iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/978-3-319-21843-4_1
Combining Multiple Knowledge Sources: A Case Study of Drug Induced Liver Injury | SpringerLink
Skip to main content
Springer Nature Link
Log in

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

  • Conference paper
  • First Online:
Data Integration in the Life Sciences (DILS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9162))

Included in the following conference series:

  • 873 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://livertox.nlm.nih.gov/Simvastatin.htm.

  2. 2.

    http://livertox.nlm.nih.gov/Atorvastatin.htm.

  3. 3.

    http://livertox.nlm.nih.gov/Zileuton.htm.

  4. 4.

    http://livertox.nlm.nih.gov/Ibuprofen.htm.

  5. 5.

    http://livertox.nlm.nih.gov/Naproxen.htm.

  6. 6.

    http://livertox.nlm.nih.gov/Ketoprofen.htm.

References

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  4. Bleakley, K., Yamanishi, Y.: Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics 25(18), 2397–2403 (2009)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. 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. 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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. Park, H., Choi, J.: V-model: a new perspective for EHR-phenotyping. BMC Medical Informatics and Decision Making, 14(90) (2014)

    Google Scholar 

  22. Robinson, P.N., Mundlos, S.: The human phenotype ontology. Clin. Genet. 77(6), 525–534 (2010)

    Article  Google Scholar 

  23. Russmann, S., Jetter, A., Kullak-Ublick, G.: Pharmacogenomics of drug-induced liver injury. Heptology 52(2), 748–761 (2010)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universidad Simón Bolívar, Caracas, Venezuela

    Alejandro Flores, Guillermo Palma & Maria-Esther Vidal

  2. University of Maryland, College Park, USA

    Casey L. Overby, Elena Zotkina & Louiqa Raschid

Authors
  1. Casey L. Overby
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alejandro Flores
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guillermo Palma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maria-Esther Vidal
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Elena Zotkina
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Louiqa Raschid
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Louiqa Raschid .

Editor information

Editors and Affiliations

  1. USC Stevens Neuroimaging and Informatics Institute, Los Angeles, California, USA

    Naveen Ashish

  2. USC Information Sciences Institute, Marina del Rey, California, USA

    Jose-Luis Ambite

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Overby, C.L., Flores, A., Palma, G., Vidal, ME., Zotkina, E., Raschid, L. (2015). Combining Multiple Knowledge Sources: A Case Study of Drug Induced Liver Injury. In: Ashish, N., Ambite, JL. (eds) Data Integration in the Life Sciences. DILS 2015. Lecture Notes in Computer Science(), vol 9162. Springer, Cham. https://doi.org/10.1007/978-3-319-21843-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-21843-4_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21842-7

  • Online ISBN: 978-3-319-21843-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation