Transcript No Slide Title
Applying Semantic Web Standards to Drug Discovery and Development Eric Neumann W3C HCLS co-chair
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 3
Where Information Advances are Most Needed
• • • Supporting Innovative Applications in R&D – – – Mol Diagnostics (Biomarkers) Molecular Mechanisms (Systems) Data Provenance, Rich Annotation Clinical Information – – – – eHealth Records + EDC Clinical Submission Documents Safety Information, Pharmacovigilance, Adverse Events Handling Biomarker evidence Standards – Central Data Sources • Genomics, Diseases, Chemistry, Toxicology – MetaData • Ontologies • Vocabularies 4
Raw Data MAGE ML CDISC Psi XML GO BioPAX ICH Decision Support Biomarker Qualification Translational Research ASN1.
XLS SAS Tables CSV
Semantic Bridge
Target Validation New Applications Safety Tox 5
Losing Connectedness in Tables
Fast Uptake and ease of use, but loose binding to entities and terms
?
Genes Tissues 6
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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Data Integration: Biology Requirements Papers Disease Proteins Genes Retention Policy Audit Trail Samples Compounds Curation Tools Ontology Experiment
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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)
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…
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Semantic Web Data Integration
R&D Scientist Dynamic, Linked, Searchable LIMS Bioinformatics Cheminformatics
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Public Data Sources
The Current Web
What the computer sees: “Dumb” links No semantics - treated just like
The Semantic Web
Machine-processable semantic information Semantic context published – making the data more informative to both humans and machines 15
The Web of Data
• • • • • URI’s are universal ID’s Distributed data references Non-locality of data NamedGraphs can help segment external references New meaning for Annotation
target pathway target gene
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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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Example: Knowledge Aggregation
18 Courtesy of BG-Medicine
Tim Berners Lee’s App View
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Semantic Web Drug DD Application Space
Therapeutics Genomics Chem Lib Biology HTS eADM E manufacturing Production NDA Clinical Studies Compound Opt DMPK 21 genes informatics Patent
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) 22
HCLS Objectives
• • • • Share use cases, applications, demonstrations, experiences Exposing collections Developing vocabularies Building / extending (where appropriate) core vocabularies for data integration 23
HCLS Activities
• • • • • • BioRDF - data as RDF BioNLP - unstructured data BioONT - ontology coordination Clinical Trials - CDISC/HL7 Scientific Publishing - evidence management Adaptive Healthcare Protocols 24
Semantic Web in R&D
Toxicogenomicist
Shared Annotations Notified of Alternatives
Progression Manager
Reporting on Progression Notify Others of Decisions
A Single Compound Scientist
Found Determinations Noted Alternatives
Open Data Format and Flexible Linking Enabled Data Integration and Collaboration
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R&D Applications in the Semantic Web Progression Manager Project Dashboard Toxicogenomicist Experiment Manager Scientist R&D Commons A Single Compound
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Other Benefits of Semantic Web
• • • • Enterprise Distributed Connectivity – Universal Resource Identifiers (URI) Authenticity – – Auditability (Sarbanes-Oxley) Authorship Non-repudibility Privacy – Encryptibility and Trust Networks Security – At any level of granularity 27
What is the Semantic Web ?
It’s Semantic
Webs
It’s Text Extraction It’s AI It’s Web 2.0
It’s Data Tracking It’s a Global Conspiracy
• http://www.w3.org/2006/Talks/0125-hclsig-em/ 28
It’s Ontologies
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 a
Web of Data Web of Document
to
The Only Open and Web-based Data Integration Model Game in Town
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Leveraging with Semantic Web
•
Benefit #1
Free Data from Applications… – Data uniquely defined by URI’s, even across multiple databases – – – Mapped through a common graph semantic model Data can be distributed (not in one location) New relations and attributes dynamically added • As easy as spreadsheets, but with semantics and web locations 30
Leveraging with Semantic Web
•
Benefit #2
All things on the Web can have semantics
added
them – Ability to define and link in ontologies – – – – to Documents Management through Links Changed data and semantics can be managed as versions Semantics can be used to define and apply policies No Need for complex Middleware 31
Leveraging with Semantic Web
•
Benefit #3
Supporting the Management of Knowledge – All data nodes and doc resources can be linked – Ability to represent Assertions and Hypotheses • Include authorship and assumptions • Use of KD45 logic – – Both Local and Global Knowledge • Scientists can upload partially validated facts View Data and Interpretations through Points-of-View (Semantic Lenses) • Share views with others 32
The Technologies: RDF
• • • • Resource Description Framework Think: "Relational Data Format" 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 Property Object
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What RDF Gets You
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Universal, semantic connectivity supports the construction of elaborate structures.
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 35
RDF vs. XML example
Wang et al., Nature Biotechnology, Sept 2005 AGML QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture.
HUPML 36
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&db=pubmed&dopt=Ab stract&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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Mapping from Current Formats
DB 41
Excel => RDF
ls:indivCell ${ rdf:type ls:GE_Cell; ls:probeHub gl:CASP2 ; ls:GE_Expected_Ratio "0.2726" ; ls:conditionHub gl:BREAST_MALIGNANT } ; ls:indivCell ${ rdf:type ls:GE_Cell; ls:probeHub gl:TNFRS ls:GE_Expected_Ratio "0.0138" ls:conditionHub gl:BREAST_MALIGNANT ls:indivCell ${ rdf:type ls:GE_Cell; ls:probeHub gl:CASP2 ls:GE_Expected_Ratio ; "0.1275" ls:conditionHub gl:BREAST_NORMAL ; ; ; } ; } ; 42 Casp2 TNFRS Breast Malig
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” Not a standards group, but a group to identify the best implementations of current SW Standards!
An Open Scientific Forum for Discussing, Capturing, and Showcasing Best Practices Co-chairs: Dr. Tonya Hongsermeier (Partners HealthCare); Eric Neumann (Teranode) 43
W3C Launches Semantic Web for HealthCare and Life Sciences Interest Group
• • • • First formal meeting: Jan 25-26, 2006 Cambridge, MA SW Supporting Vendors: Oracle, IBM, HP, Siemens, Agfa, Recent life science members: Pfizer, Merck, Partners HealthCare, Teranode, Cerebra, NIST, U Manchester, Stanford U, U Bolzano, AlzForum, Joining W3C gets you in as s group member – Early access to technology and discussions – Interaction with potential partners and clients 44
Multiple Ontologies Used Together Disease Group FOAF Person UMLS OMIM SNP Drug target ontology UniProt BioPAX Patent ontology PubChem
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Chemical entity Disease Polymorphisms Protein Extant ontologies Under development Bridge concept
Potential Linked Clinical Ontologies Clinical Obs Applications CDISC IRB RCRIM (HL7) SNOMED Disease Descriptions ICD10 Clinical Trials ontology Disease Models Pathways (BioPAX) Tox Genomics Mechanisms Molecules Extant ontologies Under development Bridge concept
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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
The Recontribution of Knowledge
Publications are usually copyrighted… Knowledge of Nature should be openly shareable!
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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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NeuroCommons First Steps
The first stage is underway: • • • Using NLP and other automated technologies, extract machine-readable representations of neuroscience-related knowledge as contained in free text and databases Assemble those representations into a graph Publish the graph with no intellectual property rights or contractual restrictions on reuse 52
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' N euronal data in
SenseLab
Neurological Disease entries in OMIM 53
Case Study: BioPAX (Pathways)
Case Study: BioPAX (Pathways)
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 56
Drug Discovery Dashboard
http://www.w3.org/2005/04/swls/BioDash Topic: GSK3beta Topic Disease: DiabetesT2 Alt Dis: Alzheimers Target: GSK3beta Cmpd: SB44121 CE: DBP Team: GSK3 Team Person: John Related Set Path: WNT 58
Bridging Chemistry and Molecular Biology Semantic Lenses: Different Views of the same data
BioPax Components Target Model urn:lsid:uniprot.org:uniprot: P49841
Apply Correspondence Rule:
if ?target.xref.lsid == ?bpx:prot.xref.lsid
then ?target.correspondsTo.?bpx:prot 59
Bridging Chemistry and Molecular Biology
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Lenses can aggregate, accentuate, or even analyze new result sets
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Behind the lens, the data can be persistently stored as RDF-OWL
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Correspondence does not need to mean “same descriptive object”, but may mean objects with identical references
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Case Study: Drug Safety ‘Safety Lenses’
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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) 62
ClinDash: Clinical Trials Browser
Subjects •
Values can be normalized across all measurables (rows)
Clinical Obs •
Samples can be aligned to their subjects using RDF rules
•
Clustering can now be done over all measureables (rows)
Expression Data 63
Case Study: Nokia
• Developer’s Forum Portal 64
Case Study: TERANODE Design Suite Supports Laboratory Data and Workflow
• Protocol Modeler – Accelerates workflow development – Eliminates database programming • Protocol Player – Guides users through workflow – – Automates data capture Automates complex data flow plates – Integrates lab data with project and enterprise data 65
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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Efficiency and Innovation: Semantic Web Applications Roadmap