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

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 pre...

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Published in:Medical & Biological Engineering & Computing Vol. 58; no. 6; pp. 1213 - 1224
Main Authors: Davoudi, Armaghan, Mahmoodian, Hamid
Format: equations & formulas research tables/charts Journal Article
Published: Springer Nature Jun2020
Online Access:View this record in EBSCOhost
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      dt: Jun2020
      vid: 58
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s11517-020-02160-6
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        atl: Stable gene selection by self-representation method in fuzzy sample classification.
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          Davoudi, Armaghan
          Mahmoodian, Hamid
        affil: Electrical Engineering Faculty, Najafabad Branch, Islamic Azad University, Najafabad, Iran
      sug:
        subj:
          Gene Expression Profiling Methods
          Resource Databases
          Logic
          Autism Spectrum Disorder
          Oligonucleotide Array Sequence Analysis Methods
          Breast Neoplasms
          Linear Regression
          Colonic Neoplasms
          Hematologic Neoplasms
          Female
          Female
      ab: 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.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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