The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research.

非编码RNA（ncRNA）和蛋白质之间的相互作用在许多生物学过程中起着重要作用，它们的生物学功能主要是通过与多种蛋白质结合来实现的。高通量生物学技术用于鉴定与特定ncRNA结合的蛋白质分子，但它们通常昂贵且耗时。深度学习提供了一种功能强大的解决方案，可通过计算预测RNA与蛋白质的相互作用。在这项工作中，我们通过使用深度学习堆叠自动编码器网络从RNA和蛋白质序列中挖掘隐藏的高级特征，并将它们输入到随机森林（RF）模型中以预测ncRNA结合，提出RPI-SAN模型。蛋白质。进一步使用堆叠组装来提高所提出方法的准确性。四个基准数据集，包括RPI2241，RPI488，RPI1807，和NPInter v2.0用于五个已建立的预测工具的无偏评估：RPI-Pred，IPMiner，RPISeq-RF，lncPro和RPI-SAN。实验结果表明，我们的RPI-SAN模型比其他方法具有更好的性能，其准确度分别为90.77％，89.7％，96.1％和99.33％。预计RPI-SAN可以用作未来生物医学研究的有效计算工具，并且可以准确预测潜在的ncRNA-蛋白质相互作用对，这为生物学研究提供了可靠的指导。和99.33％。预计RPI-SAN可以用作未来生物医学研究的有效计算工具，并且可以准确预测潜在的ncRNA-蛋白质相互作用对，这为生物学研究提供了可靠的指导。和99.33％。预计RPI-SAN可以用作未来生物医学研究的有效计算工具，并且可以准确预测潜在的ncRNA-蛋白质相互作用对，这为生物学研究提供了可靠的指导。