Accelerating Scientific Research Through Machine Learning and Graph

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Miroculus is a molecular diagnostics company that leverages the potential of microRNAs as biomarkers and has created the most easy-to-use and automated platform for their detection. MicroRNAs are small non-coding RNA molecules, whose primary role is to regulate the expression of our genes. Their discovery in circulation of body fluids such as blood plasma/serum, urine and saliva has been followed up by a multitude of studies, providing evidence that detection of specific microRNA molecules can give clues about a person’s health status and may therefore be used as biomarkers for various conditions. Loom is an up-to-date snapshot of the scientific literature landscape focused on microRNAs that we built to expedite our own research. As of today, there is no compelling way to access much of the microRNA research. By using Loom's easy-to-use, interactive UI, the researcher is able to quickly locate the relevant sentences across many publications relating specific microRNAs with her disease or gene of interest. With this tool, our objective is to provide a visually compelling and complete overview of how microRNAs relate to specific diseases and genes. At the backend, Loom is comprised of 4 microservices. The first one is a listener that fetches new publications daily that are available in the NCBI databases: PubMed for abstracts and PMC for full-text, open-access publications. Then, a natural language processor scans the publication, breaking them down into their constituent sentences and detecting mentions of microRNAs, genes and diseases. Within each sentence, a machine learning scorer evaluates the strength and type of relationship on a scale from 0 to 1 and outputs the results in a graph database. The resulting graph database is then queried in real-time by the UI to retrieve the sentences and relationships the user is interested in.

Health & Medicine

Accelerating scientiﬁc
research through Machine
Learning & Graph
Jorge Soto
CTO, Miroculus
Antonio Molins
VP Data Science, Miroculus
SAN FRANCISCO
13-14 OCTOBER 2016

1993
lin-4 in c. elegans
2000
let-7 in h. sapiens
microRNA

microRNAs are tissue specific
1993
lin-4 in c. elegans
2000
let-7 in h. sapiens

microRNA expression across different cancer types
gastrointestinal tract samples
epithelial origin samples
Jun Lu et al. MicroRNA expression profiles classify human cancers. Nature 435, 834-838(9 June 2005)
1993
lin-4 in c. elegans
2000
let-7 in h. sapiens
2002
1st link to cancer

1993
lin-4 in c. elegans
2000
let-7 in h. sapiens
2008
plasma
2002
1st 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

3 simple steps
Sample collection
20 mins

3 simple steps
Sample collection
Insert sample in a
cartridge device
20 mins 60 mins

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
2000
let-7 in h. sapiens
2008
plasma

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

Breast 
Cancer
DADS
“As shown in Fig. 3, DADS inhibited
breast cancer growth by up-
regulating MiR-34A expression.”
What has the elephant learnt so far?

Breast 
Cancer
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

- 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 architecture
Scorer
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 signature
Decision 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

jorge@miroculus.com 
antonio@miroculus.com
http://loom.bio

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