Applications of new SOM-based exploratory data analysis methods to bioinformatics are described. Cluster structures are revealed in data describing the expression of a set of yeast genes in several experimental treatments. The structures are visualized in an intuitive manner with colors: The similarity of hue corresponds to the similarity of the multivariate data. The clusters can be interpreted by visualizing changes of the data variables (expression in different treatments) at the cluster borders. The relationship between the organization of the SOM and the functional classes of the proteins encoded by the genes may additionally reveal interesting relationships between the functional classes, and substructures within them.