ABSTRACT Alzheimer's disease (AD) is the most common dementia affecting more than six million people in the United States. The complex genetic risk and the lack of disease-specific biomarkers for AD are among the most challenges that investigators and clinicians face in early prediction, diagnosis, prevention, and intervention. There is an urgent need for early identification of individuals with higher risk before the onset of symptoms. With the rapid accumulation of genetic data, researchers have developed high-performance genetic models to predict complex diseases including AD. For example, polygenic risk score (PRS), designed to estimate individual genetic liability by integrating large GWAS summary statistics and individual genotype data, has provided a potential value to predict diseases like AD. Recent artificial intelligence (AI) coupled with promising machine learning (ML) techniques have been shown to yield meaningful insights when applied to “Big Data”. Convolutional neural network (CNN), a machine learning algorithm widely used in image and object classification, has shown informative results in the medical field aiding image data analyses. However, the application of CNN to non- image data such as genetic data is limited. Lately, our group has developed an artificial image objects (AIOs) method to transform tabular data into images. Uniquely, our AIO technique not only allows us to adapt CNN algorithms to classify disease but also identify biomarkers associated with the disease. Our preliminary study is encouraging: 1). CNN with single nucleotide variant (SNV)-transformed AIOs improves disease classification in schizophrenia; 2). CNN with RNA-seq data-transformed AIOs facilitates biomarker discovery in breast cancer; 3). CNN with PRSs-transformed AIOs from multiple genetically correlated traits performs better in AD prediction, as compared with the conventional logistic regression model with PRSs from AD alone. We hypothesize that CNN models with PRSs from multiple genetically correlated traits can improve AD classification and identify the biomarkers for early prediction and therapeutic targets. To test this hypothesis, we propose the following aims: 1: To build and validate the prediction model for AD classification using CNN algorithms and mtPRS- transformed AIOs. 2: To identify and validate biomarkers specific to AD by integrating multi-omics data and CNN algorithms. The approach is innovative in that we are the first to transform PRS and SNV genetic data into AIOs and apply AI/CNN for AD classification and biomarker identification. We are also the first to integrate PRS from multiple comorbid traits for AD prediction. The application is significant because we will promote AD prevention with a high-performance prediction model that can identify high-risk individuals at an earlier stage and identify disease-specific biomarkers for drug discovery. The overall objectives of this R15 AREA grant are to 1) Develop a CNN model to identif...