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 Kushwah, Virendra Singh; Krishnan, Sivaneasan Bala; Upreti, Kamal; Kshirsagar, Pravin; Kumar, Manoj; Shankar, Uma; Radhakrishnan, Ganesh Veluswamy Title A name given to the resource Gene Expression Data-Based Interpretable Machine Learning Framework for Classifying Brain Cancer Subtypes 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 Biomedical and Pharmacology Journal;Volume;18;Issue;3;pp.2014-2023 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.13005/bpj/3233" target="_blank" rel="noreferrer noopener">https://doi.org/10.13005/bpj/3233</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018641122?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018641122?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 Kushwah V.S., Department of CSE and AI, VIT Bhopal University, Madhya Pradesh, Sehore, India; Krishnan S.B., Department. of Electrical and Electronics Engineering, Singapore Institute of Technology, Singapore; Upreti K., Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India; Kshirsagar P., Department of Electronics and Telecommunication Engineering, J D College of Engineering and Management, Nagpur, India; Kumar M., Department of Mathematics and Statistics, Gurukula Kangri University, Uttarakhand, Haridwar, India; Shankar U., Department of Management and Social Sciences, Qaiwan International University, Kurdistan, Sulaymaniyah, Iraq; Radhakrishnan G.V., Department of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, India Description An account of the resource Early detection, therapeutic stratification, and precision medicine all rely on the precise classification of brain cancer subtypes. To categorize brain tumor subtypes, we examine the application of ensemble machine learning modelsRandom Forest, XGBoost, and LightGBMusing high-dimensional gene expression data from the GSE50161 dataset (CuMiDa). The top 1000 genes were selected using variance thresholding, and models were then trained and evaluated on a stratified split of the dataset. Despite the availability of models achieving similar accuracies (~9596%) in existing works, our framework integrates SHAP-based interpretability to identify biologically significant genes, such as CDK4, EGFR, and TP53, offering dual benefits of high predictive power and explainability. The use of SHAP (SHapley Additive exPlanations) values to assess model predictions and identify physiologically important gene features revealed that key gene probes, including as CDK4, EGFR, and TP53, were significant across different tumor subtypes. This study demonstrates how SHAP and interpretable ensemble learning may be used to diagnose brain tumors with excellent classification accuracy and physiologically meaningful gene identification. Published by Oriental Scientific Publishing Company 2025. Subject The topic of the resource Biomarker Identification; Brain Cancer; Cell Lines; Gene-expression; Machine Learning; Microarray Data Publisher An entity responsible for making the resource available Oriental Scientific Publishing Company Relation A related resource ISSN: 9746242; 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 All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online