{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T16:29:49Z","timestamp":1791390589601,"version":"4.3.3"},"reference-count":38,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2019,11,13]],"date-time":"2019-11-13T00:00:00Z","timestamp":1573603200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Risk Analysis"],"published-print":{"date-parts":[[2020,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Adverse outcome pathway Bayesian networks (AOPBNs) are a promising avenue for developing predictive toxicology and risk assessment tools based on adverse outcome pathways (AOPs). Here, we describe a process for developing AOPBNs. AOPBNs use causal networks and Bayesian statistics to integrate evidence across key events. In this article, we use our AOPBN to predict the occurrence of steatosis under different chemical exposures. Since it is an expert\u2010driven model, we use external data (i.e., data not used for modeling) from the literature to validate predictions of the AOPBN model. The AOPBN accurately predicts steatosis for the chemicals from our external data. In addition, we demonstrate how end users can utilize the model to simulate the confidence (based on posterior probability) associated with predicting steatosis. We demonstrate how the network topology impacts predictions across the AOPBN, and how the AOPBN helps us identify the most informative key events that should be monitored for predicting steatosis. We close with a discussion of how the model can be used to predict potential effects of mixtures and how to model susceptible populations (e.g., where a mutation or stressor may change the conditional probability tables in the AOPBN). Using this approach for developing expert AOPBNs will facilitate the prediction of chemical toxicity, facilitate the identification of assay batteries, and greatly improve chemical hazard screening strategies.<\/jats:p>","DOI":"10.1111\/risa.13423","type":"journal-article","created":{"date-parts":[[2019,11,13]],"date-time":"2019-11-13T08:25:20Z","timestamp":1573633520000},"page":"512-523","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs)"],"prefix":"10.1111","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4977-5352","authenticated-orcid":false,"given":"Lyle D.","family":"Burgoon","sequence":"first","affiliation":[{"name":"US Army Engineer Research and Development Center  Vicksburg MS USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michelle","family":"Angrish","sequence":"additional","affiliation":[{"name":"US Environmental Protection Agency National Center for Environmental Assessment  Research Triangle Park NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natalia","family":"Garcia\u2010Reyero","sequence":"additional","affiliation":[{"name":"US Army Engineer Research and Development Center  Vicksburg MS USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathan","family":"Pollesch","sequence":"additional","affiliation":[{"name":"US Environmental Protection Agency Mid\u2010Continent Ecology Division  Duluth MN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anze","family":"Zupanic","sequence":"additional","affiliation":[{"name":"Eawag Swiss Federal Institute for Aquatic Science and Technology  Dubendorf Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward","family":"Perkins","sequence":"additional","affiliation":[{"name":"US Army Engineer Research and Development Center  Vicksburg MS USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,11,13]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/toxsci\/kfx121"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/etc.34"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.yrtph.2017.08.021"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/risa.12613"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/597134"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.2337\/db10-0869"},{"issue":"5","key":"e_1_2_7_8_1","first-page":"1157","article-title":"Rat 17 beta\u2010hydroxysteroid dehydrogenase type IV is a novel peroxisome proliferator\u2010inducible gene","volume":"50","author":"Corton J. 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