Enhancers are noncoding fragments in DNA sequences, which play an important role in gene transcription and translation. However, due to their high free scattering and positional variability, the identification and classification of enhancers have a higher level of complexity than those of coding genes. In order to solve this problem, many computer studies have been carried out in this field, but there are still some deficiencies in these prediction models. In this paper, we use various feature extraction strategies, dimension reduction technology, and a comprehensive application of machine model and recurrent neural network model to achieve an accurate prediction of enhancer identification and classification with the accuracy of was 76.7% and 84.9%, respectively. The model proposed in this paper is superior to the previous methods in performance index or feature dimension, which provides inspiration for the prediction of enhancers by computer technology in the future.

中文翻译：

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使用降维技术和循环神经网络对增强子进行识别和分类

增强子是DNA序列中的非编码片段，在基因转录和翻译中发挥重要作用。然而，由于增强子的高自由散射和位置变异性，增强子的识别和分类比编码基因具有更高的复杂性。为了解决这个问题，该领域开展了许多计算机研究，但这些预测模型仍然存在一些不足。本文采用多种特征提取策略、降维技术，综合应用机器模型和循环神经网络模型，实现了增强子识别和分类的准确预测，准确率分别为76.7%和84.9%。本文提出的模型无论在性能指标还是特征维度上均优于以往的方法，为未来利用计算机技术预测增强子提供了启发。