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Expert\u2010based approaches that qualitatively integrate multifaceted data are practiced under the term \u2019weight of evidence\u2019, whereas quantitative approaches remain rare. To address this gap we previously developed a methodology to design an Integrated Testing Strategy (ITS) in the form of a Bayesian Network (BN). This study follows up on our proof of concept work and presents an updated ITS to assess skin sensitization potency expressed as local lymph node assay (LLNA) potency classes. Modifications to the ITS structure were introduced to include better mechanistic information. The parameters of the updated ITS were calculated from an extended data set of 124 chemicals. A detailed validation analysis and a case study were carried out to demonstrate the utility of the ITS for practical application. The improved BN ITS predicted correctly 95% and 86% of chemicals in a test set (<jats:italic>n<\/jats:italic>\u2009=\u200921) for hazard and LLNA potency classes, respectively. The practical value of using the BN ITS is far more than a prediction framework when all data are available. The BN ITS can develop a hypothesis using subsets of data as small as one data point and can be queried on the value of adding additional tests before testing is commenced. The ITS represents key steps of the skin sensitization process and a mechanistically interpretable testing strategy can be developed. These features are illustrated in the manuscript via practical examples. Copyright \u00a9 2013 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/jat.2869","type":"journal-article","created":{"date-parts":[[2013,5,14]],"date-time":"2013-05-14T07:25:02Z","timestamp":1368516302000},"page":"1353-1364","source":"Crossref","is-referenced-by-count":110,"title":["Bayesian integrated testing strategy to assess skin sensitization potency: from theory to practice"],"prefix":"10.1002","volume":"33","author":[{"given":"Joanna","family":"Jaworska","sequence":"first","affiliation":[{"name":"Procter &amp; Gamble NV  100 Temselaan 1853 Strombeek \u2010 Bever Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuri","family":"Dancik","sequence":"additional","affiliation":[{"name":"Procter &amp; Gamble NV  100 Temselaan 1853 Strombeek \u2010 Bever Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Petra","family":"Kern","sequence":"additional","affiliation":[{"name":"Procter &amp; Gamble NV  100 Temselaan 1853 Strombeek \u2010 Bever Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank","family":"Gerberick","sequence":"additional","affiliation":[{"name":"Procter &amp; Gamble Company  Cincinnati OH USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Natsch","sequence":"additional","affiliation":[{"name":"Givaudan Schweiz AG  Ueberlandstrasse 138 CH\u20108600 D\u00fcbendorf Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2013,5,14]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00204-011-0693-2"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/jid.2008.209"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/etc.34"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1177\/026119291003800403"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0105-1873.2004.00278.x"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.14573\/altex.2012.1.003"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/jat.1443"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/jat.1545"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.yrtph.2012.05.013"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/toxsci\/kfp234"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addr.2012.01.006"},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tiv.2012.01.009"},{"key":"e_1_2_6_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/10915810591000631"},{"key":"e_1_2_6_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tiv.2010.10.008"},{"key":"e_1_2_6_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.taap.2010.03.009"},{"key":"e_1_2_6_17_1","doi-asserted-by":"publisher","DOI":"10.1093\/toxsci\/kfp192"},{"key":"e_1_2_6_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/toxsci\/kfh213"},{"key":"e_1_2_6_19_1","doi-asserted-by":"publisher","DOI":"10.1093\/toxsci\/kfm064"},{"key":"e_1_2_6_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-6089-0"},{"key":"e_1_2_6_21_1","doi-asserted-by":"publisher","DOI":"10.14573\/altex.2010.4.253"},{"key":"e_1_2_6_22_1","unstructured":"HeckermanD.1995.A Bayesian approach to learning causal networks.Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence pp.285\u2013295."},{"key":"e_1_2_6_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/jps.23106"},{"key":"e_1_2_6_24_1","unstructured":"ICCVAM.2008. 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