Predicting lncRNA-protein interactions with bipartite graph embedding and deep graph neural networks

被引:10
作者
Ma, Yuzhou [1 ]
Zhang, Han [1 ]
Jin, Chen [2 ]
Kang, Chuanze [1 ]
机构
[1] Nankai Univ, Coll Artificial Intelligence, Tianjin, Peoples R China
[2] Nankai Univ, Coll Comp Sci, Tianjin, Peoples R China
基金
中国国家自然科学基金;
关键词
lncRNA-protein interaction; graph neural network; bipartite graph embedding; heterogeneous graph; link prediction; RNA; METASTASIS; DISEASE;
D O I
10.3389/fgene.2023.1136672
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Background: Long non-coding RNAs (lncRNAs) play crucial roles in numerous biological processes. Investigation of the lncRNA-protein interaction contributes to discovering the undetected molecular functions of lncRNAs. In recent years, increasingly computational approaches have substituted the traditional time-consuming experiments utilized to crack the possible unknown associations. However, significant explorations of the heterogeneity in association prediction between lncRNA and protein are inadequate. It remains challenging to integrate the heterogeneity of lncRNA-protein interactions with graph neural network algorithms.Methods: In this paper, we constructed a deep architecture based on GNN called BiHo-GNN, which is the first to integrate the properties of homogeneous with heterogeneous networks through bipartite graph embedding. Different from previous research, BiHo-GNN can capture the mechanism of molecular association by the data encoder of heterogeneous networks. Meanwhile, we design the process of mutual optimization between homogeneous and heterogeneous networks, which can promote the robustness of BiHo-GNN.Results: We collected four datasets for predicting lncRNA-protein interaction and compared the performance of current prediction models on benchmarking dataset. In comparison with the performance of other models, BiHo-GNN outperforms existing bipartite graph-based methods.Conclusion: Our BiHo-GNN integrates the bipartite graph with homogeneous graph networks. Based on this model structure, the lncRNA-protein interactions and potential associations can be predicted and discovered accurately.
引用
收藏
页数:10
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