SGD Paper 
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
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.
| Status: Epub ahead of print | Type: Journal Article | PubMed ID: 22405054 |
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| Topics | Topics not linked to Genes |
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| Computational analysis | 
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| Omics |
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