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Predicting essential proteins by integrating network topology, subcellular localization information, gene expression profile and GO annotation data.

08:00 EDT 14th May 2019 | BioPortfolio

Summary of "Predicting essential proteins by integrating network topology, subcellular localization information, gene expression profile and GO annotation data."

Essential 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 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.

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This article was published in the following journal.

Name: IEEE/ACM transactions on computational biology and bioinformatics
ISSN: 1557-9964
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