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Open AccessArticle

Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information

1
School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
2
School of Mathematical Science, Heilongjiang University, Harbin 150080, China
*
Author to whom correspondence should be addressed.
Genes 2019, 10(9), 685; https://doi.org/10.3390/genes10090685
Received: 15 June 2019 / Revised: 31 August 2019 / Accepted: 3 September 2019 / Published: 6 September 2019
(This article belongs to the Special Issue Associations Between Non-Coding RNA and Diseases)
Predicting the potential microRNA (miRNA) candidates associated with a disease helps in exploring the mechanisms of disease development. Most recent approaches have utilized heterogeneous information about miRNAs and diseases, including miRNA similarities, disease similarities, and miRNA-disease associations. However, these methods do not utilize the projections of miRNAs and diseases in a low-dimensional space. Thus, it is necessary to develop a method that can utilize the effective information in the low-dimensional space to predict potential disease-related miRNA candidates. We proposed a method based on non-negative matrix factorization, named DMAPred, to predict potential miRNA-disease associations. DMAPred exploits the similarities and associations of diseases and miRNAs, and it integrates local topological information of the miRNA network. The likelihood that a miRNA is associated with a disease also depends on their projections in low-dimensional space. Therefore, we project miRNAs and diseases into low-dimensional feature space to yield their low-dimensional and dense feature representations. Moreover, the sparse characteristic of miRNA-disease associations was introduced to make our predictive model more credible. DMAPred achieved superior performance for 15 well-characterized diseases with AUCs (area under the receiver operating characteristic curve) ranging from 0.860 to 0.973 and AUPRs (area under the precision-recall curve) ranging from 0.118 to 0.761. In addition, case studies on breast, prostatic, and lung neoplasms demonstrated the ability of DMAPred to discover potential disease-related miRNAs.
Keywords: miRNA-disease associations; non-negative matrix factorization; graph regularization; projection of miRNAs and diseases; sparse characteristic of associations miRNA-disease associations; non-negative matrix factorization; graph regularization; projection of miRNAs and diseases; sparse characteristic of associations
MDPI and ACS Style

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 2019, 10, 685.

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