Cargando…

MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network

MiRNAs can regulate genes encoding specific proteins which are related to the efficacy of drugs, and predicting miRNA-drug resistance associations is of great importance. In this work, we propose an attentive multimodal graph convolution network method (AMMGC) to predict miRNA-drug resistance associ...

Descripción completa

Detalles Bibliográficos
Autores principales: Niu, Yanqing, Song, Congzhi, Gong, Yuchong, Zhang, Wen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8790023/
https://www.ncbi.nlm.nih.gov/pubmed/35095506
http://dx.doi.org/10.3389/fphar.2021.799108
_version_ 1784639897278087168
author Niu, Yanqing
Song, Congzhi
Gong, Yuchong
Zhang, Wen
author_facet Niu, Yanqing
Song, Congzhi
Gong, Yuchong
Zhang, Wen
author_sort Niu, Yanqing
collection PubMed
description MiRNAs can regulate genes encoding specific proteins which are related to the efficacy of drugs, and predicting miRNA-drug resistance associations is of great importance. In this work, we propose an attentive multimodal graph convolution network method (AMMGC) to predict miRNA-drug resistance associations. AMMGC learns the latent representations of drugs and miRNAs from four graph convolution sub-networks with distinctive combinations of features. Then, an attention neural network is employed to obtain attentive representations of drugs and miRNAs, and miRNA-drug resistance associations are predicted by the inner product of learned attentive representations. The computational experiments show that AMMGC outperforms other state-of-the-art methods and baseline methods, achieving the AUPR score of 0.2399 and the AUC score of 0.9467. The analysis demonstrates that leveraging multiple features of drugs and miRNAs can make a contribution to the miRNA-drug resistance association prediction. The usefulness of AMMGC is further validated by case studies.
format Online
Article
Text
id pubmed-8790023
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-87900232022-01-27 MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network Niu, Yanqing Song, Congzhi Gong, Yuchong Zhang, Wen Front Pharmacol Pharmacology MiRNAs can regulate genes encoding specific proteins which are related to the efficacy of drugs, and predicting miRNA-drug resistance associations is of great importance. In this work, we propose an attentive multimodal graph convolution network method (AMMGC) to predict miRNA-drug resistance associations. AMMGC learns the latent representations of drugs and miRNAs from four graph convolution sub-networks with distinctive combinations of features. Then, an attention neural network is employed to obtain attentive representations of drugs and miRNAs, and miRNA-drug resistance associations are predicted by the inner product of learned attentive representations. The computational experiments show that AMMGC outperforms other state-of-the-art methods and baseline methods, achieving the AUPR score of 0.2399 and the AUC score of 0.9467. The analysis demonstrates that leveraging multiple features of drugs and miRNAs can make a contribution to the miRNA-drug resistance association prediction. The usefulness of AMMGC is further validated by case studies. Frontiers Media S.A. 2022-01-12 /pmc/articles/PMC8790023/ /pubmed/35095506 http://dx.doi.org/10.3389/fphar.2021.799108 Text en Copyright © 2022 Niu, Song, Gong and Zhang. 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 Pharmacology
Niu, Yanqing
Song, Congzhi
Gong, Yuchong
Zhang, Wen
MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network
title MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network
title_full MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network
title_fullStr MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network
title_full_unstemmed MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network
title_short MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network
title_sort mirna-drug resistance association prediction through the attentive multimodal graph convolutional network
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8790023/
https://www.ncbi.nlm.nih.gov/pubmed/35095506
http://dx.doi.org/10.3389/fphar.2021.799108
work_keys_str_mv AT niuyanqing mirnadrugresistanceassociationpredictionthroughtheattentivemultimodalgraphconvolutionalnetwork
AT songcongzhi mirnadrugresistanceassociationpredictionthroughtheattentivemultimodalgraphconvolutionalnetwork
AT gongyuchong mirnadrugresistanceassociationpredictionthroughtheattentivemultimodalgraphconvolutionalnetwork
AT zhangwen mirnadrugresistanceassociationpredictionthroughtheattentivemultimodalgraphconvolutionalnetwork