Abstract
Clustering gene expression data has been done by many researchers. They are using traditional methods of agglomerative hierarchical clustering, which fail to use domain knowledge obtained from vast gene studies. An example of such domain knowledge is gene ontology terms for messenger RNA. We propose two methods of clustering gene expression data and different experiments using gene ontology terms. One is traditional agglomerative hierarchical clustering using similarities based on gene ontology terms. Another is algorithms using the modularity index applied to networks including gene ontology terms. Clustering results of real large-scale experiments are shown.
