Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Lakshmi Alluri, Vijaya; Pavan Kanadam, Karteeka; Josephine V L, Helen; Rajagopal, Manikandan Title A name given to the resource An Efficient Approach for Gene Selection through Parallel Bio-Inspired Algorithms and Shapley Value Analysis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Research in Industrial Engineering;Volume;14;Issue;3;pp.508-527 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.22105/riej.2025.508954.1550" target="_blank" rel="noreferrer noopener">https://doi.org/10.22105/riej.2025.508954.1550</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105012623487?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012623487?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Lakshmi Alluri V., Department of Computer Science and Engineering, Acharya Nagarjuna University, Andhra Pradesh, Guntur, India; Pavan Kanadam K., Department of Computer Applications, R.V.R &amp; J.C College of Engineering, Andhra Pradesh, Guntur, India; Josephine V L H., Department of Business Analytics, School of Business and Management, Christ University, Karnataka, Bangalore, India; Rajagopal M., Department of Lean Operations and Systems, School of Business and Management, Christ University, Karnataka, Bengaluru, India Description An account of the resource The fast development of microarray technology has significantly assisted in the use of gene expression analysis to forecast cancer subtypes. Analyzing high-dimensional microarray data is still challenging, as existing hybrid methods cannot find highly discriminative genes. This study aims to use Shapley value analysis and hybrid bio-inspired algorithms to develop a scalable, parallel gene selection technique to increase computing efficiency and classification accuracy in high-dimensional microarray data. This study used hybrid feature selection approaches inspired by bio-organisms to create a scalable parallel gene selection system. The dataset size is initially enlarged by Adaptive Synthetic Sampling (ADASYN). They use the Recursive Feature Elimination (RFE) approach to extract features and determine their Shapley values. In addition, the Whale Optimization Algorithm (WOA) works to determine which genes are most important. After that, Machine Learning (ML) techniques assist in classifying the chosen characteristics. According to the experiment results, the suggested strategy surpasses standard gene selection techniques with the same datasets, employing improved classification accuracy and reducing computing time. K-NN achieved an accuracy of 85.44%, while LR showed improved results with an accuracy of 91.72%. RF further increased accuracy to 94.69%. SVM demonstrated exceptional performance, reaching an accuracy of 97.63%. Ultimately, XGBoost excelled among all models with the highest accuracy of 98.49%, highlighting its robust ability to classify SRBCT samples effectively based on gene expression data. 2025, Ayandegan Institute of Higher Education. All rights reserved. Subject The topic of the resource Bio-inspired algorithms; High-dimensional data; Machine learning; Microarray technology; Shapley value analysis Publisher An entity responsible for making the resource available Ayandegan Institute of Higher Education Relation A related resource ISSN: 27831337; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online