TY  - JOUR
AU  - Stojić, Nataša
AU  - Erić, Slavica
AU  - Kuzmanovski, Igor
PY  - 2010
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/1357
AB  - In this work, a novel algorithm for optimization of counter-propagation artificial neural networks has been used for development of quantitative structure-activity relationships model for prediction of the estrogenic activity of endocrine-disrupting chemicals. The search for the best model was performed using genetic algorithms. Genetic algorithms were used not only for selection of the most suitable descriptors for modeling, but also for automatic adjustment of their relative importance. Using our recently developed algorithm for automatic adjustment of the relative importance of the input variables, we have developed simple models with very good generalization performances using only few interpretable descriptors. One of the developed models is in details discussed in this article. The simplicity of the chosen descriptors and their relative importance for this model helped us in performing a detailed data exploratory analysis which gave us an insight in the structural features required for the activity of the estrogenic endocrine-disrupting chemicals.
PB  - Elsevier Science Inc, New York
T2  - Journal of Molecular Graphics & Modelling
T1  - Prediction of toxicity and data exploratory analysis of estrogen-active endocrine disruptors using counter-propagation artificial neural networks
VL  - 29
IS  - 3
SP  - 450
EP  - 460
DO  - 10.1016/j.jmgm.2010.09.001
ER  - 

@article{
author = "Stojić, Nataša and Erić, Slavica and Kuzmanovski, Igor",
year = "2010",
abstract = "In this work, a novel algorithm for optimization of counter-propagation artificial neural networks has been used for development of quantitative structure-activity relationships model for prediction of the estrogenic activity of endocrine-disrupting chemicals. The search for the best model was performed using genetic algorithms. Genetic algorithms were used not only for selection of the most suitable descriptors for modeling, but also for automatic adjustment of their relative importance. Using our recently developed algorithm for automatic adjustment of the relative importance of the input variables, we have developed simple models with very good generalization performances using only few interpretable descriptors. One of the developed models is in details discussed in this article. The simplicity of the chosen descriptors and their relative importance for this model helped us in performing a detailed data exploratory analysis which gave us an insight in the structural features required for the activity of the estrogenic endocrine-disrupting chemicals.",
publisher = "Elsevier Science Inc, New York",
journal = "Journal of Molecular Graphics & Modelling",
title = "Prediction of toxicity and data exploratory analysis of estrogen-active endocrine disruptors using counter-propagation artificial neural networks",
volume = "29",
number = "3",
pages = "450-460",
doi = "10.1016/j.jmgm.2010.09.001"
}

Stojić, N., Erić, S.,& Kuzmanovski, I.. (2010). Prediction of toxicity and data exploratory analysis of estrogen-active endocrine disruptors using counter-propagation artificial neural networks. in Journal of Molecular Graphics & Modelling
Elsevier Science Inc, New York., 29(3), 450-460.
https://doi.org/10.1016/j.jmgm.2010.09.001

Stojić N, Erić S, Kuzmanovski I. Prediction of toxicity and data exploratory analysis of estrogen-active endocrine disruptors using counter-propagation artificial neural networks. in Journal of Molecular Graphics & Modelling. 2010;29(3):450-460.
doi:10.1016/j.jmgm.2010.09.001 .

Stojić, Nataša, Erić, Slavica, Kuzmanovski, Igor, "Prediction of toxicity and data exploratory analysis of estrogen-active endocrine disruptors using counter-propagation artificial neural networks" in Journal of Molecular Graphics & Modelling, 29, no. 3 (2010):450-460,
https://doi.org/10.1016/j.jmgm.2010.09.001 . .