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Kumar A 1 ,2 , Yip L 3 , Jaremek M 2 , Scheib H 3 1 I FOMIS, University of Saarland, Germany

O N C O L O G Y O N T O L O G Y. Ontological Model for Colon Carcinoma: A Case Study for Knowledge Representation in Clinical Bioinformatics. Kumar A 1 ,2 , Yip L 3 , Jaremek M 2 , Scheib H 3 1 I FOMIS, University of Saarland, Germany

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Kumar A 1 ,2 , Yip L 3 , Jaremek M 2 , Scheib H 3 1 I FOMIS, University of Saarland, Germany

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  1. O N C O L O G Y O N T O L O G Y Ontological Model for Colon Carcinoma: A Case Study for Knowledge Representation in Clinical Bioinformatics Kumar A1,2, Yip L3, Jaremek M2, Scheib H3 1IFOMIS, University of Saarland, Germany 2Ludwig-Maximilians Universität, München, Germany 3Swiss Institute for Bioinformatics, Geneva, Switzerland

  2. How the two worlds meet? O N C O L O G Y O N T O L O G Y Clinical Specific disease topics E-Health, Health support system Patient Management Patient health education ? Molecular Biology Biomedical Information on the Web Swiss-Prot, Swiss-Prot Variant pages Proteins, mutations, functions and structures

  3. How the two worlds meet? O N C O L O G Y O N T O L O G Y • Diseases • Pathological Processes • Body site for diseases • Diseases by staging • Risk factors SNOMED • Anatomy • Is-a, part-of • Granular relationship FMA • Biological Processes • Ontology • Classification GO • Swiss-Prot proteins • Annotation: function, structure, mutation

  4. How the two worlds meet? O N C O L O G Y O N T O L O G Y Disease schema Protein 3D models Swiss-Prot entries ModSNP database

  5. What is Protégé? O N C O L O G Y O N T O L O G Y • Frame based system : Allows formation of class-subclass relations, provides support for other relations between classes, compatible with OWL, XML, database standards • Support for various types of visualizations : Graphical, Web-based • Support for import/export • Support for reasoning : Description Logic based • Can give outputs in various formats • Difficulties with input

  6. Disease Representation O N C O L O G Y O N T O L O G Y • Disease classification based on Snomed CT • Various aspects considered for classification (currently present in the form of multiple inheritance) • Added from textbooks (deVita Principles of Oncology and Harrison Principles of Internal medicine) • Staging of diseases (TNM, Duke’s, Modified Asler-Coller) • Screening (Patients screened based on their level of risk) • Risk factors • Localization • Pathology • To be added: Pharmacotherapeutics, Symptoms and Signs

  7. Anatomical and Histological Representation O N C O L O G Y O N T O L O G Y • Anatomy of colon represented at Organ system, Organ, Tissue, Cell and Subcellular levels of granularity (Foundational Model of Anatomy) • Gene Ontology‘s Cellular Component axis situated within the FMA axis • Gross pathology mapped to the Carcinoma location • Information regarding Clinical procedures, Carcinoma extent, Vascular invasion, Histological pathology being added • Extensions being done to add relations like is-located-in, is-surrounded-by, etc. to make the anatomical representation deducible

  8. (246 distinct loci) Interlink between disease, LocusLink, Swiss-Prot, GO annotations O N C O L O G Y O N T O L O G Y Colon cancer/Colon carcinoma LocusLink SwissProt Gene Ontology

  9. Gene Ontology O N C O L O G Y O N T O L O G Y • Association rules found considering the Gene Ontology annotations of SWISSPROT proteins • Gene Ontology consists of three axes: • Cellular Component • Molecular Function • Biological Processes • Association between GO terms were established on the basis of these annotations • Database-based approach • Apriori-algorithm based approach • Dependency relations based on POS tagger

  10. Levels of granularity O N C O L O G Y O N T O L O G Y • Levels of granularity in human body • Organism • Organ system • Cardinal body parts • Organ • Organ part • Tissues • Cells • Subcellular organelles • Molecules • Atoms • Fundamentals behind the levels of granularity • Grains, Structure, Origin

  11. RNA binding nucleus 30 DNA binding nucleosome 31 transcriptional activator activity nucleus 31 structural constituent of ribosome cytosolic large ribosomal subunit (sensu Eukarya) 32 transmembrane receptor activity integral to plasma membrane 36 protein binding cytoplasm 36 zinc ion binding nucleus 43 protein binding nucleus 45 receptor activity integral to plasma membrane 56 G-protein coupled receptor activity integral to plasma membrane 70 DNA binding nucleus 100 antigen binding extracellular 123 transcription factor activity nucleus 171 Results O N C O L O G Y O N T O L O G Y

  12. Results (Apriori) O N C O L O G Y O N T O L O G Y • Support and Confidence are defined • ribosome <- ribosome biogenesis; protein biosynthesis (0.2%, 93.2%) • This rule says that there are 0.2% of the total annotations, put together ribosome biogenesis and protein biosynthesis, of which 93.2% (i.e. 82) are also annotated with the term ribosome. • Formal ontological relationsapplied between the entities, which would help to have deductions: has spatial projection, processual part of, facilitates, mediates, perpetrates

  13. Relations to biological pathways O N C O L O G Y O N T O L O G Y • Pathway resources: KEGG, PATH, GenePath • Protein interaction database resources: DIP, BIND, PiP, IntAct • Links to these databases possible through Swissprot and GO annotations • Associations found within GO annotations parallel the pathways and protein interactions (under verification) • Text mining resources being considered

  14. O N C O L O G Y O N T O L O G Y Ontological Model for Colon Carcinoma: A Case Study for Knowledge Representation in Clinical Bioinformatics Kumar A1,2, Yip L3, Jaremek M2, Scheib H3 1IFOMIS, University of Saarland, Germany 2Ludwig-Maximilians Universität, München, Germany 3Swiss Institute for Bioinformatics, Geneva, Switzerland

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