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A novel algorithm for identifying essential proteins by integrating subcellular localization
Conference proceeding

A novel algorithm for identifying essential proteins by integrating subcellular localization

Yetian Fan, Xiaohua Hu, Xiwei Tang, Qing Ping and Wei Wu
2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 107-110
Dec 2016
DOI: https://doi.org/10.1109/BIBM.2016.7822501

Abstract

Correlation Databases Essential proteins Fans Modified PageRank algorithm Prediction algorithms Protein-protein interaction networks Proteins Sparse matrices Subcellular localization information Gene Expression
Essential proteins play a crucial role in the survival and development process of life, as they provide all available nutrients to maintain life. Therefore, many researchers pay attention to the identification of essential proteins. As experiments methods are usually costly and time-consuming, more and more computational algorithms have been developed to discover essential proteins based on biological and topological features. Given that the subcellular localization is very important in understanding protein-protein interaction, in this paper, a novel method is proposed to predict essential proteins, which integrates the subcellular compartments information with Pearson correlation coefficient (PCC) of gene expression data. We name this method SCP in this paper. In order to evaluate the prediction performance of our method, several experiments are carried out to compare SCP with other methods. The results demonstrate that SCP has a better prediction performance of essential proteins than other methods.

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5 citations in Web of Science
5 citations in Scopus

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