Mohamad, Mohd Saberi and Omatu, Sigeru and Deris, Safaai and Misman, Muhammad Faiz and Yoshioka, Michifumi (2009) Selecting informative genes from microarray data by using hybrid methods for cancer classification. Artificial Life and Robotics, 13 (2). 414 -417. ISSN 1433-5298
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Official URL: http://dx.doi.org/10.1007/s10015-008-0534-4
Gene expression technology, namely microarrays, 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 an 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 also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results.
|Uncontrolled Keywords:||cancer classification, gene selection, geneti calgorithm, hybrid method, microarray data|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||Computer Science and Information System (Formerly known)|
|Deposited By:||Liza Porijo|
|Deposited On:||18 Jul 2011 07:59|
|Last Modified:||18 Jul 2011 07:59|
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