Building genetic networks for gene expression patterns

Wai Ki Ching; Eric S. Fung; Michael K. Ng

doi:10.1007/978-3-540-28651-6_3

Building genetic networks for gene expression patterns

Wai Ki Ching, Eric S. Fung, Michael K. Ng

Department of Mathematics

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

1 Citation (Scopus)

Abstract

Building genetic regulatory networks from time series data of gene expression patterns is an important topic in bioinformatics. Probabilistic Boolean networks (PBNs) have been developed as a model of gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and uncover the relative sensitivity of genes in their interactions with other genes. However, PBNs are unlikely used in practice because of huge number of possible predictors and their computed probabilities. In this paper, we propose a multivariate Markov chain model to govern the dynamics of a genetic network for gene expression patterns. The model preserves the strength of PBNs and reduce the complexity of the networks. Parameters of the model are quadratic with respect to the number of genes. We also develop an efficient estimation method for the model parameters. Simulation results on yeast data are given to illustrate the effectiveness of the model.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning - IDEAL 2004
Subtitle of host publication	5th International Conference, Exeter, UK, August 25-27, 2004, Proceedings
Editors	Zheng Rong Yang, Richard Everson, Hujun Yin
Publisher	Springer Berlin Heidelberg
Pages	17-24
Number of pages	8
Edition	1st
ISBN (Electronic)	9783540286516
ISBN (Print)	9783540228813
DOIs	https://doi.org/10.1007/978-3-540-28651-6_3
Publication status	Published - 13 Aug 2004
Event	5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004 - Exeter, United Kingdom Duration: 25 Aug 2004 → 27 Aug 2004 https://link.springer.com/book/10.1007/b99975

Publication series

Name	Lecture Notes in Computer Science
Volume	3177
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Name	IDEAL: International Conference on Intelligent Data Engineering and Automated Learning

Conference

Conference	5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004
Abbreviated title	IDEAL 2004
Country/Territory	United Kingdom
City	Exeter
Period	25/08/04 → 27/08/04
Internet address	https://link.springer.com/book/10.1007/b99975

Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-28651-6_3

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Ching, W. K., Fung, E. S., & Ng, M. K. (2004). Building genetic networks for gene expression patterns. In Z. R. Yang, R. Everson, & H. Yin (Eds.), Intelligent Data Engineering and Automated Learning - IDEAL 2004: 5th International Conference, Exeter, UK, August 25-27, 2004, Proceedings (1st ed., pp. 17-24). (Lecture Notes in Computer Science; Vol. 3177), (IDEAL: International Conference on Intelligent Data Engineering and Automated Learning). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_3

Ching, Wai Ki ; Fung, Eric S. ; Ng, Michael K. / Building genetic networks for gene expression patterns. Intelligent Data Engineering and Automated Learning - IDEAL 2004: 5th International Conference, Exeter, UK, August 25-27, 2004, Proceedings. editor / Zheng Rong Yang ; Richard Everson ; Hujun Yin. 1st. ed. Springer Berlin Heidelberg, 2004. pp. 17-24 (Lecture Notes in Computer Science). (IDEAL: International Conference on Intelligent Data Engineering and Automated Learning).

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abstract = "Building genetic regulatory networks from time series data of gene expression patterns is an important topic in bioinformatics. Probabilistic Boolean networks (PBNs) have been developed as a model of gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and uncover the relative sensitivity of genes in their interactions with other genes. However, PBNs are unlikely used in practice because of huge number of possible predictors and their computed probabilities. In this paper, we propose a multivariate Markov chain model to govern the dynamics of a genetic network for gene expression patterns. The model preserves the strength of PBNs and reduce the complexity of the networks. Parameters of the model are quadratic with respect to the number of genes. We also develop an efficient estimation method for the model parameters. Simulation results on yeast data are given to illustrate the effectiveness of the model.",

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Ching, WK, Fung, ES & Ng, MK 2004, Building genetic networks for gene expression patterns. in ZR Yang, R Everson & H Yin (eds), Intelligent Data Engineering and Automated Learning - IDEAL 2004: 5th International Conference, Exeter, UK, August 25-27, 2004, Proceedings. 1st edn, Lecture Notes in Computer Science, vol. 3177, IDEAL: International Conference on Intelligent Data Engineering and Automated Learning, Springer Berlin Heidelberg, pp. 17-24, 5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004, Exeter, United Kingdom, 25/08/04. https://doi.org/10.1007/978-3-540-28651-6_3

Building genetic networks for gene expression patterns. / Ching, Wai Ki; Fung, Eric S.; Ng, Michael K.
Intelligent Data Engineering and Automated Learning - IDEAL 2004: 5th International Conference, Exeter, UK, August 25-27, 2004, Proceedings. ed. / Zheng Rong Yang; Richard Everson; Hujun Yin. 1st. ed. Springer Berlin Heidelberg, 2004. p. 17-24 (Lecture Notes in Computer Science; Vol. 3177), (IDEAL: International Conference on Intelligent Data Engineering and Automated Learning).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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Ching WK, Fung ES, Ng MK. Building genetic networks for gene expression patterns. In Yang ZR, Everson R, Yin H, editors, Intelligent Data Engineering and Automated Learning - IDEAL 2004: 5th International Conference, Exeter, UK, August 25-27, 2004, Proceedings. 1st ed. Springer Berlin Heidelberg. 2004. p. 17-24. (Lecture Notes in Computer Science). (IDEAL: International Conference on Intelligent Data Engineering and Automated Learning). doi: 10.1007/978-3-540-28651-6_3

Building genetic networks for gene expression patterns

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