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

Application of machine learning algorithms to predict coronary artery calcification with a sibship-based design.

Genetic epidemiology - 1 May 2008

Sun Yan V, Bielak Lawrence F, Peyser Patricia A, Turner Stephen T, Sheedy Patrick F, Boerwinkle Eric, Kardia Sharon L R

Abstract excerpt

As part of the Genetic Epidemiology Network of Arteriopathy study, hypertensive non-Hispanic White sibships were screened using 471 single nucleotide polymorphisms (SNPs) to identify genes influencing coronary artery calcification (CAC) measured by computed tomography. Individuals with detectable CAC and CAC quantity > or =70th age- and sex-specific percentile were classified as having a high CAC burden and...

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 Calcinosis

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Coronary Vessels

Share to Databases, Factual

Share to Databases, Genetic

Share to Epidemiologic Methods

Share to ROC Curve

Share to Siblings

Source

Open in PubMed Open full text

Identifiers

PMID: 18271057
DOI: 10.1002/gepi.20309
PMCID: PMC2828904

Genes

GPR35
NOS3

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Application of machine learning algorithms to predict coronary artery calcification with a sibship-based design.PMID 18271057Read ↗

Machine learning identifies SNPs predictive of advanced coronary artery calcium in ClinSeq® and Framingham Heart Study cohortsSemantic proximity Read ↗

Integrative predictive model of coronary artery calcification in atherosclerosis.Semantic proximity Read ↗

Prediction of Coronary Artery Disease Risk Using Genetic and Phenotypic Variables.Semantic proximity Read ↗

Genotype-driven identification of a molecular network predictive of advanced coronary calcium in ClinSeq® and Framingham Heart Study cohorts.Semantic proximity Read ↗

Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection.Semantic proximity Read ↗

Identifying Distinct Cardiometabolic Groups of Coronary Artery Calcification in Patients with Coronary Artery Disease: A Machine Learning ApproachSemantic proximity Read ↗

Coronary artery calcification and its relationship to validated genetic variants for diabetes mellitus assessed in the Heinz Nixdorf recall cohort.Semantic proximity Read ↗

Tree-structured supervised learning and the genetics of hypertension.Semantic proximity Read ↗

Genome-wide association study of gene by smoking interactions in coronary artery calcification.Semantic proximity Read ↗

Autosomal genome-wide scan for coronary artery calcification loci in sibships at high risk for hypertension.Semantic proximity Read ↗

Mapping complex traits using Random Forests.Semantic proximity Read ↗

Genome-wide admixture mapping for coronary artery calcification in African Americans: the NHLBI Family Heart Study.Semantic proximity Read ↗

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