Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386827146> ?p ?o ?g. }
- W4386827146 endingPage "132577" @default.
- W4386827146 startingPage "132577" @default.
- W4386827146 abstract "Earthworms 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. This work concentrates on effect of pesticides on earthworm (Eisenia fetida) lethality. Pesticides are released into nature for agricultural development but do have unwanted effect on soil organisms. Therefore one can consider pesticides as hazardous material. It is important to know if chemical in general and pesticides specifically do have lethal effect on soil organisms. The quickest way to get this knowledge is via computational (machine learning) models. Therefore deriving, interpreting and making available machine learning models help to address environmental problems." @default.
- W4386827146 created "2023-09-19" @default.
- W4386827146 creator A5043492537 @default.
- W4386827146 creator A5051041102 @default.
- W4386827146 creator A5054790206 @default.
- W4386827146 date "2024-01-01" @default.
- W4386827146 modified "2023-10-06" @default.
- W4386827146 title "Pesticide effect on earthworm lethality via interpretable machine learning" @default.
- W4386827146 cites W1552509723 @default.
- W4386827146 cites W1983691152 @default.
- W4386827146 cites W1993249087 @default.
- W4386827146 cites W1994240917 @default.
- W4386827146 cites W1999292727 @default.
- W4386827146 cites W2006250302 @default.
- W4386827146 cites W2009356411 @default.
- W4386827146 cites W2020453632 @default.
- W4386827146 cites W2022108567 @default.
- W4386827146 cites W2025469738 @default.
- W4386827146 cites W2040372095 @default.
- W4386827146 cites W2044092609 @default.
- W4386827146 cites W2062968354 @default.
- W4386827146 cites W2064748397 @default.
- W4386827146 cites W2069699765 @default.
- W4386827146 cites W2071579644 @default.
- W4386827146 cites W2112494697 @default.
- W4386827146 cites W2116961174 @default.
- W4386827146 cites W2126105956 @default.
- W4386827146 cites W2132120579 @default.
- W4386827146 cites W2146416965 @default.
- W4386827146 cites W2160088156 @default.
- W4386827146 cites W2160476782 @default.
- W4386827146 cites W2192203593 @default.
- W4386827146 cites W2296804791 @default.
- W4386827146 cites W2328908022 @default.
- W4386827146 cites W2515577027 @default.
- W4386827146 cites W2557281874 @default.
- W4386827146 cites W2688390463 @default.
- W4386827146 cites W2710204776 @default.
- W4386827146 cites W2744167442 @default.
- W4386827146 cites W2801555904 @default.
- W4386827146 cites W2801726226 @default.
- W4386827146 cites W2880208062 @default.
- W4386827146 cites W2883962615 @default.
- W4386827146 cites W2887459817 @default.
- W4386827146 cites W2903889880 @default.
- W4386827146 cites W2911964244 @default.
- W4386827146 cites W2917577366 @default.
- W4386827146 cites W2962791909 @default.
- W4386827146 cites W2980464326 @default.
- W4386827146 cites W2984272419 @default.
- W4386827146 cites W2991711962 @default.
- W4386827146 cites W2996222501 @default.
- W4386827146 cites W3007985298 @default.
- W4386827146 cites W3086548394 @default.
- W4386827146 cites W3094704314 @default.
- W4386827146 cites W3107486919 @default.
- W4386827146 cites W3111983898 @default.
- W4386827146 cites W3205543553 @default.
- W4386827146 cites W4220948406 @default.
- W4386827146 cites W4294307752 @default.
- W4386827146 cites W4307549734 @default.
- W4386827146 cites W4320477133 @default.
- W4386827146 cites W936629205 @default.
- W4386827146 doi "https://doi.org/10.1016/j.jhazmat.2023.132577" @default.
- W4386827146 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37793249" @default.
- W4386827146 hasPublicationYear "2024" @default.
- W4386827146 type Work @default.
- W4386827146 citedByCount "0" @default.
- W4386827146 crossrefType "journal-article" @default.
- W4386827146 hasAuthorship W4386827146A5043492537 @default.
- W4386827146 hasAuthorship W4386827146A5051041102 @default.
- W4386827146 hasAuthorship W4386827146A5054790206 @default.
- W4386827146 hasConcept C118518473 @default.
- W4386827146 hasConcept C119857082 @default.
- W4386827146 hasConcept C127413603 @default.
- W4386827146 hasConcept C161176658 @default.
- W4386827146 hasConcept C164126121 @default.
- W4386827146 hasConcept C177264268 @default.
- W4386827146 hasConcept C183696295 @default.
- W4386827146 hasConcept C18903297 @default.
- W4386827146 hasConcept C199360897 @default.
- W4386827146 hasConcept C20379349 @default.
- W4386827146 hasConcept C2776332788 @default.
- W4386827146 hasConcept C2781144357 @default.
- W4386827146 hasConcept C33070731 @default.
- W4386827146 hasConcept C39432304 @default.
- W4386827146 hasConcept C41008148 @default.
- W4386827146 hasConcept C86803240 @default.
- W4386827146 hasConceptScore W4386827146C118518473 @default.
- W4386827146 hasConceptScore W4386827146C119857082 @default.
- W4386827146 hasConceptScore W4386827146C127413603 @default.
- W4386827146 hasConceptScore W4386827146C161176658 @default.
- W4386827146 hasConceptScore W4386827146C164126121 @default.
- W4386827146 hasConceptScore W4386827146C177264268 @default.
- W4386827146 hasConceptScore W4386827146C183696295 @default.
- W4386827146 hasConceptScore W4386827146C18903297 @default.
- W4386827146 hasConceptScore W4386827146C199360897 @default.
- W4386827146 hasConceptScore W4386827146C20379349 @default.