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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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Related in: MedlinePlus

The effect of λand β. Performance of TS-OCD on protein complex detection with different values of λ and β measured by f-measure with respect to MIPS on BioGrid and DIP. The x-axis denotes the value of log λ, the y-axis denotes the value of log β, and the z-axis denotes the value of f-measure.
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Fig4: The effect of λand β. Performance of TS-OCD on protein complex detection with different values of λ and β measured by f-measure with respect to MIPS on BioGrid and DIP. The x-axis denotes the value of log λ, the y-axis denotes the value of log β, and the z-axis denotes the value of f-measure.

Mentions: From Figure4 we observe that for a fixed value of λ, as the value of β increases, the f-measure increases initially and decreases after reaching the maximum. Similarly, for a fixed value of β, as the value of λ increases, f-measure increases initially and decreases after reaching the maximum. Thus both β and λ contribute to improve the performance of TS-OCD. Overall, we find that for DIP and BioGrid, λ ∈ [2(-4),2(-3)] and β ∈ [24,25] result in competitive results. On the other hand, we can find from Figure5 that TS-OCD is sensitive to τ. Overall, TS-OCD achieved best performance when τ = 0.3. In order to avoid evaluation bias and over-estimation of the performance, we do not tune the parameters for a particular dataset and fix τ = 0.3, λ = 2(-4) and β = 24 in the following experiments. Nevertheless, it is worthy to mention that better performance may be achieved if the parameters are tuned for a particular PPI dataset or for a particular complex reference set.Figure 4


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)

The effect of λand β. Performance of TS-OCD on protein complex detection with different values of λ and β measured by f-measure with respect to MIPS on BioGrid and DIP. The x-axis denotes the value of log λ, the y-axis denotes the value of log β, and the z-axis denotes the value of f-measure.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig4: The effect of λand β. Performance of TS-OCD on protein complex detection with different values of λ and β measured by f-measure with respect to MIPS on BioGrid and DIP. The x-axis denotes the value of log λ, the y-axis denotes the value of log β, and the z-axis denotes the value of f-measure.
Mentions: From Figure4 we observe that for a fixed value of λ, as the value of β increases, the f-measure increases initially and decreases after reaching the maximum. Similarly, for a fixed value of β, as the value of λ increases, f-measure increases initially and decreases after reaching the maximum. Thus both β and λ contribute to improve the performance of TS-OCD. Overall, we find that for DIP and BioGrid, λ ∈ [2(-4),2(-3)] and β ∈ [24,25] result in competitive results. On the other hand, we can find from Figure5 that TS-OCD is sensitive to τ. Overall, TS-OCD achieved best performance when τ = 0.3. In order to avoid evaluation bias and over-estimation of the performance, we do not tune the parameters for a particular dataset and fix τ = 0.3, λ = 2(-4) and β = 24 in the following experiments. Nevertheless, it is worthy to mention that better performance may be achieved if the parameters are tuned for a particular PPI dataset or for a particular complex reference set.Figure 4

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
Related in: MedlinePlus