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Stable gene selection by self-representation method in fuzzy sample classification

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Abstract

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.

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Authors and Affiliations

  1. Electrical Engineering Faculty, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Armaghan Davoudi & Hamid Mahmoodian

  2. Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Hamid Mahmoodian

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  1. Armaghan Davoudi
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  2. Hamid Mahmoodian
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Correspondence to Hamid Mahmoodian.

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Davoudi, A., Mahmoodian, H. Stable gene selection by self-representation method in fuzzy sample classification. Med Biol Eng Comput 58, 1213–1223 (2020). https://doi.org/10.1007/s11517-020-02160-6

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