IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction.
IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction.
复制标题
IPMiner：使用堆叠自动编码器进行隐藏的 ncRNA-蛋白质相互作用序列模式挖掘，以实现准确的计算预测
DOI：
10.1186/s12864-016-2931-8
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发表时间：
2016-08-09
期刊：
影响因子：
4.4
通讯作者：
Shen HB
中科院分区：
文献类型：
--
作者：
Pan X;Fan YX;Yan J;Shen HB
背景非编码 RNA (ncRNA) 在许多生物过程中发挥着至关重要的作用，例如基因转录后调控。 ncRNA 主要通过与 RNA 结合蛋白 (RBP) 相互作用发挥作用。要了解 ncRNA 的功能，一个基本步骤是确定哪种蛋白质参与其相互作用。因此，通过计算预测 RBP 是有前途的，其中主要的挑战是很难找到相互作用模式或基序。 结果在这项研究中，我们提出了一种计算方法 IPMiner (Interaction Pattern Miner) 来预测序列中的 ncRNA-蛋白质相互作用，该方法利用深度学习并使用堆叠集成进一步提高其性能。 IPMiner 的典型优点之一是它能够使用堆叠自动编码器从蛋白质和 RNA 序列的序列组成特征中挖掘隐藏的顺序交互模式，然后将学习到的隐藏特征输入到随机森林模型中。最后，使用堆叠集成来集成不同的预测器，以进一步提高预测性能。实验结果表明，IPMiner 在测试的 lncRNA-蛋白质相互作用数据集上取得了优异的性能，准确度为 0.891，灵敏度为 0.939，特异性为 0.831，精度为 0.945，Matthews 相关系数为 0.784。我们进一步在其他 RNA-蛋白质相互作用数据集上全面研究了 IPMiner，它比最先进的方法产生了更好的性能，并且在一些测试的基准数据集上性能提高了 20% 以上。此外，我们进一步将IPMiner应用于ncRNA-蛋白质网络的大规模预测，取得了良好的预测性能。结论通过集成深度神经网络和堆叠集成，IPMiner可以从简单的序列组成特征中自动学习高级抽象特征，对RNA-蛋白质检测具有很强的判别能力。 IPMiner 在我们构建的 lncRNA-蛋白质基准数据集和其他 RNA-蛋白质数据集上取得了高性能。 IPMiner 工具可在 http://www.csbio.sjtu.edu.cn/bioinf/IPMiner 获取。
BackgroundNon-coding RNAs (ncRNAs) play crucial roles in many biological processes, such as post-transcription of gene regulation. ncRNAs mainly function through interaction with RNA binding proteins (RBPs). To understand the function of a ncRNA, a fundamental step is to identify which protein is involved into its interaction. Therefore it is promising to computationally predict RBPs, where the major challenge is that the interaction pattern or motif is difficult to be found.ResultsIn this study, we propose a computational method IPMiner (Interaction Pattern Miner) to predict ncRNA-protein interactions from sequences, which makes use of deep learning and further improves its performance using stacked ensembling. One of the IPMiner’s typical merits is that it is able to mine the hidden sequential interaction patterns from sequence composition features of protein and RNA sequences using stacked autoencoder, and then the learned hidden features are fed into random forest models. Finally, stacked ensembling is used to integrate different predictors to further improve the prediction performance. The experimental results indicate that IPMiner achieves superior performance on the tested lncRNA-protein interaction dataset with an accuracy of 0.891, sensitivity of 0.939, specificity of 0.831, precision of 0.945 and Matthews correlation coefficient of 0.784, respectively. We further comprehensively investigate IPMiner on other RNA-protein interaction datasets, which yields better performance than the state-of-the-art methods, and the performance has an increase of over 20 % on some tested benchmarked datasets. In addition, we further apply IPMiner for large-scale prediction of ncRNA-protein network, that achieves promising prediction performance.ConclusionBy integrating deep neural network and stacked ensembling, from simple sequence composition features, IPMiner can automatically learn high-level abstraction features, which had strong discriminant ability for RNA-protein detection. IPMiner achieved high performance on our constructed lncRNA-protein benchmark dataset and other RNA-protein datasets. IPMiner tool is available at http://www.csbio.sjtu.edu.cn/bioinf/IPMiner .