Pan Zhengsen, Zhou Shusen, Liu Tong, Liu Chanjuan, Zang Mujun, Wang Qingjun
IEEE/ACM Trans Comput Biol Bioinform. 2023 Sep-Oct;20(5):3322-3328. doi: 10.1109/TCBB.2023.3252276. Epub 2023 Oct 9.
RNA-binding proteins are important for the process of cell life activities. High-throughput technique experimental method to discover RNA-protein binding sites is time-consuming and expensive. Deep learning is an effective theory for predicting RNA-protein binding sites. Using weighted voting method to integrate multiple basic classifier models can improve model performance. Thus, in our study, we propose a weighted voting deep learning model (WVDL), which uses weighted voting method to combine convolutional neural network (CNN), long short term memory network (LSTM) and residual network (ResNet). First, the final forecast result of WVDL outperforms the basic classifier models and other ensemble strategies. Second, WVDL can extract more effective features by using weighted voting to find the best weighted combination. And, the CNN model also can draw the predicted motif pictures. Third, WVDL gets a competitive experiment result on public RBP-24 datasets comparing with other state-of-the-art methods. The source code of our proposed WVDL can be found in https://github.com/biomg/WVDL.
RNA结合蛋白对细胞生命活动过程至关重要。通过高通量技术实验方法来发现RNA-蛋白质结合位点既耗时又昂贵。深度学习是预测RNA-蛋白质结合位点的一种有效理论。使用加权投票方法整合多个基本分类器模型可以提高模型性能。因此，在我们的研究中，我们提出了一种加权投票深度学习模型（WVDL），它使用加权投票方法来结合卷积神经网络（CNN）、长短期记忆网络（LSTM）和残差网络（ResNet）。首先，WVDL的最终预测结果优于基本分类器模型和其他集成策略。其次，WVDL可以通过加权投票找到最佳加权组合来提取更有效的特征。并且，CNN模型还可以绘制预测的基序图片。第三，与其他最先进的方法相比，WVDL在公共RBP-24数据集上获得了具有竞争力的实验结果。我们提出的WVDL的源代码可以在https://github.com/biomg/WVDL中找到。
