Combining Multiple Knowledge Sources: A Case Study of Drug Induced Liver Injury

  • Casey L. Overby
  • Alejandro Flores
  • Guillermo Palma
  • Maria-Esther Vidal
  • Elena Zotkina
  • Louiqa RaschidEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9162)


Many classes of drugs, their interaction pathways and gene targets are known to play a role in drug induced liver injury (DILI). Pharmacogenomics research to understand the impact of genetic variation on how patients respond to drugs may help explain some of the variability observed in the occurrence of adverse drug reactions (ADR) such as DILI. The goal of this project is to combine rich genotype and phenotype data to better understand these scenarios. We consider similarities between drugs, similarities between drug targets, drug-pathway-gene interactions, etc. Links to the patients will include patient drug usage, ADR, disease outcomes, etc. We will develop appropriate protocols to create these rich datasets and methods to identify patterns in graphs for explanation and prediction.


Bipartite Graph Layered Graph Semantic Knowledge Unify Medical Language System Drug Induce Liver Injury 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Anderson, P., Thor, A., Benik, J., Raschid, L., Vidal, M.E.: Pang - finding patterns in annotation graphs. In: Proceedings of the ACM Conference on the Management of Data (SIGMOD) (2012)Google Scholar
  2. 2.
    Aronson, A.R., Lang, F.-M.: An overview of metamap: historical perspective and recent advances. J. Am. Med. Inform. Assoc. 17(3), 229–236 (2010)CrossRefGoogle Scholar
  3. 3.
    Björnsson, E., Jacobsen, E.I., Kalaitzakis, E.: Hepatotoxicity associated with statins: reports of idiosyncratic liver injury post-marketing. J. Hepatol. 56(2), 374–380 (2012)CrossRefGoogle Scholar
  4. 4.
    Bleakley, K., Yamanishi, Y.: Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics 25(18), 2397–2403 (2009)CrossRefGoogle Scholar
  5. 5.
    Ding, H., Takigawa, I., Mamitsuka, H., Zhu, S.: Similarity-based machine learning methods for predicting drug-target interactions: a brief review. Briefings Bioinfor. 15, 734–747 (2013)CrossRefGoogle Scholar
  6. 6.
    Fakhraei, S., Huang, B., Raschid, L., Getoor, L.: Network-based drug-target interaction prediction with probabilistic soft logic. IEEE/ACM Trans. Comput. Biol. Bioinfor. 11, 775–787 (2014)CrossRefGoogle Scholar
  7. 7.
    Fiegenbaum, M., Silveira, F.R., Van der Sand, C.R., Van der Sand, L.C., Ferreira, M.E., Pires, R.C., Hutz, M.H.: The role of common variants of abcb1, cyp3a4, and cyp3a5 genes in lipid-lowering efficacy and safety of simvastatin treatment. Clin. Pharmacol. Ther. 78(5), 551–558 (2005)CrossRefGoogle Scholar
  8. 8.
    Hattori, M., Okuno, Y., Goto, S., et al.: Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in metabolic pathways. J. Am. Chem. Soc. 125(39), 1853–1865 (2003)CrossRefGoogle Scholar
  9. 9.
    Ho, J., Ghosh, J., Steinhubl, S., Stewart, W., Denny, J., Malin, B., Sun, J.: Limestone: high-throughput candidate phenotype generation via tensor factorization. J. Biomed. Inform. 52, 199–211 (2014)CrossRefGoogle Scholar
  10. 10.
    Hoofnagle, J.H., Serrano, J., Knoben, J.E., Navarro, V.J.: Livertox: a website on drug-induced liver injury. Hepatology 57(3), 873–874 (2013)CrossRefGoogle Scholar
  11. 11.
    Iyer, S., Harpaz, R., LePendu, P., Bauer-Mehren, A., Shah, N.: Mining clinical text for signals of adverse drug-drug interactions. JAMIA 21(2), 353–362 (2014)Google Scholar
  12. 12.
    Jiang, G., Liu, H., Solbrig, H., Chute, C.: Adepedia 2.0: integration of normalized adverse drug events (ades) knowledge from the UMLS. In: Proceedings of the AMIA Joint Summits on Translational Science, pp. 100–104 (2013)Google Scholar
  13. 13.
    Jiang, G., Wang, L., Liu, H., Solbrig, H., Chute, C.: Building a knowledge base of severe adverse drug events based on aers reporting data using semantic web technologies. Stud. Health Technol. Inform. 192, 496–500 (2013)Google Scholar
  14. 14.
    Jonquet, C., Shah, N., Youn, C., Callendar, C., Storey, M.-A., Musen, M.: Ncbo annotator: semantic annotation of biomedical data. In: International Semantic Web Conference (2009)Google Scholar
  15. 15.
    Kibbe, W.A., Arze, C., Felix, V., Mitraka, E., Bolton, E., Fu, G., Mungall, C.J., Binder, J.X., Malone, J., Vasant, D. et al.: Disease ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res. D1071–D1078 (2014)Google Scholar
  16. 16.
    Köhler, S., Doelken, S.C., Mungall, C.J., Bauer, S., Firth, H.V., Bailleul-Forestier, I., Black, G.C., Brown, D.L., Brudno, M., Campbell, J., et al.: The human phenotype ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res. 1–9 (2013)Google Scholar
  17. 17.
    Law, V., Knox, C., Djoumbou, Y., Jewison, T., Guo, A.C., Liu, Y., Maciejewski, A., Arndt, D., Wilson, M., Neveu, V., et al.: Drugbank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 42(D1), D1091–D1097 (2014)CrossRefGoogle Scholar
  18. 18.
    McKenney, J.M., Davidson, M.H., Jacobson, T.A., Guyton, J.R.: Final conclusions and recommendations of the national lipid association statin safety assessment task force. Am. J. Cardiol. 97(8), S89–S94 (2006)CrossRefGoogle Scholar
  19. 19.
    Overby, C.L., Pathak, J., Gottesman, O., Haerian, K., Perotte, A., Murphy, S., Bruce, K., Johnson, S., Talwalkar, J., Shen, Y., et al.: A collaborative approach to developing an electronic health record phenotyping algorithm for drug-induced liver injury. J. Am. Med. Inform. Assoc. pages amiajnl-2013 E243–E252 (2013)Google Scholar
  20. 20.
    Palma, G., Vidal, M.-E., Raschid, L.: Drug-target interaction prediction using semantic similarity and edge partitioning. In: Mika, P., Tudorache, T., Bernstein, A., Welty, C., Knoblock, C., Vrandečić, D., Groth, P., Noy, N., Janowicz, K., Goble, C. (eds.) ISWC 2014, Part I. LNCS, vol. 8796, pp. 131–146. Springer, Heidelberg (2014) Google Scholar
  21. 21.
    Park, H., Choi, J.: V-model: a new perspective for EHR-phenotyping. BMC Medical Informatics and Decision Making, 14(90) (2014)Google Scholar
  22. 22.
    Robinson, P.N., Mundlos, S.: The human phenotype ontology. Clin. Genet. 77(6), 525–534 (2010)CrossRefGoogle Scholar
  23. 23.
    Russmann, S., Jetter, A., Kullak-Ublick, G.: Pharmacogenomics of drug-induced liver injury. Heptology 52(2), 748–761 (2010)CrossRefGoogle Scholar
  24. 24.
    Savova, G.K., Masanz, J.J., Ogren, P.V., Zheng, J., Sohn, S., Kipper-Schuler, K.C., Chute, C.G.: Mayo clinical text analysis and knowledge extraction system (ctakes): architecture, component evaluation and applications. J. Am. Med. Inform. Assoc. 17(5), 507–513 (2010)CrossRefGoogle Scholar
  25. 25.
    Schriml, L.M., Arze, C., Nadendla, S., Chang, Y.-W.W., Mazaitis, M., Felix, V., Feng, G., Kibbe, W.A.: Disease ontology: a backbone for disease semantic integration. Nucleic Acids Res. 40(D1), D940–D946 (2012)CrossRefGoogle Scholar
  26. 26.
    Urban, T., Daly, A., Aithal, G.: Genetic basis of drug-induced liver injury: present and future. Semin. Liver Inj. 34(2), 123–133 (2014)CrossRefGoogle Scholar
  27. 27.
    Watkins, P.B., Dube, L.M., Walton-Bowen, K., Cameron, C.M., Kasten, L.E.: Clinical pattern of zileuton-associated liver injury. Drug Saf. 30(9), 805–815 (2007)CrossRefGoogle Scholar
  28. 28.
    Whirl-Carrillo, M., McDonagh, E., Hebert, J., Gong, L., Sangkuhl, K., Thorn, C., Altman, R., Klein, T.E.: Pharmacogenomics knowledge for personalized medicine. Clin. Pharmacol. Ther. 92(4), 414–417 (2012)CrossRefGoogle Scholar
  29. 29.
    Wilke, R.A., Moore, J.H., Burmester, J.K.: Relative impact of cyp3a genotype and concomitant medication on the severity of atorvastatin-induced muscle damage. Pharmacogenet. Genomics 15(6), 415–421 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Casey L. Overby
    • 2
  • Alejandro Flores
    • 1
  • Guillermo Palma
    • 1
  • Maria-Esther Vidal
    • 1
  • Elena Zotkina
    • 2
  • Louiqa Raschid
    • 2
    Email author
  1. 1.Universidad Simón BolívarCaracasVenezuela
  2. 2.University of MarylandCollege ParkUSA

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