Abstract
BackgroundProtein–protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein–protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein–protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks.ResultsIn this paper, we construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We design an objective function for exclusive clustering and overlapping clustering. We assess the quality of our proposed clustering algorithm using two gold-standard data sets.ConclusionsOur algorithm can identify protein complexes that are significantly enriched in the gold-standard data sets. Furthermore, our method surpasses three competing methods: MCL, ClusterOne, and MCODE in terms of the quality of the predicted complexes. The source code and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/GACluster.zip.
Highlights
Protein–protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level
Data source We study the protein interaction network from the yeast organism since there are abundant high-confidence data sets for its protein interaction network
We applied our clustering algorithm on the Collins protein interaction network extracted from the BioGrid data set [20]
Summary
We construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We assess the quality of our proposed clustering algorithm using two gold-standard data sets
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