Tim Clark    Harvard Medical School &  Massachusetts General Hospital Pistoia Alliance Conference         April 12, 2011Co...
  Information	  sharing	  and	  integration	     requirements	  for	  curing	  complex	  disorders.	    Web	  3.0	  and	...
  Yearly	  mortality	  (U.S.)	  	                          	     	     	            	           	  =	  642,00	  people	  ...
run experiment                  collect datadesign experiment                             interpret data                  ...
Brain. 2010 Nov;133(Pt 11):3336-3348.                                            PET imaging of PIB                       ...
SIRT2  Alzheimer                                            Huntington s   Disease                                        ...
1.  We want to organize    all the known facts in    neurobiology so we    can mash them up.2.  There are no facts    in n...
Printing Press   Web   1667          2010
We scientists do not attend professionalmeetings to present our findings excathedra, but in order to argue.               ...
  Social	  Web	  (Web	  2.0,	  read/write)	                           +  Shared	  annotation	  with	  controlled	       ...
  Information	  sharing	  within	  communities	  or	  tasks	    via	  Social	  Web	  (Web	  2.0),	  wikis	  and	  forums	...
Genes   ProteinsBiological ProcessesChemical Compounds  Antibodies    Cells  Brain anatomy    …
  Annotation	  Ontology	  (AO)	  is	  a	  domain-­‐  independent	  Web	  ontology.	      Links	  document	  fragments	  ...
Shared metadata	       Text
Dr. Paolo Ciccarese – Oct 8, 2010                                    2) Automatic annotation
Dr. Paolo Ciccarese – Oct 8, 2010
  Semantics	  on	  documents	  (SESL)	  	  	    Vocabulary	  standards	  &	  terminology	     development	  	    Docume...
    Model the thinking behind your research    Database it, web-ify it, RDF-ize it, share it    Link the Models / Hypot...
SWAN Ontology: Model of Research Statements
SWAN Ontology: Provenance of Research Statements
Dr. Paolo Ciccarese – Oct 8, 2010
Dr. Paolo Ciccarese – Oct 8, 2010
Dr. Paolo Ciccarese – Oct 8, 2010
Mons / Groth model of a nanopublication                                                       provenanc                   ...
rdf:type             <http://tinyurl.com/4h2am3a>	                              swande:Claim	                             ...
rdf:type<http://tinyurl.com/4h2am3a>	                         swande:Claim	                             dct:title         ...
rdf:type <http://tinyurl.com/4h2am3a>	                         swande:Claim	                         dct:title            ...
<http://example.info/alzswan:statement_f3556dcfc331d9b9af9d5c0cfc570ba6_event_1>	                                         ...
Hyque	  triples	     G8          pav:contributedBy                                        <http://example.info/person/2>	 ...
rdf:type   <http://tinyurl.com/4h2am3a>	                         swande:Claim	                           dct:title        ...
  The	  target	  hypothesis	  will	  be	  linked	  to:	       Pathway	  &	  target	  relation	  to	  disease,	       Ta...
  Hypotheses	  of	  therapeutic	  action	  for	    compounds	  and	  scaffolds,	  linked	  to	       Hypothesis	  /	  res...
Information ecosystem
  Curing	  complex	  medical	  disorders	  goes	  hand	  in	     hand	  with	  next-­‐gen	  biomedical	  communications	 ...
  People	       Paolo	  Ciccarese	  (Harvard)	       Maryann	  Martone	  (UCSD)	       Anita	  DeWaard	  &	  Tony	  Sc...
Dynamic Integration of Semantic Metadata in Biomedical Communications
Dynamic Integration of Semantic Metadata in Biomedical Communications
Dynamic Integration of Semantic Metadata in Biomedical Communications
Dynamic Integration of Semantic Metadata in Biomedical Communications
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Dynamic Integration of Semantic Metadata in Biomedical Communications

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Tim Clark of Harvard Medical School & Massachusetts General Hospital and chair of the W3C's scientific discourse task, gave a thorough look at applications for web 3.0, semantic metadata, and an application ontology and annotation framework for use in curing complex disorders.

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Dynamic Integration of Semantic Metadata in Biomedical Communications

  1. 1. Tim Clark Harvard Medical School & Massachusetts General Hospital Pistoia Alliance Conference April 12, 2011Copyright 2011 Massachusetts General Hospital. All rights reserved.
  2. 2.   Information  sharing  and  integration   requirements  for  curing  complex  disorders.    Web  3.0  and  semantic  metadata.     Integrating  ontologies,  documents,  data.    Annotation  Ontology  &  Annotation  Framework.    Applications     SESL,  Hypothesis  Mgmt,  Nanopublications     Open  Enterprise  Semantic  Model    Conclusion  
  3. 3.   Yearly  mortality  (U.S.)              =  642,00  people    Yearly  costs  (U.S.)                          =  $676  B  /  4.7%  GDP    Prevalence  =  5.3  M  +  76  M  +  14.4  M          =    95.7  M  people    
  4. 4. run experiment collect datadesign experiment interpret data synthesize knowledgecreate hypothesis share interpretations
  5. 5. Brain. 2010 Nov;133(Pt 11):3336-3348. PET imaging of PIB (radiolabelled compound binds amyloid beta A4 protein)     MRI imaging of brain structure showing loss of hippocampal volumeMCI progressors + non progressors = 218 subjects
  6. 6. SIRT2 Alzheimer Huntington s Disease Disease Autism chr 16p11.2 CNV -synuclein, -amlyoid chr 16p11.2 CNV Parkinson s Schizophrenia Disease Depression-synuclein, Tau dopaminergic pathway ALS CRF, glutaminergic system, DrugBipolar Disorder Addiction dopamine, amygdala …
  7. 7. 1.  We want to organize all the known facts in neurobiology so we can mash them up.2.  There are no facts in neurobiology, except uninteresting ones.3. All we have, are assertions supported by evidence, of varying quality.
  8. 8. Printing Press Web 1667 2010
  9. 9. We scientists do not attend professionalmeetings to present our findings excathedra, but in order to argue. John Polanyi, FRS, Nobel Laureate University of Manchester
  10. 10.   Social  Web  (Web  2.0,  read/write)   +  Shared  annotation  with  controlled   terminology  systems  (Sem  Web)    
  11. 11.   Information  sharing  within  communities  or  tasks   via  Social  Web  (Web  2.0),  wikis  and  forums    Information   permeability  across  pharma  R&D   projects  /  domains  /  pipeline  stages  via  shared   metadata  (semantic  annotation)    Web  3.0  improves    cross-­‐domain  Signal  to   Noise,  institutional  memory  &  data   findability  
  12. 12. Genes ProteinsBiological ProcessesChemical Compounds Antibodies Cells Brain anatomy …
  13. 13.   Annotation  Ontology  (AO)  is  a  domain-­‐ independent  Web  ontology.     Links  document  fragments  to  ontology    terms.     Metadata  separate  from  annotated  documents.      SWAN  AF  manages  document  annotation.     Interfaces  to  textmining  svcs  &  supports  curation.    Collaborating  with     NCBO,  UCSD,  Elsevier,  USC,  Manchester,  EMBL,   Colorado,  EBI,  etc…  
  14. 14. Shared metadata Text
  15. 15. Dr. Paolo Ciccarese – Oct 8, 2010 2) Automatic annotation
  16. 16. Dr. Paolo Ciccarese – Oct 8, 2010
  17. 17.   Semantics  on  documents  (SESL)        Vocabulary  standards  &  terminology   development      Document  &  data  management    Collaboratories  &  web  communities    Hypothesis  management  (SWAN)    Nanopublications  (OpenPHACTS)  
  18. 18.   Model the thinking behind your research  Database it, web-ify it, RDF-ize it, share it  Link the Models / Hypotheses to   Claims / Interpretations   Evidence (publications, experiments, data)   Supporting and contradictory claims from others   Evidence for these other claims  Web 3.0: share, compare and discuss   Manage knowledge while creating it  Can be public, private, or semi-private
  19. 19. SWAN Ontology: Model of Research Statements
  20. 20. SWAN Ontology: Provenance of Research Statements
  21. 21. Dr. Paolo Ciccarese – Oct 8, 2010
  22. 22. Dr. Paolo Ciccarese – Oct 8, 2010
  23. 23. Dr. Paolo Ciccarese – Oct 8, 2010
  24. 24. Mons / Groth model of a nanopublication provenanc context   e   Cognitive     Relate  to   BACE1   Deficits   (p)   (O)   (S)   With thanks to Barend Mons and Paul Groth…
  25. 25. rdf:type <http://tinyurl.com/4h2am3a>   swande:Claim   dct:title Intramembranous  Aβ  behaves  as   chaperones  of  other  membrane  proteins   G1 pav:authoredBy <http://example.info/person/1>   G2 foaf:name rdf:type Vincent  Marchesi   foaf:Person  pav: http://purl.org/pav/provenance/2.0/ With thanks to Paolo Ciccaresefoaf: http://xmlns.com/foaf/0.1/
  26. 26. rdf:type<http://tinyurl.com/4h2am3a>   swande:Claim   dct:title Intramembranous  Aβ  behaves  as   chaperones  of  other  membrane  G1 proteins   pav:authoredBy <http://example.info/person/1>  G2 pav:curatedBy <http://example.info/person/0>  G4 foaf:name rdf:type Gwen  Wong   foaf:Person   With thanks to Paolo Ciccarese
  27. 27. rdf:type <http://tinyurl.com/4h2am3a>   swande:Claim   dct:title Intramembranous  Aβ  behaves  as   chaperones  of  other  membrane   G1 proteins  swanrel:referencesAsSupportiveEvidence <http://example.info/citation/1>   G5 pav:contributedBy <http://example.info/person/1>   G6 With thanks to Paolo Ciccarese
  28. 28. <http://example.info/alzswan:statement_f3556dcfc331d9b9af9d5c0cfc570ba6_event_1>   rdfs:label Event  of  type  GO   "chaperone   rdf:type binding"   <http://bio2rdf.org/go:0051087>   <prefix:actor_1>   rdf:type <http://bio2rdf.org/chebi:53002>   rdfs:label “Beta amyloid” <prefix:target_1>   rdf:type <http://bio2rdf.org/mesh:D008565>   rdfs:label “Membrane protein” <prefix:location_1>   rdf:type <http://bio2rdf.org/go:0005886>   rdfs:label “Plasma membrane” G8With thanks to Nigam Shah & Paolo Ciccarese
  29. 29. Hyque  triples   G8 pav:contributedBy <http://example.info/person/2>   G9 foaf:name rdf:type Nigam  Shah   foaf:Person  
  30. 30. rdf:type <http://tinyurl.com/4h2am3a>   swande:Claim   dct:title Intramembranous  Aβ  behaves  as   chaperones  of  other  membrane   G1 proteins   swanrel:derivedFrom G8Hyque  triples  
  31. 31.   The  target  hypothesis  will  be  linked  to:     Pathway  &  target  relation  to  disease,     Target  selection  criteria,       Validation  assays  and  criteria,     Experiment  (assay)  provenance,     Experimental  data  and  computations,     Scientist  remarks,  findings  and  discussion.      Start  as  a  relatively  simple  model  and  extend  
  32. 32.   Hypotheses  of  therapeutic  action  for   compounds  and  scaffolds,  linked  to     Hypothesis  /  results  for  individual  assays,     Experiment  (assay)  provenance,     Experimental  data,     Group  annotation,       Internal  databases  etc.    Start  as  a  relatively  simple  model  and  extend  
  33. 33. Information ecosystem
  34. 34.   Curing  complex  medical  disorders  goes  hand  in   hand  with  next-­‐gen  biomedical  communications    Web  3.0  provides  the  technology  framework    Semantic  annotation,  hypothesis  management,   nanopubs:  tools  for  next-­‐gen  biomed  comms  .      Requires  /  enables  international  collaborations  of   biomedical  researchers  and  informaticians.    Open  enterprise  model  with  semantic  metadata.  
  35. 35.   People     Paolo  Ciccarese  (Harvard)     Maryann  Martone  (UCSD)     Anita  DeWaard  &  Tony  Scerri  (Elsevier)     Adam  West  &  Ernst  Dow  (Eli  Lilly  &  Co)     Carole  Goble  &  Sean  Bechhofer  (U  Manchester)     Karen  Verspoor  &  Larry  Hunter  (U  Colorado)     Gully  Burns  &  Cartik  Ramakrishnan  (USC)     David  Newman  (U  Southampton)     Nigam  Shah  (Stanford  /  NCBO)     Paul  Groth  &  Barend  Mons  (VU  Amsterdam)    Funding:  Elsevier,  NIH,  Eli  Lilly,  &  EMD  Serono  
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