{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T18:56:39Z","timestamp":1792004199748,"version":"4.4.0"},"reference-count":71,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2018,11,21]],"date-time":"2018-11-21T00:00:00Z","timestamp":1542758400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"European Union's Horizon 2020","award":["Grant agreement No. 643476"],"award-info":[{"award-number":["Grant agreement No. 643476"]}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Risk Analysis"],"published-print":{"date-parts":[[2019,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Next\u2010generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and virulence among strains. The potential of machine learning algorithms for predicting the risk\/health burden at the population level while inputting large and complex NGS data was explored with <jats:italic>Listeria monocytogenes<\/jats:italic> as a case study. Listeria data consisted of a percentage similarity matrix from genome assemblies of 38 and 207 strains of clinical and food origin, respectively. Basic Local Alignment (BLAST) was used to align the assemblies against a database of 136 virulence and stress resistance genes. The outcome variable was frequency of illness, which is the percentage of reported cases associated with each strain. These frequency data were discretized into seven ordinal outcome categories and used for supervised machine learning and model selection from five ensemble algorithms. There was no significant difference in accuracy between the models, and support vector machine with linear kernel was chosen for further inference (accuracy of 89%\u00a0[95% CI: 68%, 97%]). The virulence genes <jats:italic>FAM002725, FAM002728, FAM002729, InlF<\/jats:italic>, <jats:italic>InlJ<\/jats:italic>, <jats:italic>Inlk, IisY<\/jats:italic>, <jats:italic>IisD<\/jats:italic>, <jats:italic>IisX<\/jats:italic>, <jats:italic>IisH<\/jats:italic>, <jats:italic>IisB<\/jats:italic>, <jats:italic>lmo2026<\/jats:italic>, and <jats:italic>FAM003296<\/jats:italic> were important predictors of higher frequency of illness. <jats:italic>InlF<\/jats:italic> was uniquely truncated in the sequence type 121 strains. Most important risk predictor genes occurred at highest prevalence among strains from ready\u2010to\u2010eat, dairy, and composite foods. We foresee that the findings and approaches described offer the potential for rethinking the current approaches in MRA.<\/jats:p>","DOI":"10.1111\/risa.13239","type":"journal-article","created":{"date-parts":[[2018,11,21]],"date-time":"2018-11-21T21:15:49Z","timestamp":1542834949000},"page":"1397-1413","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next\u2010Generation Sequencing Data"],"prefix":"10.1111","volume":"39","author":[{"given":"Patrick Murigu Kamau","family":"Njage","sequence":"first","affiliation":[{"name":"Division for Epidemiology and Microbial Genomics National Food Institute, Technical University of Denmark  Kongens Lyngby Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clementine","family":"Henri","sequence":"additional","affiliation":[{"name":"Universit\u00e9 PARIS\u2010EST, Agence Nationale de S\u00e9curit\u00e9 Sanitaire de L'Alimentation, de L'Environnement et du Travail (ANSES) Laboratory for Food Safety  Maisons\u2010Alfort France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pimlapas","family":"Leekitcharoenphon","sequence":"additional","affiliation":[{"name":"Division for Epidemiology and Microbial Genomics National Food Institute, Technical University of Denmark  Kongens Lyngby Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michel\u2010Yves","family":"Mistou","sequence":"additional","affiliation":[{"name":"Universit\u00e9 PARIS\u2010EST, Agence Nationale de S\u00e9curit\u00e9 Sanitaire de L'Alimentation, de L'Environnement et du Travail (ANSES) Laboratory for Food Safety  Maisons\u2010Alfort France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rene S.","family":"Hendriksen","sequence":"additional","affiliation":[{"name":"Division for Epidemiology and Microbial Genomics National Food Institute, Technical University of Denmark  Kongens Lyngby Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tine","family":"Hald","sequence":"additional","affiliation":[{"name":"Division for Epidemiology and Microbial Genomics National Food Institute, Technical University of Denmark  Kongens Lyngby Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,11,21]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tibtech.2004.10.007"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.micinf.2007.05.003"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1009213726"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.97.1.262"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tifs.2012.04.004"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2958.2003.03945.x"},{"issue":"4","key":"e_1_2_7_9_1","first-page":"20382","article-title":"Bioinformatics in bacterial molecular epidemiology and public health: Databases, tools and the next\u2010generation sequencing revolution","volume":"18","author":"Carri\u00e7o J. 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