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

Jia Meng; Jianqiu Zhang; Yidong Chen; Yufei Huang

doi:10.1109/BIBM.2010.5706587

An iterated conditional modes solution for sparse 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

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 Iterated Conditional Modes (ICM) algorithm is developed, and a maximum a posterior (MAP) solution is calculated from multiple ICM results to avoid the local maximum problem, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can then be obtained. The proposed model's ICM algorithm and MAP solution 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 is obtained.

Original language	English
Title of host publication	Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
Pages	335-340
Number of pages	6
DOIs	https://doi.org/10.1109/BIBM.2010.5706587
Publication status	Published - 2010
Externally published	Yes
Event	2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 - Hong Kong, China Duration: 18 Dec 2010 → 21 Dec 2010

Publication series

Name	Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010

Conference

Conference	2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
Country/Territory	China
City	Hong Kong
Period	18/12/10 → 21/12/10

UN SDGs

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

SDG 3 Good Health and Well-being

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10.1109/BIBM.2010.5706587

Cite this

Meng, J., Zhang, J., Chen, Y., & Huang, Y. (2010). An iterated conditional modes solution for sparse Bayesian factor modeling of transcriptional regulatory networks. In Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 (pp. 335-340). Article 5706587 (Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010). https://doi.org/10.1109/BIBM.2010.5706587

Meng, Jia ; Zhang, Jianqiu ; Chen, Yidong et al. / An iterated conditional modes solution for sparse Bayesian factor modeling of transcriptional regulatory networks. Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010. 2010. pp. 335-340 (Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010).

@inproceedings{19daef644a0a4963999c5aa70127e2fd,

title = "An iterated conditional modes solution for sparse 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 Iterated Conditional Modes (ICM) algorithm is developed, and a maximum a posterior (MAP) solution is calculated from multiple ICM results to avoid the local maximum problem, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can then be obtained. The proposed model's ICM algorithm and MAP solution 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 is obtained.",

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

year = "2010",

doi = "10.1109/BIBM.2010.5706587",

language = "English",

isbn = "9781424483075",

series = "Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010",

pages = "335--340",

booktitle = "Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010",

note = "2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 ; Conference date: 18-12-2010 Through 21-12-2010",

}

Meng, J, Zhang, J, Chen, Y & Huang, Y 2010, An iterated conditional modes solution for sparse Bayesian factor modeling of transcriptional regulatory networks. in Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010., 5706587, Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010, pp. 335-340, 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010, Hong Kong, China, 18/12/10. https://doi.org/10.1109/BIBM.2010.5706587

An iterated conditional modes solution for sparse Bayesian factor modeling of transcriptional regulatory networks. / Meng, Jia; Zhang, Jianqiu; Chen, Yidong et al.
Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010. 2010. p. 335-340 5706587 (Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010).

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

TY - GEN

T1 - An iterated conditional modes solution for sparse 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 Iterated Conditional Modes (ICM) algorithm is developed, and a maximum a posterior (MAP) solution is calculated from multiple ICM results to avoid the local maximum problem, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can then be obtained. The proposed model's ICM algorithm and MAP solution 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 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 Iterated Conditional Modes (ICM) algorithm is developed, and a maximum a posterior (MAP) solution is calculated from multiple ICM results to avoid the local maximum problem, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can then be obtained. The proposed model's ICM algorithm and MAP solution 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 is obtained.

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U2 - 10.1109/BIBM.2010.5706587

DO - 10.1109/BIBM.2010.5706587

M3 - Conference Proceeding

AN - SCOPUS:79952387582

SN - 9781424483075

T3 - Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010

SP - 335

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BT - Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010

T2 - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010

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ER -

Meng J, Zhang J, Chen Y, Huang Y. An iterated conditional modes solution for sparse Bayesian factor modeling of transcriptional regulatory networks. In Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010. 2010. p. 335-340. 5706587. (Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010). doi: 10.1109/BIBM.2010.5706587

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

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