Improved Lasso (ILASSO) for Gene Selection and Classification in High Dimensional DNA Microarray Data

Authors

  • Isah Aliyu Kargi Department of Mathematics, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia. Department of Mathematics and Statistics Nuhu Bamalli Polytechnic p.m.b 1061, Zaria.
  • Norazlina Bint Ismail Norazlina Bint Ismail Department of Mathematics, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia.
  • Ismail Bin Mohamad Department of Mathematics, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia.

DOI:

https://doi.org/10.3991/ijoe.v17i08.24601

Keywords:

High dimension data, Penalized Adaptive Elastic net, ILasso, logistic regression, gene selection, cancer classification

Abstract


Classification and selection of gene in high dimensional microarray data has become a challenging problem in molecular biology and genetics. Penalized Adaptive likelihood method has been employed recently for classification of cancer to address both gene selection consistency and estimation of gene coefficients in high dimensional data simultaneously. Many studies from the literature have proposed the use of ordinary least squares (OLS), maximum likelihood estimation (MLE) and Elastic net as the initial weight in the Adaptive elastic net, but in high dimensional microarray data the MLE and OLS are not suitable. Likewise, considering the Elastic net as the initial weight in Adaptive elastic yields a poor performance, because the ridge penalty in the Elastic net grouped coefficient of highly correlated genes closer to each other.  As a result, the estimator fails to differentiate coefficients of highly correlated genes that have different sign being grouped together. To tackle this issue, the present study proposed Improved LASSO (ILASSO) estimator which add the ridge penalty to the original LASSO with an Adaptive weight to both    and  simultaneously. Results from the real data indicated that ILASSO has a better performance compared to other methods in terms of the number of gene selected, classification precision, Sensitivity and Specificity.

Downloads

Published

2021-08-16

How to Cite

Kargi, I. A., Ismail, N. B., & Mohamad, I. B. (2021). Improved Lasso (ILASSO) for Gene Selection and Classification in High Dimensional DNA Microarray Data. International Journal of Online and Biomedical Engineering (iJOE), 17(08), pp. 91–102. https://doi.org/10.3991/ijoe.v17i08.24601

Issue

Section

Papers