Affiliations: [a] Clinical Epidemiology, Department of Epidemiology and Public Health, University College London Medical School, London, 1-19 Torrington Place, WC1E 6BT, UK. E-mail: [email protected] | [b] Department of Computer Science, University of Ioannina, P.O. 1186, 45110, Greece, and Department Engineering of Informatics & Telecommunications, University of Western Macedonia, Greece | [x] University of Westminster, United Kingdom | [y] The University of Manchester, United Kingdom | [z] Democritus University of Thrace, Greece
Abstract: Advances in biological experiments, such as DNA microarrays, have produced large multidimensional data sets for examination and retrospective analysis. Scientists however, heavily rely on existing biomedical knowledge in order to fully analyze and comprehend such datasets. Our proposed framework relies on the Gene Ontology for integrating a priori biomedical knowledge into traditional data analysis approaches. We explore the impact of considering each aspect of the Gene Ontology individually for quantifying the biological relatedness between gene products. We discuss two figure of merit scores for quantifying the pair-wise biological relatedness between gene products and the intra-cluster biological coherency of groups of gene products. Finally, we perform cluster deterioration simulation experiments on a well scrutinized Saccharomyces cerevisiae data set consisting of hybridization measurements. The results presented illustrate a strong correlation between the devised cluster coherency figure of merit and the randomization of cluster membership.
Keywords: Data mining, bioinformatics, Gene Ontology, GO, vector space model
