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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
The probability density functions learned from the GMMs. The estimated probability density functions for the PCC values of transient interactions and stable interactions on (a) BioGrid and (b) DIP. For both BioGrid and DIP, the distribution on the left side corresponds to the estimated distribution for PCC values of transient interactions while the distribution on the right side corresponds to the estimated distribution for PCC values of stable interactions.
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Fig3: The probability density functions learned from the GMMs. The estimated probability density functions for the PCC values of transient interactions and stable interactions on (a) BioGrid and (b) DIP. For both BioGrid and DIP, the distribution on the left side corresponds to the estimated distribution for PCC values of transient interactions while the distribution on the right side corresponds to the estimated distribution for PCC values of stable interactions.

Mentions: We use Expectation Maximization (EM) algorithm to estimate the parameters of the above two Gaussian distributions for each dataset. The probability density functions learned from the BioGrid data and DIP data are shown in Figure3(a) and (b) respectively. As shown in Figure3, the two estimated distributions for each dataset are well separated. As stable interactions tend to be encoded by globally co-expressed gene pairs[27], the curve on the left side may correspond to the estimated distribution for PCC values of transient interactions while the curve on the right side may correspond to the estimated distribution for PCC values of stable interactions. From Figure3, we can find that for both BioGrid and DIP, δ ∈ (0.2,0.4) can result in a relatively low rate of misclassification errors. Thus, we consistently keep δ = 0.3 in our experiments.Figure 3


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 probability density functions learned from the GMMs. The estimated probability density functions for the PCC values of transient interactions and stable interactions on (a) BioGrid and (b) DIP. For both BioGrid and DIP, the distribution on the left side corresponds to the estimated distribution for PCC values of transient interactions while the distribution on the right side corresponds to the estimated distribution for PCC values of stable interactions.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig3: The probability density functions learned from the GMMs. The estimated probability density functions for the PCC values of transient interactions and stable interactions on (a) BioGrid and (b) DIP. For both BioGrid and DIP, the distribution on the left side corresponds to the estimated distribution for PCC values of transient interactions while the distribution on the right side corresponds to the estimated distribution for PCC values of stable interactions.
Mentions: We use Expectation Maximization (EM) algorithm to estimate the parameters of the above two Gaussian distributions for each dataset. The probability density functions learned from the BioGrid data and DIP data are shown in Figure3(a) and (b) respectively. As shown in Figure3, the two estimated distributions for each dataset are well separated. As stable interactions tend to be encoded by globally co-expressed gene pairs[27], the curve on the left side may correspond to the estimated distribution for PCC values of transient interactions while the curve on the right side may correspond to the estimated distribution for PCC values of stable interactions. From Figure3, we can find that for both BioGrid and DIP, δ ∈ (0.2,0.4) can result in a relatively low rate of misclassification errors. Thus, we consistently keep δ = 0.3 in our experiments.Figure 3

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