Artificial Intelligence Strategies for Alzheimer's Disease Research

The goal of this project is to develop artificial intelligence (AI) approaches for extracting unforeseen patterns from clinical, genetic, genomic, and imaging data that could lead to ideas for new drug development or drug repurposing. Our proposed AI methods and software will be open-source, user-friendly, and freely available for all to use. Specifically, we will analyze ADSP data sets using three novel informatics methods to tailor our automated machine learning (AutoML) tool to the analysis of Alzheimer’s disease (AD) data. First, we will develop a Multi-Modal Interaction (M2I) feature selection algorithm for identifying genetic interactions that are predictive of AD (AIM 1). Second, we will develop a Knowledge-driven Multi-omics Integration (KMI) algorithm for combining omics features for AI analysis of AD (AIM 2). Third, we will develop a Multidimensional Brain Imaging Omics (MBIO) integration framework for the joint analysis of multisource large-scale data for predicting AD. Finally, we will integrate all three biomedical informatics methods into our open-source AutoML software package and apply it to the ADSP data sets. We expect our methods will reveal new biomarkers for AD that will open the door for better treatments and clinical decision support.

The goal of this project is to develop artificial intelligence (AI) approaches for extracting unforeseen patterns from clinical, genetic, genomic, and imaging data that could lead to ideas for new drug development or drug repurposing. We will develop three biomedical informatics methods with focuses on genetics, genomics and imaging respectively. We will integrate these methods into our open-source AutoML software package, and apply it to the ADSP data sets. We expect our methods will reveal new biomarkers for AD that will open the door for better treatments and clinical decision support.