2019
DOI: 10.3390/genes10090685
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Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information

Abstract: 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-dim… Show more

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Cited by 6 publications
(3 citation statements)
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“…Chen et al [ 15 ] proposed a novel computational model BNPMDA for miRNA-disease association predictions based on bipartite network projection [ 16 ]. Xuan et al [ 17 ] developed a method DMAPred which applied non-negative matrix factorization for potential miRNA-disease association inference. DMAPred projected miRNAs and diseases into low-dimensional spaces to yield feature representations.…”
Section: Introductionmentioning
confidence: 99%
“…Chen et al [ 15 ] proposed a novel computational model BNPMDA for miRNA-disease association predictions based on bipartite network projection [ 16 ]. Xuan et al [ 17 ] developed a method DMAPred which applied non-negative matrix factorization for potential miRNA-disease association inference. DMAPred projected miRNAs and diseases into low-dimensional spaces to yield feature representations.…”
Section: Introductionmentioning
confidence: 99%
“…Deep learning technologies have been widely utilized for the prediction of miRNA–disease associations [ 22 ] and disease-related lncRNAs [ 23 , 24 ]. Owing to the development of deep learning, the indications of drug candidates are identified more accurately in recent approaches by integrating multiple sources of drug- and disease-relevant information.…”
Section: Introductionmentioning
confidence: 99%
“…However, the above semi-supervised solutions without the need for negative samples still have the limitation in initial values setting and optimal parameters of iteration selecting. Zeng et al (2016a), Li et al (2017), Chen et al (2018g), Xiao et al (2018), Xuan et al (2019a), Xuan et al (2019c), and Peng et al (2017b) utilized the matrix completion to infer the potential miRNA-disease associations. Chen et al (2018i) uncovered the potential miRNA-disease associations through integrating low-rank matrix decomposition and the sparse learning method.…”
Section: Introductionmentioning
confidence: 99%