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 Porwal, Omji; Upreti, Kamal; Kshirsagar, Pravin R.; Panwar, Sarika; Sharma, Anurag; Radhakrishnan, Ganesh V.; Jain, Rituraj Title A name given to the resource Decoding Breast Cancer Mutational Signatures: A Hybrid ElasticNetXGBoost Approach Using Gene Expression Data Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Journal of Prognostics and Health Management;Volume;17;Issue;1; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.36001/ijphm.2026.v17i1.4714" target="_blank" rel="noreferrer noopener">https://doi.org/10.36001/ijphm.2026.v17i1.4714</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105040132892?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105040132892?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 Porwal O., Faculty of Pharmacy, Qaiwan Research Center, Qaiwan International University, Kurdistan, Sulaymaniyah, 46001, Iran; Upreti K., Department of Computer Science, Christ University, Ghaziabad, 201002, India; Kshirsagar P.R., Department of Electronics &amp; Telecommunication Engineering, J D College of Engineering &amp; Management, Maharashtra, Nagpur, 441501, India; Panwar S., Departmemt of Electronics and Telecommunication Engineering, Academy of Engineering, Pune, India; Sharma A., School of Electrical and Electronic Engineering, Newcastle University, NE1 7RU, Singapore; Radhakrishnan G.V., Kalinga School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, 751024, India; Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, 360003, India Description An account of the resource TP53, PIK3CA, and MUC16 are somatic mutations that are useful in breast cancer progression and prognosis, but direct mutation profiling based on sequencing is not always practicable in practice. The data about gene expression can contain indirect transcriptomic patterns linked with mutational underlying states. This paper proposes an expression-based machine learning model to predict the status of mutations using METABRIC breast cancer cohort. Instead of directly estimating genetic changes, the suggested method estimates statistical relationships between transcriptomic phenotypes and binary somatic mutation states. A multi-stage gene features selection pipeline using variance filtering, mutual information ranking, and correlation pruning was used to reduce the number of genes (19,000). A hybrid predictive architecture was trained using these features that combined ElasticNet logistic regression and XGBoost that allowed balancing between linear regularization and nonlinear interaction modeling. The hybrid model with a combination of five-fold stratified cross validation yielded mean ROC-AUC of 0.94 (TP53), 0.92 (PIK3CA), and 0.90 (MUC16) with the stability of the calibration and equal error rates. Coefficient analysis and SHAP-based explanations were used to investigate the interpretability of the models to describe the expression patterns on mutation status. The suggested framework is a hypothesis-generating, complementary method of transcriptomic analysis, which must be reevaluated by external validation to determine the wider generalizability. 2026, International Journal of Prognostics and Health Management. All rights reserved. Publisher An entity responsible for making the resource available Prognostics and Health Management Society Relation A related resource ISSN: 21532648; 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