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Pathogenic Gene Screening of Mycobacterium tuberculosis by Literature Data Mining and Information Pathway Enrichment Analysis by Guangyu Xu, Simin Wen, Yuchen Pan, Nan Zhang, Yuanyi Wang

Background: Recent studies have unraveled mutations which have led to changes in the original conformation of functional proteins targeted by frontline drugs against Mycobacterium tuberculosis. These mutations are likely responsible for the emergence of drug-resistant strains of M. tuberculosis. Identification of new therapeutic targets is fundamental to the development of novel anti-TB drugs.
Methods: Boost evolution analysis of interactome data with use of high-throughput biological experimental technologies provides opportunities for identification of pathogenic genes and for screening out novel therapeutic targets.
Results: In this study, we identified 584 proven pathogenic genes of M. tuberculosis and new pathogenic genes via bibliometrics and relevant websites such as PubMed, KEGG, and DOOR websites. We identified 13 new genes that are most likely to be pathogenic.
Conclusions: This study may contribute to the discovery of new pathogenic genes and help unravel new functions of known pathogenic genes of M. tuberculosis.

DOI: 10.7754/Clin.Lab.2018.170935