Web Science - ISoLA 2012
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The document discusses advancements in in silico research using OWL (Web Ontology Language) to create abstract models of workflows tailored for biological informatics. It emphasizes the importance of conducting research on the web rather than over it, while demonstrating the ability to generate biological insights through automated workflows based on ontology models. The paper advocates for a paradigm shift where scientific publications become executable documents that can be reused and accessed directly in the web environment, enhancing the synergy between research discoveries and ongoing investigations.
Web Science - ISoLA 2012
- 1. Using OWL Domain Models as Abstract Workflow Models Or... Conducting in silico research in the Web from hypothesis to publication Mark Wilkinson Isaac Peral Senior Researcher in Biological Informatics Centro de Biotecnología y Genómica de Plantas, UPM, Madrid, Spain Adjunct Professor of Medical Genetics, University of British Columbia Vancouver, BC, Canada.
- 2. Context “While it took 2,300 years after the first report of angina for the condition to be commonly taught in medical curricula, modern discoveries are being disseminated at an increasingly rapid pace. Focusing on the last 150 years, the trend still appears to be linear, approaching the axis around 2025.” The Healthcare Singularity and the Age of Semantic Medicine, Michael Gillam, et al, The Fourth Paradigm: Data- Intensive Scientific Discovery Tony Hey (Editor), 2009 Slide adapted with permission from Joanne Luciano, Presentation at Health Web Science Workshop 2012, Evanston IL, USA June 22, 2012.
- 3. “The Singularity” The X-intercept is where, the moment a discovery is made, it is immediately put into practice (not only medical practice, but any research endeavour...) The Healthcare Singularity and the Age of Semantic Medicine, Michael Gillam, et al, The Fourth Paradigm: Data-Intensive Scientific Discovery Tony Hey (Editor), 2009 Slide Borrowed with Permission from Joanne Luciano, Presentation at Health Web Science Workshop 2012, Evanston IL, USA June 22, 2012.
- 4. The technology required to achieve this does not yet exist
- 5. You Are Here Scientific research would have to be conducted within a medium that immediately interpreted and disseminated the results...
- 6. You Are Here ...in a form that immediately (actively!) affected the research of others...
- 7. You Are Here ...without requiring them to be aware of these new discoveries.
- 8. To achieve this vision We must learn how to do research IN the Web Not OVER the Web
- 9. How we use the Web today
- 10. To achieve this vision We must learn how to do research IN the Web Not OVER the Web
- 11. I’d like to show you how close we now are to this vision and how we got there
- 12. Web Science 2.0
- 13. We wanted to duplicate a real, peer-reviewed, bioinformatics analysis simply by building a model in the Web describing what the answer (if one existed) would look like
- 14. ...the machine had to make every other decision on it’s own
- 15. This is the study we chose:
- 16. Gordon, P.M.K., Soliman, M.A., Bose, P., Trinh, Q., Sensen, C.W., Riabowol, K.: Interspecies data mining to predict novel ING-protein interactions in human. BMC genomics. 9, 426 (2008).
- 17. Original Study Simplified Using what is known about interactions in fly & yeast predict new interactions with your human protein of interest
- 18. Abstracted Given a protein P in Species X Find proteins similar to P in Species Y Retrieve interactors in Species Y Sequence-compare Y-interactors with Species X genome (1) Keep only those with homologue in X Find proteins similar to P in Species Z Retrieve interactors in Species Z Sequence-compare Z-interactors with (1) Putative interactors in Species X
- 19. Modeling the answer... OWL Web Ontology Language (OWL) is the language approved by the W3C for representing knowledge in the Web
- 20. Modeling the answer... Note that every word in this diagram is, in reality, a URL (because it is OWL)
- 21. Modeling the answer... The model of a Potential Interactor is published in The Web It utilizes concepts from other models published in The Web (ours and other’s) by referencing their URLs
- 22. Modeling the answer... The model of a Potential Interactor is a network of concepts distributed within the Web It will be affected by changes to those concepts We do not “own” all of those concepts!
- 23. Modeling the answer... ProbableInteractor is homologous to ( Potential Interactor from ModelOrganism1…) and Potential Interactor from ModelOrganism2…) Probable Interactor is defined in OWL as a subclass of Potential Interactor that requires homologous pairs of interacting proteins to exist in both comparator model organisms. (Effectively, an intersection)
- 24. Publish our OWL model of a Probable Interactor in the Web
- 25. Running a Web Science 2.0 Experiment In a local data-file provide the protein we are interested in and the two species we wish to use in our comparison taxon:9606 a i:OrganismOfInterest . # human uniprot:Q9UK53 a i:ProteinOfInterest . # ING1 taxon:4932 a i:ModelOrganism1 . # yeast taxon:7227 a i:ModelOrganism2 . # fly
- 26. The tricky bit is... In the abstract, the search for homology is “generic” – ANY model organism. But when the machine attempts to do the experiment, it will have to use several different and specific resources because our question specifies two different taxon:4932 a i:ModelOrganism1 . # yeast species taxon:7227 a i:ModelOrganism2 . # fly
- 27. This is the question we ask: (the query language here is SPARQL) PREFIX i: <http://sadiframework.org/ontologies/InteractingProteins.owl#> SELECT ?protein FROM <file:/local/workflow.input.n3> WHERE { ?protein a i:ProbableInteractor . } The reference (URL) to our OWL model of the answer
- 28. Our system then derives (and executes) the following workflow automatically These are different Web services! ...selected at run-time based on the same model
- 30. There are three very cool things about what you just saw...
- 31. There are three very cool things about what you just saw... The system was able to create a workflow based on an OWL model (ontology)
- 32. There are three very cool things about what you just saw... The system was able to create a COMPUTATIONAL workflow based on a BIOLOGICAL model
- 33. There are three very cool things about what you just saw... The workflow it created (i.e. the services chosen) differed depending on context taxon:4932 a i:ModelOrganism1 . # yeast taxon:7227 a i:ModelOrganism2 . # fly
- 34. We got the answer “simply” by designing a model of the answer!
- 35. How did we do that?
- 36. Design Pattern for Web Services on the Semantic Web
- 37. A Web application that answers SPARQL-DL queries Query-answering Enhanced by SADI
- 38. Demos of SADI and SHARE
- 39. What is the phenotype of every allele of the Antirrhinum majus DEFICIENS gene SELECT ?allele ?image ?desc WHERE { locus:DEF genetics:hasVariant ?allele . ?allele info:visualizedByImage ?image . ?image info:hasDescription ?desc }
- 40. What is the phenotype of every allele of the Antirrhinum majus DEFICIENS gene SELECT ?allele ?image ?desc WHERE { locus:DEF genetics:hasVariant ?allele . ?allele info:visualizedByImage ?image . ?image info:hasDescription ?desc } Note that there is no “FROM” clause! We don’t tell it where it should get the information, The machine has to figure that out by itself...
- 41. Enter that query into SHARE
- 43. SHARE examines available SADI Web Services ...and in a few seconds you get your answer.
- 44. The query results are live hyperlinks to the respective Database or images (the answer is IN the Web!)
- 45. What pathways does UniProt protein P47989 belong to? PREFIX pred: <http://sadiframework.org/ontologies/predicates.owl#> PREFIX ont: <http://ontology.dumontierlab.com/> PREFIX uniprot: <http://lsrn.org/UniProt:> SELECT ?gene ?pathway WHERE { uniprot:P47989 pred:isEncodedBy ?gene . ?gene ont:isParticipantIn ?pathway . }
- 46. What pathways does UniProt protein P47989 belong to? PREFIX pred: <http://sadiframework.org/ontologies/predicates.owl#> PREFIX ont: <http://ontology.dumontierlab.com/> PREFIX uniprot: <http://lsrn.org/UniProt:> SELECT ?gene ?pathway WHERE { uniprot:P47989 pred:isEncodedBy ?gene . ?gene ont:isParticipantIn ?pathway . }
- 47. What pathways does UniProt protein P47989 belong to? PREFIX pred: <http://sadiframework.org/ontologies/predicates.owl#> PREFIX ont: <http://ontology.dumontierlab.com/> PREFIX uniprot: <http://lsrn.org/UniProt:> SELECT ?gene ?pathway WHERE { uniprot:P47989 pred:isEncodedBy ?gene . ?gene ont:isParticipantIn ?pathway . } Note again that there is no “From” clause… I have not told SHARE where to look for the answer, I am simply asking my question
- 48. Enter that query into SHARE
- 51. Two different Two different providers of providers of pathway gene information information (KEGG and (KEGG & GO); NCBI); were found & were found & accessed accessed
- 52. The results are all links to the original data (The answer is IN the Web!)
- 53. Show me the latest Blood Urea Nitrogen and Creatinine levels of patients who appear to be rejecting their transplants (I showed you this query in ISoLA 2010… sorry for repeating myself ) PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX patient: <http://sadiframework.org/ontologies/patients.owl#> PREFIX l: <http://sadiframework.org/ontologies/predicates.owl#> SELECT ?patient ?bun ?creat FROM <http://sadiframework.org/ontologies/patients.rdf> WHERE { ?patient rdf:type patient:LikelyRejecter . ?patient l:latestBUN ?bun . ?patient l:latestCreatinine ?creat . }
- 54. Show me the latest Blood Urea Nitrogen and Creatinine levels of patients who appear to be rejecting their transplants (I showed you this query in 2010… sorry for repeating myself!) PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX patient: <http://sadiframework.org/ontologies/patients.owl#> PREFIX l: <http://sadiframework.org/ontologies/predicates.owl#> SELECT ?patient ?bun ?creat FROM <http://sadiframework.org/ontologies/patients.rdf> WHERE { ?patient rdf:type patient:LikelyRejecter . ?patient l:latestBUN ?bun . ?patient l:latestCreatinine ?creat . }
- 55. Likely Rejecter: A patient who has creatinine levels that are increasing over time - - Mark D Wilkinson’s definition
- 56. Likely Rejecter: …but there is no “likely rejecter” column or table in our database… only blood chemistry measurements at various time-points
- 57. Likely Rejecter: So the data required to answer this question DOESN’T EXIST!
- 58. ?
- 59. Enter that query into SHARE
- 60. SHARE “decomposes” the Likely Rejector OWL class into its constituent property restrictions
- 61. Each property restriction in the Class is matched with a SADI Service The matched SADI Service can generate data that has that property
- 62. SHARE chains these SADI services are into a workflow... ...the outputs from that workflow are Instances (OWL Individuals) of the Likely Rejector OWL Class
- 63. For example… SHARE utilizes SADI to discover analytical services on the Web that do linear regression analysis; required for the “increasing over time” part of the Class definition
- 64. VOILA!
- 65. SHARE examines the OWL Class Gathers, from the Web, the ontologies that are referenced by that Class then uses those ontological properties to identify which data-sources and analytical tools it must access to create data matching that Class definition
- 66. OWL
- 67. The way SHARE builds the workflow varies depending on the context of the query (i.e. which data/ontologies it reads – Mine? Yours?) and on what part of the query it is trying to answer at any given moment (which ontological concept is relevant to that clause)
- 68. And that brings us back to...
- 69. Web Science 2.0
- 70. Gordon, P.M.K., Soliman, M.A., Bose, P., Trinh, Q., Sensen, C.W., Riabowol, K.: Interspecies data mining to predict novel ING-protein interactions in human. BMC genomics. 9, 426 (2008).
- 71. derives and executes the following workflow automatically using an OWL ontology that describes the biology
- 72. The analytical tools chosen for that workflow were determined based on context even though the biological (ontological) model driving their selection was the same
- 73. i.e. The published model is re-usable
- 74. i.e. The published model is re-usable In different contexts... by different researchers
- 75. Because the model IS the experiment the published EXPERIMENT is re-usable!! Simply point the same query at your own dataset...
- 76. The scientific publication is an executable document!
- 77. Every component of the model Every component of the input data Every component of the output data is a URL Therefore the model, the question, the experiment, and the results are inherently IN the Web
- 78. Every component of the model Every component of the input data Every component of the output data is a URL The answer, and the knowledge derived from it, is immediately available to Web search engines and moreover, can instantly affect the outcome of other Web Science experiments
- 81. Change the way we think of “hypotheses”
- 82. In Web Science 2.0 Model what the world would “look like” if your hypothesis were true Then ask “is there any data that fits that model?”
- 83. Please join us! SADI and SHARE are Open-Source projects http://sadiframework.org
- 84. My New Home!
- 85. University of British Columbia Luke McCarthy – Lead Dev. Edward Kawas Everything... SADI Service auto-generator Benjamin VanderValk Ian Wood SHARE & SADI & Experimental modeling & Experimental modeling project myHeath Button Soroush Samadian Cardiovascular data modeling and queries
- 86. C-BRASS Collaborators at other sites U of New Brunswick Carleton University Dr. Chris Baker Dr. Michel Dumontier Alexandre Riazanov Marc-Alexandre Nolin Leonid Chepelev Steve Etlinger Nichaella Kieth Jose Cruz
Editor's Notes
- #3: n 1499, when Portuguese explorer Vasco da Gama returned home after completing the first-ever sea voyage from Europe to India, he had less than half of his original crew with him— scurvy had claimed the lives of 100 of the 160 men. Through-out the Age of Discovery,1 scurvy was the leading cause of death among sailors. Ship captains typically planned for the death of as many as half of their crew during long voyages. A dietary cause for scurvy was suspected, but no one had proved it. More than a century later, on a voyage from England to India in 1601, Captain James Lancaster placed the crew of one of his four ships on a regi- men of three teaspoons of lemon juice a day. By the halfway point of the trip, almost 40% of the men (110 of 278) on three of the ships had died, while on the lemon-supplied ship, every man sur- vived [1]. The British navy responded to this discovery by repeat- ing the experiment—146 years later.In 1747, a British navy physician named James Lind treated sail- ors suffering from scurvy using six randomized approaches and demonstrated that citrus reversed the symptoms. The British navy responded, 48 years later, by enacting new dietary guidelines re- quiring citrus, which virtually eradicated scurvy from the British fleet overnight. The British Board of Trade adopted similar dietary practices for the merchant fleet in 1865, an additional 70 years later. The total time from Lancaster’s definitive demonstration of how to prevent scurvy to adoption across the British Empire was 264 years [2].The translation of medical discovery to practice has thankfully improved sub- stantially. But a 2003 report from the Institute of Medicine found that the lag be- tween significant discovery and adoption into routine patient care still averages 17 years [3, 4]. This delayed translation of knowledge to clinical care has negative effects on both the cost and the quality of patient care. A nationwide review of 439 quality indicators found that only half of adults receive the care recommended by U.S. national standards [5].