Feature selection methods on gene expression microarray data for cancer classification: A systematic review

Esra'a Alhenawi; Rizik Al-Sayyed; Amjad Hudaib; Seyedali Mirjalili

doi:10.1016/j.compbiomed.2021.105051

Feature selection methods on gene expression microarray data for cancer classification: A systematic review

Comput Biol Med. 2022 Jan:140:105051. doi: 10.1016/j.compbiomed.2021.105051. Epub 2021 Nov 23.

Authors

Esra'a Alhenawi¹, Rizik Al-Sayyed², Amjad Hudaib³, Seyedali Mirjalili⁴

Affiliations

¹ King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan. Electronic address: esra_a_2008@live.com.
² King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan. Electronic address: r.alsayyed@ju.edu.jo.
³ King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan. Electronic address: ahudaib@ju.edu.jo.
⁴ Center for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, Brisbane, 4006, QLD, Australia; Yonsei Frontier Lab, Yonsei University, Seoul, South Korea. Electronic address: ali.mirjalili@gmail.com.

PMID: 34839186
DOI: 10.1016/j.compbiomed.2021.105051

Abstract

This systematic review provides researchers interested in feature selection (FS) for processing microarray data with comprehensive information about the main research directions for gene expression classification conducted during the recent seven years. A set of 132 researches published by three different publishers is reviewed. The studied papers are categorized into nine directions based on their objectives. The FS directions that received various levels of attention were then summarized. The review revealed that 'propose hybrid FS methods' represented the most interesting research direction with a percentage of 34.9%, while the other directions have lower percentages that ranged from 13.6% down to 3%. This guides researchers to select the most competitive research direction. Papers in each category are thoroughly reviewed based on six perspectives, mainly: method(s), classifier(s), dataset(s), dataset dimension(s) range, performance metric(s), and result(s) achieved.

Keywords: Embedded techniques; Ensemble; Feature selection; Filters; Hybrid; Wrappers.