A classification model for lncRNA and mRNA based on k-mers and a convolutional neural network
A classification model for lncRNA and mRNA based on k-mers and a convolutional neural network
复制标题
基于k-mers和卷积神经网络的lncRNA和mRNA分类模型
DOI：
10.1186/s12859-019-3039-3
复制
发表时间：
2019-09-13
影响因子：
3
通讯作者：
Xiao, Xinping
中科院分区：
文献类型：
--
作者：
Wen, Jianghui;Liu, Yeshu;Xiao, Xinping
研究背景长链非编码RNA（long-chain non-coding RNA，lncRNA）与许多生物活性密切相关。由于其序列结构与信使RNA（mRNA）相似，仅基于序列生物特征很难区分两者。因此，它是特别重要的，以构建一个模型，可以有效地识别lncRNA和mRNA.ResultsFirst，在lncRNA和mRNA序列之间的k-mer频率分布的差异被认为是在本文中，并将它们转化为k-mer频率矩阵。此外，具有更多物种的k-mer被相对熵筛选。然后通过输入k-mer频率矩阵和训练卷积神经网络，提出了lncRNA和mRNA序列的分类模型。最后，确定了分类模型的最佳k-mer组合，并与人类，小鼠和鸡的其他机器学习方法进行了比较。结果表明，该模型具有最高的分类精度。结论建立了基于k-mers和卷积神经网络的lncRNA和mRNA分类模型。1-mers、2-mers和3-mers模型的分类准确率最高，对人、小鼠和鸡的分类准确率分别为0.9872、0.8797和0.9963，优于随机森林、逻辑回归、决策树和支持向量机。
BackgroundLong-chain non-coding RNA (lncRNA) is closely related to many biological activities. Since its sequence structure is similar to that of messenger RNA (mRNA), it is difficult to distinguish between the two based only on sequence biometrics. Therefore, it is particularly important to construct a model that can effectively identify lncRNA and mRNA.ResultsFirst, the difference in the k-mer frequency distribution between lncRNA and mRNA sequences is considered in this paper, and they are transformed into the k-mer frequency matrix. Moreover, k-mers with more species are screened by relative entropy. The classification model of the lncRNA and mRNA sequences is then proposed by inputting the k-mer frequency matrix and training the convolutional neural network. Finally, the optimal k-mer combination of the classification model is determined and compared with other machine learning methods in humans, mice and chickens. The results indicate that the proposed model has the highest classification accuracy. Furthermore, the recognition ability of this model is verified to a single sequence.ConclusionWe established a classification model for lncRNA and mRNA based on k-mers and the convolutional neural network. The classification accuracy of the model with 1-mers, 2-mers and 3-mers was the highest, with an accuracy of 0.9872 in humans, 0.8797 in mice and 0.9963 in chickens, which is better than those of the random forest, logistic regression, decision tree and support vector machine.