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The crusade for big data in the AAL domain
- 1. The crusade for Big Data in the AAL domain Femke Ongenae
- 2. 22 Session organizers – Hi! Femke Ongenae Knowledge Engineer IBCN, UGent - iMinds Femke De Backere eCare Researcher IBCN, UGent - iMinds Griet Verhenneman Legal Researcher ICRI, KULeuven - iMinds Julie Doyle Research Fellow CASALA
- 3. 33 Ambient-Assisted Living Trend towards more personalized & context-aware healthcare services
- 4. 44 FallRisk Social-aware and context-aware multi-sensor fall detection platform
- 5. 5
- 6. 66 Enable the collection of (near) real-life profile and context data
- 8. Keynote by Dr. Gray Femke Ongenae
- 9. Data Integration in a Big Data Context Open PHACTS Case Study Alasdair J G Gray A.J.G.Gray@hw.ac.uk alasdairjggray.co.uk @gray_alasdair
- 10. Big Data @gray_alasdair Big Data Integration 11 Volume Velocity Variety Veracity http://i.kinja-img.com/gawker-media/image/upload/lvzm0afp8kik5dctxiya.jpg
- 11. Open PHACTS Use Case “Let me compare MW, logP and PSA for launched inhibitors of human & mouse oxidoreductases” Chemical Properties (Chemspider) Launched drugs (Drugbank) Human => Mouse (Homologene) Protein Families (Enzyme) Bioactivty Data (ChEMBL) … other info (Uniprot/Entrez etc.) “Let me compare MW, logP and PSA for launched inhibitors of human & mouse oxidoreductases” @gray_alasdair Big Data Integration 12
- 12. Open PHACTS Mission: Integrate Multiple Research Biomedical Data Resources Into A Single Open & Free Access Point @gray_alasdair Big Data Integration 13
- 13. Literature PubChem Genbank Patents Databases Downloads Data Integration Data Analysis Firewalled Databases Repeat @ each company x A single, shared solution. Funded under • IMI: 2011-14 • ENSO: 2014-16 Pre-competitive Data @gray_alasdair Big Data Integration 14
- 14. http://dx.doi.org/10.1016/j.websem.2014.03.003 • Cloud-Based “Production” Level System. • Secure & Private • Guided By Business Questions • Uses Semantic Web Technology • Provides REST-ful API http://dx.doi.org/10.1016/j.drudis.2013.05.008 Discovery Platform @gray_alasdair Big Data Integration 15
- 15. Scientific Results http://ceur-ws.org/Vol- 1114/Demo_Dunlop.pdf http://dx.doi.org/10.1016/j.drudis.2014.11.006 http://dx.doi.org/10.1002/minf.v31.8 http://dx.doi.org/10.1371/journal.pone.0115 460 @gray_alasdair Big Data Integration 16
- 16. OPS Discovery Platform @gray_alasdair Big Data Integration 17 Drug Discovery Platform Apps Domain API Interactive responses Production quality integration platform Method Calls Standard Web Technologies
- 17. App Ecosystem @gray_alasdair An “App Store”? Explorer Explorer2 ChemBioNavigator Target Dossier Pharmatrek Helium MOE Collector Cytophacts Utopia Garfield SciBite KNIME Mol. Data Sheets PipelinePilot scinav.it Taverna Big Data Integration 18https://www.openphacts.org/2/sci/apps.html
- 18. http://chembionavigator.com ChemBio Navigator @gray_alasdair Big Data Integration 19
- 19. @gray_alasdair Big Data Integration 20
- 20. @gray_alasdair Big Data Integration 21
- 21. API Hits @gray_alasdair Big Data Integration 22 0 10 20 30 40 50 60 Jan 2013 Feb 2013 Mar 2013 Apr 2013 May 2013 June 2013 July 2013 Aug 2013 Sept 2013 Oct 2013 Nov 2013 Dec 2013 Jan 2014 Feb 2014 Mar 2014 Apr 2014 May 2014 June 2014 July 2014 Aug 2014 Sept 2014 Oct 2014 Nov 2014 Dec 2014 Jan 2015 Feb 2015 Mar 2015 Apr 2015 May 2015 June 2015 NoofHits Millions Month Public launch of 1.2 API 1.3 API 1.4 API 1.5 API
- 22. OPS Discovery Platform Nanopub Db VoID Data Cache (Virtuoso Triple Store) Semantic Workflow Engine Linked Data API (RDF/XML, TTL, JSON) Domain Specific Services Identity Resolution Service Chemistry Registration Normalisation & Q/C Identifier Management Service Indexing CorePlatform P12374 EC2.43.4 CS4532 “Adenosine receptor 2a” VoID Db Nanopub Db VoID Db VoID Nanopub VoID Public Content Commercial Public Ontologies User Annotations Apps @gray_alasdair Big Data Integration 23
- 23. Open PHACTS Data @gray_alasdair Big Data Integration 24
- 24. John Wilbanks consulted for us A framework built around STANDARD well-understood Creative Commons licences – and how they interoperate Deal with the problems by: Interoperable licences Appropriate terms Declare expectations to users and data publishers One size won‘t fit all requirements Data Licensing (Or Lack Of!)
- 25. API: Complex Interactions @gray_alasdair Big Data Integration 26 Disease Tissue Target Compound Pathway
- 26. STANDARD_TYPE UNIT_COUNT ---------------- ------- AC50 7 Activity 421 EC50 39 IC50 46 ID50 42 Ki 23 Log IC50 4 Log Ki 7 Potency 11 log IC50 0 STANDARD_TYPE STANDARD_UNITS COUNT(*) ------------------ ------------------ -------- IC50 nM 829448 IC50 ug.mL-1 41000 IC50 38521 IC50 ug/ml 2038 IC50 ug ml-1 509 IC50 mg kg-1 295 IC50 molar ratio 178 IC50 ug 117 IC50 % 113 IC50 uM well-1 52 ~ 100 units >5000 types Implemented using the Quantities, Units, Dimension, Types Ontology (http://www.qudt.org/) Quantitative Data Challenges @gray_alasdair Big Data Integration 27
- 27. Quality Assurance @gray_alasdair Big Data Integration 28
- 28. P12047 X31045 GB:29384 Identity Mapping @gray_alasdair Big Data Integration 29 Andy Law's Third Law “The number of unique identifiers assigned to an individual is never less than the number of Institutions involved in the study” http://bioinformatics.roslin.ac.uk/lawslaws/
- 29. Gleevec®: Imatinib Mesylate @gray_alasdair Big Data Integration 30 DrugbankChemSpider PubChem Imatinib MesylateImatinib Mesylate YLMAHDNUQAMNNX-UHFFFAOYSA-N
- 30. Gleevec®: Imatinib Mesylate @gray_alasdair Big Data Integration 31 DrugbankChemSpider PubChem Imatinib MesylateImatinib Mesylate YLMAHDNUQAMNNX-UHFFFAOYSA-N Are these records the same? It depends upon your task!
- 31. Big Data Integration 32 skos:exactMatch (InChI) Strict Relaxed Analysing Browsing Structure Lens @gray_alasdair I need to perform an analysis, give me details of the active compound in Gleevec.
- 32. Big Data Integration 33 skos:closeMatch (Drug Name) skos:closeMatch (Drug Name) skos:exactMatch (InChI) Strict Relaxed Analysing Browsing Name Lens @gray_alasdair Which targets are known to interact with Gleevec?
- 33. Data Provenance @gray_alasdair Big Data Integration 35
- 34. Data Provenance @gray_alasdair Big Data Integration 36
- 35. dev.openphacts.org @gray_alasdair Big Data Integration 38
- 37. Open PHACTS Approach 1. Know your audience Web developers 2. Understand your use cases Prioritised business questions 3. Identify access pathways Identify data Identify connections Implement API @gray_alasdair Big Data Integration 40
- 38. Questions Alasdair J G Gray A.J.G.Gray@hw.ac.uk alasdairjggray.co.uk @gray_alasdair Open PHACTS contact@openphacts.org openphacts.org @open_phacts @gray_alasdair Big Data Integration 41
- 40. 4343 How do we enable an infrastructure/platform that allows the user-friendly and rapid sharing of Living Lab data?
- 41. 4444 Brainstorm: 3 Steps Generation of ideas Selection of best ideas Further detailing top ideas http://www.flandersdc.be/gps
- 42. 4545 Table 3 Data Sharing Infrastructure Table 4 Quality & Reliability of data Table 5 Data Usage Results Table 1 Privacy & Ethics Table 2 Business Models Generating ideas
- 43. 4646 Practical arrangements • Paper indicating table order • Brainstorm round: +/- 15 minutes • Moderators
- 44. 4747 Some tips!
- 45. 4848 Some tips! Delay your judgement Be open to naive and crazy ideas Openess & enthusiasm Use associative thinking Piggyback on ideas of others
- 46. 4949 Selection of ideas • Summarize 3 key ideas • How to select? – Keep the goal in mind! – Think in opportunities – What are you enthusiastic about? – Personal engagement – What is needed in the short term? – Most promising
- 47. 5050 Selection of ideas • 5 Votes • Put your name & e-mail on the sheet if you want to be involved in working out the idea!
- 48. THANK YOU FOR YOUR TIME Contact me @ Femke.Ongenae@intec.ugent.be
Editor's Notes
- A trend is emerging towards AAL services that are truly personalized. Modern AAL services need to be adapted to the needs and preferences of care receivers and they need to accurately take into account context specificities. Moreover, modern AAL services need to be designed in a way such that they offer added value to the care process.
- A trend is emerging towards AAL services that are truly personalized. Modern AAL services need to be adapted to the needs and preferences of care receivers and they need to accurately take into account context specificities. Moreover, modern AAL services need to be designed in a way such that they offer added value to the care process.
- In order to achieve true personalization and to evaluate the design of the services, large data sets based on real-life context and profiles are needed.
- Living lab environments, such as the Care Living Labs (Zorgproeftuinen) in Flanders, Karolinska Living Lab in Sweden and CASALA (Centre forAffective Solutions for Ambient Living Awareness) in Ireland, have been set up in recent years to enable the collection of such real-life context and profile data. The valorization and dissemination of context-aware and personalized AAL services could be significantly stimulated, by allowing various parties to re-use these data sets in a user-friendly manner.
- However, these data-sets are not readily available for further research or the development of novel services as several issues remain to be discussed with regard to a smart data sharing culture for AAL services, such as: How to express which types of data are available from which living lab environment? How do we achieve structured, exchangeable data? How to maintain and express the quality and reliability of the data? How can these different data sets easily be aligned? How can these different data sets easily be shared and accessed, without too much effort? How can these different data sets easily be shared and accessed, without legal constraints? How to process and synthesize the data so that it is useful and usable by various stakeholders? Can a payment model be set up for usage of the data and thus support the operation of the living lab? What about the ethics and privacy related to these data sets? Who or what should be the frontrunner in realizing this idea? How will this be organized? What can we learn from other domains where sharing of big data sets has been made possible?
- Deriving value from the data Volume: More data than you can process – relative term; complexity of processing Velocity: Data constantly being generated Variety: Multiple sources, formats, models Veracity: Accuracy of the data Open PHACTS: Not dealt with Velocity, although it is a challenge for us
- 1 of 83 business driver questions Took a team of 5 experienced researchers 6 hours to manually gather the answer Start of the project couldn’t be answered by a computer system 6 months in 30s with prototype now subsecond
- Pharma are all accessing, processing, storing & re-processing external research data Big waste of resources No competitive advantage OPS: 29 partners including many major pharma
- 83 questions ranked and top 20 taken as target
- 18 of top 20
- A platform for integrated pharmacology data Relied upon by pharma companies Public domain, commercial, and private data sources Provides domain specific API Making it easy to build multiple drug discovery applications: examples developed in the project
- Not just in-house apps
- Actively being used for different purposes Public launch April 2013 Averaging 20 million hits a month from the start of 2015 38 million in the last 30 days Heavy usage from pharma, academia, and biotech 500+ registered users
- Import data into cache Integration approach Data kept in original model but cached centrally API call translated to SPARQL query Query expressed in terms of original data Queries expanded by IMS to cover URIs of original datasets
- Data provided by many publishers Originally in many formats: relational, SD files and RDF Worked closely with publishers Data licensing was a major issue Over 3 billion triples – 12 datasets Hosted on beefy hardware; data in memory (aim) Extensive memcaching Pose complex queries to extract data
- Interactions needed to satisfy use cases Gradually added additional types of data and interactions
- No standard units Even in curated sources! Feedback issues to data providers
- Validation & Standardization Platform Developed by Royal Society of Chemistry http://bit.ly/NZF5VB
- Example drug: Gleevec Cancer drug for leukemia Lookup in three popular public chemical databases Different results Chemistry is complicated, often simplified for convenience Data is messy!
- Are these records the same? It depends on what you are doing with the data! Each captures a subtly different view of the world Chemistry is complicated, often simplified for convenience Data is messy!
- Interested in physiochemical properties of Gleevec
- Interested in biomedical and pharmacological properties sameAs != sameAs depends on your point of view Links relate individual data instances: source, target, predicate, reason. Links are grouped into Linksets which have VoID header providing provenance and justification for the link.
- Open for anybody API grouped into theme areas Two phase interaction: Resolve thing to identifier Retrieve data about the identifier
- Sustainability
- API -> queries
- 3 steps we’ll do the first two now...the others will be for after the workshop for the interested participants
- Use the paper to write on, use the post-its, one idea per post-it Make it easy for the moderator to group things