Enhancing the Efficiency of Computational Genetic Epidemiology using HPC-Driven AI Models

Genetic epidemiology significantly enhances our understanding of complex illnesses; it investigates the interaction between genetic variables and environmental factors in determining the prevalence of diseases in communities with the merging of Artificial Intelligence (AI) and High-Performance Computing (HPC) models, the industry has seen a transformational change in recent years. This work presents a new method called “High-Performance AI-GenEpi,” which is intended to improve the effectiveness of computational genetic epidemiology. Genomic Data Preprocessing (GDP), Deep Learning for Genetic Risk Prediction (DL-GRP), and Parallelized Genome-Wide Association Analysis (P-GWAA) are the three main techniques that are combined in the suggested approach. P-GWAA uses HPC to speed up genome-wide association research, DL-GRP uses deep learning to accurately predict disease risk, and GDP maximizes data quality and compatibility. It performs very well computationally, providing faster processing times for risk assessment, association analysis, and data preparation. In addition, the approach takes use of HPC's parallel processing power and produces very accurate genetic risk predictions, which makes it perfect for evaluating large genomic datasets. The efficacy of the technology is shown across a wide range of applications, such as medication repurposing, biomarker discovery, and illness risk prediction. High-Performance AI-GenEpi is leading the way in the ongoing evolution of genetic epidemiology, providing a fresh approach to effective and perceptive research in this vital area.