RNA-binding protein (RBP) is a class of proteins that bind to and accompany RNAs in regulating biological processes. An RBP may have multiple target RNAs, and its aberrant expression can cause multiple diseases. Methods have been designed to predict whether a specific RBP can bind to an RNA and the position of the binding site using binary classification model. However, most of the existing methods do not take into account the binding similarity and correlation between different RBPs. While methods employing multiple labels and Long Short Term Memory Network (LSTM) are proposed to consider binding similarity between different RBPs, the accuracy remains low due to insufficient feature learning and multi-label learning on RNA sequences. In response to this challenge, the concept of RNA-RBP Binding Network (RRBN) is proposed in this paper to provide theoretical support for multi-label learning to identify RBPs that can bind to RNAs. It is experimentally shown that the RRBN information can significantly improve the prediction of unknown RNA−RBP interactions. To further improve the prediction accuracy, we present the novel computational method iDeepMV which integrates multi-view deep learning technology under the multi-label learning framework. iDeepMV first extracts data from the views of amino acid sequence and dipeptide component based on the RNA sequences as the original view. Deep neural network models are then designed for the respective views to perform deep feature learning. The extracted deep features are fed into multi-label classifiers which are trained with the RNA−RBP interaction information for the three views. Finally, a voting mechanism is designed to make comprehensive decision on the results of the multi-label classifiers. Our experimental results show that the prediction performance of iDeepMV, which combines multi-view deep feature learning models with RNA−RBP interaction information, is significantly better than that of the state-of-the-art methods. iDeepMV is freely available at http://www.csbio.sjtu.edu.cn/bioinf/iDeepMV for academic use. The code is freely available at http://github.com/uchihayht/iDeepMV.

中文翻译：

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基于多视角深度特征和多标签学习的RNA结合蛋白识别

RNA 结合蛋白 (RBP) 是一类在调节生物过程中结合并伴随 RNA 的蛋白质。一个 RBP 可能有多个目标 RNA，其异常表达会导致多种疾病。已经设计了使用二元分类模型来预测特定 RBP 是否可以与 RNA 结合以及结合位点的位置的方法。然而，现有的大多数方法都没有考虑到不同 RBP 之间的绑定相似性和相关性。虽然提出了采用多标签和长短期记忆网络 (LSTM) 的方法来考虑不同 RBP 之间的结合相似性，但由于特征学习和 RNA 序列的多标签学习不足，准确性仍然很低。为了应对这一挑战，本文提出了RNA-RBP结合网络（RRBN）的概念，为多标签学习识别可与RNA结合的RBP提供理论支持。实验表明，RRBN 信息可以显着提高对未知 RNA-RBP 相互作用的预测。为了进一步提高预测精度，我们提出了一种新的计算方法 iDeepMV，它在多标签学习框架下集成了多视图深度学习技术。iDeepMV首先以RNA序列为原始视图，从氨基酸序列和二肽组分的视图中提取数据。然后针对各个视图设计深度神经网络模型以执行深度特征学习。提取的深度特征被输入到多标签分类器中，这些分类器使用三个视图的 RNA-RBP 交互信息进行训练。最后，设计了一种投票机制来对多标签分类器的结果进行综合决策。我们的实验结果表明，将多视图深度特征学习模型与 RNA-RBP 交互信息相结合的 iDeepMV 的预测性能明显优于最先进的方法。iDeepMV 可在 http://www.csbio.sjtu.edu.cn/bioinf/iDeepMV 免费获取，用于学术用途。该代码可在 http://github.com/uchihayht/iDeepMV 上免费获得。我们的实验结果表明，将多视图深度特征学习模型与 RNA-RBP 交互信息相结合的 iDeepMV 的预测性能明显优于最先进的方法。iDeepMV 可在 http://www.csbio.sjtu.edu.cn/bioinf/iDeepMV 免费获取，用于学术用途。该代码可在 http://github.com/uchihayht/iDeepMV 上免费获得。我们的实验结果表明，将多视图深度特征学习模型与 RNA-RBP 交互信息相结合的 iDeepMV 的预测性能明显优于最先进的方法。iDeepMV 可在 http://www.csbio.sjtu.edu.cn/bioinf/iDeepMV 免费获取，用于学术用途。该代码可在 http://github.com/uchihayht/iDeepMV 上免费获得。