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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lzfb44euzbd23pxv7vxltr3iui" style="color: black;">Journal of Applied Science and Technology Trends</a>
Microarray data plays a major role in diagnosing and treating cancer. In several microarray data sets, many gene fragments are not associated with the target diseases. A solution to the gene selection problem might become important when analyzing large gene datasets. The key task is to better represent genes through optimum accuracy in classifying the samples. Different gene classification algorithms have been provided in past studies; after all, they suffered due to the selection of several<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.38094/jastt20161">doi:10.38094/jastt20161</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xdjnq4anjjcz3jc33e6wecotya">fatcat:xdjnq4anjjcz3jc33e6wecotya</a> </span>
more »... es mostly in high-dimensional microarray data. This paper aims to review classification and feature selection with different microarray datasets focused on swarm intelligence algorithms. We explain microarray data and its types in this paper briefly. Moreover, our paper presents an introduction to most common swarm intelligence algorithms. A review on swarm intelligence algorithms in gene selection profile based on classification of Microarray Data is presented in this paper.
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