1. Linking Open Drug Data Susie Stephens, Principal Research Scientist, Eli Lilly

2. The Linked Data Cloud
Source: Chris Bizer

3. Linking Open Drug Data HCLSIG task started October 1, 2008
Primary Objectives
Survey publicly available data sets about drugs
Publish and interlink these data sets on the Web
Explore interesting questions in competitive intelligence that could be answered if the data sets are linked
Participants: Bosse Andersson, Chris Bizer, Kei Cheung, Don Doherty, Oktie Hassanzadeh, Anja Jentzsch, Scott Marshall, Eric Prud’hommeaux, Matthias Samwald, Susie Stephens, Jun Zhao

4. Assessment of Data Sources
Mark Sharp et al. A Framework for Characterizing Drug Information Sources. AMIA 2008

5. Published Data Sets LinkedCT (http://linkedct.org)
Online registry of more than 60,000 clinical trials
Published in XML
7,011,000 triples (290,000 interlinking)
DrugBank (
A repository of almost 5,000 FDA-approved drugs
Published as DrugBank DrugCards
1,153,000 triples (23,000 interlinking)
DailyMed (
High quality information about marketed drugs
Flat file representation
124,000 triples (29,600 interlinking)
Diseasome (
Information about 4,300 disorders and disease genes linked by known disorder-gene associations
88,000 triples (23,000 interlinking)

6. Classes of Links Based on common identifiers
Links present in the source data sets
Based on link discovery and record linkage techniques
String matching
E.g., “Alzheimer’s disease” in LinkedCT was matched with “Alzheimer_disease” in Diseasome
Semantic matching
E.g. “Varenicline” has the synonym “Varenicline Tartrate” and the brand names “Champix” and “Chantix”

7. Business Use Case
A neuroscience focused business manager is interested in seeing an update on new clinical trials by competitors on Alzheimer’s Disease (AD)
A phase III trial by Pfizer for a drug called Varenicline has just been listed in linkedCT
More information of interest is found in DBpedia, DailyMed, and DrugBank
DailyMed indicates the drug is already on the market for Nicotine addiction and has minimal side effects
DrugBank allows the manager to see the targets for Varenicline
Diseasome, however, indicates that the corresponding genes are only implicated in nicotine addiction, rather than AD
This suggests a more complex relationship between the diseases than just the drug target
Extending the browsing to the SWAN Knowledgebase shows that there are hypotheses relating AD to nicotine receptors through amyloid beta

8. Technical Challenges
Life sciences data is difficult to connect due to inconsistent terminology and the prevalence of synonyms, and homonyms
Refinement of tools and techniques for enabling more automatic linking of entities across data sets
Selection of ontologies to enable consistent mappings
Development a sufficiently robust platform as to enable inferencing
Provide an interface to users that supports browsing, querying, and filtering data
Persuade data providers to publish in RDF would alleviate the need for us to update data, and provide some of the interlinking

9. Next Steps
Ensure that existing data are accurately and comprehensively linked
Incorporate additional data sources into the LODD cloud that are of interest to competitive intelligence (e.g. Traditional Chinese Medicine)
Use novel link discovery tools and frameworks including Silk and LinQuer
Explore using SIOC to aggregate information as what patients are saying about drugs
Submit paper to the iTriplify Challenge

10. Task Alignment
LODD is looking to use Pharma Ontology’s work to help inform the mappings
Data converted to RDF is also loaded into BioRDF’s HCLS KB

11. Conclusions
Added 4 drug-related data sets into the cloud for competitive intelligence
Will add further data sources to the LODD cloud to enable more insights to be gleaned
Will continue to explore and test tools that are being developed for LOD