Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Lessons Learnt From Using the Machine Learning Random Forest Algorithm to Predict Virulence in Streptococcus pyogenes.

Frontiers in cellular and infection microbiology - 1 Jan 2021

Buckley Sean J, Harvey Robert J

Abstract excerpt

Group A Streptococcus is a globally significant human pathogen. The extensive variability of the GAS genome, virulence phenotypes and clinical outcomes, render it an excellent candidate for the application of genotype-phenotype association studies in the era of whole-genome sequencing. We have catalogued the distribution and diversity of the transcription regulators of GAS, and employed phylogenetics, concordance...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Genome-Wide Association Study

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Machine Learning

Share to Phenotype

Share to Plant Pathogenic Bacteria Studies

Share to Streptococcal Infections and Treatments

Share to Streptococcus pyogenes

Share to Virulence

Source

Open in PubMed Open full text

Identifiers

PMID: 35004362
DOI: 10.3389/fcimb.2021.809560
PMCID: PMC8739889

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Lessons Learnt From Using the Machine Learning Random Forest Algorithm to Predict Virulence in Streptococcus pyogenes.PMID 35004362Read ↗

Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models.Semantic proximity Read ↗

Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning.Semantic proximity Read ↗

Genome-wide association, prediction and heritability in bacteriaSemantic proximity Read ↗

microGWAS: a computational pipeline to perform large-scale bacterial genome-wide association studies.Semantic proximity Read ↗

compGWAS: a new GWAS tool allows revelation of the genetic architecture and risk stratification for the versatile pathogen Streptococcus pyogenesSemantic proximity Read ↗

BaGPipe: an automated, reproducible, and flexible pipeline for bacterial genome-wide association studiesSemantic proximity Read ↗

Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole genome sequence typing dataSemantic proximity Read ↗

Benchmarking bacterial genome-wide association study methods using simulated genomes and phenotypes.Semantic proximity Read ↗

BaGPipe: an automated, reproducible, and flexible pipeline for bacterial genome-wide association studies.Semantic proximity Read ↗

Genome-wide association, prediction and heritability in bacteria with application toStreptococcus pneumoniaeSemantic proximity Read ↗

A statistical genomics framework to trace bacterial genomic predictors of clinical outcomes in Staphylococcus aureus bacteremiaSemantic proximity Read ↗

Microbial genome-wide association studies: lessons from human GWAS.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.