液相色谱-高分辨质谱法在机器学习辅助下识别环境污染物

IF 11.8 1区 化学 Q1 CHEMISTRY, ANALYTICAL Trends in Analytical Chemistry Pub Date : 2024-09-25 DOI:10.1016/j.trac.2024.117988
Haotian Wang , Laijin Zhong , Wenyuan Su , Ting Ruan , Guibin Jiang
{"title":"液相色谱-高分辨质谱法在机器学习辅助下识别环境污染物","authors":"Haotian Wang ,&nbsp;Laijin Zhong ,&nbsp;Wenyuan Su ,&nbsp;Ting Ruan ,&nbsp;Guibin Jiang","doi":"10.1016/j.trac.2024.117988","DOIUrl":null,"url":null,"abstract":"<div><div>Chemical exposure can be linked with various adverse effects, but the causal association is still poorly understood. To meet the challenge, non-target screening (NTS) based on liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) is increasingly applied to identify known and unknown chemicals with toxicological concerns present in environmental and biological samples. In this review, we highlight that the integration of predictive toxicology in NTS workflows enables large-scale screening for emerging chemical contaminants. We summarize the applications of machine learning (ML) and deep learning (DL) in toxicity prediction with a focus on biological pathway perturbation and LC-HRMS data processing, especially in peak picking and molecular structure elucidation. The substantial progress in computational approaches allows for identifying and prioritizing emerging chemical contaminants with improved accuracy, reproducibility, and efficacy. ML and DL will become next-generation informatics tools in NTS workflows to better characterize exposure to environmental pollutants.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":null,"pages":null},"PeriodicalIF":11.8000,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning-assisted identification of environmental pollutants by liquid chromatography coupled with high-resolution mass spectrometry\",\"authors\":\"Haotian Wang ,&nbsp;Laijin Zhong ,&nbsp;Wenyuan Su ,&nbsp;Ting Ruan ,&nbsp;Guibin Jiang\",\"doi\":\"10.1016/j.trac.2024.117988\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Chemical exposure can be linked with various adverse effects, but the causal association is still poorly understood. To meet the challenge, non-target screening (NTS) based on liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) is increasingly applied to identify known and unknown chemicals with toxicological concerns present in environmental and biological samples. In this review, we highlight that the integration of predictive toxicology in NTS workflows enables large-scale screening for emerging chemical contaminants. We summarize the applications of machine learning (ML) and deep learning (DL) in toxicity prediction with a focus on biological pathway perturbation and LC-HRMS data processing, especially in peak picking and molecular structure elucidation. The substantial progress in computational approaches allows for identifying and prioritizing emerging chemical contaminants with improved accuracy, reproducibility, and efficacy. ML and DL will become next-generation informatics tools in NTS workflows to better characterize exposure to environmental pollutants.</div></div>\",\"PeriodicalId\":439,\"journal\":{\"name\":\"Trends in Analytical Chemistry\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":11.8000,\"publicationDate\":\"2024-09-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Trends in Analytical Chemistry\",\"FirstCategoryId\":\"1\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0165993624004710\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in Analytical Chemistry","FirstCategoryId":"1","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0165993624004710","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0

摘要

化学品暴露可能与各种不良影响有关,但人们对其因果关系仍然知之甚少。为了应对这一挑战,基于液相色谱-高分辨质谱(LC-HRMS)的非目标筛选(NTS)越来越多地应用于识别环境和生物样本中存在的已知和未知化学物质。在本综述中,我们将重点介绍在 NTS 工作流程中整合预测毒理学,从而实现对新兴化学污染物的大规模筛查。我们总结了机器学习(ML)和深度学习(DL)在毒性预测中的应用,重点关注生物途径扰动和 LC-HRMS 数据处理,尤其是在峰值拾取和分子结构阐明方面。计算方法的长足进步有助于识别和优先处理新兴化学污染物,并提高其准确性、可重复性和有效性。ML 和 DL 将成为 NTS 工作流程中的下一代信息学工具,以更好地描述环境污染物的暴露特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Machine learning-assisted identification of environmental pollutants by liquid chromatography coupled with high-resolution mass spectrometry
Chemical exposure can be linked with various adverse effects, but the causal association is still poorly understood. To meet the challenge, non-target screening (NTS) based on liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) is increasingly applied to identify known and unknown chemicals with toxicological concerns present in environmental and biological samples. In this review, we highlight that the integration of predictive toxicology in NTS workflows enables large-scale screening for emerging chemical contaminants. We summarize the applications of machine learning (ML) and deep learning (DL) in toxicity prediction with a focus on biological pathway perturbation and LC-HRMS data processing, especially in peak picking and molecular structure elucidation. The substantial progress in computational approaches allows for identifying and prioritizing emerging chemical contaminants with improved accuracy, reproducibility, and efficacy. ML and DL will become next-generation informatics tools in NTS workflows to better characterize exposure to environmental pollutants.
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
Trends in Analytical Chemistry
Trends in Analytical Chemistry 化学-分析化学
CiteScore
20.00
自引率
4.60%
发文量
257
审稿时长
3.4 months
期刊介绍: TrAC publishes succinct and critical overviews of recent advancements in analytical chemistry, designed to assist analytical chemists and other users of analytical techniques. These reviews offer excellent, up-to-date, and timely coverage of various topics within analytical chemistry. Encompassing areas such as analytical instrumentation, biomedical analysis, biomolecular analysis, biosensors, chemical analysis, chemometrics, clinical chemistry, drug discovery, environmental analysis and monitoring, food analysis, forensic science, laboratory automation, materials science, metabolomics, pesticide-residue analysis, pharmaceutical analysis, proteomics, surface science, and water analysis and monitoring, these critical reviews provide comprehensive insights for practitioners in the field.
期刊最新文献
Latest trends in biosensors powered by nucleic acid isothermal amplification for the diagnosis of joint infections: From sampling to identification towards the point-of-care Recommendations, trends and analytical strategies applied for biological samples collection (sampling) and storage in forensic toxicology of volatile poisons (inorganic and organic) Multimodal probes for the detection of bone cancer-related disease in biological systems: Recent advances and future prospects Analytical methods for protein kinase and inhibitor screening including kinetic evaluation Virgin olive oil authentication using mass spectrometry-based approaches: A review
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1