Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics
Cancer’s heterogeneity presents significant challenges in accurate diagnosis and effective treatment, including the complexity of identifying tumor subtypes and their diverse biological behaviors. This review examines how feature selection techniques address these challenges by improving the interpr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Biomolecules |
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| Online Access: | https://www.mdpi.com/2218-273X/15/1/81 |
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| author | Jihan Wang Zhengxiang Zhang Yangyang Wang |
| author_facet | Jihan Wang Zhengxiang Zhang Yangyang Wang |
| author_sort | Jihan Wang |
| collection | DOAJ |
| description | Cancer’s heterogeneity presents significant challenges in accurate diagnosis and effective treatment, including the complexity of identifying tumor subtypes and their diverse biological behaviors. This review examines how feature selection techniques address these challenges by improving the interpretability and performance of machine learning (ML) models in high-dimensional datasets. Feature selection methods—such as filter, wrapper, and embedded techniques—play a critical role in enhancing the precision of cancer diagnostics by identifying relevant biomarkers. The integration of multi-omics data and ML algorithms facilitates a more comprehensive understanding of tumor heterogeneity, advancing both diagnostics and personalized therapies. However, challenges such as ensuring data quality, mitigating overfitting, and addressing scalability remain critical limitations of these methods. Artificial intelligence (AI)-powered feature selection offers promising solutions to these issues by automating and refining the feature extraction process. This review highlights the transformative potential of these approaches while emphasizing future directions, including the incorporation of deep learning (DL) models and integrative multi-omics strategies for more robust and reproducible findings. |
| format | Article |
| id | doaj-art-acf815ab5eb54d10bf4992d1d8c94a0d |
| institution | Kabale University |
| issn | 2218-273X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj-art-acf815ab5eb54d10bf4992d1d8c94a0d2025-01-24T13:25:06ZengMDPI AGBiomolecules2218-273X2025-01-011518110.3390/biom15010081Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer DiagnosticsJihan Wang0Zhengxiang Zhang1Yangyang Wang2Yan’an Medical College of Yan’an University, Yan’an 716000, ChinaYan’an Medical College of Yan’an University, Yan’an 716000, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, ChinaCancer’s heterogeneity presents significant challenges in accurate diagnosis and effective treatment, including the complexity of identifying tumor subtypes and their diverse biological behaviors. This review examines how feature selection techniques address these challenges by improving the interpretability and performance of machine learning (ML) models in high-dimensional datasets. Feature selection methods—such as filter, wrapper, and embedded techniques—play a critical role in enhancing the precision of cancer diagnostics by identifying relevant biomarkers. The integration of multi-omics data and ML algorithms facilitates a more comprehensive understanding of tumor heterogeneity, advancing both diagnostics and personalized therapies. However, challenges such as ensuring data quality, mitigating overfitting, and addressing scalability remain critical limitations of these methods. Artificial intelligence (AI)-powered feature selection offers promising solutions to these issues by automating and refining the feature extraction process. This review highlights the transformative potential of these approaches while emphasizing future directions, including the incorporation of deep learning (DL) models and integrative multi-omics strategies for more robust and reproducible findings.https://www.mdpi.com/2218-273X/15/1/81tumor subtype classificationfeature selectionartificial intelligencemachine learningdeep learningmulti-omics |
| spellingShingle | Jihan Wang Zhengxiang Zhang Yangyang Wang Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics Biomolecules tumor subtype classification feature selection artificial intelligence machine learning deep learning multi-omics |
| title | Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics |
| title_full | Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics |
| title_fullStr | Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics |
| title_full_unstemmed | Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics |
| title_short | Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics |
| title_sort | utilizing feature selection techniques for ai driven tumor subtype classification enhancing precision in cancer diagnostics |
| topic | tumor subtype classification feature selection artificial intelligence machine learning deep learning multi-omics |
| url | https://www.mdpi.com/2218-273X/15/1/81 |
| work_keys_str_mv | AT jihanwang utilizingfeatureselectiontechniquesforaidriventumorsubtypeclassificationenhancingprecisionincancerdiagnostics AT zhengxiangzhang utilizingfeatureselectiontechniquesforaidriventumorsubtypeclassificationenhancingprecisionincancerdiagnostics AT yangyangwang utilizingfeatureselectiontechniquesforaidriventumorsubtypeclassificationenhancingprecisionincancerdiagnostics |