Abstract
Functional modules can be predicted using genome-wide protein–protein interactions (PPIs) from a systematic perspective. Various graph clustering algorithms have been applied to PPI networks for this task. In particular, the detection of overlapping clusters is necessary because a protein is involved in multiple functions under different conditions. graph entropy (GE) is a novel metric to assess the quality of clusters in a large, complex network. In this study, the unweighted and weighted GE algorithm is evaluated to prove the validity of predicting function modules. To measure clustering accuracy, the clustering results are compared to protein complexes and Gene Ontology (GO) annotations as references. We demonstrate that the GE algorithm is more accurate in overlapping clusters than the other competitive methods. Moreover, we confirm the biological feasibility of the proteins that occur most frequently in the set of identified clusters. Finally, novel proteins for the additional annotation of GO terms are revealed.
Highlights
A functional module is a separable entity in which the functions can be separated
The number of clusters and the average cluster size are compared in Table 1.Comparing the clustering results on the STRING protein–protein interactions (PPIs) dataset, which is a large network, the graph entropy (GE) and Markov clustering (MCL) algorithms generated a larger number of clusters than the others
The GE-based graph clustering algorithm, which iteratively performs a local search to detect an optimal cluster with the lowest GE, was recently proposed
Summary
A functional module is a separable entity in which the functions can be separated. Functional modules overlap with each other because a protein performs multiple functions under different conditions [1]. A protein complex is a multiprotein unit composed of several proteins linked by non-covalent bonds. A protein can be included as a subunit in multiple complexes of oligomeric structures. Functional modules or protein complexes can be predicted using protein–protein interactions (PPIs) from a systematic perspective. PPIs can be represented as a network, which is an undirected graph. The discovery of the entire set of functional modules from genome-wide PPI networks is an important goal of functional genomics [2]. Detecting overlapping clusters is useful for predicting functional modules at the genome scale [3]
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