Open AccessOpen source software implementation of an integrated testing strategy for skin sensitization potency based on a Bayesian network
Author(s) -
Jason R. Pirone,
Marjolein Smith,
Nicole Kleinstreuer,
Thomas A Burns,
Judy Strickland,
Yuri Dancik,
Richard K. Morris,
Lori A. Rinckel,
Warren Casey,
Joanna Jaworska
Publication year - 2014
Publication title -
altex/alternatives to animal experimentation
Language(s) - English
Resource type - Journals
eISSN - 1868-8551
pISSN - 1868-596X
DOI - 10.14573/altex.1310151
Subject(s) - skin sensitization , bayesian network , computer science , bayesian probability , open source , probabilistic logic , software , data mining , r package , sensitization , machine learning , artificial intelligence , medicine , operating system , immunology , computational science
An open-source implementation of a previously published integrated testing strategy (ITS) for skin sensitization using a Bayesian network has been developed using R, a free and open-source statistical computing language. The ITS model provides probabilistic predictions of skin sensitization potency based on in silico and in vitro information as well as skin penetration characteristics from a published bioavailability model (Kasting et al., 2008). The structure of the Bayesian network was designed to be consistent with the adverse outcome pathway published by the OECD (Jaworska et al., 2011, 2013). In this paper, the previously published data set (Jaworska et al., 2013) is improved by two data corrections and a modified application of the Kasting model. The new data set implemented in the original commercial software package and the new R version produced consistent results. The data and a fully documented version of the code are publicly available (http://ntp.niehs.nih.gov/go/its).