Journal
JOURNAL OF COMPUTATIONAL BIOLOGY
Volume 25, Issue 6, Pages 586-605Publisher
MARY ANN LIEBERT, INC
DOI: 10.1089/cmb.2017.0114
Keywords
expression value; PPI network; protein complexes; semantic similarity
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Funding
- National Science Foundation of China [61402304]
- Ministry of Education [14YJAZH046]
- Beijing Natural Science Foundation [4154065]
- Beijing Educational Committee Science and Technology Development Planned [KM201610028015]
- Science and Technology Innovation Platform, Teaching Teacher, and Connotation Development of Colleges and Universities
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Predicting protein complexes from protein-protein interaction (PPI) network is of great significance to recognize the structure and function of cells. A protein may interact with different proteins under different time or conditions. Existing approaches only utilize static PPI network data that may lose much temporal biological information. First, this article proposed a novel method that combines gene expression data at different time points with traditional static PPI network to construct different dynamic subnetworks. Second, to further filter out the data noise, the semantic similarity based on gene ontology is regarded as the network weight together with the principal component analysis, which is introduced to deal with the weight computing by three traditional methods. Third, after building a dynamic PPI network, a predicting protein complexes algorithm based on core-attachment structural feature is applied to detect complexes from each dynamic subnetworks. Finally, it is revealed from the experimental results that our method proposed in this article performs well on detecting protein complexes from dynamic weighted PPI networks.
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