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

Smooth-Threshold Multivariate Genetic Prediction with Unbiased Model Selection.

Genetic epidemiology - 1 Apr 2016

Ueki Masao, Tamiya Gen

Abstract excerpt

We develop a new genetic prediction method, smooth-threshold multivariate genetic prediction, using single nucleotide polymorphisms (SNPs) data in genome-wide association studies (GWASs). Our method consists of two stages. At the first stage, unlike the usual discontinuous SNP screening as used in the gene score method, our method continuously screens SNPs based on the output from standard univariate analysis for...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Alzheimer Disease
Share to Genome, Human

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genomics

Share to Quantitative Trait, Heritable

Share to Regression Analysis

Share to Reproducibility of Results

Share to Research Design

Source

Open in PubMed Open full text

Identifiers

PMID: 26947266
DOI: 10.1002/gepi.21958
PMCID: PMC5849235

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

Smooth-Threshold Multivariate Genetic Prediction with Unbiased Model Selection.PMID 26947266Read ↗

Machine learning for effectively avoiding overfitting is a crucial strategy for the genetic prediction of polygenic psychiatric phenotypes.Semantic proximity Read ↗

Iterative hard thresholding in genome-wide association studies: Generalized linear models, prior weights, and double sparsity.Semantic proximity Read ↗

Iterative Hard Thresholding in GWAS: Generalized Linear Models, Prior Weights, and Double SparsitySemantic proximity Read ↗

GEiPRS: a fast and powerful machine learning method for polygenic risk score prediction by leveraging genotype-environment interactions.Semantic proximity Read ↗

A smoothed version of the Lassosum penalty for fitting integrated risk modelsSemantic proximity Read ↗

Enhancing genotype-phenotype association with optimized machine learning and biological enrichment methodsSemantic proximity Read ↗

Sparse Polygenic Risk Score Inference with the Spike-and-Slab LASSOSemantic proximity Read ↗

Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data.Semantic proximity Read ↗

A Smoothed Version of the Lassosum Penalty for Fitting Integrated Risk Models Using Summary Statistics or Individual-Level Data.Semantic proximity Read ↗

Bayesian Multivariate Genetic Analysis Boosts Translational InsightsSemantic proximity Read ↗

Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics.Semantic proximity Read ↗

Winner’s curse correction and variable thresholding improve performance of polygenic risk modeling based on genome-wide association study summary-level dataSemantic proximity Read ↗

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