Selecting informative genes of lung cancers by a combination of hybrid methods

Gene expression technology namely microarray, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data have also noisy genes. It has been shown from literature reviews that selecting a small subset of informative genes can lead to an improved classification accuracy. Thus, this paper aims to select a small subset of informative genes that are most relevant for the cancer classification. To achieve this aim, an approach that involved two hybrid methods has been proposed. This approach is assessed and evaluated on one well-known microarray data set, namely the lung cancer, showing competitive results.