Graph embedding and ensemble learning for predicting gene-disease associations

2020 ◽  
Vol 23 (4) ◽  
pp. 360
Author(s):  
Zhouxin Yu ◽  
Wen Zhang ◽  
Haorui Wang ◽  
Xiaochan Wang
Keyword(s):  
Ensemble Learning ◽  
Graph Embedding ◽  
Disease Associations

scholarly journals Graph embedding ensemble methods based on the heterogeneous network for lncRNA-miRNA interaction prediction

BMC Genomics ◽  
2020 ◽  
Vol 21 (S13) ◽  
Author(s):  
Chengshuai Zhao ◽  
Yang Qiu ◽  
Shuang Zhou ◽  
Shichao Liu ◽  
Wen Zhang ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Heterogeneous Network ◽  
Sequence Similarity ◽  
Expression Patterns ◽  
Interaction Network ◽  
Ensemble Methods ◽  
Graph Embedding ◽  
Mirna Sequence ◽  
Interaction Prediction ◽  
Cellular Processes

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.

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