ArticleMicrobial genomics2019
A guide to machine learning for bacterial host attribution using genome sequence data.
Article in Microbial genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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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.
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
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 citations in OpenAlex.
- Influenza virus genotype to phenotype predictions through machine learning: a systematic review.Emerging microbes & infections · 2021Pooled it
- Genome-based source attribution using a One HealthMicrobial genomics · 2026Article
- Predicting clinical outcome ofMicrobial genomics · 2025Article
- aurora: a machine learning gwas tool for analyzing microbial habitat adaptation.Genome biology · 2025Article
- Using gut microbiome metagenomic hypervariable features for diabetes screening and typing through supervised machine learning.Microbial genomics · 2025Article
- Parameters for one health genomic surveillance of Escherichia coli from Australia.Nature communications · 2025Article
- Rapid and accurate recognition of erythrocytic stage parasites ofFrontiers in microbiology · 2025Article
- Development of a logic regression-based approach for the discovery of host- and niche-informative biomarkers inApplied and environmental microbiology · 2024Article
- Predictive phage therapy forProceedings of the National Academy of Sciences of the United States of America · 2024Article
- A Review on Microbiological Source Attribution Methods of Human Salmonellosis: From Subtyping to Whole-Genome Sequencing.Foodborne pathogens and disease · 2024Review
- Recognition of parasitic helminth eggs via a deep learning-based platform.Frontiers in microbiology · 2024Article
- Use of whole genome sequencing for surveillance and control of foodborne diseases:Frontiers in microbiology · 2024Review
- The advantage of intergenic regions as genomic features for machine-learning-based host attribution ofMicrobial genomics · 2023Article
- Plasmid permissiveness of wastewater microbiomes can be predicted from 16S rRNA sequences by machine learning.Bioinformatics (Oxford, England) · 2023Article
- Article
- Rapid geographical source attribution ofeLife · 2023Article
- Development and validation of a random forest algorithm for source attribution of animal and humanFrontiers in microbiology · 2023Article
- Genomic signatures of host adaptation in group B Salmonella enterica ST416/ST417 from harbour porpoises.Veterinary research · 2021Article
- Pathogenic potential assessment of the Shiga toxin-producingProceedings of the National Academy of Sciences of the United States of America · 2021Article
- Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
With the ever-expanding number of available sequences from bacterial genomes, and the expectation that this data type will be the primary one generated from both diagnostic and research laboratories for the foreseeable future, then there is both an opportunity and a need to evaluate how effectively computational approaches can be used within bacterial genomics to predict and understand complex phenotypes, such as pathogenic potential and host source. This article applied various quantitative methods such as diversity indexes, pangenome-wide association studies (GWAS) and dimensionality reduction techniques to better understand the data and then compared how well unsupervised and supervised machine learning (ML) methods could predict the source host of the isolates. The study uses the example of the pangenomes of 1203
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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.