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NNAN: Nearest Neighbor Attention Network to Predict Drug–Microbe Associations
Many drugs can be metabolized by human microbes; the drug metabolites would significantly alter pharmacological effects and result in low therapeutic efficacy for patients. Hence, it is crucial to identify potential drug–microbe associations (DMAs) before the drug administrations. Nevertheless, trad...
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/PMC9035839/ https://www.ncbi.nlm.nih.gov/pubmed/35479616 http://dx.doi.org/10.3389/fmicb.2022.846915 |
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author | Zhu, Bei Xu, Yi Zhao, Pengcheng Yiu, Siu-Ming Yu, Hui Shi, Jian-Yu |
author_facet | Zhu, Bei Xu, Yi Zhao, Pengcheng Yiu, Siu-Ming Yu, Hui Shi, Jian-Yu |
author_sort | Zhu, Bei |
collection | PubMed |
description | Many drugs can be metabolized by human microbes; the drug metabolites would significantly alter pharmacological effects and result in low therapeutic efficacy for patients. Hence, it is crucial to identify potential drug–microbe associations (DMAs) before the drug administrations. Nevertheless, traditional DMA determination cannot be applied in a wide range due to the tremendous number of microbe species, high costs, and the fact that it is time-consuming. Thus, predicting possible DMAs in computer technology is an essential topic. Inspired by other issues addressed by deep learning, we designed a deep learning-based model named Nearest Neighbor Attention Network (NNAN). The proposed model consists of four components, namely, a similarity network constructor, a nearest-neighbor aggregator, a feature attention block, and a predictor. In brief, the similarity block contains a microbe similarity network and a drug similarity network. The nearest-neighbor aggregator generates the embedding representations of drug–microbe pairs by integrating drug neighbors and microbe neighbors of each drug–microbe pair in the network. The feature attention block evaluates the importance of each dimension of drug–microbe pair embedding by a set of ordinary multi-layer neural networks. The predictor is an ordinary fully-connected deep neural network that functions as a binary classifier to distinguish potential DMAs among unlabeled drug–microbe pairs. Several experiments on two benchmark databases are performed to evaluate the performance of NNAN. First, the comparison with state-of-the-art baseline approaches demonstrates the superiority of NNAN under cross-validation in terms of predicting performance. Moreover, the interpretability inspection reveals that a drug tends to associate with a microbe if it finds its top-l most similar neighbors that associate with the microbe. |
format | Online Article Text |
id | pubmed-9035839 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90358392022-04-26 NNAN: Nearest Neighbor Attention Network to Predict Drug–Microbe Associations Zhu, Bei Xu, Yi Zhao, Pengcheng Yiu, Siu-Ming Yu, Hui Shi, Jian-Yu Front Microbiol Microbiology Many drugs can be metabolized by human microbes; the drug metabolites would significantly alter pharmacological effects and result in low therapeutic efficacy for patients. Hence, it is crucial to identify potential drug–microbe associations (DMAs) before the drug administrations. Nevertheless, traditional DMA determination cannot be applied in a wide range due to the tremendous number of microbe species, high costs, and the fact that it is time-consuming. Thus, predicting possible DMAs in computer technology is an essential topic. Inspired by other issues addressed by deep learning, we designed a deep learning-based model named Nearest Neighbor Attention Network (NNAN). The proposed model consists of four components, namely, a similarity network constructor, a nearest-neighbor aggregator, a feature attention block, and a predictor. In brief, the similarity block contains a microbe similarity network and a drug similarity network. The nearest-neighbor aggregator generates the embedding representations of drug–microbe pairs by integrating drug neighbors and microbe neighbors of each drug–microbe pair in the network. The feature attention block evaluates the importance of each dimension of drug–microbe pair embedding by a set of ordinary multi-layer neural networks. The predictor is an ordinary fully-connected deep neural network that functions as a binary classifier to distinguish potential DMAs among unlabeled drug–microbe pairs. Several experiments on two benchmark databases are performed to evaluate the performance of NNAN. First, the comparison with state-of-the-art baseline approaches demonstrates the superiority of NNAN under cross-validation in terms of predicting performance. Moreover, the interpretability inspection reveals that a drug tends to associate with a microbe if it finds its top-l most similar neighbors that associate with the microbe. Frontiers Media S.A. 2022-04-11 /pmc/articles/PMC9035839/ /pubmed/35479616 http://dx.doi.org/10.3389/fmicb.2022.846915 Text en Copyright © 2022 Zhu, Xu, Zhao, Yiu, Yu and Shi. 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 | Microbiology Zhu, Bei Xu, Yi Zhao, Pengcheng Yiu, Siu-Ming Yu, Hui Shi, Jian-Yu NNAN: Nearest Neighbor Attention Network to Predict Drug–Microbe Associations |
title | NNAN: Nearest Neighbor Attention Network to Predict Drug–Microbe Associations |
title_full | NNAN: Nearest Neighbor Attention Network to Predict Drug–Microbe Associations |
title_fullStr | NNAN: Nearest Neighbor Attention Network to Predict Drug–Microbe Associations |
title_full_unstemmed | NNAN: Nearest Neighbor Attention Network to Predict Drug–Microbe Associations |
title_short | NNAN: Nearest Neighbor Attention Network to Predict Drug–Microbe Associations |
title_sort | nnan: nearest neighbor attention network to predict drug–microbe associations |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9035839/ https://www.ncbi.nlm.nih.gov/pubmed/35479616 http://dx.doi.org/10.3389/fmicb.2022.846915 |
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