title: A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data.
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.
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha, Hunan, P R China. limin@mail.csu.edu.cn
authors.forename: Min
authors.initials: M
authors.lastname: Li
authors.forename: Hanhui
authors.initials: H
authors.lastname: Zhang
authors.forename: Jian-xin
authors.initials: JX
authors.lastname: Wang
authors.forename: Yi
authors.initials: Y
authors.lastname: Pan
mesh_terms: D020869:Gene Expression Profiling / Q000379:methods*; D020043:Genes, Essential / Q000235:genetics*; D008954:Models, Biological; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000235:genetics / Q000302:isolation & purification*; D040901:Proteomics / Q000379:methods*; D012441:Saccharomyces cerevisiae
chemical_list: D011506:Proteins