Modelling gene networks by a dynamic bayesian network-based model with time lag estimation

Lian En Chai; Mohd Saberi Mohamad; Safaai Deris; Chuii Khim Chong; Yee Wen Choon

doi:10.1007/978-3-642-40319-4_19

Modelling gene networks by a dynamic bayesian network-based model with time lag estimation

Lian En Chai, Mohd Saberi Mohamad, Safaai Deris, Chuii Khim Chong, Yee Wen Choon

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Due to the needs to discover the immense information and understand the underlying mechanism of gene regulations, modelling gene regulatory networks (GRNs) from gene expression data has attracted the interests of numerous researchers. To this end, the dynamic Bayesian network (DBN) has emerged as a popular method in GRNs modelling as it is able to model time-series gene expression data and feedback loops. Nevertheless, the commonly found missing values in gene expression data, the inability to take account of the transcriptional time lag, and the redundant computation time caused by the large search space, frequently inhibits the effectiveness of DBN in modelling GRNs from gene expression data. This paper proposes a DBN-based model (IST-DBN) with missing values imputation, potential regulators selection, and time lag estimation to tackle the aforementioned problems. To evaluate the performance of IST-DBN, we applied the model on the S. cerevisiae cell cycle time-series expression data. The experimental results revealed IST-DBN has decreased computation time and better accuracy in identifying gene-gene relationships when compared with existing DBN-based model and conventional DBN. Furthermore, we expect the resultant networks from IST-DBN to be applied as a general framework for potential gene intervention research.

Original language	English
Title of host publication	Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops
Subtitle of host publication	DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers
Pages	214-222
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-642-40319-4_19
Publication status	Published - 2013
Externally published	Yes
Event	17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013 - Gold Coast, QLD, Australia Duration: Apr 14 2013 → Apr 17 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7867 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
Country/Territory	Australia
City	Gold Coast, QLD
Period	4/14/13 → 4/17/13

Keywords

Dynamic bayesian network
Gene regulatory networks
Missing values imputation
Network inference
Time-series gene expression data

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-40319-4_19

Cite this

Chai, L. E., Mohamad, M. S., Deris, S., Chong, C. K., & Choon, Y. W. (2013). Modelling gene networks by a dynamic bayesian network-based model with time lag estimation. In Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers (pp. 214-222). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7867 LNAI). https://doi.org/10.1007/978-3-642-40319-4_19

Modelling gene networks by a dynamic bayesian network-based model with time lag estimation. / Chai, Lian En; Mohamad, Mohd Saberi; Deris, Safaai et al.
Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers. 2013. p. 214-222 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7867 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chai, LE, Mohamad, MS, Deris, S, Chong, CK & Choon, YW 2013, Modelling gene networks by a dynamic bayesian network-based model with time lag estimation. in Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7867 LNAI, pp. 214-222, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013, Gold Coast, QLD, Australia, 4/14/13. https://doi.org/10.1007/978-3-642-40319-4_19

Chai LE, Mohamad MS, Deris S, Chong CK, Choon YW. Modelling gene networks by a dynamic bayesian network-based model with time lag estimation. In Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers. 2013. p. 214-222. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-40319-4_19

Chai, Lian En ; Mohamad, Mohd Saberi ; Deris, Safaai et al. / Modelling gene networks by a dynamic bayesian network-based model with time lag estimation. Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers. 2013. pp. 214-222 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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AB - Due to the needs to discover the immense information and understand the underlying mechanism of gene regulations, modelling gene regulatory networks (GRNs) from gene expression data has attracted the interests of numerous researchers. To this end, the dynamic Bayesian network (DBN) has emerged as a popular method in GRNs modelling as it is able to model time-series gene expression data and feedback loops. Nevertheless, the commonly found missing values in gene expression data, the inability to take account of the transcriptional time lag, and the redundant computation time caused by the large search space, frequently inhibits the effectiveness of DBN in modelling GRNs from gene expression data. This paper proposes a DBN-based model (IST-DBN) with missing values imputation, potential regulators selection, and time lag estimation to tackle the aforementioned problems. To evaluate the performance of IST-DBN, we applied the model on the S. cerevisiae cell cycle time-series expression data. The experimental results revealed IST-DBN has decreased computation time and better accuracy in identifying gene-gene relationships when compared with existing DBN-based model and conventional DBN. Furthermore, we expect the resultant networks from IST-DBN to be applied as a general framework for potential gene intervention research.

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Modelling gene networks by a dynamic bayesian network-based model with time lag estimation

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Keywords

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