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

Rare variants detection with kernel machine learning based on likelihood ratio test.

PloS one - 1 Jan 2014

Zeng Ping, Zhao Yang, Zhang Liwei, Huang Shuiping, Chen Feng

Abstract excerpt

This paper mainly utilizes likelihood-based tests to detect rare variants associated with a continuous phenotype under the framework of kernel machine learning. Both the likelihood ratio test (LRT) and the restricted likelihood ratio test (ReLRT) are investigated. The relationship between the ker...

Read the complete abstract on PubMed

Topics

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

Share to Apoptosis Regulatory Proteins
Share to Artificial Intelligence
Share to Basic Helix-Loop-Helix Transcription Factors

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Likelihood Functions

Share to Sample Size

Share to Vascular Endothelial Growth Factor Receptor-1

Share to Vascular Endothelial Growth Factor Receptor-2

Source

Open in PubMed Open full text

Identifiers

PMID: 24675868
DOI: 10.1371/journal.pone.0093355
PMCID: PMC3968153

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026. Prepared record publication-24675868-2026-09-04.2. 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

Rare variants detection with kernel machine learning based on likelihood ratio test.PMID 24675868Read ↗

Likelihood ratio tests in rare variant detection for continuous phenotypes.Semantic proximity Read ↗

Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method.Semantic proximity Read ↗

Boosting Gene Mapping Power and Efficiency with Efficient Exact Variance Component Tests of Single Nucleotide Polymorphism Sets.Semantic proximity Read ↗

Detecting Disease Association With Rare Variants Using Weighted EntropySemantic proximity Read ↗

Generalized functional linear models for gene-based case-control association studies.Semantic proximity Read ↗

Detecting disease association with rare variants using weighted entropy.Semantic proximity Read ↗

Rare variant association test in family-based sequencing studies.Semantic proximity Read ↗

Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples.Semantic proximity Read ↗

A maximum kernel-based association test to detect the pleiotropic genetic effects on multiple phenotypes.Semantic proximity Read ↗

Identifying complex gene-gene interactions: a mixed kernel omnibus testing approach.Semantic proximity Read ↗

Prioritizing individual genetic variants after kernel machine testing using variable selection.Semantic proximity Read ↗

Association test based on SNP set: logistic kernel machine based test vs. principal component analysis.Semantic proximity Read ↗

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