Reference: Li M, et al. (2012) A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data. BMC Syst Biol 6(1):15

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Abstract

ABSTRACT: BACKGROUND: Identification of essential proteins is always a challenging task for it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which has produced unprecedented opportunities for detecting proteins' essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based centrality measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value. RESULTS: In this paper, we propose a new centrality measure, named PeC, based on the integration of protein-protein interaction and gene expression data. The performance of PeC is validated based on the protein-protein interaction network of Saccharomyces cerevisiae. The experimental results show that the predicted precision of PeC clearly exceeds that of the other ten previously proposed centrality measures: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality(SC), Eigenvector Centrality(EC), Information Centrality(IC), Bottle Neck (BN), Density of Maximum Neighborhood Component (DMNC), Local Average Connectivity-based method (LAC), and Sum of ECC (SoECC). Especially, the improvements of PeC compared with the classic centrality measures (BC, CC, SC, EC, and BN) are more than 50% when predicting no more than 500 proteins. CONCLUSIONS: We demonstrate that the integration of protein-protein interaction network and gene expression data can help to improve the precision of predicting essential proteins. The new centrality measure, PeC, is an effective essential protein discovery method.

Reference Type
Journal Article
Authors
Li M, Zhang H, Wang J, Pan Y
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