N⁶-methyladenosine (m⁶A) is closely related to various life processes and diseases. The detection of genomic-level m⁶A sites plays an important role in explaining its biological mechanism. However, the current mainstream m⁶A sites detection method has limited precision. In this paper, a novel m⁶A sites detection method called “Dm6A-TSVM” is proposed. The feature vectors are firstly extracted from the mRNA sequences according to their nucleotide chemical property and position statistical distribution characteristics. Then the two kinds of features are combined, and the m⁶A sites detection model is constructed by the twin support vector machines method. Finally, based on the standard yeast dataset, the cross-validation experimental method is used to verify Dm6A-TSVM. The results demonstrate that the Dm6A-TSVM method is significantly better than the current mainstream m⁶A sites detection method, and its accuracy (ACC) value reaches 82.81%.

N ^6-甲基腺苷（m ⁶ A）与各种生命过程和疾病密切相关。基因组水平的m ⁶ A位点的检测在解释其生物学机制中起着重要作用。然而，当前主流的m ⁶ A位点检测方法精度有限。本文提出了一种新颖的m ⁶ A站点检测方法“ Dm6A-TSVM”。首先根据其核苷酸的化学性质和位置统计分布特征从mRNA序列中提取特征载体。然后这两种功能组合，并且米⁶通过双支持向量机方法构建站点检测模型。最后，基于标准酵母数据集，使用交叉验证实验方法验证Dm6A-TSVM。结果表明，Dm6A-TSVM方法明显优于目前主流的m ⁶ A位点检测方法，其准确度（ACC）值达到82.81％。