Machine Learning-Driven Cross-Species Toxicity Prediction for Advancing Ecologically Relevant PFAS Water Quality Criteria

Machine Learning-Driven Cross-Species Toxicity Prediction for Advancing Ecologically Relevant PFAS Water Quality Criteria

Traditional toxicity testing cannot keep pace with the rapid growth of synthetic chemicals, creating major data gaps that hinder the development of water quality criteria (WQC) for emerging contaminants. This study developed a machine learning model integrating compound- and organism-related feature...

Повний опис

Збережено в:
Бібліографічні деталі
Автор: Weigang Liang (734333) (author)
Інші автори: Jingya Li (309241) (author), Xiaolei Wang (139592) (author), John P. Giesy (302766) (author), Xiaoli Zhao (118708) (author)
Опубліковано: 2025
Предмети:
Ecology
Inorganic Chemistry
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Chemical Sciences not elsewhere classified
potentially reflecting inter
30 representative per
water quality criteria
informed species selection
5 </ sub
species toxicity prediction
species prediction
species sensitivity
existing criteria
acute toxicity
synthetic chemicals
study developed
related features
regional differences
regional comparisons
rapid growth
polyfluoroalkyl substances
perfluorooctanoic acid
perfluorooctanesulfonic acid
north america
model predicted
machine learning
hazardous concentrations
emerging contaminants
dominant predictors
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