TAILIEUCHUNG - Báo cáo sinh học: " Research Article Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 538919 18 pages doi 2010 538919 Research Article Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model Jia Meng 1 Jianqiu Michelle Zhang 1 Yuan Alan Qi 2 Yidong Chen 3 4 and Yufei Huang1 3 4 1 Department of Electrical and Computer Engineering University of Texas at San Antonio San Antonio TX 78249-0669 USA 2 Departments of Computer Science and Statistics Purdue University West Lafayette IN 47907 USA 3 Department of Epidemiology and Biostatistics UT Health Science Center at San Antonio San Antonio TX 78229 USA 4 Greehey Children s Cancer Research Institute UT Health Science Center at San Antonio San Antonio TX 78229 USA Correspondence should be addressed to Yufei Huang Received 2 April 2010 Accepted 11 June 2010 Academic Editor Ulisses Braga-Neto Copyright 2010 Jia Meng et al. 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. 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

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