NCPMDA statistics

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Citations per year

Number of citations per year for the bioinformatics software tool NCPMDA
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Tool usage distribution map

This map represents all the scientific publications referring to NCPMDA per scientific context
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NCPMDA specifications

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Unique identifier OMICS_20554
Name NCPMDA
Alternative name Network Consistency Projection for miRNA-Disease Associations

Maintainer


This tool is not maintained anymore.

Publication for Network Consistency Projection for miRNA-Disease Associations

NCPMDA citations

 (2)
library_books

Global Similarity Method Based on a Two tier Random Walk for the Prediction of microRNA–Disease Association

2018
Sci Rep
PMCID: 5915491
PMID: 29691434
DOI: 10.1038/s41598-018-24532-7

[…] ease association prediction method based on random walk, namely, NetGS. However, too many parameters are present in these two methods. Gu et al. designed a network conformance method, which is called NCPMDA, to predict miRNA–disease association. This method is nonparametric, and it can simultaneously predict the miRNA–disease association among all diseases. No negative samples are needed in this m […]

library_books

A Semi Supervised Learning Algorithm for Predicting Four Types MiRNA Disease Associations by Mutual Information in a Heterogeneous Network

2018
Genes
PMCID: 5867860
PMID: 29498680
DOI: 10.3390/genes9030139

[…] s. Nevertheless, the performance is not particularly satisfactory. Recently, Gu et al. [] proposed a global and effective method to infer the associations between miRNAs and diseases, which is called network consistency projection for miRNA-disease associations (NCPMDA). NCPMDA is a non-parametric approach and takes full advantage of various molecular data, including miRNA functional similarity ne […]


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NCPMDA institution(s)
College of Information Science and Engineering, Hunan University, Changsha, Hunan, China; Department of Computer Science, State University of New York, New Paltz, NY, USA
NCPMDA funding source(s)
Supported by the Program for New Century Excellent Talents in university (Grant No. NCET-10- 0365); National Nature Science Foundation of China (Grant Nos 60973082, 11171369, 61272395, and 61370171); National Nature Science Foundation of Hunan Province (Grant No. 12JJ2041); the Planned Science and Technology project of Hunan Province (Grant Nos 2009FJ3195 and 2012FJ1012); the Fundamental Research Funds for the Central universities, Hunan university.

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