In this work, we introduce a new method for uncertainty quantification in ToxCast data. We explore how unavoidable uncertainties in the data result in uncertainties in concentration-response parameters such as potency and efficacy. These uncertainties are then extended throughout to the analysis and interpretation of results for risk assessment. By quantifying these uncertainties through the analysis stages we increase the confidence in the data interpretation and allow for a more robust risk assessment. We also flag chemicals and assays for manual inspection, removal, and retesting so that data quality and model outputs can be further improved.

This dataset is associated with the following publication: Watt, E., and R. Judson. Uncertainty Quantification in ToxCast High Throughput Screening. PLoS Computational Biology. Public Library of Science, San Francisco, CA, USA, 13(7): 1-23, (2018).