ArticlePLoS genetics2021
Machine learning to predict the source of campylobacteriosis using whole genome data.
Article in PLoS genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed.
- Genomic signatures of host adaptation in Pasteurella multocida from poultry, cattle, swine, and cats: insights from interpretable machine learning.Poultry science · 2026Article
- AcceleratingProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Protocols for genomic epidemiology and source attribution of enteric bacteria causing diarrhoeal disease across Africa.BMJ open · 2026Article
- GeneScanner: profiling genetic variation across bacterial populations.Microbial genomics · 2026Article
- Sources of Human Campylobacteriosis Cases in Estonia and the Genomic Associations with Broiler Chicken Meat Isolates.Pathogens (Basel, Switzerland) · 2026Article
- Genome-based source attribution using a One HealthMicrobial genomics · 2026Article
- Everything is everywhere but Escherichia coli adapts to different niches.The ISME journal · 2026Article
- Seasonal dynamics, antimicrobial resistance, and genetic lineages of thermotolerantScience in One Health · 2026Article
- Genomic insights and survival dynamics ofFrontiers in microbiology · 2026Article
- Predicting clinical outcome ofMicrobial genomics · 2025Article
- The European Union One Health 2024 Zoonoses Report.EFSA journal. European Food Safety Authority · 2025Article
- Evidence of diversification and geographic separation in a lineage ofMicrobial genomics · 2025Article
- Spatiotemporal analysis and risk prediction of foodborne diseases based on meteorological risk factors: a case study of Wuxi city, China.BMC infectious diseases · 2025Article
- Machine learning and statistical inference in microbial population genomics.Genome biology · 2025Review
- Transmission pathways of Campylobacter jejuni between humans and livestock in rural Ethiopia are highly complex and interdependent.Gut pathogens · 2025Article
- Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning.Scientific reports · 2025Article
- Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Source attribution of humanFrontiers in microbiology · 2025Article
- Machine learning to attribute the source of Campylobacter infections in the United States: A retrospective analysis of national surveillance data.The Journal of infection · 2024Article
- Foodborne bacterial pathogens: genome-based approaches for enduring and emerging threats in a complex and changing world.Nature reviews. Microbiology · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Campylobacteriosis is among the world's most common foodborne illnesses, caused predominantly by the bacterium Campylobacter jejuni. Effective interventions require determination of the infection source which is challenging as transmission occurs via multiple sources such as contaminated meat, poultry, and drinking water. Strain variation has allowed source tracking based upon allelic variation in multi-locus sequence typing (MLST) genes allowing isolates from infected individuals to be attributed to specific animal or environmental reservoirs. However, the accuracy of probabilistic attribution models has been limited by the ability to differentiate isolates based upon just 7 MLST genes. Here, we broaden the input data spectrum to include core genome MLST (cgMLST) and whole genome sequences (WGS), and implement multiple machine learning algorithms, allowing more accurate source attribution. We increase attribution accuracy from 64% using the standard iSource population genetic approach to 71% for MLST, 85% for cgMLST and 78% for kmerized WGS data using the classifier we named aiSource. To gain insight beyond the source model prediction, we use Bayesian inference to analyse the relative affinity of C. jejuni strains to infect humans and identified potential differences, in source-human transmission ability among clonally related isolates in the most common disease causing lineage (ST-21 clonal complex). Providing generalizable computationally efficient methods, based upon machine learning and population genetics, we provide a scalable approach to global disease surveillance that can continuously incorporate novel samples for source attribution and identify fine-scale variation in transmission potential.
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Registered trials
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