Zhou Yuan-Ke, Shen Zi-Ang, Yu Han, Luo Tao, Gao Yang, Du Pu-Feng
College of Intelligence and Computing, Tianjin University, Tianjin, China.
School of Medicine, Nankai University, Tianjin, China.
Front Genet. 2020 Jan 22;10:1341. doi: 10.3389/fgene.2019.01341. eCollection 2019.
Long non-coding RNAs (lncRNAs) play important roles in various biological processes, where lncRNA-protein interactions are usually involved. Therefore, identifying lncRNA-protein interactions is of great significance to understand the molecular functions of lncRNAs. Since the experiments to identify lncRNA-protein interactions are always costly and time consuming, computational methods are developed as alternative approaches. However, existing lncRNA-protein interaction predictors usually require prior knowledge of lncRNA-protein interactions with experimental evidences. Their performances are limited due to the number of known lncRNA-protein interactions. In this paper, we explored a novel way to predict lncRNA-protein interactions without direct prior knowledge. MiRNAs were picked up as mediators to estimate potential interactions between lncRNAs and proteins. By validating our results based on known lncRNA-protein interactions, our method achieved an AUROC (Area Under Receiver Operating Curve) of 0.821, which is comparable to the state-of-the-art methods. Moreover, our method achieved an improved AUROC of 0.852 by further expanding the training dataset. We believe that our method can be a useful supplement to the existing methods, as it provides an alternative way to estimate lncRNA-protein interactions in a heterogeneous network without direct prior knowledge. All data and codes of this work can be downloaded from GitHub (https://github.com/zyk2118216069/LncRNA-protein-interactions-prediction).
长链非编码RNA（lncRNA）在各种生物过程中发挥着重要作用，这些过程通常涉及lncRNA与蛋白质的相互作用。因此，识别lncRNA与蛋白质的相互作用对于理解lncRNA的分子功能具有重要意义。由于识别lncRNA与蛋白质相互作用的实验总是成本高昂且耗时，因此开发了计算方法作为替代方法。然而，现有的lncRNA与蛋白质相互作用预测器通常需要具有实验证据的lncRNA与蛋白质相互作用的先验知识。由于已知lncRNA与蛋白质相互作用的数量有限，它们的性能受到限制。在本文中，我们探索了一种无需直接先验知识即可预测lncRNA与蛋白质相互作用的新方法。挑选miRNA作为介导物来估计lncRNA与蛋白质之间的潜在相互作用。通过基于已知的lncRNA与蛋白质相互作用验证我们的结果，我们的方法实现了0.821的受试者工作特征曲线下面积（AUROC），这与现有方法相当。此外，通过进一步扩大训练数据集，我们的方法实现了0.852的改进AUROC。我们相信，我们的方法可以作为现有方法的有益补充，因为它提供了一种在无直接先验知识的异质网络中估计lncRNA与蛋白质相互作用的替代方法。这项工作的所有数据和代码都可以从GitHub（https://github.com/zyk2118216069/LncRNA-protein-interactions-prediction）下载。
