A Machine Learning Model for Food Source Attribution of Listeria monocytogenes

dc.contributor.authorTanui, Collins K.
dc.contributor.authorBenefo, Edmund O.
dc.contributor.authorKaranth, Shraddha
dc.contributor.authorPradhan, Abani K.
dc.date.accessioned2023-10-25T16:15:06Z
dc.date.available2023-10-25T16:15:06Z
dc.date.issued2022-06-16
dc.description.abstractDespite its low morbidity, listeriosis has a high mortality rate due to the severity of its clinical manifestations. The source of human listeriosis is often unclear. In this study, we investigate the ability of machine learning to predict the food source from which clinical Listeria monocytogenes isolates originated. Four machine learning classification algorithms were trained on core genome multilocus sequence typing data of 1212 L. monocytogenes isolates from various food sources. The average accuracies of random forest, support vector machine radial kernel, stochastic gradient boosting, and logit boost were found to be 0.72, 0.61, 0.7, and 0.73, respectively. Logit boost showed the best performance and was used in model testing on 154 L. monocytogenes clinical isolates. The model attributed 17.5 % of human clinical cases to dairy, 32.5% to fruits, 14.3% to leafy greens, 9.7% to meat, 4.6% to poultry, and 18.8% to vegetables. The final model also provided us with genetic features that were predictive of specific sources. Thus, this combination of genomic data and machine learning-based models can greatly enhance our ability to track L. monocytogenes from different food sources.
dc.description.urihttps://doi.org/10.3390/pathogens11060691
dc.identifierhttps://doi.org/10.13016/dspace/bnna-li96
dc.identifier.citationTanui, C.K.; Benefo, E.O.; Karanth, S.; Pradhan, A.K. A Machine Learning Model for Food Source Attribution of Listeria monocytogenes. Pathogens 2022, 11, 691.
dc.identifier.urihttp://hdl.handle.net/1903/31111
dc.language.isoen_US
dc.publisherMDPI
dc.relation.isAvailableAtCollege of Agriculture & Natural Resourcesen_us
dc.relation.isAvailableAtNutrition & Food Scienceen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.subjectListeria monocytogenes
dc.subjectfood source attribution
dc.subjectwhole-genome sequencing
dc.subjectmachine learning
dc.subjectpredictive modeling
dc.titleA Machine Learning Model for Food Source Attribution of Listeria monocytogenes
dc.typeArticle
local.equitableAccessSubmissionNo

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
pathogens-11-00691.pdf
Size:
459.65 KB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.55 KB
Format:
Item-specific license agreed upon to submission
Description: