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ECO R European Centre for Ontological Research Basic Introduction to Ontology-based Language Technology (LT) for the Biomedical Sciences (1st year Biomedicine, UG, Belgium) Werner Ceusters European Centre for Ontological Research Universität des Saarlandes Saarbrücken, Germany ECO R European Centre for Ontological Research Purpose of this lecture • Introduce some keywords • Give just a taste for ontology-based LT in Biomedicine • Induce interest for further research ECO R European Centre for Ontological Research • • • • • • 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 ECO Biomedical Data Mining R European Centre for Ontological Research 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 ECO DNA Analysis: Examples R European Centre for Ontological Research • 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 ECO Task descriptions R • Sequence similarity searching European Centre for Ontological Research • • • • • • • • • • • • • • – 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 Stevens R, Goble C, Baker P, and Brass A. A Classification of Tasks in Bioinformatics. Bioinformatics 2001: 17 (2):180-188. ECO R European Centre for Ontological Research 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 other biomarker 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 ECO R European Centre for Ontological Research BUT !!! • Majority of data buried in –huge amounts of texts –Incompatibly annotated databases ECO R European Centre for Ontological Research 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. ECO R European Centre for Ontological Research Objectives of LT in Biomedical 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 ECO R Knowledge discovery and use European Centre for Ontological Research ECO R European Centre for Ontological Research Text Mining Technologies for Biomedicine Hi Artificial Manual Knowledge Intelligence Representation Cyc Riboweb Information Extraction Fastus Structure Mining Primary Literature Reading Keyword-based Retrieval PubMed Low Low Cost effectiveness Hi Hinrich Schütze, Novation Biosciences Russ Altman, Stanford University ECO R European Centre for Ontological Research 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] ECO R European Centre for Ontological Research Text-based knowledge 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 ECO R European Centre for Ontological Research Swanson Effects B Substance A Fish oil High blood viscosity Platelet aggregation Disease C Raynaud’s disease ECO R European Centre for Ontological Research Protein-Protein Interaction extracted from texts by C. Blaschke ECO R Steps of Knowledge Discovery European Centre for Ontological Research • 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 ECO R European Centre for Ontological Research Functional components for text-based feature generation system • Basic use components: end-user – Corpus Management tool – Parser – Export module • Management components: – – – – – Corpus editor Grammar building workbench Domain Ontology editor Parser generator Linguistic ontology (multi-lingual use) super user super user super user exporter exporter ECO R European Centre for Ontological Research What does it take to 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 ECO R European Centre for Ontological Research 22 page full paper A “statistics only system” ABSTRACT ONLY ECO R Relative Concept/Node identification (real) European Centre for Ontological Research 0,4 0,35 concepts 0,3 Statistic analysis is powerful, but not enough 0,25 0,2 0,15 0,1 0,05 nodes 0 0 500 1000 1500 2000 2500 Nr of words 3000 3500 4000 4500 5000 ECO R European Centre for Ontological Research Clean separation of knowledge for deep understanding The Galen view: – – – – – linguistic knowledge conceptual knowledge pragmatic knowledge criteria knowledge terminological knowledge The LT view: – – – – – – phonologic knowledge morphologic knowledge syntactic knowledge semantic knowledge pragmatic knowledge world knowledge ECO R One word – multiple meanings European Centre for Ontological Research • Abbreviation Extraction (Schwartz 2003) – Extracts short and long form pairs Short form Long form AA Alcoholic Anonymous American Americans Arachidonic acid arachidonic acid amino acid amino acids anaemia anemia : ECO R European Centre for Ontological Research • Corpus Syntactic variant detection – 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] ECO R European Centre for Ontological Research Results: “antiinflammatory effect” Generation Probability Generated Variants Frequency 1.0 (input) antiinflammatory effect 7 0.462 anti-inflammatory effect 33 0.393 antiinflammatory effects 6 0.356 Antiinflammatory effect 0 0.286 antiinflammatory-effect 0 0.181 anti-inflammatory effects 23 : : : ECO R Results: “tumour necrosis factor alpha” European Centre for Ontological Research Generation Probability Generated Variants Frequenc y 1.0 (Input) tumour necrosis factor alpha 15 0.492 tumor necrosis factor alpha 126 0.356 tumour necrosis factor-alpha 30 0.235 Tumour necrosis factor alpha 2 0.175 tumor necrosis factor alpha 182 0.115 Tumor necrosis factor alpha 8 : : : ECO R European Centre for Ontological Research 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 PROTEIN DNA but recruits another B cell-specific coactivator to increase transcriptional activity of class II promoters in B cells . CELLTYPE DNA Identify and classify Junichi Tsujii ECO Text mining and classification R European Centre for Ontological Research Generalised Possession Human Haspossessor 1 2 IS-A 1 IS-A Healthcare phenomenon Haspossessed 1 Having a healthcare phenomenon IS-A 2 Is-possessor-of Patient 3 Has-Healthcarephenomenon IS-A Malignant neoplasm IS-A 3 Cancer patient lung carcinoma Mr. Smith has a pulmonary carcinoma ECO R Data integration approaches European Centre for Ontological Research at least, the beginnings of ... • • • • • • Protein interaction databases Small molecule databases Genome databases Pathway databases Protein databases Enzyme databases Gene Ontology ECO R European Centre for Ontological Research ECO R System Integrationapproaches approaches Data Integration European Centre for Ontological Research 1. 2. 3. 4. 5. 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 ECO R European Centre for Ontological Research CoMeDIAS (France) ECO R European Centre for Ontological Research GenesTraceTM: Biological Knowledge Discovery via Structured Terminology ECO R European Centre for Ontological Research 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 niveau des veines péronières gauche. Absence d’opacification des 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> ECO Towards Machine Readable R European Centre for Ontological Research Semantics Form Data about Structure Meaning Function Usage Style Type Definition Document Type Definition Information Type Definition Knowledge Type Definition Workflow Type Definition Bold Centred Align Left Title Paragraph Heading1 Subject isPartOf Date Utility affectedBy Actor Formalism Cases Static Dynamic Standard Blink Play After_value Receive Protect Layout Outline Content Behaviour Receival Maintenance Archival Process Hao Ding, Ingeborg T. Sølvberg ECO R Triadic models of meaning: European Centre for Ontological Research The Semiotic/Semantic triangle Reference: Concept / Sense / Model / View Sign: Language/ Term/ Symbol Referent: Reality/ Object ECO R There is ontology and “ontology” European Centre for Ontological Research • 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.” concept definition term referent ECO R European Centre for Ontological Research Why are concepts not 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. ECO R European Centre for Ontological Research Or you get nonsense: Definition of “cancer gene” ECO Take home message: R Language Technology requires European Centre for Ontological Research a 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