Machine learning-based prediction of drug response in ischemia reperfusion animal model.

ABSTRACT: Myocardial ischemia is a major global contributor to mortality. While reperfusion therapy remains the most effective treatment, it paradoxically leads to myocardial ischemia-reperfusion (MI/R) injury, resulting in irreversible cardiac damage for which no effective interventions currently exist. This underscores the pressing need to unravel the pathogenesis of MI/R injury and devise new therapeutic strategies. In this study, supervised machine learning models, including logistic regression (LR), support vector machines (SVM), random forests (RF), neural networks (NN), and k-nearest neighbors (kNN), were utilized to predict treatment response. The models incorporated molecular and biochemical features to evaluate three drugs: trans-Anethole (TNA), pentoxifylline (PTX), and cyanidin-3-O-gluco

SUBMITTER: Abd ElGwad AM 

PROVIDER: S-EPMC12672590 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature