An iterated conditional mode solution for Bayesian factor modeling of transcriptional regulatory networks

Jia Meng; Jianqiu Zhang; Yidong Chen; Yufei Huang

doi:10.1109/GENSIPS.2010.5719679

An iterated conditional mode solution for Bayesian factor modeling of transcriptional regulatory networks

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

1 Citation (Scopus)

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) coupled with its ICM solution is proposed. BSCRFM models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF regulated genes and it admits prior knowledge from existing database regarding TF regulated target genes. An efficient ICM algorithm is developed and a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can be obtained. The proposed model and the ICM algorithm are evaluated on the simulated systems and results demonstrated the validity and effectiveness of the proposed approach. The proposed model is also applied to the breast cancer microarray data and a TF regulated network regarding ER status is obtained.

Original language	English
Title of host publication	2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010
DOIs	https://doi.org/10.1109/GENSIPS.2010.5719679
Publication status	Published - 2010
Externally published	Yes
Event	2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010 - Cold Spring Harbor, NY, United States Duration: 10 Nov 2010 → 12 Nov 2010

Publication series

Name	2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010

Conference

Conference	2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010
Country/Territory	United States
City	Cold Spring Harbor, NY
Period	10/11/10 → 12/11/10

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SDG 3 Good Health and Well-being

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10.1109/GENSIPS.2010.5719679

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Meng, J., Zhang, J., Chen, Y., & Huang, Y. (2010). An iterated conditional mode solution for Bayesian factor modeling of transcriptional regulatory networks. In 2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010 Article 5719679 (2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010). https://doi.org/10.1109/GENSIPS.2010.5719679

@inproceedings{53ad1463849240098dbff662f307a3db,

title = "An iterated conditional mode solution for Bayesian factor modeling of transcriptional regulatory networks",

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) coupled with its ICM solution is proposed. BSCRFM models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF regulated genes and it admits prior knowledge from existing database regarding TF regulated target genes. An efficient ICM algorithm is developed and a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can be obtained. The proposed model and the ICM algorithm are evaluated on the simulated systems and results demonstrated the validity and effectiveness of the proposed approach. The proposed model is also applied to the breast cancer microarray data and a TF regulated network regarding ER status is obtained.",

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

year = "2010",

doi = "10.1109/GENSIPS.2010.5719679",

language = "English",

isbn = "9781612847924",

series = "2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010",

booktitle = "2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010",

note = "2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010 ; Conference date: 10-11-2010 Through 12-11-2010",

}

Meng, J, Zhang, J, Chen, Y & Huang, Y 2010, An iterated conditional mode solution for Bayesian factor modeling of transcriptional regulatory networks. in 2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010., 5719679, 2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010, 2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010, Cold Spring Harbor, NY, United States, 10/11/10. https://doi.org/10.1109/GENSIPS.2010.5719679

An iterated conditional mode solution for Bayesian factor modeling of transcriptional regulatory networks. / Meng, Jia; Zhang, Jianqiu; Chen, Yidong et al.
2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010. 2010. 5719679 (2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010).

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

TY - GEN

T1 - An iterated conditional mode solution for Bayesian factor modeling of transcriptional regulatory networks

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) coupled with its ICM solution is proposed. BSCRFM models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF regulated genes and it admits prior knowledge from existing database regarding TF regulated target genes. An efficient ICM algorithm is developed and a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can be obtained. The proposed model and the ICM algorithm are evaluated on the simulated systems and results demonstrated the validity and effectiveness of the proposed approach. The proposed model is also applied to the breast cancer microarray data and a TF regulated network regarding ER status is obtained.

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) coupled with its ICM solution is proposed. BSCRFM models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF regulated genes and it admits prior knowledge from existing database regarding TF regulated target genes. An efficient ICM algorithm is developed and a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can be obtained. The proposed model and the ICM algorithm are evaluated on the simulated systems and results demonstrated the validity and effectiveness of the proposed approach. The proposed model is also applied to the breast cancer microarray data and a TF regulated network regarding ER status is obtained.

UR - https://www.scopus.com/pages/publications/79952809349

U2 - 10.1109/GENSIPS.2010.5719679

DO - 10.1109/GENSIPS.2010.5719679

M3 - Conference Proceeding

AN - SCOPUS:79952809349

SN - 9781612847924

T3 - 2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010

BT - 2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010

T2 - 2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010

Y2 - 10 November 2010 through 12 November 2010

ER -

Meng J, Zhang J, Chen Y, Huang Y. An iterated conditional mode solution for Bayesian factor modeling of transcriptional regulatory networks. In 2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010. 2010. 5719679. (2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010). doi: 10.1109/GENSIPS.2010.5719679

An iterated conditional mode solution for Bayesian factor modeling of transcriptional regulatory networks

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