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It offers a comprehensive view of the transcriptome, making it a more expansive technique in comparison with micro-array. Genes that discriminate malignancy and normal can be deduced using quantitative gene expression. However, this data is a high-dimensional dense matrix; each sample has a dimension of more than 20,000 genes. Dealing with this data poses challenges. This paper proposes RBNRO-DE (Relief Binary NRO based on Differential Evolution) for handling the gene selection strategy on (rnaseqv2 illuminahiseq rnaseqv2 un edu Level 3 RSEM genes normalized) with more than 20,000 genes to pick the best informative genes and assess them through 22 cancer datasets. The <jats:italic>k<\/jats:italic>-nearest Neighbor (<jats:italic>k<\/jats:italic>-NN) and Support Vector Machine (SVM) are applied to assess the quality of the selected genes. Binary versions of the most common meta-heuristic algorithms have been compared with the proposed RBNRO-DE algorithm. In most of the 22 cancer datasets, the RBNRO-DE algorithm based on <jats:italic>k<\/jats:italic>-NN and SVM classifiers achieved optimal convergence and classification accuracy up to 100% integrated with a feature reduction size down to 98%, which is very evident when compared to its counterparts, according to Wilcoxon\u2019s rank-sum test (5% significance level).<\/jats:p>","DOI":"10.1186\/s40537-024-00902-z","type":"journal-article","created":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T15:02:12Z","timestamp":1712156532000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Gene selection via improved nuclear reaction optimization algorithm for cancer classification in high-dimensional data"],"prefix":"10.1186","volume":"11","author":[{"given":"Amr A. 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