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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.

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Comparison with other static protein complex detection methods. Comparison results on two PPI networks in terms of PR and f-measure with respect to CYC2008. (a) DIP data. (b) BioGrid data.
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Fig6: Comparison with other static protein complex detection methods. Comparison results on two PPI networks in terms of PR and f-measure with respect to CYC2008. (a) DIP data. (b) BioGrid data.

Mentions: We also apply TS-OCD on static PPI networks, i.e., discard the smooth regularization term in the objective function (5) and take the static PPI networks as input (we denote it as OCD). For fair comparison, optimal parameters are also set for OCD to generate its best results. In addition, we discard their predicted complexes with less than three proteins, for all the 7 methods. Figure6 shows the comparative performance of 7 different algorithms on two PPI networks with respect to benchmark complex set CYC2008. Moreover, Table1 shows the size distribution of complexes detected by various algorithms, and the values of recall and precision for each algorithm.Figure 6


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 static protein complex detection methods. Comparison results on two PPI networks in terms of PR and f-measure 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

Fig6: Comparison with other static protein complex detection methods. Comparison results on two PPI networks in terms of PR and f-measure with respect to CYC2008. (a) DIP data. (b) BioGrid data.
Mentions: We also apply TS-OCD on static PPI networks, i.e., discard the smooth regularization term in the objective function (5) and take the static PPI networks as input (we denote it as OCD). For fair comparison, optimal parameters are also set for OCD to generate its best results. In addition, we discard their predicted complexes with less than three proteins, for all the 7 methods. Figure6 shows the comparative performance of 7 different algorithms on two PPI networks with respect to benchmark complex set CYC2008. Moreover, Table1 shows the size distribution of complexes detected by various algorithms, and the values of recall and precision for each algorithm.Figure 6

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