Dr. Dennis Wang discusses possible ways to enable ML methods to be more powerful for discovery and to reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale. The talk by Dr. Dennis Wang was followed by a panel discussion with Mr. Albert Wang, M. Eng., Head, IT Business Partner, Translational Research & Technologies, Bristol-Myers Squibb.

1. 17 January, 2020
Looking beyond the hype: Applied
AI and machine learning in
translational medicine
Panelists: Dr Dennis Wang, Senior Lecturer and Group Leader in Genomic Medicine,
Dept of Computer Science and Neuroscience, University of Sheffield
Mr. Albert Wang, M. Eng., Head, IT Business Partner, Translational
Research & Technologies, Bristol-Myers Squibb
Moderator: Vladimir Makarov

2. This webinar is being recorded

3. ©PistoiaAlliance
Introduction to Today’s Speakers
Dr Dennis Wang, Senior Lecturer and Group Leader in Genomic
Medicine, Dept of Computer Science and Neuroscience,
University of Sheffield
Mr. Albert Wang, M. Eng., Head, IT Business Partner,
Translational Research & Technologies, Bristol-Myers Squibb

4. Dennis Wang, PhD
Depts. Computer Science & Neuroscience,
University of Sheffield and NIHR Sheffield BRC
Looking beyond the hype: Applied
AI and machine learning in
translational medicine
Dennis.Wang@sheffield.ac.uk
trans-bioinformatics.com #gotbioinformatics

5. Translational Medicine
“Translational medicine builds on basic research advances - studies of biological processes using cell
cultures, for example, or animal models - and uses them to develop new therapies or medical
procedures.” - Science Translational Medicine
£2 bil. spent
How can we reduce time and resources?

6. Gene
Therapy
Suite
Laser Capture
Microdissection
Live Cellular
Imaging
Confocal
Microscopy Cellular Biology
Functional Genomics
Neuropathology
Neurobiology
Drug Discovery
Suite
Neurogenetics
RNA Biology
Molecular Biology
Electrophysiology
6
Computational
Biology
Underpinning Basic Science Facilities at the University of Sheffield for the Sheffield BRC
Sheffield Institute for
Nucleic Acid Biology
Interfaculty Life Course
Biology Bateson Centre
Institute for Insilico
Medicine
INSIGNEO
Medical Advanced
Manufacturing Research
Centre
Wolfson Light
Microscopy facility
Pre Clinical Imaging
Academic Unit of
Radiology: Neuroimaging
SITraN - a translational laboratory

7. Pipelines: Clinical vs Research
Patient
Assay
Pipeline
InterpretResults Patient
Clinical
Research
Sample
Experiment
Pipeline
Experiment
Pipeline
Experiment
Pipeline
Results
Results
Results
InterpretHypothesis
Results &
Publication

8. How can we standardise and automate
data analysis?
Nature Outlook, 25 Sept 2019

9. AI
ML
Artificial Intelligence has been broadly defined as the
science and engineering of making intelligent machines,
especially intelligent computer programs (John McCarthy,
2007)
Machine Learning is an artificial intelligence technique
that can be used to design and train software algorithms to
learn from and act on data.
FDA definition
https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device
AI and ML

10. Precision medicine with machine learning
Toh, Dondelinger and Wang. EBioMedicine 2019

11. Drug Discovery Pipeline
Phase
IIa
Phase
IIb
Pre-
clinical
Dev
Phase I
Phase
III
OptimiseLeadHit
Tar
get
LC
M
Pre-clinical testing of your
drug
Expansion of
medical use
Identifying your
drug
Identifying your
target
Clinical testing of
your drug
1. Genomic profiling and
linking to disease
2. Drug design and screening 3. Patient stratification using
imaging and clinical records

12. 1. Genomic profiling
Google’s DeepVariant. Poplin et al. Nat Biotech 2018

13. Discordant variant predictions
T Alioto, et al. Nature Comm. 2015
• Variant prediction algorithms
rarely agree.
• Higher concordance if combining
variant calls
• No best practice for which callers
to combine (we use intersect of
three)
Variants identified

14. 1. Linking variants to disease:
mathematical modeling
Silverbush et al. Cancer Res. 2016

15. Simulating cells after perturbation
• Mathematical model
of all pathways
relevant to drug
resistant cells
• Mutations encoded
into parameters
• Run models and
predict protein
expression

16. Machine learning approach to predicting
cellular response
Menden, Wang et al. Nature Communications 2019

17. 2. Learning from drug screening data

18. Crowd-sourcing and benchmarking
• Post a question to whole scientific community, withhold the answer
• Evaluate submissions against the gold-standard with appropriate scoring
• Analyze results
Challenge
Train
Test
Pose
Challenge
to the
Community
Design Open Challenge Scoring
Tested ~900 drug combinations across 85 cancer cell lines
https://www.synapse.org/DrugCombinationChallenge

19. Performance of ML algorithms
• Crowd-sourced 76 machine-learning algorithms
• Measured correlation between predicted vs observed response
• Replicates were highly variable and lacked gold-standard labelled
data
Menden, Wang et al. Nature Communications 2019

20. Certain drugs are always difficult to predict

21. 3. Patient stratification
Keshava, Toh, et al. NPJ Systems Biology and Applications
2019
B Wang, et al. Nature Methods 2014

22. Two lung cancer patients
Patient 137
Patient 148
• Female, 46-year old
• non-smoker
• Stage 3
• lung adenocarcinoma,
• did not respond to chemo
• EGFR mutation
• Female, 54-year old
• non-smoker
• Stage 3
• lung adenocarcinoma,
• did not respond to chemo
• EGFR mutation
EGFR inhibitor
gefitinib
EGFR inhibitor
gefitinib

23. Learning from multi-omics data
EGFR
148
similarity score
148
MET
137
137
Similarity score:
phospho-protein + RNA expression +
copy number + mutation
Self Organizing Map
Stewart, E. et al. J. Clin. Onc. 2015
Wang et al. Int. J. Cancer, 2016

24. Patient-derived models

25. Clinical data: Unstructured vs Structured
Clinical data
Hospital
Episode
Statistics &
Linked EHR
? ? biomarker ?? outcome?
?

26. Biggest challenges for ML in translational
research
Processing data is
resource intensive
and time consuming
Describing why the
algorithm made its
decision

27. Train Visualise
R Shiny for describing ML outputs

28. Follow Best Practices from the Clinic
• Documentation
• Provenance
• Version control
• Full traceability
• Validation
• Audits
• Participation in benchmarking
• Community championing of best practices (eg. PCS
framework https://arxiv.org/abs/1901.08152)

29. “Building a community and
developing best practices for
healthcare data science”
SITraN Bioinformatics
Dr Emily Chambers
Dr Emmanuel Jemmah
Kat Koler
Dr Matt Parker
Dr Mark Dunning
Mohammed Rajab
Dr Nat Ilenkovan
Niamh Errington
Sokratis Kariotis
Tim Freeman
Dr Tzen S Toh
Hiring: Cancer Genomics
Collaborators
Dr Dana Silverbush, Tel Aviv Univ
Prof Jasmin Fisher, University College London
Dr Michael Menden, Helmholtz Zentrum
Dr Nirmal Keshava, Cerevel Therapeutics
Jonathan Dry, AstraZeneca
Dr Nhu-An Pham, Princess Margaret Cancer Centre
Prof Ming-Sound Tsao, University of Toronto

30. Poll Question 1:
What are the areas of Research where the utilization
of AI seems the most promising? Choose one or more
A. Disease biology understanding
B. Identification of new targets
C. Identification of new biomarkers
D. Patient stratification
E. Predictive toxicology

31. Poll Question 2:
What factors limit the use of AI for research in your
organization the most? Choose one or more
A. Interpretability of results
B. Data availability
C. Reproducibility of results
D. Regulatory restrictions

32. ©PistoiaAlliance
Panel Discussion and Audience Q&A
Please use the Question function in GoToWebinar

33. ©PistoiaAlliance
Upcoming Webinars
1. February 6th, 2020: Using AI and Predictive
Analytics to Optimize Clinical Trial Design, Planning
and Delivery (tentative, speakers TBD)
2. April 7th, 2020: Darren Green, Director of Molecular
Design & Senior Fellow at GlaxoSmithKline
(tentative, title TBD)
Please suggest other topics and speakers

34. info@pistoiaalliance.org @pistoiaalliance www.pistoiaalliance.org
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