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.
Highlights
In the past several years, large-scale proteomics experiments have produced many proteinprotein interaction (PPI) data sets from different organisms [1]
The notion of the overlapping protein complexes is easy to understand, constructing an effective algorithm for overlapping protein complexes is highly non-trivial for two reasons: firstly, the number of protein complexes is unknown for a given PPI network; secondly, a protein complex should contain many reliable interactions within its subunit, and it should be well-separated from the rest of the PPI networks [1]
In order to solve the three dimensional datasets clustering problems in PPI networks and the weaknesses of c-means clustering, we present a novel method based on rough-fuzzy clustering (RFC) to detect overlapping protein complexes in PPI networks
Summary
In the past several years, large-scale proteomics experiments have produced many PPI data sets from different organisms [1]. These data sets are generally represented as undirected weighted or unweighted networks with proteins as a set of nodes and interactions as a set of edges. Each of them has limitations: some algorithms only work in unweighted networks, and can be applied to weighted data sets only after binarizing them by deleting edges whose weights are below a given threshold, while others need to assign the number of complexes firstly [8,9]. The notion of the overlapping protein complexes is easy to understand, constructing an effective algorithm for overlapping protein complexes is highly non-trivial for two reasons: firstly, the number of protein complexes is unknown for a given PPI network; secondly, a protein complex should contain many reliable interactions within its subunit, and it should be well-separated from the rest of the PPI networks [1]
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