Abstract

Background We propose a new method for supervising learning from gene expression data. We call it \Tree Harvesting". This technique starts with a hierarchical clustering of genes, and models the outcome variable as a sum of the average expression proles of chosen clusters, and their products. It can be applied to many dierent kinds of outcome measures, such as censored survival times, or a response falling in two or more classes (e.g. cancer classes). The method can discover genes that have strong eects on their own, and genes that interact with other genes. Results We illustrate the method on data from a lymphoma study, and on a dataset containing samples from 8 dierent cancers. It identied some interesting gene clusters and interactions between genes. Conclusions Tree Harvesting is a potentially useful tool for exploration of gene expression data and identication of interesting clusters of genes worthy of further investigation. Depts. of Statistics, and Hea...

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