This was an Invited Talk on my PhD research at the NASA Ames Research Center Intelligent Systems Division

1. Semantic Web Methods for Biomedical Data Integration:
An Application in Pharmacovigilance
Invited Talk at NASA Ames Research Center
14th August 2018
M A U L I K R . KA M D A R
Stanford Center for Biomedical Informa:cs Research
maulikrk@stanford.edu

2. {Seman?c} Web Enthusiast in Biomedicine
Indian Ins:tute of Technology (IIT) Kharagpur
Dual Degree in Biotechnology (2007 – 2012)
2 hKp://onto-apps.stanford.edu/

3. {Seman?c} Web Enthusiast in Biomedicine
Google Summer of Code Program
Drupal and Reactome (2010 – 2012)
3 hKp://onto-apps.stanford.edu/

4. {Seman?c} Web Enthusiast in Biomedicine
4 hKp://onto-apps.stanford.edu/

5. {Seman?c} Web Enthusiast in Biomedicine
Digital Enterprise Research Ins:tute, NUI Galway
Linked Data Researcher (2012 – 2014)
5 hKp://onto-apps.stanford.edu/

6. {Seman?c} Web Enthusiast in Biomedicine
6 hKp://onto-apps.stanford.edu/

7. {Seman?c} Web Enthusiast in Biomedicine
7 hKp://onto-apps.stanford.edu/

8. {Seman?c} Web Enthusiast in Biomedicine
8 hKp://onto-apps.stanford.edu/

9. {Seman?c} Web Enthusiast in Biomedicine
Stanford University School of Medicine
PhD Candidate, Biomedical Informa?cs (2014 – 2018)
9 hKp://onto-apps.stanford.edu/

10. {Seman?c} Web Enthusiast in Biomedicine
10 hKp://onto-apps.stanford.edu/

11. {Seman?c} Web Enthusiast in Biomedicine
11 hKp://onto-apps.stanford.edu/

12. {Seman?c} Web Enthusiast in Biomedicine
12 hKp://onto-apps.stanford.edu/

13. {Seman?c} Web Enthusiast in Biomedicine
13 hKp://onto-apps.stanford.edu/

14. 14

15. What does it mean for data and knowledge to be linked?
PDGFR
Gleevec
has-target
15
PDGFR
is-implicated-in
Glioma

16. What does it mean for data and knowledge to be linked?
PDGFR
Gleevec
has-target
PDGFR
is-implicated-in
Glioma
16

17. What does it mean for data and knowledge to be linked?
PDGFR
Gleevec
has-target
PDGFR
is-implicated-in
Glioma
possibly-associated-with
17

18. What does it mean for data and knowledge to be linked?
PDGFR
Gleevec
has-target
possibly-associated-with
“493.25 g/mol”
has-molecular-weight
Ca2+ Signaling Pathway
is-par6cipant-in
18
PDGFR
is-implicated-in
Glioma

19. 19
hKp://lod-cloud.net
Linked Open
Data Cloud

20. 20
hKp://lod-cloud.net
Linked Open
Data Cloud

21. Life Sciences Linked Open Data Cloud
21

22. Life Sciences Linked Open Data Cloud
Linked Data
22

23. Life Sciences Linked Open Data Cloud
Linked Data Ontologies
23

24. The Seman?c Web Technology Stack
24
Berners Lee, Scien?fic American 2001 Tim Berners-Lee: The next Web of open, linked data (TED Talk 2009)

25. The Seman?c Web Technology Stack
25
Berners Lee, Scien?fic American 2001 Tim Berners-Lee: The next Web of open, linked data (TED Talk 2009)
Linked Data

26. Seman?c Web: Publishing Data as a Graph
589.25
mol_weight
Gleevec (Mol. Wt.: 589.25 g/mol, Half-Life: 18 hours)
inhibits PDGFR, involved in signal transduc?on.
Gleevec
Resource Descrip?on Framework (RDF)
Inhibits
target name
type
GO:0007165
(Signal
Transduc?on)
process
PDGFR
KEGG: D01441 hMp://bio2rdf.org/kegg:D01441
Uniform Resource Iden?fier
26

27. Seman?c Web: Publishing Data as a Graph
589.25
mol_weight
Gleevec (Mol. Wt.: 589.25 g/mol, 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)
Inhibits
target name
type
GO:0007165
(Signal
Transduc?on)
process
PDGFR
KEGG: D01441 hMp://bio2rdf.org/kegg:D01441
hMp://bio2rdf.org/drugbank:DB00619
Uniform Resource Iden?fier
27

28. SPARQL Graph Query Language
BCL2
Viagra
has-target
PDGFR
Gleevec
has-target
PDGFR
is-implicated-in
Glioma
Resource Descrip?on Framework (RDF)
has-target
SPARQL Query Language
28

29. SPARQL Graph Query Language
BCL2
Viagra
has-target
PDGFR
Gleevec
has-target
PDGFR
is-implicated-in
Glioma
Resource Descrip?on Framework (RDF)
has-target
SPARQL Query Language
?protein ?drug
has-target
29

30. SPARQL Graph Query Language
BCL2
Viagra
has-target
PDGFR
Gleevec
has-target
PDGFR
is-implicated-in
Glioma
Resource Descrip?on Framework (RDF)
has-target
SPARQL Query Language
?protein ?drug
has-target
PDGFR
Gleevec
BCL2
Viagra
BCL2 Gleevec
30

31. Seman?c Web: Querying the Graph
< 1000
mol_weight
?dr1
What are the half-lives of drugs that have
Mol. Wt < 1000 g/mol and inhibit proteins
involved in signal transduc?on?
SPARQL Query Language
Inhibits
?pr target name
type
GO:0007165
(Signal
Transduc?on)
process
31

32. Seman?c Web: Querying the Graph
< 1000
mol_weight
?hl half-life
x-ref
?dr2
?dr1
What are the half-lives of drugs that have
Mol. Wt < 1000 g/mol and inhibit proteins
involved in signal transduc?on?
SPARQL Query Language
Inhibits
?pr target name
type
GO:0007165
(Signal
Transduc?on)
process
32

33. Himmelstein, Daniel ScoK, et al. "Systema?c integra?on of biomedical knowledge priori?zes drugs for repurposing." Elife 6 (2017). 33

34. Himmelstein, Daniel ScoK, et al. "Systema?c integra?on of biomedical knowledge priori?zes drugs for repurposing." Elife 6 (2017). 34

35. Himmelstein, Daniel ScoK, et al. "Systema?c integra?on of biomedical knowledge priori?zes drugs for repurposing." Elife 6 (2017). 35

36. 36

37. A paMern-based query federa:on architecture can
address seman:c heterogeneity in the LOD Cloud
to integrate mul?ple data and knowledge sources for
discovering novel implicit associa:ons in biomedicine.
37

38. Phlegra is a spider genus of the Sal?cidae family, commonly termed jumping spiders.
PhLeGrA – Linked Graph Analy?cs in Pharmacology
Query Federation Module
Mapping
Rules
Data
Model
Drug Protein Pathway
Adverse
Reaction
Graph
Analytics
Module
Pattern
Miner
Inputs-
Outcomes
Visualization Interface
Life Sciences
Linked Open Data
(LSLOD) Cloud
38

39. 39
Manifesta?ons of
Seman?c Heterogeneity
PaKern-based
Query Federa?on
Associa?on Discovery
In Pharmacovigilance
Themes of my PhD Research

40. 40
Manifesta?ons of
Seman?c Heterogeneity
PaKern-based
Query Federa?on
Associa?on Discovery
In Pharmacovigilance
Themes of my PhD Research

41. Linked Data Principles
• Use URIs as names for things.
• Use HTTP URIs so that people can look up those names.
• When someone looks up a URI, provide useful informa?on, using the standards.
• Refer to the en??es using their URIs when publishing data and knowledge.
41

42. No one follows the Fourth Linked Data Principle …
• Discovery of informa?on stored in other sources (e.g.
drug—protein interac?ons, disease treatments)
• Reuse-based ontology and Linked Data design
• Seman:c Heterogeneity is inversely propor:onal to
Reuse across Ontologies and Linked Data
• Use URIs as names for things.
• Use HTTP URIs so that people can look up those names.
• When someone looks up a URI, provide useful informa?on, using the standards.
• Refer to the en::es using their URIs when publishing data and knowledge.
42

43. Binding to RNA
(GRO#BindingToRNA)
GO:0003723
URI xref
RNA Binding
(GO:0003723)
Gene Expression
Ontology (GEXO)
Gene Regula:on
Ontology (GEXO)
Gene Ontology (GO)
Reuse can occur through either
explicit use of the same URI or
mapping two terms with equivalence aKribute
43

44. PaKern Miner – Data-driven Profiling of LSLOD
Query Federation Module
Mapping
Rules
Data
Model
Drug Protein Pathway
Adverse
Reaction
Graph
Analytics
Module
Pattern
Miner
Inputs-
Outcomes
Visualization Interface
Life Sciences
Linked Open Data
(LSLOD) Cloud
44

45. Systema?c Analyses of Reuse and Similarity Overlap
90+ Linked Data Sources
• 57,000+ classes,
4,700+ object proper?es and
8,400+ data proper?es
• 100,000+ edges
• 2,000 sample instances
for each class
377 Biomedical Ontologies
• 5M+ Class terms
Extractor Mapper
PaMern Miner
45

46. Minimal Reuse across the Linked Datasets
46
~100 schema elements
present in > 10 sources
10,000+ schema elements
present in only 1 LD source

47. Minimal Reuse across Biomedical Ontologies
Most ontologies reuse less than 5% of their terms
This reuse exists only from popular ontologies (e.g. Gene Ontology)
Kamdar, M. R., Tudorache, T., & Musen, M. A. (2017). A systema?c analysis of term reuse and term overlap across
biomedical ontologies. Seman6c web, 8(6), 853-871. IMIA YEARBOOK HONOURABLE MENTION. 47

48. Intent for Reuse: Use of similar, but not actual URIs
48
Gleevec
KEGG: D01441
Gleevec
ChEMBL: 1642
purl.obolibrary.org/
obo/CHEBI/31690
bio2rdf.org/
chebi:31690
x-ref x-ref

49. Label Mismatch: Different labels for classes, rela?ons and aKributes
Gleevec
molecular-weight
493.61 Gleevec
mol_weight
589.25
(clinical features) (biological features)
49

50. Model Mismatch: Different graph paKerns to capture granularity
(clinical features) (biological features)
Gleevec PDGFR
drug-target
Gleevec
Inhibits
PDGFR
target
name
type
PubMed: 21152856
source
50

51. Manifesta?ons of Seman?c Heterogeneity
• Lack of reuse across biomedical ontologies and
biomedical Linked Data sources
• Reuse only from popular vocabularies or ontologies
• Intent for Reuse (not actual reuse) that can be
tackled using simple conversion rules
• Label and Model Mismatch that can make simple
tasks (e.g. extract drug—protein target interac?ons
from mul?ple sources) difficult.
51

52. 52
Manifesta?ons of
Seman?c Heterogeneity
PaKern-based
Query Federa?on
Associa?on Discovery
In Pharmacovigilance
Themes of my PhD Research

53. Query Federa:on: Rewri?ng and execu?ng
queries across different sources
QUERY FEDERATION
Drug
v molecular-weight < 1000
v target
v process = “GO:0007165”
v half-life
Schwarte, et al. ISWC 2012
What are the half-lives of drugs that
have Mol. Wt < 1000 g/mol and inhibit
proteins involved in signal transduc?on?
53

54. Query Federa:on: Rewri?ng and execu?ng
queries across different sources
QUERY FEDERATION
Drug
v molecular-weight < 1000
v target
v process = “GO:0007165”
v half-life
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”
What are the half-lives of drugs that
have Mol. Wt < 1000 g/mol and inhibit
proteins involved in signal transduc?on?
54

55. Seman?c Heterogeneity hinders Query Federa?on
55
Intent for Reuse Label Mismatch Model Mismatch

56. PaKern-based Query Federa?on to tackle these …
Query Federation Module
Mapping
Rules
Data
Model
Drug Protein Pathway
Adverse
Reaction
Graph
Analytics
Module
Pattern
Miner
Inputs-
Outcomes
Visualization Interface
Life Sciences
Linked Open Data
(LSLOD) Cloud
56

57. Intui:on: Using graph paKerns for query federa?on
?Drug DrugBank:drug-target ?Protein
?Drug KEGG:target ?blank KEGG:link ?Protein
Mapping Rules:
?Drug hasTarget ?Protein
57

58. Intui:on: Using graph paKerns for query federa?on
?Drug DrugBank:drug-target ?Protein
?Drug KEGG:target ?blank KEGG:link ?Protein
Mapping Rules:
What are the half-lives of drugs that have Mol. Wt < 1000 g/mol and
inhibit proteins involved in signal transduc?on?
?s a <Drug>
?s <hasMolWt> ?mw
?s <hasTarget> ?protein
?s <hasHalfLife> ?hl
?mw < 1000 g/mol
?protein <hasGO> <GO:0007165>
?s a <Drug>
{?s <molecular-weight> ?mw}
?s <drug-target> ?protein
{?s <half-life> ?hl}
?mw < 1000 g/mol
?s a <Drug>
?s <mol_wt> ?mw
{?s <target> ?protein_blank
?protein_blank <link> ?protein}
?protein <hasGO> <GO:0007165>
Query
Rewrite Query
Rewri:ng
?Drug hasTarget ?Protein
58

59. PaKern-based Query Federa?on Method
Query
Federation
Module
LSLOD Cloud
D1 D2 D3 D4
R2
R1
Mapping
Rules
Data
Model
Drug
?Drug1 rdf:type DrugBank:Drug
?Drug2 rdf:type KEGG:Drug
OPTIONAL {?Drug1 DrugBank:x-kegg ?Drug2}
OPTIONAL {?Drug1 DrugBank:x-atc ?atc}
OPTIONAL {?Drug2 KEGG:x-atc ?atc}
Drug hasTarget Protein
?Drug DrugBank:drug-target ?Protein
?Drug KEGG:target ?blank KEGG:link ?Protein
?s a <Drug>
?s <hasMolWt> ?mw
?s <hasTarget> ?protein
?s <hasHalfLife> ?hl
?mw < 1000 g/mol
?protein <hasGO> <GO:0007165>
R3
59

60. PaKern-based Query Federa?on Method
Federator /
Query Rewriter
QFP3
Reconciliator/
Integrator
QFP4
Parser
QFP1
Source Selector
QFP2
Query
Federation
Module
?s a <drugbank:Drug>
{?s <molecular-weight> ?mw}
?s <drug-target> ?protein
{?s <half-life> ?hl}
?mw < 1000 g/mol
{?s DrugBank:x-kegg ?kegg}
{?s DrugBank:x-atc ?atc}
…
?s a <kegg:Drug>
?s <mol_wt> ?mw
{?s <target> ?blank
?blank <link> ?protein}
?protein <hasGO> <GO:0007165>
{?s KEGG:x-atc ?atc}
…
LSLOD Cloud
D1 D2 D3 D4
R2
R1
Mapping
Rules
Data
Model
Drug
?Drug1 rdf:type DrugBank:Drug
?Drug2 rdf:type KEGG:Drug
OPTIONAL {?Drug1 DrugBank:x-kegg ?Drug2}
OPTIONAL {?Drug1 DrugBank:x-atc ?atc}
OPTIONAL {?Drug2 KEGG:x-atc ?atc}
Drug hasTarget Protein
?Drug DrugBank:drug-target ?Protein
?Drug KEGG:target ?blank KEGG:link ?Protein
?s a <Drug>
?s <hasMolWt> ?mw
?s <hasTarget> ?protein
?s <hasHalfLife> ?hl
?mw < 1000 g/mol
?protein <hasGO> <GO:0007165>
R3
60

61. PaKern-based Query Federa?on Method
Federator /
Query Rewriter
QFP3
Reconciliator/
Integrator
QFP4
Parser
QFP1
Source Selector
QFP2
Query
Federation
Module
?s a <drugbank:Drug>
{?s <molecular-weight> ?mw}
?s <drug-target> ?protein
{?s <half-life> ?hl}
?mw < 1000 g/mol
{?s DrugBank:x-kegg ?kegg}
{?s DrugBank:x-atc ?atc}
…
?s a <kegg:Drug>
?s <mol_wt> ?mw
{?s <target> ?blank
?blank <link> ?protein}
?protein <hasGO> <GO:0007165>
{?s KEGG:x-atc ?atc}
…
LSLOD Cloud
D1 D2 D3 D4
R2
R1
Mapping
Rules
Data
Model
Drug
?Drug1 rdf:type DrugBank:Drug
?Drug2 rdf:type KEGG:Drug
OPTIONAL {?Drug1 DrugBank:x-kegg ?Drug2}
OPTIONAL {?Drug1 DrugBank:x-atc ?atc}
OPTIONAL {?Drug2 KEGG:x-atc ?atc}
Drug hasTarget Protein
?Drug DrugBank:drug-target ?Protein
?Drug KEGG:target ?blank KEGG:link ?Protein
?s a <Drug>
?s <hasMolWt> ?mw
?s <hasTarget> ?protein
?s <hasHalfLife> ?hl
?mw < 1000 g/mol
?protein <hasGO> <GO:0007165>
Gleevec, PDGFR, 18 hours
R3
61

62. Reduc?on in SPARQL
Query Complexity
62
PREFIX <drugbank>: <hKp://bio2rdf.org/drugbank_vocabulary:>
PREFIX <kegg>: <hKp://bio2rdf.org/kegg_vocabulary:>
PREFIX <phlegra>: …
CONSTRUCT {
?drug phlegra:hasTarget ?protein .
} WHERE {
{GRAPH <DrugBank> {
?drug a drugbank:Drug .
OPTIONAL {?drug dc:?tle ??tle} .
OPTIONAL {?drug drugbank:x-kegg ?kegg} .
OPTIONAL {?drug drugbank:x-atc ?atc}.
OPTIONAL {?drug drugbank:x-pharmgkb ?pharmgkb} .
?s a drugbank:Target-Rela?on;
drugbank:drug ?drug;
drugbank:target ?protein
…
}} UNION {GRAPH <KEGG> {
?drug a kegg:Drug .
OPTIONAL {?drug dc:?tle ??tle} .
OPTIONAL {?drug kegg:x-drugbank ?drugbank} .
OPTIONAL {?drug kegg:x-atc ?atc}.
OPTIONAL {?drug kegg:x-pharmgkb ?pharmgkb} .
?drug kegg:target ?targetlink.
?targetlink kegg:link ?protein;
…
}} UNION {GRAPH <PharmGKB> {
?drug a pharmgkb:Drug .
OPTIONAL {?drug dc:?tle ??tle} .
OPTIONAL {?drug pharmgkb:x-drugbank ?drugbank} .
OPTIONAL {?drug pharmgkb:x-kegg ?kegg} .
OPTIONAL {?drug pharmgkb:x-atc ?atc}.
?s1 pharmgkb:associa?on ?assoc;
a pharmgkb:gene-drug-Associa?on;
pharmgkb:drug ?drug;
pharmgkb:gene ?protein
…
}} UNION {SERVICE <CTD> {
?protein a ctd:Gene ;
ctd:x-pharmgkb ?pharmgkb .
OPTIONAL {?protein dc:?tle ??tle} .
?x a ctd:Chemical-Gene-Associa?on;
ctd:gene ?protein;
ctd:chemical ?drug;
}}
}
?drug a phlegra:Drug .
?protein a phlegra:Protein .
?drug phlegra:hasTarget ?protein .
Query using the PhLeGrA architecture
Exemplary Federated SPARQL Construct Query

63. Unique drug—protein target rela?ons
exist across mul?ple Data Sources
63

64. PaKern-based Query Federa?on
• A way to address the manifesta?ons of seman?c
heterogeneity using mapping rules that transform
user-provided query to source-specific queries.
• Generate knowledge networks, on a use-case basis,
using simple queries from a common data model.
• Discover novel informa?on (e.g. protein targets,
publica?ons, assays, characteris?cs) on a given en?ty
(e.g. Gleevec) from mul?ple sources.
64

65. 65
Manifesta?ons of
Seman?c Heterogeneity
PaKern-based
Query Federa?on
Associa?on Discovery
In Pharmacovigilance
Themes of my PhD Research

66. Pharmacovigilance: Detec?ng drug-drug interac?ons
and the resultant adverse reac?ons
Jane P.F. Bai and Darrell R. Abernethy. Annual review of pharmacology and toxicology 53 (2013) 66

67. Mechanism-based pharmacovigilance
Jane P.F. Bai and Darrell R. Abernethy. Annual review of pharmacology and toxicology 53 (2013) 67

68. Applica?ons in pharmacovigilance
Query Federation Module
Mapping
Rules
Data
Model
Drug Protein Pathway
Adverse
Reaction
Graph
Analytics
Module
Pattern
Miner
Inputs-
Outcomes
Visualization Interface
Life Sciences
Linked Open Data
(LSLOD) Cloud
68

69. A k-par?te network of drugs and ADRs
69 Kamdar, M. R., & Musen, M. A. (2017). PhLeGrA: Graph analy?cs in pharmacology over the web of life sciences
linked open data. In Proceedings of the 26th Interna6onal Conference on World Wide Web (pp. 321-329).

70. This k-par:te network is generated using
4 LD sources and 3 ontologies in < 1 day
70 Kamdar, M. R., & Musen, M. A. (2017). PhLeGrA: Graph analy?cs in pharmacology over the web of life sciences
linked open data. In Proceedings of the 26th Interna6onal Conference on World Wide Web (pp. 321-329).
Concept Type Count
Drug 2,759
Protein 19,903
Pathway 301
Adverse Drug Reac?on 3,890
Rela:on Type Count
Drug hasTarget Protein 249,001
Drug hasEnzyme Protein 2,062
Drug hasTransporter Protein 919
Protein isPresentIn Pathway 25,480
Pathway isImplicatedIn ADR 46,300

71. Network-based Apriori algorithm to discover
novel associa?ons from FAERS database
Dataset BCPNN GPS Network-based
Rela:ve Repor:ng Ra:o
OMOP 0.70 0.70 0.72
EU-ADR 0.75 0.76 0.78
Iyer, et al. 0.81 0.83 0.82
Kamdar, M. R., & Musen, M. A. (2017). Mechanism-based Pharmacovigilance over the Life Sciences Linked Open
Data Cloud. Proceedings of the AMIA Annual Symposium 2017. DISTINGUISHED PAPER AWARD.
71
3 million case reports
from US FDA Adverse
Event Repor?ng
System (2013 – 2015)

72. Associa?on Discovery in Pharmacovigilance
• A k-par?te network of drugs, proteins, pathways,
phenotypes is generated using PhLeGrA architecture.
• FAERS safety reports mined in conjunc?on with the
k-par?te network to predict drug—ADR associa?ons
for pharmacovigilance, with underlying mechanisms.
72

73. Phlegra is a spider genus of the Sal?cidae family, commonly termed jumping spiders.
PhLeGrA – Linked Graph Analy?cs in Pharmacology
Query Federation Module
Mapping
Rules
Data
Model
Drug Protein Pathway
Adverse
Reaction
Graph
Analytics
Module
Pattern
Miner
Inputs-
Outcomes
Visualization Interface
Life Sciences
Linked Open Data
(LSLOD) Cloud
73

74. Acknowledgments
• Mark Musen
• Russ Altman
• Jure Leskovec
• Axel Polleres
• Ross Shachter
• Tania Tudorache
• Csongor Nyulas
• MaKhew Horridge
• Simon Walk
• Rafael Gonçalves
• Josef Hardi
• Marcos Mar?nez
• Mar?n O’Connor
• John Graybeal
• Clement Jonquet
• Michel Dumon?er
• Suzanne Tamang
• Nigam Shah
• Richard Boyce
• Husham Sharifi
• Rainer Winnenberg
• Erik Van Mulligen
• Juan Banda
• Amrapali Zaveri
• Steve Bagley
• Joan Menees
• Lichy Han
• Alejandro Schuler
• Michelle Wu
• Alice Yu
74
Thanks to
Dr. Richard Keller and the
NASA Ames Research
Team for hos?ng me!
Collaborators and Mentors

75. 75
Ques?ons?
@maulikkamdar
maulikrk@stanford.edu
stanford.edu/~maulikrk/