expression data

Introduction • We have studied the application of a new clustering and information visualization methodology to functional genomics. • The learning metrics principle is a new approach to finding important aspects of data. It is assumed that changes of gene expression are important only to the extent that they cause changes in certain auxiliary data, in this study the functional classes of the genes. • We cluster the yeast genes based on their expression in a set of knock-out mutation experiments. (PD data set [1].) • The SOM computed in the new metric is more accurate in modeling the functional classes of the genes. The visualizations are as intuitive as in the usual inner product (correlation) metric.