Welcome to iDNA-ABT server

DNA methylation plays an important role in epigenetic modification, the occurrence, and the development of diseases. Therefore, the identification of DNA methylation sites is critical for better understanding and revealing their functional mechanisms. To date, several machine learning and deep learning methods have been developed for the prediction of different methylation types. However, they still highly rely on manual features, which can largely limit the high-latent information extraction. Moreover, most of them are designed for one specific methylation type, and therefore cannot predict multiple methylation sites in multiple species simultaneously.

In this study, we propose iDNA-ABT, an advanced deep learning model that utilizes adaptive embedding based on bidirectional transformers for language understanding (BERT) together with a novel transductive information maximization (TIM) loss. Benchmarking results show that our proposed iDNA-ABT performs significantly better than the state-of-the-art methods in the prediction of three different DNA methylations. Importantly, our model shows great generalization ability in different species, demonstrating that our model can adaptively capture the cross-species differences and improve the predictive performance.

If you think iDNA-ABT is useful, please kindly cite the following paper:

iDNA-ABT:advanced deep learning model for detecting DNA methylation with adaptive features and transductive information maximization

Webserver update:

May, 5th, 2021: the first version of iDNA-ABT server was established.

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