Jiangnan University.
School of Artificial Intelligence and Computer Science of Jiangnan University, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (LCNBI) and ZJLab.
Brief Bioinform. 2021 May 20;22(3). doi: 10.1093/bib/bbaa174.
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.
RNA 结合蛋白（RBP）是一类能与 RNA 结合并伴随其调控生物过程的蛋白质。一个 RBP 可能有多个靶 RNA，其异常表达可导致多种疾病。目前已经设计了一些方法，使用二分类模型来预测特定的 RBP 是否能与 RNA 结合以及结合位点的位置。然而，大多数现有的方法没有考虑不同 RBP 之间的结合相似性和相关性。虽然已经提出了使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，但由于对 RNA 序列的特征学习和多标签学习不足，准确性仍然较低。针对这一挑战，本文提出了 RNA-RBP 结合网络（RRBN）的概念，为多标签学习识别能与 RNA 结合的 RBP 提供了理论支持。实验表明，RRBN 信息可以显著提高未知 RNA-RBP 相互作用的预测能力。为了进一步提高预测准确性，我们提出了新的计算方法 iDeepMV，它在多标签学习框架下集成了多视图深度学习技术。iDeepMV 首先从原始视图（基于 RNA 序列的氨基酸序列和二肽成分视图）中提取数据，然后为各自的视图设计深度神经网络模型进行深度特征学习。提取的深度特征被输入到多标签分类器中，这些分类器是使用 RNA-RBP 相互作用信息进行训练的。最后，设计了一种投票机制，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，iDeepMV 结合了多视图深度特征学习模型和 RNA-RBP 相互作用信息，其预测性能明显优于最先进的方法。iDeepMV 可在学术上免费使用：http://www.csbio.sjtu.edu.cn/bioinf/iDeepMV。代码可在：http://github.com/uchihayht/iDeepMV 上免费获取。
