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A Hybrid BPSO-CGA Approach for Gene Selection and Classification of Microarray Data

Li-Yeh Chuang, Cheng-Huei Yang, Jung-Chike Li, Cheng-Hong Yang
<span title="">2012</span> <i title="Mary Ann Liebert Inc"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/id6xbd2o6fhy7pts2djgwhtmvy" style="color: black;">Journal of Computational Biology</a> </i> &nbsp;
A hybrid method of binary particle swarm optimization (BPSO) and a combat genetic algorithm (CGA) is to perform the microarray data selection.  ...  The selection of relevant genes from microarray data poses a formidable challenge to researchers due to the high-dimensionality of features, multiclass categories being involved, and the usually small  ...  ACKNOWLEDGMENTS This work was partly supported by the National Science Council in Taiwan (grants NSC94-2622-E-151-025-CC3, NSC94-2311-B037-001, NSC93-2213-E-214-037, and NSC92-2213-E-214-036).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1089/cmb.2010.0064">doi:10.1089/cmb.2010.0064</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/21210743">pmid:21210743</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3244808/">pmcid:PMC3244808</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sz7g5b3itvdhzl23ys2ump7bgq">fatcat:sz7g5b3itvdhzl23ys2ump7bgq</a> </span>
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