REVIEW article
Front. Big Data
Sec. Data Mining and Management
Volume 8 - 2025 | doi: 10.3389/fdata.2025.1624507
Navigating the Microarray Landscape: A Comprehensive Review of Feature Selection Techniques and Their Applications
Provisionally accepted
Fang-Ling Wang1
Yanjie Ren2
Mahadi Bahari3
AZURAH A SAMAH1Zuraini Binti Ali Shah1Norfadzlan Bin Yusup4Rozita Abdul Jalil5Azizah Mohamad6Nurulhuda Firdaus Mohd Azmi7Azlan Mohd Zain1*- 1Faculty of Computing, Universiti Teknologi Malaysia, skudai, Malaysia
- 2Hebei Institute of Mechanical and Electrical Technology, Xingtai, China
- 3Faculty of Management, Universiti Teknologi Malaysia, Skudai, Malaysia
- 4Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan, Malaysia
- 5Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia, Johor, Malaysia
- 6Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuhraya Tun Razak, Gambang, 26300 Kuantan, Pahang, Malaysia, Pahang, Malaysia
- 7Faculty of Artificial Intelligence, Universiti Teknologi Malaysia, Skudai, 81310, Johor, Malaysia, Johor, Malaysia
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This review systematically summarizes recent advances in microarray feature selection techniques and their applications in biomedical research. It addresses the challenges posed by the high dimensionality and noise of microarray data, aiming to integrate the strengths and limitations of various methods while exploring their applicability across different scenarios. By identifying gaps in current research, highlighting underexplored areas, and proposing clear directions for future studies, this review seeks to inspire academics to develop novel techniques and applications. Furthermore, it provides a comprehensive evaluation of feature selection methods, offering both a theoretical foundation and practical guidance to help researchers select the most suitable approaches for their specific research questions. Emphasizing the importance of interdisciplinary collaboration, the study underscores the potential of feature selection in transformative applications such as personalized medicine, cancer diagnosis, and drug discovery. Through this review, not only does it provide in-depth theoretical support for the academic community, but also practical guidance for the practical field, which significantly contributes to the overall improvement of microarray data analysis technology.
Keywords: Cancer classification, Feature Selection, Microarray data, machine learning, gene expression analysis
Received: 08 May 2025; Accepted: 16 Jun 2025.
Copyright: © 2025 Wang, Ren, Bahari, A SAMAH, Ali Shah, Yusup, Jalil, Mohamad, Azmi and Zain. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Azlan Mohd Zain, Faculty of Computing, Universiti Teknologi Malaysia, skudai, Malaysia
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.