Medical School
Twin Cities
Alzheimer’s disease (AD) and AD-related dementias (ADRD) are complex multifactorial processes where epigenetic and biochemical changes occur many years before the onset of clinical symptoms. During the last decade, large amounts of high-throughput molecular data including genetic variants, and epigenetic and transcriptomic data from blood and brain tissues have improved our understanding of complex molecular mechanisms associated with pathways of AD/ADRD. The application of deep learning methods to analyze integrated multi-omics data may be a powerful approach to elucidate the biological mechanisms in AD. This project aims to develop an integrated multi-omics prediction model for dementia utilizing an end-to-end deep learning classifier model. The explainable deep learning model will allow researchers to interpret the biological importance of deep representations of multi-omics features by optimizing a prediction model for dementia.