UNet with Attention Networks: A Novel Deep Learning Approach for DNA Methylation Prediction in HeLa Cells

Background: The purpose of the proposed study is to investigate the efficacy of UNet in predicting Deoxyribonucleic Acid methylation patterns in a cervical cancer cell line. The application of deep learning to analyse the factors affecting methylation in the context of cervical cancer has not yet been fully explored. Methods: A comprehensive performance evaluation has been conducted based on multiple window sizes of DNA sequences. For this purpose, three different parameter-analysis techniques, namely, autoencoders, Generative Adversarial Networks, and Multi-Head Attention Networks, were used. This work presents a novel framework for methylation prediction in promoter regions of various genes. Results and Conclusions: Experimental results have proved that attention networks in association with UNet achieved a significant accuracy level of 91.01% along with a sensitivity of 89.65%, specificity of around 92.35%, and an area under curve of 0.910 on ENCODE database. The proposed model outperformed three state-of-the-art models: Convolutional Neural Network, Transfer Learning, and Feed Forward Neural Network with K-Nearest Neighbour. Moreover, validation of the model in five gene promoters achieved an accuracy of 81.60% with an area under curve score of 0.814, a p-value of 3.62x10-19, and Cohen's Kappa value of 0.631. This novel approach has led to a better understanding of epigenetic variables and their implications in cervical cancer, offering potential insights into therapeutic strategies.