ArticleMicroorganisms2020
Biological Machine Learning Combined with
Article in Microorganisms, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 23 citations in OpenAlex.
- Apparent expansion of virulentVirulence · 2026Article
- Genomic diversity and host-specificity inFrontiers in microbiology · 2026Article
- Beyond AMR and virulence databases: genome wide associations of closely relatedFrontiers in microbiology · 2026Article
- Machine learning based on pangenome-wide association studies reveals the impact of host source on the zoonotic potential of closely related bacterial pathogens.Communications biology · 2025Article
- Comparison of virulence and resistance genes inMicrobiology spectrum · 2025Article
- Antimicrobial resistance ofApplied and environmental microbiology · 2025Article
- WGS of intrauterinemBio · 2024Article
- Antimicrobial Resistance Genes in Respiratory Bacteria from Weaned Dairy Heifers.Pathogens (Basel, Switzerland) · 2024Article
- Whole genome sequence analysis reveals high genomic diversity and potential host-driven adaptations among multidrug-resistantFrontiers in microbiology · 2024Article
- New Insights into Foodborne Bacteria-Host Interactions: Evolving Research and Discoveries.Microorganisms · 2023Article
- Harmonization of supervised machine learning practices for efficient source attribution of Listeria monocytogenes based on genomic data.BMC genomics · 2023Article
- Comparison of Source Attribution Methodologies for Human Campylobacteriosis.Pathogens (Basel, Switzerland) · 2023Article
- WGS-Based Lineage and Antimicrobial Resistance Pattern ofAntibiotics (Basel, Switzerland) · 2022Article
- Source Attribution of Human Campylobacteriosis Using Whole-Genome Sequencing Data and Network Analysis.Pathogens (Basel, Switzerland) · 2022Article
- Analyses of publicly availableVirulence · 2021Article
- Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning.Trends in microbiology · 2021Review
- Foodborne Pathogen Campylobacter.Microorganisms · 2021Article
- The Complete Genome of Nocardia seriolae MH196537 and Intra-Species Level as Analyzed by Comparative Genomics Based on Random Forest Algorithm.Current microbiology · 2021Article
- Phylogenetic and Biogeographic Patterns of Vibrio parahaemolyticus Strains from North America Inferred from Whole-Genome Sequence Data.Applied and environmental microbiology · 2021Article
- Identification of Nitrogen Fixation Genes inMicroorganisms · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Highly dimensional data generated from bacterial whole-genome sequencing is providing an unprecedented scale of information that requires an appropriate statistical analysis framework to infer biological function from populations of genomes. The application of genome-wide association study (GWAS) methods is an appropriate framework for bacterial population genome analysis that yields a list of candidate genes associated with a phenotype, but it provides an unranked measure of importance. Here, we validated a novel framework to define infection mechanism using the combination of GWAS, machine learning, and bacterial population genomics that ranked allelic variants that accurately identified disease. This approach parsed a dataset of 1.2 million single nucleotide polymorphisms (SNPs) and indels that resulted in an importance ranked list of associated alleles of
Indexed as
Identifiers
What OpenQuestion holds
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.