Extending Molecular Subtyping in Alzheimer’s using Machine Learning Techniques and Alternative Splicing Data

This work demonstrates the potential of AS in further describing putative molecular subtypes of AD and aims to highlight AS events in specific areas of interest that distinguish such subtypes from each other, further developing the emerging picture of the diverse molecular underpinnings of AD and hopefully contributing to more precise and effective medical interventions. Refining of the machine learning portion of the work to achieve the clearest delineation possible between clusters will facilitate a clearer understanding of the molecular signatures that characterise each patient cluster.