Gene reduction and machine learning algorithms for cancer classification based on microarray gene expression data: A comprehensive review

作者：

Highlights：

• A comprehensive review on microarray gene expression reduction is proposed.

• A novel taxonomy of up-to-date gene reduction algorithms is presented.

• The strength and limitations of each type of gene reduction algorithms are provided.

• Provide an outline of the accurately used type of machine learning algorithms.

• Analyze the performance of the state-of-the-art algorithms.

摘要

•A comprehensive review on microarray gene expression reduction is proposed.•A novel taxonomy of up-to-date gene reduction algorithms is presented.•The strength and limitations of each type of gene reduction algorithms are provided.•Provide an outline of the accurately used type of machine learning algorithms.•Analyze the performance of the state-of-the-art algorithms.

论文关键词：Microarray gene expression,Data reduction,Feature selection,Feature extraction,Machine learning,Cancer classification

论文评审过程：Received 3 February 2022, Revised 22 September 2022, Accepted 28 September 2022, Available online 6 October 2022, Version of Record 12 October 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118946