AI-Driven Prediction of Hereditary Diseases from Genetic Sequences
DOI:
https://doi.org/10.48001/978-81-966500-2-5-3Keywords:
Hereditary Diseases, Genetic Sequences, ResNet-50, Convolutional neural networks(CNN), Recurrent Neural NetworkAbstract
This study explores AI-driven algorithms for predicting hereditary diseases from genetic sequences. Using machine learning, we analyze genetic data to identify patterns and mutations linked to specific conditions. The ResNet-50 convolutional neural network (CNN) model is employed to capture spatial relationships, while recurrent neural networks (RNNs) address sequential data. Preliminary results show an accuracy of 92%, significantly improving predictive accuracy over traditional methods, with high sensitivity and specificity. This advancement enhances genetic screening and personalized medicine, promising better patient outcomes and reduced healthcare costs.
Downloads
References
Abdallah, S., Sharifa, M., I.KH. ALMADHOUN, M. K., Khawar, M. M., Shaikh, U., Balabel, K. M., Saleh, I., Manzoor, A., Mandal, A. K., Ekomwereren, O., Khine, W. M., & Oyelaja, O. T. (2023). The Impact of Artificial Intelligence on Optimizing Diagnosis and Treatment Plans for Rare Genetic Disorders. Cureus. https://doi.org/10.7759/cureus.46860
Choon, Y. W., Choon, Y. F., Nasarudin, N. A., Al Jasmi, F., Remli, M. A., Alkayali, M. H., & Mohamad, M. S. (2023). Artificial intelligence and database for NGS based diagnosis in rare disease. Frontiers in Genetics, 14. https://doi.org/10.3389/fgene.2023.1258083
De Paoli, F., Nicora, G., Berardelli, S., Gazzo, A., Bellazzi, R., Magni, P., Rizzo, E., Limongelli, I., & Zucca, S. (2023). Digenic variant interpretation with hypothesis driven explainable AI. bioRxiv, 2023.10.02.560464. http://biorxiv.org/content/early/2023/10/03/2023.10.02.560464.abstract
Devaki, A., & Rao, C. V. (2022). An Ensemble Framework for Improving Brain Stroke Prediction Performance. 2022 1st International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2022. https://doi.org/10.1109/ICEEICT53079.2022.9768579
H Patel, U., & Mathur, R. (2024). AI-Driven Bioinformatics for Genomic Sequencing: Explore how AI and Machine Learning Techniques are Revolutionizing the Analysis 36 of Genomic Data, Leading to Breakthroughs in Personalized Medicine and Genetic Engineering. International Journal of Innovative Science and Research Technology(IJISRT), 2685–2689. https://doi.org/10.38124/ijisrt/ijisrt24may2112
Mohammed, R. K., Alrawi, A. T. H., & Dawood, A. J. (2023). Optimizing genetic prediction: Define-by-run DL approach in DNA sequencing. Journal of Intelligent Systems, 32(1). https://doi.org/10.1515/jisys-2023-0130
Raza, A., Rustam, F., Siddiqui, H. U. R., Diez, I. d. l. T., Garcia-Zapirain, B., Lee, E., & Ashraf, I. (2023). Predicting Genetic Disorder and Types of Disorder Using Chain Classifier Approach. Genes, 14(1). https://doi.org/10.3390/genes14010071
Sadichchha Naik, Disha Nevare, Amisha Panchal, & Dr. Chhaya Pawar. (2022). Prediction of Genetic Disorders using Machine Learning. International Journal of Scientific Research in Science and Technology, 01–09. https://doi.org/10.32628/ijsrst229273
Tran, T. N., Fong, C., Pichotta, K., Luthra, A., Shen, R., Chen, Y., Waters, M., Kim, S., Riely, G., Chakravarty, D., Schultz, N., & Jee, J. (2024). AI-derived predictions improve identification of real-world cancer driver mutations. Cancer Research, 84(6Supplement), 1252–1252. https://doi.org/10.1158/1538-7445.am2024-1252
Yadav, S., Mp, S., & Yadav, D. K. (2023). Predictive Analytics and AI for Personalized Treatment Plans in Genetic Heart Diseases. 3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023. https://doi.org/10.1109/ICTBIG59752.2023.10456227
