A Hybrid Prediction Method for Plant lncRNA-Protein Interaction
A Hybrid Prediction Method for Plant lncRNA-Protein Interaction
复制标题
植物 lncRNA-蛋白质相互作用的混合预测方法
DOI：
10.3390/cells8060521
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发表时间：
2019-06-01
期刊：
影响因子：
6
通讯作者：
Meng, Jun
中科院分区：
文献类型：
--
作者：
Wekesa, Jael Sanyanda;Luan, Yushi;Meng, Jun
长链非蛋白质编码RNA（lncRNA）的鉴定和分析由于其在生物过程中的作用而在转录组研究中普遍存在。特别是，lncRNA-蛋白质相互作用与基因表达调控和细胞过程（如植物中的病原体抗性）具有合理的相关性。虽然已经在动物中研究了lncRNA-蛋白质相互作用，但在植物中还没有广泛的研究。本文提出了一种新的植物lncRNA-蛋白质相互作用预测方法PLRPIM，它结合了深度学习和浅层机器学习方法。最佳特征子集的选择和后续的有效压缩是深度学习模型的重大挑战。该方法采用k-mer和提取高层次的抽象序列为基础的特征，使用堆叠稀疏自动编码器。在提取特征的基础上，采用随机森林（RF）和光梯度增强机（LGBM）的融合方法建立预测模型。在拟南芥和玉米数据集上评估了性能。实验结果表明，PLRPIM的优越性与其他预测工具在两个数据集上。基于5倍交叉验证，我们获得89.98%和93.44%的准确度，0.954和0.982 AUC拟南芥和玉米，分别。PLRPIM能有效预测lncRNA与蛋白质的相互作用对，为lncRNA相关研究包括功能预测提供了有力的工具。
Long non-protein-coding RNAs (lncRNAs) identification and analysis are pervasive in transcriptome studies due to their roles in biological processes. In particular, lncRNA-protein interaction has plausible relevance to gene expression regulation and in cellular processes such as pathogen resistance in plants. While lncRNA-protein interaction has been studied in animals, there has yet to be extensive research in plants. In this paper, we propose a novel plant lncRNA-protein interaction prediction method, namely PLRPIM, which combines deep learning and shallow machine learning methods. The selection of an optimal feature subset and subsequent efficient compression are significant challenges for deep learning models. The proposed method adopts k-mer and extracts high-level abstraction sequence-based features using stacked sparse autoencoder. Based on the extracted features, the fusion of random forest (RF) and light gradient boosting machine (LGBM) is used to build the prediction model. The performances are evaluated on Arabidopsis thaliana and Zea mays datasets. Results from experiments demonstrate PLRPIM’s superiority compared with other prediction tools on the two datasets. Based on 5-fold cross-validation, we obtain 89.98% and 93.44% accuracy, 0.954 and 0.982 AUC for Arabidopsis thaliana and Zea mays, respectively. PLRPIM predicts potential lncRNA-protein interaction pairs effectively, which can facilitate lncRNA related research including function prediction.