Wang Fangling, Zain Azlan Mohd, Ren Yanjie, Bahari Mahadi, Samah Azurah A, Ali Shah Zuraini Binti, Yusup Norfadzlan Bin, Jalil Rozita Abdul, Mohamad Azizah, Azmi Nurulhuda Firdaus Mohd
Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia.
Hebei Institute of Mechanical and Electrical Technology, Xingtai, China.
Front Big Data. 2025 Jul 10;8:1624507. doi: 10.3389/fdata.2025.1624507. eCollection 2025.
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.
本综述系统地总结了微阵列特征选择技术的最新进展及其在生物医学研究中的应用。它探讨了微阵列数据的高维度和噪声所带来的挑战,旨在综合各种方法的优缺点,同时探索它们在不同场景中的适用性。通过识别当前研究中的差距,突出未充分探索的领域,并为未来研究提出明确方向,本综述旨在激励学者开发新技术和应用。此外,它对特征选择方法进行了全面评估,提供了理论基础和实践指导,以帮助研究人员为其特定研究问题选择最合适的方法。该研究强调跨学科合作的重要性,强调特征选择在个性化医疗、癌症诊断和药物发现等变革性应用中的潜力。通过本综述,它不仅为学术界提供了深入的理论支持,也为实践领域提供了实践指导,这对微阵列数据分析技术的整体提升做出了重大贡献。