Extracting very simple diagnostic rules from microarray data

Abstract

We present an approach to deriving very simple classification rules from microarray data by first selecting very small gene subsets that can ensure highly accurate classification of cancers. Finding such minimum gene subsets can greatly reduce the computational load and "noise" arising from irrelevant genes. The derived simple classification rules allow for accurate diagnosis without the need for any classifiers. This work can simplify gene expression tests by including only a very small number of genes rather than thousands or tens of thousands of genes, which can significantly bring down the cost for cancer testing. These studies also call for further investigations into possible biological relationship between these small number of genes and cancer development and treatment. For example, we report the following simple, and yet 100% accurate, diagnostic rules involving only 2 genes to separate the 3 types of lymphoma patients: the patient has diffuse large B-cell lymphoma (DLBCL), if and only if the expression level of gene GENE1622X is greater than -0.75; the patient has chronic lymphocytic leukaemia (CLL), if and only if the expression level of gene GENE540X is less than -1; and the patient has follicular lymphoma (FL) otherwise, i.e., if and only if the expression level of gene GENE1622X is less than -0.75 and the expression level of gene GENE540X is greater than -1.