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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Mohamad, M.S. Omatu, S. Yoshioka, M. Deris, S. |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Comput. Sci. & Intell. Syst., Osaka Prefecture Univ., Sakai (Mohamad, M.S.; Omatu, S.; Yoshioka, M.) |
| Abstract | Recent advances in microarray technology allow scientists to measure expression levels of thousands of genes simultaneously in human tissue samples. This technology has been increasingly used in cancer research because of its potential for classification of the tissue samples based only on gene expression levels. A major problem in these microarray data is that the number of genes greatly exceeds the number of tissue samples. Moreover, these data have a noisy nature. It has been shown from literature review 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 is most relevant for the cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed on two well-known microarray data. The experimental results have shown that the gene subsets are very small in size and yield better classification accuracy as compared with other previous works as well as four methods experimented in this work. In addition, a list of informative genes in the best subsets is also presented for biological usage. |
| Starting Page | 603 |
| Ending Page | 608 |
| File Size | 308768 |
| Page Count | 6 |
| File Format | |
| ISBN | 9780769531366 |
| DOI | 10.1109/AMS.2008.71 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-05-13 |
| Publisher Place | Malaysia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | gene selection hybrid method Computational modeling Computer simulation Genetic mutations microarray data Data engineering Gene expression cancer classification Computer science Asia Hybrid intelligent systems gene expression Cancer Tumors |
| Content Type | Text |
| Resource Type | Article |
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