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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...

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Autores principales: Guan, Yong-Jian, Yu, Chang-Qing, Li, Li-Ping, You, Zhu-Hong, Ren, Zhong-Hao, Pan, Jie, Li, Yue-Chao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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.
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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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