IATA-Bayesian Network Model for Skin Sensitization Data

Metadata Updated: January 18, 2020

Since the publication of the Adverse Outcome Pathway (AOP) for skin sensitization, there have been many efforts to develop systematic approaches to integrate the information generated from different key events for decision making. The types of information characterizing key events in an AOP can be generated from in silico, in chemico, in vitro or in vivo approaches. Integration of this information and interpretation for decision making are known as integrated approaches to testing and assessment or IATA. One such IATA that has been developed was published by Jaworska et al (2013) which describes a Bayesian network model known as ITS-2. The current work evaluated the performance of ITS-2 using a stratified cross validation approach. We also characterized the impact of refinements to the network by replacing the most significant component, the output from a commercial expert system TIMES-SS with structural alert information readily generated from the freely available OECD QSAR Toolbox. Lack of any structural alert flags or TIMES-SS predictions, yielded a sensitization potential prediction of 79% +3%/-4%. If the TIMES-SS prediction was replaced by an indicator for the presence of a structural alert, the network predictivity increased to 84% +2%/-4%, which was only slightly less than found for the original network (89% [HTML_REMOVED]2%). The local applicability domain of the original ITS-2 network was also evaluated using reaction mechanistic domains to better understand what types of chemicals ITS-2 was able to make the best predictions for [HTML_REMOVED] i.e. a local validity domain analysis. We ultimately found that the original network was successful at predicting which chemicals would be sensitizers, but not at predicting their relative potency.

This dataset is associated with the following publication: Fitzpatrick, J., and G. Patlewicz. (SAR AND QSAR IN ENVIRONMENTAL RESEARCH) Application of IATA - A case study in evaluating the global and local performance of a Bayesian Network model for Skin Sensitization. SAR AND QSAR IN ENVIRONMENTAL RESEARCH. Taylor & Francis, Inc., Philadelphia, PA, USA, 28(4): 297-310, (2017).

Access & Use Information

Public: This dataset is intended for public access and use. License: See this page for license information.

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References

https://doi.org/10.1080/1062936x.2017.1311941

Dates

Metadata Created Date October 3, 2017
Metadata Updated Date January 18, 2020

Metadata Source

Harvested from EPA ScienceHub

Additional Metadata

Resource Type Dataset
Metadata Created Date October 3, 2017
Metadata Updated Date January 18, 2020
Publisher U.S. EPA Office of Research and Development (ORD)
Unique Identifier https://doi.org/10.23719/1395040
Maintainer
Ann Richard
Maintainer Email
Public Access Level public
Bureau Code 020:00
Schema Version https://project-open-data.cio.gov/v1.1/schema
Harvest Object Id bbb5f0d0-fb3f-4e22-a200-108c0fc56068
Harvest Source Id cf9b0004-f9fd-420e-bade-a86839e82acf
Harvest Source Title EPA ScienceHub
License https://pasteur.epa.gov/license/sciencehub-license.html
Data Last Modified 2017-08-07
Program Code 020:095
Publisher Hierarchy U.S. Government > U.S. Environmental Protection Agency > U.S. EPA Office of Research and Development (ORD)
Related Documents https://doi.org/10.1080/1062936x.2017.1311941
Source Datajson Identifier True
Source Hash e8d8071a289fda7fc0d1306d23c32a36c647e1fe
Source Schema Version 1.1

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