# Recognition of mRNA N4 Acetylctidine (ac4C) by Using Non-Deep vs. Deep Learning
## 1 Introduction  
Ac4C (N4-acetylctidine) is commonly thought to be a conservative, chemically modified nucleoside found on tRNA and rRNA. Recent research has revealed extensive ac4C modifications in human and yeast mRNA. Ac4C aids in the correct reading of codons during translation, improving translation efficiency and mRNA stability. At the moment, ac4C research employs a variety of detection methods. Ac4C synthesis is linked to N-acetyltransferase 10 (NAT10) and its helpers, such as putative tRNA acetyltransferase (TAN1) for tRNA ac4C and small nucleolar RNA (snoRNA) for rRNA-ac4C. Ac4C has also been linked to the onset, progression, and prognosis of a number of human diseases.  
The RNA also regulates N4-acetylctidine (ac4C), which has been improved in over 160 different ways [1]. This is the only instance of increased cytidine acetylation in eukaryotic mRNA [2]. [PERSON] et al. discovered the role of ac4C in mRNA regulation and translation efficiency promotion [3]. A study of mRNA half-lives revealed a link between acetylation and stability and in addition, N4-acetylctidine increases the number of translations in cytidine wobble sites [3]. In addition, ac4C has been connected to several human disease progression, prediction, and development [4]. [PERSON] et al. recently demonstrated that N4-acetylctidine catalyzing (ac4C), as a modification to the mRNA, has engaged NAT10 acetyltransferasease [3]. The entire mapping of ac4C's transcriptome displays several acetylates in a coding sequence. The NAT10, which is related to the mRNA down control, decreases ac4C detection at the mapped mRNA location. To establish the role of ac4C in the mRNA translation law, acetylated residues were thus added to the repertory of mRNA, and PACES data were amended [5; 6; 7; 8; 9; 10; 11; 12]. Although mRNA vaccines are a promising solution to humanity's most recent grand challenge, the technology is still in its early stages. If these nucleotide-based drugs are to continue to be successful, manufacturers must find ways to reduce the headaches associated with their production.  
This article describes an ac4C site change theoretical model based on deep learning-ac4C. Nuclear chemicals (NCP), nuclear density (DN), \(k\)-mer, single hot coding, and pseudo-potential electron-ion interaction are among the data sets included in the benchmarking data set. DL-ac4C was calculated using a variety of parameters, including precision, sensitivity, and specificity, all of which are commonly used in the field of bioinformatics [8; 9; 10; 11; 12; 13]. Cross-validation with measurement parameters was used for DL-ac4C controls. Furthermore, there are uneven data sets [10; 13] for the receiver operating curve (ROC) and the recall curve (PRC). As a result, the ROC and PRC are used to select the best vector representation and deep learning classification. In this paper, we used sequence one hot coding data and applied deep and none deep learning models to check the genes predict ac4C sites in human mRNA and predict the accuracy, and we proposed a PACES predictor for the classification of ac4C sites in human mRNA. The effects of PACES are still evolving; in this study, we propose a computer model that focuses on a deep learning approach to assessing mRNA DL-ac4C sites; we compare our model to none deep learning models, regression, SVM, and BMAML, and find that our model predicts ac4C sites in human mRNA better.