Dynamic Semantic Metadata in Biomedical Communications

Tim ClarkHarvard Medical School &

Massachusetts General Hospital

April 12, 2011Copyright 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

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.

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 LaureateUniversity 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…

TextShared metadata

2) Automatic annotation

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Dr.

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Semantics on documents (SESL) Vocabulary standards & terminology

development Document & data managementCollaboratories & web communitiesHypothesis 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

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Cognitive

Deficits(S)

BACE1(O)

Relate to(p)

provenancecontext

With thanks to Barend Mons and Paul Groth…

Mons / Groth model of a nanopublication

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



rdf:type

dct:title

G1


pav:authoredBy

G2


pav:curatedBy

G4

Gwen Wong

foaf:name

foaf:Person

rdf:type

swande:Claim



rdf:type

dct:title

G1


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 "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

Hyque triples

G8


pav:contributedBy

Nigam Shah

foaf:name

foaf:Person

rdf:typeG9

swande:Claim



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

Dynamic Semantic Metadata in Biomedical Communications

Technology

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