Gibbs Sampling Based Banoian Biclustering of Gene Expression Data

Daoyuan Chen; Qinyi Liu; Jia Meng; Jionglong Su

doi:10.1109/CISP-BMEI51763.2020.9263631

Gibbs Sampling Based Banoian Biclustering of Gene Expression Data

Daoyuan Chen, Qinyi Liu, Jia Meng, Jionglong Su

Xi'an Jiaotong-Liverpool University

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

Abstract

This paper proposes a rigorous Bayes model to infer biclusters of microarray data formed by gene sets and condition sets. The model employs few fine-tune threshold parameters and handles missing data by statistically inferring them in Gibbs sampling. The proposed model outperforms others on simulated data and discovered meaningful local patterns, 63% of which were corroborated by biological evidence.

Original language	English
Title of host publication	Proceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020
Editors	Qiang Zheng, Xiaopeng Zheng, Xiangfu Zhao, Weiqing Yan, Nan Zhang, Lipo Wang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	790-795
Number of pages	6
ISBN (Electronic)	9780738105451
DOIs	https://doi.org/10.1109/CISP-BMEI51763.2020.9263631
Publication status	Published - 17 Oct 2020
Event	13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020 - Virtual, Chengdu, China Duration: 17 Oct 2020 → 19 Oct 2020

Publication series

Name	Proceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020

Conference

Conference	13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020
Country/Territory	China
City	Virtual, Chengdu
Period	17/10/20 → 19/10/20

Keywords

Bayesian inference
Biclustering
Gibbs sampling
Multivariate Gaussian distribution

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10.1109/CISP-BMEI51763.2020.9263631

Cite this

Chen, D., Liu, Q., Meng, J., & Su, J. (2020). Gibbs Sampling Based Banoian Biclustering of Gene Expression Data. In Q. Zheng, X. Zheng, X. Zhao, W. Yan, N. Zhang, & L. Wang (Eds.), Proceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020 (pp. 790-795). Article 9263631 (Proceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISP-BMEI51763.2020.9263631

Chen, Daoyuan ; Liu, Qinyi ; Meng, Jia et al. / Gibbs Sampling Based Banoian Biclustering of Gene Expression Data. Proceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020. editor / Qiang Zheng ; Xiaopeng Zheng ; Xiangfu Zhao ; Weiqing Yan ; Nan Zhang ; Lipo Wang. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 790-795 (Proceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020).

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title = "Gibbs Sampling Based Banoian Biclustering of Gene Expression Data",

abstract = "This paper proposes a rigorous Bayes model to infer biclusters of microarray data formed by gene sets and condition sets. The model employs few fine-tune threshold parameters and handles missing data by statistically inferring them in Gibbs sampling. The proposed model outperforms others on simulated data and discovered meaningful local patterns, 63% of which were corroborated by biological evidence.",

keywords = "Bayesian inference, Biclustering, Gibbs sampling, Multivariate Gaussian distribution",

author = "Daoyuan Chen and Qinyi Liu and Jia Meng and Jionglong Su",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020 ; Conference date: 17-10-2020 Through 19-10-2020",

year = "2020",

month = oct,

day = "17",

doi = "10.1109/CISP-BMEI51763.2020.9263631",

language = "English",

series = "Proceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "790--795",

editor = "Qiang Zheng and Xiaopeng Zheng and Xiangfu Zhao and Weiqing Yan and Nan Zhang and Lipo Wang",

booktitle = "Proceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020",

}

Chen, D, Liu, Q, Meng, J & Su, J 2020, Gibbs Sampling Based Banoian Biclustering of Gene Expression Data. in Q Zheng, X Zheng, X Zhao, W Yan, N Zhang & L Wang (eds), Proceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020., 9263631, Proceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020, Institute of Electrical and Electronics Engineers Inc., pp. 790-795, 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020, Virtual, Chengdu, China, 17/10/20. https://doi.org/10.1109/CISP-BMEI51763.2020.9263631

Gibbs Sampling Based Banoian Biclustering of Gene Expression Data. / Chen, Daoyuan; Liu, Qinyi; Meng, Jia et al.
Proceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020. ed. / Qiang Zheng; Xiaopeng Zheng; Xiangfu Zhao; Weiqing Yan; Nan Zhang; Lipo Wang. Institute of Electrical and Electronics Engineers Inc., 2020. p. 790-795 9263631 (Proceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020).

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

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T1 - Gibbs Sampling Based Banoian Biclustering of Gene Expression Data

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AU - Liu, Qinyi

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AU - Su, Jionglong

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AB - This paper proposes a rigorous Bayes model to infer biclusters of microarray data formed by gene sets and condition sets. The model employs few fine-tune threshold parameters and handles missing data by statistically inferring them in Gibbs sampling. The proposed model outperforms others on simulated data and discovered meaningful local patterns, 63% of which were corroborated by biological evidence.

KW - Bayesian inference

KW - Biclustering

KW - Gibbs sampling

KW - Multivariate Gaussian distribution

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Chen D, Liu Q, Meng J , Su J. Gibbs Sampling Based Banoian Biclustering of Gene Expression Data. In Zheng Q, Zheng X, Zhao X, Yan W, Zhang N, Wang L, editors, Proceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 790-795. 9263631. (Proceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020). doi: 10.1109/CISP-BMEI51763.2020.9263631

Gibbs Sampling Based Banoian Biclustering of Gene Expression Data

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