A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network

Zhong Y, Xuan P, Wang X, et al.
Oxford University Press
Published 2018
Publication Date:
2018-03-06
Publisher:
Oxford University Press
Print ISSN:
1367-4803
Electronic ISSN:
1460-2059
Topics:
Biology
Computer Science
Medicine
Published by:
_version_ 1836398817083129857
autor Zhong Y, Xuan P, Wang X, et al.
beschreibung Motivation Identification of disease-associated miRNAs (disease miRNAs) is critical for understanding disease etiology and pathogenesis. Since miRNAs exert their functions by regulating the expression of their target mRNAs, several methods based on the target genes were proposed to predict disease miRNA candidates. They achieved only limited success as they all suffered from the high false-positive rate of target prediction results. Alternatively, other prediction methods were based on the observation that miRNAs with similar functions tend to be associated with similar diseases and vice versa. The methods exploited the information about miRNAs and diseases, including the functional similarities between miRNAs, the similarities between diseases, and the associations between miRNAs and diseases. However, how to integrate the multiple kinds of information completely and consider the biological characteristic of disease miRNAs is a challenging problem. Results We constructed a bilayer network to represent the complex relationships among miRNAs, among diseases and between miRNAs and diseases. We proposed a non-negative matrix factorization based method to rank, so as to predict, the disease miRNA candidates. The method integrated the miRNA functional similarity, the disease similarity and the miRNA-disease associations seamlessly, which exploited the complex relationships within the bilayer network and the consensus relationship between multiple kinds of information. Considering the correlation between the candidates related to various diseases, it predicted their respective candidates for all the diseases simultaneously. In addition, the sparseness characteristic of disease miRNAs was introduced to generate more reliable prediction model that excludes those noisy candidates. The results on 15 common diseases showed a superior performance of the new method for not only well-characterized diseases but also new ones. A detailed case study on breast neoplasms, colorectal neoplasms, lung neoplasms and 32 other diseases demonstrated the ability of the method for discovering potential disease miRNAs. Availability and implementation The web service for the new method and the list of predicted candidates for all the diseases are available at http://www.bioinfolab.top . Contact xuanping@hlju.edu.cn or zhang@hlju.edu.cn or lijzh@hit.edu.cn Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.
citation_standardnr 6180445
datenlieferant ipn_articles
feed_id 2184
feed_publisher Oxford University Press
feed_publisher_url http://global.oup.com/
insertion_date 2018-03-06
journaleissn 1460-2059
journalissn 1367-4803
publikationsjahr_anzeige 2018
publikationsjahr_facette 2018
publikationsjahr_intervall 7984:2015-2019
publikationsjahr_sort 2018
publisher Oxford University Press
quelle Bioinformatics
relation https://academic.oup.com/bioinformatics/article/34/2/267/4101941?rss=1
search_space articles
shingle_author_1 Zhong Y, Xuan P, Wang X, et al.
shingle_author_2 Zhong Y, Xuan P, Wang X, et al.
shingle_author_3 Zhong Y, Xuan P, Wang X, et al.
shingle_author_4 Zhong Y, Xuan P, Wang X, et al.
shingle_catch_all_1 A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network
Motivation Identification of disease-associated miRNAs (disease miRNAs) is critical for understanding disease etiology and pathogenesis. Since miRNAs exert their functions by regulating the expression of their target mRNAs, several methods based on the target genes were proposed to predict disease miRNA candidates. They achieved only limited success as they all suffered from the high false-positive rate of target prediction results. Alternatively, other prediction methods were based on the observation that miRNAs with similar functions tend to be associated with similar diseases and vice versa. The methods exploited the information about miRNAs and diseases, including the functional similarities between miRNAs, the similarities between diseases, and the associations between miRNAs and diseases. However, how to integrate the multiple kinds of information completely and consider the biological characteristic of disease miRNAs is a challenging problem. Results We constructed a bilayer network to represent the complex relationships among miRNAs, among diseases and between miRNAs and diseases. We proposed a non-negative matrix factorization based method to rank, so as to predict, the disease miRNA candidates. The method integrated the miRNA functional similarity, the disease similarity and the miRNA-disease associations seamlessly, which exploited the complex relationships within the bilayer network and the consensus relationship between multiple kinds of information. Considering the correlation between the candidates related to various diseases, it predicted their respective candidates for all the diseases simultaneously. In addition, the sparseness characteristic of disease miRNAs was introduced to generate more reliable prediction model that excludes those noisy candidates. The results on 15 common diseases showed a superior performance of the new method for not only well-characterized diseases but also new ones. A detailed case study on breast neoplasms, colorectal neoplasms, lung neoplasms and 32 other diseases demonstrated the ability of the method for discovering potential disease miRNAs. Availability and implementation The web service for the new method and the list of predicted candidates for all the diseases are available at http://www.bioinfolab.top . Contact xuanping@hlju.edu.cn or zhang@hlju.edu.cn or lijzh@hit.edu.cn Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.
Zhong Y, Xuan P, Wang X, et al.
Oxford University Press
1367-4803
13674803
1460-2059
14602059
shingle_catch_all_2 A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network
Motivation Identification of disease-associated miRNAs (disease miRNAs) is critical for understanding disease etiology and pathogenesis. Since miRNAs exert their functions by regulating the expression of their target mRNAs, several methods based on the target genes were proposed to predict disease miRNA candidates. They achieved only limited success as they all suffered from the high false-positive rate of target prediction results. Alternatively, other prediction methods were based on the observation that miRNAs with similar functions tend to be associated with similar diseases and vice versa. The methods exploited the information about miRNAs and diseases, including the functional similarities between miRNAs, the similarities between diseases, and the associations between miRNAs and diseases. However, how to integrate the multiple kinds of information completely and consider the biological characteristic of disease miRNAs is a challenging problem. Results We constructed a bilayer network to represent the complex relationships among miRNAs, among diseases and between miRNAs and diseases. We proposed a non-negative matrix factorization based method to rank, so as to predict, the disease miRNA candidates. The method integrated the miRNA functional similarity, the disease similarity and the miRNA-disease associations seamlessly, which exploited the complex relationships within the bilayer network and the consensus relationship between multiple kinds of information. Considering the correlation between the candidates related to various diseases, it predicted their respective candidates for all the diseases simultaneously. In addition, the sparseness characteristic of disease miRNAs was introduced to generate more reliable prediction model that excludes those noisy candidates. The results on 15 common diseases showed a superior performance of the new method for not only well-characterized diseases but also new ones. A detailed case study on breast neoplasms, colorectal neoplasms, lung neoplasms and 32 other diseases demonstrated the ability of the method for discovering potential disease miRNAs. Availability and implementation The web service for the new method and the list of predicted candidates for all the diseases are available at http://www.bioinfolab.top . Contact xuanping@hlju.edu.cn or zhang@hlju.edu.cn or lijzh@hit.edu.cn Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.
Zhong Y, Xuan P, Wang X, et al.
Oxford University Press
1367-4803
13674803
1460-2059
14602059
shingle_catch_all_3 A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network
Motivation Identification of disease-associated miRNAs (disease miRNAs) is critical for understanding disease etiology and pathogenesis. Since miRNAs exert their functions by regulating the expression of their target mRNAs, several methods based on the target genes were proposed to predict disease miRNA candidates. They achieved only limited success as they all suffered from the high false-positive rate of target prediction results. Alternatively, other prediction methods were based on the observation that miRNAs with similar functions tend to be associated with similar diseases and vice versa. The methods exploited the information about miRNAs and diseases, including the functional similarities between miRNAs, the similarities between diseases, and the associations between miRNAs and diseases. However, how to integrate the multiple kinds of information completely and consider the biological characteristic of disease miRNAs is a challenging problem. Results We constructed a bilayer network to represent the complex relationships among miRNAs, among diseases and between miRNAs and diseases. We proposed a non-negative matrix factorization based method to rank, so as to predict, the disease miRNA candidates. The method integrated the miRNA functional similarity, the disease similarity and the miRNA-disease associations seamlessly, which exploited the complex relationships within the bilayer network and the consensus relationship between multiple kinds of information. Considering the correlation between the candidates related to various diseases, it predicted their respective candidates for all the diseases simultaneously. In addition, the sparseness characteristic of disease miRNAs was introduced to generate more reliable prediction model that excludes those noisy candidates. The results on 15 common diseases showed a superior performance of the new method for not only well-characterized diseases but also new ones. A detailed case study on breast neoplasms, colorectal neoplasms, lung neoplasms and 32 other diseases demonstrated the ability of the method for discovering potential disease miRNAs. Availability and implementation The web service for the new method and the list of predicted candidates for all the diseases are available at http://www.bioinfolab.top . Contact xuanping@hlju.edu.cn or zhang@hlju.edu.cn or lijzh@hit.edu.cn Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.
Zhong Y, Xuan P, Wang X, et al.
Oxford University Press
1367-4803
13674803
1460-2059
14602059
shingle_catch_all_4 A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network
Motivation Identification of disease-associated miRNAs (disease miRNAs) is critical for understanding disease etiology and pathogenesis. Since miRNAs exert their functions by regulating the expression of their target mRNAs, several methods based on the target genes were proposed to predict disease miRNA candidates. They achieved only limited success as they all suffered from the high false-positive rate of target prediction results. Alternatively, other prediction methods were based on the observation that miRNAs with similar functions tend to be associated with similar diseases and vice versa. The methods exploited the information about miRNAs and diseases, including the functional similarities between miRNAs, the similarities between diseases, and the associations between miRNAs and diseases. However, how to integrate the multiple kinds of information completely and consider the biological characteristic of disease miRNAs is a challenging problem. Results We constructed a bilayer network to represent the complex relationships among miRNAs, among diseases and between miRNAs and diseases. We proposed a non-negative matrix factorization based method to rank, so as to predict, the disease miRNA candidates. The method integrated the miRNA functional similarity, the disease similarity and the miRNA-disease associations seamlessly, which exploited the complex relationships within the bilayer network and the consensus relationship between multiple kinds of information. Considering the correlation between the candidates related to various diseases, it predicted their respective candidates for all the diseases simultaneously. In addition, the sparseness characteristic of disease miRNAs was introduced to generate more reliable prediction model that excludes those noisy candidates. The results on 15 common diseases showed a superior performance of the new method for not only well-characterized diseases but also new ones. A detailed case study on breast neoplasms, colorectal neoplasms, lung neoplasms and 32 other diseases demonstrated the ability of the method for discovering potential disease miRNAs. Availability and implementation The web service for the new method and the list of predicted candidates for all the diseases are available at http://www.bioinfolab.top . Contact xuanping@hlju.edu.cn or zhang@hlju.edu.cn or lijzh@hit.edu.cn Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.
Zhong Y, Xuan P, Wang X, et al.
Oxford University Press
1367-4803
13674803
1460-2059
14602059
shingle_title_1 A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network
shingle_title_2 A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network
shingle_title_3 A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network
shingle_title_4 A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network
timestamp 2025-06-30T23:33:06.326Z
titel A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network
titel_suche A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network
topic W
SQ-SU
WW-YZ
uid ipn_articles_6180445