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Inferring potential small molecule–miRNA association based on triple layer heterogeneous network

Recently, many biological experiments have indicated that microRNAs (miRNAs) are a newly discovered small molecule (SM) drug targets that play an important role in the development and progression of human complex diseases. More and more computational models have been developed to identify potential...

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Autores principales: Qu, Jia, Chen, Xing, Sun, Ya-Zhou, Li, Jian-Qiang, Ming, Zhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6020102/
https://www.ncbi.nlm.nih.gov/pubmed/29943160
http://dx.doi.org/10.1186/s13321-018-0284-9
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author Qu, Jia
Chen, Xing
Sun, Ya-Zhou
Li, Jian-Qiang
Ming, Zhong
author_facet Qu, Jia
Chen, Xing
Sun, Ya-Zhou
Li, Jian-Qiang
Ming, Zhong
author_sort Qu, Jia
collection PubMed
description Recently, many biological experiments have indicated that microRNAs (miRNAs) are a newly discovered small molecule (SM) drug targets that play an important role in the development and progression of human complex diseases. More and more computational models have been developed to identify potential associations between SMs and target miRNAs, which would be a great help for disease therapy and clinical applications for known drugs in the field of medical research. In this study, we proposed a computational model of triple layer heterogeneous network based small molecule–MiRNA association prediction (TLHNSMMA) to uncover potential SM–miRNA associations by integrating integrated SM similarity, integrated miRNA similarity, integrated disease similarity, experimentally verified SM–miRNA associations and miRNA–disease associations into a heterogeneous graph. To evaluate the performance of TLHNSMMA, we implemented global and two types of local leave-one-out cross validation as well as fivefold cross validation to compare TLHNSMMA with one previous classical computational model (SMiR-NBI). As a result, for Dataset 1, TLHNSMMA obtained the AUCs of 0.9859, 0.9845, 0.7645 and 0.9851 ± 0.0012, respectively; for Dataset 2, the AUCs are in turn 0.8149, 0.8244, 0.6057 and 0.8168 ± 0.0022. As the result of case studies shown, among the top 10, 20 and 50 potential SM-related miRNAs, there were 2, 7 and 14 SM–miRNA associations confirmed by experiments, respectively. Therefore, TLHNSMMA could be effectively applied to the prediction of SM–miRNA associations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13321-018-0284-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-60201022018-07-13 Inferring potential small molecule–miRNA association based on triple layer heterogeneous network Qu, Jia Chen, Xing Sun, Ya-Zhou Li, Jian-Qiang Ming, Zhong J Cheminform Research Article Recently, many biological experiments have indicated that microRNAs (miRNAs) are a newly discovered small molecule (SM) drug targets that play an important role in the development and progression of human complex diseases. More and more computational models have been developed to identify potential associations between SMs and target miRNAs, which would be a great help for disease therapy and clinical applications for known drugs in the field of medical research. In this study, we proposed a computational model of triple layer heterogeneous network based small molecule–MiRNA association prediction (TLHNSMMA) to uncover potential SM–miRNA associations by integrating integrated SM similarity, integrated miRNA similarity, integrated disease similarity, experimentally verified SM–miRNA associations and miRNA–disease associations into a heterogeneous graph. To evaluate the performance of TLHNSMMA, we implemented global and two types of local leave-one-out cross validation as well as fivefold cross validation to compare TLHNSMMA with one previous classical computational model (SMiR-NBI). As a result, for Dataset 1, TLHNSMMA obtained the AUCs of 0.9859, 0.9845, 0.7645 and 0.9851 ± 0.0012, respectively; for Dataset 2, the AUCs are in turn 0.8149, 0.8244, 0.6057 and 0.8168 ± 0.0022. As the result of case studies shown, among the top 10, 20 and 50 potential SM-related miRNAs, there were 2, 7 and 14 SM–miRNA associations confirmed by experiments, respectively. Therefore, TLHNSMMA could be effectively applied to the prediction of SM–miRNA associations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13321-018-0284-9) contains supplementary material, which is available to authorized users. Springer International Publishing 2018-06-26 /pmc/articles/PMC6020102/ /pubmed/29943160 http://dx.doi.org/10.1186/s13321-018-0284-9 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Qu, Jia
Chen, Xing
Sun, Ya-Zhou
Li, Jian-Qiang
Ming, Zhong
Inferring potential small molecule–miRNA association based on triple layer heterogeneous network
title Inferring potential small molecule–miRNA association based on triple layer heterogeneous network
title_full Inferring potential small molecule–miRNA association based on triple layer heterogeneous network
title_fullStr Inferring potential small molecule–miRNA association based on triple layer heterogeneous network
title_full_unstemmed Inferring potential small molecule–miRNA association based on triple layer heterogeneous network
title_short Inferring potential small molecule–miRNA association based on triple layer heterogeneous network
title_sort inferring potential small molecule–mirna association based on triple layer heterogeneous network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6020102/
https://www.ncbi.nlm.nih.gov/pubmed/29943160
http://dx.doi.org/10.1186/s13321-018-0284-9
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