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

Integrating dynamic mixed-effect modelling and penalized regression to explore genetic association with pharmacokinetics.

Pharmacogenetics and genomics - 1 May 2015

Bertrand Julie, De Iorio Maria, Balding David J

Abstract excerpt

CONTEXT: In a previous work, we have shown that penalized regression approaches can allow many genetic variants to be incorporated into sophisticated pharmacokinetic (PK) models in a way that is both computationally and statistically efficient. The phenotypes were the individual model parameter e...

Read the complete abstract on PubMed

Topics

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

Share to Computer Simulation
Share to HapMap Project
Share to High-Throughput Screening Assays

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Inactivation, Metabolic

Share to Models, Statistical

Share to Models, Theoretical

Share to Pharmacogenetics

Share to Pharmacokinetics

Source

Open in PubMed Open full text

Identifiers

PMID: 25751396
DOI: 10.1097/FPC.0000000000000127
PMCID: PMC4387202

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026. Prepared record publication-25751396-2026-09-04.2. 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

Integrating dynamic mixed-effect modelling and penalized regression to explore genetic association with pharmacokinetics.PMID 25751396Read ↗

Multiple single nucleotide polymorphism analysis using penalized regression in nonlinear mixed-effect pharmacokinetic models.Semantic proximity Read ↗

Comparison of Nonlinear Mixed Effects Models and Noncompartmental Approaches in Detecting Pharmacogenetic Covariates.Semantic proximity Read ↗

Penalized linear mixed models for structured genetic data.Semantic proximity Read ↗

Combined Analysis of Phase I and Phase II Data to Enhance the Power of Pharmacogenetic Tests.Semantic proximity Read ↗

Efficient penalized generalized linear mixed models for variable selection and genetic risk prediction in high-dimensional data.Semantic proximity Read ↗

An efficient and robust method for analyzing population pharmacokinetic data in genome-wide pharmacogenomic studies: a generalized estimating equation approach.Semantic proximity Read ↗

Integration of high-throughput genotyping data into pharmacometric analyses using nonlinear mixed effects modeling.Semantic proximity Read ↗

Rare variants analysis using penalization methods for whole genome sequence data.Semantic proximity Read ↗

Design of pharmacokinetic studies for latent covariates.Semantic proximity Read ↗

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

Integrative analysis of gene-environment interactions under a multi-response partially linear varying coefficient model.Semantic proximity Read ↗

Pharmacogenetics and population pharmacokinetics: impact of the design on three tests using the SAEM algorithm.Semantic proximity Read ↗

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