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Tim Clark
    Harvard Medical School &
  Massachusetts General Hospital


 Pistoia Alliance Conference
         April 12, 2011
Copyright 2011 Massachusetts General Hospital. All rights reserved.
  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	
  
  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	
  	
  
run experiment                  collect data




design experiment                             interpret data
                    synthesize knowledge




create hypothesis                          share interpretations
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 volume




MCI progressors + non progressors         = 218 subjects
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,         Drug
Bipolar Disorder                                             Addiction
                          dopamine, amygdala …
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.
Printing Press   Web




   1667          2010
We scientists do not attend professional
meetings to present our findings ex
cathedra, but in order to argue.
                      John Polanyi, FRS, Nobel Laureate
                      University of Manchester
  Social	
  Web	
  (Web	
  2.0,	
  read/write)	
  

                         +
  Shared	
  annotation	
  with	
  controlled	
  
       terminology	
  systems	
  (Sem	
  Web)	
  
	
  
  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 	
  
Dynamic Integration of Semantic Metadata in Biomedical Communications
Genes

   Proteins

Biological Processes

Chemical Compounds

  Antibodies

    Cells

  Brain anatomy

    …
  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…	
  
Shared metadata	

       Text
Dr. Paolo Ciccarese – Oct 8, 2010




                                    2) Automatic annotation
Dr. Paolo Ciccarese – Oct 8, 2010
Dynamic Integration of Semantic Metadata in Biomedical Communications
Dynamic Integration of Semantic Metadata in Biomedical Communications
  Semantics	
  on	
  documents	
  (SESL)	
  	
  	
  
  Vocabulary	
  standards	
  &	
  terminology	
  
   development	
  	
  
  Document	
  &	
  data	
  management	
  
  Collaboratories	
  &	
  web	
  communities	
  
  Hypothesis	
  management	
  (SWAN)	
  
  Nanopublications	
  (OpenPHACTS)	
  
    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
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
                      context	
  
                                                           e	
  




        Cognitive	
  	
  
                                    Relate	
  to	
                 BACE1	
  
         Deficits	
                     (p)	
                        (O)	
  
           (S)	
  




   With thanks to Barend Mons and Paul Groth…
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 Ciccarese
foaf: http://xmlns.com/foaf/0.1/
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
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
<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”
  G8
With thanks to Nigam Shah & Paolo Ciccarese
Hyque	
  triples	
  

   G8
          pav:contributedBy
                                        <http://example.info/person/2>	
  
 G9
                                   foaf:name                rdf:type

               Nigam	
  Shah	
                              foaf:Person	
  
rdf:type
   <http://tinyurl.com/4h2am3a>	
                         swande:Claim	
  

                         dct:title

                             Intramembranous	
  Aβ	
  behaves	
  as	
  
                             chaperones	
  of	
  other	
  membrane	
  
 G1                          proteins	
  

      swanrel:derivedFrom

 G8
Hyque	
  triples	
  
  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	
  
  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	
  
Dynamic Integration of Semantic Metadata in Biomedical Communications
Information ecosystem
  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.	
  
  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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Dynamic Integration of Semantic Metadata in Biomedical Communications

  • 1. Tim Clark Harvard Medical School & Massachusetts General Hospital Pistoia Alliance Conference April 12, 2011 Copyright 2011 Massachusetts General Hospital. All rights reserved.
  • 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.   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. run experiment collect data design experiment interpret data synthesize knowledge create hypothesis share interpretations
  • 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 volume MCI progressors + non progressors = 218 subjects
  • 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, Drug Bipolar Disorder Addiction dopamine, amygdala …
  • 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. Printing Press Web 1667 2010
  • 9. We scientists do not attend professional meetings to present our findings ex cathedra, but in order to argue. John Polanyi, FRS, Nobel Laureate University of Manchester
  • 10.   Social  Web  (Web  2.0,  read/write)   +   Shared  annotation  with  controlled   terminology  systems  (Sem  Web)    
  • 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  
  • 13. Genes Proteins Biological Processes Chemical Compounds Antibodies Cells Brain anatomy …
  • 14.   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…  
  • 16. Dr. Paolo Ciccarese – Oct 8, 2010 2) Automatic annotation
  • 17. Dr. Paolo Ciccarese – Oct 8, 2010
  • 20.   Semantics  on  documents  (SESL)         Vocabulary  standards  &  terminology   development       Document  &  data  management     Collaboratories  &  web  communities     Hypothesis  management  (SWAN)     Nanopublications  (OpenPHACTS)  
  • 21.   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
  • 22. SWAN Ontology: Model of Research Statements
  • 23. SWAN Ontology: Provenance of Research Statements
  • 24. Dr. Paolo Ciccarese – Oct 8, 2010
  • 25. Dr. Paolo Ciccarese – Oct 8, 2010
  • 26. Dr. Paolo Ciccarese – Oct 8, 2010
  • 27. 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…
  • 28. 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 Ciccarese foaf: http://xmlns.com/foaf/0.1/
  • 29. 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
  • 30. 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
  • 31. <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” G8 With thanks to Nigam Shah & Paolo Ciccarese
  • 32. Hyque  triples   G8 pav:contributedBy <http://example.info/person/2>   G9 foaf:name rdf:type Nigam  Shah   foaf:Person  
  • 33. rdf:type <http://tinyurl.com/4h2am3a>   swande:Claim   dct:title Intramembranous  Aβ  behaves  as   chaperones  of  other  membrane   G1 proteins   swanrel:derivedFrom G8 Hyque  triples  
  • 34.   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  
  • 35.   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  
  • 38.   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.  
  • 39.   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