DNA Methylation Recognition Using Hybrid Deep Learning with Dual Nucleotide Visualization Fusion Feature Encoding

While many machine and deep learning methods have been developed for predicting different types of DNA methylation, common feature encoding methods have not fully extracted the potential information in DNA sequences, influencing the prediction accuracy of the models. Furthermore, many methods focus solely on a single type of methylation, necessitating the development of robust universal predictors. Therefore, this study proposes a novel and efficient method for DNA methylation prediction, named DeepDNA-DNVFF. For sequence encoding, a new dual nucleotide visual fusion feature encoding (DNVFF) method is proposed by improving and integrating two-dimensional DNA visualization techniques. The hybrid deep learning model used in DeepDNA-DNVFF integrates CNN, BiLSTM, and an attention mechanism to enhance the model's ability to capture long-range dependencies. The results show that compared with traditional encoding methods, DNVFF can more effectively extract the latent feature information from DNA sequences. Compared to other existing advanced methods, DeepDNA-DNVFF excelled beyond the state-of-the-art method in 10 out of 17 species datasets, with the best Matthews correlation coefficient approximately 1.24% higher. DeepDNA-DNVFF effectively predicts DNA methylation sites, offering valuable insights for researchers to understand gene regulatory mechanisms and identify potential disease biomarkers.