Predicting chemical hazard across taxa through machine learning.

Details

Serval ID
serval:BIB_5F64A3E03C04
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Predicting chemical hazard across taxa through machine learning.
Journal
Environment international
Author(s)
Wu J., D'Ambrosi S., Ammann L., Stadnicka-Michalak J., Schirmer K., Baity-Jesi M.
ISSN
1873-6750 (Electronic)
ISSN-L
0160-4120
Publication state
Published
Issued date
05/2022
Peer-reviewed
Oui
Volume
163
Pages
107184
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
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.
Keywords
Animals, Machine Learning, Mammals, Neural Networks, Computer, Reproducibility of Results, Structure-Activity Relationship, Acute toxicity, Animal testing, Ecotoxicology, Fish, In vivo testing, Machine learning, RASAR
Pubmed
Open Access
Yes
Create date
03/05/2022 9:40
Last modification date
02/11/2022 6:41
Usage data