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Ensemble Deep Learning Based on Multi-level Information Enhancement and Greedy Fuzzy Decision for Plant miRNA–lncRNA Interaction Prediction

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Abstract

MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are both non-coding RNAs (ncRNAs) and their interactions play important roles in biological processes. Computational methods, such as machine learning and various bioinformatics tools, can predict potential miRNA–lncRNA interactions, which is significant for studying their mechanisms and biological functions. A growing number of RNA interaction predictors for animal have been reported, but they are unreliable for plant due to the differences of ncRNAs in animal and plant. It is urgent to build a reliable plant predictor, especially for cross-species. This paper proposes an ensemble deep learning model based on multi-level information enhancement and greedy fuzzy decision (PmliPEMG) for plant miRNA–lncRNA interaction prediction. The fusion complex features, multi-scale convolutional long short-term memory networks, and attention mechanism are adopted to enhance the sample information at the feature, scale, and model levels, respectively. An ensemble deep learning model is built based on a novel method (greedy fuzzy decision) which greatly improves the efficiency. The multi-level information enhancement and greedy fuzzy decision are verified to have the positive effects on prediction performance. PmliPEMG can be applied to the cross-species prediction. It shows better performance and stronger generalization ability than state-of-the-art predictors and may provide valuable references for related research.

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Code availability

RNAfold in ViennaRNA Package is downloaded at https://www.tbi.univie.ac.at/RNA/. PmliPEMG is freely available at https://github.com/kangzhai/PmliPEMG.

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Funding

This work was supported by the National Natural Science Foundation of China (Nos. 61872055, 32072592, and 31872116).

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Authors and Affiliations

  1. School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Qiang Kang, Jun Meng & Wenhao Shi

  2. School of Bioengineering, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Yushi Luan

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  1. Qiang Kang
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Correspondence to Jun Meng.

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Kang, Q., Meng, J., Shi, W. et al. Ensemble Deep Learning Based on Multi-level Information Enhancement and Greedy Fuzzy Decision for Plant miRNA–lncRNA Interaction Prediction. Interdiscip Sci Comput Life Sci 13, 603–614 (2021). https://doi.org/10.1007/s12539-021-00434-7

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