气候变化领域集成服务门户(CCPortal): Graph attention convolutional neural network model for chemical poisoning of honey bees’ prediction

CCPortal

DOI	10.1016/j.scib.2020.04.006
	Graph attention convolutional neural network model for chemical poisoning of honey bees’ prediction
	Wang F.; Yang J.-F.; Wang M.-Y.; Jia C.-Y.; Shi X.-X.; Hao G.-F.; Yang G.-F.
发表日期	2020
ISSN	20959273
起始页码	1184
结束页码	1191
卷号	65 期号:14
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
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
语种	英语
来源期刊	Science Bulletin
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/207201
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
推荐引用方式 GB/T 7714	Wang F.,Yang J.-F.,Wang M.-Y.,等. Graph attention convolutional neural network model for chemical poisoning of honey bees’ prediction[J],2020,65(14).
APA	Wang F..,Yang J.-F..,Wang M.-Y..,Jia C.-Y..,Shi X.-X..,...&Yang G.-F..(2020).Graph attention convolutional neural network model for chemical poisoning of honey bees’ prediction.Science Bulletin,65(14).
MLA	Wang F.,et al."Graph attention convolutional neural network model for chemical poisoning of honey bees’ prediction".Science Bulletin 65.14(2020).