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Abstract
The primary goal of this study was to describe and compare the criteria used to assess carcinogenic activity. The statistically-based predictive quantitative structure–activity relationship (QSAR) models based on the counter propagation artificial neural network (CPANN) algorithm, and knowledge-based expert systems based on a decision tree structural alert (SA) approach (Toxtree application), were considered. The integration of the QSAR (CPANN models) and SAR (Toxtree SA application) approach contributed to the mechanistic understanding of the QSAR model considered. The mapping technique inherent to CPANN Kohonen enables us to relate the similarities or dissimilarities within a congeneric set of chemicals with particular SAs for carcinogenicity. The focus of our investigations was the similarities and dissimilarities of the features used in the QSAR and SAR methods. Due to the complexity of the carcinogenic endpoint, the integration of different approaches allows the models to be improved and provides a valuable technique for evaluating the safety of chemicals.
Acknowledgements
This paper was presented at the 7th CMTPI conference in Seoul, 8–12 October 2013. The financial support of the European Union through the CAESAR project (SSPI–022674), and also of the Slovenian Ministry of Higher Education, Science and Technology (grant P1–017), is gratefully acknowledged. We wish to thank Marjan Tusar for assistance with traceann toolbox for Matlab for CPANN modelling.