F. Como
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
F. Como has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
A. Bassan
S-IN Soluzioni Informatiche S.r.l., via G. Ferrari 14, 36100 Vicenza, Italy
1 shared publicationE. Benfenati
IRCSS Istituto di Ricerche Farmacologiche Mario Negri, via La Masa 19, 20146 Milano, Italy
1 shared publicationE. Carnesecchi
European Food Safety Authority, Via Carlo Magno 1A, 43126 Parma, Italy
1 shared publicationJ. Richardson
European Food Safety Authority, Via Carlo Magno 1A, 43126 Parma, Italy
1 shared publicationJ.L. Dorne
European Food Safety Authority, Via Carlo Magno 1A, 43126 Parma, Italy
1 shared publicationM. Novič
A National Institute of Chemistry , Ljubljana , Slovenia
1 shared publication
Latest Publications
- 1 January 2017
- Published by Elsevier in Chemosphere
- Vol. 166, 438-444
Predicting acute contact toxicity of pesticides in honeybees (Apis mellifera) through a k-nearest neighbor model
- 1 January 2017
- Published by Elsevier in Chemosphere
- Vol. 166, 438-444
Predicting acute contact toxicity of pesticides in honeybees (Apis mellifera) through a k-nearest neighbor model
- 20 June 2016
- Published by Taylor & Francis in SAR and QSAR in Environmental Research
- Vol. 27 (7) , 501-519
Robust modelling of acute toxicity towards fathead minnow (Pimephales promelas)using counter-propagation artificial neural networks and genetic algorithm
Large worldwide use of chemicals has caused great concern about their possible adverse effects on human health, flora and fauna. Increased production of new chemicals has also increased demand for their risk assessment. Traditionally, results from animal tests have been used to assess toxicity of chemicals. However, such methods are ethically questionable since they involve killing and causing suffering of the test animals. Therefore, new in silico methods are being sought to replace the traditional in vivo and in vitro testing methods. In this article we report on one method that can be used to build robust models for the prediction of compounds’ properties from their chemical structure. The method has been developed by combining a genetic algorithm, a counter-propagation artificial neural network and cross-validation. It has been tested using existing data on toxicity to fathead minnow (Pimephales promelas). The results show that the method may give reliable results for chemicals belonging to the applicability domain of the developed models. Therefore, it can aid the risk assessment of chemicals and consequently reduce demand for animal tests.
- 20 June 2016
- Published by Taylor & Francis in SAR and QSAR in Environmental Research
- Vol. 27 (7) , 501-519
Robust modelling of acute toxicity towards fathead minnow (Pimephales promelas)using counter-propagation artificial neural networks and genetic algorithm
Large worldwide use of chemicals has caused great concern about their possible adverse effects on human health, flora and fauna. Increased production of new chemicals has also increased demand for their risk assessment. Traditionally, results from animal tests have been used to assess toxicity of chemicals. However, such methods are ethically questionable since they involve killing and causing suffering of the test animals. Therefore, new in silico methods are being sought to replace the traditional in vivo and in vitro testing methods. In this article we report on one method that can be used to build robust models for the prediction of compounds’ properties from their chemical structure. The method has been developed by combining a genetic algorithm, a counter-propagation artificial neural network and cross-validation. It has been tested using existing data on toxicity to fathead minnow (Pimephales promelas). The results show that the method may give reliable results for chemicals belonging to the applicability domain of the developed models. Therefore, it can aid the risk assessment of chemicals and consequently reduce demand for animal tests.