A Bayesian Hidden Markov Model for Motif Discovery Through Joint Modeling of Genomic Sequence and ChIP‐Chip Data

DOI10.1111/J.1541-0420.2008.01180.XzbMath1180.62169OpenAlexW2050708422WikidataQ33408346 ScholiaQ33408346MaRDI QIDQ5850957

Mayetri Gupta, Jonathan A. Gelfond, Joseph G. Ibrahim

Publication date: 21 January 2010

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc2794970

zbMATH Keywords

data augmentationgene regulationtiling arraytranscription factor binding site

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Medical applications (general) (92C50) Biochemistry, molecular biology (92C40) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (1)

Bayesian hidden Markov models to identify RNA–protein interaction sites in PAR‐CLIP

Uses Software

• TileMap

Cites Work

• Bayesian Models for Multiple Local Sequence Alignment and Gibbs Sampling Strategies
• A Flexible and Powerful Bayesian Hierarchical Model for ChIP-Chip Experiments
• Integrating quantitative information from ChIP-chip experiments into motif finding
• Discovery of Conserved Sequence Patterns Using a Stochastic Dictionary Model
• Mixture Modeling for Genome‐Wide Localization of Transcription Factors
• ChIP‐chip: Data, Model, and Analysis

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