Kotli, M.; Piir, G.; Maran, U. Pesticide effect on earthworm lethality via interpretable machine learning. Journal of Hazardous Materials 2023

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Kotli, M.; Piir, G.; Maran, U. Pesticide effect on earthworm lethality via interpretable machine learning. Journal of Hazardous Materials 2023

QDB archive DOI: 10.15152/QDB.258   DOWNLOAD

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Property toxicity_class: Acute toxicity to earthworm [mg/kg] i

rf: Random Forest

Random forest (classification)

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NameTypenAccuracy
toxicity_class.traintraining5240.979
toxicity_class.testexternal validation1310.802
toxicity_class.oobinternal validation5240.781

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dc.date.accessioned2023-08-18T08:26:20Z
dc.date.available2023-08-18T08:26:20Z
dc.date.issued2023-08-18
dc.identifier.urihttp://hdl.handle.net/10967/258
dc.identifier.urihttp://dx.doi.org/10.15152/QDB.258
dc.description.abstractEarthworms are among the most important animals (invertebrates) for soil health. Many chemical substances released into nature for agricultural development, such as pesticides, may have unwanted effects on those organisms. However, it is essential to understand the extent of the impact of chemicals on soil health first and then make the proper decisions for regulatory or commercial purposes. We hypothesize that there is an expressible quantitative structure-activity relationship (QSAR) between the structure of pesticide compounds and the acute toxicity effect of earthworm species Eisenia fetida. The description of this relationship allows for a better assessment of the impact of chemicals on the said earthworm. To describe this relationship, a dataset of chemicals was collected from open-access sources to develop a mathematical model. A novel approach, combining genetic algorithm and Bayesian optimization, was used to select structural features into the model and to optimize model parameters. The final QSAR classification model was created with the Random Forest algorithm and exhibited good prediction Accuracy of 0.78 on training set and 0.80 on test set. The model representation follows FAIR principles and is available on QsarDB.org.en_US
dc.publisherMihkel Kotli
dc.publisherGeven Piir
dc.publisherUko Maran
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleKotli, M.; Piir, G.; Maran, U. Pesticide effect on earthworm lethality via interpretable machine learning. Journal of Hazardous Materials 2023
qdb.property.endpoint3. Ecotoxic effects 3.8. Toxicity to earthwormsen_US
qdb.property.speciesEisenia fetidaen_US
qdb.descriptor.applicationDragon 6.0.40en_US
qdb.prediction.applicationscikit-learn 1.1.3en_US
bibtex.entryarticleen_US
bibtex.entry.authorKotli, Mihkel
bibtex.entry.authorPiir, Geven
bibtex.entry.authorMaran, Uko
bibtex.entry.doi10.1016/j.jhazmat.2023.132577
bibtex.entry.journalJournal of Hazardous Materialsen_US
bibtex.entry.titlePesticide effect on earthworm lethality via interpretable machine learningen_US
qdb.model.typeRandom forest (classification)en_US
qdb.descriptor.calculationrf


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