Integrating the GO Graph Structure in Scoring the Significance of Gene Ontology Terms

TL;DRAbstract

The analysis of gene expression data is an important tool for understanding mechanisms of living systems. Microarray experiments provide large amounts of data, but it is difficult to understand biological processes or molecular functions from such data on their own. Clustering methods based on expression data have been used to deal with this problem, but the biological relevance of the results is limited. New methods have been proposed in which annotations from the Gene Ontology (GO) database are integrated into the analysis in order to gain biological understanding. Gene classes obtained from GO are scored with respect to their significance in datasets from microarray experiments. Current methods of this type do not incorporate the structure of the GO database when computing statistical quantities. In this thesis we developed methods that make use of this topology in order to improve the biological insight obtained from gene expression. By ignoring the structure of the GO, the current

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