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“Semantic Webs” and “The Semantic Web”: Services, Resources and Technologies for Clinical Care and Biomedical Researc

“Semantic Webs” and “The Semantic Web”: Services, Resources and Technologies for Clinical Care and Biomedical Research. Alan Rector School of Computer Science / Northwest Institute of Bio-Health Informatics rector@cs.man.ac.uk www.co-ode.org www.clinical-escience.org www.opengalen.org.

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“Semantic Webs” and “The Semantic Web”: Services, Resources and Technologies for Clinical Care and Biomedical Researc

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  1. “Semantic Webs” and “The Semantic Web”: Services, Resources and Technologies for Clinical Care and Biomedical Research Alan RectorSchool of Computer Science / Northwest Institute of Bio-Health Informaticsrector@cs.man.ac.uk www.co-ode.orgwww.clinical-escience.orgwww.opengalen.org

  2. Semantic Web and Webs • The Semantic Web • A Global Information Resource • Discoverable • Collaborative • Trust to be negotiated • Semantic Webs • Resources for Virtual Organisations • Discoverable • Collaborative • Faithful and trusted • Interworking • BioMedicine is network of virtual organisations • For care • For Research

  3. Semantic Web Technology • New ways to deliver information services • Service oriented computing • Easy interworking of heterogeneous systems • SOAP • Semantically rich computing • Workflows • “Macros on steroids” • Discovering appropriate services. • Knowledge representation • “Ontologies and metadata with everything!” • Data on its own means nothing • New standards for things we have been doing • RDF(S), OWL, WSDL, xxML, SCUFL, … … … • New standard resources • Genes, proteins, pathways, … … …

  4. … Standards for everything … and E-Science / E-Health … and digital libraries … and … and • RDF, RDFS, OWL, SWRL, WSDL, SOAP, … • W3C Healthcare and Life Sciences Special Interest Group • ISO 11179 • Dublin Core • SKOS • … What about medical standards?HL7? CEN? ISO? SNOMED? …? Do we have to do it on our own? It’s a big open world out there!

  5. …and E-Science / Semantic Grid • E-Science • Large scale collaborative science • Collections based research • Using information rather than gathering data • Often Uses Grids but not about Grids • Image processing, Text mining, Neuro Computing • Need Cycles and Petabytes • Workflows, Information organisation, social computing • Need connectivity & collaboration

  6. Three themes for this talk • Information discovery • Joining up healthcare delivery and biomedical research • Factoring huge problems into manageable chunks • Workflows & Service Oriented Architectures • Rich semantics, metadata and ontologies

  7. Theme 1:Discovering Information • Adding meaning • That machines can process • That people can understand • From specifying “how to do it” tospecifying “what to do” • To find it it must be described • Metadata & annotations • To describe it you need a language; • RDF(S), OWL, SWRL, … • For the language you need words - • “Ontologies” and Terminologies

  8. The promise of the Semantic WebThe Syntactic Web is easily confused… Find images of Steve Furber Carole Goble … Alan Rector… Rev. Alan M. Gates, Associate Rector of the Church of the Holy Spirit, Lake Forest, Illinois

  9. What information can we see… WWW2002 The eleventh international world wide web conference Sheraton waikiki hotel Honolulu, hawaii, USA 7-11 may 2002 1 location 5 days learn interact Registered participants coming from australia, canada, chile denmark, france, germany, ghana, hong kong, india, ireland, italy, japan, malta, new zealand, the netherlands, norway, singapore, switzerland, the united kingdom, the united states, vietnam, zaire Register now On the 7th May Honolulu will provide the backdrop of the eleventh international world wide web conference. This prestigious event … Speakers confirmed Tim berners-lee Tim is the well known inventor of the Web, … Ian Foster Ian is the pioneer of the Grid, the next generation internet …

  10. What information can a machine see… WWW2002 The eleventh international world wide web conference Sheraton waikiki hotel Honolulu, hawaii, USA 7-11 may 2002 1 location 5 days learn interact Registered participants coming from australia, canada, chile denmark, france, germany, ghana, hong kong, india, ireland, italy, japan, malta, new zealand, the netherlands, norway, singapore, switzerland, the united kingdom, the united states, vietnam, zaire Register now On the 7th May Honolulu will provide the backdrop of the eleventh international world wide web conference. This prestigious event … Speakers confirmed Tim berners-lee Tim is the well known inventor of the Web, … Ian Foster Ian is the pioneer of the Grid, the next generation internet …

  11. Solution: XML markup with “meaningful” tags? <name>WWW2002 The eleventh international world wide webcon</name> <location>Sheraton waikiki hotel Honolulu, hawaii, USA</location> <date>7-11 may 2002</date> <slogan>1 location 5 days learn interact</slogan> <participants>Registered participants coming from australia, canada, chile denmark, france, germany, ghana, hong kong, india, ireland, italy, japan, malta, new zealand, the netherlands, norway, singapore, switzerland, the united kingdom, the united states, vietnam, zaire</participants> <introduction>Register now On the 7th May Honolulu will provide the backdrop of the eleventh international world wide web conference. This prestigious event … Speakers confirmed</introduction> <speaker>Tim berners-lee</speaker> <bio>Tim is the well known inventor of the Web,</bio>…

  12. Still the Machine only sees… <name>WWW2002 The eleventh international world wide webc</name> <location>Sheraton waikiki hotel Honolulu, hawaii, USA</location> <date>7-11 may 2002</date> <slogan>1 location 5 days learn interact</slogan> <participants>Registered participants coming from australia, canada, chile denmark, france, germany, ghana, hong kong, india, ireland, italy, japan, malta, new zealand, the netherlands, norway, singapore, switzerland, the united kingdom, the united states, vietnam, zaire</participants> <introduction>Register now On the 7th May Honolulu will provide the backdrop of the eleventh international world wide web conference. This prestigious event … Speakers confirmed</introduction> <speaker>Tim berners-lee</speaker> <bio>Tim is the well known inventor of the W</bio> <speaker>Ian Foster</speaker> <bio>Ian is the pioneer of the Grid, the ne</bio>

  13. Need to Add “Semantics” • Annotations • In languages that machines can process • Using terminology that people have agreed & machines can process

  14. Competitive/ComplementaryTechnologies - machine learning & text mining National Centre for Text Mining (NaCTeM) http://www-tsujii.is.s.u-tokyo.ac.jp/info-pubmed/

  15. Or web miningthere’s no lack of text out there

  16. Web-Discovery of information • Four competing technologies • Semantic Web • Or hand built ontologies • OBO, FMA, SNOMED? , other … • Social computing • Open Directory, Wikipedia, FLIKR, FoF, … • Web mining • Google (& other web search) • Text mining • Just becoming widely available, especially in biology • All of pubmed abstracts about to be minable for relations • National Centre for Text Mining - NaCTeM

  17. Theme II:Joining up healthcare delivery and Biomedical Research The CLEF Vision www.clinical-escience.org

  18. Knowledgeenrichment Knowledgeenrichment HazardMonitoring Integrate &Aggregate Integrate &Aggregate PseudonymisedRepository PseudonymisedRepository ExtractInformation ExtractInformation Ethical oversightcommittee Depersonalise Depersonalise Chronicle Chronicle PrivacyEnhancementTechnologies Summarise & FormulateQueries Summarise & FormulateQueries Construct‘Chronicle’ Construct‘Chronicle’ PseudonymiseIn Hospital PseudonymiseIn Hospital Individual Summaries & Queries Individual Summaries & Queries PrivacyEnhancementTechnologies ReidentifyBy Hospital HazardMonitoring ReidentifyBy Hospital Data Access Cycle Data Acquisition Cycle Joining up care and Research:The CLEF Vision

  19. The Chronicle • A semantically rich summary of our best understanding of the patient • Inferred from data and metadata • Combined from many sources on semantic webs

  20. (Increasing detail) Low haemoglobins over a period = anaemia Coreferences Time Clinical pragmatics Simplification Abstraction

  21. R R R R Grade III infiltrating ductal carcinoma left breast Died RADIO CHEMO TAMOXIFEN ARIMIDEX Nodes Liver Spleen Kidney Bone Nodes Liver Spleen Kidney Bone Staging CT S S S S S S S S S S S S S T1>N1>M0 T1N3cM0 T1>N3cM1 >Stage IIA Stage IIIc Stage IV Recurrence 1975 1980 1985 1990 1995 2000 Inferred best view of the patient history -from whatever sources - the CLEF Chronicle

  22. Privacy and Security • The great barrier to clinical use • Web/Grid security a key topic • For policy • How safe is safe? • What is the risk from medical information • Your credit card company knows how much you drink! • What counts as informed consent? Consent for what? • Benefits vs risks • Technology • Authentication - who are you? • Authorisation - what are you doing? what are you allowed to do in that role? • Accounting - who pays? How much?

  23. Theme III:Factoring huge problems • Medicine is big and complicated • & full of niches • How to beat the combinatorial explosion • Workflows • myGrid & Taverna • Ontologies • Protégé & CO-ODE • www.co-ode.org

  24. Predicted Actual The combinatorial explosion source of the scaling problem • It keeps happening! • “Simple” brute force solutions do not scale up! • Conditions x tasks x setting x users x media • Huge number of forms to author • Software CHAOS

  25. Effort per term What we might accept What we would like Things to build Combination of things to be done & time to do each thing • Terms and forms needed • Increases exponentially • Effort per form or form • Must decrease tocompensate • To give the effectiveness we want • Or might accept

  26. New ways of factoring problems • Better ways to build from “Lego” • Better ways of indexing and cataloguing • Keys • Rich semantics • Discover rather than call • Machine undersatndable • Service oriented architectures • Workflows • Metadata and Provenance • Data on its own is meaningless • What is in the repository? • What studies have used it? • What is known of its reliability? • ???…???…??? • Terminology and ontology

  27. Workflows in Biomedical Research • “Macros on steroids” • Specify what rather than how • Describe the resources and tasks (RDF, WSDS, …) • Break big problems down into little steps • Reduce effort from days to hours for bioscientists • Can we move them to medical care

  28. Query nucleotide sequence Experiment RepeatMasker BLASTwrapper GenBank Accession No Promotor Prediction URL inc GB identifier TF binding Prediction Translation/sequence file. Good for records and publications prettyseq Regulation Element Prediction GenBank Entry Amino Acid translation Sort for appropriate Sequences only Identifies PEST seq epestfind Identify regulatory elements in genomic sequence Seqret Identifies FingerPRINTS pscan MW, length, charge, pI, etc Nucleotide seq (Fasta) pepstats 6 ORFs Predicts Coiled-coil regions RepeatMasker pepcoil tblastn Vs nr, est, est_mouse, est_human databases. Blastp Vs nr Coding sequence GenScan BlastWrapper Restriction enzyme map restrict SignalP TargetP PSORTII sixpack Predicts cellular location transeq CpG Island locations and % cpgreport Identifies functional and structural domains/motifs InterPro RepeatMasker Repetitive elements ORFs Hydrophobic regions Pepwindow? Octanol? Blastn Vs nr, est databases. ncbiBlastWrapper

  29. 12181 acatttctac caacagtgga tgaggttgtt ggtctatgtt ctcaccaaat ttggtgttgt 12241 cagtctttta aattttaacc tttagagaag agtcatacag tcaatagcct tttttagctt 12301 gaccatccta atagatacac agtggtgtct cactgtgatt ttaatttgca ttttcctgct 12361 gactaattat gttgagcttg ttaccattta gacaacttca ttagagaagt gtctaatatt 12421 taggtgactt gcctgttttt ttttaattgg gatcttaatt tttttaaatt attgatttgt 12481 aggagctatt tatatattct ggatacaagt tctttatcag atacacagtt tgtgactatt 12541 ttcttataag tctgtggttt ttatattaat gtttttattg atgactgttt tttacaattg 12601 tggttaagta tacatgacat aaaacggatt atcttaacca ttttaaaatg taaaattcga 12661 tggcattaag tacatccaca atattgtgca actatcacca ctatcatact ccaaaagggc 12721 atccaatacc cattaagctg tcactcccca atctcccatt ttcccacccc tgacaatcaa 12781 taacccattt tctgtctcta tggatttgcc tgttctggat attcatatta atagaatcaa Analysis via ‘Cut and Paste’

  30. Workflows C B A A: Identification of overlapping sequence B: Characterisation of nucleotide sequence C: Characterisation of protein sequence

  31. Description needs a language:Ontologies and Terminologies • Biologists manage quite well • Open Biological Ontologies • The Gene Ontology, Micro-array / Gene Expression Database, etc. • Little legacy • It all started in 1980 • Fanatically open and collaborative • Medicine has chaos and “the coding wars” • SNOMED (International,-RT, -CT), ICD, LOINC, DICOM, MEDDRA, NCI, ICPC, Read/CT (v1,v2, & v3), GALEN, NANDA,… • It all started in 1880 • Closed and proprietary

  32. No longer a unique problemNew standards and interest • Logicians and Computer Scientists from the mainstream • OWL, RDF, … • Ontologists from Philosophy • 3000 years of analysis • much of which is relevant • …but medicine is big and complicated … and combinatorially explosive • A prime source of combinatorial explosions

  33. How to defuse the “exploding bicycle” • 1972 ICD-9 (E826) 8 • READ-2 (T30..) 81 • READ-3 87 • 1999 ICD-10 ……

  34. 1999 ICD10: 587 codes • V31.22 Occupant of three-wheeled motor vehicle injured in collision with pedal cycle, person on outside of vehicle, nontraffic accident, while working for income • W65.40 Drowning and submersion while in bath-tub, street and highway, while engaged in sports activity • X35.44 Victim of volcanic eruption, street and highway, while resting, sleeping, eating or engaging in other vital activities

  35. Defusing the exploding bicycle:500 codes in pieces • 10 things to hit… • Pedestrian / cycle / motorbike / car / HGV / train / unpowered vehicle / a tree / other • 5 roles for the injured… • Driving / passenger / cyclist / getting in / other • 5 activities when injured… • resting / at work / sporting / at leisure / other • 2 contexts… • In traffic / not in traffic V12.24 Pedal cyclist injured in collision with two- or three-wheeled motor vehicle, unspecified pedal cyclist, nontraffic accident, while resting, sleeping, eating or engaging in other vital activities

  36. Structured Data Entry File Edit Help Cycling Accident What you hit Your Role Activity Location Conceptual Lego… it could be...Goodbye to picking lists…

  37. Hello to Intelligent Forms

  38. And generated language

  39. hand extremity body abnormal normal Semantic Technology: Logic as the clips for “Conceptual Lego” gene protein polysacharide cell expression chronic Lung acute infection inflammation bacterium deletion polymorphism ischaemic virus mucus

  40. Logic as the clips for “Conceptual Lego” “SNPolymorphismofCFTRGene causing Defect in MembraneTransport of Chloride Ion causing Increase in Viscosity of Mucus in CysticFibrosis…” “Handwhich isanatomically normal”

  41. Protein Gene in humans Disease caused by abnormality inFunction ofProtein coded bygene in humans Protein coded bygene in humans Function ofProtein coded bygene in humans Build complex representations from modularisedprimitives Species Genes Function Disease

  42. …but whatever the technology, how will people interpret it?

  43. Inter-rater variability ART & ARCHITECTURE THESAURUS (AAT) Domain: art, architecture, decorative arts, material culture Content: 125,000 terms Structure: 7 facets, 33 polyhierarchies Associated concepts (beauty, freedom, socialism) Physical attributes (red, round, waterlogged) Style/Period (French, impressionist, surrealist) Agents: (printmaker, architect, jockey) Activities: (analysing, running, painting) Materials (iron, clay, emulsifier) Objects: (gun, house, painting, statue, arm) Synonyms Links to ‘associated’ terms Access: lexical string match; hierarchical view

  44. X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X Inter-rater variability Headcloth Cloth Scarf Model Person Woman Adults Standing Background Brown Blue Chemise Dress Tunics Clothes Suitcase Luggage Attache case Brass Instrument French Horn Horn Tuba

  45. It happens in medicine New codes added per Dr per year • READ CODE Practice A Practice B • Sore Throat Symptom 0.6 117 • Visual Acuity 0.4 644 • ECG General 2.2 300 • Ovary/Broad Ligament Op 7.8 809 • Specific Viral Infections 1.4 556 • Alcohol Consumption 0 106 • H/O Resp Disease 0 26 • Full Blood Count 0 838

  46. The “coding wars”: UMLS helps • US National Library of Medicine • De facto common registry for vocabularies • Metathesaurus • 1.8 million concepts • categorised by semantic net types • Semantic Net • 135 Types • 54 Links • Specialist Lexicon • Now a key web resource • Source of reference IDs • CUIs and LUIs • LSIDs elsewhere in biology

  47. Code Code SUI LUI SUI Code LUI SUI CUI Code LUI SUI Code Unified Medical Language System • Concept Unique Identifiers (CUIs) • Lexical Unique Identifiers (LUIs) • String Unique Identifers (SUIs) Never buildanythingwithout cross references to CUIs and LUIs!

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