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Using Semantics to personalize medical research


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My presentation to BIoIT World Congress 2010, Boston MA, April, 2010. The slideshow describes the SADI Semantic Web Service framework, and how we are using SADI to begin personalizing the data exploration and analysis experience for biomedical and clinical researchers

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Using Semantics to personalize medical research

  1. 1. Personalizing medical research using Semantics<br />Mark Wilkinson, PI Bioinformatics, Heart + Lung Institute @ St. Paul’s Hospital<br />Vancouver, BC, Canada<br />
  2. 2. Web Servicesvs.Semantic Web<br />
  3. 3. Web Servicesare not “connected” to the Semantic WebWhy?<br />
  4. 4. Web ServicesXML + XML SchemaSemantic WebRDF + OWL<br />
  5. 5. Web ServicesPOST of SOAP-XMLSemantic WebGET of RDF-XML<br />
  6. 6. Web ServicesNo (rigorous) semanticsSemantic WebRich, flexible semantics<br />
  7. 7. Web Services&Semantic WebFundamentally and deeply different Web technologies! <br />
  8. 8.
  9. 9. >1000 X more data in the “Deep Web” than in Web pagesPrimarily databases and analytical algorithms<br />
  10. 10. Accessing these databases and analytical algorithms “transparently”, based on individual researcher’s <br />ideas, beliefs, preferenceswill help us personalize medical research<br />
  11. 11. Mark Butler (2003) Is the semantic web hype? Hewlett Packard laboratories presentation at MMU, 2003-03-12<br />
  12. 12. Semantic Web?(my definition)<br />An information system where machines can receive information from one source, re-interpret it, and correctly use it for a purpose that the source had not anticipated.<br />
  13. 13. Re-interpretation<br />Correct re-use<br />Both are critical to the personalization of research<br />
  14. 14. Building a personalized Semantic Web…<br />Step-by-step…<br />
  15. 15. Semantic Automated Discovery and Integration<br />Founding partner<br />
  16. 16. SADI“best-practices” for Semantic Web Service provision<br />
  17. 17. standards-compliant<br />
  18. 18. Lightweight(only 2 “rules”)<br />
  19. 19. Rules come from observations:<br />
  20. 20. SADI Observation #1:<br />Web Services in Bioinformatics create implicitbiologicalrelationships between their input and output<br />
  21. 21. SADI Observation #1:<br />
  22. 22. SADI Best Practice #1<br />Make the implicit explicit…<br />A Web Service should create triples linking the input data to the output data, thus explicitly describing the semantic relationship between them <br />
  23. 23. SADI Best Practice #1<br />This is what bioinformatics Web Services implicitly do anyway! Easy to implement this as a best-practice<br />
  24. 24. SADI Observation #2:GET and POST<br />GET guarantees the response relates to the request URI in a very precise and predictable way<br />POST does not…<br />
  25. 25. SADI Observation #2:GET and POST<br />That’s why Web Services have a fundamentally different behaviour than the Semantic Web<br />
  26. 26. SADI Observation #2:GET and POST<br />We can fix that!<br />(without breaking any existing rules or standards!)<br />
  27. 27. SADI Best Practice #2<br />SUBJECT URI of the output graph (triples)<br /> is the sameas the SUBJECT URI of the input graph (triples)<br />(the output is “about” the input... Now explicitly!)<br />
  28. 28. Consequence<br />The “Semantics” of our interaction with the Web Service are now explicitandidentical to the “Semantics” of GET<br />
  29. 29. SADI Web Service Interfaces<br />Service Interfaces defined by two OWL classes:<br />
  30. 30. SADI Web Service Interfaces<br />OWL Class #1: My Input Class<br />
  31. 31. SADI Web Service Interfaces<br />OWL Class #2: My Output Class<br />
  32. 32. SADI Web Service Interfaces<br />My Service consumes OWL Individuals of Class #1 and returns OWL Individuals of Class #2<br />…but the URI of those two individuals is the same!(see best practice #2)<br />
  33. 33. How do we discover services?<br />Since input and output are about the same “thing”, we can automatically determine what a service doesby comparing the Input and Output OWL classes<br />
  34. 34. How do we find services?<br />Automatically index services in a registry based on what properties (predicates) Services add to their respective input data-types<br />
  35. 35. EXAMPLE<br /> Input Data: BRCA1 rdf:type Gene ID<br /> Output Data: BRCA1 hasDNASequence AGCTTAGCCA…<br /> Registry Index: Service provides “hasDNASequence” property to Gene IDs<br />
  36. 36. Now we can answer questions like <br />“what is the DNA sequence of BRCA1?”<br /> Discover a service that creates the DNA Sequence property of gene identifiers<br />
  37. 37. Okay, enough tech gobbledygookWhat will this do for ME?<br />
  38. 38. Demo #1<br />
  39. 39. Imagine there is a “virtual database” containing all of the data from all of the databases,together with the output ofevery conceivable analysis<br />
  40. 40. How do we query that database?<br />
  41. 41. “SHARE”Semantic Health And Research EnvironmentSADI client application<br />
  42. 42.
  43. 43. What pathways does UniProt protein P47989 belong to?<br />PREFIX pred: <><br />PREFIX ont: <><br />PREFIX uniprot: <><br />SELECT ?gene ?pathway <br />WHERE { <br /> uniprot:P47989 pred:isEncodedBy ?gene . <br /> ?gene ont:isParticipantIn ?pathway . <br />}<br />
  44. 44.
  45. 45.
  46. 46.
  47. 47. Recapwhat we just saw<br />A standard SPARQL query was entered into SHARE, a SADI-aware query engine<br />
  48. 48. Recapwhat we just saw<br />The query was interpreted to extract the properties being queried and these were passed to SADI for Web Service discovery<br />
  49. 49. Recapwhat we just saw<br />SADIsearched-for, found, and accessed all databases and/or analytical tools capable of generating those properties<br />
  50. 50. Recapwhat we just saw<br />We posed, and answered a complex database query <br />WITHOUT A DATABASE<br />(in fact, the data didn’t even have to exist...)<br />
  51. 51. Cool!<br />
  52. 52. …but I’m supposed to be personalizing research…<br />Let’s make this a little more personal by bringing in Ontologies<br />
  53. 53. My Definition of Ontology (for this talk)<br />Ontologies explicitly define the things that exist in “the world” based on what propertieseach kind of thing must have<br />
  54. 54. Ontology Spectrum<br />Frames<br />(Properties)<br />Thesauri<br />“narrower<br />term”<br />relation<br />Selected<br />Logical<br /> Constraints<br />(disjointness, <br />inverse, …) <br />Catalog/<br />ID<br />Formal<br />is-a<br />Informal<br />is-a<br />Formal<br />instance<br />General<br />Logical<br />constraints<br />Terms/<br />glossary<br />Value Restrs.<br />
  55. 55. Demo #2<br />Discover instances of OWL classes <br />from data that doesn’t exist…<br />
  56. 56. Show me patients whose creatinine level is increasing over time, along with their latest BUN and creatinine levels.<br />PREFIX regress: <> <br />PREFIX patients: <> <br />PREFIX pred: <> <br />SELECT ?patient ?bun ?creat<br />FROM <><br />WHERE {<br /> ?patient patients:creatinineLevels ?collection . <br /> ?collection regress:hasRegressionModel ?model . <br /> ?model regress:slope ?slope<br /> FILTER (?slope > 0) .<br /> ?patient pred:latestBUN ?bun . <br /> ?patient pred:latestCreatinine ?creat . <br />}<br />
  57. 57.
  58. 58.
  59. 59.
  60. 60. Show me patients with elevated creatinine along with their latest BUN and creatinine levels<br />PREFIX rdf: <> <br />PREFIX patients: <> <br />PREFIX pred: <> <br />SELECT ?patient ?bun ?creat<br />FROM <><br />WHERE {<br /> ?patient rdf:typepatients:ElevatedCreatininePatient .<br /> ?patient pred:latestBUN ?bun . <br /> ?patient pred:latestCreatinine ?creat . <br />}<br />
  61. 61. Start burrowing through the OWL class  find that we need aregression model OWL class<br />
  62. 62. Regression models have features like slopes and intercepts… and so onThe class is completely decomposed until a set of required Services are discoveredcapable of creating all these necessary properties<br />
  63. 63. Successful decomposition of the OWL class to discover <br />the need for a LinearRegression Web Service, and so on<br />
  64. 64. VOILA!<br />
  65. 65. OWL Class restrictions converted into workflows<br />SPARQL queries converted into workflows<br />Reasoning happening in parallel with query executionData fulfilling OWL models is discovered, or generated through running analytical tools<br />SADI and CardioSHARE<br />
  66. 66. This SADI/SHARE behaviour is completely novel<br />
  67. 67. I still don’t seewhy this is“Personal”??<br />
  68. 68. Show me patients whose creatinine level is increasing over time, along with their latest BUN and creatinine<br />SELECT ?patient ?bun ?creat<br />FROM <><br />WHERE {<br /> ?patient rdf:typepatients:ElevatedCreatininePatient .<br /> ?patient pred:latestBUN ?bun . <br /> ?patient pred:latestCreatinine ?creat . <br />}<br />
  69. 69. I created a small ontologydescribing my definition ofan Elevated Creatinine Patient<br />
  70. 70. … it was MY ontology!<br />
  71. 71. I can re-use it<br />
  72. 72. I can modify it as I change my world-view<br />
  73. 73. I can publish it for others to use<br />
  74. 74. Others can modify it to fit THEIR world-view<br />
  75. 75. My personal world-view is being dynamically resolved againstglobal data and knowledge<br />
  76. 76. …but it’s bigger than that…<br />
  77. 77. “Elevated Creatinine Patient”<br />
  78. 78. I made that up! It came out of my head!<br />
  79. 79. What’s a fancy word for a world-view that you make-up?<br />Hypothesis<br />
  80. 80. Current Research We believe that ontologies and hypothesesare, in some ways, the same “thing”……simply assertions about individuals that may or may not existIf an instance of a class exists, then the hypothesis is “validated”<br />
  81. 81. Recap<br />SADI Semantic Web Services generate triples; the predicates of those triples are indexed... Period.<br />For a given query, determine which properties are available, and which need to be discovered/generated<br />Find services that generate the properties we need<br />
  82. 82. Semantic Web<br />An information system where machines can receive information from one source, re-interpret it, and correctly use it for a purpose that the source had not anticipated.<br />My Purpose!!<br />
  83. 83. What SADI + SHARE supports<br />Re-interpretation<br />We constantly compare the collection of properties, gathered from third-parties worldwide, to whatever world-model (query/ontology) we wish to view it through.<br />MY world model<br />
  84. 84. What SADI + SHARE supports<br />Novel re-use<br />There is no way for the provider to dictate how their data should be used, or how it should be interpreted. They simply add their properties into the “cloud” and those properties are used in whatever way is appropriate for me.<br />
  85. 85. Important “wins”<br />Data remains distributed – no warehouse!<br />Data is not “exposed” as a SPARQL endpoint  greater provider-control over computational resources<br />Yet data appears to be a SPARQL endpoint… no modification of SPARQL or reasoner required.<br />
  86. 86. And all this because SADI simply requires <br />that the input URI <br />is the same as the output URI<br />
  87. 87. Join us!<br />We have recently received funding from CANARIEto assist and train service providersin deploying their own SADI Semantic Web Services<br />Come join us – we’re having a lot of fun!!<br /><br />
  88. 88. Credits<br />Benjamin VanderValk (SADI & CardioSHARE)<br />Luke McCarthy (SADI & CardioSHARE)<br />SoroushSamadian (CardioSHARE)<br />Microsoft Research<br />Fin <br />This presentation available on SlideShare: keywords ‘wilkinson’ ‘BioIT-2010’<br />
  89. 89. Credits<br />Benjamin VanderValk(SADI & CardioSHARE)<br />Luke McCarthy (SADI & CardioSHARE)<br />SoroushSamadian(CardioSHARE)<br />
  90. 90. Microsoft Research<br />