Modeling acute toxicity of chemicals to Daphnia magna: A probabilistic neural network approach

Environmental Toxicology and Chemistry

Volumn 20, Issue 2, 2001, Pages 420-431

  Kaiser, Klaus L E ^a   Niculescu, Stefan P ^b  

^a ENVIRONMENT CANADA   (Canada)

^b TerraBase Inc   (Canada)

Author keywords

Daphnia magna; Median lethal concentration; Neural network validation; Probabilistic neural network; Toxicity modeling

Indexed keywords

OCTANOL; WATER;

ACUTE TOXICITY; ARTICLE; ARTIFICIAL NEURAL NETWORK; DAPHNIA; MATHEMATICAL MODEL; PARAMETER; PRIORITY JOURNAL; PROBABILITY; SOLUBILITY; VALIDATION PROCESS;

DAPHNIA; DAPHNIA MAGNA;

EID: 0035145572     PISSN: 07307268     EISSN: None     Source Type: Journal    
DOI: 10.1002/etc.5620200225     Document Type: Article

References (22)

1. The toxicity thresholds of various substances found in industrial wastes as determined by the use of Daphnia magna
2. A note of caution to users of ECOSAR
3. Ability of ECOSAR, TOPKAT, neural networks, and ASTER to predict toxicity of chemicals to aquatic biota
4. Classification of environmentally occurring chemicals using structural fragments and PLS discriminant analysis
5. Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow (Pimephales promelas)
6. Classifying environmental pollutants. 1. Structure-activity relationships for prediction of aquatic toxicity
7. Neural network modeling of Vibrio fischeri toxicity data with structural physico-chemical parameters and molecular indicator variables
8. Influence of data preprocessing and kernel selection on probabilistic neural network modeling of the acute toxicity of chemicals to the fathead minnow and Vibrio fischeri bacteria
9. Using probabilistic neural networks to model the toxicity of chemicals to the fathead minnow (Pimephales promelas): A study based on 865 compounds
10. Modeling the toxicity of chemicals to Tetrahymena pyriformis using molecular fragment descriptors and probabilistic neural networks
11. Estimation of a multivariate density
12. On estimation of probability density function and mode
13. Probabilistic neural networks