Current Bioinformatics

Yi-Ping Phoebe Chen
Department of Computer Science and Information Technology
La Trobe University
Melbourne
Australia

Back

A New Integration-Centric Algorithm of Identifying Essential Proteins Based on Topology Structure of Protein-Protein Interaction Network and Complex Information

Author(s): Jiawei Luo, Ling Ma.

Abstract:

Essential proteins are necessary for the survival and development of organism. Many computational approaches have been proposed for predicting essential proteins based on protein-protein interaction (PPI) network. In this paper, we propose a new centrality algorithm for identifying essential proteins, named CSC algorithm. CSC algorithm integrates topology character of PPI network and in-degree of proteins in complexes. We use CSC algorithm to identify the essential proteins in PPI network of Saccharomyces cerevisiae. The results show that the ratio of identified essential proteins on CSC algorithm is higher than other ten centrality methods: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), Bottle Neck (BN), Local Average Connectivity-based method (LAC), Sum of ECC (SoECC) and PeC. Particularly, the identification accuracy of CSC algorithm is more than 40% over the six classic centrality measures (DC, BC, CC, SC, EC, IC).

Keywords: Centrality measures, clustering coefficient, essential proteins, protein-protein interaction, protein complex, topology.

Order Reprints Order Eprints Rights & PermissionsPrintExport

Article Details

VOLUME: 8
ISSUE: 3
Year: 2013
Page: [380 - 385]
Pages: 6
DOI: 10.2174/1574893611308030014
Price: $58