Graph embedding ensemble methods based on the heterogeneous network for lncRNA-miRNA interaction prediction
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RESEARCH
Open Access
Graph embedding ensemble methods based on the heterogeneous network for lncRNA-miRNA interaction prediction Chengshuai Zhao1, Yang Qiu1, Shuang Zhou2, Shichao Liu1, Wen Zhang1* and Yanqing Niu3* From 2019 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2019) San Diego, CA, USA. 18-21 November 2019
Abstract Background: Researchers discover LncRNA–miRNA regulatory paradigms modulate gene expression patterns and drive major cellular processes. Identification of lncRNA-miRNA interactions (LMIs) is critical to reveal the mechanism of biological processes and complicated diseases. Because conventional wet experiments are time-consuming, labor-intensive and costly, a few computational methods have been proposed to expedite the identification of lncRNA-miRNA interactions. However, little attention has been paid to fully exploit the structural and topological information of the lncRNA-miRNA interaction network. Results: In this paper, we propose novel lncRNA-miRNA prediction methods by using graph embedding and ensemble learning. First, we calculate lncRNA-lncRNA sequence similarity and miRNA-miRNA sequence similarity, and then we combine them with the known lncRNA-miRNA interactions to construct a heterogeneous network. Second, we adopt several graph embedding methods to learn embedded representations of lncRNAs and miRNAs from the heterogeneous network, and construct the ensemble models using two ensemble strategies. For the former, we consider individual graph embedding based models as base predictors and integrate their predictions, and develop a method, named GEEL-PI. For the latter, we construct a deep attention neural network (DANN) to integrate various graph embeddings, and present an ensemble method, named GEEL-FI. The experimental results demonstrate both GEEL-PI and GEEL-FI outperform other state-of-the-art methods. The effectiveness of two ensemble strategies is validated by further experiments. Moreover, the case studies show that GEEL-PI and GEEL-FI can find novel lncRNA-miRNA associations. Conclusion: The study reveals that graph embedding and ensemble learning based method is efficient for integrating heterogeneous information derived from lncRNA-miRNA interaction network and can achieve better performance on LMI prediction task. In conclusion, GEEL-PI and GEEL-FI are promising for lncRNA-miRNA interaction prediction. Keywords: lncRNA-miRNA interactions, Graph embedding, Ensemble learning, Attention mechanism * Correspondence: [email protected]; [email protected] 1 College of Informatics, Huazhong Agricultural University, Wuhan 430070, China 3 School of Mathematics and Statistics, South-Central University for Nationalities, Wuhan 430074, China Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as y
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