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

A random forest approach to the detection of epistatic interactions in case-control studies.

BMC bioinformatics - 30 Jan 2009

Jiang Rui, Tang Wanwan, Wu Xuebing, Fu Wenhui

Abstract excerpt

BACKGROUND: The key roles of epistatic interactions between multiple genetic variants in the pathogenesis of complex diseases notwithstanding, the detection of such interactions remains a great challenge in genome-wide association studies. Although some existing multi-locus approaches have shown their successes in small-scale case-control data, the "combination explosion" course prohibits their applications to...

Read the complete abstract on PubMed

Topics

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

Share to Artificial Intelligence
Share to Case-Control Studies
Share to Epistasis, Genetic

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Predisposition to Disease

Share to Genome, Human

Share to Genomics

Share to Polymorphism, Single Nucleotide

Share to Selection, Genetic

Source

Open in PubMed Open full text

Identifiers

PMID: 19208169
DOI: 10.1186/1471-2105-10-S1-S65
PMCID: PMC2648748

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A random forest approach to the detection of epistatic interactions in case-control studies.PMID 19208169Read ↗

SNPInterForest: a new method for detecting epistatic interactions.Semantic proximity Read ↗

A hybrid framework for genome wide epistasis discovery.Semantic proximity Read ↗

Screening large-scale association study data: exploiting interactions using random forests.Semantic proximity Read ↗

A forest-based approach to identifying gene and gene gene interactions.Semantic proximity Read ↗

A screening methodology based on Random Forests to improve the detection of gene-gene interactions.Semantic proximity Read ↗

Detecting gene-gene interactions using a permutation-based random forest methodSemantic proximity Read ↗

Multigenic modeling of complex disease by random forests.Semantic proximity Read ↗

GenEpi: Gene-based Epistasis Discovery Using Machine LearningSemantic proximity Read ↗

GenEpi: gene-based epistasis discovery using machine learning.Semantic proximity Read ↗

A comparison of methods for interpreting random forest models of genetic association in the presence of non-additive interactions.Semantic proximity Read ↗

Variable selection method for the identification of epistatic models.Semantic proximity Read ↗

Epistatic module detection for case-control studies: a Bayesian model with a Gibbs sampling strategy.Semantic proximity Read ↗

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