using deep learning for product innovation in the personalized care space
TRANSCRIPT
Artificial Intelligence in Health
Shalini Ananda, PhD [email protected]
http://quantifiedskin.com1061 Market St. #511 San Francisco, CA 94103
http://quantifiedskin.com
Deep Learning Teaches Computers to Think
Our brain’s neocortex utilizes layers to create representation from low level inputs to transform them into meaningful assertions.
Deep learning enables computers to do the same.
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Deep Learning Assists in Feature Engineering
Raw DataFeature
ExtractorFeatures
Algorithm (machine learning)
(train or predict)
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Deep Learning Assists in Feature Engineering
Raw DataFeature
ExtractorFeatures
Algorithm (machine learning)
(train or predict)
Deep learning
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Easy to Tell These Are Cookies
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Necessary to Have a Deep Understanding of Material Science
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What If a Computer Could Think Like a Researcher?
Published experiments contain magnitudes of errors, but there are meaningful patterns if you know what to look for
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We Can Teach Computers to Learn the Relevant Scientific Data
Ever increasing number of experiments
Experimentation steps
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Humans Use a Logical Approach to Experimentation
1
Elucidating the role of a material or molecular
system
i.e. Targeting and sustained
release
2
Estimating the effect of a material system in the
state of action
i.e. Biodistribution and half-life
3
Design of material
Run experiment
Prepare for next iteration
Human’s can’t physically test all possibilities
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Need for Reducing Irrelevant Experiments
Experiments must factor characteristics such as:
•Targeting modalities •Charge modifications •Shape, and more…
If there are 10 nanoparticles and 6 possible parameters to change and 43 variants; that’s over:
10258 experiments
Not enough time to run every experiment.Smart materials
Lets use nanoparticles as an example
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Our Validation Approach
1
We outreached to researchers working on
smart materials.
The researchers provided us characterization input data.
Three step process
We conducted 152 blind experiments with 31 institutions.
A few of the institutions we worked with
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Our Validation Approach
1
We outreached to researchers working on
smart materials.
The researchers provided us characterization input data.
2
Our team ran the characterization inputs
through our deep learning engine, NuSilico™.
We predicted biodistribution and half-life outputs.
3
Researchers ran their experiments
(avg. length 4 months).
We compared NuSilico™’s outputs to the researcher’s
experimental data.
Three step process
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Our Results - Our Predictions vs Observed Empirical Experiments
From 152 experiments with 31 institutions
R2 = 0.9370 R2 = 0.9656
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Deep Learning Is Better for Feature Selection than SVM
recall: 61.40
precision: 66.80
AUC: 84.01
SVM polynomial kernel deep learning
recall: 76.80
precision: 88.10
AUC: 93.70
In terms of sensitivity/recall, ROC, and precision where the laplacian score was utilized as the feature selection method for building the model. We compare SVM polynomial kernel to deep learning
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Our Simulations Produced Accurate Models in a Fraction of the Time
We have repeatably demonstrated an order of magnitude time improvement using deep learning
Over 39 months
Just 1 month
Current researching methods
What we’ve demonstrated with deep learning
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Building Personalized Therapies - Systematically
1
Determine the target cells.
What are you trying to achieve physically?
Three step process
2
Understand the building blocks.
Train the computer to test outcomes of experiments.
3
Use deep learning to simulate and rank the best
designs.
Test orders of magnitude of experiments to find the most optimal design path.
Thank You