Dynamic Integration of Semantic Metadata in Biomedical Communications
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The document discusses the integration of information sharing and semantic technologies in biomedical research to address complex disorders, highlighting the challenges and needs in neurobiology. It emphasizes the role of Web 3.0, semantic annotation, and collaborative frameworks to organize and share data effectively among researchers. Key contributors and collaborations in this initiative are also identified, underlining the importance of international cooperation in advancing biomedical communications.
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…
- 15. Shared metadata Text
- 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