Prediction of toxicity and data exploratory analysis of estrogen-active endocrine disruptors using counter-propagation artificial neural networks
Само за регистроване кориснике
2010
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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 requir...ed for the activity of the estrogenic endocrine-disrupting chemicals.
Кључне речи:
Endocrine disruptors toxicity / Estrogenic activity prediction / Data exploratory analysis / SAR / QSAR / Counter-propagation artificial neural networksИзвор:
Journal of Molecular Graphics & Modelling, 2010, 29, 3, 450-460Издавач:
- Elsevier Science Inc, New York
DOI: 10.1016/j.jmgm.2010.09.001
ISSN: 1093-3263
PubMed: 20952233
WoS: 000285402500017
Scopus: 2-s2.0-78649522324
Институција/група
PharmacyTY - 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 . .