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 met...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Frontiers Media S.A
2025
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/48832/1/fdata-1-1624507.pdf http://ir.unimas.my/id/eprint/48832/ https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1624507/full https://doi.org/10.3389/fdata.2025.1624507 |
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| Summary: | 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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