IEEE/ACM Trans Comput Biol Bioinform. 2020 Sep-Oct;17(5):1741-1750. doi: 10.1109/TCBB.2019.2910513. Epub 2019 Apr 11.
RNA-Protein binding plays important roles in the field of gene expression. With the development of high throughput sequencing, several conventional methods and deep learning-based methods have been proposed to predict the binding preference of RNA-protein binding. These methods can hardly meet the need of consideration of the dependencies between subsequence and the various motif lengths of different translation factors (TFs). To overcome such limitations, we propose a predictive model that utilizes a combination of multi-scale convolutional layers and bidirectional gated recurrent unit (GRU) layer. Multi-scale convolution layer has the ability to capture the motif features of different lengths, and bidirectional GRU layer is able to capture the dependencies among subsequence. Experimental results show that the proposed method performs better than four state-of-the-art methods in this field. In addition, we investigate the effect of model structure on model performance by performing our proposed method with a different convolution layer and a different number of kernel size. We also demonstrate the effectiveness of bidirectional GRU in improving model performance through comparative experiments.
RNA-蛋白质结合在基因表达领域中起着重要作用。随着高通量测序技术的发展，已经提出了几种传统方法和基于深度学习的方法来预测 RNA-蛋白质结合的结合偏好。这些方法很难满足考虑子序列之间的依赖关系和不同翻译因子 (TF) 的各种基序长度的需求。为了克服这些限制，我们提出了一种利用多尺度卷积层和双向门控循环单元 (GRU) 层相结合的预测模型。多尺度卷积层具有捕获不同长度基序特征的能力，而双向 GRU 层能够捕获子序列之间的依赖关系。实验结果表明，该方法在该领域的四个最先进的方法中的表现更好。此外，我们通过使用不同的卷积层和不同的核大小来执行我们的方法，研究了模型结构对模型性能的影响。我们还通过对比实验证明了双向 GRU 在提高模型性能方面的有效性。
