Construction of Complex Features for Computational Predicting ncRNA-Protein Interaction
Construction of Complex Features for Computational Predicting ncRNA-Protein Interaction
复制标题
构建用于计算预测 ncRNA-蛋白质相互作用的复杂特征
DOI：
10.3389/fgene.2019.00018
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发表时间：
2019-02-01
影响因子：
3.7
通讯作者：
Fu, Yueyue
中科院分区：
文献类型：
--
作者：
Dai, Qiguo;Guo, Maozu;Fu, Yueyue
非编码RNA（Non-coding RNA，ncRNA）在许多重要的调控过程中起着重要的作用。许多ncRNA通过RNA-蛋白质复合物的形式发挥其调节功能。因此，确定ncRNA和蛋白质之间的相互作用是理解ncRNA功能的基础。在昂贵的实验技术成本的压力下，开发精确的计算预测模型已成为鉴定ncRNA-蛋白质相互作用的不可或缺的方法。一个强有力的ncRNA-蛋白质相互作用预测模型需要一个好的表征相互作用的特征集。本文提出了一种新的方法来产生复杂的特征来表征ncRNA-蛋白质相互作用（命名为CFRP）。为了获得ncRNA-蛋白质相互作用的全面描述，通过非线性变换从ncRNA和蛋白质序列的传统k-mer特征生成复杂特征。为了进一步降低复杂特征的维数，通过随机森林选择一组有区别的特征。为了验证该方法的性能，在几个广泛使用的公共数据集上进行了一系列的实验。与传统的k-mer特征相比，CFRP复合体特征能够提高ncRNA-蛋白质相互作用预测模型的性能。同时，将基于CFRP的预测模型与现有的几种预测方法进行了比较，结果表明，基于CFRP的预测模型在评价指标上优于其他方法。综上所述，CFRP产生的复杂特征有利于建立一个强大的预测模型的ncRNA-蛋白质相互作用。
Non-coding RNA (ncRNA) plays important roles in many critical regulation processes. Many ncRNAs perform their regulatory functions by the form of RNA-protein complexes. Therefore, identifying the interaction between ncRNA and protein is fundamental to understand functions of ncRNA. Under pressures from expensive cost of experimental techniques, developing an accuracy computational predictive model has become an indispensable way to identify ncRNA-protein interaction. A powerful predicting model of ncRNA-protein interaction needs a good feature set of characterizing the interaction. In this paper, a novel method is put forward to generate complex features for characterizing ncRNA-protein interaction (named CFRP). To obtain a comprehensive description of ncRNA-protein interaction, complex features are generated by non-linear transformations from the traditional k-mer features of ncRNA and protein sequences. To further reduce the dimensions of complex features, a group of discriminative features are selected by random forest. To validate the performances of the proposed method, a series of experiments are carried on several widely-used public datasets. Compared with the traditional k-mer features, the CFRP complex features can boost the performances of ncRNA-protein interaction prediction model. Meanwhile, the CFRP-based prediction model is compared with several state-of-the-art methods, and the results show that the proposed method achieves better performances than the others in term of the evaluation metrics. In conclusion, the complex features generated by CFRP are beneficial for building a powerful predicting model of ncRNA-protein interaction.