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Bayesian Modeling of ChIP-chip Data Through a High-Order Ising Model

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  • Qianxing Mo
  • Faming Liang

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  • Qianxing Mo & Faming Liang, 2010. "Bayesian Modeling of ChIP-chip Data Through a High-Order Ising Model," Biometrics, The International Biometric Society, vol. 66(4), pages 1284-1294, December.
    • Handle: RePEc:bla:biomet:v:66:y:2010:i:4:p:1284-1294
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2009.01379.x
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    References listed on IDEAS

    as
    1. Sündüz Keleş, 2007. "Mixture Modeling for Genome-Wide Localization of Transcription Factors," Biometrics, The International Biometric Society, vol. 63(1), pages 10-21, March.
    2. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
    3. Raphael Gottardo & Wei Li & W. Evan Johnson & X. Shirley Liu, 2008. "A Flexible and Powerful Bayesian Hierarchical Model for ChIP–Chip Experiments," Biometrics, The International Biometric Society, vol. 64(2), pages 468-478, June.
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    Cited by:

    1. Jonghyun Yun & Tao Wang & Guanghua Xiao, 2014. "Bayesian hidden Markov models to identify RNA–protein interaction sites in PAR-CLIP," Biometrics, The International Biometric Society, vol. 70(2), pages 430-440, June.

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