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Mining Biomedical Literature and Ontologies for Drug Repositioning Discovery

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Advances in Knowledge Discovery and Data Mining (PAKDD 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8444))

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Abstract

Drug development is time-consuming, costly, and risky. Approximate 80% to 90% of drug development projects fail before they ever get into clinical trials. To reduce the high risk of failure for drug development, pharmaceutical companies are exploring the drug repositioning approach for drug development. Previous studies have shown the feasibility of using computational methods to help extract plausible drug repositioning candidates, but they all encountered some limitations. In this study, we propose a novel drug-repositioning discovery method that takes into account multiple information sources, including more than 18,000,000 biomedical research articles and some existing ontologies that cover detailed relations between drugs, proteins and diseases. We design two experiments to evaluate our proposed drug repositioning discovery method. Overall, our evaluation results demonstrate the capability and superiority of our proposed drug repositioning method for discovering potential, novel drug-disease relationships.

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References

  1. Adams, C.P., Brantner, V.V.: Estimating the cost of new drug development: Is it really $802 million? Health Aff. 25(2), 420–428 (2006)

    Article  Google Scholar 

  2. National Institutes of Health. NIH Announces New Program to Develop Therapeutics for Rare and Neglected Diseases (May 20, 2009), http://rarediseases.info.nih.gov/files/TRND%20Press%20Release.pdf

  3. Ashburn, T.T., Thor, K.B.: Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 3(8), 673–683 (2004)

    Article  Google Scholar 

  4. McBride, W.G.: Thalidomide and congenital abnormalities. Lancet 278(7216), 1358 (1961)

    Article  Google Scholar 

  5. Stephens, T.D., Brynner, R.: Dark Remedy: The Impact of Thalidomide and Its Revival as a Vital Medicine. Perseus Publishing, Cambridge (2001)

    Google Scholar 

  6. Celgene Corporation: 2005 Annual Report. Celgene Corporation, Summit, NJ (2006)

    Google Scholar 

  7. Celgene Corporation: 2008 Annual Report on Form 10-K. Celgene Corporation, Summit, NJ (2009)

    Google Scholar 

  8. Celgene Corporation: 2012 Annual Report on Form 10-K. Celgene Corporation, Summit, NJ (2013)

    Google Scholar 

  9. Dudley, J.T., Deshpande, T., Butte, A.J.: Exploiting drug-disease relationships for computational drug repositioning. Brief. Bioinformatics 12, 303–311 (2011)

    Article  Google Scholar 

  10. Hurle, M.R., Yang, L., Xie, Q., Rajpal, D.K., Sanseau, P., Agarwal, P.: Computational drug repositioning: From data to therapeutics. Clin. Pharmacol. Ther. 93(4), 335–341 (2013)

    Article  Google Scholar 

  11. 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 8(4), e60618 (2013)

    Google Scholar 

  12. Kim, S., Jin, D., Lee, H.: Predicting Drug-Target Interactions Using Drug-Drug Interactions. PLoS One 8(11), e80129 (2013)

    Google Scholar 

  13. Wu, Z., Wang, Y., Chen, L.: Network-based drug repositioning. Mol. Biosyst. 9, 1268–1281 (2013)

    Article  Google Scholar 

  14. Swanson, D.R., Smalheiser, N.R.: An interactive system for finding complementary literatures: A stimulus to scientific discovery. Artif. Intell. 91(2), 183–203 (1997)

    Article  MATH  Google Scholar 

  15. Knox, C., Law, V., Jewison, T., Liu, P., Ly, S., Frolkis, A., Wishart, D.S.: DrugBank 3.0: A comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res. 39(suppl. 1), D1035–D1041 (2011)

    Google Scholar 

  16. Hamosh, A., Scott, A.F., Amberger, J.S., Bocchini, C.A., McKusick, V.A.: Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33(suppl. 1), D514–D517 (2005)

    Google Scholar 

  17. Davis, A., King, B.L., Mockus, S., Murphy, C.G., Saraceni-Richards, C., Rosenstein, M., Mattingly, C.J.: The Comparative Toxicogenomics Database: Update 2013. Nucleic Acids Res. 39(suppl. 1), D1067–D1072 (2013)

    Google Scholar 

  18. Yetisgen-Yildiz, M., Pratt, W.: A new evaluation methodology for literature-based discovery systems. J. Biomed. Inform. 42(4), 633–643 (2009)

    Article  Google Scholar 

  19. Swanson, D.R.: Fish oil, Raynaud’s syndrome, and undiscovered public knowledge. Perspect. Biol. Med. 30(1), 7–18 (1986)

    Google Scholar 

  20. Weeber, M., Klein, H., de Jong-van den Berg, L.T., Vos, R.: Using concepts in literature-based discovery: Simulating Swanson’s Raynaud–fish oil and migraine–magnesium discoveries. J. Am. Soc. Inf. Sci. Technol. 52(7), 548–557 (2001)

    Article  Google Scholar 

  21. Wren, J.D., Bekeredjian, R., Stewart, J.A., Shohet, R.V., Garner, H.R.: Knowledge discovery by automated identification and ranking for implicit relationships. Bioinformatics 20(3), 389–398 (2004)

    Article  Google Scholar 

  22. Lee, S., Choi, J., Park, K., Song, M., Lee, D.: Discovering context-specific relationships from biological literature by using multi-level context terms. BMC Medical Informatics and Decision Making (BMC Med. Inform. Decis. Mak.) 12(suppl. 1), S1 (2012)

    Google Scholar 

  23. Srinivasan, P.: Text mining: Generating hypotheses from MEDLINE. J. Am. Soc. Inf. Sci. Technol. 55(5), 396–413 (2004)

    Article  Google Scholar 

  24. Hristovski, D., Peterlin, B., Mitchell, J.A., Humphrey, S.M.: Using literature-based discovery to identify disease candidate genes. Int. J. Med. Inform. 74, 289–298 (2005)

    Article  Google Scholar 

  25. Frijters, R., van Vugt, M., Smeets, R., van Schaik, R., de Vlieg, J., Alkema, W.: Literature mining for the discovery of hidden connections between drugs, genes and diseases. PLoS Comput. Biol. 6(9), e1000943 (2010)

    Google Scholar 

  26. Lee, H.S., Bae, T., Lee, J.-H., Kim, D., Oh, Y., Jang, Y., Kim, S.: Rational drug repositioning guided by an integrated pharmacological network of protein, disease and drug. BMC Syst. Biol. 6(1), 80 (2012)

    Article  Google Scholar 

  27. Campillos, M., Kuhn, M., Gavin, A.-C., Jensen, L.J., Bork, P.: Drug target identification using side-effect similarity. Science 321(5886), 263–266 (2008)

    Article  Google Scholar 

  28. Yang, L., Agarwal, P.: Systematic drug repositioning based on clinical side-effects. PLOS ONE 6(12), e28025 (2011)

    Google Scholar 

  29. Cheng, F., Liu, C., Jiang, J., Lu, W., Li, W., Liu, G., Tang, Y.: Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput. Biol. 8(5), e1002503 (2012)

    Google Scholar 

  30. Li, J., Lu, Z.: A new method for computational drug repositioning. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 1–4. IEEE Press, Philadelphia (2012)

    Google Scholar 

  31. Qu, X.A., Gudivada, R.C., Jegga, A.G., Neumann, E.K., Aronow, B.J.: Inferring novel disease indications for known drugs by semantically linking drug action and disease mechanism relationships. BMC Bioinformatics 10(suppl. 5), S4 (2009)

    Google Scholar 

  32. Li, J., Zhu, X., Chen, J.Y.: Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts. PLoS Comput. Biol. 5(7), e1000450 (2009)

    Google Scholar 

  33. Cilibrasi, R.L., Vitányi, P.M.: The Google similarity distance. IEEE Trans. Knowl. Data Eng. 19(3), 370–383 (2007)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information Management, National Taiwan University, Taipei, Taiwan, R.O.C.

    Chih-Ping Wei, Kuei-An Chen & Lien-Chin Chen

Authors
  1. Chih-Ping Wei
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  2. Kuei-An Chen
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  3. Lien-Chin Chen
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Editor information

Editors and Affiliations

  1. National Cheng Kung University, Tainan, Taiwan, R.O.C.

    Vincent S. Tseng  & Hung-Yu Kao  & 

  2. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Tu Bao Ho

  3. Nanjing University, China

    Zhi-Hua Zhou

  4. National Chengchi University, Taipei, Taiwan, R.O.C.

    Arbee L. P. Chen

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© 2014 Springer International Publishing Switzerland

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Wei, CP., Chen, KA., Chen, LC. (2014). Mining Biomedical Literature and Ontologies for Drug Repositioning Discovery. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8444. Springer, Cham. https://doi.org/10.1007/978-3-319-06605-9_31

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  • DOI: https://doi.org/10.1007/978-3-319-06605-9_31

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06604-2

  • Online ISBN: 978-3-319-06605-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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