Werner Ceusters European Centre for Ontological Research Universität des Saarlandes

1. Basic Introduction toOntology-basedLanguage Technology (LT)for the Biomedical Sciences(1st year Biomedicine, UG, Belgium) Werner Ceusters European Centre for Ontological Research Universität des Saarlandes Saarbrücken, Germany

2. Purpose of this lecture • Introduce some keywords • Give just a taste for ontology-based LT in Biomedicine • Induce interest for further research

3. Biomedicine: A Great Area for LT • Educated users • High utility of NLP • Doesn’t require solution to general problem • Complex and interesting (not just IE) • Recent surge in data • Knowledge bases available Hinrich Schütze, Novation Biosciences Russ Altman, Stanford University

4. Biomedical Data Mining and DNA Analysis • DNA sequences: 4 basic building blocks (nucleotides): adenine (A), cytosine (C), guanine (G), and thymine (T). • Gene: a sequence of hundreds of individual nucleotides arranged in a particular order • Humans have around 100,000 genes • Tremendous number of ways that the nucleotides can be ordered and sequenced to form distinct genes • Semantic integration of heterogeneous, distributed genome databases • Current: highly distributed, uncontrolled generation and use of a wide variety of DNA data • Data cleaning and data integration methods developed in data mining will help Jiawei Han and Micheline Kamber

5. DNA Analysis: Examples • Similarity search and comparison among DNA sequences • Compare the frequently occurring patterns of each class (e.g., diseased and healthy) • Identify gene sequence patterns that play roles in various diseases • Association analysis: identification of co-occurring gene sequences • Most diseases are not triggered by a single gene but by a combination of genes acting together • Association analysis may help determine the kinds of genes that are likely to co-occur together in target samples • Path analysis: linking genes to different disease development stages • Different genes may become active at different stages of the disease • Develop pharmaceutical interventions that target the different stages separately • Visualization tools and genetic data analysis Jiawei Han and Micheline Kamber

6. Task descriptions • Sequence similarity searching • Nucleic acid vs nucleic acid 28 • Protein vs protein 39 • Translated nucleic acid vs protein 6 • Unspecified sequence type 29 • Search for non-coding DNA 9 • Functional motif searching 35 • Sequence retrieval 27 • Multiple sequence alignment 21 • Restriction mapping 19 • Secondary and tertiary structure prediction 14 • Other DNA analysis including translation 14 • Primer design 12 • ORF analysis 11 • Literature searching 10 • Phylogenetic analysis 9 • Protein analysis 10 • Sequence assembly 8 • Location of expression 7 • Miscellaneous 7 • Total315 Stevens R, Goble C, Baker P, and Brass A. A Classificationof Tasks in Bioinformatics. Bioinformatics 2001: 17 (2):180-188.

7. Three major challenges • Analyse massive amounts of data: • Eg: high throughput technologies based upon cDNA or oligonucleotide microarrays for analysis of gene expression, analysis of sequence polymorphisms and mutations,and sequencing • Appropriately link clinical histories to molecular or otherbiomarker data generated by genomic and proteomic technologies. • Development of user-friendly computer-based platforms • that can be accessed and utilized by the average researcher for searching, retrieval, manipulation, and analysis of information from large-scale datasets

8. BUT !!! • Majority of data buried in • huge amounts of texts • Incompatibly annotated databases

9. Text overload • According to a conservative estimate, the number of digital libraries is more than 105. • [Norbert Fuhr 03] • Google indexed over 4.28 billion web pages; • from Google press release. • But, any single engine is prevented from indexing more than one-third of the “indexable web”. • from Science.Vol.285, Nr.5426.

10. Objectives of LT inBiomedical Informatics • Make large volumes of scientific texts better accessable • Assist annotation of genome and phenome to allow better linking of the data • CSB: Computational Systems Biology • Link biomedical data with patient record data

11. Knowledge discovery and use

12. Text Mining Technologiesfor Biomedicine Hi Artificial Intelligence Cyc Manual Knowledge Representation Riboweb Information Extraction Fastus Structure Mining Primary Literature Reading Utility Keyword-based Retrieval PubMed Low Low Cost effectiveness Hi Hinrich Schütze, Novation Biosciences Russ Altman, Stanford University

13. Scientists in areas such as molecular biology and biochemistry aim to discover new biological entities and their functions. Typical cases could be discoveries of the implications of new proteins and genes in an already known process, or implication of proteins with previously characterized functions in a separate process. The use of available information (published papers, etc.) is a key step for the discovery process, since in many cases weak or indirect evidences about possible relations hidden in the literature are used to substantiate working hypothesis that are experimentally explored. [C.Blaschke, A.Valencia: 2001]

14. Text-basedknowledge discovery • Goal: Finding “new” biomedical scientific knowledge through the combination of existing knowledge as represented in the medical literature • Motivation: Prevention of re-inventing the wheel, re-usage of specific knowledge outside the original domain of discovery

15. High blood viscosity Platelet aggregation Fish oil Raynaud’s disease Swanson Effects B Substance A Disease C

16. Protein-Protein Interaction extracted from texts by C. Blaschke

17. Steps of Knowledge Discovery • Training data gathering • Feature generation • k-grams, domain know-how, ... • Feature selection • Entropy, 2, CFS, t-test, domain know-how... • Feature integration • SVM, ANN, PCL, CART, C4.5, kNN, ... Some classifiers/learning methods Limsoon Wong

18. Functional componentsfor text-basedfeature generation system • Basic use components: end-user • Corpus Management tool • Parser • Export module • Management components: • Corpus editor super user • Grammar building workbench super user • Domain Ontology editor super user • Parser generator exporter • Linguistic ontology (multi-lingual use) exporter

19. What does it taketo build such a system ? • Short term: single domain • Corpus collection & analysis • Domain model design & implementation • Grammar Development • Corpus Manipulation Engine • Integration in Biomining package • Long term: generic system • Grammar Building Workbench • Parser Generator • Documentation

20. 22 page full paper ABSTRACT ONLY A “statistics only system”

21. Relative Concept/Node identification (real) Statistic analysis is powerful, but not enough concepts nodes

22. The Galen view: linguistic knowledge conceptual knowledge pragmatic knowledge criteria knowledge terminological knowledge Clean separation of knowledgefor deep understanding • The LT view: • phonologic knowledge • morphologic knowledge • syntactic knowledge • semantic knowledge • pragmatic knowledge • world knowledge

23. One word – multiple meanings • Abbreviation Extraction （Schwartz 2003） • Extracts short and long form pairs

24. Syntactic variant detection • Corpus • MEDLINE: the largest collection of abstracts in the biomedical domain • Rule learning • 83,142 abstracts • Obtained rules: 14,158 • Evaluation • 18,930 abstracts • Count the occurrences of each generated variant. Tsuruoka, et.al. 03 SIGIR]

25. Results: “antiinflammatory effect”

26. Results: “tumour necrosis factor alpha”

27. PROTEIN DNA CELLTYPE DNA Identify and classify Biomedical NE Task(Collier Coling00,Kazama ACL02, Kim ISMB02) • Recognize “names” in the text • Technical terms expressing proteins, genes, cells, etc. Thus, CIITA not only activates the expression of class II genes but recruits another B cell-specific coactivator to increase transcriptional activity of class II promoters in B cells . Junichi Tsujii

28. Generalised Possession Healthcare phenomenon Human Has- possessor Has- possessed IS-A 1 1 2 1 IS-A Having a healthcare phenomenon IS-A 2 Is-possessor-of Patient Has-Healthcare-phenomenon Malignant neoplasm IS-A 3 IS-A 3 Cancer patient lung carcinoma Mr. Smithhasa pulmonary carcinoma Text mining and classification

29. Annotation Gene Ontology Data integration approaches at least, the beginnings of ... • Protein interaction databases • Small molecule databases • Genome databases • Pathway databases • Protein databases • Enzyme databases

31. System Integration approaches Data Integration approaches • Data Warehousing : • Data from various data sources are converted, merged and stored in a centralized DBMS. (Examples) Integrated Genomic Database • Hyperlinking approaches: • Where links are set up between related information and data sources. SRS, Entrez (NCBI) • Standardization: • Efforts which address the need for a common metadata model for various application domains. • Integration systems: • Systems that can gather and integrate information from multiple sources. Some of these systems have a Mediator-Wrapper Architecture others are language based systems like Bio-Kleisli. • Federated Database: • Cooperating, yet autonomous, databases map their individual schema’s to a single global schema. Operations are preformed against the federated schema. Steve Brady

32. CoMeDIAS (France)

33. GenesTraceTM: Biological Knowledge Discovery via Structured Terminology

34. The XML misconception <?XML version="1.0" ?> <?XML:stylesheet type="text/XSL" href="cr-radio.xsl" ?> <CR-RADIOLOGIE><ENTETE> <INFORMATION-SERVICE> <HOPITAL>Groupe hospitalier Léonard Devintscie</HOPITAL> <SERVICE>Radiologie Centrale</SERVICE><MEDECIN>Dr. Bouaud</MEDECIN> <TITRE-EXAMEN>Phlébographie des membres inférieurs</TITRE-EXAMEN> </INFORMATION-SERVICE> <INFORMATION-DEMANDE> <SERVICE>Sce Pr. Charlet</SERVICE><MEDECIN>Dr. Brunie</MEDECIN> <DATE>29-10-99</DATE> </INFORMATION-DEMANDE> <INFORMATION-PATIENT ID="236784020"><NOM>Donald</NOM> <PRENOM>Duck</PRENOM></INFORMATION-PATIENT></ENTETE> <BODY> <INDICATION>Suspicion de phlébite de jambe gauche</INDICATION> <TECHNIQUE>Ponction bilatérale d’une veine du dos du pied et injection de 180cc de produit de contraste</TECHNIQUE> <RESULTATS>image lacunaire endoluminale visible au niveaudes veines péronières gauche. Absence d’opacificationdes veines tibiales antérieures et postérieures gauches.Les veines illiaques et la veine cave inférieure sont libres. </RESULTATS> <CONCLUSION>Trombophlébite péronière et probablement tibiale antérieure et postérieure gauche.</CONCLUSION> </BODY> </CR-RADIOLOGIE>

35. Towards Machine ReadableSemantics Form Structure Meaning Function Usage Workflow Type Definition Document Type Definition Knowledge Type Definition Style Type Definition Information Type Definition Data about Formalism CSS XML RDF OWL ? Cases Static Dynamic Bold Centred Align Left Blink Title Paragraph Heading1 Play Subject isPartOf Date After_value Utility affectedBy Receive Protect Actor Receival Maintenance Archival Standard Layout Outline Content Behaviour Process Hao Ding, Ingeborg T. Sølvberg

36. Triadic models of meaning: The Semiotic/Semantic triangle Reference: Concept / Sense / Model / View Sign: Language/ Term/ Symbol Referent: Reality/ Object

37. There is ontology and “ontology” • Ontology in Information Science: • “An ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents.” • Ontology in Philosophy: • “Ontology is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality.”

38. Why are conceptsnot enough? • Why must our theory address also the referents in reality? • Because referents are observable fixed points in relation to which we can work out how the concepts used by different communities relate to each other ; • Because only by looking at referents can we establish the degree to which concepts are good for their purpose.

39. Or you get nonsense:Definition of “cancer gene”

40. Take home message:Language Technology requiresa clean separation of knowledge AND (the right sort of) ontology Pragmatic knowledge: what users usually say or think, what they consider important, how to integrate in software Knowledge of classification and coding systems: how an expression has been classified by such a system Knowledge of definitions and criteria: how to determine if a concept applies to a particular instance Surface linguistic knowledge: how to express the concepts in any given language Conceptual knowledge: the knowledge of sensible domain concepts Ontology: what exists and how what exists relates to each other