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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| Title | An Iterative GASVM-Based Method: Gene Selection and Classification of Microarray Data. |
| File name | Mohamad2009-An_Iterative_GASVM-BasedMethod_Gene_Selection_and_Classification_of_Microarray_Data..pdf |
| File size | 152089 |
| SHA1 | 8a76a96a7007dbcf0d5d97cc62f1c54ee1e2405e |
| Content type | Adobe PDF |
Description
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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