Abstract
Identifying essential proteins is important for understandingthe minimal requirements for cellular survival and development.Numerouscomputational methodshave been proposed to identify essential proteins from protein-protein interaction (PPI) network.However most of methods only use the PPI network topology information. HartGT indicated that essentiality is a product of theprotein complex rather than the individual protein. Based on these, we propose a new method ECC to identify essential proteins by integration of subgraph centrality (SC) of PPI network and protein complexes information.We apply ECC method and six centrality methods on the yeast PPI network. The experimental results show that the performance of ECCis much better than that of six centrality methods, whichmeans that the prediction of essential proteins based on both networktopology and protein complexes set is much better than that only based on networktopology. Moreover, ECC has a significant improvement in predictionof low-connectivity essential proteins.
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Ren, J., Wang, J., Li, M., Wang, H., Liu, B. (2011). Prediction of Essential Proteins by Integration of PPI Network Topology and Protein Complexes Information. In: Chen, J., Wang, J., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2011. Lecture Notes in Computer Science(), vol 6674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21260-4_6
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DOI: https://doi.org/10.1007/978-3-642-21260-4_6
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