Tagged: Polygenic Risk Scores

Projects

• Deep Learning, Sequencing Technologies & Polygenic Scores: Alzheimer’s Disease Risk Prediction and Classification Review — This work reviews traditional genome-wide association studies (GWAS) and weighted polygenic risk scores (PRS) as methods for predicting the onset of Alzheimer’s Disease (AD), then examines machine learning (ML) and deep learning (DL) approaches. Reviewed studies include the use of random forests, support vector machines, and various neural network architectures. We identify persistent challenges encountered throughout the survey, including dataset diversity, model explainability, and regulatory compliance. The work concludes by cautiously proposing a multi-phase framework for clinical adoption of selective ML and DL methods into existing NHS genomic testing pipelines over a seven-year timeline, emphasising quality control, SHAP-based interpretability, and robust validation before any scaled deployment.