In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 17, No. 7 ( 2021-7-12), p. e1009165-
Abstract:
miRNAs belong to small non-coding RNAs that are related to a number of complicated biological processes. Considerable studies have suggested that miRNAs are closely associated with many human diseases. In this study, we proposed a computational model based on Similarity Constrained Matrix Factorization for miRNA-Disease Association Prediction (SCMFMDA). In order to effectively combine different disease and miRNA similarity data, we applied similarity network fusion algorithm to obtain integrated disease similarity (composed of disease functional similarity, disease semantic similarity and disease Gaussian interaction profile kernel similarity) and integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity and miRNA Gaussian interaction profile kernel similarity). In addition, the L 2 regularization terms and similarity constraint terms were added to traditional Nonnegative Matrix Factorization algorithm to predict disease-related miRNAs. SCMFMDA achieved AUCs of 0.9675 and 0.9447 based on global Leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, the case studies on two common human diseases were also implemented to demonstrate the prediction accuracy of SCMFMDA. The out of top 50 predicted miRNAs confirmed by experimental reports that indicated SCMFMDA was effective for prediction of relationship between miRNAs and diseases.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1009165
DOI:
10.1371/journal.pcbi.1009165.g001
DOI:
10.1371/journal.pcbi.1009165.g002
DOI:
10.1371/journal.pcbi.1009165.g003
DOI:
10.1371/journal.pcbi.1009165.g004
DOI:
10.1371/journal.pcbi.1009165.g005
DOI:
10.1371/journal.pcbi.1009165.t001
DOI:
10.1371/journal.pcbi.1009165.t002
DOI:
10.1371/journal.pcbi.1009165.t003
DOI:
10.1371/journal.pcbi.1009165.s001
DOI:
10.1371/journal.pcbi.1009165.s002
DOI:
10.1371/journal.pcbi.1009165.s003
DOI:
10.1371/journal.pcbi.1009165.s004
DOI:
10.1371/journal.pcbi.1009165.s005
DOI:
10.1371/journal.pcbi.1009165.s006
DOI:
10.1371/journal.pcbi.1009165.s007
DOI:
10.1371/journal.pcbi.1009165.s008
DOI:
10.1371/journal.pcbi.1009165.s009
DOI:
10.1371/journal.pcbi.1009165.r001
DOI:
10.1371/journal.pcbi.1009165.r002
DOI:
10.1371/journal.pcbi.1009165.r003
DOI:
10.1371/journal.pcbi.1009165.r004
DOI:
10.1371/journal.pcbi.1009165.r005
DOI:
10.1371/journal.pcbi.1009165.r006
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2193340-6
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