translational medicine from a semantic web perspective eric neumann w3c june 16, 2006

Translational Medicine from a Semantic Web Perspective

Eric NeumannW3C June 16, 2006

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Drug Discoveryand Medicine

Hygieia, G. Klimt

• Health

• Practice

• Safety

• Prevention

• Privacy

• Knowledge

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Data ExpansionLarge Data SetsVariables >> Samples

Many New Data TypesWhich Formats?

Combine

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Where Information Advances are Most Needed

• Supporting Innovative Applications in R&D– Translational Medicine (Biomarkers)– Molecular Mechanisms (Systems)– Data Provenance, Rich Annotation

• Clinical Information– eHealth Records, EDC, Clinical Submission Documents– Safety Information, Pharmacovigilance, Adverse Events,

Biomarker data

• Standards– Central Data Sources

• Genomics, Diseases, Chemistry, Toxicology

– MetaData• Ontologies

• Vocabularies

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Knowledge

“--is the human acquired capacity (both potential and actual) to take effective action in varied and uncertain situations.”

How does this translate into using Information Systems better in support of Innovation?

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Knowledge Predictiveness

• Knowledge of Target Mechanisms• Knowledge of Toxicity • Knowledge of Patient-Drug Profiles

Drug Discovery Challenges

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Current Challenges: Drug Discovery

• Business– Costly, lengthy drug discovery process (12-14 years)– Poor funding to find new uses for existing therapies (ie antibiotics)– Insufficient economic drivers for certain disease areas– Discovery and clinical trials design not well aligned with anticipating adverse

effect detection• Post-launch surveillance is weak

• Science & Technology– Counteracting the legacy of “Silos”– How to break away from the DD “conveyor belt model” to the “Translation

model” • gaining and sharing insights throughout the process

– The Benefit of New Targets for New Diseases– How to best identify safety and efficacy issues early on, so that cost and failure

are reduced• A D3 Knowledge-base: Drugability and Safety

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The Big Picture -

Hard to understand from just a few Points of View

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10Complete view tells a very different Story

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Distributed Nature of R&D

Silos of Data…

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Static,Untagged,

Disjoint

Existing Web Data Throttles the R&D Potential

R&D ScientistIntegrating

Data Manually

LIMS Bioinformatics Cheminformatics Public Data Sources

Dolor Sit Amet ConsectetuerLacreet Dolore Euismod VolutpatLacreet Dolore Magna Volutpat

Nibh Euismod Tincidunt Aliguam Erat

Dolor Sit Amet ConsectetuerLacreet Dolore Euismod VolutpatLacreet Dolore Magna Volutpat

Nibh Euismod Tincidunt Aliguam Erat

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Data Integration: Biology Requirements

Disease Proteins GenesPapers

RetentionPolicy

AuditTrail

Curation Tools Ontology Experiment

Assays

Compounds

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Semantic Web Data Integration

R&D Scientist

Bioinformatics CheminformaticsLIMS Public Data Sources

Dynamic,Linked,

Searchable

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DecisionDecisionSupportSupport

TranslationalTranslationalResearchResearch

ToxicityToxicity

NewNewApplicationsApplications

SafetySafety

TargetTargetValidationValidation

BiomarkerBiomarkerQualificationQualification

GOGO

BioPAXBioPAX

ICHICH

Raw DataRaw Data

MAGE MLMAGE ML

ASN1.ASN1.

XLSXLS

Psi XMLPsi XML

CSVCSV

SAS TablesSAS Tables

CDISCCDISC

Semantic BridgeSemantic Bridge

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Key Technologies Pharmaceuticals use to Exchanging Knowledge

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New Regulatory Issues Confronting Pharmaceuticals

from Innovation or Stagnation, FDA Report March 2004

Tox/EfficacyADME Optim

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Key Functionality

• Ubiquity– Same identifiers for anything from anywhere

• Discoverability– Global search on any entity

• Interoperability– => Application independence:

“Recombinant Data”

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Additional Functionality

• Provenance– Origin and history of data and annotations

• Scalability– Over all potentially relevant data and content

• Authentication/Security– Single user and team identity and granular data security– Non-repudiation of authorship– Encryption of graphs– Policy Awareness

• Data Preservation– Long-term persistence by minimizing API needs

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Translational Research and Personalized Medicine

Research Practice

Clinical

Biological

Biomedical Research

ClinicalPractice

ClinicalResearch

PersonalizedMedicine

TranslationalMedicine

-Two significant areas of HCLS activity- Span most areas of activity

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HCLS Framework:Biomedical Research

• Molecular, Cellular and Systems Biology/Physiology– Organism as an integrated an interacting network of genes, proteins and

biochemical reactions

– Human body as a system of interacting organs

• Molecular Cell Biology/Genomic and Proteomic Research– Gene Sequencing, Genotyping, Protein Structures

– Cell Signaling and other Pathways

• Biomarker Research– Discovery of genes and gene products that can be used to measure disease

progression or impacts of drug

• Pharmaco-genomics– Impact of genetic inheritance on

• Drug Discovery and Translational Research– Use of preclinical research to identify promising drug candidates

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HCLS Framework:Clinical Research

• Clinical Trials– Determination of efficacy, impact and safety of drugs for particular

diseases

• Pharmaco-vigilance/ADE Surveillance– Monitoring of impacts of drugs on patients, especially safety and adverse

event related information

• Patient Cohort Identification and Management– Identifying patient cohorts for drug trials is a challenging task

• Translational Research– Test theories emerging from pre-clinical experimentation on disease

affected human subjects

• Development of EHRs/EMRs for both clinical research and practice– Currently EHRs/EMRs focussed on clinical workflow processes– Re-using that information for clinical research and trials is a challenging

task

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Translational Research

• Improve communication between basic and clinical science so that more therapeutic insights may be derived from new scientific ideas - and vice versa.

• Testing of theories emerging from preclinical experimentation on disease-affected human subjects.

• Information obtained from preliminary human experimentation can be used to refine our understanding of the biological principles underpinning the heterogeneity of human disease and polymorphism(s).

• http://www.translational-medicine.com/info/about

• Reference NIH Digital Roadmap activity

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Personalized Medicine

• Propagation of insights from Genomic research into clinical practice

• Impact of new Molecular diagnostic tests hitting the market– How can they be incorporated into clinical care?– How does one update current clinical guidelines to incorporate the use of these

tests– How can one enable novel clinical decision support?

• How can phenotypic characteristics and genomic markers be used to:– Stratify patient populations– “Personalize” clinical care

• Genetic test results as risk factors• Therapeutic use of genomic markers

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Ecosystem: Current State

PharmaceuticalCompanies

Clinical ResearchOrganizations (CROs)

FDANational InstitutesOf Health

Hospitals

Universities,Academic MedicalCenters (AMCs)

Characterized by silos with uncoordinated supply chains leading to inefficiencies in the system

Center forDiseaseControl

Hospitals Doctors

Payors

Patients

Patients,Public

Patients

Patients

Biomedical ResearchClinical Practice

Clinical Trials/Research Clinical Practice

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Ecosystem: Goal State

/* Need to expand this to include Healthcare and Biomedical Research Players as well… Show an integrated picture with “continuous” information flow */

/* Need to expand this with Biomedical Research + Clinical Practice */

Biomedical Research Clinial Practice

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Use Case Flow: Drug Discovery and Development

Qualified Targets

Lead Generation

Toxicity & Safety

Biomarkers

PharmacogenomicsClinical Trials

Molecular Mechanisms

Lead Optimization

KD

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Drug Discovery & Development Knowledge

Qualified Targets

Lead Generation Toxicity &

Safety

Biomarkers

Pharmacogenomics

Clinical Trials

Molecular Mechanisms

Lead Optimization

Launch

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Semantic Web Drug DD Application Space

Genomics

Therapeutics

Biology

HTS

NDA

Compound Opt

safety

eADME

DMPK

informatics

manufacturing

genes

ClinicalStudies

Patent

Chem Lib

Production

Critical Path

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Opportunities for Semantics in HealthCare

• Enhanced interoperability via:– Semantic Tagging

– Grounding of concepts in Standardized Vocabularies

– Complex Definitions

• Semantics-based Observation Capture• Inference on Diseases

– Phenotypes

– Genetics

– Mechanisms

• Semantics-based Clinical Decision Support– Guided Data Interpretation

– Guided Ordering

• Semantics-based Knowledge Management

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Text

UnstructuredData Types

Structuredand Complex Data

Types

Histology Profiling

Data Semantics in the Life Sciences

Publications

Image + Text

Publications + data

Text + data items

genomics

Gene expression

Data Items

Data Items

Clinical Findings

CategoricalTaxonomicData Items

Pathways, Biomarkers

ComplexObjects

Clinical trials

ComplexObjects withCategorical/TaxonomicData Items

Systems Biology

CompositeObjects withEmbedded“process”

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RDB => RDF

Virtualized RDF

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Use-Case: COSA

Ro

w S

em

an

tic

<rd

f:ty

pe

Su

bje

ct>

Data Set

Column Semantic <rdf:type Gene>

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Use-Case:Experimental Design Definition

TreatmentW

ControlTime

PointsStaining

VisibleMicroscopy

FluorescentMicroscopy

CulturedCells

TreatmentZ

ImageAnalysis

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Case Study: Drug Safety ‘Safety Lenses’

• Lenses can ‘focus data in specific ways– Hepatoxicity, genotoxicity, hERG, metabolites

• Can be “wrapped” around statistical tools• Aggregate other papers and findings (knowledge) in

context with a particular project• Align animal studies with clinical results• Support special “Alert-channels” by regulators for

each different toxicity issue• Integrate JIT information on newly published

mechanisms of actions

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Courtesy of BG-Medicine

Example:Knowledge Aggregation

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Case Study: Omics

ApoA1 …

… is produced by the Liver

… is expressed less in Atherosclerotic Liver

… is correlated with DKK1

… is cited regarding Tangier’s disease

… has Tx Reg elements like HNFR1

Subject Verb Object

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Scenario: Biomarker Qualification

• Biomarker Roles– Disease– Toxicity– Efficacy

• Molecular and cytological markers– Tissue-specific– High content screening derived information– Different sets associated with different predictive tools

• Statistical discrimination based on selected samples– Predictive power– Alternative cluster prediction algorithms– Support qualifications from multiple studies (comparisons)

• Causal mechanisms– Pathways– Population variation

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BioMarker Semantics

DiseasePathways

Significance&

Strength

+Samples -Samples

Biomarker Set

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Scenario: Toxicity• Mechanisms

– Tissue-selective, Species-specific– Pathways, Off-Targets– Metabolites, PK sensitivity

• Evidence– Biomarkers

• In vitro assays (cell lines), Animal models, Clinical Phase 1

– Literature

• Population Variation– Drug Metabolism to toxic forms (CYP, SULT, UGT) – Target interaction variability

– Potential vs. Demonstrated

• Predictions– Data Mining Patterns– Computational Modeling

• Working Solutions– Chemical modifications– Dosing, Reformulation– Documented animal <=> human similarity and variation

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Knowledge Mining using Semantic Web

“Gene Prioritization through Data Fusion”

- Aerts et al, 2006, Nature

-Use of quantitative and qualitative information for statistical ranking.

-Can be used to identify novel genes involved in diseases

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QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

<bp:PATHWAYSTEP rdf:ID="xDshToXGSK3bPathwayStep"><bp:next-step rdf:resource="#xGSK3bToBetaCateninPathwayStep"/><bp:step-interactions>

<bp:MODULATION rdf:ID="xDshToXGSK3b"><bp:keft rdf:resource="#xDsh"/><bp:right rdf:resource="#xGSK-3beta"/><bp:participants rdf:resource="#xGSK-3beta"/><bp:name rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> Dishevelled to GSK3beta</bp:name><bp:direction rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> IRREVERSIBLE-LEFT-TO-RIGHT</bp: direction ><bp:control-type rdf:datatype="http://www.w3.org/2001/XMLSchema#string">

INHIBITION</bp: control-type ><bp: participants rdf:resource="#xDsh"/>

</bp: MODULATION > </bp: step-interactions > </bp: PATHWAYSTEP >

Case Study: BioPAX (Pathways)

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<bp:PATHWAYSTEP rdf:ID="xDshToXGSK3bPathwayStep"><bp:next-step rdf:resource="#xGSK3bToBetaCateninPathwayStep"/><bp:step-interactions>

<bp:MODULATION rdf:ID="xDshToXGSK3b"><bp:keft rdf:resource="#xDsh"/><bp:right rdf:resource="#xGSK-3beta"/><bp:participants rdf:resource="#xGSK-3beta"/><bp:name rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> Dishevelled to GSK3beta</bp:name><bp:direction rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> IRREVERSIBLE-LEFT-TO-RIGHT</bp: direction ><bp:control-type rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> INHIBITION</bp: control-type ><bp: participants rdf:resource="#xDsh"/>

</bp: MODULATION > </bp: step-interactions > </bp: PATHWAYSTEP >

Case Study: BioPAX (Pathways)

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<bp:PATHWAYSTEP rdf:ID="xDshToXGSK3bPathwayStep"><bp:next-step rdf:resource="#xGSK3bToBetaCateninPathwayStep"/><bp:step-interactions>

<bp:MODULATION rdf:ID="xDshToXGSK3b"><bp:keft rdf:resource="#xDsh"/><bp:right rdf:resource="#xGSK-3beta"/><bp:participants rdf:resource="#xGSK-3beta"/><bp:name rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> Dishevelled to GSK3beta</bp:name><bp:direction rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> IRREVERSIBLE-LEFT-TO-RIGHT</bp: direction ><bp:control-type rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> INHIBITION</bp: control-type ><drug:affectedBy rdf:resource=”http://pharma.com/cmpd/CHIR99102"/><bp: participants rdf:resource="#xDsh"/>

</bp: MODULATION > </bp: step-interactions > </bp: PATHWAYSTEP >

Case Study: BioPAX (Pathways)

Modulation

CHIR99102

affectedBy

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Potential Linked Clinical Ontologies

Clinical Trialsontology

RCRIM(HL7)

Genomics

CDISC

IRB

Applications

Molecules

Clinical Obs

ICD10

Pathways(BioPAX)

DiseaseModels

Extant ontologies

Mechanisms

Under development

Bridge concept

SNOMED

DiseaseDescriptions

Tox

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Case Study: Drug Discovery Dashboards

• Dashboards and Project Reports• Next generation browsers for semantic

information via Semantic Lenses• Renders OWL-RDF, XML, and HTML

documents• Lenses act as information aggregators

and logic style-sheets

add { ls:TheraTopic hs:classView:TopicView}

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Drug Discovery Dashboardhttp://www.w3.org/2005/04/swls/BioDash

Topic: GSK3beta Topic

Target: GSK3beta

Disease: DiabetesT2

Alt Dis: Alzheimers

Cmpd: SB44121

CE: DBP

Team: GSK3 Team

Person: John

Related Set

Path: WNT

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Bridging Chemistry and Molecular Biology

urn:lsid:uniprot.org:uniprot:P49841

Semantic Lenses: Different Views of the same data

Apply Correspondence Rule:if ?target.xref.lsid == ?bpx:prot.xref.lsidthen ?target.correspondsTo.?bpx:prot

BioPax Components

Target Model

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•Lenses can aggregate, accentuate, or even analyze new result sets

• Behind the lens, the data can be persistently stored as RDF-OWL

• Correspondence does not need to mean “same descriptive object”, but may mean objects with identical references

Bridging Chemistry and Molecular Biology

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Pathway Polymorphisms

•Merge directly onto pathway graph

•Identify targets with lowest chance of genetic variance

•Predict parts of pathways with highest functional variability

•Map genetic influence to potential pathway elements

•Select mechanisms of action that are minimally impacted by polymorphisms

Non-synonymous polymorphisms from db-SNP

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Knowledge Channels

<item rdf:about="http://www.connotea.org/user/hannahr/uri/48e905bdb66310af85ad2e8503628e01"><title>High Mda-7 expression promotes malignant cell survival and p38 MAP kinase activation in chronic lymphocytic leukemia.</title><link>http://www.connotea.org/user/hannahr/uri/48e905bdb66310af85ad2e8503628e01</link><description>Posted by hannahr to CLLSignalling&#x26;Processes on Thu Jan 19 2006</description><dc:creator>hannahr</dc:creator><dc:date>2006-01-19T11:24:03Z</dc:date><dc:subject>CLLSignalling&#x26;Processes</dc:subject><connotea:uri>

<dc:title>High Mda-7 expression promotes malignant cell survival and p38 MAP kinase activation in chronic lymphocytic leukemia.</dc:title>

<dc:creator>A Sainz-Perez</dc:creator> <dc:creator>H Gary-Gouy</dc:creator> <dc:identifier> <connotea:PubMedID> <connotea:idValue>16408101</connotea:idValue> <rdf:value>PMID: 16408101</rdf:value> </connotea:PubMedID> </dc:identifier> <dc:date>2006-01-12</dc:date> <prism:publicationName>Leukemia</prism:publicationName> <prism:issn>0887-6924</prism:issn>

</connotea:uri></item>

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Knowledge Channels

<item rdf:about="http://www.connotea.org/user/hannahr/uri/48e905bdb66310af85ad2e8503628e01"><title>High Mda-7 expression promotes malignant cell survival and p38 MAP kinase activation in chronic lymphocytic leukemia.</title><link>http://www.connotea.org/user/hannahr/uri/48e905bdb66310af85ad2e8503628e01</link><description>Posted by hannahr to CLLSignalling&#x26;Processes on Thu Jan 19 2006</description><dc:creator>hannahr</dc:creator><dc:date>2006-01-19T11:24:03Z</dc:date><dc:subject>CLLSignalling&#x26;Processes</dc:subject> <kn:nugget rdf:resource=“#N251”>

<tn:expert>Giles Day </tn:expert> <tn:topic>pf#P38</tn:topic> <tn:kChannel>pf#Kinases</tn:kChannel > <tn:comment>This paper suggests a mechanism for P38 protection of CLL B-cells</tn:comment >

</kn:nugget ><connotea:uri>

<dc:title>High Mda-7 expression promotes malignant cell survival and p38 MAP kinase activation in chronic lymphocytic leukemia.</dc:title>

<dc:creator>A Sainz-Perez</dc:creator> <dc:creator>H Gary-Gouy</dc:creator> <dc:identifier> <connotea:PubMedID> <connotea:idValue>16408101</connotea:idValue> <rdf:value>PMID: 16408101</rdf:value> </connotea:PubMedID> </dc:identifier> <dc:date>2006-01-12</dc:date> <prism:publicationName>Leukemia</prism:publicationName> <prism:issn>0887-6924</prism:issn>

</connotea:uri></item>

P38 paper

N251

Giles Day

pf#P38

Pf#Kinases

nugget

expert

topic

kChannel

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Case Study: Drug Safety ‘Safety Lenses’

• Lenses can ‘focus data in specific ways– Hepatoxicity, genotoxicity, hERG, metabolites

• Can be “wrapped” around statistical tools• Aggregate other papers and findings (knowledge) in

context with a particular project• Align animal studies with clinical results• Support special “Alert-channels” by regulators for

each different toxicity issue• Integrate JIT information on newly published

mechanisms of actions

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GeneLogic GeneExpress Data

• Additional relations and aspects can be defined additionally

Diseased Tissue

Links to OMIM (RDF)

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Bar View of GeneExpress

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ClinDash: Clinical Trials Browser

Clinical Obs

Expression Data

Subjects

•Values can be normalized across all measurables (rows)

•Samples can be aligned to their subjects using RDF rules

•Clustering can now be done over all measureables (rows)

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W3C Launches Semantic Web for HealthCare and Life Sciences Interest Group

• Interest Group formally launched Nov 2005: http://www.w3.org/2001/sw/hcls

• First Domain Group for W3C - “…take SW through its paces”

• An Open Scientific Forum for Discussing, Capturing, and Showcasing Best Practices

• Recent life science members: Pfizer, Merck, Partners HealthCare, Teranode, Cerebra, NIST, U Manchester, Stanford U, AlzForum

• SW Supporting Vendors: Oracle, IBM, HP, Siemens, AGFA,

• Co-chairs: Dr. Tonya Hongsermeier (Partners HealthCare); Eric Neumann (Teranode)

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HCLS Objectives

• Share use cases, applications, demonstrations, experiences

• Exposing collections

• Developing vocabularies

• Building / extending (where appropriate) core vocabularies for data integration

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HCLS Activities

• BioRDF - data + NLP as RDF• BioONT - ontology coordination • Scientific Publishing - evidence management• Adaptive Clinical Protocols and Pathways • Clinical Trials

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BioRDF: NeuroCommons.org

The Neurocommons project, a collaboration between Science Commons and the Teranode Corporation, is creating a free, public Semantic Web for neurological research. The project has three distinct goals:

1. To demonstrate that scientific impact and innovation is directly related to the freedom to legally reuse and technically transform scientific information.

2. To establish a legal and technical framework that increases the impact of investment in neurological research in a public and clearly measurable manner.

3. To develop an open community of neuroscientists, funders of neurological research, technologists, physicians, and patients to extend the Neurocommons work in an open, collaborative, distributed manner.

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BioRDF: Reagents

RDF resources that describes various kinds of experimental reagents, starting with antibodies:

•Initial RDF that captures: Gene, the fact that this is an antibody, various kinds of pages about the antibody, such as vendor documentation, and any other properties that are explicitly captured in the source material•Work with the Ontology task force to identify appropriate ontologies and vocabularies to use in the RDF.•Write queries against the RDF to answer questions of the sort posed on the Alzforum's

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BioRDF: NCBI

• NCBI Data: URIs and as RDF• Terminology Integration: NLM’s UMLS, MESH

– SNOMED

• Olivier Bodensreider

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BioRDF Neuro Tasks

• Aggregate facts and models around Parkinson’s Disease

• BIRN / Human Brain Project• SWAN: scientific annotations and

evidence• Use RDF and OWL to describe

– ’Brain Connectivity'– Neuronal data in SenseLab

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What does RDF get you?

• Structure is not format-rigid (i.e. tree)– Semantics not implicit in Syntax– No new parsers need to be defined for new data

• Entities can be anywhere on the web (URI)• Define semantics into graph structures

(ontologies)– Use rules to test data consistency and extract important

relations

• Data can be merged into complete graphs• Multiple ontologies supported

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RDF vs. XML example

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

Wang et al., Nature Biotechnology, Sept 2005

AGML

HUPML

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RDF Stripe Mode

Node>Edge>Node>Edge….

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RDF Graph

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gsk:KENPAL rdf:type :Compound ; dc:source http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&#38;db=pubmed&#38;dopt=Abstract&#38;list_uids=14698171 ;

chemID “3820” ;

clogP “2.4” ;

kA “e-8” ;

mw “327.17” ;

ic50 { rdf:type :IC50 ; value “23” ; units :nM ; forTarget gsk:GSK3beta } ;

chemStructure “C16H11BrN2O” ;

rdfs:label “kenpaullone” ;

synonym “bromo-paullone” ;

smiles “C1C2=C(C3=CC=CC=C3NC1=O)NC4=C2C=C(C=C4)B” ;

inChI “1/C16H11BrN2O/c17-9-5-6-14-11(7-9)12-8-15(20)18-13-4-2-1-3-10(13)16(12)19- 14/h1-7,19H,8H2,(H,18,20)/f/h18H” ;

xref http://pubchem.ncbi.nlm.nih.gov/summary/summary.cgi?cid=3820 .

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Multiple Ontologies Used Together

Drug targetontologyFOAF

Patentontology

OMIM

Person

Group

Chemicalentity

Disease

SNP

BioPAX

UniProt

Extant ontologies

Protein

Under development

Bridge concept

UMLS

DiseasePolymorphisms

PubChem

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Case Studies

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Case Study: NeuroCommons.org

• Public Data & Knowledge for CNS

• R&D Forum

• Available for industry and academia

• All based on Semantic Web Standards

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NeuroCommons.org

The Neurocommons project, a collaboration between Science Commons and the Teranode Corporation, is creating a free, public Semantic Web for neurological research. The project has three distinct goals:

1. To demonstrate that scientific impact and innovation is directly related to the freedom to legally reuse and technically transform scientific information.

2. To establish a legal and technical framework that increases the impact of investment in neurological research in a public and clearly measurable manner.

3. To develop an open community of neuroscientists, funders of neurological research, technologists, physicians, and patients to extend the Neurocommons work in an open, collaborative, distributed manner.

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HCLS Neuro Tasks

• Aggregate facts and models around Parkinson’s Disease

• SWAN: scientific annotations and evidence• Use RDF and OWL to describe

– Brain scans in the The Whole Brain Atlas– Neural entries in NCBI’s Entrez Gene Database– ’Brain Connectivity'– Neuronal data in SenseLab– Neurological Disease entries in OMIM

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Conclusions:Key Semantic Web Principles

• Plan for change • Free data from the application that

created it • Lower reliance on overly complex

Middleware• The value in "as needed" data integration

• Big wins come from many little ones • The power of links - network effect • Open-world, open solutions are cost

effective • Importance of "Partial Understanding"

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What is the Semantic Web ?

• http://www.w3.org/2006/Talks/0125-hclsig-em/

It’s AI

It’s Web 2.0

It’sOntologies

It’s DataTracking

It’s a Global Conspiracy

It’s SemanticWebs

It’s TextExtraction

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W3C Roadmap

• Semantic Web foundation specifications – RDF, RDF Schema and OWL are W3C

Recommendations as of Feb 2004

• Standardization work is underway in Query, Best Practices and Rules

• Goal of moving from a Web of Document to a Web of Data

The Only Open and Web-based Data Integration Model Game in Town

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The Current Web

What the computer sees: “Dumb” links

No semantics - <a href> treated just like <bold>

Minimal machine-processable information

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The Semantic Web

Machine-processable semantic information

Semantic context published – making the data more informative to both humans and machines

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Google Graphs

Ranking Sites based on Topology

Associate Word frequencies with ranked sites

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The Technologies: RDF

• Resource Description Framework• W3C standard for making statements of fact

or belief about data or concepts• Descriptive statements are expressed as

triples: (Subject, Verb, Object)– We call verb a “predicate” or a “property”

Subject ObjectProperty

<Patient HB2122> <shows_sign> <Disease Pneumococcal_Meningitis>

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Universal, semantic connectivity supports the construction of elaborate structures.

What RDF Gets You

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Losing Connectedness in Tables

Casp2

Colon

?

Fast Uptake and ease of use, but loose binding to entities and terms

Casp2

Endodermal

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Data Integration?

• Querying Databases is not sufficient

• Data needs to include the Context of Local Scientists

• Concepts and Vocabulary need to be associated

• More about Sociology than Technology

Information Knowledge

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Standards- Why Not?

• Good when there’s a majority of agreement• By vendors, for vendors?• Mainly about Data Packing-- should be more

about Semantics (user-defined)• API dominated (Time trapped)• Ease and Expressivity• Too often they’re Brittle and Slow to develop• “They’re great, that’s why there are so many of

them”

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Data Integration Enables Business Integration: Efficiency and Innovation

• Searching

• Visualization

• Analysis

• Reporting

• Notification

• Navigation

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Searching…

#1 way for finding information in companies…