导航微阵列景观:特征选择技术及其应用的全面回顾。

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Frontiers in Big Data Pub Date : 2025-07-10 eCollection Date: 2025-01-01 DOI:10.3389/fdata.2025.1624507
Fangling Wang, Azlan Mohd Zain, Yanjie Ren, Mahadi Bahari, Azurah A Samah, Zuraini Binti Ali Shah, Norfadzlan Bin Yusup, Rozita Abdul Jalil, Azizah Mohamad, Nurulhuda Firdaus Mohd Azmi
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引用次数: 0

摘要

本文系统地综述了近年来微阵列特征选择技术及其在生物医学研究中的应用。它解决了微阵列数据的高维和噪声带来的挑战,旨在整合各种方法的优点和局限性,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,突出未开发的领域,并为未来的研究提出明确的方向,本综述旨在激励学者开发新的技术和应用。此外,本文还对特征选择方法进行了综合评价,为研究人员选择最适合其具体研究问题的方法提供了理论基础和实践指导。该研究强调了跨学科合作的重要性,强调了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。通过本文的综述,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,对微阵列数据分析技术的整体提升有重要贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Navigating the microarray landscape: a comprehensive review of feature selection techniques and their applications.

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.

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来源期刊
CiteScore
5.20
自引率
3.20%
发文量
122
审稿时长
13 weeks
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