Use this URL to cite or link to this record in EThOS:
Title: A novel framework for integrating a priori domain knowledge into traditional data analysis in the context of bioinformatics
Author: Denaxas, Spiridon Christoforos
ISNI:       0000 0001 3421 9615
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2008
Availability of Full Text:
Access from EThOS:
Recent advances in experimental technology have given scientists the ability to perform large-scale multidimensional experiments involving large data sets. As a direct implication, the amount of data that is being generated is rising in an exponential manner. However, in order to fully scrutinize and comprehend the results obtained from traditional data analysis approaches, it has been proven that a priori domain knowledge must be taken into consideration. Infusing existing knowledge into data analysis operations however is a non-trivial task which presents a number of challenges. This research is concerned into utilizing a structured ontology representing the individual elements composing such large data sets for assessing the results obtained. More specifically, statistical natural language processing and information retrieval methodologies are used in order to provide a seamless integration of existing domain knowledge in the context of cluster analysis experiments on gene product expression patterns. The aim of this research is to produce a framework for integrating a priori domain knowledge into traditional data analysis approaches. This is done in the context of DNA microarrays and gene expression experiments. The value added by the framework to the existing body of research is twofold. First, the framework provides a figure of merit score for assessing and quantifying the biological relatedness between individual gene products. Second, it proposes a mechanism for evaluating the results of data clustering algorithms from a biological point of view.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available