Discovering high-order patterns of gene expression levels.

This paper reports the discovery of statistically significant association patterns of gene expression levels from microarray data. By association patterns, we mean certain gene expression intensity intervals having statistically significant associations among themselves and with the tissue classes, such as cancerous and normal tissues. We describe how the significance of the associations among gene expression levels can be evaluated using a statistical measure in an objective manner.

Attribute clustering for grouping, selection, and classification of gene expression data.

This paper presents an attribute clustering method which is able to group genes based on their interdependence so as to mine meaningful patterns from the gene expression data. It can be used for gene grouping, selection, and classification. The partitioning of a relational table into attribute subgroups allows a small number of attributes within or across the groups to be selected for analysis.