Schlicker, Andreas

Ontology-based Similarity Measures and their Application in Bioinformatics

Genome-wide sequencing projects of many different organisms produce large
numbers of sequences that are functionally characterized using experimental and
bioinformatics methods. Following the development of the first bio-ontologies,
knowledge of the functions of genes and proteins is increasingly made available
in a standardized format. This allows for devising approaches that directly
exploit functional information using semantic and functional similarity
measures. This thesis addresses different aspects of the development and
application of such similarity measures.

First, we analyze semantic and functional similarity measures and apply them for
investigating the functional space in different taxa. Second, a new software
program and a new database are described, which overcome limitations of existing
tools and simplify the utilization of similarity measures for different
applications.

Third, we delineate two applications of our functional similarity measures. We
utilize them for analyzing domain and protein interaction datasets and derive
thresholds for grouping predicted domain interactions into low- and
high-confidence subsets. We also present the new MedSim method for
prioritization of candidate disease genes, which is based on the observation
that genes and proteins contributing to similar diseases are functionally
related. We demonstrate that the MedSim method performs at least as well as more
complex state-of-the-art methods and significantly outperforms current methods
that also utilize functional annotation.

•  dissertation_andreas_schlicker.pdf