Siena, Aprile 2016
Machine learning applied to vaccine research
Alessandro Brozzi, PhD Exploratory Data Analytics
Data Science & Clinical Systems
GSK in a nutshell
Ethical medicines
Main areas:
• Breathing apparatus
• Cardiovascular – metabolic
• Immunology and infectious diseases
• Central nervous system
• Dermatology
• Urology
• HIV
• Rare diseases
Vaccines
World leader in the prevention in childhood and adulthood.
Over 30 vaccines, including:
• diphteria, tetanus, pertussis, hepatitis
A+B, polio, haemophilus influenzae, parotitis, meningitis, rotavirus, HPV and flu
Consumer Healthcare
Main areas :
• dermatology (eg Physiogel)
• oral hygiene (eg Iodosan, Aquafresh)
• nutrition (eg Horlicks)
• OTC (eg Zovirax, NiQuitin)
2
4 billion
packs in 2014
860 million
doses of vaccines in 2014
18 billion
packs in 2014
Our global presence
Hamilton
Marietta
Ste Foy
Rixensart
Wavre
Marburg
Rosia/Siena
Singapore
Shanghai Tian Yuan (JV)
Nashik
Ankleshwar Gödollö
Saint-Amand-Les-Eaux
Dresden
Moscow
Rockville
R&D Hubs Manufacturing
Facilities
Research and Development
Siena
75 clinical
studies in 2014
R&D Center
generated some of the most innovative vaccines, included
MenB
€1,3 billion
investments in R&D managed from
Siena between 2006-2015
182 people in
Research
163 in
Development
Introduction
In scope:
- present the biological problem to be addressed by ML - present results of a case study
Out of scope
- the mathematical and theoretical aspects behind ML - the formal comparisons between ML models
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What is in scope and out of scope of this presentation
Essential bibliography
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A vaccine is to convince our immune system to treat as an invading pathogen an harmless substance
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What is a vaccine?
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Microrganisms: bacteria and viruses
Who is an invading pathogen?
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Penetrance and multiplication
How a so small organism might harm
Staphylococcus aureus
tonsillitis
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Immune system cells and antibodies
How our organism defend itself
Four most common types of vaccines
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subunit vaccines
attenuated microrganism killed microrganisms
fractions of microrganisms
pathogen
harmless
Yes ok, but which subunit?
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Car metaphor
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mechanical pieces = proteins
> 2000 subunits
Experimental procedure to select subunit candidates
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time 5 -15 years
other assays
Experimental standard procedure
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experimental result
etc…
candidate
Main issues:
Time consuming High costs
Pathogen specific
The advent of genomics
DNA sequence and protein information
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In-silico pipeline
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length
number of structures
localization
ATFLPRYNDIRQQFYHNFRGKW WCFCQNDMVQMEYRALIKSVAD YDMGLRSFKKTRGMHPMKQYYG LMEVMQQAYDAIECTSPSRDFG GFDICVRFAWEYKADAYMYAPK TEQIVLPTFN
hydrophobicity
other features
Bioinformatic programs
Data matrix
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length 100
150
30
20
# helices 3
0
1
3
localization membrane
membrane
nuclear
nuclear
experimental outcome
Independent variables
Machine learning
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Breiman, 2001
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nature X
(independent variables) Y
(outcome)
unknown X
Y
Neural networks SVM
Random forests Naïve Bayes
f(x)
Siena, Aprile 2016
Case study
Study in silico vaccine candidates
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University of Technology, Sidney
Dataset
• organisms of 4 different species
• 923 proteins of known experimental results to train the models
• 140 proteins of known experimental results to test the models
• 7 protein features
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General characteristics
Data matrix
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[ 923 ] …
[ 7 ]
Results
Single rule
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Only numerical features
feature exp.
protein
Duble rule
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Machine learning algorithms
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Results
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method sensitivity specificity
single rule 0,96 0,73
double rule 0,43 0,97
method sensitivity specificity
neural networks 0,97 0,97
naïve bayes 1 0,98
k-nearest neighbor 0,92 0,97
random forest 1 0,99
adaptive boosting 1 0,98
decision tree 1 0,97
svm 0,93 0,98
General overview
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Conclusions
• Strong need to use information gathered in the past to guide experiments in the future
• Need for a procedure general for every pathogen
• ML is better than basic analyses
• A pool of algorithms might be a solution to increase efficiency Issues:
• Commonly dealing with very rectangular matrices (p >> n)
• Heterogeneous input data: categorical and numerical
• Noise effect and feature selections
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In the future
• Make the program that infer protein features more precise
• Effort to unify in a single repository all the experimental data available
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