Literature DB >> 12653515

Tuning neural and fuzzy-neural networks for toxicity modeling.

P Mazzatorta1, E Benfenati, C-D Neagu, G Gini.   

Abstract

The need for general reliable models for predicting toxicity has led to the use of artificial intelligence. We applied neural and fuzzy-neural networks with the QSAR approach. We underline how the networks have to be tuned on the data sets generally involved in modeling toxicity. This study was conducted on 562 organic compounds in order to establish models for predictive the acute toxicity in fish.

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Year:  2003        PMID: 12653515     DOI: 10.1021/ci025585q

Source DB:  PubMed          Journal:  J Chem Inf Comput Sci        ISSN: 0095-2338


  2 in total

1.  QSTR with extended topochemical atom (ETA) indices. VI. Acute toxicity of benzene derivatives to tadpoles (Rana japonica).

Authors:  Kunal Roy; Gopinath Ghosh
Journal:  J Mol Model       Date:  2005-10-26       Impact factor: 1.810

2.  Improving nitrogen removal using a fuzzy neural network-based control system in the anoxic/oxic process.

Authors:  Mingzhi Huang; Yongwen Ma; Jinquan Wan; Yan Wang; Yangmei Chen; Changkyoo Yoo
Journal:  Environ Sci Pollut Res Int       Date:  2014-06-13       Impact factor: 4.223

  2 in total

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