My slides on "Query Federation over the Life Sciences Linked Open Data Cloud: Methods, Challenges and Applications"

1. Query Federa*on over the
Life Sciences Linked Open Data Cloud
Maulik R. Kamdar
Musen Lab
BMIR Research in progress Talk
November 10, 2016

3. The Data and Knowledge Discovery BoMleneck
Biomedical Queries
List drugs that have Mol. Wt < 1000 and
inhibit proteins involved in signal
transduc*on. Men*on their half-life.
List an*neoplas*c agents that target
EGFR or PDGFR, with literature cita*ons
and their downstream targets.
3
Desirable Drugs
Molecular characteris*cs
Protein Targets
Downstream Genes …
Biomedical Informa*cs
Research Methods
OpenPHACTS. Williams, et al. Drug Discovery Today, 2012

4. Systems Pharmacology
Zhao, Shan, and Ravi Iyengar. Annual review of pharmacology and toxicology 52 (2012): 505.

5. Isolated Databases and Knowledgebases
DISTRIBUTED DATA and KNOWLEDGE
5
• Formats (XML, CSV, MySQL Database, etc.)
• En*ty Nota*ons (Ensembl, Entrez, HGNC, etc.)
• Schemas (Small Compound, Compound, etc.)

6. Seman*c Web Technologies
6
Berners Lee, Scien*fic American 2001 Tim Berners-Lee: The next Web of open, linked data (TED Talk 2009)

7. Seman*c Web: Publishing Data as a Graph
7
589.25
mol_weight
Gleevec (Mol. Wt.: 589.25, Half-Life: 18 hours)
inhibits PDGFR, involved in signal transduc*on.
“18 hours”
half-life
x-ref
Gleevec
DrugB: DB00619
Gleevec
Resource Descrip*on Framework (RDF)
Kyoto Encyclopedia of
Genes and Genomes (KEGG)
Inhibits
target name
type
GO:0007165
(Signal
Transduc*on)
process
PDGFR
DrugBank
KEGG: D01441
h<p://bio2rdf.org/kegg:D01441
h<p://bio2rdf.org/drugbank:DB00619
Uniform Resource Iden*fier

8. Seman*c Web: Querying the Graph
589.25
mol_weight
PDGFR
Gleevec (Mol. Wt.: 589.25, Half-Life: 18 hours)
inhibits PDGFR, involved in signal transduc*on.
“18 hours”
half-life
x-ref
Gleevec
DrugB: DB00615
Gleevec
KEGG: D01441
< 1000
mol_weight
? half-life
x-ref
?
?
List drugs that have Mol. Wt < 1000 and
inhibit proteins involved in signal
transduc*on. Men*on their half-life.
Resource Descrip*on Framework (RDF) SPARQL Query Language
KEGG
DrugBank DrugBank
8
Inhibits
target name
type
GO:0007165
(Signal
Transduc*on)
process
Inhibits
? target name
type
GO:0007165
(Signal
Transduc*on)
process
KEGG

9. Life Sciences Linked Open Data Cloud
Cyganiak, Richard et al. 2014
9

10. Callahan, A., et al., 2013.
Saleem, M., Kamdar, MR. et al., 2014.
Jupp, S., et al., 2014.
Noy, NF., et al., 2009.
10
Life Sciences Linked Open Data Cloud (LSLOD)

11. LSLOD Query Federa*on
• Challenges mining the LSLOD cloud
• Current methods
• Rule-based method (RIP)
What this talk is about …
LSLOD Applica*ons
• Biomedical Ques*on-Answering
• Systems Pharmacology (RIP)

12. LSLOD Query Federa*on
• Challenges mining the LSLOD cloud
• Current methods
• Rule-based method (RIP)
What this talk is about …
LSLOD Applica*ons
• Biomedical Ques*on-Answering
• Systems Pharmacology (RIP)

13. Challenges mining the LSLOD cloud
• Isolated SPARQL endpoints or
RDF Dumps
• Different URI nota*ons, with no
explicit x-refs links
• h)p://bio2rdf.org/uniprot:P45059
• h)p://purl.uniprot.org/uniprot/P45059
• Heterogeneity between the
SemanSc Web datasets
• Technical Issues: Malformed
URIs, unavailable SPARQL
endpoints, etc.
• h)p://bio2rdf.org/kegg:map00010
h)p://bio2rdf.org/kegg:00010
• h)p://bio2rdf.org/go:0030307”

14. Heterogeneity between Seman*c Web Datasets
14
Gleevec PDGFR
drug-target
Gleevec
Inhibits
PDGFR
target
name
type
PubMed: 21152856
source
Model Mismatch: Different graph paMerns to capture granularity
Gleevec
molecular_weight
493.61 Gleevec
mol_weight
589.25
Label Mismatch: Different labels for classes, rela*ons and aMributes
DrugBank
DrugBank KEGG
KEGG

15. Data Warehousing: Transforming data under
one uniform schema and uniform nota*ons
15
WAREHOUSING
OpenPHACTS. Williams, 2012
Data Graphs
✓ Efficient query execu*on
✓ Complete results
✗ Data copies
✗ Inflexible, not scalable

16. Query FederaSon: Execu*ng different por*ons
of queries across different sources
QUERY FEDERATION
Drug
v molecular-weight < 1000
v target
v process = “GO:0007165”
v half-life
16 Schwarte, et al. ISWC 2012
Drug
v molecular-weight < 1000
v target
v half-life
Drug
v molecular-weight < 1000
v target
v process = “GO:0007165”
DrugBank KEGG
List drugs that have Mol. Wt < 1000
and inhibit proteins involved in signal
transduc*on. Men*on their half-life.

17. Reconcilia*on during Federa*on
Gleevec
molecular_weight
493.61 Gleevec
mol_weight
589.25
DrugBank KEGG
Informa*on on the same en*ty in two different sources may be different.

18. Reconcilia*on during Federa*on
Drug hasTarget Protein
Unique rela*ons for a given rela*on type, in each source.
Similar rela*ons in mul*ple sources, that need to be reconciled.

19. PREFIX drugbank: <hMp://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/>
PREFIX kegg: <hMp://bio2rdf.org/ns/kegg#>
PREFIX purl: <hMp://purl.org/dc/elements/1.1/>
PREFIX bio2RDF: <hMp://bio2rdf.org/ns/bio2rdf#>
SELECT DISTINCT ?drug ?halflife WHERE {
SERVICE <hMp://www4.wiwiss.fu-berlin.de/drugbank/sparql> {
?drug a drugbank:Drug .
?drug drugbank:molecular-weight ?drugbankMolwt.
?drug drugbank:half-life ?halflife.
?drug bio2RDF:inchi ?drugbankInchi .
?drug drugbank:target ?drugbankProtein.
?drug purl:*tle ?drugBankName .
}
?keggDrug bio2RDF:drug-target ?keggProtein .
?keggDrug bio2RDF:inchi ?keggInchi .
………………………………………………
FILTER (?drugbankInchi = ?keggInchi)
FILTER (?drugbankMolwt < 1000)
}
19
Query FederaSon: The SPARQL SERVICE keyword
Saleem, et al. Journal of Web Seman*cs, 2016.
Also, SPARQL is just messy.

20. Rewri*ng during Query Federa*on

21. Query FederaSon: The SPARQL ASK method
Schwarte, et al. ISWC 2012
?s a <Drug>
?s <hasMolWt> ?mw
?s <hasTarget> ?protein
?s <hasHalfLife> ?hl
?mw < 1000
?protein <hasGO> <GO:0007165>
ASK {?s <hasMolWt> ?mw}
ASK {?s <hasTarget> ?protein}
…
ASK {?protein <hasGO> <GO:0007165>}

22. Vocabulary of Interlinked Datasets
Linked Data on the Web Workshop (LDOW 09), in conjunc*on with WWW 09

23. Query FederaSon: The VoID method
Gorlitz, et al. ISWC 2014
?s a <Drug>
?s <hasMolWt> ?mw
?s <hasTarget> ?protein
?s <hasHalfLife> ?hl
?mw < 1000
?protein <hasGO> <GO:0007165>
?s a <Drug> DrugBank
?s <hasMolWt> ?mw DrugBank
?s <hasTarget> ?protein KEGG
?s <hasTarget> ?protein DrugBank

24. Heterogeneity between Seman*c Web Datasets
24
Gleevec
molecular_weight
493.61 Gleevec
mol_weight
589.25
Label Mismatch: Different labels for classes, rela*ons and aMributes
DrugBank KEGG

25. • Query federa*on offers a scalable, flexible approach towards
data integra*on without the flaws of data warehousing
• However, the current methods using only SERVICE, ASK or
VOID descrip*ons are not suitable over LSLOD
• Combining query federa*on with schema mapping …

26. Mapping source schemas to an ontology
Callahan, et al. Journal of Biomedical Seman*cs 2013
ComparaSve
Toxicogenomics
Database
SemanSc Science
Integrated Ontology
Saccharomyces
Genome Database

27. Cancer Chemopreven*on Ontology in OWL Lite
27 Zeginis, D. et al. Seman*c Web Journal, 2013 hMp://bioportal.bioontology.org/ontologies/CANCO

28. 28
Query Federa*on: Using Rule Templates
Hasnain, A., Kamdar MR, et al. ISWC 2014
Canco:Drug
Canco:molecular_weight
1) Drugbank:Drug
Drugbank:molecular_weight
2) KEGG:Drug
KEGG:mol_wt
CANCO

29. Linking methods to generate Rule Templates
• Naïve matching:
– CANCO:Drug ç Drugbank:Drug
• Named en*ty matching:
– CANCO:Molecule ç ChEBI:Compound
• Domain matching:
– CANCO:Molecule ç CTD:Chemical
• If {InchiKey(CTD:Chemicaluri)} ≈ {InchiKey(ChEBI:Compounduri)}
• RegEx/ID matching:
– Uniprot:Protein ç Bio2RDF:Uniprot_Resource
• If {Regex(Uniprot:Proteinuri)} = {Regex(Bio2RDF:Uniprot_Resourceuri)}
• h)p://bio2rdf.org/uniprot:P45059, h)p://purl.uniprot.org/uniprot/P45059

30. Par*ally solves the Label Mismatch problem but …
Gleevec PDGFR
drug-target
Gleevec
Inhibits
PDGFR
target
name
type
PubMed: 21152856
source
Model Mismatch: Different graph paMerns to capture granularity
DrugBank KEGG
PDGFR Query Results:
Drug hasTarget ?Protein ç DrugBank:Drug DrugBank:drug-target ?Protein
Drug hasTarget ?Protein ç KEGG:Drug KEGG:target ?Protein
Mapping Rules:

31. Using Graph PaMerns for Query Rewri*ng
?Drug hasTarget ?Protein ç ?Drug DrugBank:drug-target ?Protein
?Drug hasTarget ?Protein ç ?Drug KEGG:target ?blank KEGG:link ?Protein
Mapping Rules:
List drugs that have Mol. Wt < 1000 and inhibit proteins
involved in signal transduction. Mention their half-life.
?s a <Drug>
?s <hasMolWt> ?mw
?s <hasTarget> ?protein
?s <hasHalfLife> ?hl
?mw < 1000
?protein <hasGO> <GO:0007165>
?s a <Drug>
{?s <molecular_weight> ?mw_blank
?mw_blank <value> ?mw}
?s <drug-target> ?protein
{?s <half-life> ?hl_blank
?hl_blank <value> ?hl}
?mw < 1000
?s a <Drug>
?s <mol_wt> ?mw
{?s <target> ?protein_blank
?protein_blank <link> ?protein}
?protein <hasGO> <GO:0007165>
Query
Rewrite Query RewriSng

32. Include more complex paMerns…
Small Molecule
CTD:Chemical
< 900
mol_weight
CHEBI:Compound
< 900
molecularWeight
value unit
Da

33. • Query federaSon, combined with schema mapping methods,
may provide an alterna*ve approach for the LSLOD cloud
• Query mul*ple sources without being concerned of the
underlying heterogeneity.
• Pa<ern-based mapping rules can aid in genera*ng complex
constructs a prior, as well as aid in en*ty reconcilia*on.
• Manual construc*on of such PaMern-based mapping rules --
using a visual interface and recommenda*on
• How to automate …

34. DrugBank
KEGG
Domain Model
DrugBank
KEGG
Future Work: How to learn these mapping rules …
34
Inhibits
type
name target
name target
target
drug-target
drug-target
calculated
Drug target Protein
Pa<ern ranking
Number of Nodes
Number of Edges
No. of Blank nodes
Distribu*on of nodes
Start node
Sim(EpaMern, Emodel)
...
Logis*c Regression
name
? target
?
drug-target
? ?

35. Ongoing Work: Evalua*on of the method
• ComparaSve evaluaSon:
– FedX,
– SPLENDID and
– SPLENDID augmented with the query rewri*ng component
• Metrics:
– Query complexity
– Source selec*on *me
– Query execu*on *me, etc.
• Benchmarks:
– LargeRDFBench (Billion Triples Benchmark consis*ng
Linked TCGA, DrugBank, KEGG, Affymetrix)
• Involve domain users …

36. LSLOD Query Federa*on
• Challenges mining the LSLOD cloud
• Current methods
• Rule-based method (RIP)
What this talk is about …
LSLOD Applica*ons
• Biomedical Ques*on-Answering
• Systems Pharmacology (RIP)

37. Applica*on:
Systems Pharmacology

38. Systems Pharmacology
Zhao, Shan, and Ravi Iyengar. Annual review of pharmacology and toxicology 52 (2012): 505.

39. Underlying mechanisms for drug-drug interac*ons.
Jia et al. Nature reviews Drug discovery, 8(2):111–128, 2009

40. Data Model
Concept
E1 Drug
E2 Protein
E3 Pathway
E4 Adverse Drug
Reac*on
RelaSon
R1 Drug hasTarget Protein
R2 Drug hasEnzyme Protein
R3 Drug hasTransporter Protein
R4 Protein isPresentIn Pathway
R5 Pathway isImplicatedIn ADR

41. Manually generated rules
Source Graph Pa<ern (R1)
Drugbank E1 <--drug-- Target-RelaSon --target--> E2
PharmGKB E1 <--drug-- gene-drug-AssociaSon --gene--> E2
KEGG E1 --target--> _:blank --link--> E2
ComparaSve
Toxicogenomics
E1 <--chemical-- Chemical-Gene-AssociaSon --gene--> E2

42. PhLeGrA – Linked Graph Analy*cs in Pharmacology

43. Graph Analy*cs Module
• AssociaSon: {Drug}n --> ADR
• 2-state Hidden CondiSonal Random Field model over the
k-par*te network, with (k-2) hidden layers.
– Discrimina*ve probabilis*c graphical model
– Unobserved en**es on the associa*on path
– No assump*on on independence of inputs
– QuaMoni, Ariadna, et al. IEEE Trans. Pa)ern Anal. Mach. Intell. 2007.
• Inputs – Outcomes database to train the HCRF.
– US FDA Adverse Event Repor*ng System (FAERS)
– Drugs, Adverse Reac*ons, Indica*ons, Doses etc.
– Text pre-processing using UMLS terminologies
– X = Drugs, Y = ADRs, H = {Proteins, Pathways}

44. Network sta*s*cs
Generated in < 1 day

45. Seman*c Web and Systems Pharmacology
R1: Drug hasTarget Protein E1: Drug
• Similar and complete unique en**es and rela*ons exist between data sources
• Necessary to get the complete picture, but also determine sources of noise
• “Will the correct drugs please stand up?” - Southan et al. GCC 2016

46. AUROC curves for some ADRs

47. Web applica*on to explore underlying mechanisms
hMp://onto-apps.stanford.edu/phlegra

48. Future Work …
• How to test the significance of the associa*ons discovered?
• Implement other models (e.g. simple path enrichment)
– Deal with repor*ng bias.
• Use exis*ng “Silver standards” such as OMOP, EU-ADR,
Drugbank DDIs, MedSpan, etc.
• Relevance of the associa*ons, as well as the correctness of
the underlying mechanisms.
• Importance of the sources to create such networks.

49. Applica*on:
Biomedical Ques*on-Answering

50. ReVeaLD: Real-*me Visual Explorer and
Aggregator of Linked Data
Kamdar, Maulik R., et al. Journal of biomedical informa*cs 2014.

51. To summarize …
• Exci*ng opportuni*es to seamlessly query and integrate data
and knowledge from isolated sources.
• Query federa*on can aid towards biomedical ques*on
answering and systems pharmacology
• The data collected, or the networks generated, can be used in
downstream analy*cs approaches (e.g. Protein/Drug
structures in Autodock Vina)
• Make Seman*c Web more usable for biomedical researchers!

52. Acknowledgments
Musen Lab
- Mark Musen
- Tania Tudorache
- Csongor Nyulas
- MaMhew Horridge
- Rafael Gonçalves
- Josef Hardi
- Marcos Mar*nez
- Mar*n O’Connor
- John Graybeal
- Alex Screnchuk
And others …
52
Michel Dumon*er
Russ Altman
Rainer Winnenberg
Juan Banda
Amrapali Zaveri
BMI Students
Saleem M.
Ali Hasnain
Axel Ngonga
Helena Deus
Jonas Almeida
Stefan Decker
“Discovery informa[cs is in its infancy. Search engines are grappling with the need
for deep search, but it is doub^ul they will fulfill the needs of the biomedical research
community when it comes to finding and analyzing the appropriate datasets …”
- Philip Bourne, Associate Director for Data Science, NIH, 2014

53. Ebola-KB Dashboard
53
Kamdar, MR., et al. Database 2015.

54. Linked TCGA
54
Saleem, M., Kamdar MR, et al. Web Seman*cs, 2014.

55. Pathway-based drug reposi*oning
55
Li, et al. BMC Bioinforma*cs 2013