Zhang Guishan, Dai Zhiming, Dai Xianhua
School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, China.
School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China.
Front Genet. 2020 Jan 8;10:1303. doi: 10.3389/fgene.2019.01303. eCollection 2019.
Accurate prediction of guide RNA (gRNA) on-target efficacy is critical for effective application of CRISPR/Cas9 system. Although some machine learning-based and convolutional neural network (CNN)-based methods have been proposed, prediction accuracy remains to be improved. Here, firstly we improved architectures of current CNNs for predicting gRNA on-target efficacy. Secondly, we proposed a novel hybrid system which combines our improved CNN with support vector regression (SVR). This CNN-SVR system is composed of two major components: a merged CNN as the front-end for extracting gRNA feature and an SVR as the back-end for regression and predicting gRNA cleavage efficiency. We demonstrate that CNN-SVR can effectively exploit features interactions from feed-forward directions to learn deeper features of gRNAs and their corresponding epigenetic features. Experiments on commonly used datasets show that our CNN-SVR system outperforms available state-of-the-art methods in terms of prediction accuracy, generalization, and robustness. Source codes are available at https://github.com/Peppags/CNN-SVR.
准确预测引导RNA（gRNA）的靶向效率对于CRISPR/Cas9系统的有效应用至关重要。尽管已经提出了一些基于机器学习和卷积神经网络（CNN）的方法，但预测准确性仍有待提高。在此，首先我们改进了当前用于预测gRNA靶向效率的CNN架构。其次，我们提出了一种新颖的混合系统，该系统将我们改进的CNN与支持向量回归（SVR）相结合。这个CNN-SVR系统由两个主要部分组成：一个合并的CNN作为提取gRNA特征的前端，以及一个SVR作为用于回归和预测gRNA切割效率的后端。我们证明，CNN-SVR可以有效地利用前馈方向的特征交互来学习gRNA及其相应表观遗传特征的更深层次特征。在常用数据集上的实验表明，我们的CNN-SVR系统在预测准确性、泛化能力和鲁棒性方面优于现有的最先进方法。源代码可在https://github.com/Peppags/CNN-SVR获取。
