Abstract Meiotic recombination is a biological process which plays a crucial role in genetic evolution. Therefore, the ability of machine learning models in extracting desire information embedded in DNA sequences has drawn a great deal of attention among biologists. Recently, several attempts have been made to address this problem, however, the performance results still need to be improved. The current study aims to investigate the relationship between natural language processing model and supervised learning in classifying DNA sequences. The idea is to treat DNA sequences by FastText model, including sub-word information and then use them as features in a suitable supervised learning algorithm. To the end, this hybrid approach helps us classify DNA recombination spots with achieved sensitivity of 90%, specificity of 94.76%, accuracy of 92.6%, and MCC of 0.851. These results have suggested that our newly proposed method is superior to other methods on the same benchmark dataset. This study, therefore, could shed the light on developing the prediction models for recombination spots in particular, and DNA sequences in general.

中文翻译：

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一种基于序列的方法，通过使用 FastText 中的超参数优化和支持向量机来识别酿酒酵母中的重组点

摘要 减数分裂重组是一个在遗传进化中起重要作用的生物学过程。因此，机器学习模型提取嵌入在 DNA 序列中的欲望信息的能力引起了生物学家的极大关注。最近，已经进行了多次尝试来解决这个问题，但是，性能结果仍然需要改进。目前的研究旨在研究自然语言处理模型与监督学习在 DNA 序列分类中的关系。这个想法是通过 FastText 模型处理 DNA 序列，包括子词信息，然后将它们用作合适的监督学习算法中的特征。最后，这种混合方法帮助我们对 DNA 重组点进行分类，实现了 90% 的灵敏度、94.76% 的特异性、92.6% 的准确度、和 MCC 为 0.851。这些结果表明，我们新提出的方法优于相同基准数据集上的其他方法。因此，这项研究可以为开发重组点的预测模型以及一般的 DNA 序列提供启示。