Two-stage Gene Selection and Classification for a High-Dimensional Microarray Data

Authors

  • Masithoh Yessi Rochayani Universitas Brawijaya, Indonesia
  • Umu Sa'adah Universitas Brawijaya, Indonesia
  • Ani Budi Astuti Universitas Brawijaya, Indonesia

DOI:

https://doi.org/10.15575/join.v5i1.569

Keywords:

Classification and Regression, Feature selection, Gene expression, High-dimensional, Microarray, Tree

Abstract

Microarray technology has provided benefits for cancer diagnosis and classification. However, classifying cancer using microarray data is confronted with difficulty since the dataset has high dimensions. One strategy for dealing with the dimensionality problem is to make a feature selection before modeling. Lasso is a common regularization method to reduce the number of features or predictors. However, Lasso remains too many features at the optimum regularization parameter. Therefore, feature selection can be continued to the second stage. We proposed Classification and Regression Tree (CART) for feature selection on the second stage which can also produce a classification model. We used a dataset which comparing gene expression in breast tumor tissues and other tumor tissues. This dataset has 10,936 predictor variables and 1,545 observations. The results of this study were the proposed method able to produce a few numbers of selected genes but gave high accuracy. The model also acquired in line with the Oncogenomics Theory by the obtained of GATA3 to split the root node of the decision tree model. GATA3 has become an important marker for breast tumors.

Author Biographies

Masithoh Yessi Rochayani, Universitas Brawijaya

Department of Statistics, Faculty of Mathematics and Natural Sciences

Umu Sa'adah, Universitas Brawijaya

Department of Statistics, Faculty of Mathematics and Natural Sciences

Ani Budi Astuti, Universitas Brawijaya

Department of Statistics, Faculty of Mathematics and Natural Sciences

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Published

2020-07-16

Issue

Vol. 5 No. 1 (2020)

Section

Article

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