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Detecting temporal protein complexes from dynamic protein-protein interaction networks.

Ou-Yang L, Dai DQ, Li XL, Wu M, Zhang XF, Yang P - BMC Bioinformatics (2014)

Bottom Line: TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point.Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

View Article: PubMed Central - PubMed

Affiliation: Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China. stsddq@mail.sysu.edu.cn.

ABSTRACT

Background: Proteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization.

Results: In this study, we propose a novel computational method to predict temporal protein complexes. Particularly, we first construct a series of dynamic PPI networks by joint analysis of time-course gene expression data and protein interaction data. Then a Time Smooth Overlapping Complex Detection model (TS-OCD) has been proposed to detect temporal protein complexes from these dynamic PPI networks. TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point. Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.

Conclusions: Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

Show MeSH
Comparison with other temporal protein complex detection methods. Comparison results on dynamic PPI networks with respect to CYC2008. (a) DIP data. (b) BioGrid data.
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Fig7: Comparison with other temporal protein complex detection methods. Comparison results on dynamic PPI networks with respect to CYC2008. (a) DIP data. (b) BioGrid data.

Mentions: Figure7 illustrates the comparison among all the above algorithms with respect to CYC2008 (the detailed comparative results of various algorithms are listed in Additional file1: Table S2). We observe that TS-OCD achieves best performance than existing algorithms consistently in terms of the two measures across DIP and BioGrid data (similar results obtained with respect to MIPS benchmark in Additional file1: Figure S3). Moreover, some existing algorithms combined with our NMF model obtain notable gains in prediction accuracy on the dynamic PPI networks. For example, ClusterONE achieves 0.360 f-measure on the static DIP data, but it increases to 0.427 on the dynamic DIP data. Similarly, SPICi achieves 0.355 f-measure on the static BioGrid data, but it increases to 0.402 on the dynamic BioGrid data. Therefore, the information contained in dynamic PPI networks are indeed useful and they complement to the static PPI data for better complex detection.Figure 7


Detecting temporal protein complexes from dynamic protein-protein interaction networks.

Ou-Yang L, Dai DQ, Li XL, Wu M, Zhang XF, Yang P - BMC Bioinformatics (2014)

Comparison with other temporal protein complex detection methods. Comparison results on dynamic PPI networks with respect to CYC2008. (a) DIP data. (b) BioGrid data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4288635&req=5

Fig7: Comparison with other temporal protein complex detection methods. Comparison results on dynamic PPI networks with respect to CYC2008. (a) DIP data. (b) BioGrid data.
Mentions: Figure7 illustrates the comparison among all the above algorithms with respect to CYC2008 (the detailed comparative results of various algorithms are listed in Additional file1: Table S2). We observe that TS-OCD achieves best performance than existing algorithms consistently in terms of the two measures across DIP and BioGrid data (similar results obtained with respect to MIPS benchmark in Additional file1: Figure S3). Moreover, some existing algorithms combined with our NMF model obtain notable gains in prediction accuracy on the dynamic PPI networks. For example, ClusterONE achieves 0.360 f-measure on the static DIP data, but it increases to 0.427 on the dynamic DIP data. Similarly, SPICi achieves 0.355 f-measure on the static BioGrid data, but it increases to 0.402 on the dynamic BioGrid data. Therefore, the information contained in dynamic PPI networks are indeed useful and they complement to the static PPI data for better complex detection.Figure 7

Bottom Line: TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point.Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

View Article: PubMed Central - PubMed

Affiliation: Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China. stsddq@mail.sysu.edu.cn.

ABSTRACT

Background: Proteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization.

Results: In this study, we propose a novel computational method to predict temporal protein complexes. Particularly, we first construct a series of dynamic PPI networks by joint analysis of time-course gene expression data and protein interaction data. Then a Time Smooth Overlapping Complex Detection model (TS-OCD) has been proposed to detect temporal protein complexes from these dynamic PPI networks. TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point. Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.

Conclusions: Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

Show MeSH