EMDL-ac4C: identifying N4-acetylcytidine based on ensemble two-branch residual connection DenseNet and attention - Jia, Jianhua; Wei, Zhangying; Cao, Xiaojing (Frontiers in Genetics, 2023)
N4-acetylcytidine (ac4C) is a critical acetylation modification that has an essential function in protein translation and is associated with a number of human diseases. The process of identifying ac4C sites by biological experiments is too cumbersome and costly. And the performance of several existing computational models needs to be improved. Therefore, we propose a new deep learning tool EMDL-ac4C to predict ac4C sites, which uses a simple one-hot encoding for a unbalanced dataset using a downsampled ensemble deep learning network to extract important features to identify ac4C sites. The base learner of this ensemble model consists of a modified DenseNet and Squeeze-and-Excitation Networks. In addition, we innovatively add a convolutional residual structure in parallel with the dense block to achieve the effect of two-layer feature extraction. The average accuracy (Acc), mathews correlation coefficient (MCC), and area under the curve (AUC) of EMDL-ac4C on ten independent testing sets are 80.84%, 61.77%, and 87.94%, respectively.Multiple experimental comparisons indicate that EMDL-ac4C outperforms existing predictors. It also offers some useful reference values for the subsequent related studies. One may view the code and the dataset at https://github.com/13133989982/EMDL-ac4C.