Complex Detection in PPI Network Using Genes Expression Information

Author(s): Zehua Zhang, Jijun Tang*, Fei Guo*.

Journal Name: Current Proteomics

Volume 15 , Issue 2 , 2018

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Graphical Abstract:


Background: Identifying of protein complexes from PPI networks has become a key problem to elucidate protein functions and identify signaling and biological processes in a cell.

Objective: Accurate determination of complexes in PPI networks is crucial for understanding principles of cellular organization.

Method: We propose a novel method to identify protein complexes on PPI networks. First, we use Markov Cluster Algorithm with an edge-weighting scheme to calculate complexes on PPI networks. Second, we design a new co-expression analysis method to measure each protein complex, based on differential co-expression information.

Results: To evaluate our method, we experiment on two yeast PPI networks. On DIP network, our method has Precision and F-Measure values of 0.5014 and 0.5219, which improves upon Precision and F-Measure values of 0.2896 and 0.3211 for COACH, 0.4252 and 0.3675 for ClusterONE. On MIPS network, our method has F-Measure values of 0.3597, which improves upon F-Measure values of 0.2497 for COACH, 0.3326 for ClusterONE.

Conclusion: Our method achieves better results than some state-of-the-art methods for identifying protein complexes on dynamic PPI networks, with the prediction improved.

Keywords: Co-expression information, complex detection, gene expression, genes, markov cluster algorithm, PPI network.

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Article Details

Year: 2018
Page: [119 - 127]
Pages: 9
DOI: 10.2174/1570164614666171030161237
Price: $58

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