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论文类型：期刊论文

发表时间：2017-12-01

发表刊物：BMC BIOINFORMATICS

收录刊物：SCIE、EI、PubMed

卷号：18

期号：Suppl 13

页面范围：470

ISSN号：1471-2105

关键字：Essential proteins; Subcellular localization information; Modified PageRank algorithm; Protein-protein interaction networks

摘要：Background: Essential proteins are indispensable to the survival and development process of living organisms. To understand the functional mechanisms of essential proteins, which can be applied to the analysis of disease and design of drugs, it is important to identify essential proteins from a set of proteins first. As traditional experimental methods designed to test out essential proteins are usually expensive and laborious, computational methods, which utilize biological and topological features of proteins, have attracted more attention in recent years. Protein-protein interaction networks, together with other biological data, have been explored to improve the performance of essential protein prediction.
   Results: The proposed method SCP is evaluated on Saccharomyces cerevisiae datasets and compared with five other methods. The results show that our method SCP outperforms the other five methods in terms of accuracy of essential protein prediction.
   Conclusions: In this paper, we propose a novel algorithm named SCP, which combines the ranking by a modified PageRank algorithm based on subcellular compartments information, with the ranking by Pearson correlation coefficient (PCC) calculated from gene expression data. Experiments show that subcellular localization information is promising in boosting essential protein prediction.