MSFSP: A Novel MiRNA-Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection

Overview

Journal Front Genet

Date 2020 May 20

PMID 32425980

Citations 7

Authors

Yi Zhang

Min Chen

Xiaohui Cheng

Hanyan Wei

Affiliations

Soon will be listed here.

Abstract

Growing evidences have indicated that microRNAs (miRNAs) play a significant role relating to many important bioprocesses; their mutations and disorders will cause the occurrence of various complex diseases. The prediction of miRNAs associated with underlying diseases computational approaches is beneficial to identify biomarkers and discover specific medicine, which can greatly reduce the cost of diagnosis, cure, prognosis, and prevention of human diseases. However, how to further achieve a more reliable prediction of potential miRNA-disease associations with effective integration of different biological data is a challenge for researchers. In this study, we proposed a computational model by using a federated method of combined multiple-similarities fusion and space projection (MSFSP). MSFSP firstly fused the integrated disease similarity (composed of disease semantic similarity, disease functional similarity, and disease Hamming similarity) with the integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity, and miRNA Hamming similarity). Secondly, it constructed the weighted network of miRNA-disease associations from the experimentally verified Boolean network of miRNA-disease associations by using similarity networks. Finally, it calculated the prediction results by weighting miRNA space projection scores and the disease space projection scores. Leave-one-out cross-validation demonstrated that MSFSP has the distinguished predictive accuracy with area under the receiver operating characteristics curve (AUC) of 0.9613 better than that of five other existing models. In case studies, the predictive ability of MSFSP was further confirmed as 96 and 98% of the top 50 predictions for prostatic neoplasms and lung neoplasms were successfully validated by experimental evidences and supporting experimental evidences were also found for 100% of the top 50 predictions for isolated diseases.

Citing Articles

KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning.

Luo H, Yang H, Zhang G, Wang J, Luo J, Yan C Front Pharmacol. 2025; 16:1525029.

PMID: 40008124 PMC: 11850324. DOI: 10.3389/fphar.2025.1525029.

Synchronous Mutual Learning Network and Asynchronous Multi-Scale Embedding Network for miRNA-Disease Association Prediction.

Sun W, Zhang P, Zhang W, Xu J, Huang Y, Li L Interdiscip Sci. 2024; 16(3):532-553.

PMID: 38310628 DOI: 10.1007/s12539-023-00602-x.

Hessian Regularized [Formula: see text]-Nonnegative Matrix Factorization and Deep Learning for miRNA-Disease Associations Prediction.

Han G, Gao Q, Peng L, Tang J Interdiscip Sci. 2023; 16(1):176-191.

PMID: 38099958 DOI: 10.1007/s12539-023-00594-8.

Predicting potential miRNA-disease associations based on more reliable negative sample selection.

Guo R, Chen H, Wang W, Wu G, Lv F BMC Bioinformatics. 2022; 23(1):432.

PMID: 36253735 PMC: 9575264. DOI: 10.1186/s12859-022-04978-3.

A message passing framework with multiple data integration for miRNA-disease association prediction.

Dong T, Schrader J, Mucke S, Khosla M Sci Rep. 2022; 12(1):16259.

PMID: 36171337 PMC: 9519928. DOI: 10.1038/s41598-022-20529-5.

References

Liang C, Yu S, Luo J . Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs. PLoS Comput Biol. 2019; 15(4):e1006931. PMC: 6459551. DOI: 10.1371/journal.pcbi.1006931. View

Jiang L, Xiao Y, Ding Y, Tang J, Guo F . Discovering Cancer Subtypes via an Accurate Fusion Strategy on Multiple Profile Data. Front Genet. 2019; 10:20. PMC: 6370730. DOI: 10.3389/fgene.2019.00020. View

Wang B, Mezlini A, Demir F, Fiume M, Tu Z, Brudno M . Similarity network fusion for aggregating data types on a genomic scale. Nat Methods. 2014; 11(3):333-7. DOI: 10.1038/nmeth.2810. View

Li J, Wu Z, Cheng F, Li W, Liu G, Tang Y . Computational prediction of microRNA networks incorporating environmental toxicity and disease etiology. Sci Rep. 2014; 4:5576. PMC: 4081875. DOI: 10.1038/srep05576. View

Luo J, Xiao Q . A novel approach for predicting microRNA-disease associations by unbalanced bi-random walk on heterogeneous network. J Biomed Inform. 2017; 66:194-203. DOI: 10.1016/j.jbi.2017.01.008. View

Zhu L, Zhao J, Wang J, Hu C, Peng J, Luo R . MicroRNAs Are Involved in the Regulation of Ovary Development in the Pathogenic Blood Fluke Schistosoma japonicum. PLoS Pathog. 2016; 12(2):e1005423. PMC: 4752461. DOI: 10.1371/journal.ppat.1005423. View

Chen X, Wu Q, Yan G . RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction. RNA Biol. 2017; 14(7):952-962. PMC: 5546566. DOI: 10.1080/15476286.2017.1312226. View

Chen X, Cheng J, Yin J . Predicting microRNA-disease associations using bipartite local models and hubness-aware regression. RNA Biol. 2018; 15(9):1192-1205. PMC: 6284580. DOI: 10.1080/15476286.2018.1517010. View

Luo J, Ding P, Liang C, Cao B, Chen X . Collective Prediction of Disease-Associated miRNAs Based on Transduction Learning. IEEE/ACM Trans Comput Biol Bioinform. 2016; 14(6):1468-1475. DOI: 10.1109/TCBB.2016.2599866. View

10.

Liu Y, Zeng X, He Z, Zou Q . Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources. IEEE/ACM Trans Comput Biol Bioinform. 2016; 14(4):905-915. DOI: 10.1109/TCBB.2016.2550432. View

11.

Xiao Q, Luo J, Liang C, Cai J, Ding P . A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations. Bioinformatics. 2017; 34(2):239-248. DOI: 10.1093/bioinformatics/btx545. View

12.

Huang Z, Shi J, Gao Y, Cui C, Zhang S, Li J . HMDD v3.0: a database for experimentally supported human microRNA-disease associations. Nucleic Acids Res. 2018; 47(D1):D1013-D1017. PMC: 6323994. DOI: 10.1093/nar/gky1010. View

13.

Xuan P, Zhang Y, Zhang T, Li L, Zhao L . Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information. Genes (Basel). 2019; 10(9). PMC: 6770973. DOI: 10.3390/genes10090685. View

14.

Carthew R, Sontheimer E . Origins and Mechanisms of miRNAs and siRNAs. Cell. 2009; 136(4):642-55. PMC: 2675692. DOI: 10.1016/j.cell.2009.01.035. View

15.

Iorio M, Ferracin M, Liu C, Veronese A, Spizzo R, Sabbioni S . MicroRNA gene expression deregulation in human breast cancer. Cancer Res. 2005; 65(16):7065-70. DOI: 10.1158/0008-5472.CAN-05-1783. View

16.

Zhang L, Chen X, Yin J . Prediction of Potential miRNA-Disease Associations Through a Novel Unsupervised Deep Learning Framework with Variational Autoencoder. Cells. 2019; 8(9). PMC: 6770222. DOI: 10.3390/cells8091040. View

17.

Chen X, Huang L, Xie D, Zhao Q . EGBMMDA: Extreme Gradient Boosting Machine for MiRNA-Disease Association prediction. Cell Death Dis. 2018; 9(1):3. PMC: 5849212. DOI: 10.1038/s41419-017-0003-x. View

18.

Chen M, He X, Duan S, Deng Y . A Novel Gene Selection Method Based on Sparse Representation and Max-Relevance and Min-Redundancy. Comb Chem High Throughput Screen. 2017; 20(2):158-163. DOI: 10.2174/1386207320666170126114051. View

19.

Xu C, Ping Y, Li X, Zhao H, Wang L, Fan H . Prioritizing candidate disease miRNAs by integrating phenotype associations of multiple diseases with matched miRNA and mRNA expression profiles. Mol Biosyst. 2014; 10(11):2800-9. DOI: 10.1039/c4mb00353e. View

20.

Kozomara A, Griffiths-Jones S . miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2013; 42(Database issue):D68-73. PMC: 3965103. DOI: 10.1093/nar/gkt1181. View