Integrating Genomic Data and Genetic Risk Factors With AI for Predicting Susceptibility to Alzheimer's Disease

Abstract

Alzheimer's disease (AD) is a complicated neurodegenerative disease that has a vast effect on people, families, and society. despite a long time of research, there is no effective treatment for AD, highlighting the want for accurate and early prediction of disease susceptibility. recent advancements in genomic sequencing strategies have led to an explosion of facts, including genetic hazard variants associated with the advert. however, the widespread quantity of facts provides a mission in identifying meaningful patterns and predicting sickness chance. artificial intelligence (AI) has emerged as an effective device in integrating and studying massive genomic datasets, offering insights into complicated illnesses consisting of ad. In this evaluation, we speak about the contemporary country of integrating genomic records and genetic risk factors with AI for predicting susceptibility to AD. We can additionally explore the challenges and future directions in utilizing this approach for early detection and customized treatment of AD.