MD-MLI: Prediction of miRNA-lncRNA Interaction by Using Multiple Features and Hierarchical Deep Learning
MD-MLI: Prediction of miRNA-lncRNA Interaction by Using Multiple Features and Hierarchical Deep Learning
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MD-MLI：利用多重特征和分层深度学习预测 miRNA-lncRNA 相互作用
DOI：
10.1109/tcbb.2020.3034922
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
段晓东
中科院分区：
文献类型：
--
作者：
宋金淼;田生伟;禹龙;杨启萌;邢艳;张超;代启国;段晓东
长非编码RNA(LncRNA)可以与microRNA(MiRNA)相互作用，在抑制或激活靶基因的表达和肿瘤的发生发展中发挥重要作用。积累的研究主要集中在miRNA-lncRNA相互作用的预测上，主要涉及生物实验和机器学习方法。这些方法周期长、成本高，需要过多的人工干预。提出了一种数据驱动的分层深度学习框架，该框架由胶囊网络、具有注意机制的独立递归神经网络和双向长短期记忆网络组成。该框架结合了不同网络的优点，利用原始序列的多个序列衍生特征和二级结构特征来挖掘特征之间的依赖关系，致力于获得更好的结果。在实验中，采用五次交叉验证对模型的性能进行了评估，并将Zea Mays数据集与不同的模型进行了比较，以获得更好的分类效果。此外，利用高粱、青冈和苔藓数据集对模型进行了检验，准确率分别达到0.9850、0.9859和0.9777，验证了模型良好的泛化能力。
Long non-coding RNA(lncRNA) can interact with microRNA(miRNA) and play an important role in inhibiting or activating the expression of target genes and the occurrence and development of tumors. Accumulating studies focus on the prediction of miRNA-lncRNA interaction, and mostly are concerned with biological experiments and machine learning methods. These methods are found with long cycles, high costs, and requiring over much human intervention. In this paper, a data-driven hierarchical deep learning framework was proposed, which was composed of a capsule network, an independent recurrent neural network with attention mechanism and bi-directional long short-term memory network. This framework combines the advantages of different networks, uses multiple sequencederived features of the original sequence and features of secondary structure to mine the dependency between features, and devotes to obtain better results. In the experiment, five-fold cross-validation was used to evaluate the performance of the model, and the zea mays data set was compared with the different model to obtain better classification effect. In addition, sorghum, brachypodium distachyon and bryophyte data sets were used to test the model, and the accuracy reached 0.9850, 0.9859 and 0.9777, respectively, which verified the model's good generalization ability.