Data for drug discovery and healthcare is often trapped in silos which hampers effective interpretation and reuse. To remedy this, such data needs to be linked both internally and to external sources to make a FAIR data landscape which can power semantic models and knowledge graphs.

1. Knowledge graphs and semantic models
for drug discovery and healthcare
on Thurs, 23rd April 2020 at 16:00 BST
Hosted by: Ian Harrow, Pistoia Alliance
Speaker: Ilaria Maresi, The Hyve
FAIR/OM projects Community of Interest webinar series

2. This webinar is being recorded

3. Audience Q&A
Please use the questions box

4. ©PistoiaAlliance
Ilaria Maresi
• Ilaria is a Data Engineer at The Hyve, specialising
in Semantic Modelling and Knowledge Graphs with
applications in healthcare and drug discovery.
• As a mathematician by training, Ilaria came to the
bioinformatics field through her interest in the
intersection of biology, mathematics and
engineering.
• In her free time, she tries to get away from her
computer, and enjoys cooking and spending time
outside.

5. ©PistoiaAlliance
Knowledge graphs and semantic models for
drug discovery and healthcare
Ilaria Maresi, Data Engineer
ilaria@thehyve.nl

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Agenda
● About me
● The problem
● What are semantic models and knowledge graphs?
○ Creating
○ Querying
● Semantic models in action
○ Clinical trials
○ Drug discovery landscape
● Wrap up
● Q&A

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We enable open science by developing
and implementing open source solutions
and FAIRifying data in life sciences

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About me
Data Engineer at The Hyve
● Semantic modelling
● Knowledge graphs
● ETL pipelines
● FAIR data services
Utrecht, The Netherlands

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The problem

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Drug discovery process
Source: PhRMA Biopharmaceutical R&D: The Process Behind New Medicines https://www.phrma.org/en/Report/Biopharmaceutical-R-
and-D-The-Process-Behind-New-Medicines

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Differing and unlinked identifiers
Source: PhRMA Biopharmaceutical R&D: The Process Behind New Medicines https://www.phrma.org/en/Report/Biopharmaceutical-R-
and-D-The-Process-Behind-New-Medicines
CMP102401
acetylsalicylic
acid
Aspirin
Query
I want to see the
trajectory of
compound xxx

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Differing terminology
Source: PhRMA Biopharmaceutical R&D: The Process Behind New Medicines https://www.phrma.org/en/Report/Biopharmaceutical-R-
and-D-The-Process-Behind-New-Medicines
omics
experiment
lab test
genomics
assay
Query
I want to see all
the genomics
data related to
compound xxx

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Differing terminology
Source: PhRMA Biopharmaceutical R&D: The Process Behind New Medicines https://www.phrma.org/en/Report/Biopharmaceutical-R-
and-D-The-Process-Behind-New-Medicines
non-small cell
lung cancer
NSCLC
non-small cell
malignant
neoplasm of
lung
Query
I want to see all
terminated studies
for non-small cell
lung cancer that
study compound
xxx
terminated
recruiting
terminated

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What are semantic models and
Knowledge Graphs?

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Semantic models are ...
“Semantic models of data sources represent the implicit meaning of
the data by specifying the concepts and the relationships within the
data. Such models are the key ingredients to automatically publish
the data into knowledge graphs”
– USC semantic modelling

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What about knowledge graphs?
A knowledge graph is several things:
● Database: contains actual data
● Graph: data items and concepts are connected via relationships
(i.e nodes and edges)
● Semantic: the meaning of the data is encoded in the graph,
allowing for meaning to be inferred
● Alive: constantly refreshed with new data & can be extended
and revised as new data comes in

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The power of a knowledge graph
● Linking concepts that are same or similar
○ Model level – entities
○ Data (instance) level – identifiers
● Harmonising data sources without transforming or forcing a
common standard on the data
● The more diverse your data, the more powerful your KG will be
○ Google Knowledge Graph
○ Diffbot
○ Wikidata

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A knowledge graph example
DBPedia:
● Community effort to extract
structured content from the
information created in
various Wikimedia projects
● Data coming in various
formats and from various
sources
● KG unifies data and enables
querying

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Using RDF to create semantic models
● Resource Description Framework (RDF)
● Information encoded in triples
● Almost everything needs a Uniform Resource Identifier (URI)
Subject Object
predicate
schema:Patient schema:MedicalCondition
schema:healthCondition
patient_1231 AnginaPectoris
schema:healthCondition

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Using RDF to create semantic models
patient_1231 AnginaPectoris
schema:healthCondition
Heart
schema:associatedAnatomy
AnatomicalStructure
rdf:type
12/12/1950
schema:birthDate
Ranolazine
schema:drug
Ranexa
schema:nonProprietaryName

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Querying a knowledge graph
● SPARQL Protocol and RDF Query Language (SPARQL)

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Querying a knowledge graph
For a single gene (CDX2) these are all its associated identifiers in wikidata:
Ensembl ID
Entrez ID
NCBI Gene ID

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Linked data is FAIR(er) data
● Findable: uniform resource identifiers that persist across an
organization
● Accessible: triple store access can be open or only to select users
● Interoperable: RDF gives meaning to data to both humans and
machines (include community standard ontologies!)
● Reusable: RDF is independent of tools/systems and can encode
provenance of (meta)data

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Semantic models in action

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A semantic model of clinical studies
Problem
○ Data across stages of
clinical studies exists
mostly in silos and is not
harmonised
○ Cross domain analytics
is currently burdensome
○ Unclear what processes
data has gone through
Goals
○ Data Conformance
Layer to semantically
represent source data
on clinical trials
○ Leverage external data
○ Represent provenance
in model

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The solution
● 1300+ triples
● 65 classes
● 153 properties
● 13 ontologies

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Ontologies and interoperability
● Use ontologies that fit
domain and data
● BioPortal
● It’s possible to use
multiple ontologies in
one model!
● FAIR principles I2 & I3

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The solution
● Harmonising terms (clinical trial
vs. clinical study vs. medical trial)
● Metadata annotation (alt labels
for compound)
● Controlled vocabularies
(indication)
● Provenance (what activity
generated the dataset and
when)

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Knowledge graph: from R&D to market
Problem
○ “How much data do we
have and how is it
connected?”
○ Data across stages of R&D
is unlinked
Goals
○ Understand quantity
and qualitative details
of existing data assets
○ Map relationships
between data assets

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The solution
● Semantic model representing data flows and major entities
● Encode provenance
Compound
registry
Electronic
Lab
Notebook
Data
Warehouse
Outside
vendor
compound_id
assay_results
assay_results

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The solution
● Instantiate semantic model with (meta)data
● Queryable knowledge graph with ~14M triples
● Different “views”
● Evolving model

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The solution
Query: how many
genomics assays exist
across all sectors of
my organisation?
RNA Seq
experiment
Single-cell
RNA-seq
assay
Whole
genome
sequencing
WGS
experiment
RSQ_12965
AssayRNA_31
SC4050_EM
8019240201G
Exp_WGS_4128019240202G

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The solution
Query: how many
genomics assays exist
across all sectors of
my organisation?
RNA Seq
experiment
Single-cell
RNA-seq
assay
Whole
genome
sequencing
Genomics
Assay
WGS
experiment
RSQ_12965
AssayRNA_31
SC4050_EM 8019240201G Exp_WGS_412
owl:sameAs
rdf:type rdf:type rdf:type rdf:type
rdfs:subClassOf rdfs:subClassOf
rdfs:subClassOf rdfs:subClassOf
owl:equivalentClass
8019240202G
rdf:type

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Wrap up

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Potential pitfalls
● RDF is flexible – triples could contradict your model and inference could easily
propagate wrong information in graph
○ Use SHACL (Shapes Constraint Language) validation
● Overloading triple store = slow query times
○ Restrict triples to important information
● URI schema needs to be maintained
○ Automated URI generation and URI update
● Sometimes Knowledge Graph is overkill
○ Semantic models or common data model may suffice

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Why you should consider KGs
● Connecting data and representing as a KG enables:
○ Querying across an organisation
○ Querying across stages of drug discovery
○ Adoption of common terminologies
○ Cleaner data
○ Clear provenance of data
● Linked data is FAIR(er) data!

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Why you should consider KGs
● Machine learning and knowledge graphs
○ ML can be used to develop a KG
○ Conversely: KG can be used for ML algorithm:
“...knowledge graphs are a step towards enabling machines to more
deeply understand data ... that don’t fit neatly into the rows and columns
of a relational database” [1]
[1] https://www.forbes.com/sites/bernardmarr/2019/06/26/knowledge-graphs-and-machine-learning-the-future-of-ai-analytics/#7b825f0c3a36

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Materials
Data engineering tools for knowledge graphs
● Data Discovery Toolkit
● Model Repository
● Knowledge Base
● Data Mapping Framework
● Data Visualization Framework
FAIR data governance is like a fractal
● Purpose and scope
○ Metrics & KPIs
○ New insights
● Retrospective vs prospective
A Data Engineer’s Guide to Semantic Models (coming soon!)
● Getting started on semantic models
● RDF, SPARQL, SHACL
Common Data Models for FAIR biomedical data
● Choosing data models
● OMOP CDM
Leveraging the OMOP Common Data Model for
Clinical Trials
● Repurposing OMOP for clinical trials

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Audience Q&A
Please use the questions box

40. Semantics of data matrices
and the STATO ontology
Join us for the next FAIR/OM CoI webinar:
Speaker: Philippe Rocca-Serra, Oxford University
Thurs 28th May at 16:00 BST

41. info@pistoiaalliance.org @pistoiaalliance www.pistoiaalliance.org
Thanks for your attention