N6-methyladenosine (m6A) modifications are one the most frequently occurring RNA post transcriptional modifications. These modifications perform vital roles in different biological processes, including, localization and translation of proteins, X chromosome inactivation, cell stability, microRNA regulation, and reprogramming etc. Any abnormal change in m6A sites may lead to several abnormalities, including, cancer, brain-related disorders and many other life threatening diseases. Precise detection of m6A modifications is crucial for the diagnosis and treatment of these diseases. Existing methods suffer from the problem of inefficient detection of m6A sites, especially in yeast transcriptomes (due to varied structure) and inability of the computational techniques to capture the encoded information surrounding the m6A sites. In this work, we propose a novel method (called m6A-pred predictor) that utilizes a fusion of characteristics including, statistical, and chemical properties of the nucleotides, to precisely predict the presence of m6A sites in RNA sequences. The fusion of multiple types of features results in a high dimensional vector which is further optimized using an evolutionary algorithm. Finally, the random forest classifier is used to detect m6A sites by using the most discriminative features. The results, benchmarked on yeast transcriptomes, indicate that m6A-pred predictor outperforms all the previously reported predictors, notably, with an accuracy value of 78.58%, specificity value of 79.65% and Matthews correlation coefficient of 0.5717.

N6-甲基腺苷（m6A）修饰是转录后修饰中最常见的RNA之一。这些修饰在不同的生物学过程中起着至关重要的作用，包括蛋白质的定位和翻译，X染色体失活，细胞稳定性，microRNA调节和重编程等。m6A位点的任何异常变化都可能导致几种异常，包括癌症，脑癌，相关疾病和许多其他威胁生命的疾病。准确检测m6A修饰对于这些疾病的诊断和治疗至关重要。现有方法存在m6A位点检测效率低下的问题，特别是在酵母转录组中（由于结构变化）以及计算技术无法捕获m6A位点周围的编码信息的问题。在这项工作中m6A预测因子）利用特征的融合，包括核苷酸的统计和化学特性，以精确预测RNA序列中m6A位点的存在。多种类型的特征的融合产生了高维向量，可以使用进化算法对其进行进一步优化。最后，随机森林分类器通过使用最具区分性的特征来检测m6A站点。以酵母转录组为基准的结果表明，m6A预测的预测因子优于所有先前报道的预测因子，其准确度值为78.58％，特异性值为79.65％，马修斯相关系数为0.5717。