Dynamic Semantic Metadata in Biomedical Communications
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Dynamic Semantic Metadata in Biomedical Communications

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1st Annual Conference of the Pistoia Alliance

1st Annual Conference of the Pistoia Alliance
Keynote talk Apr 12 2011 by Tim Clark

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Dynamic Semantic Metadata in Biomedical Communications Dynamic Semantic Metadata in Biomedical Communications Presentation Transcript

  • Tim Clark Harvard Medical School & Massachusetts General Hospital April 12, 2011 Copyright 2010 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.
    • 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
  • create hypothesis design experiment run experiment collect data interpret data share interpretations synthesize knowledge
  • MCI progressors non progressors PET imaging of PIB (radiolabelled compound binds amyloid beta A4 protein) MRI imaging of brain structure showing loss of hippocampal volume Brain. 2010 Nov;133(Pt 11):3336-3348 . = 218 subjects +
  • dopaminergic pathway α-synuclein, β-amlyoid α-synuclein, Tau chr 16p11.2 CNV chr 16p11.2 CNV CRF, glutaminergic system, dopamine, amygdala … Alzheimer Disease Parkinson ’s Disease Schizophrenia Autism Bipolar Disorder Drug Addiction Huntington ’s Disease ALS Depression SIRT2
    • We want to organize all the known facts in neurobiology so we can mash them up.
    • There are no “facts” in neurobiology, except uninteresting ones.
    • 3. All we have, are assertions supported by evidence, of varying quality.
  • 1667 2010 Printing Press Web
  • 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”
  •  
  • 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…
  • Text Shared metadata
  • 2) Automatic annotation Dr. Paolo Ciccarese – Oct 8, 2010
  • Dr. Paolo Ciccarese – Oct 8, 2010
  •  
  •  
    • 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
  •  
  •  
  • Dr. Paolo Ciccarese – Oct 8, 2010
  • Dr. Paolo Ciccarese – Oct 8, 2010
  • Dr. Paolo Ciccarese – Oct 8, 2010
  • With thanks to Barend Mons and Paul Groth… Mons / Groth model of a nanopublication Cognitive Deficits (S) BACE1 (O) Relate to (p) provenance context
  • swande:Claim <http://tinyurl.com/4h2am3a> Intramembranous Aβ behaves as chaperones of other membrane proteins rdf:type dct:title G1 <http://example.info/person/1> pav:authoredBy Vincent Marchesi foaf:name foaf:Person rdf:type pav: http://purl.org/pav/provenance/2.0/ foaf: http://xmlns.com/foaf/0.1/ G2
  • swande:Claim <http://tinyurl.com/4h2am3a> Intramembranous Aβ behaves as chaperones of other membrane proteins rdf:type dct:title G1 <http://example.info/person/1> pav:authoredBy G2 <http://example.info/person/0> pav:curatedBy G4 Gwen Wong foaf:name foaf:Person rdf:type
  • swande:Claim <http://tinyurl.com/4h2am3a> Intramembranous Aβ behaves as chaperones of other membrane proteins rdf:type dct:title G1 <http://example.info/person/1> pav:contributedBy <http://example.info/citation/1> swanrel:referencesAsSupportiveEvidence G5 G6
  • G8 <http://example.info/alzswan:statement_f3556dcfc331d9b9af9d5c0cfc570ba6_event_1> <http://bio2rdf.org/go:0051087> rdf:type Event of type GO &quot;chaperone binding&quot; rdfs:label <prefix:actor_1> <prefix:target_1> <prefix:location_1> <http://bio2rdf.org/chebi:53002> <http://bio2rdf.org/mesh:D008565> <http://bio2rdf.org/go:0005886> rdf:type rdf:type rdf:type rdfs:label “Beta amyloid” rdfs:label “Membrane protein” rdfs:label “Plasma membrane” With many thanks to Nigam Shah, Stanford University
  • Hyque triples G8 <http://example.info/person/2> pav:contributedBy Nigam Shah foaf:name foaf:Person rdf:type G9
  • swande:Claim <http://tinyurl.com/4h2am3a> Intramembranous Aβ behaves as chaperones of other membrane proteins rdf:type dct:title G1 Hyque triples G8 swanrel:derivedFrom
    • 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
  •  
  • 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)
      • Karen Verspoor & Larry Hunter (Colorado)
      • Adam West & Ernst Dow (Eli Lilly)
      • Carole Goble (Manchester)
      • Nigam Shah (Stanford / NCBO)
      • Paul Groth (VU Amsterdam)
    • Funding: Elsevier, NIH, Eli Lilly, & EMD Serono