Unravelling Genetic Insights in Alzheimer's Disease (AD) Using Unsupervised Machine Learning Model

We aim to develop interpretable machine learning models that provide accurate predictions and insights into the biological relevance of identified genes. Apply state-of-the-art feature selection techniques to pinpoint influential genes while mitigating noise and improving the robustness of findings. Integrate multi-omics data and use pathway-level analysis to provide a holistic understanding of the biological networks and mechanisms driving AD. Leverage advanced ML techniques such as graph neural networks, transfer learning, and attention-based models to improve the analysis of complex, high-dimensional data. Use large-scale and diverse datasets to ensure generalizability across populations and address biases in existing studies. Validate findings using independent datasets and, where possible, collaborate with experimentalists to test predictions in biological models. By addressing these gaps, our work seeks to push the boundaries of AD research and contribute to discovering novel biomarkers, therapeutic targets, and disease-modifying interventions. In doing so, we hope to advance precision medicine in Alzheimer’s disease and provide new tools for early diagnosis and treatment. This interdisciplinary approach, combining machine learning with biology, has the potential to transform how we study and combat this devastating disease.