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Ora Lassila                        Amit Sheth
•  Principal Architect (Nokia      •  LexisNexis Ohio Eminent
   Mobile Solutions); also an         Scholar, Director, Ohio Center of
   advisor to Nokia’s top mgmt        Excellence in Knowledge-
•  Elected member of W3C’s            enabled Computing (Kno.e.sis),
   Advisory Board since 1998          Wright State University
•  Earlier: Research Fellow        •  Educator, researcher,
   (Nokia Research), W3C Fellow       entrepreneur – 2 companies,
   (MIT), Project Manager (CMU),      products, deployed apps, W3C
   entrepreneur, etc.                 and biomedical community
•  Ph.D from Helsinki University      standards
   of Technology (CS)              •  Earlier: UGA, Telcordia, Unisys,
•  http://www.lassila.org/            Honeywell
                                   •  http://knoesis.org/amit
• Semantic Web
                    Ora
  • some background
• Semantic Web in use
             Amit
  • examples of applications in
    traditional clinical care to
    translational medicine
• Challenges (and promise)
        Ora (technical)
  • what makes this difficult
     Amit (health)
  • why do we want to pursue it
    anyway
• Often characterized as the “next
  generation of the World Wide Web”
  •  Web content amenable to automation
  •  (current content intended for humans…)
• Often characterized as the “next
  generation of the World Wide Web”
  •  Web content amenable to automation
  •  (current content intended for humans…)
• In reality, the Semantic Web is a vision of the
  future of (personal) computing
  •  machines working on behalf of their human users
  •  more autonomy, handling of unanticipated situations
• Heavy reliance of knowledge representation &
  reasoning
  •  also multi-agent systems, other AI-based technologies
• At the core, the Semantic Web is about
  •  describing things (objects, concepts, services, …)
  •  querying the descriptions
  •  reasoning about the descriptions
• As such, it is knowledge representation
  •  for the Web
  •  (or KR using standardized Web technologies)


• (in comparison, the “old Web” was really about
  documents and finding them…)
• Motivated by the need for automation
  •  automation requires interoperability (via standards)
  •  heavy process, high up-front investment
  •  (alternative: hand-crafted but “brittle” programs…)
• Interoperability achieved by exposing meaning
  •  accessible semantics
  •  note: interoperability of any two systems can be
     achieved via engineering, but this does not scale
• Automation → autonomy
  •  prevailing paradigm: agent-based systems
  •  implies reasoning, planning, interoperable
          representations of knowledge
• Contrary to “Web 2.0”, Semantic Web aims at
  achieving many things “ad hoc”
  •  e.g., ad hoc mash-ups by non-computer savvy people
• Shared (and accessible) semantics is the key to
  interoperability
• Semantic Web introduces a fundamentally
  different approach to standardization
  •  standardize how to say things and not what to say
  •  ontological techniques allow “delayed semantic
     commitment”
• Semantic Web is built in a layered manner
• Not everybody needs all the layers

                                        …

                                   Queries: SPARQL, Rules: RIF
  Semantic Web
                           Rich ontologies: OWL

                     Simple data models & taxonomies: RDF Schema

               Uniform metamodel: RDF + URI

         Encoding structure: XML

   Encoding characters : Unicode
• Achieve for data what Web did to documents
• Relationship with the original Semantic Web
  vision: no AI, no agents, no autonomy
• Interoperability is still very important
  •  interoperability of formats
  •  interoperability of semantics
• Enables interchange of large data sets
  •  (thus very useful in, say, collaborative research)
• Semantic Web vision is largely predicated on
  the availability of data
  •  Linked Data is a movement that gets us there
Tech assimilated in life
                                    Web of Sensors, Devices/IoT
Situations,                         - 40 billion sensors, 5 billion mobile connections
                    2007
Events                                                                    Web 3.0
Objects                         Web of people
                                  - social networks, user-created casual content

Patterns         Web of resources                                       Web 2.0
                  - data, service, data, mashups
Keywords
         Web of databases
1997       - dynamically generated pages
           - web query interfaces
       Web of pages
        - text, manually created links                     Web 1.0
        - extensive navigation
...needs a connection                          Hypothesis Validation
                                               Experiment design
                                               Predictions
                                               Personalized medicine

                          Biomedical Informatics




      Etiology                                         Genome      More advanced capabilities for
      Pathogenesis                                Transcriptome
      Clinical findings
                                                                            search,
                                                      Proteome
      Diagnosis                            Genbank Metabolome               integration,
                          Pubmed
      Prognosis                                       Physiome              analysis,
      Treatment                                           ...ome            linking to new insights
                                         Uniprot
                            Clinical                                        and discoveries!
                            Trials.gov




     Medical Informatics                   Bioinformatics
text




                                         User-contributed
 Scientific         Health                                  NCBI
                                         Content (Informal)                   Clinical Data    Laboratory
 Literature         Information                             Public Datasets
                                         Experts:                                              Data
                    Services             GeneRifs
                                         WikiGene
 PubMed             Elsevier                               Genome,
                                                                                               Lab tests,
 300 Documents                           Consumer:         Protein DBs        Personal
                    iConsult                                                                   RTPCR,
 Published Online                        Blogs             new sequences      health history
                                                           daily                               Mass spec
 each day                                Social Networks




Search, browsing, complex query, integration, workflow,
analysis, hypothesis validation, decision support.
• W3C Semantic Web Health Care & Life
  Sciences Interest Group:
  http://www.w3.org/2001/sw/hcls/
• Clinical Observations Interoperability: EMR +
  Clinical Trials:
  http://esw.w3.org/HCLS/
  ClinicalObservationsInteroperability
• National Center for Biomedical Ontologies:
  http://bioportal.bioontology.org/
• Status: In use continuously since 01/2006
• Where: Athens Heart Center & its partners and
  labs
• What: Use of semantic Web technologies for
  clinical decision support
Examples demonstrating use of Semantic Web for Health Care
and Life Sciences research projects and operational clinical or
research applications
Details: http://knoesis.org/library/resource.php?id=00004
Annotate ICD9s                Annotate Doctors


                                     Lexical Annotation               Insurance
                                                                      Formulary

                                  Level 3 Drug
                                  Interaction




Demo at: http://knoesis.org/library/demos/                           Drug Allergy
formulary_
        non_drug_           interaction_     property                   formulary
        reactant            property
                                                                                              indication
                    indication_                         property
                                                                              owl:thing
monograph           property
_ix_class                           prescription                                             interaction_
                                    _drug_                                                   with_non_
                brandname_                               prescription
                                    brand_name                                               drug_reactant
prescription    individual                               _drug                interaction
_drug_
property                      brandname_
               brandname_     composite        prescription                                 interaction_
               undeclared                      _drug_                                       with_mono
                                                                          interaction_
                                               generic                                      graph_ix_cl
                                                                          with_prescri
  cpnum_                     generic_                                                       ass
                                                                          ption_drug
  group                      composite
                                                   generic_
                                                   individual
• Status: Completed research
• Where: NIH
• What: queries across integrated data sources
  •  Enriching data with ontologies for integration, querying,
     and automation
  •  Ontologies beyond vocabularies: the power of
     relationships
Gene name
                                                               Glycosyltransferase
Interactions                                          GO


                           gene

    Sequence                                         PubMed
                           OMIM


               Congenital muscular dystrophy
    Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07
    http://knoesis.org/library/resource.php?id=00014
(GeneID: 9215)

                                           has_associated_disease

                                             Congenital muscular
                                             dystrophy,
                                             type 1D


                                           has_molecular_function


                                              Acetylglucosaminyl-
                                              transferase activity


Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07
SELECT DISTINCT ?t ?g ?d {
             ?t is_a GO:0016757 .
                                      glycosyltransferase
             ?g has molecular function ?t .                     GO:0016757
             ?g has_associated_phenotype ?b2 .
             ?b2 has_textual_description ?d .                       isa
           FILTER (?d, “muscular distrophy”, “i”) . GO:0008194“congenital”,GO:0016758
                                                    FILTER (?d,            “i”) }


                                                                                       acetylglucosaminyl-
                                                                          GO:0008375
                                                                                       transferase




                                      has_molecular_function                           acetylglucosaminyl-
                                                                          GO:0008375
                                                                                       transferase
     LARGE           EG:9215
                                                                                       Muscular dystrophy,
                                                                          MIM:608840
                                    has_associated_phenotype                           congenital, type 1D
From medinfo paper.
Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07
• Status: Completed research
• Where: NIH
• What: Understanding the genetic basis of
  nicotine dependence. Integrate gene and
  pathway information and show how three complex
  biological queries can be answered by the
  integrated knowledge base.
• How: Semantic Web technologies (especially RDF,
  OWL, and SPARQL) support information
  integration and make it easy to create semantic
  mashups (semantically integrated resources).
• NIDA study on nicotine dependency
• List of candidate genes in humans
• Analysis objectives include:
  o Find interactions between genes
  o Identification of active genes – maximum number of
    pathways
  o Identification of genes based on anatomical locations
• Requires integration of genome and biological
  pathway information
Genome and pathway information
  integration
                                           KEGG
                        Reactome
                                             • pathway
                               • pathway     • protein    HumanCyc
                               • protein     • pmid      • pathway
                               • pmid
                                                         • protein
                                                         • pmid


                                  Entrez Gene
                            • GO ID
                                                             • HomoloGene ID


                    GeneOntology                                  HomoloGene




http://knoesis.org/library/resource.php?id=00221
Entrez
           Knowledge
           Model
           (EKoM)




BioPAX
ontology
• Status: Research prototype – in regular lab use
• Where: Center for Tropical and Emerging
  Global Diseases (CTEGD), UGA
• What: Semantics and Services Enabled
  Problem Solving Environment for Trypanosoma
  cruzi
• Who: Kno.e.sis, UGA, NCBO
Ohio Center of Excellence in Knowledge-enabled Computing
 (Kno.e.sis), Wright State University

Tarleton Research Group, Center for Tropical and Emerging
 Global Diseases(CTEGD), University of Georgia

Large Scale Distributed Information Systems (LSDIS).
 University of Georgia

National Center for Biological Ontologies (NCBO),
 Stanford University

The Wellcome Trust Sanger Institute, Cambridge, UK

The Oswaldo Cruz Institute (Fiocruz), Brazil
• T. cruzi is a protozoan parasite
  that causes Chagas Disease or
  American trypanosomiasis
• Chagas disease is the leading
  cause of death in Latin America
  where around 18 million people
  are infected with this parasite    T. Brucei surrounded by red blood cells
                                     in a smear of infected blood.
• Related parasites include,         (Copyright: Jürgen Berger and Dr. Peter
                                     Overath, Max Planck Institute for
  Trypanosoma brucei and             Developmental Biology, Tübengen)

  Leishmania major that causes
  African trypanosomiasis and
  leishmaniasis, respectively.
Trykipedia - a Wiki-based platform for collaboration of Parasite Research Community
•  Data Resources
    Internal lab data (from Tarleton Research Group)
        Gene Knockout, Strain Creation, Microarray, and Proteome
    External databases (TriTrypDB, ProtozoaDB, Drug Bank, etc. )
•  Ontologies
    Parasite Lifecycle Ontology (PLO)
    Parasite Experiment Ontology (PEO)
•  PKR supports complex biological queries related to T.cruzi
   drugs, vaccination, or gene knockout targets; for example,
    Find all genes with proteomic expression in mammalian lifecycle stage with GPI anchor
    or signal peptide predictions.
    Find genes annotated as potential vaccine candidates.
    Find all genes with proteomic expression evidence in the mammalian host lifecycle
    stages for T. cruzi
Gene
                  Name



               Sequence
               Extraction
                              Gene Knockout and Strain Creation*
                                            Related Queries from Biologists
Drug            3‘ & 5’
Resistant       Region
Plasmid
                                                   Gene Name
             Plasmid
             Construction
                  •  List all groups in the lab that used
   T.Cruzi
                Knockout
                Construct
                                               a Target Region Plasmid?
   sample
       Plasmid



              Transfection
                                            •  Which ?researcher created a new
                                               strain of the parasite (with ID =
               Transfecte
                d Sample
                                               66)?
                                            •  An experiment was not successful
                  Drug
                Selection
                      Cloned Sample

                Selected
                Sample
                                               – has this experiment been
                   Cell
                 Cloning
                                               conducted earlier? What were the
                Cloned
                                               results?
                Sample



                   *T.cruzi Semantic Problem Solving Environment Project, Courtesy of D.B.
                   Weatherly and Flora Logan, Tarleton Lab, University of Georgia
Complex queries can also include:
- on-the-fly Web services execution to retrieve additional data
-  inference rules to make implicit knowledge explicit
1.  Describe drug user’s knowledge, attitudes, and
     behaviors related to illicit use of OxyContin®
2.  Describe temporal patterns of non-medical use of
    OxyContin® tablets as discussed on Web-based
    forums
3.  Collaboration between Kno.e.sis and CITAR (Center
    for Interventions, Treatment and Addictions Research)
    at Wright State Univ.
• Volatile nature of execution environments
  •  May have an impact on multiple activities/ tasks in the
     workflow
• HF Pathway
  •  New information about diseases, drugs becomes
     available
  •  Affects treatment plans, drug-drug interactions
• Need to incorporate the new knowledge into
  execution
  •  capture the constraints and relationships between
     different tasks activities
New knowledge about
treatment found during
the execution of the pathway



New knowledge about drugs,
drug drug interactions
Diabetes mellitus adversely affects the outcomes in patients with myocardial infarction (MI), due in part to the exacerbation of left
 ventricular (LV) remodeling. Although angiotensin II type 1 receptor blocker (ARB) has been demonstrated to be effective in the
 treatment of heart failure, information about the potential benefits of ARB on advanced LV failure associated with diabetes is lacking.
 To induce diabetes, male mice were injected intraperitoneally with streptozotocin (200 mg/kg). At 2 weeks, anterior MI was created by
 ligating the left coronary artery. These animals received treatment with olmesartan (0.1 mg/kg/day; n = 50) or vehicle (n = 51) for 4
 weeks. Diabetes worsened the survival and exaggerated echocardiographic LV dilatation and dysfunction in MI. Treatment of diabetic
 MI mice with olmesartan significantly improved the survival rate (42% versus 27%, P < 0.05) without affecting blood glucose, arterial
 blood pressure, or infarct size. It also attenuated LV dysfunction in diabetic MI. Likewise, olmesartan attenuated myocyte hypertrophy,
 interstitial fibrosis, and the number of apoptotic cells in the noninfarcted LV from diabetic MI. Post-MI LV remodeling and failure in
 diabetes were ameliorated by ARB, providing further evidence that angiotensin II plays a pivotal role in the exacerbated heart failure
 after diabetic MI.




                                                                   possibly
                                         ARB                       plays role in
                                                                                          heart failure
Angiotensin II type 1 receptor blocker attenuates exacerbated left ventricular remodeling and failure in diabetes-associated myocardial infarction.,
       Matsusaka H, et. al.
Disease

                                                                        possibly
                                                                        plays role in


                                                                     Angiotension
                                                                     Receptor Blocker
                                                                     (ARB)


Ontology: A Framework for Schema-Driven Relationship Discovery from Unstructured Text, Ramakrishnan, et. al., ISWC 2006, LNCS 4273, pp. 583-596
•  Matching medical requirements with availability of
   medical resources (Mumbai, India)
  •  Project HERO Helpline for Emergency Response Operations
  •  For patients seeking for immediate medical help

•  Medical awareness in rural India
  •  mMitra, info. service during pregnancy and childhood
     emergency



 Medical
                                                   Medical
                            Information
Emergency 
                                                 Resourc
                               bridge
                                    
                                                              es
• Any specific problem (typically) has a specific
  solution that does not require Semantic Web
  technologies
• Q: Why then is the Semantic Web attractive?
  A: For future-proofing


  Semantic Web can be a solution to
  those problems and situations that
         we are yet to define
• Cultural resistance (“this smacks of AI…”)
• Unfamiliar technology (e.g., reasoning)
• Often implies complex representational models
  •  procedural programs vs. declarative data
• Unclear business models
• Also, actual technical challenges
  •  scalability of query processing
  •  complexity (and thus scalability) of reasoning
  •  scalability of access control
  •  …
• (merely an observation of what you may
  encounter…)




                                           Source: Mindlab, U of Maryland
• What makes Semantic
  Web attractive and worth pursuing is…
an Dictionary)
                        (Source: Oxford Americ



• Serendipity in interoperability
  •  can we interoperate with systems, devices and/or
     services we knew nothing about at design time?
• Serendipity in information reuse
  •  with accessible semantics, this becomes easier…
• Serendipity in information integration
  •  can information from independent sources be combined?
  •  even simple forms of reasoning can help
• Semantic Web was designed to
  •  accommodate different points of view
  •  be flexible about what it can express (not preferential
     towards any particular domain or application)
• Combining information in new ways
  •  we cannot anticipate all the possible ways in which
     information is used, combined
  ⇒  there is value to merely making information (data)
       available
  •  using Semantic Web technologies lowers the threshold
     for “serendipitous reuse”
Insurance,          Clinical Care
Financial Aspects                        Follow up,
                                         Lifestyle




  Genetic Tests…
  Profiles                            Social Media
                    Clinical Trials
NIH                 FDA               CDC
          (Research)




  Universities,        Pharmaceutical
    AMCs               Companies

                                             Patients, Public




                         CROs             Hospitals Doctors




                                              Payors            From FDA, CDC

Translation 1: Genomic Research and Clinical Practice
Translation 2: Clinical Research and Clinical Practice
                         Slide by: Vipul Kashyap
• For each component in 360-degree health care,
  we have data, processes, knowledge and
  experience. Interoperability solutions need to
  encompass all these!
  •  Possibly largest growth in data will be in sensors (eg
     Body Area Networks, Biosensors) and social content.
     Extensive use of mobile phones.




    Credit: ece.virginia.edu
• Semantic Web is an “interoperability
  technology”
• Linked Data is a step in the right direction
• Many examples of viable usage of Semantic
  Web technologies
• Words of warning about deployment
• For health, Semantic Web provides the needed
  interoperability, and can accommodate all
  necessary “points of view”
• Significant research challenges remain as
  Health presents the most complex domain
• Researchers: Satya Sahoo, Dr. Priti Parikh,
  Pablo Mendes, Cartic Ramakrishnan, and
  Kno.e.sis team
• Collaborators: Athens Heart Center (Dr.
  Agrawal), NLM (Olivier Bodenreider), CCRC-
  UGA (Will York), UGA (Tarleton),
  Bioinformatics-WSU (Raymer)
• Funding: NIH/NCRR, NIH/NLBHI (R01), NSF

               http://knoesis.org
1.     A. Sheth, S. Agrawal, J. Lathem, N. Oldham, H. Wingate, P. Yadav, and K. Gallagher,
      Active Semantic Electronic Medical Record, Intl Semantic Web Conference, 2006.
2.     Satya Sahoo, Olivier Bodenreider, Kelly Zeng, and Amit Sheth,
      An Experiment in Integrating Large Biomedical Knowledge Resources with RDF: Application to
      Associating Genotype and Phenotype Information
      WWW2007 HCLS Workshop, May 2007.
3.     Satya S. Sahoo, Kelly Zeng, Olivier Bodenreider, and Amit Sheth,
      From "Glycosyltransferase to Congenital Muscular Dystrophy: Integrating Knowledge from NCBI
      Entrez Gene and the Gene Ontology, Amsterdam: IOS, August 2007, PMID: 17911917, pp.
      1260-4
4.     Satya S. Sahoo, Olivier Bodenreider, Joni L. Rutter, Karen J. Skinner , Amit P. Sheth,
      An ontology-driven semantic mash-up of gene and biological pathway information: Application to
      the domain of nicotine dependence, Journal of Biomedical Informatics, 2008.
5.     Cartic Ramakrishnan, Krzysztof J. Kochut, and Amit Sheth, "
      A Framework for Schema-Driven Relationship Discovery from Unstructured Text", Intl Semantic
      Web Conference, 2006, pp. 583-596
6.     Satya S. Sahoo, Christopher Thomas, Amit Sheth, William S. York, and Samir Tartir, "
      Knowledge Modeling and Its Application in Life Sciences: A Tale of Two Ontologies", 15th
      International World Wide Web Conference (WWW2006), Edinburgh, Scotland, May 23-26, 2006.
7.     Satya S. Sahoo, Olivier Bodenreider, Pascal Hitzler, Amit Sheth and Krishnaprasad
      Thirunarayan, '
      Provenance Context Entity (PaCE): Scalable provenance tracking for scientific RDF data.’
      SSDBM, Heidelberg, Germany 2010.
•      Papers: http://knoesis.org/library
•      Demos at: http://knoesis.wright.edu/library/demos/

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Semantic Web for 360-degree Health: State-of-the-Art & Vision for Better Interoperability

  • 1.
  • 2. Ora Lassila Amit Sheth •  Principal Architect (Nokia •  LexisNexis Ohio Eminent Mobile Solutions); also an Scholar, Director, Ohio Center of advisor to Nokia’s top mgmt Excellence in Knowledge- •  Elected member of W3C’s enabled Computing (Kno.e.sis), Advisory Board since 1998 Wright State University •  Earlier: Research Fellow •  Educator, researcher, (Nokia Research), W3C Fellow entrepreneur – 2 companies, (MIT), Project Manager (CMU), products, deployed apps, W3C entrepreneur, etc. and biomedical community •  Ph.D from Helsinki University standards of Technology (CS) •  Earlier: UGA, Telcordia, Unisys, •  http://www.lassila.org/ Honeywell •  http://knoesis.org/amit
  • 3. • Semantic Web Ora • some background • Semantic Web in use Amit • examples of applications in traditional clinical care to translational medicine • Challenges (and promise) Ora (technical) • what makes this difficult Amit (health) • why do we want to pursue it anyway
  • 4.
  • 5. • Often characterized as the “next generation of the World Wide Web” •  Web content amenable to automation •  (current content intended for humans…)
  • 6. • Often characterized as the “next generation of the World Wide Web” •  Web content amenable to automation •  (current content intended for humans…) • In reality, the Semantic Web is a vision of the future of (personal) computing •  machines working on behalf of their human users •  more autonomy, handling of unanticipated situations • Heavy reliance of knowledge representation & reasoning •  also multi-agent systems, other AI-based technologies
  • 7. • At the core, the Semantic Web is about •  describing things (objects, concepts, services, …) •  querying the descriptions •  reasoning about the descriptions • As such, it is knowledge representation •  for the Web •  (or KR using standardized Web technologies) • (in comparison, the “old Web” was really about documents and finding them…)
  • 8. • Motivated by the need for automation •  automation requires interoperability (via standards) •  heavy process, high up-front investment •  (alternative: hand-crafted but “brittle” programs…) • Interoperability achieved by exposing meaning •  accessible semantics •  note: interoperability of any two systems can be achieved via engineering, but this does not scale • Automation → autonomy •  prevailing paradigm: agent-based systems •  implies reasoning, planning, interoperable representations of knowledge
  • 9. • Contrary to “Web 2.0”, Semantic Web aims at achieving many things “ad hoc” •  e.g., ad hoc mash-ups by non-computer savvy people • Shared (and accessible) semantics is the key to interoperability • Semantic Web introduces a fundamentally different approach to standardization •  standardize how to say things and not what to say •  ontological techniques allow “delayed semantic commitment”
  • 10. • Semantic Web is built in a layered manner • Not everybody needs all the layers … Queries: SPARQL, Rules: RIF Semantic Web Rich ontologies: OWL Simple data models & taxonomies: RDF Schema Uniform metamodel: RDF + URI Encoding structure: XML Encoding characters : Unicode
  • 11. • Achieve for data what Web did to documents • Relationship with the original Semantic Web vision: no AI, no agents, no autonomy • Interoperability is still very important •  interoperability of formats •  interoperability of semantics • Enables interchange of large data sets •  (thus very useful in, say, collaborative research) • Semantic Web vision is largely predicated on the availability of data •  Linked Data is a movement that gets us there
  • 12. Tech assimilated in life Web of Sensors, Devices/IoT Situations, - 40 billion sensors, 5 billion mobile connections 2007 Events Web 3.0 Objects Web of people - social networks, user-created casual content Patterns Web of resources Web 2.0 - data, service, data, mashups Keywords Web of databases 1997 - dynamically generated pages - web query interfaces Web of pages - text, manually created links Web 1.0 - extensive navigation
  • 13.
  • 14. ...needs a connection Hypothesis Validation Experiment design Predictions Personalized medicine Biomedical Informatics Etiology Genome More advanced capabilities for Pathogenesis Transcriptome Clinical findings search, Proteome Diagnosis Genbank Metabolome integration, Pubmed Prognosis Physiome analysis, Treatment ...ome linking to new insights Uniprot Clinical and discoveries! Trials.gov Medical Informatics Bioinformatics
  • 15. text User-contributed Scientific Health NCBI Content (Informal) Clinical Data Laboratory Literature Information Public Datasets Experts: Data Services GeneRifs WikiGene PubMed Elsevier Genome, Lab tests, 300 Documents Consumer: Protein DBs Personal iConsult RTPCR, Published Online Blogs new sequences health history daily Mass spec each day Social Networks Search, browsing, complex query, integration, workflow, analysis, hypothesis validation, decision support.
  • 16. • W3C Semantic Web Health Care & Life Sciences Interest Group: http://www.w3.org/2001/sw/hcls/ • Clinical Observations Interoperability: EMR + Clinical Trials: http://esw.w3.org/HCLS/ ClinicalObservationsInteroperability • National Center for Biomedical Ontologies: http://bioportal.bioontology.org/
  • 17. • Status: In use continuously since 01/2006 • Where: Athens Heart Center & its partners and labs • What: Use of semantic Web technologies for clinical decision support
  • 18. Examples demonstrating use of Semantic Web for Health Care and Life Sciences research projects and operational clinical or research applications
  • 20. Annotate ICD9s Annotate Doctors Lexical Annotation Insurance Formulary Level 3 Drug Interaction Demo at: http://knoesis.org/library/demos/ Drug Allergy
  • 21. formulary_ non_drug_ interaction_ property formulary reactant property indication indication_ property owl:thing monograph property _ix_class prescription interaction_ _drug_ with_non_ brandname_ prescription brand_name drug_reactant prescription individual _drug interaction _drug_ property brandname_ brandname_ composite prescription interaction_ undeclared _drug_ with_mono interaction_ generic graph_ix_cl with_prescri cpnum_ generic_ ass ption_drug group composite generic_ individual
  • 22. • Status: Completed research • Where: NIH • What: queries across integrated data sources •  Enriching data with ontologies for integration, querying, and automation •  Ontologies beyond vocabularies: the power of relationships
  • 23. Gene name Glycosyltransferase Interactions GO gene Sequence PubMed OMIM Congenital muscular dystrophy Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07 http://knoesis.org/library/resource.php?id=00014
  • 24. (GeneID: 9215) has_associated_disease Congenital muscular dystrophy, type 1D has_molecular_function Acetylglucosaminyl- transferase activity Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07
  • 25. SELECT DISTINCT ?t ?g ?d { ?t is_a GO:0016757 . glycosyltransferase ?g has molecular function ?t . GO:0016757 ?g has_associated_phenotype ?b2 . ?b2 has_textual_description ?d . isa FILTER (?d, “muscular distrophy”, “i”) . GO:0008194“congenital”,GO:0016758 FILTER (?d, “i”) } acetylglucosaminyl- GO:0008375 transferase has_molecular_function acetylglucosaminyl- GO:0008375 transferase LARGE EG:9215 Muscular dystrophy, MIM:608840 has_associated_phenotype congenital, type 1D From medinfo paper. Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07
  • 26. • Status: Completed research • Where: NIH • What: Understanding the genetic basis of nicotine dependence. Integrate gene and pathway information and show how three complex biological queries can be answered by the integrated knowledge base. • How: Semantic Web technologies (especially RDF, OWL, and SPARQL) support information integration and make it easy to create semantic mashups (semantically integrated resources).
  • 27. • NIDA study on nicotine dependency • List of candidate genes in humans • Analysis objectives include: o Find interactions between genes o Identification of active genes – maximum number of pathways o Identification of genes based on anatomical locations • Requires integration of genome and biological pathway information
  • 28. Genome and pathway information integration KEGG Reactome • pathway • pathway • protein HumanCyc • protein • pmid • pathway • pmid • protein • pmid Entrez Gene • GO ID • HomoloGene ID GeneOntology HomoloGene http://knoesis.org/library/resource.php?id=00221
  • 29.
  • 30. Entrez Knowledge Model (EKoM) BioPAX ontology
  • 31.
  • 32. • Status: Research prototype – in regular lab use • Where: Center for Tropical and Emerging Global Diseases (CTEGD), UGA • What: Semantics and Services Enabled Problem Solving Environment for Trypanosoma cruzi • Who: Kno.e.sis, UGA, NCBO
  • 33. Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Wright State University Tarleton Research Group, Center for Tropical and Emerging Global Diseases(CTEGD), University of Georgia Large Scale Distributed Information Systems (LSDIS). University of Georgia National Center for Biological Ontologies (NCBO), Stanford University The Wellcome Trust Sanger Institute, Cambridge, UK The Oswaldo Cruz Institute (Fiocruz), Brazil
  • 34. • T. cruzi is a protozoan parasite that causes Chagas Disease or American trypanosomiasis • Chagas disease is the leading cause of death in Latin America where around 18 million people are infected with this parasite T. Brucei surrounded by red blood cells in a smear of infected blood. • Related parasites include, (Copyright: Jürgen Berger and Dr. Peter Overath, Max Planck Institute for Trypanosoma brucei and Developmental Biology, Tübengen) Leishmania major that causes African trypanosomiasis and leishmaniasis, respectively.
  • 35. Trykipedia - a Wiki-based platform for collaboration of Parasite Research Community
  • 36. •  Data Resources  Internal lab data (from Tarleton Research Group)  Gene Knockout, Strain Creation, Microarray, and Proteome  External databases (TriTrypDB, ProtozoaDB, Drug Bank, etc. ) •  Ontologies  Parasite Lifecycle Ontology (PLO)  Parasite Experiment Ontology (PEO) •  PKR supports complex biological queries related to T.cruzi drugs, vaccination, or gene knockout targets; for example,  Find all genes with proteomic expression in mammalian lifecycle stage with GPI anchor or signal peptide predictions.  Find genes annotated as potential vaccine candidates.  Find all genes with proteomic expression evidence in the mammalian host lifecycle stages for T. cruzi
  • 37. Gene Name Sequence Extraction Gene Knockout and Strain Creation* Related Queries from Biologists Drug 3‘ & 5’ Resistant Region Plasmid Gene Name Plasmid Construction •  List all groups in the lab that used T.Cruzi Knockout Construct a Target Region Plasmid? sample Plasmid Transfection •  Which ?researcher created a new strain of the parasite (with ID = Transfecte d Sample 66)? •  An experiment was not successful Drug Selection Cloned Sample Selected Sample – has this experiment been Cell Cloning conducted earlier? What were the Cloned results? Sample *T.cruzi Semantic Problem Solving Environment Project, Courtesy of D.B. Weatherly and Flora Logan, Tarleton Lab, University of Georgia
  • 38. Complex queries can also include: - on-the-fly Web services execution to retrieve additional data -  inference rules to make implicit knowledge explicit
  • 39. 1.  Describe drug user’s knowledge, attitudes, and behaviors related to illicit use of OxyContin® 2.  Describe temporal patterns of non-medical use of OxyContin® tablets as discussed on Web-based forums 3.  Collaboration between Kno.e.sis and CITAR (Center for Interventions, Treatment and Addictions Research) at Wright State Univ.
  • 40.
  • 41. • Volatile nature of execution environments •  May have an impact on multiple activities/ tasks in the workflow • HF Pathway •  New information about diseases, drugs becomes available •  Affects treatment plans, drug-drug interactions • Need to incorporate the new knowledge into execution •  capture the constraints and relationships between different tasks activities
  • 42. New knowledge about treatment found during the execution of the pathway New knowledge about drugs, drug drug interactions
  • 43.
  • 44. Diabetes mellitus adversely affects the outcomes in patients with myocardial infarction (MI), due in part to the exacerbation of left ventricular (LV) remodeling. Although angiotensin II type 1 receptor blocker (ARB) has been demonstrated to be effective in the treatment of heart failure, information about the potential benefits of ARB on advanced LV failure associated with diabetes is lacking. To induce diabetes, male mice were injected intraperitoneally with streptozotocin (200 mg/kg). At 2 weeks, anterior MI was created by ligating the left coronary artery. These animals received treatment with olmesartan (0.1 mg/kg/day; n = 50) or vehicle (n = 51) for 4 weeks. Diabetes worsened the survival and exaggerated echocardiographic LV dilatation and dysfunction in MI. Treatment of diabetic MI mice with olmesartan significantly improved the survival rate (42% versus 27%, P < 0.05) without affecting blood glucose, arterial blood pressure, or infarct size. It also attenuated LV dysfunction in diabetic MI. Likewise, olmesartan attenuated myocyte hypertrophy, interstitial fibrosis, and the number of apoptotic cells in the noninfarcted LV from diabetic MI. Post-MI LV remodeling and failure in diabetes were ameliorated by ARB, providing further evidence that angiotensin II plays a pivotal role in the exacerbated heart failure after diabetic MI. possibly ARB plays role in heart failure Angiotensin II type 1 receptor blocker attenuates exacerbated left ventricular remodeling and failure in diabetes-associated myocardial infarction., Matsusaka H, et. al.
  • 45. Disease possibly plays role in Angiotension Receptor Blocker (ARB) Ontology: A Framework for Schema-Driven Relationship Discovery from Unstructured Text, Ramakrishnan, et. al., ISWC 2006, LNCS 4273, pp. 583-596
  • 46. •  Matching medical requirements with availability of medical resources (Mumbai, India) •  Project HERO Helpline for Emergency Response Operations •  For patients seeking for immediate medical help •  Medical awareness in rural India •  mMitra, info. service during pregnancy and childhood emergency Medical Medical Information Emergency Resourc bridge es
  • 47.
  • 48. • Any specific problem (typically) has a specific solution that does not require Semantic Web technologies • Q: Why then is the Semantic Web attractive? A: For future-proofing Semantic Web can be a solution to those problems and situations that we are yet to define
  • 49. • Cultural resistance (“this smacks of AI…”) • Unfamiliar technology (e.g., reasoning) • Often implies complex representational models •  procedural programs vs. declarative data • Unclear business models • Also, actual technical challenges •  scalability of query processing •  complexity (and thus scalability) of reasoning •  scalability of access control •  …
  • 50. • (merely an observation of what you may encounter…) Source: Mindlab, U of Maryland • What makes Semantic Web attractive and worth pursuing is…
  • 51. an Dictionary) (Source: Oxford Americ • Serendipity in interoperability •  can we interoperate with systems, devices and/or services we knew nothing about at design time? • Serendipity in information reuse •  with accessible semantics, this becomes easier… • Serendipity in information integration •  can information from independent sources be combined? •  even simple forms of reasoning can help
  • 52. • Semantic Web was designed to •  accommodate different points of view •  be flexible about what it can express (not preferential towards any particular domain or application) • Combining information in new ways •  we cannot anticipate all the possible ways in which information is used, combined ⇒  there is value to merely making information (data) available •  using Semantic Web technologies lowers the threshold for “serendipitous reuse”
  • 53. Insurance, Clinical Care Financial Aspects Follow up, Lifestyle Genetic Tests… Profiles Social Media Clinical Trials
  • 54. NIH FDA CDC (Research) Universities, Pharmaceutical AMCs Companies Patients, Public CROs Hospitals Doctors Payors From FDA, CDC Translation 1: Genomic Research and Clinical Practice Translation 2: Clinical Research and Clinical Practice Slide by: Vipul Kashyap
  • 55. • For each component in 360-degree health care, we have data, processes, knowledge and experience. Interoperability solutions need to encompass all these! •  Possibly largest growth in data will be in sensors (eg Body Area Networks, Biosensors) and social content. Extensive use of mobile phones. Credit: ece.virginia.edu
  • 56. • Semantic Web is an “interoperability technology” • Linked Data is a step in the right direction • Many examples of viable usage of Semantic Web technologies • Words of warning about deployment • For health, Semantic Web provides the needed interoperability, and can accommodate all necessary “points of view” • Significant research challenges remain as Health presents the most complex domain
  • 57. • Researchers: Satya Sahoo, Dr. Priti Parikh, Pablo Mendes, Cartic Ramakrishnan, and Kno.e.sis team • Collaborators: Athens Heart Center (Dr. Agrawal), NLM (Olivier Bodenreider), CCRC- UGA (Will York), UGA (Tarleton), Bioinformatics-WSU (Raymer) • Funding: NIH/NCRR, NIH/NLBHI (R01), NSF http://knoesis.org
  • 58. 1.  A. Sheth, S. Agrawal, J. Lathem, N. Oldham, H. Wingate, P. Yadav, and K. Gallagher, Active Semantic Electronic Medical Record, Intl Semantic Web Conference, 2006. 2.  Satya Sahoo, Olivier Bodenreider, Kelly Zeng, and Amit Sheth, An Experiment in Integrating Large Biomedical Knowledge Resources with RDF: Application to Associating Genotype and Phenotype Information WWW2007 HCLS Workshop, May 2007. 3.  Satya S. Sahoo, Kelly Zeng, Olivier Bodenreider, and Amit Sheth, From "Glycosyltransferase to Congenital Muscular Dystrophy: Integrating Knowledge from NCBI Entrez Gene and the Gene Ontology, Amsterdam: IOS, August 2007, PMID: 17911917, pp. 1260-4 4.  Satya S. Sahoo, Olivier Bodenreider, Joni L. Rutter, Karen J. Skinner , Amit P. Sheth, An ontology-driven semantic mash-up of gene and biological pathway information: Application to the domain of nicotine dependence, Journal of Biomedical Informatics, 2008. 5.  Cartic Ramakrishnan, Krzysztof J. Kochut, and Amit Sheth, " A Framework for Schema-Driven Relationship Discovery from Unstructured Text", Intl Semantic Web Conference, 2006, pp. 583-596 6.  Satya S. Sahoo, Christopher Thomas, Amit Sheth, William S. York, and Samir Tartir, " Knowledge Modeling and Its Application in Life Sciences: A Tale of Two Ontologies", 15th International World Wide Web Conference (WWW2006), Edinburgh, Scotland, May 23-26, 2006. 7.  Satya S. Sahoo, Olivier Bodenreider, Pascal Hitzler, Amit Sheth and Krishnaprasad Thirunarayan, ' Provenance Context Entity (PaCE): Scalable provenance tracking for scientific RDF data.’ SSDBM, Heidelberg, Germany 2010. •  Papers: http://knoesis.org/library •  Demos at: http://knoesis.wright.edu/library/demos/