4mCFSNet: A Feature Fusion-Based Multi-Scale Representations for Predicting DNA N4-Methylcytosine Sites.

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Abstract

N4-methylcytosine (4mC) is a very important epigenetic modification that regulates DNA expression, repair and replication. Traditional experimental methods for 4mC site detection are both time consuming and laborious. Therefore, the development of computational methods is necessary. But mining the internal information of DNA sequences remains a great challenge. In this paper, we propose a novel 4mC deep learning prediction method, named 4mCFSNet. Firstly, we encode the sequences using one-hot. Secondly, we construct multi-scale fusion modules to fully extract biological sequence information by overlapping multi-scale channel input features. Finally, we use fully connected layers and class weights for multi-species classification prediction. The average MCC of our proposed method on six species is about 2% higher than the optimal method, and the average ACC is about 1% higher.