Prediction of essential proteins by integration of PPI network topology and protein complexes information

Authors:
Jun Ren;Jianxin Wang;Min Li;Huan Wang;Binbin Liu
Affiliations:
School of Information Science and Engineering, Central South University, Changsha, China and College of Information Science and Technology, Hunan Agricultural University, Changsha, China;School of Information Science and Engineering, Central South University, Changsha, China;School of Information Science and Engineering, Central South University, Changsha, China;School of Information Science and Engineering, Central South University, Changsha, China;School of Information Science and Engineering, Central South University, Changsha, China
Venue:
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
Year:
2011

Citing 3
Cited 0

A Unified Scoring Scheme for Detecting Essential Proteins in Protein Interaction Networks

ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Complex discovery from weighted PPI networks

Bioinformatics
Essential proteins discovery from weighted protein interaction networks

ISBRA'10 Proceedings of the 6th international conference on Bioinformatics Research and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Identifying essential proteins is important for understanding the minimal requirements for cellular survival and development. Numerous computational methods have been proposed to identify essential proteins from protein-protein interaction (PPI) network. However most of methods only use the PPI network topology information. HartGT indicated that essentiality is a product of the protein complex rather than the individual protein. Based on these, we propose a new method ECC to identify essential proteins by integration of subgraph centrality (SC) of PPI network and protein complexes information. We apply ECC method and six centrality methods on the yeast PPI network. The experimental results show that the performance of ECC is much better than that of six centrality methods, which means that the prediction of essential proteins based on both network topology and protein complexes set is much better than that only based on network topology. Moreover, ECC has a significant improvement in prediction of low-connectivity essential proteins.