors used data from a voluntary association to construct a new formal model for a traditional anthropological problem, fission in small groups, where the process leading to fission is viewed as an unequal flow of sentiments and information across the ties in a social network.
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