Earticle

초록

영어

Identification of essential proteins plays a significant role in understanding minimal requirements for the cellular survival and development. Experimental methods for the identification of essential proteins are always costly, time-consuming, and laborious. High throughput technologies have resulted in a large number of protein-protein interaction data, which provided a stepping stone for predicting essential proteins using computational approaches. 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 noise of network. In this paper, we propose a naive essential protein discovery method, named PMN, based on the integration of weighted interactome network and functional modules. The performance of PMN is validated based on the PPI network of Saccharomyces cerevisiae. Experimental results show that PMN significantly outperforms the classical centrality measures. The results also uncover relationship between the modularity and essentiality of proteins.

목차

Abstract
 1. Introduction
 2. Method
 3. Results and Discussion
  3.1. Experimental Data
  3.2. Compare PMN with other Methods
  3.3. Analysis of the Differences between PMN and other Methods
 4. Conclusions
 Acknowledgments
 References

키워드

저자

• Yi Pan [ Department of Mathematics and Computer Science, Changsha University, Changsha, Hunan 410022, China ]
• Sai Hu [ Department of Mathematics and Computer Science, Changsha University, Changsha, Hunan 410022, China ]
• Bihai Zhao [ Department of Mathematics and Computer Science, Changsha University, Changsha, Hunan 410022, China ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605