%0 Journal Article
%T An Entropy-based gene selection method for cancer classification using microarray data
%A Xiaoxing Liu
%A Arun Krishnan
%A Adrian Mondry
%J BMC Bioinformatics
%D 2005
%I BioMed Central
%R 10.1186/1471-2105-6-76
%X The selected gene set should be small enough to allow diagnosis even in regular clinical laboratories and ideally identify genes involved in cancer-specific regulatory pathways. Here an entropy-based method is proposed that selects genes related to the different cancer classes while at the same time reducing the redundancy among the genes.The present study identifies a subset of features by maximizing the relevance and minimizing the redundancy of the selected genes. A merit called normalized mutual information is employed to measure the relevance and the redundancy of the genes. In order to find a more representative subset of features, an iterative procedure is adopted that incorporates an initial clustering followed by data partitioning and the application of the algorithm to each of the partitions. A leave-one-out approach then selects the most commonly selected genes across all the different runs and the gene selection algorithm is applied again to pare down the list of selected genes until a minimal subset is obtained that gives a satisfactory accuracy of classification.The algorithm was applied to three different data sets and the results obtained were compared to work done by others using the same data setsThis study presents an entropy-based iterative algorithm for selecting genes from microarray data that are able to classify various cancer sub-types with high accuracy. In addition, the feature set obtained is very compact, that is, the redundancy between genes is reduced to a large extent. This implies that classifiers can be built with a smaller subset of genes.DNA microarrays have become ubiquitous in analyzing the expression profiles of genes in the hope to distinguish between various disease types, such as discriminating between various cancer sub-types. Differential expression of genes is analyzed statistically and genes are assigned to various classes which may (or not) enhance the understanding of underlying biological processes. Alternatively, a r
%U http://www.biomedcentral.com/1471-2105/6/76