# Recognition of mRNA N4 Acetylctidine (ac4C) by Using Non-Deep vs. Deep Learning
## 2 Background  
In the age of big data, it is more important than ever to convert large amounts of data into concrete knowledge [14], and bioinformatics is certainly not similar to that in these trends. Biomedical data of various types, such as omics, image, and signal data, have been accumulated in significant quantities, and industry and academia have been drawn to them because of their high bio- and health-related potential. For example, IBM has Watson for Oncology, the Patient Medical Information Review, and the Care Forum [15; 16], whereas Google recently released DeepMind Health, a massive AlphaGo success store.  
Deep learning algorithms were used in biotech to extract information from large data sets [17; 18; 19; 20; 21]. Hidden Markov models, Gaussian networks, Bayesian networks, and other common supporting vector algorithms have also been developed in proteomic, genomic, and biological systems [23]. Traditional machine learning algorithms, by manipulating raw data, prevent the development of raw materials with extensive field expertise [24] that are suitable for high abstraction. The researcher is working extremely hard; a new learning algorithm based on large amounts of data, computing power, and complex algorithms was recently developed. Deep learning algorithms have surpassed previous limitations and made significant advances in a variety of fields, including bioinformatics. This in-depth bioinformatics training is not an exception.  
Researchers in the Omics field use genetic information such as genomes, transcriptomes, and proteomes to address bio informing problems. The raw, relatively inexpensive and easy to achieve with the next generation of technology for sequencing, deoxyribonucleic acid (DNA), ribonucleic acid (RNA) and amino acid is one of the most popular intrinsically in omics. Deep research algorithms are also used to reduce problems with complex biological data and improve results such as derived sequential characteristics (PSSM) [25; 26], physical-chemical characteristics [27; 28], Atchley factors (FAC) [28] and structural features of one dimension [29; 30]. Furthermore, it employs data from micro-array genes based on problem characteristics. This protein touch map depicts distances between amino acid pairs in their three-dimensional structure.  
One of the most frequently studied issues is whether protein structure predicts secondary structure or contact map [31; 32; 33; 34; 35; 36; 37; 38; 39; 40]. Gene expression regulation [41; 42; 43; 44; 45; 46; 47; 48] can also be used in superfamily or subcellular studies involving splice junctions or RNA proteins, as well as protein classification [49; 50]. Furthermore, using anomaly classification, omics data have been used to detect cancer [51]  
These preliminary results confirmed that mRNA can express specific proteins in situ and induce antigen-specific cellular and humoral immunity. However, the field was neglected for nearly ten years until the potential of in vivo mRNA application, i.e., the induction of specific cytotoxic T lymphocytes and antibodies, was discovered. Due to the labile nature of mRNA, which makes experiments with unmodified mRNA extremely difficult unless strict mRNA handling precautions are strictly followed, progress in the mRNA field has been slow. Instead, due to the fact that DNA is more stable than RNA, the emphasis has shifted to DNA-based drugs. N4 acetylcytidine (ac4C) is a post-transcriptional modification in mRNA that plays an important role in mRNA stability control and translation. For conventional laboratory experiments, the ac4C method of mRNA changes is still not simple, time consuming, or cost effective. As a result, we created DL-ac4C, a deep learning model for ac4C recognition based on CNN. In the alternative scenario, model families are well-suited to working with large datasets with a large number of available samples, particularly in biological domains.