Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics

Overview

Journal Biomolecules

Publisher MDPI

Date 2025 Jan 25

PMID 39858475

Authors

Jihan Wang

Zhengxiang Zhang

Yangyang Wang

Affiliations

Soon will be listed here.

Abstract

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.

References

Potlitz F, Link A, Schulig L . Advances in the discovery of new chemotypes through ultra-large library docking. Expert Opin Drug Discov. 2023; 18(3):303-313. DOI: 10.1080/17460441.2023.2171984. View

Cai Z, Xu D, Zhang Q, Zhang J, Ngai S, Shao J . Classification of lung cancer using ensemble-based feature selection and machine learning methods. Mol Biosyst. 2014; 11(3):791-800. DOI: 10.1039/c4mb00659c. View

Chen J, Dhahbi J . Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature selection methods. Sci Rep. 2021; 11(1):13323. PMC: 8233431. DOI: 10.1038/s41598-021-92725-8. View

Cantor E, Guauque-Olarte S, Leon R, Chabert S, Salas R . Knowledge-slanted random forest method for high-dimensional data and small sample size with a feature selection application for gene expression data. BioData Min. 2024; 17(1):34. PMC: 11389072. DOI: 10.1186/s13040-024-00388-8. View

Wang J, Bo T, Jonassen I, Myklebost O, Hovig E . Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data. BMC Bioinformatics. 2003; 4:60. PMC: 302113. DOI: 10.1186/1471-2105-4-60. View

Mendonca-Neto R, Li Z, Fenyo D, Silva C, Nakamura F, Nakamura E . A Gene Selection Method Based on Outliers for Breast Cancer Subtype Classification. IEEE/ACM Trans Comput Biol Bioinform. 2021; 19(5):2547-2559. DOI: 10.1109/TCBB.2021.3132339. View

Cho H, Kim S, Jeong M, Park Y, Ziegler T, Jones D . Genetic algorithm-based feature selection in high-resolution NMR spectra. Expert Syst Appl. 2011; 35(3):967-975. PMC: 3070267. DOI: 10.1016/j.eswa.2007.08.050. View

Haznedar B, Arslan M, Kalinli A . Optimizing ANFIS using simulated annealing algorithm for classification of microarray gene expression cancer data. Med Biol Eng Comput. 2021; 59(3):497-509. DOI: 10.1007/s11517-021-02331-z. View

Ma M, Liu R, Wen C, Xu W, Xu Z, Wang S . Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms. Eur Radiol. 2021; 32(3):1652-1662. DOI: 10.1007/s00330-021-08271-4. View

10.

Gligorijevic V, Przulj N . Methods for biological data integration: perspectives and challenges. J R Soc Interface. 2015; 12(112). PMC: 4685837. DOI: 10.1098/rsif.2015.0571. View

11.

Maulik U, Mukhopadhyay A, Chakraborty D . Gene-expression-based cancer subtypes prediction through feature selection and transductive SVM. IEEE Trans Biomed Eng. 2012; 60(4):1111-7. DOI: 10.1109/TBME.2012.2225622. View

12.

Pozzoli S, Soliman A, Bahri L, Branca R, Girdzijauskas S, Brambilla M . Domain expertise-agnostic feature selection for the analysis of breast cancer data. Artif Intell Med. 2020; 108:101928. DOI: 10.1016/j.artmed.2020.101928. View

13.

Sugai Y, Kadoya N, Tanaka S, Tanabe S, Umeda M, Yamamoto T . Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients. Radiat Oncol. 2021; 16(1):80. PMC: 8086112. DOI: 10.1186/s13014-021-01810-9. View

14.

Miller H, van Berkel V, Frieboes H . Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data. Metabolomics. 2022; 18(8):57. PMC: 9737952. DOI: 10.1007/s11306-022-01918-3. View

15.

Goh W, Wong L . Evaluating feature-selection stability in next-generation proteomics. J Bioinform Comput Biol. 2016; 14(5):1650029. DOI: 10.1142/S0219720016500293. View

16.

Mostafa G, Mahmoud H, Abd El-Hafeez T, E ElAraby M . The power of deep learning in simplifying feature selection for hepatocellular carcinoma: a review. BMC Med Inform Decis Mak. 2024; 24(1):287. PMC: 11452940. DOI: 10.1186/s12911-024-02682-1. View

17.

Boehm K, Aherne E, Ellenson L, Nikolovski I, Alghamdi M, Vazquez-Garcia I . Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer. Nat Cancer. 2022; 3(6):723-733. PMC: 9239907. DOI: 10.1038/s43018-022-00388-9. View

18.

Huang M, Ye X, Imakura A, Sakurai T . Sequential reinforcement active feature learning for gene signature identification in renal cell carcinoma. J Biomed Inform. 2022; 128:104049. DOI: 10.1016/j.jbi.2022.104049. View

19.

Luo J, Feng Y, Wu X, Li R, Shi J, Chang W . ForestSubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest. BMC Bioinformatics. 2023; 24(1):289. PMC: 10354904. DOI: 10.1186/s12859-023-05412-y. View

20.

Taghizadeh E, Heydarheydari S, Saberi A, JafarpoorNesheli S, Rezaeijo S . Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods. BMC Bioinformatics. 2022; 23(1):410. PMC: 9526906. DOI: 10.1186/s12859-022-04965-8. View