miTAR: a hybrid deep learning-based approach for predicting miRNA targets.
miTAR: a hybrid deep learning-based approach for predicting miRNA targets.
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DOI：
10.1186/s12859-021-04026-6
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发表时间：
2021-02-27
影响因子：
3
通讯作者：
Lee JH
中科院分区：
文献类型：
--
作者：
Gu T;Zhao X;Barbazuk WB;Lee JH
microRNAs （miRNAs）已被证明在广泛的生物过程中发挥重要作用。已经开发了许多计算方法来识别mirna的靶标。然而，这些方法中的大多数依赖于预定义的特征，这些特征需要大量的努力和资源来计算，并且在预测miRNA靶点时往往被证明是次优的。我们开发了一种新的基于深度学习的混合方法，能够以更高的精度预测miRNA靶点。该方法集成了擅长学习空间特征的卷积神经网络（cnn）和识别序列特征的循环神经网络（rnn）。因此，我们的方法具有学习miRNA:target固有的空间和序列特征的优势。我们的方法的输入是可以毫不费力地获得的原始mirna和基因序列。我们将我们的方法应用于最近miRNA靶标预测研究的两个人类数据集，并训练了两个模型。我们证明，根据测试数据集上的评估指标，这两个模型始终优于以前的方法。将我们的方法与目前在独立数据集上可用的替代方法进行比较，表明我们的方法在性能上有了实质性的改进。我们还用多个证据表明，在小数据集上，我们的方法比其他方法更健壮。我们的研究是第一个对多种现有基于dl的miRNA靶标预测方法进行比较的研究。此外，我们检查了CNN和RNN之间的最大池化层的贡献，并证明它提高了我们所有模型的性能。最后，建立了一个统一的模型，该模型对拟合不同的输入数据集具有鲁棒性。我们提出了一种新的基于dl的方法来预测miRNA靶标，并证明我们的方法优于当前的替代方法。我们提供了一个易于使用的工具，miTAR，网址是https://github.com/tjgu/miTAR。此外，我们的分析结果支持最大池化通常有利于混合模型，并可能防止混合模型的过拟合。
microRNAs (miRNAs) have been shown to play essential roles in a wide range of biological processes. Many computational methods have been developed to identify targets of miRNAs. However, the majority of these methods depend on pre-defined features that require considerable efforts and resources to compute and often prove suboptimal at predicting miRNA targets. We developed a novel hybrid deep learning-based (DL-based) approach that is capable of predicting miRNA targets at a higher accuracy. This approach integrates convolutional neural networks (CNNs) that excel in learning spatial features and recurrent neural networks (RNNs) that discern sequential features. Therefore, our approach has the advantages of learning both the intrinsic spatial and sequential features of miRNA:target. The inputs for our approach are raw sequences of miRNAs and genes that can be obtained effortlessly. We applied our approach on two human datasets from recently miRNA target prediction studies and trained two models. We demonstrated that the two models consistently outperform the previous methods according to evaluation metrics on test datasets. Comparing our approach with currently available alternatives on independent datasets shows that our approach delivers substantial improvements in performance. We also show with multiple evidences that our approach is more robust than other methods on small datasets. Our study is the first study to perform comparisons across multiple existing DL-based approaches on miRNA target prediction. Furthermore, we examined the contribution of a Max pooling layer in between the CNN and RNN and demonstrated that it improves the performance of all our models. Finally, a unified model was developed that is robust on fitting different input datasets. We present a new DL-based approach for predicting miRNA targets and demonstrate that our approach outperforms the current alternatives. We supplied an easy-to-use tool, miTAR, at https://github.com/tjgu/miTAR. Furthermore, our analysis results support that Max Pooling generally benefits the hybrid models and potentially prevents overfitting for hybrid models.