Predicting miRNA-disease associations based on graph attention network with multi-source information

BMC Bioinformatics

Jiawei Luo¹  Qiu Xiao²  Yuejin Zhang³  Guanghui Li³  Tao Fang³  Cheng Liang⁴ 

[1] College of Computer Science and Electronic Engineering, Hunan University;College of Information Science and Engineering, Hunan Normal University;School of Information Engineering, East China Jiaotong University;School of Information Science and Engineering, Shandong Normal University;

关键词: miRNA-disease associations;    Graph attention network;    Feature fusion;    Random forest;   

DOI  :  10.1186/s12859-022-04796-7

来源: DOAJ

【 摘 要 】

Abstract Background There is a growing body of evidence from biological experiments suggesting that microRNAs (miRNAs) play a significant regulatory role in both diverse cellular activities and pathological processes. Exploring miRNA-disease associations not only can decipher pathogenic mechanisms but also provide treatment solutions for diseases. As it is inefficient to identify undiscovered relationships between diseases and miRNAs using biotechnology, an explosion of computational methods have been advanced. However, the prediction accuracy of existing models is hampered by the sparsity of known association network and single-category feature, which is hard to model the complicated relationships between diseases and miRNAs. Results In this study, we advance a new computational framework (GATMDA) to discover unknown miRNA-disease associations based on graph attention network with multi-source information, which effectively fuses linear and non-linear features. In our method, the linear features of diseases and miRNAs are constructed by disease-lncRNA correlation profiles and miRNA-lncRNA correlation profiles, respectively. Then, the graph attention network is employed to extract the non-linear features of diseases and miRNAs by aggregating information of each neighbor with different weights. Finally, the random forest algorithm is applied to infer the disease-miRNA correlation pairs through fusing linear and non-linear features of diseases and miRNAs. As a result, GATMDA achieves impressive performance: an average AUC of 0.9566 with five-fold cross validation, which is superior to other previous models. In addition, case studies conducted on breast cancer, colon cancer and lymphoma indicate that 50, 50 and 48 out of the top fifty prioritized candidates are verified by biological experiments. Conclusions The extensive experimental results justify the accuracy and utility of GATMDA and we could anticipate that it may regard as a utility tool for identifying unobserved disease-miRNA relationships.

【 授权许可】

Unknown