In recent years, microarray technology and gene expression profiles have been widely used to detect, predict, or classify the samples of various diseases. The presence of large genes in these profiles and the small number of samples are known challenges in this field and are widely considered in previous papers. In previous studies, other topics such as the noise of microarray data or the dependence of selected genes on samples have been less considered. Therefore, we have tried to address these two issues by using a fuzzy classifier and stability index of selected genes, respectively. The proposed method is based on the regression function between the genes and class labels which is determined by the self-representing method. This regression function is determined individually for each class of the database. To minimize the effect of noise in microarray data, a fuzzy classifier is applied in the proposed model. Four databases of gene expression profiles are examined in this article, and the results indicate that the proposed model has a relative advantage over the previous methods. Graphical abstract.

中文翻译：

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在模糊样本分类中通过自我表示法进行稳定的基因选择。

近年来，微阵列技术和基因表达谱已被广泛用于检测，预测或分类各种疾病的样本。这些配置文件中大基因的存在和少量样品是该领域已知的挑战，并且在先前的论文中被广泛考虑。在以前的研究中，较少考虑其他主题，例如微阵列数据的噪音或所选基因对样品的依赖性。因此，我们试图分别通过使用模糊分类器和所选基因的稳定性指数来解决这两个问题。所提出的方法基于基因和类别标签之间的回归函数，该函数由自我表示方法确定。针对数据库的每个类别分别确定此回归函数。为了使噪声对微阵列数据的影响最小，在该模型中应用了模糊分类器。本文检查了四个基因表达谱数据库，结果表明所提出的模型比以前的方法具有相对优势。图形概要。