Uncovering transcriptional regulatory networks by sparse Bayesian factor model

Jia Meng; Jianqiu Zhang; Yidong Chen; Yufei Huang

doi:10.1109/ICOSP.2010.5656704

Uncovering transcriptional regulatory networks by sparse Bayesian factor model

Jia Meng^*
, Jianqiu Zhang
, Yidong Chen
, Yufei Huang

^*Corresponding author for this work

University of Texas at San Antonio
University of Texas Health Science Center at San Antonio

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

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 ER+ status and Estrogen Receptor negative ER- status, respectively.

Original language	English
Title of host publication	ICSP2010 - 2010 IEEE 10th International Conference on Signal Processing, Proceedings
Pages	1785-1788
Number of pages	4
DOIs	https://doi.org/10.1109/ICOSP.2010.5656704
Publication status	Published - 2010
Externally published	Yes
Event	2010 IEEE 10th International Conference on Signal Processing, ICSP2010 - Beijing, China Duration: 24 Oct 2010 → 28 Oct 2010

Publication series

Name	International Conference on Signal Processing Proceedings, ICSP

Conference

Conference	2010 IEEE 10th International Conference on Signal Processing, ICSP2010
Country/Territory	China
City	Beijing
Period	24/10/10 → 28/10/10

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Bayesian sparse factor model
Component
Correlated non-negative factor
Dirichlet process mixture (DPM)
Gibbs sampling
Rectified Gaussian mixture
Transcriptional regulatory network

Access to Document

10.1109/ICOSP.2010.5656704

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@inproceedings{d016a5b203bf46c8b7a86d31c5e417f3,

title = "Uncovering transcriptional regulatory networks by sparse Bayesian factor model",

abstract = "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 ER+ status and Estrogen Receptor negative ER- status, respectively.",

keywords = "Bayesian sparse factor model, Component, Correlated non-negative factor, Dirichlet process mixture (DPM), Gibbs sampling, Rectified Gaussian mixture, Transcriptional regulatory network",

author = "Jia Meng and Jianqiu Zhang and Yidong Chen and Yufei Huang",

year = "2010",

doi = "10.1109/ICOSP.2010.5656704",

language = "English",

isbn = "9781424458981",

series = "International Conference on Signal Processing Proceedings, ICSP",

pages = "1785--1788",

booktitle = "ICSP2010 - 2010 IEEE 10th International Conference on Signal Processing, Proceedings",

note = "2010 IEEE 10th International Conference on Signal Processing, ICSP2010 ; Conference date: 24-10-2010 Through 28-10-2010",

}

Meng, J, Zhang, J, Chen, Y & Huang, Y 2010, Uncovering transcriptional regulatory networks by sparse Bayesian factor model. in ICSP2010 - 2010 IEEE 10th International Conference on Signal Processing, Proceedings., 5656704, International Conference on Signal Processing Proceedings, ICSP, pp. 1785-1788, 2010 IEEE 10th International Conference on Signal Processing, ICSP2010, Beijing, China, 24/10/10. https://doi.org/10.1109/ICOSP.2010.5656704

Uncovering transcriptional regulatory networks by sparse Bayesian factor model. / Meng, Jia; Zhang, Jianqiu; Chen, Yidong et al.
ICSP2010 - 2010 IEEE 10th International Conference on Signal Processing, Proceedings. 2010. p. 1785-1788 5656704 (International Conference on Signal Processing Proceedings, ICSP).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Uncovering transcriptional regulatory networks by sparse Bayesian factor model

AU - Meng, Jia

AU - Zhang, Jianqiu

AU - Chen, Yidong

AU - Huang, Yufei

PY - 2010

Y1 - 2010

N2 - 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 ER+ status and Estrogen Receptor negative ER- status, respectively.

AB - 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 ER+ status and Estrogen Receptor negative ER- status, respectively.

KW - Bayesian sparse factor model

KW - Component

KW - Correlated non-negative factor

KW - Dirichlet process mixture (DPM)

KW - Gibbs sampling

KW - Rectified Gaussian mixture

KW - Transcriptional regulatory network

UR - https://www.scopus.com/pages/publications/78651066798

U2 - 10.1109/ICOSP.2010.5656704

DO - 10.1109/ICOSP.2010.5656704

M3 - Conference Proceeding

AN - SCOPUS:78651066798

SN - 9781424458981

T3 - International Conference on Signal Processing Proceedings, ICSP

SP - 1785

EP - 1788

BT - ICSP2010 - 2010 IEEE 10th International Conference on Signal Processing, Proceedings

T2 - 2010 IEEE 10th International Conference on Signal Processing, ICSP2010

Y2 - 24 October 2010 through 28 October 2010

ER -

Uncovering transcriptional regulatory networks by sparse Bayesian factor model

Abstract

Publication series

Conference

UN SDGs

Keywords

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