Application of Whole-Genome Sequences and Machine Learning in Source Attribution of Salmonella Typhimurium.

Risk Analysis : an Official Publication of the Society for Risk Analysis
Nanna MunckTine Hald

Abstract

Prevention of the emergence and spread of foodborne diseases is an important prerequisite for the improvement of public health. Source attribution models link sporadic human cases of a specific illness to food sources and animal reservoirs. With the next generation sequencing technology, it is possible to develop novel source attribution models. We investigated the potential of machine learning to predict the animal reservoir from which a bacterial strain isolated from a human salmonellosis case originated based on whole-genome sequencing. Machine learning methods recognize patterns in large and complex data sets and use this knowledge to build models. The model learns patterns associated with genetic variations in bacteria isolated from the different animal reservoirs. We selected different machine learning algorithms to predict sources of human salmonellosis cases and trained the model with Danish Salmonella Typhimurium isolates sampled from broilers (n = 34), cattle (n = 2), ducks (n = 11), layers (n = 4), and pigs (n = 159). Using cgMLST as input features, the model yielded an average accuracy of 0.783 (95% CI: 0.77-0.80) in the source prediction for the random forest and 0.933 (95% CI: 0.92-0.94) for the logit boost algori...Continue Reading

References

Dec 16, 2000·Epidemiology and Infection·S UzzauJ E Olsen
Mar 19, 2004·Risk Analysis : an Official Publication of the Society for Risk Analysis·Tine HaldTimour Koupeev
Apr 21, 2009·Molecular Biology and Evolution·Morgan N PriceAdam P Arkin
May 7, 2009·Foodborne Pathogens and Disease·Sara M PiresUNKNOWN Med-Vet-Net Workpackage 28 Working Group
May 20, 2009·Bioinformatics·Heng Li, Richard Durbin
Jun 3, 2009·Risk Analysis : an Official Publication of the Society for Risk Analysis·Petra MullnerNigel Peter French
Jun 10, 2009·Bioinformatics·Heng LiUNKNOWN 1000 Genome Project Data Processing Subgroup
Jun 1, 2011·BMC Proceedings·Joseph O OgutuTorben Schulz-Streeck
Nov 1, 2011·Bioinformatics·Daniel J Stekhoven, Peter Bühlmann
Jan 11, 2013·BMC Genomics·Pimlapas LeekitcharoenphonFrank M Aarestrup
May 8, 2015·Nature Reviews. Genetics·Maxwell W Libbrecht, William Stafford Noble
Jul 2, 2015·Risk Analysis : an Official Publication of the Society for Risk Analysis·K GlassM D Kirk
Jul 26, 2015·Veterinary Research·Gustavo MachadoLuis Gustavo Corbellini
Apr 6, 2018·PLoS Genetics·Nabil-Fareed AlikhanMark Achtman
Nov 30, 2019·Microbial Genomics·Nadejda LupolovaDavid L Gally

❮ Previous
Next ❯

Citations

Jan 21, 2021·Annual Review of Food Science and Technology·Xiangyu DengAbigail L Horn
May 29, 2021·Avian Pathology : Journal of the W.V.P.A·Jai W MehatRoberto M La Ragione
Jun 28, 2021·Current Opinion in Microbiology·Mark P Stevens, Robert A Kingsley

❮ Previous
Next ❯

Methods Mentioned

BETA
phylogenetic profiles

Software Mentioned

FastTree
Boruta R package
Excel
SAMTools
missForest
cgMLST
Call SNPs
BioNumerics
R
Infer Phylogeny ( CSI )

Related Concepts

Related Feeds

Botulism (ASM)

Botulism is a rare but serious paralytic illness caused by a nerve toxin that is produced by the bacterium clostridium botulinum. Discover the latest research on botulism here.

Botulism

Botulism is a rare but serious paralytic illness caused by a nerve toxin that is produced by the bacterium clostridium botulinum. Discover the latest research on botulism here.