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Accelerating scientific research through Machine Learning & Graph Jorge Soto CTO, Miroculus Antonio Molins VP Data Science, Miroculus SAN FRANCISCO 13-14 OCTOBER 2016
| Date post: | 16-Apr-2017 |
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Health & Medicine |
| Upload: | neo4j-the-fastest-and-most-scalable-native-graph-database |
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Accelerating scientific research through Machine Learning & GraphJorge Soto CTO, Miroculus Antonio Molins VP Data Science, Miroculus
SAN FRANCISCO13-14 OCTOBER 2016
microRNAs
DNA
mRNA
DNA
mRNAPROTEIN
DNA
mRNAPROTEIN
DNA
microRNA
1993lin-4 in c. elegans
2000let-7 in h. sapiens
microRNA
microRNAs are tissue specific
1993lin-4 in c. elegans
2000let-7 in h. sapiens
microRNA expression across different cancer types
gastrointestinal tract samplesepithelial origin samples
Jun Lu et al. MicroRNA expression profiles classify human cancers. Nature 435, 834-838(9 June 2005)
1993lin-4 in c. elegans
2000let-7 in h. sapiens
20021st link to cancer
1993lin-4 in c. elegans
2000let-7 in h. sapiens
2008plasma
20021st link to cancer
microRNAs found cell-free in biofluids
Highly Stable
Organ/Tissue Specific
Detectable in blood
microRNA as an ideal biomarker
microRNAs reflect your physiology
Red blood cells
Liver
Muscle
Heart
Red blood cells
Liver
Muscle
Heart
microRNAs reflect your physiology
Red blood cells
Liver
Muscle
Heart
microRNAs reflect your physiology
Red blood cells
Liver
Muscle
Heart
microRNAs reflect your physiology
3 simple steps
Sample collection
20 mins
Assay sensitivity
3 simple steps
Sample collection Insert sample in a cartridge device
20 mins 60 mins
Digital microfluidics technology
3 simple steps
Sample collection Automated workflow and data analysis
real time
Insert sample in a cartridge device
20 mins 60 mins
Adaptated from Nair et al, Am J Epid, 2014
Tissue VS circulating microRNA related publications
2000let-7 in h. sapiens
2008plasma
BreastCancer
miR-34a
DADS
Retrieve Read LearnSearch Choose
Search Choose
Retrieve Read Learn
Retrieve Read Learn
Retrieve Read Learn
Retrieve Read Learn
Retrieve Read Learn
Retrieve Read Learn
Retrieved
1,000,000+articles
192,496,883 lines
199,639,090 sentences
111,382,775 concept mentions
What has the elephant learnt so far?
“As shown in Fig. 3, DADS inhibited breast cancer growth by up-regulating MiR-34A expression.”
What has the elephant learnt so far?
“As shown in Fig. 3, DADS inhibited breast cancer growth by up-regulating MiR-34A expression.”
What has the elephant learnt so far?
DADS
BreastCancer
DADS
“As shown in Fig. 3, DADS inhibited breast cancer growth by up-regulating MiR-34A expression.”
What has the elephant learnt so far?
BreastCancer
miR-34A
DADS
“As shown in Fig. 3, DADS inhibited breast cancer growth by up-regulating MiR-34A expression.”
What has the elephant learnt so far?
Distant supervision for relationship classification
Blog post in MSFT dev site
Distant supervision for relationship classification
Blog post in MSFT dev site
Distant supervision for relationship classification
Blog post in MSFT dev site
Distant supervision for relationship classification
Blog post in MSFT dev site
Distant supervision for relationship classification
Blog post in MSFT dev site
www.loom.bio
- connect to NCBI databases (pubmed and pmc) and fetch new publications
- identify when microRNAs are mentioned in relationship to genes or diseases- split the results into sentences
NLP
I can...Listener
I can...
Loom architectureScorer
I can...- score between 0 to 1 the accuracy of the relations between the entities using machine learning
Graph
I can...
- store the relationships and their score in a graph database- be queried about each node and their relationships
55
Weiland et al, RNA biology, 2012
When discovery > validation
“Most clinical research therefore fails to be useful not because of its findings but because of its design” - JPA Ioannidis, PLOS Medicine, 2016
Unmet clinical need for stomach cancer patients
In collaboration with:
Inclusion criteria Individuals suspected of stomach cancer eligible for endoscopies.
Collection All samples collected from 2010 to 2013.
Machine-learned model
Samples split 50/50 in two groups doubly balanced per country, gender, diagnosis, subtype and stage.
Cohort distribution 650 samples including the entire cascade of the disease.
Multi-center Samples collected in Chile, Lithuania and Latvia.
Clinical study design
Proprietary 7-microRNA diagnostic signature
Proprietary 7-microRNA diagnostic signatureDecision boundary set to maximize
accuracy for the observed prevalence
Robust regardless of stage Good performance across ethnicities
Decision boundary set to maximize accuracy for the observed prevalence
Proprietary 7-microRNA diagnostic signature
-+
Without Miroculus With Miroculus
NPV = 99.8%
Miroculus test compared to gold standard
Ideal biomarker
Cost effective, simple and accurate detection
Future of diagnostics
Enabling technology
Advanced data analysis
