Mixture Modeling for Genome‐Wide Localization of Transcription Factors

DOI10.1111/j.1541-0420.2005.00659.xzbMath1206.62170OpenAlexW1993304320WikidataQ38302536 ScholiaQ38302536MaRDI QIDQ5427393

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Publication date: 20 November 2007

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2005.00659.x

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Biochemistry, molecular biology (92C40)

Related Items (9)

Gamma-based clustering via ordered means with application to gene-expression analysis ⋮ A Flexible and Powerful Bayesian Hierarchical Model for ChIP-Chip Experiments ⋮ Modeling epigenetic modifications under multiple treatment conditions ⋮ Bayesian hidden Markov models to identify RNA–protein interaction sites in PAR‐CLIP ⋮ Hypothesis testing, power and sample size determination for between group comparisons in fMRI experiments ⋮ The importance of distinct modeling strategies for gene and gene-specific treatment effects in hierarchical models for microarray data ⋮ Doubly stochastic continuous-time hidden Markov approach for analyzing genome tiling arrays ⋮ Bayesian Modeling of ChIP-chip Data Through a High-Order Ising Model ⋮ A Bayesian Hidden Markov Model for Motif Discovery Through Joint Modeling of Genomic Sequence and ChIP‐Chip Data

Uses Software

• TileMap
• COMODE

Cites Work

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• Choice of a null distribution in resampling-based multiple testing
• Supervised Detection of Regulatory Motifs in DNA Sequences
• Bayesian Robust Inference for Differential Gene Expression in Microarrays with Multiple Samples
• Detecting differential gene expression with a semiparametric hierarchical mixture method

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