Tese de doutoramento, Informática (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2014Biomedical research has evolved into a data-intensive science, where
prodigious amounts of data can be collected from disparate resources
at any time. However, the value of data can only be leveraged through
its analysis, which ultimately results in the acquisition of knowledge.
In domains such as translational medicine, data integration and interoperability
are key requirements for an efficient data analysis.
The semantic web and its technologies have been proposed as a solution
for the problems of data integration and interoperability. One of
the tools of the semantic web is the representation of domain knowledge
with ontologies, which provide a formal description of that knowledge
in a structured manner.
The thesis underlying this work is that the representation of domain
knowledge in ontologies can be exploited to improve the current
knowledge about a disease, as well as improve the diagnosis and
prognosis processes. The following two objectives were defined to validate
this thesis: 1) to create a semantic model that represents and
integrates the heterogeneous sources of data necessary for the characterization
of a disease and of its prognosis process, exploiting semantic
web technologies and existing ontologies; 2) to develop a methodology
that exploits the knowledge represented in existing ontologies to
improve the results of knowledge exploration methods obtained with
translational medicine datasets.
The first objective was accomplished and resulting in the following
contributions: the methodology for the creation of a semantic model
in the OWL language; a semantic model of the disease hypertrophic
cardiomyopathy; and a review on the exploitation of semantic web
resources in translation medicine systems. In the case of the second objective, also accomplished, the contributions are the adaptation of a
standard enrichment analysis to use data from patients; and the application
of the adapted enrichment analysis to improve the predictions
made with a translational medicine dataset.Fundação para a Ciência e a Tecnologia (FCT, SFRH/BD/65257/2009
