Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Aging and Longevity in Inbred Mouse Strains

Handle URI:
http://hdl.handle.net/10754/627831
Title:
Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Aging and Longevity in Inbred Mouse Strains
Authors:
Alghamdi, Sarah M. ( 0000-0001-5544-7166 )
Abstract:
In biomedical research, ontologies are widely used to represent knowledge as well as to annotate datasets. Many of the existing ontologies cover a single type of phenomena, such as a process, cell type, gene, pathological entity or anatomical structure. Consequently, there is a requirement to use multiple ontologies to fully characterize the observations in the datasets. Although this allows precise annotation of different aspects of a given dataset, it limits our ability to use the ontologies in data analysis, as the ontologies are usually disconnected and their combinations cannot be exploited. Motivated by this, here we present novel ontology design methods for combining pathology and anatomy concepts. To this end, we use a dataset of mouse models which has been characterized through two ontologies: one of them is the mouse pathology ontology (MPATH) covering pathological lesions while the other is the mouse anatomy ontology (MA) covering the anatomical site of the lesions. We propose four novel ontology design patterns for combining these ontologies, and use these patterns to generate four ontologies in a data-driven way. To evaluate the generated ontologies, we utilize these in ontology-based data analysis, including ontology enrichment analysis and computation of semantic similarity. We demonstrate that there are significant differences between the four ontologies in different analysis approaches. In addition, when using semantic similarity to confirm the hypothesis that genetically identical mice should develop more similar diseases, the generated combined ontologies lead to significantly better analysis results compared to using each ontology individually. Our results reveal that using ontology design patterns to combine different facets characterizing a dataset can improve established analysis methods.
Advisors:
Hoehndorf, Robert ( 0000-0001-8149-5890 )
Committee Member:
Gao, Xin ( 0000-0002-7108-3574 ) ; Bajic, Vladimir B. ( 0000-0001-5435-4750 )
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Program:
Computer Science
Issue Date:
13-May-2018
Type:
Thesis
Appears in Collections:
Theses

Full metadata record

DC FieldValue Language
dc.contributor.advisorHoehndorf, Roberten
dc.contributor.authorAlghamdi, Sarah M.en
dc.date.accessioned2018-05-13T09:00:39Z-
dc.date.available2018-05-13T09:00:39Z-
dc.date.issued2018-05-13-
dc.identifier.urihttp://hdl.handle.net/10754/627831-
dc.description.abstractIn biomedical research, ontologies are widely used to represent knowledge as well as to annotate datasets. Many of the existing ontologies cover a single type of phenomena, such as a process, cell type, gene, pathological entity or anatomical structure. Consequently, there is a requirement to use multiple ontologies to fully characterize the observations in the datasets. Although this allows precise annotation of different aspects of a given dataset, it limits our ability to use the ontologies in data analysis, as the ontologies are usually disconnected and their combinations cannot be exploited. Motivated by this, here we present novel ontology design methods for combining pathology and anatomy concepts. To this end, we use a dataset of mouse models which has been characterized through two ontologies: one of them is the mouse pathology ontology (MPATH) covering pathological lesions while the other is the mouse anatomy ontology (MA) covering the anatomical site of the lesions. We propose four novel ontology design patterns for combining these ontologies, and use these patterns to generate four ontologies in a data-driven way. To evaluate the generated ontologies, we utilize these in ontology-based data analysis, including ontology enrichment analysis and computation of semantic similarity. We demonstrate that there are significant differences between the four ontologies in different analysis approaches. In addition, when using semantic similarity to confirm the hypothesis that genetically identical mice should develop more similar diseases, the generated combined ontologies lead to significantly better analysis results compared to using each ontology individually. Our results reveal that using ontology design patterns to combine different facets characterizing a dataset can improve established analysis methods.en
dc.language.isoenen
dc.subjectontology evaluationen
dc.subjectModel organismen
dc.subjectAgingen
dc.subjectPhenotypeen
dc.subjectontology design patternen
dc.titleOntology Design Patterns for Combining Pathology and Anatomy: Application to Study Aging and Longevity in Inbred Mouse Strainsen
dc.typeThesisen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
thesis.degree.grantorKing Abdullah University of Science and Technologyen
dc.contributor.committeememberGao, Xinen
dc.contributor.committeememberBajic, Vladimir B.en
thesis.degree.disciplineComputer Scienceen
thesis.degree.nameMaster of Scienceen
dc.person.id144194en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.