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


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.

SUBMITTER: Chen L 

PROVIDER: S-EPMC2914056 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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

Chen Lina L   Wang Hong H   Zhang Liangcai L   Li Wan W   Wang Qian Q   Shang Yukui Y   He Yuehan Y   He Weiming W   Li Xu X   Tai Jingxie J   Li Xia X  

BMC bioinformatics 20100722


<h4>Background</h4>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.<h4>Results</h4>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 un  ...[more]

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