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

Using approximate Bayesian computation to estimate tuberculosis transmission parameters from genotype data.

Genetics - 1 Jul 2006

Tanaka Mark M, Francis Andrew R, Luciani Fabio, Sisson S A

Abstract excerpt

Tuberculosis can be studied at the population level by genotyping strains of Mycobacterium tuberculosis isolated from patients. We use an approximate Bayesian computational method in combination with a stochastic model of tuberculosis transmission and mutation of a molecular marker to estimate th...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Bayes Theorem
Share to Computational Biology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Markers

Share to Genotype

Share to Risk Factors

Share to San Francisco

Share to Tuberculosis

Source

Open in PubMed Open full text

Identifiers

PMID: 16624908
DOI: 10.1534/genetics.106.055574
PMCID: PMC1526704

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026. Prepared record publication-16624908-2026-09-04.2. Manifest publication-graph-cohort-2026-09-04.2.

Source receipt: PubMed via Helena Literature Corpus, retrieved 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Using approximate Bayesian computation to estimate tuberculosis transmission parameters from genotype data.PMID 16624908Read ↗

Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth–death modelsSemantic proximity Read ↗

Resolving outbreak dynamics using Approximate Bayesian Computation for stochastic birth-death modelsSemantic proximity Read ↗

Inferring epidemiological parameters on the basis of allele frequencies.Semantic proximity Read ↗

A field-validated approach using surveillance and genotyping data to estimate tuberculosis attributable to recent transmission in the United States.Semantic proximity Read ↗

On the Identifiability of Transmission Dynamic Models for Infectious Diseases.Semantic proximity Read ↗

On the identifiability of transmission dynamic models for infectious diseasesSemantic proximity Read ↗

Approximate Bayesian Computation Algorithms for Estimating Network Model ParametersSemantic proximity Read ↗

Sampling Bias in the Molecular Epidemiology of TuberculosisSemantic proximity Read ↗

An evaluation of indices for quantifying tuberculosis transmission using genotypes of pathogen isolates.Semantic proximity Read ↗

Using genetic data to identify transmission risk factors: statistical assessment and application to tuberculosis transmissionSemantic proximity Read ↗

How close is close enough? Exploring matching criteria in the estimation of recent transmission of tuberculosis.Semantic proximity Read ↗

Estimating change rates of genetic markers using serial samples: applications to the transposon IS6110 in Mycobacterium tuberculosis.Semantic proximity Read ↗

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