Plant miRNA-lncRNA Interaction Prediction with the Ensemble of CNN and IndRNN
Plant miRNA-lncRNA Interaction Prediction with the Ensemble of CNN and IndRNN
复制标题
使用 CNN 和 IndRNN 组合预测植物 miRNA-lncRNA 相互作用
DOI：
10.1007/s12539-019-00351-w
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发表时间：
2020
影响因子：
4.8
通讯作者：
Chanjuan Liu
中科院分区：
文献类型：
--
作者：
Peng Zhang;Jun Meng;Yushi Luan;Chanjuan Liu
非编码RNA（Non-coding RNA，ncRNA）在动植物的生物学活动中起着重要的调控作用，其中代表性的有microRNA（microRNA，miRNA）和长链非编码RNA（long non-coding RNA，lncRNA）。最近的研究发现，预测miRNA和lncRNA之间的相互作用是阐明其功能机制的首要任务。传统的基于特征的分类方法由于数据规模小、噪声量大、人为因素的限制，其预测精度和可靠性往往受到影响。此外，植物ncRNA结构复杂。本文提出了一种基于卷积神经网络（CNN）和独立递归神经网络（IndRNN）的集成深度学习模型，用于预测植物miRNA和lncRNA之间的相互作用，即CIRNN。该模型利用CNN自动探索基因序列的功能特征，利用IndRNN获取序列特征的表示，并学习序列间的依赖关系，克服了传统特征工程中人为因素造成的不准确性。实验结果表明，该模型在处理大规模数据，尤其是长序列数据时，上级浅层机器学习和现有的深度学习模型.
Non-coding RNA (ncRNA) plays an important role in regulating biological activities of animals and plants, and the representative ones are microRNA (miRNA) and long non-coding RNA (lncRNA). Recent research has found that predicting the interaction between miRNA and lncRNA is the primary task for elucidating their functional mechanisms. Due to the small scale of data, a large amount of noise, and the limitations of human factors, the prediction accuracy and reliability of traditional feature-based classification methods are often affected. Besides, the structure of plant ncRNA is complex. This paper proposes an ensemble deep-learning model based on convolutional neural network (CNN) and independently recurrent neural network (IndRNN) for predicting the interaction between miRNA and lncRNA of plants, namely, CIRNN. The model uses CNN to explore the functional features of gene sequences automatically, leverages IndRNN to obtain the representation of sequence features, and learns the dependencies among sequences; thus, it overcomes the inaccuracy caused by human factors in traditional feature engineering. The experiment results show that the proposed model is superior to shallow machine-learning and existing deep-learning models when dealing with large-scale data, especially for the long sequence.