Graph neural networks for biomedical insights - revealing markers of Alzheimer's disease from multi-modal molecular data
Alzheimer’s disease (AD), the predominant form of dementia worldwide, is a progressive neurodegenerative disease characterized by severe cognitive decline. Despite the widespread prevalence of AD, and significant investment into AD research, our understanding of this disease is still evolving owing to the complex interplay of the genetic and molecular mechanisms involved. Effective therapeutic development for AD requires a deeper understanding of these underlying interactions. In this talk, we first demonstrate how graph neural networks are utilized to integrate multi-modal molecular data (i.e. multi-omics data) to build predictive models of AD. We then apply machine learning interpretability methods to our models to generate insights into the disease’s underlying drivers and reveal key biological interactions. We discuss the technical challenges encountered, the limitations of our current approach, and potential future directions for advancing research in this field.