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  • Research Article
  • Open Access

Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model

  • 1,
  • 1,
  • 2,
  • 3, 4 and
  • 1, 3, 4Email author
EURASIP Journal on Advances in Signal Processing20102010:538919

  • Received: 2 April 2010
  • Accepted: 11 June 2010
  • Published:


The problem of uncovering transcriptional regulation by transcription factors (TFs) based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM) is proposed that models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF-regulated genes. The model admits prior knowledge from existing database regarding TF-regulated target genes based on a sparse prior and through a developed Gibbs sampling algorithm, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can be obtained. The proposed model and the Gibbs sampling algorithm were evaluated on the simulated systems, and results demonstrated the validity and effectiveness of the proposed approach. The proposed model was then applied to the breast cancer microarray data of patients with Estrogen Receptor positive ( ) status and Estrogen Receptor negative ( ) status, respectively.


  • Breast Cancer
  • Estrogen Receptor
  • Transcriptional Regulation
  • Microarray Data
  • Quantum Information

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Authors’ Affiliations

Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249-0669, USA
Departments of Computer Science and Statistics, Purdue University, West Lafayette, IN 47907, USA
Department of Epidemiology and Biostatistics, UT Health Science Center at San Antonio, San Antonio, TX 78229, USA
Greehey Children's Cancer Research Institute, UT Health Science Center at San Antonio, San Antonio, TX 78229, USA


© Jia Meng et al. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.