ArticlePLoS genetics2018
Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica.
Article in PLoS genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
58 citing papers in PubMed, 1 synthesis or guideline pooled it, 106 citations in OpenAlex.
- Computational paradigms for antimicrobial resistance prediction: integrating multi-omics, structural modeling, and foundation artificial intelligence systems.Briefings in bioinformatics · 2026Pooled it
- Genomic signatures of host adaptation in Pasteurella multocida from poultry, cattle, swine, and cats: insights from interpretable machine learning.Poultry science · 2026Article
- Sequential gene loss promotes expansion of monophasic Salmonella Typhimurium ST34.Nature microbiology · 2026Article
- Pan genome clustering identifies a novel mosaic prophage specific to Salmonella Enteritidis lineage associated with the invasive disease in India.PLoS neglected tropical diseases · 2026Article
- Distinct genomic trajectory among invasive Salmonella Typhimurium ST313 infections.Nature communications · 2026Article
- GWAS and Machine Learning Screening of Genomic Determinants Underlying Host Adaptation in Swine and ChickenMicroorganisms · 2026Article
- A Clinically Silent Resistance Phenotype That PromotesbioRxiv : the preprint server for biology · 2026Article
- BIOGEN: evidence-grounded multi-agent reasoning framework for transcriptomic interpretation in antimicrobial resistance.Frontiers in bioinformatics · 2026Article
- Machine learning and statistical inference in microbial population genomics.Genome biology · 2025Review
- Genomic census of invasive nontyphoidal Salmonella infections reveals global and local human-to-human transmission.Nature medicine · 2025Article
- Article
- A genomic and phenotypic investigation of pigeon-adaptive Salmonella.PLoS pathogens · 2025Article
- Potential links between human bloodstream infection by Salmonella enterica serovar Typhimurium and international transmission to Colombia.PLoS neglected tropical diseases · 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
- Genomic epidemiology and public health implications of zoonotic monophasicFrontiers in cellular and infection microbiology · 2025Article
- Comparative genomics reveals key adaptive mechanisms in pathogen host-niche specialization.Frontiers in microbiology · 2025Article
- Transmission dynamics for invasive Non-TyphoidalWellcome open research · 2025Article
- Genomic and Transcriptomic Comparison Between Invasive Non-TyphoidalMicroorganisms · 2024Article
- Impact of COVID-19-related nonpharmaceutical interventions on diarrheal diseases and zoonotichLife · 2024Article
- Generalizability of machine learning in predicting antimicrobial resistance in E. coli: a multi-country case study in Africa.BMC genomics · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 3 countries.
Funding
Abstract
Emerging pathogens are a major threat to public health, however understanding how pathogens adapt to new niches remains a challenge. New methods are urgently required to provide functional insights into pathogens from the massive genomic data sets now being generated from routine pathogen surveillance for epidemiological purposes. Here, we measure the burden of atypical mutations in protein coding genes across independently evolved Salmonella enterica lineages, and use these as input to train a random forest classifier to identify strains associated with extraintestinal disease. Members of the species fall along a continuum, from pathovars which cause gastrointestinal infection and low mortality, associated with a broad host-range, to those that cause invasive infection and high mortality, associated with a narrowed host range. Our random forest classifier learned to perfectly discriminate long-established gastrointestinal and invasive serovars of Salmonella. Additionally, it was able to discriminate recently emerged Salmonella Enteritidis and Typhimurium lineages associated with invasive disease in immunocompromised populations in sub-Saharan Africa, and within-host adaptation to invasive infection. We dissect the architecture of the model to identify the genes that were most informative of phenotype, revealing a common theme of degradation of metabolic pathways in extraintestinal lineages. This approach accurately identifies patterns of gene degradation and diversifying selection specific to invasive serovars that have been captured by more labour-intensive investigations, but can be readily scaled to larger analyses.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.