A go-driven semantic similarity measure for quantifying the biological relatedness of gene products

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.