Abstract
Finding protein complexes and their functions is essential work for understanding biological process. However, one of the difficulties in inferring protein complexes from protein-protein interaction(PPI) network originates from the fact that protein interactions suffer from high false positive rate. We propose a complex finding algorithm which is not strongly dependent on topological traits of the protein interaction network. Our method exploits a new measure, GECSS(Gene Expression Condition Set Similarity) which considers mRNA expression data for a set of PPI. The complexes we found exhibit a higher match with reference complexes than the existing methods. Also we found several novel protein complexes, which are significantly enriched on Gene Ontology database.
This work was supported by National Research Foundation of Korea funded by the Korean Government under Grant (No. 2010-0003965).
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Yeu, Y., Ahn, J., Yoon, Y., Park, S. (2011). Protein Complex Discovery from Protein Interaction Network with High False-Positive Rate. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2011. Lecture Notes in Computer Science, vol 6623. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20389-3_19
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DOI: https://doi.org/10.1007/978-3-642-20389-3_19
Publisher Name: Springer, Berlin, Heidelberg
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