Neural networks for effect prediction in environmental and health issues using large datasets
Klaus L E Kaiser · QSAR & Combinatorial Science · 2003
Abstract 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.