Hidden Markov Models for Microarray Time Course Data in Multiple Biological Conditions

DOI10.1198/016214505000000394zbMath1171.62359OpenAlexW2037893131MaRDI QIDQ3434196

Christina Kendziorski, Ming Yuan

Publication date: 23 April 2007

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1198/016214505000000394

zbMATH Keywords

hidden Markov modelsgene expressionmicroarraystime course

Mathematics Subject Classification ID

Related Items (22)

Dynamical modeling for non-Gaussian data with high-dimensional sparse ordinary differential equations ⋮ A multivariate empirical Bayes statistic for replicated microarray time course data ⋮ High dimensional extension of the growth curve model and its application in genetics ⋮ Gamma-based clustering via ordered means with application to gene-expression analysis ⋮ Strong law of large numbers for hidden Markov chains indexed by Cayley trees ⋮ An empirical Bayes change-point model for transcriptome time-course data ⋮ Identifying temporally differentially expressed genes through functional principal components analysis ⋮ A statistical analysis of memory CD8 T cell differentiation: An application of a hierarchical state space model to a short time course microarray experiment ⋮ Distributions associated with general runs and patterns in hidden Markov models ⋮ Hidden Markov Models With Applications in Cell Adhesion Experiments ⋮ Partially Hidden Markov Model for Time-Varying Principal Stratification in HIV Prevention Trials ⋮ A hidden spatial-temporal Markov random field model for network-based analysis of time course gene expression data ⋮ Regularization method for predicting an ordinal response using longitudinal high-dimensional genomic data ⋮ Strong law of large numbers for hidden Markov chains indexed by an infinite tree with uniformly bounded degrees ⋮ The importance of distinct modeling strategies for gene and gene-specific treatment effects in hierarchical models for microarray data ⋮ Bayesian Hierarchical Modeling for Time Course Microarray Experiments ⋮ On Gene Ranking Using Replicated Microarray Time Course Data ⋮ Flexible temporal expression profile modelling using the Gaussian process ⋮ Time-course data prediction for repeatedly measured gene expression ⋮ Bayesian modeling of factorial time-course data with applications to a bone aging gene expression study ⋮ Local false discovery rate based methods for multiple testing of one-way classified hypotheses ⋮ A Markov random field-based approach to characterizing human brain development using spatial-temporal transcriptome data

This page was built for publication: Hidden Markov Models for Microarray Time Course Data in Multiple Biological Conditions