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
run experiment collect datadesign experiment interpret data synthesize knowledgecreate hypothesis share interpretations
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
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 …
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.
We scientists do not attend professionalmeetings to present our findings excathedra, 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
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
rdf:type <http://tinyurl.com/4h2am3a> swande:Claim dct:title Intramembranous Aβ behaves as chaperones of other membrane G1 proteins swanrel:derivedFrom G8Hyque triples
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
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