PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of bmcsysbioBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Systems Biology
 
BMC Syst Biol. 2012; 6: 15.
Published online Mar 10, 2012. doi:  10.1186/1752-0509-6-15
PMCID: PMC3325894
A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data
Min Li,corresponding author1,2 Hanhui Zhang,1 Jian-xin Wang,corresponding author1 and Yi Pancorresponding author1,2
1School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China
2Department of Computer Science, Georgia State University, Atlanta, GA 30302-4110, USA
corresponding authorCorresponding author.
Min Li: limin/at/mail.csu.edu.cn; Hanhui Zhang: hhzhang/at/csu.edu.cn; Jian-xin Wang: jxwang/at/mail.csu.edu.cn; Yi Pan: pan/at/cs.gsu.edu
Received October 24, 2011; Accepted March 10, 2012.
Abstract
Background
Identification of essential proteins is always a challenging task since 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 have 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 fifteen 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), Sum of ECC (SoECC), Range-Limited Centrality (RL), L-index (LI), Leader Rank (LR), Normalized α-Centrality (NC), and Moduland-Centrality (MC). Especially, the improvement of PeC over the classic centrality measures (BC, CC, SC, EC, and BN) is 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 improve the precision of predicting essential proteins. The new centrality measure, PeC, is an effective essential protein discovery method.
Articles from BMC Systems Biology are provided here courtesy of
BioMed Central