Gene selection for microarray gene expression classification using Bayesian Lasso quantile regression

doi:10.1016/j.compbiomed.2018.04.018

Gene selection for microarray gene expression classification using Bayesian Lasso quantile regression

2018-06-01

27

PRE

AI

Z

Zakariya Yahya Algamal *

R

Rahim Alhamzawi

H

Haithem Taha Mohammad Ali

DOI：10.1016/j.compbiomed.2018.04.018

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Abstract

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Gene selection has been proven to be an effective way to improve the results of many classification methods. However, existing gene selection techniques in binary classification regression are sensitive to outliers of the data, heteroskedasticity or other anomalies of the latent response. In this paper, we propose a new Bayesian hierarchical model to overcome these problems in a relatively straightforward way. In particular, we propose a new Bayesian Lasso method that employs a skewed Laplace distribution for the errors and a scaled mixture of uniform distribution for the regression parameters, together with Bayesian MCMC estimation. Comprehensive comparisons between our proposed gene selection method and other competitor methods are performed experimentally, depending on four benchmark gene expression datasets. The experimental results prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy.

Keywords:

Gene selection

Lasso

Quantile regression

Classification

Bayesian hierarchical model

Gene selection for microarray gene expression classification using Bayesian Lasso quantile regression

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