With all the fast pace where artificial intelligence (AI) and bioinformatics are growing quickly to make real-time genomic data interpretation and disease prediction more and more realistic, they present so many important challenges to the core areas of precision medicine and healthcare, and this is why. Given the need and a lack of such framework, this paper proposes an AI optimized bioinformatics framework by inserting quantum enhanced deep learning, federated learning, edge computing, AI augmented knowledge graphs and blockchain technology together to support efficient, scalable, and private genomic analysis. Based on quantum neural networks, the framework accelerates processing of genetic sequences, while taking federated learning into use to secure the patient data and at the same time enable decentralized model training. At the edge of the Internet, care is taken to have lightweight deep learning model deployment on healthcare devices for reduced latency and computational overhead in real-time disease prediction. Furthermore, an AI-based genomic knowledge graph is used to further improve disease association studies by mapping gene mutations to possible health risks using graph neural network, and view of genetic disorders as a whole. A blockchain-based genomic data sharing mechanism is used for ensuring integrity and security of data with the help of tamper-proof and transparent transactions, in compliance with the regulatory standards. Aided by the proposed approach, the disease prediction accuracy is improved significantly, the data privacy and computational efficiency are improved greatly, and the conventional bioinformatic methods are greatly weakened. This is an innovative framework anticipated to change genomic research, lead to personal medical, rare diseases diagnostic, and epidemiological studies to revolutionize how we use genetic data in predictive health care solutions.
Volume 12 | Issue 3
Pages: 746-752