The present study shows that an ICA-based method can, e®ectively and blindly, classify a vast amount of gene expression data into biologically meaningful groups. Speci¯cally, we show (1) that genes, whose expression data are sampled at di®erent times, can be classi¯ed into several groups, based on the correlation of each gene with independent component curves over time, and (2) that these classi¯ed groups by ICA-based method have a good match with the classi¯ed groups that are determined by use of domain knowledge and considered to be a benchmark. These results suggest that the ICA-based method can be a powerful approach to discover unknown gene functions. 1
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.