N⁶-methyladenosine (m⁶A) is a well-studied and most common interior messenger RNA (mRNA) modification that plays an important function in cell development. N⁶A is found in all kingdoms​ of life and many other cellular processes such as RNA splicing, immune tolerance, regulatory functions, RNA processing, and cancer. Despite the crucial role of m⁶A in cells, it was targeted computationally, but unfortunately, the obtained results were unsatisfactory. It is imperative to develop an efficient computational model that can truly represent m⁶A sites. In this regard, an intelligent and highly discriminative computational model namely: m6A-word2vec is introduced for the discrimination of m⁶A sites. Here, a concept of natural language processing in the form of word2vec is used to represent the motif of the target class automatically. These motifs (numerical descriptors) are automatically targeted from the human genome without any clear definition. Further, the extracted feature space is then forwarded to the convolution neural network model as input for prediction. The developed computational model obtained 83.17%, 92.69%, and 90.50% accuracy for benchmark datasets $S_1$, $S_2$, and $S_3$, respectively, using a 10-fold cross-validation test. The predictive outcomes validate that the developed intelligent computational model showed better performance compared to existing computational models. It is thus greatly estimated that the introduced computational model “m6A-word2vec” may be a supportive and practical tool for elementary and pharmaceutical research such as in drug design along with academia.

N ⁶ -甲基腺苷 (m ⁶ A) 是一种经过充分研究且最常见的内部信使 RNA (mRNA) 修饰，在细胞发育中发挥重要作用。N ⁶ A 存在于所有生命王国和许多其他细胞过程中，例如 RNA 剪接、免疫耐受、调节功能、RNA 加工和癌症。尽管 m ⁶ A 在细胞中起着至关重要的作用，但它在计算上是有针对性的，但不幸的是，获得的结果并不令人满意。开发一种能够真正代表 m ⁶ A 位点的高效计算模型势在必行。对此，引入了一种智能且高判别性的计算模型，即：m6A-word2vec 用于对 m ⁶的判别一个站点。这里使用word2vec形式的自然语言处理概念来自动表示目标类的motif。这些基序（数字描述符）是从人类基因组中自动定位的，没有任何明确的定义。此外，提取的特征空间然后被转发到卷积神经网络模型作为预测的输入。开发的计算模型为基准数据集获得了 83.17%、92.69% 和 90.50% 的准确率${秒}_1$, ${秒}_2$， 和 ${秒}_3$，分别使用 10 折交叉验证测试。预测结果验证了开发的智能计算模型与现有计算模型相比表现出更好的性能。因此，极大地估计引入的计算模型“m6A-word2vec”可能是基础和药物研究的支持和实用工具，例如药物设计和学术界。