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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Using Machine Learning to Classify Bioactivity for 3486 Per- and Polyfluoroalkyl Substances (PFASs) from the OECD List
Weixiao Cheng, Carla A. Ng
Environmental Science & Technology (2019) Vol. 53, Iss. 23, pp. 13970-13980
Open Access | Times Cited: 101

Showing 1-25 of 101 citing articles:

Per‐ and Polyfluoroalkyl Substance Toxicity and Human Health Review: Current State of Knowledge and Strategies for Informing Future Research
Suzanne E. Fenton, Alan Ducatman, Alan R. Boobis, et al.
Environmental Toxicology and Chemistry (2020) Vol. 40, Iss. 3, pp. 606-630
Open Access | Times Cited: 1294
PFAS Exposure Pathways for Humans and Wildlife: A Synthesis of Current Knowledge and Key Gaps in Understanding
Amila O. De Silva, James M. Armitage, Thomas A. Bruton, et al.
Environmental Toxicology and Chemistry (2020) Vol. 40, Iss. 3, pp. 631-657
Open Access | Times Cited: 529
Assessing the Ecological Risks of Per‐ and Polyfluoroalkyl Substances: Current State‐of‐the Science and a Proposed Path Forward
Gerald T. Ankley, P.M. Cureton, Robert A. Hoke, et al.
Environmental Toxicology and Chemistry (2020) Vol. 40, Iss. 3, pp. 564-605
Open Access | Times Cited: 269
Differentiating Solutes with Precise Nanofiltration for Next Generation Environmental Separations: A Review
Yangying Zhao, Tiezheng Tong, Xiaomao Wang, et al.
Environmental Science & Technology (2021) Vol. 55, Iss. 3, pp. 1359-1376
Closed Access | Times Cited: 250
Strategies for grouping per- and polyfluoroalkyl substances (PFAS) to protect human and environmental health
Ian T. Cousins, Jamie C. DeWitt, Juliane Glüge, et al.
Environmental Science Processes & Impacts (2020) Vol. 22, Iss. 7, pp. 1444-1460
Open Access | Times Cited: 218
Insight into the binding model of per- and polyfluoroalkyl substances to proteins and membranes
Lihui Zhao, Miaomiao Teng, Xiaoli Zhao, et al.
Environment International (2023) Vol. 175, pp. 107951-107951
Open Access | Times Cited: 75
Machine Learning and Artificial Intelligence in Toxicological Sciences
Zhoumeng Lin, Wei-Chun Chou
Toxicological Sciences (2022) Vol. 189, Iss. 1, pp. 7-19
Open Access | Times Cited: 70
Effect of Enterohepatic Circulation on the Accumulation of Per- and Polyfluoroalkyl Substances: Evidence from Experimental and Computational Studies
Huiming Cao, Zhen Zhou, Zhe Hu, et al.
Environmental Science & Technology (2022) Vol. 56, Iss. 5, pp. 3214-3224
Closed Access | Times Cited: 69
Artificial Intelligence in Pharmaceutical Sciences
Mingkun Lu, Jiayi Yin, Qi Zhu, et al.
Engineering (2023) Vol. 27, pp. 37-69
Open Access | Times Cited: 64
Environmental Sources, Chemistry, Fate, and Transport of Per‐ and Polyfluoroalkyl Substances: State of the Science, Key Knowledge Gaps, and Recommendations Presented at the August 2019 SETAC Focus Topic Meeting
Jennifer L. Guelfo, Stephen H. Korzeniowski, Marc A. Mills, et al.
Environmental Toxicology and Chemistry (2021) Vol. 40, Iss. 12, pp. 3234-3260
Open Access | Times Cited: 92
Predicting Micropollutant Removal by Reverse Osmosis and Nanofiltration Membranes: Is Machine Learning Viable?
Nohyeong Jeong, Tai-Heng Chung, Tiezheng Tong
Environmental Science & Technology (2021) Vol. 55, Iss. 16, pp. 11348-11359
Closed Access | Times Cited: 83
Prediction of pharmacological activities from chemical structures with graph convolutional neural networks
Miyuki Sakai, Kazuki Nagayasu, Norihiro Shibui, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 64
Legacy and Emerging Per- and Polyfluoroalkyl Substances Behave Distinctly in Spatial Distribution and Multimedia Partitioning: A Case Study in the Pearl River, China
Weizong Li, Huizhen Li, Dainan Zhang, et al.
Environmental Science & Technology (2022) Vol. 56, Iss. 6, pp. 3492-3502
Closed Access | Times Cited: 50
XGBoost model as an efficient machine learning approach for PFAS removal: Effects of material characteristics and operation conditions
Elika Karbassiyazdi, Fatemeh Fattahi, Negin Yousefi, et al.
Environmental Research (2022) Vol. 215, pp. 114286-114286
Closed Access | Times Cited: 38
How the Structure of Per- and Polyfluoroalkyl Substances (PFAS) Influences Their Binding Potency to the Peroxisome Proliferator-Activated and Thyroid Hormone Receptors—An In Silico Screening Study
Dominika Kowalska, Anita Sosnowska, Natalia Buławska, et al.
Molecules (2023) Vol. 28, Iss. 2, pp. 479-479
Open Access | Times Cited: 22
Current applications and future impact of machine learning in emerging contaminants: A review
Lang Lei, Ruirui Pang, Zhibang Han, et al.
Critical Reviews in Environmental Science and Technology (2023) Vol. 53, Iss. 20, pp. 1817-1835
Closed Access | Times Cited: 22
Suspect, Nontarget Screening, and Toxicity Prediction of Per- and Polyfluoroalkyl Substances in the Landfill Leachate
Chao Feng, Yuanjie Lin, Sunyang Le, et al.
Environmental Science & Technology (2024) Vol. 58, Iss. 10, pp. 4737-4750
Closed Access | Times Cited: 10
Graph Convolutional Network-Enhanced Model for Screening Persistent, Mobile, and Toxic and Very Persistent and Very Mobile Substances
Qiming Zhao, Yuting Zheng, Yu Qiu, et al.
Environmental Science & Technology (2024) Vol. 58, Iss. 14, pp. 6149-6157
Closed Access | Times Cited: 9
Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions
Lingxuan Meng, Beihai Zhou, Haijun Liu, et al.
The Science of The Total Environment (2024) Vol. 946, pp. 174201-174201
Closed Access | Times Cited: 9
Environmental impact of PFAS: Filling data gaps using theoretical quantum chemistry and QSPR modeling
Michalina Mudlaff, Anita Sosnowska, Leonid Gorb, et al.
Environment International (2024) Vol. 185, pp. 108568-108568
Open Access | Times Cited: 8
Machine learning–enhanced molecular network reveals global exposure to hundreds of unknown PFAS
X. Y. Wang, Nanyang Yu, Zhaoyu Jiao, et al.
Science Advances (2024) Vol. 10, Iss. 21
Open Access | Times Cited: 8
Sorption of PFOS in 114 Well-Characterized Tropical and Temperate Soils: Application of Multivariate and Artificial Neural Network Analyses
Anthony C. Umeh, Ravi Naidu, Sonia Shilpi, et al.
Environmental Science & Technology (2021) Vol. 55, Iss. 3, pp. 1779-1789
Closed Access | Times Cited: 53
A database framework for rapid screening of structure-function relationships in PFAS chemistry
An Su, Krishna Rajan
Scientific Data (2021) Vol. 8, Iss. 1
Open Access | Times Cited: 46
Predicting the risk of GenX contamination in private well water using a machine-learned Bayesian network model
Javad Roostaei, Sarah Colley, Riley Mulhern, et al.
Journal of Hazardous Materials (2021) Vol. 411, pp. 125075-125075
Open Access | Times Cited: 42
Evaluating the capability of municipal solid waste separation in China based on AHP-EWM and BP neural network
Hao Xi, Zhiheng Li, Jingyi Han, et al.
Waste Management (2021) Vol. 139, pp. 208-216
Closed Access | Times Cited: 41
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