Prediction of acute aquatic toxicity in Tetrahymena pyriformis--'Eco-Derek', a knowledge-based system approach.

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Prediction of acute aquatic toxicity in Tetrahymena pyriformis--'Eco-Derek', a knowledge-based system approach.

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2013 nî lūn-bûn

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2013年の論文

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2013年学术文章

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2013年学术文章

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2013年学术文章

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2013年学术文章

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2013年学术文章

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2013年学术文章

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2013年學術文章

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2013年學術文章

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Prediction of acute aquatic to ...... owledge-based system approach.

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Prediction of acute aquatic to ...... owledge-based system approach.

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Item

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Prediction of acute aquatic to ...... owledge-based system approach.

@en

Prediction of acute aquatic to ...... owledge-based system approach.

@nl

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Prediction of acute aquatic to ...... owledge-based system approach.

@en

Prediction of acute aquatic to ...... owledge-based system approach.

@nl

P1476

Prediction of acute aquatic to ...... owledge-based system approach.

@en

P2093

M P Payne

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W G Button

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P2860

QSARs for the aquatic toxicity of aromatic aldehydes from Tetrahymena data.

In silico tools for sharing data and knowledge on toxicity and metabolism: derek for windows, meteor, and vitic.

QSAR analysis of the toxicity of nitroaromatics in Tetrahymena pyriformis: structural factors and possible modes of action.

Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis.

Diphenylthiourea, a common rubber chemical, is bioactivated to potent skin sensitizers.

Thiol reactivity and its impact on the ciliate toxicity of α,β-unsaturated aldehydes, ketones, and esters.

Experimental reactivity parameters for toxicity modeling: application to the acute aquatic toxicity of SN2 electrophiles to Tetrahymena pyriformis.

Predictive toxicology modeling: protocols for exploring hERG classification and Tetrahymena pyriformis end point predictions.

Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection.

In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods.

P304

439-460

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scholarly article

P356

10.1080/1062936X.2013.783507

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2013-04-19T00:00:00Z

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23600431

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