{"title":"Gene selection via improved nuclear reaction optimization algorithm for cancer classification in high-dimensional data","authors":"","doi":"10.1186/s40537-024-00902-z","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>RNA Sequencing (RNA-Seq) has been considered a revolutionary technique in gene profiling and quantification. 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 <em>k</em>-nearest Neighbor (<em>k</em>-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 <em>k</em>-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’s rank-sum test (5% significance level).</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"82 1","pages":""},"PeriodicalIF":8.6000,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Big Data","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1186/s40537-024-00902-z","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
RNA Sequencing (RNA-Seq) has been considered a revolutionary technique in gene profiling and quantification. 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 k-nearest Neighbor (k-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 k-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’s rank-sum test (5% significance level).
期刊介绍：
The Journal of Big Data publishes high-quality, scholarly research papers, methodologies, and case studies covering a broad spectrum of topics, from big data analytics to data-intensive computing and all applications of big data research. It addresses challenges facing big data today and in the future, including data capture and storage, search, sharing, analytics, technologies, visualization, architectures, data mining, machine learning, cloud computing, distributed systems, and scalable storage. The journal serves as a seminal source of innovative material for academic researchers and practitioners alike.
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