Current drug targets · 2026
predALZ: An Ensemble Learning Framework for Identifying Genetic Biomarkers in Familial Alzheimer's Disease.
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Karim Ayesha, Alturise Fahad, Alkhalifah Tamim, Khan Yaser Daanial
Аннотация
INTRODUCTION: Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder with a substantial genetic contribution, especially in the early-onset form. Mutations in genes like APP, PSEN1, and PSEN2 serve as crucial biomarkers, indicating a heightened risk of developing AD. Leveraging these genetic markers, we introduce predALZ, a prediction model designed to enhance early detection of familial AD through genomic sequence analysis.
METHODS: The model integrates data derived from genome-wide association studies (GWAS) and employs advanced feature encoding techniques to generate a robust representation of genomic patterns. A diverse ensemble of classifiers, namely XGBoost, Random Forest, LightGBM, and ExtraTrees, is employed to train the predALZ model, utilizing the generated feature vector for training.
RESULTS: The predALZ framework achieved 94% accuracy on an independent test set and approximately 96% accuracy in cross-validation for Alzheimer-related driver gene prediction. The ensemble model also yielded consistently high sensitivity, specificity, and Matthews correlation coefficient values, indicating stable and reliable classification performance.
DISCUSSION: The model's effectiveness was further rigorously validated through a comprehensive evaluation, using metrics such as accuracy, sensitivity, specificity, and Matthew's correlation coefficient. The study underscores the predictor's remarkable performance, consistently achieving 94% accuracy in an independent Test and ~96% in cross-validation.
CONCLUSION: These findings highlight predALZ's potential for application in predictive diagnostics and targeted therapeutic development for Alzheimer's disease.
METHODS: The model integrates data derived from genome-wide association studies (GWAS) and employs advanced feature encoding techniques to generate a robust representation of genomic patterns. A diverse ensemble of classifiers, namely XGBoost, Random Forest, LightGBM, and ExtraTrees, is employed to train the predALZ model, utilizing the generated feature vector for training.
RESULTS: The predALZ framework achieved 94% accuracy on an independent test set and approximately 96% accuracy in cross-validation for Alzheimer-related driver gene prediction. The ensemble model also yielded consistently high sensitivity, specificity, and Matthews correlation coefficient values, indicating stable and reliable classification performance.
DISCUSSION: The model's effectiveness was further rigorously validated through a comprehensive evaluation, using metrics such as accuracy, sensitivity, specificity, and Matthew's correlation coefficient. The study underscores the predictor's remarkable performance, consistently achieving 94% accuracy in an independent Test and ~96% in cross-validation.
CONCLUSION: These findings highlight predALZ's potential for application in predictive diagnostics and targeted therapeutic development for Alzheimer's disease.