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The knowledge-driven exploration of integrated biomedical knowledge sources facilitates the       generation of new hypoth...
Context• Chagas disease    – Infectious disease caused by Trypanosomacruzi      (T. cruzi) parasite    – Affects 8-10 mill...
Context10/21/2011             3
Why integrate two?• Synergistically exploit the two sources      – If literature indicates certain finding (for the same  ...
The knowledge-driven integrated  exploitation of complementary  sources, which can facilitate generation  of new hypothese...
Use case• Data sources  – Text data from PubMed  – Experimental data about Chagas disease• Objectives/capabilities  – Quer...
Background Knowledge• Two knowledge bases were linked/partly  integrated      – The Biomedical Knowledge Repository (BKR) ...
Knowledge source - BKR• Created at NLM by Dr. Olivier Bodenreider and  Dr. Thomas Rindflesch• ~ 20 million predications fr...
Knowledge source - PKRCreated by T.cruziproject at Kno.e.sis, WSU with  CTEGD, UGA primarily for semantic analysis of para...
Linking the two knowledge bases• Orthology relation      – Gene function is usually conserved across species10/21/2011    ...
How to explore the integrated                  knowledge bases?• Knowledge-driven exploration process• Two steps of the pr...
Example of Exploration process• Sample chain10/21/2011                             12
Exploration process – Demo       iExplorevideo demo is available athttp://www.youtube.com/watch?v=-7-BgBDTSjc10/21/2011   ...
Exploration process -                Interpretation• Sample chain         -> Hypothesis                         The T. cru...
Significance• Biologically      – Biologists follow the exploration process to        generate novel hypotheses      – Can...
Technologies• Semantic Web      – RDF, SPARQL standards      – Virtuoso triple store• iExplore development      – Client s...
Conclusion• Integrate knowledge bases      – BKR      – PKR• Knowledge-driven exploration      – Two-step process: navigat...
Future work• Validation of novel hypotheses      – Collaborating with UGA researchers in the        biomedical aspect     ...
Acknowledgements    Dr. Thomas Rindflesch   Dr. CarticRamakrishnan    May Cheh                Dr. Priti Parikh    Dr. Clem...
10/21/2011   20
Further information, please visit http://knoesis.wright.edu/iExplore10/21/2011                                            ...
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The knowledge-driven exploration of integrated biomedical knowledge sources facilitates the generation of new hypotheses

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To be presented at Linked Science, Oct 24, 2011

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The knowledge-driven exploration of integrated biomedical knowledge sources facilitates the generation of new hypotheses

  1. 1. The knowledge-driven exploration of integrated biomedical knowledge sources facilitates the generation of new hypothesesVinh Nguyen, Olivier Bodenreider, Todd Mining, AmitSheth
  2. 2. Context• Chagas disease – Infectious disease caused by Trypanosomacruzi (T. cruzi) parasite – Affects 8-10 million people in South America, about 20,000 deaths per year – Finding drug targets and vaccine10/21/2011 2
  3. 3. Context10/21/2011 3
  4. 4. Why integrate two?• Synergistically exploit the two sources – If literature indicates certain finding (for the same or other organism [mice, rat], for same or different process [e.g. for gene expression], does my data support the same finding? – I find a pattern in my data, or a pathway component – is this reported in literature by someone in the same or similar context?10/21/2011 4
  5. 5. The knowledge-driven integrated exploitation of complementary sources, which can facilitate generation of new hypotheses for additional investigations and insights.10/21/2011 5
  6. 6. Use case• Data sources – Text data from PubMed – Experimental data about Chagas disease• Objectives/capabilities – Query experimental and literature (text) together – Find new insights into Chagas disease 10/21/2011 6
  7. 7. Background Knowledge• Two knowledge bases were linked/partly integrated – The Biomedical Knowledge Repository (BKR) [NLM] – The Parasite Knowledge Repository for Chagas disease (PKR)10/21/2011 7
  8. 8. Knowledge source - BKR• Created at NLM by Dr. Olivier Bodenreider and Dr. Thomas Rindflesch• ~ 20 million predications from 6 million PubMed articles by SemRep• Example10/21/2011 8
  9. 9. Knowledge source - PKRCreated by T.cruziproject at Kno.e.sis, WSU with CTEGD, UGA primarily for semantic analysis of parasite experimental data from/including:• The expression level of T. cruzi genes (from Microarray analysis data)• The presence of proteins encoded by T. cruzi genes (using Proteome analysis)• Ontologies for annotation – Parasite Experiment Ontology (PEO) – Parasite Lifecycle Ontology (PLO)10/21/2011 9
  10. 10. Linking the two knowledge bases• Orthology relation – Gene function is usually conserved across species10/21/2011 10
  11. 11. How to explore the integrated knowledge bases?• Knowledge-driven exploration process• Two steps of the process – Navigate the chain of named relationships • Graph expansion • Graph restriction – Interpret the chain to infer new hypothesis10/21/2011 11
  12. 12. Example of Exploration process• Sample chain10/21/2011 12
  13. 13. Exploration process – Demo iExplorevideo demo is available athttp://www.youtube.com/watch?v=-7-BgBDTSjc10/21/2011 13
  14. 14. Exploration process - Interpretation• Sample chain -> Hypothesis The T. cruzi orthologs of human genes inhibited by itraconazole may also be inhibited by itraconazole and thus would be candidates for further studies into the mechanism(s) of action of the drug itraconazole on T. cruzi10/21/2011 14
  15. 15. Significance• Biologically – Biologists follow the exploration process to generate novel hypotheses – Can easily be generalized to other diseases• Technically – Technical details (e.g., SPARQL queries) hidden from the user – Scalable over millions of predications10/21/2011 15
  16. 16. Technologies• Semantic Web – RDF, SPARQL standards – Virtuoso triple store• iExplore development – Client side: Flex application – Server side: Java10/21/2011 16
  17. 17. Conclusion• Integrate knowledge bases – BKR – PKR• Knowledge-driven exploration – Two-step process: navigation and interpretation – Assist biologists to explore the integrated knowledge bases10/21/2011 17
  18. 18. Future work• Validation of novel hypotheses – Collaborating with UGA researchers in the biomedical aspect – Incorporate the validation into iExplore• Automate the interpretation step – Learning the way biologists use their background knowledge to interpret the chain of named relationship10/21/2011 18
  19. 19. Acknowledgements Dr. Thomas Rindflesch Dr. CarticRamakrishnan May Cheh Dr. Priti Parikh Dr. Clement McDonald Joshua Dotson Dr. BastienRance SarasiLalithsena Jonathan Mortensen John Nguyen Thang Nguyen Lister Hill Center10/21/2011 19
  20. 20. 10/21/2011 20
  21. 21. Further information, please visit http://knoesis.wright.edu/iExplore10/21/2011 21

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