Clustering longitudinal profiles using P-splines and mixed effects models applied to time-course gene expression data
type: Item
instance of: scholarly article
title: Clustering longitudinal profiles using P-splines and mixed effects models applied to time-course gene expression data
zbMATH Open document ID: 1471.62045
author: John Hinde
author: E. Holian
author: Norma Coffey
published in: Computational Statistics and Data Analysis
publication date: 2018-11-08T00:00:00Z
Mathematics Subject Classification ID: 62-08
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62P10
zbMATH DE Number: 6975369
zbMATH Keywords: longitudinal profiles
zbMATH Keywords: time-course gene expression
zbMATH Keywords: clustering
zbMATH Keywords: mixed effects model
zbMATH Keywords: finite mixture model
describes a project that uses: SemiPar
describes a project that uses: fda (R)
MaRDI profile type: Publication
full work available at URL: j.csda.2013.04.001
OpenAlex ID: W2036937228
cites work: Mixture of linear mixed models for clustering gene expression profiles from repeated microarray experiments
cites work: Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
cites work: Model-Based Clustering, Discriminant Analysis, and Density Estimation
cites work: Q4315271
cites work: Algorithm AS 136: A K-Means Clustering Algorithm
cites work: Identifiability of models for clusterwise linear regression
cites work: Methods for merging Gaussian mixture components
cites work: Clustering for Sparsely Sampled Functional Data
cites work: Functional cluster analysis via orthonormalized Gaussian basis expansions and its application
cites work: Self-organizing maps.
cites work: Rejection Control and Sequential Importance Sampling
cites work: Penalized Clustering of Large-Scale Functional Data With Multiple Covariates
cites work: Modelling high-dimensional data by mixtures of factor analyzers
cites work: The Clustering of Regression Models Method with Applications in Gene Expression Data
cites work: Cluster analysis of gene expression dynamics
cites work: Functional data analysis.
cites work: Semiparametric Regression
cites work: Clustering of time-course gene expression data using functional data analysis
cites work: Tight Clustering: A Resampling‐Based Approach for Identifying Stable and Tight Patterns in Data
cites work: Q3996207
DOI: 10.1016/J.CSDA.2013.04.001
Recommended article: Model-Based Clustering of Longitudinal Data: Application to Modeling Disease Course and Gene Expression Trajectories
Recommended article: Clustering gene expression time course data using mixtures of multivariate \(t\)-distributions
Recommended article: Mixture of linear mixed models for clustering gene expression profiles from repeated microarray experiments
Recommended article: Clustering of time-course gene expression data using functional data analysis
Recommended article: Q4706544
Recommended article: Clustering of temporal gene expression data by regularized spline regression and an energy based similarity measure
Recommended article: A unified mixed effects model for gene set analysis of time course microarray experiments
description: scientific article; zbMATH DE number 6975369