Rare genetic diseases are very challenging to diagnose, with the average child waiting for diagnosis for 5 years. Next generation genetic sequencing data may hold the key to diagnosis, however analysis can become a paramount task with multiple factors affecting conclusions. Dx29, an AI-assisted platform facilitates this task, allowing the physician to drive the analysis. Dx29 is a free platform developed by Foudation29, in close collaboration with academic groups.

1. Dx29: genetic rare disease
diagnosis support with AI
Pablo Botas
Head of Science, Foundation29

2. O R G A N I Z A T I O N
P L A T I N U M S P O N S O R S
Thank you!
C O L L A B O R A T O R S

3. pablo.botas@foundation29.org
Rare genetic diseases are very challenging to diagnose, with the
average child waiting for diagnosis for 5 years. Next generation genetic
sequencing data may hold the key to diagnosis, however analysis can
become a paramount task with multiple factors affecting conclusions.
Dx29, an AI-assisted platform facilitates this task, allowing the
physician to drive the analysis. Dx29 is a free platform developed by
Foudation29, in close collaboration with academic groups
Pablo Botas
Head of Science at Foundation29

4. https://globalgenes.org/rare-facts/
1 in 10
People
affected by
Rare Disease
3 of 10
Children with a Rare
Disease won’t live to
see their 5th birthday
8 in 10
Have a genetic
origin
4.8 Years
For patients to
receive a diagnosis
95%
of Rare Diseases
lack approved
treatment
10000
Rare Diseases
Rare disease facts
4

5. 5

6. Lack of diagnosis is lack of data
• Lack of diagnosis is about lack of signal
• Doctors can’t let you know where you are
• Your journey starts by collecting information
6

7. Diagnosis navigator
7

8. Diagnosis is about finding a route
• Navigation requires a map and data coordinates
• Inaccurate navigation has very high costs!!
8
Required coordinates:
1. Symptoms
2. Genetic Variants

9. Icons by different authors from www.flaticon.com
Symptom-based path-finding (diagnosis)
 Rare diseases have complex phenotypes
 Differential diagnosis based on subtle
details
 Rare diseases: inexperience
 Disease evolution is gradual
 Symptom identification is hard
 Short time per patient, independently
of complexity
9

10. 10
Licensed under CC BY-SA-NC

11. Icons by different authors from www.flaticon.com
Genotype-based path-finding (diagnosis)
 Genetic testing is a black box
 Mutation analysis is often manual
 Symptom identification is hard!
 EMR is not a good communication tool
 Difficult consideration of new symptoms
11

12. : AI as the GPS powering the navigation
12

13. New patient
to study
The navigator
DNA data
Symptom extraction
using NLP
1
Variant
prioritization
supported by the
phenotype
2
Candidate
symptoms based
on the mutations
3
Candidate
conditions for
assessment
4 13

14. 1. Symptom identification
 Dx29 uses NCR1,2, developed by the
Centre for Computational Medicine at
SickKids, Toronto
 Deep NN to classify concepts into Human
Phenotype Ontology (HPO)3 terms
EMR, electronic medical records; NN, neural network; NLP, natural language processing
1. Github. 2019. Available from: https://github.com/ccmbioinfo/NeuralCR (Accessed 11 June 2019);
2. Arbabi A, et al. 2019; 7(2):e12596. doi: 10.2196/12596.
3. Köhler S, et al. Nucleic Acids Res 2018;47:D1018–D1027 14

15. How to compute on concepts?

16. Rank mutation
pathogenicity based
on public databases:
Exomiser1,2
1. Robinson PN, et al. Genome Res 2014;24:340–34; 2. Github. 2019. Available from: https://github.com/exomiser/Exomiser
(Accessed 11 June 2019); 3. Zhou X, et al. Nat Commun 2014;5:4212; 4. Wang Genomics Lab. 2019. Available from: http://phenolyzer.wglab.org/ (Accessed 11
June 2019); 5. Yang H, et al. Nat Methods 2015;12:841–843
2. Variant analysis

17. 1. Robinson PN, et al. Genome Res 2014;24:340–34; 2. Github. 2019. Available from: https://github.com/exomiser/Exomiser
(Accessed 11 June 2019); 3. Zhou X, et al. Nat Commun 2014;5:4212; 4. Wang Genomics Lab. 2019. Available from: http://phenolyzer.wglab.org/ (Accessed 11
June 2019); 5. Yang H, et al. Nat Methods 2015;12:841–843
What if no genetic
data is available?
Build list of candidate genes
based on disease networks3 :
Phenolyzer4,5
2. Variant analysis

18. 3. Engage with patient: What is missing?
18

19. 4. Results analysis
19

20. • Open platform to deploy and run diagnosis models
• Data available to academia to develop and test new approaches
• Open-source code and philosophy
• Data donor concept implementation
• Diagnosis of complex cases based on clustering using ML
Overall platform summary
20

21. • Very useful in areas with
no access to genetic testing
• Cloud-based, no installation
required
• Available (beta) at dx29.ai
• A single variant prioritisation
algorithm
doesn’t work for all conditions
• NLP is an ongoing challenge
• Phenotype quality in
public databases is poor
• Seamless UX is required for a
complex process
• Patients need their own
navigator
• Very good feedback from
physicians
• Exploring symptoms is useful and
innovative
• High performance: academia

22. This is great, but what about the data used … ?
22

23. The need for high quality data

24. Think
about
it!!
24
How are
diseases
named?

25. • PSR means the object is a pulsar.
• The J reveals that a coordinate system known as J2000 is used.
• 1302 and 6350 are coordinates like the latitude and longitude.
PSR J1302-6350

26. Dravet Syndrome
Lennox-Gastaut Syndrome
Allan–Herndon–Dudley Syndrome
SCN1A
MECP2
CDKL5
STXBP1
26
Rett Syndrome

27. West
Dravet
LGSSCN1A
?
A real case:
27

28. 28

29. SCN1A
Dravet Syndrome
Febrile Seizures
Temporal Lobe Epilepsy
Myoclonic Astatic Epilepsy
Lennox-Gastaut Syndrome
Migrating Partial Epilepsy of Infancy (MMPSI)
Autism
http://epilepsygenetics.net/the-epilepsiome/scn1a-this-is-what-you-need-to-know/
29

30. 30

31. 3 2581703423 4457570

32. Let’s do a better job!!
https://deepai.org/publication/embedding-complexity-in-the-data-representation-instead-of-in-the-model-a-case-study-using-heterogeneous-medical-data

33. How to compute on concepts?

35. Data locker of patient-owned data, for
the development of new algorithms.
Patients with complex conditions
(multiple genes, big genomic
changes) need an approach based on
mixed learning
: navigator for patients
35

36. 36
Final notes
1. Key technologies: NGS + AI
2. NGS+AI makes healthcare personalized, accurate and affordable
3. NGS+AI require development of standards and platforms
4. Seamless UX is vital for technology adoption
5. Transformation of healthcare into information-centered process
6. Rare diseases offer the perfect niche for testing: want to help?
7. Foundation29 is laying the basis for such transformation

37. Thanks and …
See you soon!
Thanks also to the sponsors.
Without whom this would not have been posible.
O R G A N I Z A T I O N
P L A T I N U M S P O N S O R S
C O L L A B O R A T O R S