GANLDA: Graph attention network for lncRNA-disease associations prediction

被引:74
作者
Lan, Wei [1 ]
Wu, Ximin [1 ]
Chen, Qingfeng [1 ]
Peng, Wei [2 ]
Wang, Jianxin [3 ]
Chen, Yiping Phoebe [4 ]
机构
[1] Guangxi Univ, Sch Comp Elect & Informat, Nanning, Peoples R China
[2] Kunming Univ Sci & Technol, Network Ctr, Kunming, Yunnan, Peoples R China
[3] Cent South Univ, Sch Comp Sci & Engn, Hunan Prov Key Lab Bioinformat, Changsha, Peoples R China
[4] La Trobe Univ, Dept Comp Sci & Informat Technol, Melbourne, Vic 3086, Australia
基金
中国国家自然科学基金;
关键词
lncRNA-disease associations; Principal component analysis; Graph attention network; Multi-layer perceptron; LONG NONCODING RNAS; PROMOTES GASTRIC-CANCER; PROSTATE-CANCER; COMPLEX DISEASES; PROGRESSION; DATABASE; EXPRESSION; KNOCKDOWN; ANRIL;
D O I
10.1016/j.neucom.2020.09.094
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Increasing studies have indicated that long non-coding RNAs (lncRNAs) play important roles in many physiological and pathological pathways. Identifying lncRNA-disease associations not only contributes to the understanding of biological processes, but also provides new strategies for the diagnosis and prevention of diseases. In this article, an end to end computational model based on graph attention network (GANLDA) is proposed to predict associations between lncRNAs and diseases. In our method, it combines heterogeneous data of lncRNA and disease as original features. Then, the principal component analysis (PCA) is used to reduce the noise of the original features. Further, the graph attention network is utilized to extract the useful information from features of lncRNA and disease. Finally, the multi-layer perceptron is employed to infer lncRNA-disease associations. The experimental results show GANLDA outperforms than other four state-of-the-art methods in 10-fold cross validation and devono test. The case studies also demonstrate that GANLDA is an effective method for lncRNA-disease associations identification. (c) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页码:384 / 393
页数:10
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