Predicting chemical hazard across taxa through machine learning.

Autor: Wu J; Eawag, Überlandstrasse 133, CH-8600 Dübendorf, Switzerland; Department of Environmental Engineering, ETHZ, Zurich, Switzerland. Electronic address: jimeng.wu@eawag.ch., D'Ambrosi S; Department of Statistics, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, RM, Italy., Ammann L; Swiss Federal Institute for Forest, Snow, and Landscape Research WSL, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerland., Stadnicka-Michalak J; Eawag, Überlandstrasse 133, CH-8600 Dübendorf, Switzerland., Schirmer K; Eawag, Überlandstrasse 133, CH-8600 Dübendorf, Switzerland; School of Architecture, Civil and Environmental Engineering, EPFL, Lausanne, Switzerland. Electronic address: kristin.schirmer@eawag.ch., Baity-Jesi M; Eawag, Überlandstrasse 133, CH-8600 Dübendorf, Switzerland. Electronic address: marco.baityjesi@eawag.ch.
Jazyk: angličtina
Zdroj: Environment international [Environ Int] 2022 May; Vol. 163, pp. 107184. Date of Electronic Publication: 2022 Mar 17.
DOI: 10.1016/j.envint.2022.107184
Abstrakt: We applied machine learning methods to predict chemical hazards focusing on fish acute toxicity across taxa. We analyzed the relevance of taxonomy and experimental setup, showing that taking them into account can lead to considerable improvements in the classification performance. We quantified the gain obtained throught the introduction of taxonomic and experimental information, compared to classification based on chemical information alone. We used our approach with standard machine learning models (K-nearest neighbors, random forests and deep neural networks), as well as the recently proposed Read-Across Structure Activity Relationship (RASAR) models, which were very successful in predicting chemical hazards to mammals based on chemical similarity. We were able to obtain accuracies of over 93% on datasets where, due to noise in the data, the maximum achievable accuracy was expected to be below 96%. The best performances were obtained by random forests and RASAR models. We analyzed metrics to compare our results with animal test reproducibility, and despite most of our models "outperform animal test reproducibility" as measured through recently proposed metrics, we showed that the comparison between machine learning performance and animal test reproducibility should be addressed with particular care. While we focused on fish mortality, our approach, provided that the right data is available, is valid for any combination of chemicals, effects and taxa.
(Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: