Computational genetic epidemiology: Leveraging HPC for large-scale AI models based on Cyber Security
Rajeev Rajeev, Tushar Pandey, Rajeev Shrivastava, Rajesh Tiwari, S Anjali Devi, Neerugatti Varipallay vishwanath
Abstract
To better understand disease susceptibility and prevention, computational genetic epidemiology is leading research. This paper introduces GenomeMinds, a breakthrough method for scaling large-scale AI models for disease risk prediction. HPC was used to develop the method. GenomeMinds is compared to six standard methods to demonstrate its benefits. GenomeMinds' incredible potential is shown by real-world performance assessments. These measures evaluate data processing speed, forecast accuracy, scalability, computer efficiency, privacy, and ethics. GenomeMinds benefits are shown via scatter plots, which visually compare data. According to the data, GenomeMinds may revolutionize computational genetic epidemiology by doing well across all criteria. GenomeMinds has faster data processing, better prediction accuracy, stronger scalability, higher computational efficiency, enhanced privacy and security, and a comprehensive ethical awareness.