RNA 5-methylcytosine (m⁵C) sites perform a major role in numerous biological processes and commonly reported in both DNA and RNA cellular. The enzymatic mechanism and biological functions of m⁵C sites in DNA remain the focusing area of researchers for last few decades. Likewise, the investigators also targeted m⁵C sites in RNA due to its cellular functions, positioning and formation mechanism. Currently, several rudimentary roles of the m⁵C in RNA have been explored, but a lot of improvements are still under consideration. Initially, the identification of RNA methylcytosine sites was carried out via experimental methods, which were very hard, erroneous and time consuming owing to partial availability of recognized structures. Looking at the significance of m⁵C role in RNA, scientists have diverted their attention from structure to sequence-based prediction. In this regards, an intelligent computational model is proposed in order to identify m⁵C sites in RNA with high precision. Three RNA sequences formulation methods namely: pseudo dinucleotide composition,pseudo trinucleotide composition and pseudo tetra nucleotide composition are applied to extract variant and high profound numerical features. In a sequel, the vector spaces are fused to build a hybrid space in order to compensate the weakness of each other. Various learning hypotheses are examined to select the best operational engine, which can truly identify the pattern of the target class. The strength and generalization of the proposed model are measured using two different cross validation tests. The reported outcomes reveal that the proposed model achieved 3% better accuracy than that of the highest present approach in the literature so far.

中文翻译：

---

iRNA-PseTNC：使用伪核苷酸组成的杂交载体空间鉴定RNA 5-甲基胞嘧啶位点

RNA 5-甲基胞嘧啶（m ⁵ C）位点在许多生物学过程中起着重要作用，并且在DNA和RNA细胞中都普遍报道。在过去的几十年中，DNA中m ⁵ C位点的酶促机制和生物学功能仍然是研究人员关注的重点。同样，由于其细胞功能，定位和形成机制，研究人员还针对RNA中的m ⁵ C位点。目前，m ^5的几个基本角色已经研究了RNA中的C，但仍在考虑许多改进。最初，RNA甲基胞嘧啶位点的鉴定是通过实验方法进行的，由于部分获得了公认的结构，因此非常困难，错误且耗时。鉴于m ⁵ C在RNA中的作用的重要性，科学家将注意力从结构转移到了基于序列的预测。在这方面，提出了一种智能计算模型以识别m ⁵RNA中的C位点具有很高的精确度。采用三种RNA序列的配制方法：假二核苷酸组成，假三核苷酸组成和假四核苷酸组成，提取具有较高数值特征的变异体。在续集中，矢量空间被融合以构建混合空间，以弥补彼此的弱点。检查各种学习假设以选择最佳的操作引擎，该引擎可以真正识别目标类别的模式。使用两个不同的交叉验证测试来衡量所提出模型的强度和一般性。报告的结果表明，与迄今为止文献中最高的方法相比，该模型的准确性提高了3％。