File Entry: An Iterative GASVM-Based Method: Gene Selection and Classification of Microarray Data.

Created: 2012-05-11 02:00:37
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Version created on: 2012-05-11 02:00:37


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 Microarray technology has provided biologists with the ability to

measure the expression levels of thousands of genes in a single experiment. One
of the urgent issues in the use of microarray data is the selection of a smaller
subset of genes from the thousands of genes in the data that contributes to a disease.
This selection process is difficult due to many irrelevant genes, noisy
genes, and the availability of the small number of samples compared to the
huge number of genes (higher-dimensional data). In this study, we propose an
iterative method based on hybrid genetic algorithms to select a near-optimal
(smaller) subset of informative genes in classification of the microarray data.
The experimental results show that our proposed method is capable in selecting
the near-optimal subset to obtain better classification accuracies than other related
previous works as well as four methods experimented in this work. Additionally,
a list of informative genes in the best gene subsets is also presented for
biological usage.

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