DOI: 10.1016/j.scib.2020.04.006
论文题名: Graph attention convolutional neural network model for chemical poisoning of honey bees’ prediction
作者: Wang F. ; Yang J.-F. ; Wang M.-Y. ; Jia C.-Y. ; Shi X.-X. ; Hao G.-F. ; Yang G.-F.
刊名: Science Bulletin
ISSN: 20959273
出版年: 2020
卷: 65, 期: 14 起始页码: 1184
结束页码: 1191
语种: 英语
中文关键词: Deep learning
; Graph attention convolutional neural networks
; Honey bees toxicity
; Molecular design
; Pesticide
英文关键词: Chemical contamination
; Convolution
; Deep learning
; Food supply
; Forecasting
; Pesticides
; Risk assessment
; Accurate prediction
; Chemical exposure
; Chemical poisoning
; Chemical toxicity
; Correlation coefficient
; Environmental risk assessment
; Machine learning models
; Molecular similarity
; Convolutional neural networks
英文摘要: The impact of pesticides on insect pollinators has caused worldwide concern. Both global bee decline and stopping the use of pesticides may have serious consequences for food security. Automated and accurate prediction of chemical poisoning of honey bees is a challenging task owing to a lack of understanding of chemical toxicity and introspection. Deep learning (DL) shows potential utility for general and highly variable tasks across fields. Here, we developed a new DL model of deep graph attention convolutional neural networks (GACNN) with the combination of undirected graph (UG) and attention convolutional neural networks (ACNN) to accurately classify chemical poisoning of honey bees. We used a training dataset of 720 pesticides and an external validation dataset of 90 pesticides, which is one order of magnitude larger than the previous datasets. We tested its performance in two ways: poisonous versus non-poisonous and GACNN versus other frequently-used machine learning models. The first case represents the accuracy in identifying bee poisonous chemicals. The second represents performance advantages. The GACNN achieved ~6% higher performance for predicting toxic samples and more stable with ~7% Matthews Correlation Coefficient (MCC) higher compared to all tested models, demonstrating GACNN is capable of accurately classifying chemicals and has considerable potential in practical applications. In addition, we also summarized and evaluated the mechanisms underlying the response of honey bees to chemical exposure based on the mapping of molecular similarity. Moreover, our cloud platform (http://beetox.cn) of this model provides low-cost universal access to information, which could vitally enhance environmental risk assessment. © 2020 Science China Press Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/170057
Appears in Collections: 气候变化与战略
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作者单位: Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, 430079, China; International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan, 430079, China; Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, 300072, China
Recommended Citation:
Wang F.,Yang J.-F.,Wang M.-Y.,et al. Graph attention convolutional neural network model for chemical poisoning of honey bees’ prediction[J]. Science Bulletin,2020-01-01,65(14)