Accelerating Scientific Research Through Machine Learning and Graph
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.
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Accelerating Scientific Research Through Machine Learning and Graph
- 1. Accelerating scientific research through Machine Learning & Graph Jorge Soto CTO, Miroculus Antonio Molins VP Data Science, Miroculus SAN FRANCISCO 13-14 OCTOBER 2016
- 3. microRNAs
- 4. DNA
- 5. mRNA DNA
- 8. 1993 lin-4 in c. elegans 2000 let-7 in h. sapiens microRNA
- 9. microRNAs are tissue specific 1993 lin-4 in c. elegans 2000 let-7 in h. sapiens
- 10. 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
- 11. 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
- 12. Highly Stable Organ/Tissue Specific Detectable in blood microRNA as an ideal biomarker
- 13. microRNAs reflect your physiology Red blood cells Liver Muscle Heart
- 14. Red blood cells Liver Muscle Heart microRNAs reflect your physiology
- 15. Red blood cells Liver Muscle Heart microRNAs reflect your physiology
- 16. Red blood cells Liver Muscle Heart microRNAs reflect your physiology
- 20. 3 simple steps Sample collection 20 mins
- 23. 3 simple steps Sample collection Insert sample in a cartridge device 20 mins 60 mins
- 25. 3 simple steps Sample collection Automated workflow and data analysis real time Insert sample in a cartridge device 20 mins 60 mins
- 28. Adaptated from Nair et al, Am J Epid, 2014 Tissue VS circulating microRNA related publications 2000 let-7 in h. sapiens 2008 plasma
- 40. Retrieve Read LearnSearch Choose
- 41. Search Choose Retrieve Read Learn Retrieve Read Learn Retrieve Read Learn Retrieve Read Learn Retrieve Read Learn Retrieve Read Learn
- 42. 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?
- 43. “As shown in Fig. 3, DADS inhibited breast cancer growth by up- regulating MiR-34A expression.” What has the elephant learnt so far?
- 44. “As shown in Fig. 3, DADS inhibited breast cancer growth by up- regulating MiR-34A expression.” What has the elephant learnt so far? DADS
- 45. 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?
- 46. 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?
- 47. Distant supervision for relationship classification Blog post in MSFT dev site
- 48. Distant supervision for relationship classification Blog post in MSFT dev site
- 49. Distant supervision for relationship classification Blog post in MSFT dev site
- 50. Distant supervision for relationship classification Blog post in MSFT dev site
- 51. Distant supervision for relationship classification Blog post in MSFT dev site
- 52. 52 [cypher]
- 54. www.loom.bio
- 55. - 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
- 57. Weiland et al, RNA biology, 2012 When discovery > validation
- 58. “Most clinical research therefore fails to be useful not because of its findings but because of its design” - JPA Ioannidis, PLOS Medicine, 2016
- 60. Unmet clinical need for stomach cancer patients
- 62. 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
- 63. Proprietary 7-microRNA diagnostic signature
- 64. Proprietary 7-microRNA diagnostic signature Decision boundary set to maximize accuracy for the observed prevalence
- 65. Robust regardless of stage Good performance across ethnicities Decision boundary set to maximize accuracy for the observed prevalence Proprietary 7-microRNA diagnostic signature
- 66. - + Without Miroculus With Miroculus NPV = 99.8% Miroculus test compared to gold standard
- 67. Ideal biomarker Cost effective, simple and accurate detection Future of diagnostics Enabling technology Advanced data analysis