Paper Info
Title
Predicting Essential Proteins by Integrating Network Topology, Subcellular Localization Information, Gene Expression Profile and GO Annotation Data
Abstract
AbstractEssential proteins are indispensable for maintaining normal cellular functions. Identification of essential proteins from Protein-protein interaction (PPI) networks has become a hot topic in recent years. Traditionally biological experimental based approaches are time-consuming and expensive, although lots of computational based methods have been developed in the past years; however, the prediction accuracy is still unsatisfied. In this research, by introducing the protein sub-cellular localization information, we define a new measurement for characterizing the protein's subcellular localization essentiality, and a new data fusion based method is developed for identifying essential proteins, named TEGS, based on integrating network topology, gene expression profile, GO annotation information, and protein subcellular localization information. To demonstrate the efficiency of the proposed method TEGS, we evaluate its performance on two $Saccharomyces cerevisiae$Saccharomycescerevisiae datasets and compare with other seven state-of-the-art methods (DC, BC, NC, PeC, WDC, SON, and TEO) in terms of true predicted number, jackknife curve, and precision-recall curve. Simulation results show that the TEGS outperforms the other compared methods in identifying essential proteins. The source code of TEGS is freely available at https://github.com/wzhangwhu/TEGS.
Year
DOI
Venue
2020
10.1109/TCBB.2019.2916038
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Keywords
DocType
Volume
Protein-protein interaction network, essential proteins, high throughput data, data fusion
Journal
17
Issue
ISSN
Citations 
6
1545-5963
1
PageRank 
References 
Authors
0.35
0
3
Name
Order
Citations
PageRank
Wei Zhang144072.00
Jia Xu221.03
Xiufen Zou327225.44