FSFT6mA: a feature-synthesis fine-tuning framework for DNA 6mA site prediction

DNA N6-methyladenine (6mA) is an important epigenetic modification that plays a critical role in gene expression regulation and has been associated with diverse biological processes and diseases. Accurate identification of 6mA sites is essential for understanding its functional significance. Although an increasing number of computational approaches have been proposed, they almost exclusively rely on sequence-derived features. The potential of novel feature representations to further enhance predictive performance remains an important research problem.

In this study, we propose FSFT6mA, a novel deep learning-based framework designed to improve 6mA site prediction through feature synthesis. The model is initially trained on the original datasets using a deep convolutional neural network. Subsequently, a Generative Adversarial Network (GAN) is employed to generate synthetic features from intermediate network layers, which are then used to fine-tune the well-trained model in the first stage.

Incorporating GAN-generated features leads to notable performance gains, improving MCC by 2.6% on A. thaliana and 1.9% on D. melanogaster compared with the base models without synthetic features. Independent validation experiments demonstrate that FSFT6mA achieves superior performance compared to existing state-of-the-art predictors, attaining AUC values of 0.969 and 0.968 on A. thaliana and D. melanogaster, respectively.

These results indicate that FSFT6mA is an accurate tool for DNA 6mA site prediction. The data and the codes used in this study are freely accessible on GitHub (https://github.com/YuHong-Jin/FSFT6mA).