计算机科学
鉴定(生物学)
支持向量机
软件
假阳性率
集合(抽象数据类型)
电流(流体)
机器学习
数据挖掘
人工智能
操作系统
生物
生态学
电气工程
程序设计语言
工程类
作者
X. Y. Wang,Nanyang Yu,Zhaoyu Jiao,Laihui Li,Hongxia Yu,Si Wei
出处
期刊:Science Advances
[American Association for the Advancement of Science (AAAS)]
日期:2024-05-23
卷期号:10 (21)
标识
DOI:10.1126/sciadv.adn1039
摘要
Unknown forever chemicals like per- and polyfluoroalkyl substances (PFASs) are difficult to identify. Current platforms designed for metabolites and natural products cannot capture the diverse structural characteristics of PFAS. Here, we report an automatic PFAS identification platform (APP-ID) that screens for PFAS in environmental samples using an enhanced molecular network and identifies unknown PFAS structures using machine learning. Our networking algorithm, which enhances characteristic fragment matches, has lower false-positive rate (0.7%) than current algorithms (2.4 to 46%). Our support vector machine model identified unknown PFAS in test set with 58.3% accuracy, surpassing current software. Further, APP-ID detected 733 PFASs in real fluorochemical wastewater, 39 of which are previously unreported in environmental media. Retrospective screening of 126 PFASs against public data repository from 20 countries show PFAS substitutes are prevalent worldwide.
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