The rapid development and spread of analytical tools in the biomedical sciences has produced a variety of information about all sorts of biological components and their functions. Though important individually, their biological characteristics need to be understood in relation to the interactions they have with other biological components, which requires the integration of vast amounts of complex, semantically-rich, heterogenous data. Traditional systems are inadequate at accurately modelling and handling data at this scale and complexity, making solutions that speed up the integration and querying of such data a necessity. In this talk, we present various approaches being used in organisations to build biomedical computational pipelines to address these problems using tools such as Machine Learning and TypeDB. In particular, we discuss how to create an accurate and scalable semantic representation of molecular level biomedical data by presenting examples from drug discovery, precision medicine and competitive intelligence. Speaker: Tomás Sabat Tomás is the Chief Operating Officer at Vaticle, dedicated to building a strongly-typed database for intelligent systems. He works directly with TypeDB's open source and enterprise users so they can fulfil their potential with TypeDB and change the world. He focuses mainly in life sciences, cyber security, finance and robotics.

1. Tomás Sabat Stöfsel, COO
Building Biomedical Knowledge Graphs for In-
Silico Drug Discovery

2. 360 real time patient views

3. 360 real time patient views In silico clinical trials

4. 360 real time patient views In silico clinical trials
Hyper-personalised medicine

5. 360 real time patient views In silico clinical trials
Hyper-personalised medicine De novo drug design

6. 360 real time patient views In silico clinical trials
Hyper-personalised medicine De novo drug design
Cell and gene therapy

7. 360 real time patient views In silico clinical trials
Hyper-personalised medicine De novo drug design
Cell and gene therapy Ageing research

8. 360 real time patient views In silico clinical trials
Hyper-personalised medicine De novo drug design
Cell and gene therapy Ageing research
Immunotherapy

9. 360 real time patient views In silico clinical trials
Hyper-personalised medicine De novo drug design
Cell and gene therapy Ageing research
Immunotherapy mRNA technology

10. 2025

11. 2025

12. The future of biology is computational

13. Nearly 90% of PIs said they are or
will soon be working with large
data sets

14. 65TB
PER WEEK

15. What is computational biology?

16. Is there any area of biology that doesn’t involve
computation?

17. What data are we talking about?

18. Structured Data

19. Public Data
Structured Data
Internal Data
Legacy data
Lab data

20. Unstructured Data (academic papers, etc)
Structured Data
Public Data Internal Data
Legacy data
Lab data

21. Structured Data
Unstructured Data (academic papers, etc)
Public Data Internal Data
Legacy data
Lab data

22. Disease Drug

23. Pathway Cell type
Protein
Transcript Gene
Disease Drug
Phenotype

24. Variant Drug
Tissue
Phenotype
Sample
Protein
Trial Receptor
Cell
Chromosome
Virus
Disease
Gene
SNP
Transcript
Bacteria
Cell-type
Compound
Protein
Organism
Pathway
GPCR
Kinase
Protein complex
Drug-class
Publication
Author
Organisation
Journal
Metabolite

25. Variant Drug
Tissue
Phenotype
Sample
Protein
Trial Receptor
Cell
Chromosome
Virus
Disease
Gene
SNP
Transcript
Bacteria
Cell-type
Compound
Protein
Organism
Pathway
GPCR
Kinase
Protein complex
Drug-class
Publication
Author
Organisation
Journal
Metabolite
TOO COMPLEX

27. It’s very hard to model and integrate heterogeneous datasets

28. Why is it so hard to model biomedical data?

29. Data Modelling

30. Choose the major entities
Data Modelling
protein drug

31. protein
Choose the major entities
Identify the relationship types
drug
interaction
Data Modelling

32. Choose the major entities
Identify the relationship types
Determine which attributes belong to which entities
protein
uniprot-id
drug
chembl-id
interaction
Data Modelling

33. Choose the major entities
Identify the relationship types
Determine which attributes belong to which entities
Normalise
protein
uniprot-id
drug
chembl-id
interaction
Data Modelling

34. SSN Pnumber Hours Ename Pname Plocation
FD1
FD2
FD3
SSN Pnumber Hours
FD1
SSN Ename
FD2
Pnumber Pname Plocation
FD3
2NF Normalisation
Ename Ssn Bdate Address Dnumber Dname Dmgr_ssn
3NF Normalisation
Ename Ssn Bdate Address Dnumber Dnumber Dname Dmgr_ssn

35. So why TypeDB?

36. Choose the major entities
Identify the relationship types
Determine which attributes belong to which entities
Normalise
protein
uniprot-id
drug
chembl-id
interaction
Data Modelling

37. Choose the major entities
Identify the relationship types
Determine which attributes belong to which entities
Normalise
X
protein
uniprot-id
drug
chembl-id
interaction
Data Modelling

38. protein
uniprot-id
drug
chembl-id
interaction
Data Modelling

39. protein
uniprot-id
drug
chembl-id
interaction
define
protein sub entity,
owns uniprot-id,
plays interaction:interacted;
drug sub entity,
owns chembl-id,
plays interaction:interacting;
interaction sub relation,
relates interacting,
relates interacted;
uniprot-id sub attribute, value string;
chembl-id sub attribute, value string;
No need to normalise our data!
Data Modelling

40. protein
uniprot-id
drug
chembl-id
kinase ion-channel
interaction
Data Modelling

41. drug
chembl-id
owns
protein
uniprot-id
drug
chembl-id
kinase ion-channel
interaction
define
protein sub entity,
owns uniprot-id,
plays interaction:interacted;
kinase sub protein;
ion-channel sub protein;
drug sub entity,
owns chembl-id,
plays interaction:interacting;
interaction sub relation,
relates interacting,
relates interacted;
uniprot-id sub attribute, value string;
chembl-id sub attribute, value string;
Data Modelling

42. drug
ion-channel
kinase
interaction
interacted
interacting
interacting
interacted
interaction
Return kinases and ion-channels connected to drugs

43. interaction
protein
interacting interacted
match
$drug isa drug, has chembl-id "CHEMBL1193654";
$protein isa protein;
(interacted: $protein, interacting: $drug) isa interaction;
get $protein;
drug
interacting interacted
Return kinases and ion-channels connected to drugs

44. gene
encode
protein
source
$gene isa gene;
$protein isa protein;
$source isa source;
(encoding: $gene, encoded: $protein,
sourced: $source);
Ternary Relations
encoding encoded
sourced

45. protein
ppi
protein
locates
tissue
interacting interacting
located locating
$protein1 isa protein;
$protein2 isa protein;
$ppi (interacting: $protein1,
interacting: $protein2) isa protein-
interaction;
$tissue isa tissue;
(located: $ppi, locating: $tissue) isa
locates;
Nested Relations

46. protein disease
gene
encode assoc
assoc
associating
encoding associating
associated
associated
encoded
rule gene-protein-disease:
when {
(encoding: $x, encoded: $y) isa encode;
(associating: $y, associated: $z) isa
protein-disease-association;
} then {
(associating: $x, associated: $z) isa
gene-disease-association;
};
Rule: Infer gene to disease associations

47. Why do these constructs matter?

48. Because it makes your lives so much easier!

49. We can finally model the real world as we perceive reality

50. How do we build?

51. Drug Discovery
Data Harmonisation
Precision Medicine
Competitive Intelligence
Data Management
Supply Chain Optimisation
Clinical Trial
Cohort Selection
Disease Understanding

52. Who have run clinical trials on Ebola who also own patents?
What are the most likely gene targets for Melanoma?
Given someone’s biological and genetic profile, what clinical trials are they
eligible for?
Questions we can ask

53. Architecture

54. Architecture
Public Data
Unstructured data
Legacy data
Structured Data
Lab data
Internal Data

55. Architecture
Connectors
Public Data
Unstructured data
Structured Data
Legacy data
Lab data
Internal Data

56. Architecture
TypeDB
Loader
Custom
Loaders
Connectors
…
Public Data
Unstructured data
Structured Data
Legacy data
Lab data
Internal Data

57. Architecture
Client Drivers
(Python, Java,
NodeJS, etc)
Public Data
Unstructured data
Structured Data
Legacy data
Lab data
Internal Data
TypeDB
Loader
Custom
Loaders
Connectors
…

58. Architecture
Text Mining
coreNLP
…
Client Drivers
(Python, Java,
NodeJS, etc)
Public Data
Unstructured data
Structured Data
Legacy data
Lab data
Internal Data
TypeDB
Loader
Custom
Loaders
Connectors
…

59. Text Mining
coreNLP
…
Dashboards
Analytics
Chatbots
…
Applications
Client Drivers
(Python, Java,
NodeJS, etc)
Architecture
Public Data
Unstructured data
Structured Data
Legacy data
Lab data
Internal Data
TypeDB
Loader
Custom
Loaders
Connectors
…

60. Can we automatically map SQL, RDF or JSON to a TypeDB schema?

61. Can we automatically map SQL, RDF or JSON to a TypeDB schema?
We could, but we shouldn’t.

62. For example, if it’s already normalised in 3NF, we won’t benefit from TypeDB’s schema.

63. Genes
disease-name mesh-id ensembl-id …
… … … …
Diseases
ensembl-id symbol mesh-id …
… … … …
Loading tabular data

64. Genes
disease-name mesh-id ensembl-id …
… … … …
Diseases
ensembl-id symbol mesh-id …
… … … …
gene
symbol
owns
ensembl-id
owns
Loading tabular data

65. Genes
disease-name mesh-id ensembl-id …
… … … …
Diseases
ensembl-id symbol mesh-id …
… … … …
gene
symbol
owns
ensembl-id
owns
disease
mesh-id
owns
name
owns
Loading tabular data

66. Genes
disease-name mesh-id ensembl-id …
… … … …
Diseases
ensembl-id symbol mesh-id …
… … … …
associated-gene
gene-disease-
association
gene
symbol
owns
ensembl-id
owns
associated-disease
disease
mesh-id
owns
name
owns
Loading tabular data

67. Loading JSON

68. disease
Loading JSON

69. disease
person
person
Loading JSON

70. disease
person
person
publication
authorship
authorship
Loading JSON

71. disease
person
person
publication
authorship
publishing journal
Loading JSON
authorship

72. person mention
publication disease
authorship
person
publishing journal
end
start
Loading JSON
authorship

73. Competitive Intelligence
Drug Discovery
Precision Medicine
Given someone’s biological and genetic profile, what clinical trials are they
eligible for?
What are the most likely gene targets for Melanoma?
Who have run clinical trials on Ebola who also own patents?

74. Competitive Intelligence
Drug Discovery
Precision Medicine
Who have run clinical trials on Ebola who also own patents?

75. Competitive Intelligence Drug Discovery Precision Medicine

76. Competitive Intelligence Drug Discovery Precision Medicine
Public Data
Structured Data
Molecular
Disease
…

77. Competitive Intelligence Drug Discovery Precision Medicine
Public Data
Unstructured data
Structured Data
Molecular
Clinical Trials
Patents
Disease
…

78. Competitive Intelligence Drug Discovery Precision Medicine
Public Data
Unstructured data
Structured Data
Molecular
Clinical Trials
Patents
Disease
…

79. Competitive Intelligence Drug Discovery Precision Medicine
Public Data
Unstructured data
Structured Data
TypeDB
Loader
Custom
Loaders
Connectors
…
Molecular
Clinical Trials
Patents
Disease
…

80. Competitive Intelligence Drug Discovery Precision Medicine
Text Mining
coreNLP
…
Public Data
Unstructured data
Structured Data
TypeDB
Loader
Custom
Loaders
Connectors
…
Molecular
Clinical Trials
Patents
Disease
…

81. Client Drivers
(Python, Java,
NodeJS, etc)
Competitive Intelligence Drug Discovery Precision Medicine
Text Mining
coreNLP
…
Public Data
Unstructured data
Structured Data
TypeDB
Loader
Custom
Loaders
Connectors
…
Molecular
Clinical Trials
Patents
Disease
…

82. Who have run clinical trials on Ebola who also own patents?
Client Drivers
(Python, Java,
NodeJS, etc)
Competitive
Insights
Output
Competitive Intelligence Drug Discovery Precision Medicine
Text Mining
coreNLP
…
Public Data
Unstructured data
Structured Data
TypeDB
Loader
Custom
Loaders
Connectors
…
Molecular
Clinical Trials
Patents
Disease
…

83. Who have run clinical trials on Ebola who also own patents?
person
Competitive Intelligence Drug Discovery Precision Medicine

84. Who have run clinical trials on Ebola who also own patents?
patent person
clinical-trial
disease
name: “Ebola”
Competitive Intelligence Drug Discovery Precision Medicine

85. Who have run clinical trials on Ebola who also own patents?
patent person
clinical-trial investigation
disease
name: “Ebola”
Competitive Intelligence Drug Discovery Precision Medicine
investigator
investigated

86. Who have run clinical trials on Ebola who also own patents?
patent person
clinical-trial investigation
study disease
name: “Ebola”
Competitive Intelligence Drug Discovery Precision Medicine
investigator
studied
investigated
studying

87. Who have run clinical trials on Ebola who also own patents?
patent
ownership
person
clinical-trial investigation
study disease
name: “Ebola”
Competitive Intelligence Drug Discovery Precision Medicine
investigator
investigated
studying
studied
owned owner

88. Who have run clinical trials on Ebola who also own patents?
patent
ownership
person
clinical-trial investigation
study disease
name: “Ebola”
Competitive Intelligence Drug Discovery Precision Medicine
match
$person isa person;
investigator
investigated
studying
studied
owned owner

89. Who have run clinical trials on Ebola who also own patents?
patent
ownership
person
clinical-trial investigation
study disease
name: “Ebola”
Competitive Intelligence Drug Discovery Precision Medicine
match
$person isa person;
$patent isa patent;
investigator
investigated
studying
studied
owned owner

90. Who have run clinical trials on Ebola who also own patents?
patent
ownership
person
clinical-trial investigation
study disease
name: “Ebola”
Competitive Intelligence Drug Discovery Precision Medicine
match
$person isa person;
$patent isa patent;
$trial isa clinical-trial;
investigator
investigated
studying
studied
owned owner

91. Who have run clinical trials on Ebola who also own patents?
patent
ownership
person
clinical-trial investigation
study disease
name: “Ebola”
Competitive Intelligence Drug Discovery Precision Medicine
match
$person isa person;
$patent isa patent;
$trial isa clinical-trial;
$disease isa disease;
investigator
investigated
studying
studied
owned owner

92. Who have run clinical trials on Ebola who also own patents?
patent
ownership
person
clinical-trial investigation
study disease
name: “Ebola”
Competitive Intelligence Drug Discovery Precision Medicine
match
$person isa person;
$patent isa patent;
$trial isa clinical-trial;
$disease isa disease, has name "Ebola";
investigator
investigated
studying
studied
owned owner

93. Who have run clinical trials on Ebola who also own patents?
patent
ownership
person
clinical-trial investigation
study disease
name: “Ebola”
Competitive Intelligence Drug Discovery Precision Medicine
match
$person isa person;
$patent isa patent;
$trial isa clinical-trial;
$disease isa disease, has name "Ebola";
(owner: $person, owned: $patent) isa ownership;
investigator
investigated
studying
studied
owned owner

94. Who have run clinical trials on Ebola who also own patents?
patent
ownership
person
clinical-trial investigation
study disease
name: “Ebola”
Competitive Intelligence Drug Discovery Precision Medicine
match
$person isa person;
$patent isa patent;
$trial isa clinical-trial;
$disease isa disease, has name "Ebola";
(owner: $person, owned: $patent) isa ownership;
(investigator: $person, investigated: $trial) isa
investigation;
investigator
investigated
studying
studied
owned owner

95. Who have run clinical trials on Ebola who also own patents?
patent
ownership
person
clinical-trial investigation
study disease
name: “Ebola”
Competitive Intelligence Drug Discovery Precision Medicine
match
$person isa person;
$patent isa patent;
$trial isa clinical-trial;
$disease isa disease, has name "Ebola";
(owner: $person, owned: $patent) isa ownership;
(investigator: $person, investigated: $trial) isa
investigation;
(studying: $trial, studied: $disease) isa study;
investigator
investigated
studying
studied
owned owner

96. Who have run clinical trials on Ebola who also own patents?
patent
ownership
person
clinical-trial investigation
study disease
name: “Ebola”
Competitive Intelligence Drug Discovery Precision Medicine
match
$person isa person;
$patent isa patent;
$trial isa clinical-trial;
$disease isa disease, has name "Ebola";
(owner: $person, owned: $patent) isa ownership;
(investigator: $person, investigated: $trial) isa
investigation;
(studying: $trial, studied: $disease) isa study;
get $person;
investigator
investigated
studying
studied
owned owner

97. Who have run clinical trials on Ebola who also own patents?
patent
ownership
person
clinical-trial investigation
study disease
match
$person isa person, has name $name;
$patent isa patent;
$trial isa clinical-trial;
$disease isa disease, has name "Ebola";
(owner: $person, owned: $patent) isa ownership;
(investigator: $person, investigated: $trial) isa
investigation;
(studying: $trial, studied: $disease) isa study;
get $name;
name: “Ebola”
Competitive Intelligence Drug Discovery Precision Medicine
investigator
investigated
studying
studied
owned owner

98. Competitive Intelligence
Drug Discovery
Precision Medicine
What are the most likely gene targets for Melanoma?

99. Competitive Intelligence Drug Discovery Precision Medicine

100. Competitive Intelligence Drug Discovery Precision Medicine
Public Data
Unstructured data
Structured Data
Legacy data
Lab data
Internal Data

101. Competitive Intelligence Drug Discovery Precision Medicine
Client Drivers
(Python, Java,
NodeJS, etc)
Text Mining
coreNLP
…
Public Data
Unstructured data
Structured Data
Legacy data
Lab data
Internal Data
TypeDB
Loader
Custom
Loaders
Connectors
…

102. Competitive Intelligence Drug Discovery Precision Medicine
Client Drivers
(Python, Java,
NodeJS, etc) KGCN
Text Mining
coreNLP
…
Public Data
Unstructured data
Structured Data
Legacy data
Lab data
Internal Data
TypeDB
Loader
Custom
Loaders
Connectors
…

103. Query Result, a subgraph
Graph
Learning
Algorithm
Learner
TypeQL Query Subgraph Predictions
match
$p isa protein;
$d isa disease, has disease-
group "Cancer", has disease-id
$did;
$t isa tissue;
$g isa gene, has gene-id $gid;
($p, $t);
($t, $d);
($g, $t);

104. Competitive Intelligence Drug Discovery Precision Medicine
Client Drivers
(Python, Java,
NodeJS, etc) KGCN
List of targets
Output
What are the most likely gene targets for Melanoma?
Text Mining
coreNLP
…
Public Data
Unstructured data
Structured Data
Legacy data
Lab data
Internal Data
TypeDB
Loader
Custom
Loaders
Connectors
…

105. > match $g isa gene, has gene-id $gid;
$d isa disease, has disease-name "melanoma";
What are the most likely gene targets for Melanoma?

106. > match $g isa gene, has gene-id $gid;
$d isa disease, has disease-name "melanoma";
($g, $d) isa gene-disease-association, has kgcn-prob $p;
What are the most likely gene targets for Melanoma?

107. > match $g isa gene, has gene-id $gid;
$d isa disease, has disease-name "melanoma";
($g, $d) isa gene-disease-association, has kgcn-prob $p;
get $gid; sort desc $p;
What are the most likely gene targets for Melanoma?

108. > match $g isa gene, has gene-id $gid;
$d isa disease, has disease-name "melanoma";
($g, $d) isa gene-disease-association, has kgcn-prob $p;
get $gid; sort desc $p;
{$gid "DDXIIL1" isa gene-id;}
{$gid "WASH7P" isa gene-id;}
{$gid "MIR1302-10" isa gene-id;}
{$gid "MIR1302-11" isa gene-id;}
{$gid "OR4F5" isa gene-id;}
{$gid "FAM138D" isa gene-id;}
{$gid "FAM41C" isa gene-id;}
{$gid "NOC2L" isa gene-id;}
{$gid "HES4" isa gene-id;}
{$gid "RNF223" isa gene-id;}
{$gid "TNFRSF4" isa gene-id;}
...
What are the most likely gene targets for Melanoma?

109. Competitive Intelligence
Drug Discovery
Precision Medicine
Given someone’s biological and genetic profile, what
clinical trials are they eligible for?

110. Competitive Intelligence Drug Discovery Precision Medicine
Public Data
Unstructured data
Structured Data
Molecular
Clinical Trials
…
Precision DBs
…

111. Competitive Intelligence Drug Discovery Precision Medicine
Public Data
Unstructured data
Structured Data
Molecular
Clinical Trials
…
Precision DBs
…

112. Competitive Intelligence Drug Discovery Precision Medicine
Public Data
Unstructured data
Structured Data
Molecular
Clinical Trials
…
Precision DBs
…

113. Public Data
Unstructured data
Structured Data
Molecular
Clinical Trials
…
Precision DBs
…
Competitive Intelligence Drug Discovery Precision Medicine

114. Competitive Intelligence Drug Discovery Precision Medicine
Text Mining
coreNLP
…
TypeDB
Loader
Custom
Loaders
Connectors
…
Public Data
Unstructured data
Structured Data
Molecular
Clinical Trials
…
Precision DBs
…
Competitive Intelligence Drug Discovery Precision Medicine

115. Client Drivers
(Python, Java,
NodeJS, etc)
Competitive Intelligence Drug Discovery Precision Medicine
Text Mining
coreNLP
…
TypeDB
Loader
Custom
Loaders
Connectors
…
Public Data
Unstructured data
Structured Data
Molecular
Clinical Trials
…
Precision DBs
…

116. Client Drivers
(Python, Java,
NodeJS, etc)
Competitive Intelligence Drug Discovery Precision Medicine
Personalised-
therapies
Output
Text Mining
coreNLP
…
TypeDB
Loader
Custom
Loaders
Connectors
…
Public Data
Unstructured data
Structured Data
Molecular
Clinical Trials
…
Precision DBs
…

117. Client Drivers
(Python, Java,
NodeJS, etc)
Personalised-
therapies
Output
Competitive Intelligence Drug Discovery Precision Medicine
Given someone’s biological and genetic profile, what clinical trials are they
eligible for?
Text Mining
coreNLP
…
TypeDB
Loader
Custom
Loaders
Connectors
…
Public Data
Unstructured data
Structured Data
Molecular
Clinical Trials
…
Precision DBs
…

118. trial
personalised-
therapy
person
match
$person isa person, has name "Alice";
$trial isa clinical-trial, has nct-id $nct;
($person, $trial) isa personalised-therapy;
get $nct;
Competitive Intelligence Drug Discovery Precision Medicine
Given someone’s biological and genetic profile, what clinical trials are they
eligible for?

119. Given someone’s biological and genetic profile, what clinical trials are they
eligible for?
Relevance for a clinical trial Eligibility for a clinical trial
Patient has the same gene and variant
mentioned in the clinical trial
Patient is within the right age bracket and
gender for the trial
Competitive Intelligence Drug Discovery Precision Medicine

120. trial
personalised-
therapy
person
Given someone’s biological and genetic profile, what clinical trials are they
eligible for?
Competitive Intelligence Drug Discovery Precision Medicine

121. trial
personalised-
therapy
relevant-
trial
person
Given someone’s biological and genetic profile, what clinical trials are they
eligible for?
Competitive Intelligence Drug Discovery Precision Medicine

122. trial
personalised-
therapy
eligible-trial
relevant-
trial
person
Given someone’s biological and genetic profile, what clinical trials are they
eligible for?
Competitive Intelligence Drug Discovery Precision Medicine

123. trial
personalised-
therapy
eligible-trial
relevant-
trial
rule personalised-patient-therapy:
when {
($person, $trial) isa eligible-trial-participant;
($person, $trial) isa relevant-trial-participant;
} then {
($person, $trial) isa personalised-therapy;
};
Given someone’s biological and genetic profile, what clinical trials are they
eligible for?
Competitive Intelligence Drug Discovery Precision Medicine

124. Given someone’s biological and genetic profile, what clinical trials are they
eligible for?
Relevance for a clinical trial
Patient has the same gene and variant
mentioned in the clinical trial
relevant-
trial
Competitive Intelligence Drug Discovery Precision Medicine

125. trial person
relevant-
trial
Competitive Intelligence Drug Discovery Precision Medicine

126. trial person
variant assoc
mention
relevant-
trial
Competitive Intelligence Drug Discovery Precision Medicine

127. trial person
gene assoc
variant assoc
mention
mention
relevant-
trial
Competitive Intelligence Drug Discovery Precision Medicine

128. trial person
gene
symbol: $gs
assoc
variant assoc
symbol: $vs
mention
mention
relevant-
trial
rule trial-participant-relevance:
when {
$person isa person;
$gene isa gene;
$variant isa variant;
$trial isa clinical-trial;
($person, $gene);
($person, $variant);
($trial, $gene);
($trial, $variant);
} then {
($person, $trial) isa relevant-trial;
};
Competitive Intelligence Drug Discovery Precision Medicine

129. Given someone’s biological and genetic profile, what clinical trials are they
eligible for?
Eligibility for a clinical trial
Patient is within the right age bracket and
gender for the trial
eligible-trial
Competitive Intelligence Drug Discovery Precision Medicine

130. trial person
eligible-trial
Competitive Intelligence Drug Discovery Precision Medicine

131. trial person
disease assoc
assoc
eligible-trial
Competitive Intelligence Drug Discovery Precision Medicine

132. trial person
disease assoc
assoc
max-age
gender
eligible-trial
age
min-age
greater than
less than
Competitive Intelligence Drug Discovery Precision Medicine

133. trial person
disease assoc
assoc
eligible-trial
max-age
gender
age
min-age
greater than
less than
rule trial-participant-eligibility:
when {
$person isa person, has age $age, has gender $gender;
$trial isa clinical-trial,
has min-age <= $age,
has max-age >= $age,
has gender = $gender;
$disease isa disease;
($disease, $person);
($disease, $trial);
} then {
($person, $trial) isa eligible-trial;
};
Competitive Intelligence Drug Discovery Precision Medicine

134. Given someone’s biological and genetic profile, what clinical trials are they
eligible for?
Relevance for a clinical trial Eligibility for a clinical trial
Patient has the same gene and variant
mentioned in the clinical trial
Patient is within the right age bracket and
gender for the trial
Competitive Intelligence Drug Discovery Precision Medicine

135. Drug Discovery
Data Harmonisation
Precision Medicine
Competitive Intelligence
Data Management
Supply Chain Optimisation
Clinical Trial
Cohort Selection
Disease Understanding

136. TypeDB Bio
github.com/typedb-osi/typedb-bio