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

The knowledge-driven exploration of integrated biomedical knowledge sources facilitates the generation of new hypothesesVinh Nguyen, Olivier Bodenreider, Todd Mining, AmitSheth

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

Context10/21/2011 3

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

The knowledge-driven integrated exploitation of complementary sources, which can facilitate generation of new hypotheses for additional investigations and insights.10/21/2011 5

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

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

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

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

Linking the two knowledge bases• Orthology relation – Gene function is usually conserved across species10/21/2011 10

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

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 13

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

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

Technologies• Semantic Web – RDF, SPARQL standards – Virtuoso triple store• iExplore development – Client side: Flex application – Server side: Java10/21/2011 16

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

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

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

10/21/2011 20

Further information, please visit http://knoesis.wright.edu/iExplore10/21/2011 21

http://knoesis.wright.edu	204
http://knoesis1.wright.edu	54
http://knoesis.org	15
http://localhost	3
http://tcruzi.cs.wright.edu	3
http://knoesis20.cs.wright.edu	2
http://www.knoesis.org	1

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

by Kno.e.sis Center, Wright State University

Accessibility

Categories

Upload Details

Usage Rights

7 Embeds 282

Statistics

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