Your SlideShare is downloading. ×
0
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
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
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
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
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
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
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
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
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
Dynamic Integration of Semantic Metadata in Biomedical Communications
Dynamic Integration of Semantic Metadata in Biomedical Communications
Dynamic Integration of Semantic Metadata in Biomedical Communications
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Dynamic Integration of Semantic Metadata in Biomedical Communications

626

Published on

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 …

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.

Published in: Technology, Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
626
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
9
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Tim Clark Harvard Medical School & Massachusetts General Hospital Pistoia Alliance Conference April 12, 2011Copyright 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 datadesign experiment interpret data synthesize knowledgecreate 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 volumeMCI 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, DrugBipolar 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 professionalmeetings to present our findings excathedra, 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  
  • 12. Genes ProteinsBiological ProcessesChemical Compounds Antibodies Cells Brain anatomy …
  • 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. Shared metadata Text
  • 15. Dr. Paolo Ciccarese – Oct 8, 2010 2) Automatic annotation
  • 16. Dr. Paolo Ciccarese – Oct 8, 2010
  • 17.   Semantics  on  documents  (SESL)        Vocabulary  standards  &  terminology   development      Document  &  data  management    Collaboratories  &  web  communities    Hypothesis  management  (SWAN)    Nanopublications  (OpenPHACTS)  
  • 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. SWAN Ontology: Model of Research Statements
  • 20. SWAN Ontology: Provenance of Research Statements
  • 21. Dr. Paolo Ciccarese – Oct 8, 2010
  • 22. Dr. Paolo Ciccarese – Oct 8, 2010
  • 23. Dr. Paolo Ciccarese – Oct 8, 2010
  • 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. 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. 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. 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. <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. Hyque  triples   G8 pav:contributedBy <http://example.info/person/2>   G9 foaf:name rdf:type Nigam  Shah   foaf:Person  
  • 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.   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.   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. Information ecosystem
  • 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.   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  

×