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Lookup NU author(s): Dr Anurag SharmaORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2026, International Journal of Prognostics and Health Management. All rights reserved.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.
Author(s): Porwal O, Upreti K, Kshirsagar PR, Panwar S, Sharma A, Radhakrishnan GV, Jain R
Publication type: Article
Publication status: Published
Journal: International Journal of Prognostics and Health Management
Year: 2026
Volume: 17
Issue: 1
Online publication date: 18/04/2026
Acceptance date: 02/04/2018
Date deposited: 08/06/2026
ISSN (electronic): 2153-2648
Publisher: Prognostics and Health Management Society
URL: https://doi.org/10.36001/ijphm.2026.v17i1.4714
DOI: 10.36001/ijphm.2026.v17i1.4714
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