Zhang, Y.; Li, Y.; Li, Y.; Zhao, L.; Yang, Y.
Interpretable Machine Learning Models and Symbolic Regressions Reveal Transfer of Per- and Polyfluoroalkyl Substances (PFASs) in Plants: A New Small-Data Machine Learning Method to Augment Data and Obtain Predictive Equations. Toxics 2025, 13, 579.
https://doi.org/10.3390/toxics13070579
AMA Style
Zhang Y, Li Y, Li Y, Zhao L, Yang Y.
Interpretable Machine Learning Models and Symbolic Regressions Reveal Transfer of Per- and Polyfluoroalkyl Substances (PFASs) in Plants: A New Small-Data Machine Learning Method to Augment Data and Obtain Predictive Equations. Toxics. 2025; 13(7):579.
https://doi.org/10.3390/toxics13070579
Chicago/Turabian Style
Zhang, Yuan, Yanting Li, Yang Li, Lin Zhao, and Yongkui Yang.
2025. "Interpretable Machine Learning Models and Symbolic Regressions Reveal Transfer of Per- and Polyfluoroalkyl Substances (PFASs) in Plants: A New Small-Data Machine Learning Method to Augment Data and Obtain Predictive Equations" Toxics 13, no. 7: 579.
https://doi.org/10.3390/toxics13070579
APA Style
Zhang, Y., Li, Y., Li, Y., Zhao, L., & Yang, Y.
(2025). Interpretable Machine Learning Models and Symbolic Regressions Reveal Transfer of Per- and Polyfluoroalkyl Substances (PFASs) in Plants: A New Small-Data Machine Learning Method to Augment Data and Obtain Predictive Equations. Toxics, 13(7), 579.
https://doi.org/10.3390/toxics13070579
