Three-layer heterogeneous network based on the integration of CircRNA information for MiRNA-disease association prediction

被引：0
作者：
Qu J. [1 ]
Liu S. [1 ]
Li H. [1 ]
Zhou J. [2 ]
Bian Z. [3 ]
Song Z. [1 ]
Jiang Z. [2 ]
机构：
[1] Changzhou University, School of Computer Science and Artificial Intelligence, Jiangsu, Changzhou
[2] Shaoxing University, School of Computer Science and Engineering, Zhejiang, Shaoxing
[3] Jiangnan University, School of AI & Computer Science, Jiangsu, Wuxi
来源：
PeerJ Computer Science | 2024年 / 10卷
基金：
中国国家自然科学基金;
关键词：
Association prediction; CircRNAs; Diseases; Heterogeneous network; MicroRNAs;
D O I：
10.7717/PEERJ-CS.2070
中图分类号：
学科分类号：
摘要：
Increasing research has shown that the abnormal expression of microRNA (miRNA) is associated with many complex diseases. However, biological experiments have many limitations in identifying the potential disease-miRNA associations. Therefore, we developed a computational model of Three-Layer Heterogeneous Network based on the Integration of CircRNA information for MiRNA-Disease Association prediction (TLHNICMDA). In the model, a disease-miRNA-circRNA heterogeneous network is built by known disease-miRNA associations, known miRNA-circRNA interactions, disease similarity, miRNA similarity, and circRNA similarity. Then, the potential disease-miRNA associations are identified by an update algorithm based on the global network. Finally, based on global and local leave-one-out cross validation (LOOCV), the values of AUCs in TLHNICMDA are 0.8795 and 0.7774. Moreover, the mean and standard deviation of AUC in 5-fold cross-validations is 0.8777 +/−0.0010. Especially, the two types of case studies illustrated the usefulness of TLHNICMDA in predicting disease-miRNA interactions. © (2024), Qu et al.
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页码：1 / 30
页数：29
相关论文
共 81 条
[21]  
Ding Y, Lei X, Liao B, Wu FX., Predicting miRNA-disease associations based on multi-view variational graph auto-encoder with matrix factorization, IEEE Journal of Biomedical and Health Informatics, 26, 1, pp. 446-457, (2022)
[22]  
Dong W, Qiu C, Zhang H, Wang J, Cui Q, Yin Y, Raya K., Human MicroRNA oncogenes and tumor suppressors show significantly different biological patterns: from functions to targets, PLOS ONE, 5, 9, (2010)
[23]  
Feng H, Jin D, Li J, Li Y, Zou Q, Liu T., Matrix reconstruction with reliable neighbors for predicting potential MiRNA-disease associations, Briefings in Bioinformatics, 24, 1, (2023)
[24]  
Feng Y, Kang Y, He Y, Liu J, Yu Z., MicroRNA-99a acts as a tumor suppressor and is downregulated in bladder cancer, BMC Urology, 14, (2014)
[25]  
Fu Y, Yang R, Zhang L, Fu X., HGECDA: a heterogeneous graph embedding model for CircRNA-disease association prediction, IEEE Journal of Biomedical and Health Informatics, 27, 10, pp. 5177-5186, (2023)
[26]  
Gottardo F, Chang GL, Ferracin M, Calin GA, Fassan M, Bassi P, Sevignani C, Byrne D, Negrini M, Pagano F., Micro-RNA profiling in kidney and bladder cancers, Urologic Oncology, 25, 5, pp. 387-392, (2007)
[27]  
Grover A, Leskovec J., node2vec: scalable feature learning for networks, KDD, 2016, pp. 855-864, (2016)
[28]  
Han B, Chao J, Yao H., Circular RNA and its mechanisms in disease: from the bench to the clinic, Pharmacology & Therapeutics, 187, 22–37, pp. 31-44, (2018)
[29]  
He XQ, Dong Y, Wu CW, Zhao Z, Ng S, Chan F, Sung J, Yu J., 1052 MicroRNA-218 inhibits cell cycle progression and promotes apoptosis in colon cancer cells by downregulating oncogene BMI1, Gastroenterology, 142, pp. S-185, (2012)
[30]  
He H, Garcia EA., Learning from imbalanced data, IEEE Transactions on Knowledge and Data Engineering, 21, 9, pp. 1263-1284, (2009)
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