iDNA-OpenPrompt: OpenPrompt learning model for identifying DNA methylation - Yu, Xia; Ren, Jia; Long, Haixia; Zeng, Rao; Zhang, Guoqiang; Bilal, Anas; Cui, Yani (Frontiers in Genetics, 2024)
This study introduces the iDNA-OpenPrompt model, leveraging the novel OpenPrompt learning framework. The model combines a prompt template, prompt verbalizer, and Pre-trained Language Model (PLM) to construct the prompt-learning framework for DNA methylation sequences. Moreover, a DNA vocabulary library, BERT tokenizer, and specific label words are also introduced into the model to enable accurate identification of DNA methylation sites. An extensive analysis is conducted to evaluate the predictive, reliability, and consistency capabilities of the iDNA-OpenPrompt model. The experimental outcomes, covering 17 benchmark datasets that include various species and three DNA methylation modifications (4mC, 5hmC, 6mA) , consistently indicate that our model surpasses outstanding performance and robustness approaches.