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

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  • Jonathan A. L. Gelfond
  • Mayetri Gupta
  • Joseph G. Ibrahim

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  • Jonathan A. L. Gelfond & Mayetri Gupta & Joseph G. Ibrahim, 2009. "A Bayesian Hidden Markov Model for Motif Discovery Through Joint Modeling of Genomic Sequence and ChIP-Chip Data," Biometrics, The International Biometric Society, vol. 65(4), pages 1087-1095, December.
  • Handle: RePEc:bla:biomet:v:65:y:2009:i:4:p:1087-1095
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2008.01180.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. Ming Zheng & Leah O. Barrera & Bing Ren & Ying Nian Wu, 2007. "ChIP-chip: Data, Model, and Analysis," Biometrics, The International Biometric Society, vol. 63(3), pages 787-796, September.
    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.
    4. Sunduz Keles & Mark van der Laan & Sandrine Dudoit & Simon Cawley, 2004. "Multiple Testing Methods For ChIP-Chip High Density Oligonucleotide Array Data," U.C. Berkeley Division of Biostatistics Working Paper Series 1147, Berkeley Electronic Press.
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