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Uncovering packaging features of co-regulated modules based on human protein interaction and transcriptional regulatory networks.

Chen L, Wang H, Zhang L, Li W, Wang Q, Shang Y, He Y, He W, Li X, Tai J, Li X - BMC Bioinformatics (2010)

Bottom Line: Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways).Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods.Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China. chenlina_2004@yahoo.com.cn

ABSTRACT

Background: Network co-regulated modules are believed to have the functionality of packaging multiple biological entities, and can thus be assumed to coordinate many biological functions in their network neighbouring regions.

Results: Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways). Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods.

Conclusions: Dysfunctions in co-regulated interactions often occur in the development of cancer. Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes. This was extended to causal dysfunctions of some complexes maintained by several physically interacting proteins, thus coordinating several metabolic pathways that directly underlie cancer.

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Related in: MedlinePlus

Performance information of CRAIN, Connected, Biconnected, MCL and CPM.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2914056&req=5

Figure 3: Performance information of CRAIN, Connected, Biconnected, MCL and CPM.

Mentions: Figure 3 is a histogram of three measures: sensitivity, specificity and the summary measurement F-measure, for each algorithm. The results indicated that the F-score of our method was superior to the other methods. This suggested that CRAIN could return co-regulated modules with more affluent biological meanings.


Uncovering packaging features of co-regulated modules based on human protein interaction and transcriptional regulatory networks.

Chen L, Wang H, Zhang L, Li W, Wang Q, Shang Y, He Y, He W, Li X, Tai J, Li X - BMC Bioinformatics (2010)

Performance information of CRAIN, Connected, Biconnected, MCL and CPM.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2914056&req=5

Figure 3: Performance information of CRAIN, Connected, Biconnected, MCL and CPM.
Mentions: Figure 3 is a histogram of three measures: sensitivity, specificity and the summary measurement F-measure, for each algorithm. The results indicated that the F-score of our method was superior to the other methods. This suggested that CRAIN could return co-regulated modules with more affluent biological meanings.

Bottom Line: Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways).Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods.Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China. chenlina_2004@yahoo.com.cn

ABSTRACT

Background: Network co-regulated modules are believed to have the functionality of packaging multiple biological entities, and can thus be assumed to coordinate many biological functions in their network neighbouring regions.

Results: Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways). Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods.

Conclusions: Dysfunctions in co-regulated interactions often occur in the development of cancer. Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes. This was extended to causal dysfunctions of some complexes maintained by several physically interacting proteins, thus coordinating several metabolic pathways that directly underlie cancer.

Show MeSH
Related in: MedlinePlus