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

Multi-model inference using mixed effects from a linear regression based genetic algorithm.

BMC bioinformatics - 27 Mar 2014

Van der Borght Koen, Verbeke Geert, van Vlijmen Herman

Abstract excerpt

BACKGROUND: Different high-dimensional regression methodologies exist for the selection of variables to predict a continuous variable. To improve the variable selection in case clustered observations are present in the training data, an extension towards mixed-effects modeling (MM) is requested, but may not always be straightforward to implement.In this article, we developed such a MM extension (GA-MM-MMI) 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 Databases, Genetic
Share to Drug Resistance, Viral

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Least-Squares Analysis

Share to Linear Models

Share to Pyrrolidinones

Share to Raltegravir Potassium

Source

Open in PubMed Open full text

Identifiers

PMID: 24669828
DOI: 10.1186/1471-2105-15-88
PMCID: PMC3987104

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

Multi-model inference using mixed effects from a linear regression based genetic algorithm.PMID 24669828Read ↗

Quantitative prediction of integrase inhibitor resistance from genotype through consensus linear regression modeling.Semantic proximity Read ↗

Genomic Machine Learning Meta-regression: Insights on Associations of Study Features with Reported Model PerformanceSemantic proximity Read ↗

Asymptotically exact fit for linear mixed model in genetic association studies.Semantic proximity Read ↗

Efficient multivariate linear mixed model algorithms for genome-wide association studies.Semantic proximity Read ↗

Cross-validated stepwise regression for identification of novel non-nucleoside reverse transcriptase inhibitor resistance associated mutations.Semantic proximity Read ↗

Using iterative ridge regression to explore associations between conditioned variables.Semantic proximity Read ↗

Optimising machine learning prediction of minimum inhibitory concentrations in <i>Klebsiella pneumoniae</i>Semantic proximity Read ↗

Multiobjective multifactor dimensionality reduction to detect SNP-SNP interactions.Semantic proximity Read ↗

Modeling sequence-sequence interactions for drug response.Semantic proximity Read ↗

Prediction of HIV-1 drug susceptibility phenotype from the viral genotype using linear regression modeling.Semantic proximity Read ↗

High-dimensional pharmacogenetic prediction of a continuous trait using machine learning techniques with application to warfarin dose prediction in African Americans.Semantic proximity Read ↗

Improving Genotype Imputation in High-Dimensional Pharmacogenomics Using Multiple Imputation: Evaluation with Machine Learning Approaches.Semantic proximity Read ↗

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