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Data-driven modeling and prediction of acute toxicity of pesticide residues

This paper outlines and implements a concept for developing alternative tools for toxicity modeling and prediction of chemical compounds to be used for evaluation and authorization purposes of public regulatory bodies to help minimizing animal tests, costs, and time associated with registration and risk assessment processes. Starting from a general problem description we address and introduce concepts of multileveled self-organization for high-dimensional modeling, model validation, model combining, and decision support within the frame of a knowledge discovery from noisy data.