Diagnosis of the disease using an ant colony gene selection method based on information gain ratio using fuzzy rough sets

Document Type : Research Article

Authors

Faculty of Mathematics and Computer, Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran

Abstract

With the advancement of metagenome data mining science has become focused on microarrays. Microarrays are datasets with a large number of genes that are usually irrelevant to the output class; hence, the process of gene selection or feature selection is essential. So, it follows that you can remove redundant genes and increase the speed and accuracy of classification. After applying the gene selection, the dataset is reduced and detection of differentially abundant genes facilitated with more accuracy. This will, in turn, increases the power of genes which are correctly detected statistically differentially abundant in two or more phenotypes. The method presented in this study is a two-stage method for functional analysis of metagenomes.  The first stage uses a combination of the filter and wrapper gene selection method, which includes the ant colony algorithm and utilizes fuzzy rough sets to calculate the information gain ratio as an evaluation measure in the ant colony algorithm. The set of features from the first stage is used as input in the second stage, and then the negative binomial distribution is used to detect genes which are statistically differentially abundant in two or more phenotypes. Applying the proposed method on a microarray dataset it becomes clear that the proposed method increases the accuracy of the classifier and selects a subset of genes that have a minimum length and maximum accuracy.

Highlights

  • Gene selection as a preprocessing phase is very important in the diagnosis of diseases.
  • By applying a two-stage gene selection method, the accuracy of detecting diseases process was increased. 
  • By detecting the genes which were statistically differentially abundant in different phenotypes, the genes that related to healthy or diseases were detected.

Keywords

References

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Journal of Particle Science and Technology
Volume 3, Issue 4
December 2017
Pages 175-186

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History

  • Receive Date: 11 December 2017
  • Revise Date: 17 January 2018
  • Accept Date: 24 January 2018

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