{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T01:17:45Z","timestamp":1790644665213,"version":"4.1.0"},"reference-count":60,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T00:00:00Z","timestamp":1681257600000},"content-version":"am","delay-in-days":365,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#am"},{"start":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T00:00:00Z","timestamp":1649721600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Risk Analysis"],"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Estimating microbial dose\u2013response is an important aspect of a food safety risk assessment. In recent years, there has been considerable interest to advance these models with potential incorporation of gene expression data. The aim of this study was to develop a novel machine learning model that considers the weights of expression of <jats:italic>Salmonella<\/jats:italic> genes that could be associated with illness, given exposure, in hosts. Here, an elastic net\u2010based weighted Poisson regression method was proposed to identify <jats:italic>Salmonella enterica<\/jats:italic> genes that could be significantly associated with the illness response, irrespective of serovar. The best\u2010fit elastic net model was obtained by 10\u2010fold cross\u2010validation. The best\u2010fit elastic net model identified 33 gene expression\u2013dose interaction terms that added to the predictability of the model. Of these, nine genes associated with <jats:italic>Salmonella<\/jats:italic> metabolism and virulence were found to be significant by the best\u2010fit Poisson regression model (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.05). This method could improve or redefine dose\u2013response relationships for illness from relative proportions of significant genes from a microbial genetic dataset, which would help in refining endpoint and risk estimations.<\/jats:p>","DOI":"10.1111\/risa.13924","type":"journal-article","created":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T18:12:13Z","timestamp":1649787133000},"page":"440-450","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Development of a novel machine learning\u2010based weighted modeling approach to incorporate <i>Salmonella enterica<\/i> heterogeneity on a genetic scale in a dose\u2013response modeling framework"],"prefix":"10.1111","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6970-8539","authenticated-orcid":false,"given":"Shraddha","family":"Karanth","sequence":"first","affiliation":[{"name":"Department of Nutrition and Food Science University of Maryland  College Park Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8461-9617","authenticated-orcid":false,"given":"Abani K.","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Department of Nutrition and Food Science University of Maryland  College Park Maryland USA"},{"name":"Center for Food Safety and Security Systems University of Maryland  College Park Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,4,12]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2014.00070"},{"key":"e_1_2_7_3_1","unstructured":"Anderson C. 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(2012).National Salmonella surveillance annual report 2012. Atlanta GA. U.S. Centers for Disease Control and Prevention.https:\/\/www.cdc.gov\/ncezid\/dfwed\/pdfs\/salmonella\u2010annual\u2010report\u20102012\u2010508c.pdf"},{"key":"e_1_2_7_11_1","unstructured":"C.D.C. United States Centers for Disease Control and Prevention. (2019).Reports of Salmonella outbreak investigations from 2019. U.S. Centers for Disease Control and Prevention.https:\/\/www.cdc.gov\/salmonella\/outbreaks\u20102019.html"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1214\/009053606000001523"},{"key":"e_1_2_7_13_1","unstructured":"Chesaniuk M.(2021).Chapter 19: Logistic and Poisson regression.https:\/\/ademos.people.uic.edu\/Chapter19.html"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1128\/MMBR.00024-08"},{"key":"e_1_2_7_15_1","unstructured":"Dataquest. 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