A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method.

A large number of studies have shown that the variation and disorder of miRNAs are important causes of diseases. The recognition of disease-related miRNAs has become an important topic in the field of biological research. However, the identification of disease-related miRNAs by biological experiment...

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Autores principales:	Ang Li, Yingwei Deng, Yan Tan, Min Chen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/977d0b04fd354d278de8fd05a6bdec51
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spelling	oai:doaj.org-article:977d0b04fd354d278de8fd05a6bdec512021-12-02T20:10:28ZA novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method.1932-620310.1371/journal.pone.0252971https://doaj.org/article/977d0b04fd354d278de8fd05a6bdec512021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252971https://doaj.org/toc/1932-6203A large number of studies have shown that the variation and disorder of miRNAs are important causes of diseases. The recognition of disease-related miRNAs has become an important topic in the field of biological research. However, the identification of disease-related miRNAs by biological experiments is expensive and time consuming. Thus, computational prediction models that predict disease-related miRNAs must be developed. A novel network projection-based dual random walk with restart (NPRWR) was used to predict potential disease-related miRNAs. The NPRWR model aims to estimate and accurately predict miRNA-disease associations by using dual random walk with restart and network projection technology, respectively. The leave-one-out cross validation (LOOCV) was adopted to evaluate the prediction performance of NPRWR. The results show that the area under the receiver operating characteristic curve(AUC) of NPRWR was 0.9029, which is superior to that of other advanced miRNA-disease associated prediction methods. In addition, lung and kidney neoplasms were selected to present a case study. Among the first 50 miRNAs predicted, 50 and 49 miRNAs have been proven by in databases or relevant literature. Moreover, NPRWR can be used to predict isolated diseases and new miRNAs. LOOCV and the case study achieved good prediction results. Thus, NPRWR will become an effective and accurate disease-miRNA association prediction model.Ang LiYingwei DengYan TanMin ChenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252971 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ang Li Yingwei Deng Yan Tan Min Chen A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method.
description	A large number of studies have shown that the variation and disorder of miRNAs are important causes of diseases. The recognition of disease-related miRNAs has become an important topic in the field of biological research. However, the identification of disease-related miRNAs by biological experiments is expensive and time consuming. Thus, computational prediction models that predict disease-related miRNAs must be developed. A novel network projection-based dual random walk with restart (NPRWR) was used to predict potential disease-related miRNAs. The NPRWR model aims to estimate and accurately predict miRNA-disease associations by using dual random walk with restart and network projection technology, respectively. The leave-one-out cross validation (LOOCV) was adopted to evaluate the prediction performance of NPRWR. The results show that the area under the receiver operating characteristic curve(AUC) of NPRWR was 0.9029, which is superior to that of other advanced miRNA-disease associated prediction methods. In addition, lung and kidney neoplasms were selected to present a case study. Among the first 50 miRNAs predicted, 50 and 49 miRNAs have been proven by in databases or relevant literature. Moreover, NPRWR can be used to predict isolated diseases and new miRNAs. LOOCV and the case study achieved good prediction results. Thus, NPRWR will become an effective and accurate disease-miRNA association prediction model.
format	article
author	Ang Li Yingwei Deng Yan Tan Min Chen
author_facet	Ang Li Yingwei Deng Yan Tan Min Chen
author_sort	Ang Li
title	A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method.
title_short	A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method.
title_full	A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method.
title_fullStr	A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method.
title_full_unstemmed	A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method.
title_sort	novel mirna-disease association prediction model using dual random walk with restart and space projection federated method.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/977d0b04fd354d278de8fd05a6bdec51
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A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method.

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