An efficient feature selection and classification system for microarray cancer data using genetic algorithm and deep belief networks-Reference-Cited by-同舟云学术

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An efficient feature selection and classification system for microarray cancer data using genetic algorithm and deep belief networks

Published:2024-03-19 Issue: Volume: Page:
ISSN:1573-7721
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Lawrence Morolake Oladayo^ORCID,Jimoh Rasheed Gbenga,Yahya Waheed Babatunde

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11042-024-18802-y.pdf

Reference121 articles.

1. Dashtban M, Balafar M, Suravajhala P (2018) Gene selection for tumor classification using a novel bio-inspired multi-objective approach. Genomics 110(1):10–17

2. Dashtban M, Balafar M (2017) Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts. Genomics 109(2):91–107

3. Wessels LF, Reinders MJ, Hart AA, Veenman CJ, Dai H, He YD, Veer LJVT (2005) A protocol for building and evaluating predictors of disease state based on microarray data. Bioinformatics 21(19):3755–3762

4. Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A, Benítez J, Herrera F (2014) A review of microarray datasets and applied feature selection methods. Inf Sci 282:111–135. https://doi.org/10.1016/j.ins.2014.05.042

5. Gumaei A, Sammouda R, Al-Rakhami M, AlSalman H, El-Zaart A (2021) Feature selection with ensemble learning for prostate cancer diagnosis from microarray gene expression. Health Inform J 27(1):1460458221989402

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1. Deciphering Key Genes in Colon Cancer Through Deep Learning Techniques;2024 Third International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN);2024-07-18

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