Citation:

Shah, I., T. Tate, AND G. Patlewicz. Generalised Read-Across Prediction using genra-py. BIOINFORMATICS. Oxford University Press, Cary, NC, 37(19):3380-3381, (2021). https://doi.org/10.1093/bioinformatics/btab210

Impact/Purpose:

Read-across (RAX) is a widely used data gap filling approach and the authors have developed a data-driven tool, called GenRA, to support expert-driven RAX. This work describes a stand-alone Python 3 package, called genra-py, which enables end-users to conduct hazard identification and point of departure (POD) estimation using GenRA.

Description:

Motivation Generalized Read-Across (GenRA) is a data-driven approach to estimate physico-chemical, biological or eco-toxicological properties of chemicals by inference from analogues. GenRA attempts to mimic a human expert’s manual read-across reasoning for filling data gaps about new chemicals from known chemicals with an interpretable and automated approach based on nearest-neighbors. A key objective of GenRA is to systematically explore different choices of input data selection and neighborhood definition to objectively evaluate predictive performance of automated read-across estimates of chemical properties. Results We have implemented genra-py as a python package that can be freely used for chemical safety analysis and risk assessment applications. Automated read-across prediction in genra-py conforms to the scikit-learn machine learning library's estimator design pattern, making it easy to use and integrate in computational pipelines. We demonstrate the data-driven application of genra-py to address two key human health risk assessment problems namely: hazard identification and point of departure estimation.

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