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Updated: May 12, 2026

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans
09:01

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

Published on: March 14, 2019

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在中预测口腔急性动物毒性使用共识机器学习

Sebastian Schieferdecker1, Florian Rottach1, Esther Vock1

  • 1Boehringer Ingelheim Pharma GmbH & Co. KG, 88397 Biberach, Germany.

Journal of chemical information and modeling
|March 18, 2024
PubMed
概括

这项研究开发了一种新的计算模型,用于使用机器学习预测急性口腔毒性 (AOT). 这种方法为GHS下的化学品分类和标签提供了一种可靠的无动物替代方案.

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Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation16:02

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

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Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

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相关概念视频

Toxicity Testing in Animals01:23

Toxicity Testing in Animals

Toxicity tests in animals are grounded on two main assumptions: first, the effects observed in laboratory animals can be extrapolated to humans, especially when adjusted for body surface area; second, high-dose exposure in animals is essential to identify potential human hazards from lower doses. This is based on the quantal dose-response concept, which faces the challenge of extrapolating results from relatively few test animals to much larger human populations. For example, a 0.01% incidence...