When are two entries about a concept in different datasets the same? If they have the same name, properties, or some other criteria? The choice depends upon the application to which the data will be put. However, existing Linked Data approaches provide a single global view over the data with no way of varying the notion of equivalence to be applied. In this presentation, I will introduce Scientific lenses, an approach that enables applications to vary the equivalence conditions between linked datasets. They have been deployed in the Open PHACTS Discovery Platform – a large scale data integration platform for drug discovery. To cater for different use cases, the platform allows the application of different lenses which vary the equivalence rules to be applied based on the context and interpretation of the links.

1. Scientific Lenses over
Linked Data
An approach to support
multiple integrated views
Alasdair J G Gray
A.J.G.Gray@hw.ac.uk
alasdairjggray.co.uk
@gray_alasdair

2. Open PHACTS Use Case
“Let me compare MW, logP
and PSA for launched
inhibitors of human &
mouse oxidoreductases”
 Chemical Properties (Chemspider)
 Launched drugs (Drugbank)
 Human => Mouse (Homologene)
 Protein Families (Enzyme)
 Bioactivty Data (ChEMBL)
 … other info (Uniprot/Entrez etc.)
16 October 2014 Scientific Lenses – A. J. G. Gray 1

3. Discovery Platform
Apps
Method
Calls
Domain API
Drug Discovery Platform
Interactive
responses
Production quality
integration platform
16 October 2014 Scientific Lenses – A. J. G. Gray 2

4. App Ecosystem An “App Store”?
Explorer Explorer2 ChemBioNavigator Target Dossier Pharmatrek Helium
MOE Collector Cytophacts Utopia Garfield SciBite
KNIME Mol. Data Sheets PipelinePilot scinav.it Taverna
http://www.openphactsfoundation.org/apps.html
16 October 2014

5. API Hits
April 2013 – March 2014: 15.8m
April 2014 – Sept 2014: 14m
Total: 29.8 million
16 October 2014 Scientific Lenses – A. J. G. Gray 4

6. Linked Data API
Drug
Target Pathway
Disease (1.4)
https://dev.openphacts.org/
16 October 2014 Scientific Lenses – A. J. G. Gray 5

7. Open PHACTS Data
Source Initial Records Triples Properties
ChEMBL 1,481,473 304,360,749 77
DrugBank 19,628 517,584 74
UniProt 564,246 405,473,138 82
ENZYME 6,187 73,838 2
ChEBI 40,575 1,673,863 2
GeneOntology 38,137 2,447,682 26
GOA 661,232 1,765,622,393 15
ChemSpider 1,361,568 215,193,441 23
ConceptWiki 2,828,966 4,291,131 1
WikiPathways 946 1,949,074 34
16 October 2014 Scientific Lenses – A. J. G. Gray 6

8. Dataset Descriptions in the
Open Pharmacological
Space
14 January 2013
Being replaced by W3C
HCLS community profile
http://tiny.cc/hcls-datadesc-ed
OPS Dataset Descriptions – A. J.
G. Gray 7

9. OPS Discovery Platform
Linked Data API (RDF/XML, TTL, JSON)
Semantic Workflow Engine
VoID
Nanopub
Db
Data Cache
(Virtuoso Triple Store)
Domain
Specific
Services
Identity
Resolution
Service
Chemistry
Registration
Normalisation
& Q/C
Identifier
Management
Service
Indexing
Core Platform
“Adenosine
receptor 2a”
P12374
EC2.43.4
CS4532
VoID
Db
VoID
Nanopub
Db
VoID
Db
VoID
Nanopub
Public Content Commercial
Public Ontologies
User
Annotations
Apps

10. Multiple Identities
Andy Law's Third Law
“The number of unique identifiers assigned to an individual is
never less than the number of Institutions involved in the study”
http://bioinformatics.roslin.ac.uk/lawslaws/
GB:29384
P12047
X31045
16 October 2014 Scientific Lenses – A. J. G. Gray
9
Are these the
same thing?

11. Gleevec®: Imatinib Mesylate
Imatinib
Imatinib MesylateMesylate
YLMAHDNUQAMNNX-UHFFFAOYSA-N
ChemSpider Drugbank PubChem
16 October 2014 Scientific Lenses – A. J. G. Gray 10

12. Gleevec®: Imatinib Mesylate
Imatinib
Are these records the same?
It depends upon your task!
Imatinib MesylateMesylate
YLMAHDNUQAMNNX-UHFFFAOYSA-N
ChemSpider Drugbank PubChem
16 October 2014 Scientific Lenses – A. J. G. Gray 11

13. Genes == Proteins?
BRCA1: Chromosome 17
Breast cancer type 1
susceptibility protein
http://en.wikipedia.org/wiki/File:Protei
n_BRCA1_PDB_1jm7.png
http://en.wikipedia.org/wiki/File:BRCA1
_en.png
16 October 2014 Scientific Lenses – A. J. G. Gray 12

14. Genes == Proteins?
BRCA1: Chromosome 17
Breast cancer type 1
susceptibility protein
http://en.wikipedia.org/wiki/File:Protei
n_BRCA1_PDB_1jm7.png
http://en.wikipedia.org/wiki/File:BRCA1
_en.png
Are these records the same?
It depends upon your task!
16 October 2014 Scientific Lenses – A. J. G. Gray 13

15. Example Use Cases
I need to perform an
analysis, give me details
of the active compound
in Gleevec.
Which targets are
known to interact
with Gleevec?
16 October 2014 Scientific Lenses – A. J. G. Gray 14

16. Structure Lens
I need to perform an analysis, give me
Strict Relaxed
Analysing Browsing
skos:exactMatch
(InChI)
Scientific Lenses – A. J. G. Gray 15
16 October 2014
details of the active compound in
Gleevec.

17. Name Lens
Which targets are known to interact
Strict Relaxed
Analysing Browsing
skos:closeMatch
(Drug Name)
skos:exactMatch
(InChI)
skos:closeMatch
(Drug Name)
Scientific Lenses – A. J. G. Gray 16
16 October 2014
with Gleevec?

18. What is a Scientific Lens?
A lens defines a conceptual view over the data
 Specifies operational equivalence conditions
Consists of:
 Identifier (URI)
 Title
(dct:title)
 Description
(dct:description)
 Documentation link
(dcat:landingPage)
 Creator
(pav:createdBy)
 Timestamp
(pav:createdOn)
 Equivalence rules
(bdb:linksetJustification)
16 October 2014 Scientific Lenses – A. J. G. Gray 17

19. Lens Effects: Ibuprofen
Ibuprofen consists of two equally active stereoisomers.
• Stereoisomers not always represented in data
Users wish to retrieve information for any stereoisomer.
CHEMBL427526
CHEMBL521
CHEMBL175
16 October 2014 Scientific Lenses – A. J. G. Gray 18

20. Default Lens
Ibuprofen consists of two equally active stereoisomers.
• Stereoisomers not always represented in data
Users wish to retrieve information for any stereoisomer.
16 October 2014 Scientific Lenses – A. J. G. Gray 19

21. Stereoisomer Lens
Ibuprofen consists of two equally active stereoisomers.
• Stereoisomers not always represented in data
Users wish to retrieve information for any stereoisomer.
16 October 2014 Scientific Lenses – A. J. G. Gray 20

22. Mapping Generation
✔
ops:OPS437281
has_stereoundefined_parent
[ci:CHEMINF_000456]
ops:OPS380297
is_stereoisomer_of
[ci:CHEMINF_000461]
ops:OPS380292
Other relationships
• has part
• is tautomer of
• uncharged counterpart
• isotope
…
16 October 2014 Scientific Lenses – A. J. G. Gray 21

23. Initial Connectivity
Datasets 37
Linksets 104
Links 7,096,712
Justifications 7
16 October 2014 Scientific Lenses – A. J. G. Gray 22

24. Compound Information
Scientific Lenses – A. J. G. Gray 23
16 October 2014

25. Proceed with Caution!
16 October 2014 Scientific Lenses – A. J. G. Gray 24

26. Co-reference
Computation
Rules ensure
 Unrestricted
transitivity within
conceptual type
 Restrict crossing
conceptual types
Based on justifications
Provenance captured
0..*
0..*
0..*
0..1
0..1
16 October 2014 Scientific Lenses – A. J. G. Gray 25

27. Initial Connectivity
Datasets 37
Linksets 104
Links 7,096,712
Justification
s
7
16 October 2014 Scientific Lenses – A. J. G. Gray 26

28. Inferred Connectivity
Datasets 37
Linksets 883
Links 17,383,846
Justifications 7
16 October 2014 Scientific Lenses – A. J. G. Gray 27

29. BridgeDb
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30. Lenses: Under the hood
GRAPH <http://rdf.chemspider.com> {
cw:979b545d-f9a9 cheminf:logd ?logd .
?iri cheminf:logd ?logd .
FILTER (?iri = cw:979b545d-f9a9 ||
?iri = cs:2157 ||
?iri = chembl:1280 ||
?iri = db:db00945 )
}
GRAPH <http://…
Q, L1 Q’
Query
Expander
Service
Identity
Mapping
Service
(BridgeDB)
Mappings
Profiles
cw:979b545d-f9a9, L1
[cw:979b545d-f9a9,
cs:2157,
chembl:1280,
db:db00945]
• Can also be achieved through UNION
• IMS call adds overhead
16 October 2014 Scientific Lenses – A. J. G. Gray 29

31. Experiment
Is it feasible to use a stand-off
mapping service?
 Base lines (no external call):
 “Perfect” URIs
 Linked data querying
 Expansion approaches (external service
call):
 FILTER by Graph
 UNION by Graph
C. Y. A. Brenninkmeijer, C. A. Goble, A. J. G. Gray, P. T. Groth, A. Loizou, S.
Pettifer: Including Co-referent URIs in a SPARQL Query. COLD 2013.
http://ceur-ws.org/Vol-1034/BrenninkmeijerEtAl_COLD2013.pdf

32. “Perfect” URI Baseline
WHERE {
GRAPH <chemspider> {
cs:2157 cheminf:logp ?logp .
}
GRAPH <chembl> {
chembl_mol:m1280 cheminf:mw ?mw .
}
}
16 October 2014 Scientific Lenses – A. J. G. Gray 31

33. Linked Data Baseline
WHERE {
GRAPH <chemspider> {
cs:2157 cheminf:logp ?logp .
}
GRAPH <chembl> {
?chemblid cheminf:mw ?mw .
}
cs:2157 skos:exactMatch ?chemblid .
}
16 October 2014 Scientific Lenses – A. J. G. Gray 32

34. Queries
Drawn from Open PHACTS API:
1. Simple compound information (1)
2. Compound information (1)
3. Compound pharmacology (M)
4. Simple target information (1)
5. Target information (1)
6. Target pharmacology (M)
16 October 2014 Scientific Lenses – A. J. G. Gray 33

35. Queries
Drawn from Open PHACTS API:
1. Simple compound information (1)
2. Compound information (1)
3. Compound pharmacology (M)
4. Simple target information (1)
5. Target information (1)
6. Target pharmacology (M)
16 October 2014 Scientific Lenses – A. J. G. Gray 34

36. Data:
167,783,592 triples
Mappings:
2,114,584 triples
Lenses:
1
Experiment Data
16 October 2014 Scientific Lenses – A. J. G. Gray 35

37. Average execution times

38. Average execution times
0.018

39. Q6: Target Pharmacology

40. Explorer Screenshot
16 October 2014 Scientific Lenses – A. J. G. Gray 45

41. Explorer Screenshot
16 October 2014 Scientific Lenses – A. J. G. Gray 46

42. Conclusions
 Scientific data is complex and messy
 Requires flexibility in linking
 Equivalence depends upon context
 Lenses provide support for operation
equivalence
 Chemical structures support automatic
computing of links with justification
16 October 2014 Scientific Lenses – A. J. G. Gray 47

43. Acknowledgements
Royal Society of Chemistry
 Colin Batchelor
 Karen Karapetyan
 Jon Steele
 Valery Tkachenko
 Antony Williams
University of Manchester
 Christian Brenninkmeijer
 Ian Dunlop
 Carole Goble
 Steve Pettifer
 Robert Stevens
Swiss Institute for Bioinformatics
 Christine Chichester
European Bioinformatics Institute
 Mark Davies
 Anna Gaulton
 John Overington
University of Vienna
 Daniela Digles
Maastricht University
 Chris Evelo
 Andra Waagmeester
 Egon Willighagen
VU University of Amsterdam
 Paul Groth
 Antonis Loizou
Connected Discovery
 Lee Harland
16 October 2014 Scientific Lenses – A. J. G. Gray 48

44. Questions
Alasdair J G Gray
A.J.G.Gray@hw.ac.uk
alasdairjggray.co.uk
@gray_alasdair
Open PHACTS
pmu@openphacts.org
openphacts.org
@open_phacts