## 1 Introduction  
DNA methylation is the process of adding methyl groups to specific regions of DNA and leads to genetic changes in gene expression [1], and this process can regulate gene expression and shutdown without altering the nucleotide sequence. Therefore, the activity of genes can be regulated by controlling the process of DNA methylation, thus turning off certain gene activity or inducing the reactivation and expression of certain genes [2; 3; 4; 5]. Hence, it is closely relevant for the study of cancer, aging, or for the study of regulating virulence and antibiotic resistance in prokaryotes [6; 7; 8]. Each DNA sequence is composed of multiple nucleotide bases, and there are four types of bases: adenine (A), thymine (T), cytosine (C), and guanine (G). Depending on where the methylation group binds to a base in the DNA sequences, DNA base methylation can be divided into various types: N6-methyladenine (6 mA), which occurs at the N-6 position of adenine; N7-methylguanosine (7 mG), which occurs at the N-7 position of guanine; 5-methylcytosine (5 mC) and N4-methylcytosine (4 mC), which occur at the C-5 and C-4 position of cytosine, respectively; and so on. Among them, the DNA 5 mC sites, 6 mA sites, and 4 mC sites are the most common methylation processes, which are widely found in eukaryotes and prokaryotes [9; 10; 11; 12]. Compared with the studies for 5 mC and 6 mA, the detection of 4 mC is relatively difficult due to the lack of adequate assays. Additionally, as the 4 mC modification site is a member of the restriction endonuclease modification (RM) system, which not only protects host DNA from degradation by restriction enzymes [13], but also controls DNA replication and cell cycle and corrects DNA replication errors, among other functions [14], the study of accurate identification of the 4 mC site is crucial. Some wet experiment techniques have been proposed to accurately identify these 4 mC sites, for example, single-molecule real-time sequencing technology (SMRT) for identifying DNA 4 mC modification sites, and the 4 mC-TAB-seq method that can accurately identify 4 mC modification sites without interference from 5 mC modification [15; 16]. However, the identification of 4 mC sites through these wet experimental techniques is very time consuming and costly. Therefore, the development of bioinformatics tools for the accurate and efficient identification of 4 mC sites by computational methods is a useful complement to the experimental method. iDNA4 mC [17] is the first prediction tool proposed by [PERSON] and [PERSON] to predict 4 mC modification sites using machine learning methods. The positive samples containing 4 mC sites were obtained from the MethSMRT database [18], involving six species of _Escherichia coli_, _Geobacter pickeringii_, _Geoalkalibacter subterraneus_, _Caenorhabditis elegans_, _Arabidopsis thaliana_, and _Drosophila melanogaster_, and finally, a high-quality balanced dataset was constructed using the sliding window method (the optimal window length is 41 bp). Subsequently, based on the benchmark dataset of iDNA4 mC predictors, [PERSON]'s team used electron ion interaction potential (EIIP) and position-specific trinucleotide propensity (PSTNP) feature expression methods to propose the second predictor 4 mCPred [19] to identify 4 mC modification sites. The results showed that, compared to the iDNA4 mC predictor, the predictor's overall performance had improved. Based on the first two predictors, [PERSON] and his team proposed the 4 mcPred-SVM [20] predictor based on the feature representation method of sequence information, in which the feature selection optimization strategy was used to screen out the optimal model corresponding to the model. Feature subsets were used to obtain representative features, and then SVM and the obtained features were used for supervised training to obtain optimal models of different species, and the effect was improved. Later, [PERSON] et al. adopted a feature representation learning scheme.