College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
Comput Math Methods Med. 2020 Oct 18;2020:8852258. doi: 10.1155/2020/8852258. eCollection 2020.
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.
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着重要作用。然而，由于其高度自由散射和位置可变性，增强子的识别和分类比编码基因更为复杂。为了解决这个问题，该领域已经进行了许多计算机研究，但这些预测模型仍然存在一些缺陷。在本文中，我们使用了各种特征提取策略、降维技术以及综合应用机器模型和递归神经网络模型，分别实现了 76.7%和 84.9%的增强子识别和分类的准确预测。与之前的方法相比，本文提出的模型在性能指标或特征维度上都具有优势，为未来计算机技术对增强子的预测提供了启示。
