Neural networks for effect prediction in environmental and health issues using large datasets

Neural network methodologies allow the modeling of non-linear relationships. This makes them useful tools for the analysis of larger data sets of non-congeneric compounds with unknown or varying modes of action. This brief review describes recent advances and their applications to sets of several hundred to over 1 000 compounds, modeling acute toxicity data for several aquatic species, including fish, ciliate, bacteria, and non-acute toxicity data for a mammalian species endpoint, i.e. estrogen receptor binding assay data.

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