DOI QR코드

DOI QR Code

Extraction of specific common genetic network of side effect pair, and prediction of side effects for a drug based on PPI network

Hwang, Youhyeon;Oh, Min;Yoon, Youngmi

  • 투고 : 2016.01.20
  • 심사 : 2016.01.29
  • 발행 : 2016.01.30

초록

In this study, we collect various side effect pairs which are appeared frequently at many drugs, and select side effect pairs that have higher severity. For every selected side effect pair, we extract common genetic networks which are shared by side effects' genes and drugs' target genes based on PPI(Protein-Protein Interaction) network. For this work, firstly, we gather drug related data, side effect data and PPI data. Secondly, for extracting common genetic network, we find shortest paths between drug target genes and side effect genes based on PPI network, and integrate these shortest paths. Thirdly, we develop a classification model which uses this common genetic network as a classifier. We calculate similarity score between the common genetic network and genetic network of a drug for classifying the drug. Lastly, we validate our classification model by means of AUC(Area Under the Curve) value.

키워드

Drug;Side effect;Genetic network;Bioinformatics;Data mining;PPI;Classification model

참고문헌

  1. Classen D C, Pestotnik S L, Evans R S, Lloyd J F, Burke J P. "Adverse drug events in hospitalized patients. Excess length of stay, extra costs, and attributable mortality.", JAMA, Vol. 277, No. 4, pp. 301-6, Jan. 1997. https://doi.org/10.1001/jama.1997.03540280039031
  2. Song mi Lee, "Drug leads to disease", Sodam publisher, Oct. 2007.
  3. Kuhn, Michael, et al. "A side effect resource to capture phenotypic effects of drugs.", Molecular systems biology, Vol. 6, No. 1, pp. 343, Jan. 2010.
  4. Perucca, Piero, and Frank G. Gilliam. "Adverse effects of antiepileptic drugs.", The Lancet Neurology, Vol. 11, No. 9, pp. 792-802, Sep. 2012. https://doi.org/10.1016/S1474-4422(12)70153-9
  5. Atias, Nir, and Roded Sharan. "An algorithmic framework for predicting side effects of drugs.", Journal of Computational Biology, Vol. 18, No. 3, pp. 207-218, March 2011. https://doi.org/10.1089/cmb.2010.0255
  6. Kuhn, Michael, et al. "Systematic identification of proteins that elicit drug side effects.", Molecular systems biology, Vol. 9, No. 1, pp. 663, April 2013.
  7. Gottlieb, Assaf, et al. "Ranking Adverse Drug Reactions With Crowdsourcing.", Journal of medical Internet research, Vol. 17, No. 3, March 2015.
  8. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J. "DrugBank: a comprehensive resource for in silico drug discovery and exploration.", Nucleic Acids Res., Vol. 1 No. 34, pp. 668-72, Jan. 2006.
  9. Davis AP et al., "The Comparative Toxicogenomics Database's 10th year anniversary: update 2015.", Nucleic Acids Res., Oct. 2014.
  10. Kuhn M, Letunic I, Jensen LJ, Bork P. "The SIDER database of drugs and side effects.", Nucleic Acids Res., Oct. 2015.
  11. Chatr-Aryamontri A. et al., "The BioGRID interaction database: 2015 update." Nucleic Acids Research., Nov. 2014.
  12. Salwinski, Lukasz, et al. "The database of interacting proteins: 2004 update." Nucleic acids research, Vol. 32, D449-D451, Jan. 2004. https://doi.org/10.1093/nar/gkh086
  13. Orchard, Sandra, et al. "The MIntAct project-IntAct as a common curation platform for 11 molecular interaction databases." Nucleic acids research, Nov. 2013.
  14. Licata, Luana, et al. "MINT, the molecular interaction database: 2012 update." Nucleic acids research, Vol. 40, No. D1, pp. D857-D861, Jan. 2012. https://doi.org/10.1093/nar/gkr930
  15. Silberberg, Yael, et al. "Large-scale elucidation of drug response pathways in humans.", Journal of Computational Biology, Vol. 19, No. 2, pp. 163-174, Feb. 2012. https://doi.org/10.1089/cmb.2011.0264

과제정보

연구 과제 주관 기관 : National Research Foundation of Korea(NRF)