典型文献
Application of machine learning algorithms to screen potential biomarkers under cadmium exposure based on human urine metabolic profiles
文献摘要：
Exposure to environmental cadmium increases the health risk of residents.Early urine metabolic detec-tion using high-resolution mass spectrometry and machine learning algorithms would be advantageous to predict the adverse health effects.Here,we conducted machine learning approaches to screen poten-tial biomarkers under cadmium exposure in 403 urine samples.In positive and negative ionization mode,4207 and 3558 features were extracted,respectively.We compared seven machine learning algorithms and found that the extreme gradient boosting(XGBoost)and random forest(RF)classifiers showed bet-ter accuracy and predictive performance than others.Following 5-fold cross-validation,the value of area under curve(AUC)was both 0.93 for positive and negative ionization modes in XGBoost classifier.In the RF classifier,AUC were 0.80 and 0.84 for positive and negative ionization modes,respectively.We then identified a biomarker panel based on XGBoost and RF classifiers.The incorporation of machine learning models into urine analysis using high-resolution mass spectrometry could allow a convenient assessment of cadmium exposure.
文献关键词：
中图分类号：
作者姓名：
Ting Zeng;Yanshan Liang;Qingyuan Dai;Jinglin Tian;Jinyao Chen;Bo Lei;Zhu Yang;Zongwei Cai
作者机构：
Food Science and Technology Program,Beijing Normal University-Hong Kong Baptist University United International College,Zhuhai 519087,China;State Key Laboratory of Environmental and Biological Analysis,Department of Chemistry,Hong Kong Baptist University,Hong Kong,China;Department of Nutrition,Food Safety and Toxicology,West China School of Public Health,Sichuan University,Chengdu 610041,China
文献出处：
引用格式：
[1]Ting Zeng;Yanshan Liang;Qingyuan Dai;Jinglin Tian;Jinyao Chen;Bo Lei;Zhu Yang;Zongwei Cai-.Application of machine learning algorithms to screen potential biomarkers under cadmium exposure based on human urine metabolic profiles)[J].中国化学快报（英文版）,2022(12):5184-5188
A类：
B类：
Application,machine,learning,algorithms,screen,potential,biomarkers,under,cadmium,exposure,human,urine,metabolic,profiles,Exposure,environmental,increases,health,risk,residents,Early,detec,using,high,resolution,mass,spectrometry,would,advantageous,adverse,effects,Here,conducted,approaches,samples,In,positive,negative,ionization,features,were,extracted,respectively,We,compared,seven,found,that,extreme,gradient,boosting,XGBoost,random,forest,RF,classifiers,showed,bet,ter,accuracy,predictive,performance,than,others,Following,fold,cross,validation,value,area,curve,was,both,modes,then,identified,panel,incorporation,models,into,analysis,could,allow,convenient,assessment
AB值：
0.540885
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