Volume 13 , Issue 2 , PP: 182-190, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Vadali Pitchi Raju 1 , Tushar Kumar Pandey 2 , Rajeev Shrivastava 3 * , Rajesh Tiwari 4 , S. Anjali Devi 5 , Neerugatti Varipallay vishwanath 6
Doi: https://doi.org/10.54216/JCIM.130214
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.
Computational Genetic Epidemiology , Disease Risk Prediction , AI Models , Data Processing , Predictive Accuracy , Scalability , Computational Efficiency , Cyber Security.
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