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Ontologies & Machine Learning v2 - SciBIte Lab Of The Future 2019

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My presentation at this years lab of the future event at the genome campus in November 2019. Updated from the previous one to condense into a 20 minute slot. Talks about the role of ontologies in FAIR data environments and how #cleandata helps AI & deep learning processes. But also how machine learning can help us build more intelligent systems to deal with scientific content.

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Ontologies & Machine Learning v2 - SciBIte Lab Of The Future 2019

  1. 1. Are Ontologies Relevant In A Machine Learning World? Dr. Lee Harland Chief Scientific Officer, SciBite Limited Lab Of The Future October 2019 https://scibite.com | https://www.slideshare.net/scibitely/
  2. 2. The Promised Data Lake….
  3. 3. The Real Data Lake….
  4. 4. ….Therefore, we are confident that the true cost of not having FAIR research data is much higher than the estimated €10.2bn per year...
  5. 5. What it feels like…
  6. 6. Computers Don’t Understand Text….
  7. 7. Ontologies: The Bedrock Of Good Data Stewardship “Things Not Strings” Machine Understandable Human Validated (Consensus/Authority) Stable Identifiers Define A Domain = “Semantic”
  8. 8. Transform Search, LIMS, ELN, Regulatory & more: Synonym Independent Search (Viagra=Sildenafil) Ontology Search (”Find projects on Kinases”)…. Connection Search (“Drugs that cause inflammation”) Beautiful Data – build a data integration legacy Empower Machine Learning PAXIP1 Potentiates the Combination of WEE1 ….. SciBite - An Infrastructure Company For This…
  9. 9. AZ Ontology-Driven Search At Massive Scale http://sinequa/com https://www.sinequa.com/live- webcast-unlock-wealth-rd- data/ Ack: Nick Brown, AZ
  10. 10. F.A.I.R @ BMS with Smart Semantic Forms • Public Bioassay Ontology • Augmented with BMS-specific terms • Users can suggest new assays etc. • Reactive, semantic form fields CREDIT: BMS AIMS Team
  11. 11. Major examples of ontology-based clean-data AI
  12. 12. The good news is I have discovered inefficiencies… …The bad news is that you are one of them. https://timoelliott.com/blog/cartoons/artificial-intelligence-cartoons AI Is Here
  13. 13. Examples: Named Entity Recognition/Ontology Enrichment & Question Answering
  14. 14. Expanding models in virtuous circles Often made as softer, open-textured cheeses, the addition of blue-mould causes veins to form. Classic examples are Stilton, Roquefort & Gorgonzola. The British way of making cheese, where moisture is driven out by acidifying milk gives Cheddar, Lancashire & red Leicester. Often made as softer, open-textured cheeses, the addition of blue-mould causes veins to form. Classic examples are Stilton, Roquefort & Gorgonzola. The British way of making cheese, where moisture is driven out by acidifying milk gives Cheddar, Lancashire & red Leicester. Scientific Corpus Identify Known Ontology Concepts Train model Run Model Manual Evaluation 'false' positives Add true positives to vocabs TERMiteTERMite New model Often made as softer, open-textured cheeses, the addition of blue- mould causes veins to form. Classic examples are Stilton, Roquefort & Gorgonzola. The British way of making cheese, where moisture is driven out by acidifying milk gives Cheddar, Lancashire & red Leicester.
  15. 15. A Helping Hand For Ontology Building
  16. 16. Natural Language Interfaces Machine Learning Toolkit
  17. 17. Natural Query as an API
  18. 18. • Large numbers of disorganised documents (i.e. CRO documents) • Need to align these to internal taxonomy of categories (e.g. M4 hierarchy from FDA) • Also need to identify key pieces of metadata (e.g. what is the study compound? Title? Assay… etc ) • Manual process, incredibly time consuming Pfizer Acquisition Challenge CREDIT: Pfizer Computational Sciences http://www.bio- itworld.com/2018/08/08/a- new-machine-learning- approach-to-document- classification-a-pfizer/scibite- collaboration.aspx
  19. 19. LifeArc Horizon Scanning
  20. 20. © 2018 SciBite Limited Ontologies, FAIR & Machine Learning form a powerful model for data stewardship
  21. 21. Acknowledgements AZ: R&D Search Team, Integrative Informatics Team, Sinequa, Nick Brown Pfizer: Computational Sciences CoE, Steve Penn BMS: Aims Team LifeArc Team Many colleagues at SciBite involved in this work
  22. 22. Thanks! Slides @ https://www.slideshare.net/scibitely Visit Us At http://scibite.com

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