An Entropy-based gene selection method for cancer classification using microarray data

Background: Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach; yet the selection of nonredundant but relevant genes is difficult. 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. Results: 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 applica...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Xiaoxing Liu, Arun Krishnan, Adrian Mondry
• Keywords: BMCBI 2005, Commonly Selected Genes, Gene Expression Data, Relevant Genes