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Generating Drug-Gene Association for Vibrio Cholerae using Ontological Profile Similarity

Affiliations

  • Department of Computer Science and Engineering, Sathyabama University, Chennai - 600119, Tamil Nadu, India

Abstract


Objective: The measure of biomedical literature has been expanding quickly in the most recent decade. Text mining systems can analysis this vast scale information, shed light onto complex medication instruments and concentrate connection data that can bolster computational polypharmacology. The key idea of the paper is to find the drug candidate for the disease cholera, which is caused due to the organism Vibrio cholerae. Method: The technique estimates the Pointwise Mutual Information (PMI) among protein name obtained from the UniprotKB and the Medical Subject Headings that contain drug terms. Based on the PMI scores, gene/protein profiles and drug are produced and candidate drug-gene/protein associations are constructed when evaluating the relevancy of their profiles. Findings: The association between protein and drug is found and the drug candidates are proposed. Applications: The similar technique can be applied to find the drug candidate for various diseases which reduces the drug release cost in the market.

Keywords

Association, Corpus, MEDLINEDatabase, MESH, Ontology, Text Mining

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References


  • Kissa M, Tsatsaronis G, Schroeder M. Prediction of drug gene associations via ontological profile similarity with application to drug repositioning. Methods. 2015 Mar; 74:71–82.
  • Weeber M. Advances in literature-based discovery. Journal of the American Society for Information Science and Technology. 2003; 54(10):913–25.
  • Takarabe M, KoteraM, Nishimura Y, Goto S, Yamanishi Y. Drug target prediction using adverse event report systems: Apharmacogenomic approach. Bioinformatics. 2012; 28(18):611–8.
  • Chang J. Using machine learning to extract drug and gene relationships from text. [Doctoral Dissertation]. 2004.
  • Yamanishi Y, Kotera M, Kanehisa M, Goto S. Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics. 2010; 26(12):246–54.
  • Chen B, Ying D, David W. Assessing drug target association using semantic linked data. PLoS Computational Biology. 2012 Jul; 8(7):1–10.
  • Wu Y, Liu M, Zheng WJ, Zhao Z, Xu H. Ranking gene-drug relationships in biomedical literature using latent dirichlet allocation. Pacific Symposium on Biocomputing; 2012. p.422–33.
  • Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, DjoumbouY. DrugBank 3.0: A comprehensive resource for omics research on drugs. Nucleic Acids Research. 2011; 39:1035–41.
  • Cheng F, Liu C, Jiang J, Lu W, Li W, Liu G, Zhou W, Huang J, Tang Y. Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Computational Biology.2012 May; 8(5):1–12.
  • Emig D, Ivliev A, Pustovalova O, Lancashire L, Bureeva S, Nikolsky Y, Bessarabova M. Drug target prediction and repositioning using an integrated network-based approach. PLoS One. 2013; 8(4):606–18.
  • Vogt I, Jordi M. Drug‐target networks. Molecular Informatics, 2010; 29(2):10–4.
  • Srinivasan P. Text mining: Generating hypotheses from MEDLINE. Journal of the American Society for Information Science and Technology. 2004 Mar; 55(5):396–413.
  • Vazquez M, Krallinger M, Leitner F, Valencia A. Text mining for drugs and chemical compounds: Methods, tools and applications. Molecular Informatics. 2011; 30(6‐7):506–19.

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