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BNEMDI: A Novel MicroRNA–Drug Interaction Prediction Model Based on Multi-Source Information With a Large-Scale Biological Network
As a novel target in pharmacy, microRNA (miRNA) can regulate gene expression under specific disease conditions to produce specific proteins. To date, many researchers leveraged miRNA to reveal drug efficacy and pathogenesis at the molecular level. As we all know that conventional wet experiments suf...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9334674/ https://www.ncbi.nlm.nih.gov/pubmed/35910223 http://dx.doi.org/10.3389/fgene.2022.919264 |
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author | Guan, Yong-Jian Yu, Chang-Qing Li, Li-Ping You, Zhu-Hong Ren, Zhong-Hao Pan, Jie Li, Yue-Chao |
author_facet | Guan, Yong-Jian Yu, Chang-Qing Li, Li-Ping You, Zhu-Hong Ren, Zhong-Hao Pan, Jie Li, Yue-Chao |
author_sort | Guan, Yong-Jian |
collection | PubMed |
description | As a novel target in pharmacy, microRNA (miRNA) can regulate gene expression under specific disease conditions to produce specific proteins. To date, many researchers leveraged miRNA to reveal drug efficacy and pathogenesis at the molecular level. As we all know that conventional wet experiments suffer from many problems, including time-consuming, labor-intensity, and high cost. Thus, there is an urgent need to develop a novel computational model to facilitate the identification of miRNA–drug interactions (MDIs). In this work, we propose a novel bipartite network embedding-based method called BNEMDI to predict MDIs. First, the Bipartite Network Embedding (BiNE) algorithm is employed to learn the topological features from the network. Then, the inherent attributes of drugs and miRNAs are expressed as attribute features by MACCS fingerprints and k-mers. Finally, we feed these features into deep neural network (DNN) for training the prediction model. To validate the prediction ability of the BNEMDI model, we apply it to five different benchmark datasets under five-fold cross-validation, and the proposed model obtained excellent AUC values of 0.9568, 0.9420, 0.8489, 0.8774, and 0.9005 in ncDR, RNAInter, SM2miR1, SM2miR2, and SM2miR MDI datasets, respectively. To further verify the prediction performance of the BNEMDI model, we compare it with some existing powerful methods. We also compare the BiNE algorithm with several different network embedding methods. Furthermore, we carry out a case study on a common drug named 5-fluorouracil. Among the top 50 miRNAs predicted by the proposed model, there were 38 verified by the experimental literature. The comprehensive experiment results demonstrated that our method is effective and robust for predicting MDIs. In the future work, we hope that the BNEMDI model can be a reliable supplement method for the development of pharmacology and miRNA therapeutics. |
format | Online Article Text |
id | pubmed-9334674 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93346742022-07-30 BNEMDI: A Novel MicroRNA–Drug Interaction Prediction Model Based on Multi-Source Information With a Large-Scale Biological Network Guan, Yong-Jian Yu, Chang-Qing Li, Li-Ping You, Zhu-Hong Ren, Zhong-Hao Pan, Jie Li, Yue-Chao Front Genet Genetics As a novel target in pharmacy, microRNA (miRNA) can regulate gene expression under specific disease conditions to produce specific proteins. To date, many researchers leveraged miRNA to reveal drug efficacy and pathogenesis at the molecular level. As we all know that conventional wet experiments suffer from many problems, including time-consuming, labor-intensity, and high cost. Thus, there is an urgent need to develop a novel computational model to facilitate the identification of miRNA–drug interactions (MDIs). In this work, we propose a novel bipartite network embedding-based method called BNEMDI to predict MDIs. First, the Bipartite Network Embedding (BiNE) algorithm is employed to learn the topological features from the network. Then, the inherent attributes of drugs and miRNAs are expressed as attribute features by MACCS fingerprints and k-mers. Finally, we feed these features into deep neural network (DNN) for training the prediction model. To validate the prediction ability of the BNEMDI model, we apply it to five different benchmark datasets under five-fold cross-validation, and the proposed model obtained excellent AUC values of 0.9568, 0.9420, 0.8489, 0.8774, and 0.9005 in ncDR, RNAInter, SM2miR1, SM2miR2, and SM2miR MDI datasets, respectively. To further verify the prediction performance of the BNEMDI model, we compare it with some existing powerful methods. We also compare the BiNE algorithm with several different network embedding methods. Furthermore, we carry out a case study on a common drug named 5-fluorouracil. Among the top 50 miRNAs predicted by the proposed model, there were 38 verified by the experimental literature. The comprehensive experiment results demonstrated that our method is effective and robust for predicting MDIs. In the future work, we hope that the BNEMDI model can be a reliable supplement method for the development of pharmacology and miRNA therapeutics. Frontiers Media S.A. 2022-07-15 /pmc/articles/PMC9334674/ /pubmed/35910223 http://dx.doi.org/10.3389/fgene.2022.919264 Text en Copyright © 2022 Guan, Yu, Li, You, Ren, Pan and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Guan, Yong-Jian Yu, Chang-Qing Li, Li-Ping You, Zhu-Hong Ren, Zhong-Hao Pan, Jie Li, Yue-Chao BNEMDI: A Novel MicroRNA–Drug Interaction Prediction Model Based on Multi-Source Information With a Large-Scale Biological Network |
title | BNEMDI: A Novel MicroRNA–Drug Interaction Prediction Model Based on Multi-Source Information With a Large-Scale Biological Network |
title_full | BNEMDI: A Novel MicroRNA–Drug Interaction Prediction Model Based on Multi-Source Information With a Large-Scale Biological Network |
title_fullStr | BNEMDI: A Novel MicroRNA–Drug Interaction Prediction Model Based on Multi-Source Information With a Large-Scale Biological Network |
title_full_unstemmed | BNEMDI: A Novel MicroRNA–Drug Interaction Prediction Model Based on Multi-Source Information With a Large-Scale Biological Network |
title_short | BNEMDI: A Novel MicroRNA–Drug Interaction Prediction Model Based on Multi-Source Information With a Large-Scale Biological Network |
title_sort | bnemdi: a novel microrna–drug interaction prediction model based on multi-source information with a large-scale biological network |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9334674/ https://www.ncbi.nlm.nih.gov/pubmed/35910223 http://dx.doi.org/10.3389/fgene.2022.919264 |
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