Dynamic Semantic Metadata in Biomedical Communications
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1) The document discusses challenges in curing complex medical disorders and proposes that semantic annotation, hypothesis management, and nanopublications can help address these challenges by enabling improved information sharing and integration across research communities. 2) It describes various technologies and frameworks like the Annotation Ontology, SWAN Annotation Framework, and nanopublications that can help researchers semantically annotate documents, manage hypotheses, and publish and share interpretations. 3) International collaborations between researchers and informaticians are seen as important to building the information ecosystem needed to make progress on curing complex diseases.
Dynamic Semantic Metadata in Biomedical Communications
- 1. Tim Clark Harvard Medical School & Massachusetts General Hospital April 12, 2011 Copyright 2010 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.
- 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. create hypothesis design experiment run experiment collect data interpret data share interpretations synthesize knowledge
- 5. 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 +
- 6. 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
- 7. 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.
- 8. 1667 2010 Printing Press Web
- 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. 2) Automatic annotation Dr. Paolo Ciccarese – Oct 8, 2010
- 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)
- 22. 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
- 25. Dr. Paolo Ciccarese – Oct 8, 2010
- 26. Dr. Paolo Ciccarese – Oct 8, 2010
- 27. Dr. Paolo Ciccarese – Oct 8, 2010
- 28. With thanks to Barend Mons and Paul Groth… Mons / Groth model of a nanopublication Cognitive Deficits (S) BACE1 (O) Relate to (p) provenance context
- 29. 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
- 30. 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
- 31. 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
- 32. G8 <http://example.info/alzswan:statement_f3556dcfc331d9b9af9d5c0cfc570ba6_event_1> <http://bio2rdf.org/go:0051087> rdf:type Event of type GO "chaperone binding" 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
- 33. Hyque triples G8 <http://example.info/person/2> pav:contributedBy Nigam Shah foaf:name foaf:Person rdf:type G9
- 34. 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
- 35. 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
- 36. 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
- 39. 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.
- 40. 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