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Issue 6, 2013
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Computationally identifying virulence factors based on KEGG pathways

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Abstract

Virulence factors are molecules that play very important roles in enhancing the pathogen’s capability in causing diseases. Many efforts were made to investigate the mechanism of virulence factors using in silico methods. In this study, we present a novel computational method to predict virulence factors by integrating proteinprotein interactions in a STRING database and biological pathways in the KEGG. Three specific species were studied according to their records in the VFDB. They are Campylobacter jejuni NCTC 11168, Escherichia coli O6 : K15 : H31 536 (UPEC) and Pseudomonas aeruginosa PAO1. The prediction accuracies reached were 0.9467, 0.9575 and 0.9180, respectively. Metabolism pathways, flagellar assembly and chemotaxis may be of importance for virulence based on the analysis of the optimal feature sets we obtained. We hope this can provide some insight and guidance for related research.

Graphical abstract: Computationally identifying virulence factors based on KEGG pathways

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Publication details

The article was received on 14 Jan 2013, accepted on 08 Mar 2013 and first published on 08 Mar 2013


Article type: Paper
DOI: 10.1039/C3MB70024K
Mol. BioSyst., 2013,9, 1447-1452

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    Computationally identifying virulence factors based on KEGG pathways

    W. Cui, L. Chen, T. Huang, Q. Gao, M. Jiang, N. Zhang, L. Zheng, K. Feng, Y. Cai and H. Wang, Mol. BioSyst., 2013, 9, 1447
    DOI: 10.1039/C3MB70024K

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