给定化学物质对给定水生物种的生态毒性是多少?使用推荐系统技术预测物种和化学品之间的相互作用。

IF 2.3 3区 环境科学与生态学 Q3 CHEMISTRY, MULTIDISCIPLINARY
M Viljanen, J Minnema, P N H Wassenaar, E Rorije, W Peijnenburg
{"title":"给定化学物质对给定水生物种的生态毒性是多少?使用推荐系统技术预测物种和化学品之间的相互作用。","authors":"M Viljanen,&nbsp;J Minnema,&nbsp;P N H Wassenaar,&nbsp;E Rorije,&nbsp;W Peijnenburg","doi":"10.1080/1062936X.2023.2254225","DOIUrl":null,"url":null,"abstract":"<p><p>Ecotoxicological safety assessment of chemicals requires toxicity data on multiple species, despite the general desire of minimizing animal testing. Predictive models, specifically machine learning (ML) methods, are one of the tools capable of solving this apparent contradiction as they allow to generalize toxicity patterns across chemicals and species. However, despite the availability of large public toxicity datasets, the data is highly sparse, complicating model development. The aim of this study is to provide insights into how ML can predict toxicity using a large but sparse dataset. We developed models to predict LC50-values, based on experimental LC50-data covering 2431 organic chemicals and 1506 aquatic species from the ECOTOX-database. Several well-known ML techniques were evaluated and a new ML model was developed, inspired by recommender systems. This new model involves a simple linear model that learns low-rank interactions between species and chemicals using factorization machines. We evaluated the predictive performances of the developed models based on two validation settings: 1) predicting unseen chemical-species pairs, and 2) predicting unseen chemicals. The results of this study show that ML models can accurately predict LC50-values in both validation settings. Moreover, we show that the novel factorization machine approach can match well-tuned, complex, ML approaches.</p>","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"765-788"},"PeriodicalIF":2.3000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"What is the ecotoxicity of a given chemical for a given aquatic species? Predicting interactions between species and chemicals using recommender system techniques.\",\"authors\":\"M Viljanen,&nbsp;J Minnema,&nbsp;P N H Wassenaar,&nbsp;E Rorije,&nbsp;W Peijnenburg\",\"doi\":\"10.1080/1062936X.2023.2254225\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Ecotoxicological safety assessment of chemicals requires toxicity data on multiple species, despite the general desire of minimizing animal testing. Predictive models, specifically machine learning (ML) methods, are one of the tools capable of solving this apparent contradiction as they allow to generalize toxicity patterns across chemicals and species. However, despite the availability of large public toxicity datasets, the data is highly sparse, complicating model development. The aim of this study is to provide insights into how ML can predict toxicity using a large but sparse dataset. We developed models to predict LC50-values, based on experimental LC50-data covering 2431 organic chemicals and 1506 aquatic species from the ECOTOX-database. Several well-known ML techniques were evaluated and a new ML model was developed, inspired by recommender systems. This new model involves a simple linear model that learns low-rank interactions between species and chemicals using factorization machines. We evaluated the predictive performances of the developed models based on two validation settings: 1) predicting unseen chemical-species pairs, and 2) predicting unseen chemicals. The results of this study show that ML models can accurately predict LC50-values in both validation settings. Moreover, we show that the novel factorization machine approach can match well-tuned, complex, ML approaches.</p>\",\"PeriodicalId\":21446,\"journal\":{\"name\":\"SAR and QSAR in Environmental Research\",\"volume\":\" \",\"pages\":\"765-788\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SAR and QSAR in Environmental Research\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1080/1062936X.2023.2254225\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/9/6 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SAR and QSAR in Environmental Research","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1080/1062936X.2023.2254225","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/9/6 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

化学品的生态毒理学安全评估需要多个物种的毒性数据,尽管人们普遍希望尽量减少动物试验。预测模型,特别是机器学习(ML)方法,是能够解决这一明显矛盾的工具之一,因为它们可以概括化学品和物种的毒性模式。然而,尽管有大型公共毒性数据集,但数据高度稀疏,使模型开发复杂化。本研究的目的是深入了解ML如何使用大型但稀疏的数据集预测毒性。我们根据ECOTOX数据库中2431种有机化学品和1506种水生物种的实验LC50数据,开发了预测LC50值的模型。对几种著名的ML技术进行了评估,并在推荐系统的启发下开发了一个新的ML模型。这个新模型涉及一个简单的线性模型,该模型使用因子分解机学习物种和化学物质之间的低阶相互作用。我们基于两个验证设置评估了所开发的模型的预测性能:1)预测看不见的化学物质对,2)预测看看不到的化学物质。本研究的结果表明,ML模型可以准确预测两种验证设置下的LC50值。此外,我们还证明了新的因子分解机方法可以匹配调整良好的复杂ML方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
What is the ecotoxicity of a given chemical for a given aquatic species? Predicting interactions between species and chemicals using recommender system techniques.

Ecotoxicological safety assessment of chemicals requires toxicity data on multiple species, despite the general desire of minimizing animal testing. Predictive models, specifically machine learning (ML) methods, are one of the tools capable of solving this apparent contradiction as they allow to generalize toxicity patterns across chemicals and species. However, despite the availability of large public toxicity datasets, the data is highly sparse, complicating model development. The aim of this study is to provide insights into how ML can predict toxicity using a large but sparse dataset. We developed models to predict LC50-values, based on experimental LC50-data covering 2431 organic chemicals and 1506 aquatic species from the ECOTOX-database. Several well-known ML techniques were evaluated and a new ML model was developed, inspired by recommender systems. This new model involves a simple linear model that learns low-rank interactions between species and chemicals using factorization machines. We evaluated the predictive performances of the developed models based on two validation settings: 1) predicting unseen chemical-species pairs, and 2) predicting unseen chemicals. The results of this study show that ML models can accurately predict LC50-values in both validation settings. Moreover, we show that the novel factorization machine approach can match well-tuned, complex, ML approaches.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
5.20
自引率
20.00%
发文量
78
审稿时长
>24 weeks
期刊介绍: SAR and QSAR in Environmental Research is an international journal welcoming papers on the fundamental and practical aspects of the structure-activity and structure-property relationships in the fields of environmental science, agrochemistry, toxicology, pharmacology and applied chemistry. A unique aspect of the journal is the focus on emerging techniques for the building of SAR and QSAR models in these widely varying fields. The scope of the journal includes, but is not limited to, the topics of topological and physicochemical descriptors, mathematical, statistical and graphical methods for data analysis, computer methods and programs, original applications and comparative studies. In addition to primary scientific papers, the journal contains reviews of books and software and news of conferences. Special issues on topics of current and widespread interest to the SAR and QSAR community will be published from time to time.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信