This Article   Bibliographic References   Add to:  Accurate
Cancer Classification Using Expressions of Very Few Genes
January-March 2007 (vol. 4 no. 1) pp. 40-53 We aim at finding
the smallest set of genes that can ensure highly accurate
classification of cancers from microarray data by using
supervised machine learning algorithms. The significance of
finding the minimum gene subsets is three-fold: 1) It greatly
reduces the computational burden and "noise” arising from
irrelevant genes. In the examples studied in this paper, finding
the minimum gene subsets even allows for extraction of simple
diagnostic rules which lead to accurate diagnosis without the
need for any classifiers. 2) It simplifies gene expression tests
to include only a very small number of genes rather than
thousands of genes, which can bring down the cost for cancer
testing significantly. 3) It calls for further investigation
into the possible biological relationship between these small
numbers of genes and cancer development and treatment.