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- W4200575562 abstract "The aim of this chapter is to present different counter-propagation artificial neural networks (CPNN) models developed for the prediction of endpoints relevant for fish toxicity assessment. Examples of CPNN models for acute fish toxicity, read-across, and outlier detection will be discussed. Examples of CPNN optimization approaches and QSAR fish toxicity modeling in combination with molecular modeling for the screening of active pharmaceutical ingredients are also presented. A short description of the CPNN learning algorithm is given to better understand the theoretical background of the method and the examples given in the text." @default.
- W4200575562 created "2021-12-31" @default.
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- W4200575562 date "2021-10-13" @default.
- W4200575562 modified "2023-10-16" @default.
- W4200575562 title "Counter‐Propagation Neural Networks for Modeling and Read Across in Aquatic (Fish) Toxicity" @default.
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- W4200575562 doi "https://doi.org/10.1002/9781119681397.ch8" @default.
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