Speaker: Richard Wendell, Founder & CEO, tellic

1. Automating data curation with AI
for biomedical graph applications
Data Science for Life™

2. About us
Life Science
Data
Life
Sciences
Informatics Data
ScienceData Science For Life
• 2015
• New York City
• Developed in partnership
with 100’s of users at
dozens of pharma,
biotech & institutions
tellic graph
©2020 TELLIC LLC CONFIDENTIAL AND PROPRIETARY 2

3. Our advisory board
Former Chief Medical
Officer, Vertex
Chief Marketing & Digital
Experience Officer, Nielsen
Healthcare Information
security & cyber
protection executive
Former President &
Managing Director,
EMD Serono
Jeffrey Chodakewitz Greg Daniel
Christine Sublett Gary Zieziula
Jessica Federer
Former Chief Digital
Officer, Bayer
Founder & CEO, tellic
Former Fortune 500
Chief Data Officer
Richard Wendell
©2020 TELLIC LLC CONFIDENTIAL AND PROPRIETARY 3

4. “It’s difficult for people to manually go through more
than 20,000 articles and synthesize their findings...Our
hope is that AI can be used to help find answers to a key
set of questions about COVID-19.”
Anthony Goldbloom
Co-Founder & CEO Kaggle
https://www.whitehouse.gov/briefings-statements/call-action-tech-community-new-machine-readable-covid-19-dataset/?utm_source=COVID+Digest&utm_campaign=3058521bef-COVID_MARCH_16&utm_medium=email&utm_term=0_e8fe02e35b-3058521bef-232951809
“It would take many years and a large number of people
to manually read each of these academic papers
manually….This is an opportunity for NLP developers and
AI experts to …rapidly find solutions with the potential to
stop the pandemic and save lives around the world.”
Cami Russo
Author, The Future Brain
https://www.psychologytoday.com/us/blog/the-future-brain/202003/white-house-calls-ai-experts-help-covid-19-research
March 20, 2020: The AI and NLP opportunity

5. Science, June 14, 2020. https://science.sciencemag.org/
“…about 75% of the papers don’t
mention search terms used by
CORD-19’s creators, such as
‘coronavirus,’ in their titles,
abstracts, or keywords…”,
“these articles might only be
tangentially related to COVID-19…”
“…fewer than half the papers
provided full text, necessary for
comprehensive data mining by AI
programs.”
June 14, 2020: The challenges with data curation

6. Agenda
The Challenges
The tellic Solution
The Impact
Raw biomedical data is highly unrefined
knowledgen – automate biomedical graph creation with AI
tellic data platform – a unified internal & external graph data asset

7. The challenges

8. A biomedical graph is comparable to an architectural blueprint
©2020 TELLIC LLC CONFIDENTIAL AND PROPRIETARY 8

9. Raw Refined Raw Refined Raw Refined
Raw biomedical data is highly unrefined at three sequential levels
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250X 2X 20X
1. Entity 2. Relationship 3. Property

10. 1. Entity-level data can be 250X unrefined
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1.2M string disease entities -> 4,500 MESH concepts
(250X unrefined)
“Increased FH is associated with
decreased symptoms and increased
lifespan in patients with HbSC disease.”
“Children with SCD have high levels of
fetal hemoglobin (HbF) in early life.”
Sickle Cell
Anemia?
Raw data (string entities) Concepts (MESH)
“They have determined the PAI-1 4G/5G
genotypes… in 2 groups of patients with
coronary artery disease (CAD): one
without malignant arrhythmias and one
with a history of SCD.”
Sudden Cardiac
Death?
Spondylocostal Dysostosis?
Systematic
Carnitine
Deficiency?
SCD gene?

11. 2. Relationship-level data can be 2X unrefined
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79M sentences with >1 gene co-occurrence
-> 29M have meaningful relationships
(2X unrefined)
“BCL11A and KLF1 are
transcriptional repressors of
HBG2 gene expression…”
“SNPs of HBG2, HBS1L-MYB and
BCL11A can be used for the rapid
screening of disease severity in
newborns and to provide more
personalized care…”
Raw data (includes co-occurrences) Relationship strength
17%
92%

12. 3. Adding properties to RDF can explode data rows by 20X
©2020 TELLIC LLC CONFIDENTIAL AND PROPRIETARY 12
Representing the LPG graph on the left in RDF
requires 20X more triples and data rows
Resource Description FrameworkGraph with Edge Properties

13. The
tellic
Solution

14. knowledgen
has three key stages within its pipeline
©2020 TELLIC LLC CONFIDENTIAL AND PROPRIETARY 14
Using Machine Learning, AI, and Label Property Graph
knowledgen
Normalize entities
to unique concepts
within an ontology
using Machine
Learning
Quantify
relationships
based on meaning,
not syntax using AI
Compress billions
of relationships to
millions of rows
with LPG
250X à 1 2X à 1 20X à 1
Property Graph
Labeling
Automated
Ontology Mapping
Relationship Strength
Quantification1 2 3
Text data with
Natural language
processing (NER)
Structured
data
Annotated
images
RDF
triples

15. 1
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Disease
Sudden Cardiac Death
Systematic Carnitine Deficiency
Anemia, Sickle Cell
Spondylocostal Dysostosis
MeSH Unique ID: D000755
Gene“BCL11A” BCL11A
HGNC ID: 13221
“Increased levels of
fetal hemoglobin (HbF)”
Phenotype
Fetal hemoglobin
measurement
EFO ID: 0004576
“Increased FH is associated with decreased
symptoms and increased lifespan in
patients with HbSC disease.”
“Children with SCD have high levels of
fetal hemoglobin (HbF) in early life.”
“They have determined the PAI-1 4G/5G genotypes…
in 2 groups of patients with coronary artery
disease (CAD): one without malignant arrhythmias
and one with a history of SCD.”
MeSH Unique ID: D016757
Automated Ontology Mapping

16. Primary Ontology Supported Ontologies
Disorder MeSH
SnoMedCT, ICD-X, UMLS,
NCI, EFO
Gene HGNC
Ensembl, NCBI, UniProt,
OpenTargets, GO, GWAS
Catalog
Phenotype
Experimental Factor
Ontology (EFO)
HPO, OMIM
Variant
Human Genome
Variation Society
(HGVS), dbSNP
Drug ChEMBL
NDC, Orange/Purple Book,
IUPAC, DrugBank
©2020 TELLIC LLC CONFIDENTIAL AND PROPRIETARY 16
1
Automates
learning of new
custom ontologies
1 Automated Ontology Mapping

17. 2
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Relationship Strength Quantification
“Increased levels of fetal
hemoglobin are associated with
decreased symptoms and
increased lifespan in patients
with sickle cell disease
(SCD)”
Sickle Cell Disease
MeSH Unique ID: D000755
Fetal hemoglobin
measurement
EFO ID: 0004576
“Children with HbSS SCD and
HBFG were excluded from the
study, but children with the
traits for hemoglobin S,
hemoglobin C, thalassemia, and
iron deficiency were not.”
8%98%

18. 3
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Property Graph Labeling
98%
Example node properties:
• Ontology: MeSH
• Ontology ID: D000755
• Entity Type: Disease
• Therapeutic Area: Hemic & Lymphatic Diseases
• Synonym: Sickle Cell Disease
Example relationship properties:
• Source: PubMed
• Document ID: 25931013
• Journal: Experimental Hematology
• Impact Factor: 2.82
• Date Published: Oct 2016
• Strength: 98%

19. Automate generation of your company’s biomedical knowledge graph
©2020 TELLIC LLC CONFIDENTIAL AND PROPRIETARY 19
14B graph triples in highly
compressed LPG format
tellic data
Unrefined proprietary
/ internal data
Unrefined public data
(optional) automate curation
of your internal data into unified
internal + external graph database
GWASAssays
& Trials
Lab
notebooks
tellic graph
Automated
biomedical data
curation
software using
AI / NLP
1 2 3

20. 680M+ semantic relationships
618M+ relationships from scientific research
32M+ relationships from patents
30M+ relationships from clinical trials
408K unique entities
Patents
49M+ unique documents
14B+ triples transformed into 680M rows
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tellic data
Coming Soon…
Tissue Type Cell Type

21. 1 2 3
Solutions Architecture
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Internal data
External data
Control processesData repository
Curation pipeline
Hybrid graph index
knowledgentellic graph
APIs
Custom UIs
Data science
platforms
Analytics &
reporting
Cloud On-Prem
tellic graph

22. tellic graphDemo

23. The Impact

24. “tellic’s technology has
put [us] on a path to
reduce the time it takes
scientists to retrieve
relevant research by 50%”
VP R&D Data Strategy, top 5
pharma company
“Without tellic graph we wouldn’t
understand the relationships that
we find in literature. We have
repeatedly found pathway patterns
which has led us to learn a lot more
about genes in these pathways”
R&D specialist, emerging biotech
“It took us about two weeks at [my previous company] to get down
to this list of targets. Here we did it quickly during a meeting”
VP Translational Medicine, emerging biotech
What our Customers are saying…

25. Science, June 14, 2020. https://science.sciencemag.org/
"tellic graph.c19 enables
our researchers to
surface insights from
millions of research
articles in minutes. This
process can take years
with traditional
methods."
Martin Graham, CEO
Empire Discovery Institute
https://seekingalpha.com/pr/17824325-tellic-gives-scientists-free-
access-to-ai-tool-accelerates-covidminus-19-research

26. Learn more about refining biomedical data:
https://info.tellic.com/unrefined-data-challenge
For additional information please contact info@tellic.com | +1 212.221.8612 | tellic.com

27. Questions & Answers
Please submit your questions using the questions tab.