Inferring gene regulatory networks from microarray time series data using transfer entropy

Thai Quang Tung; Taewoo Ryu; Kwang H. Lee; Doheon Lee

doi:10.1109/CBMS.2007.60

Inferring gene regulatory networks from microarray time series data using transfer entropy

Thai Quang Tung^*, Taewoo Ryu, Kwang H. Lee, Doheon Lee

^*Corresponding author for this work

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

34 Scopus citations

Abstract

Reverse engineering of gene regulatory networks from microarray time series data has been a challenging problem due to the limit of available data. In this paper, a new approach is proposed based on the concept of transfer entropy. Using this information theoretic measure, causal relations between pairs of genes are assessed to draw a causal network. A heuristic rule is then applied to differentiate direct and indirect causality. Simulation on a synthetic network showed that the transfer entropy can identify both linear and nonlinear causality. Application of the method in a biological data identified many causal interactions with biological information supports.

Original language	English (US)
Title of host publication	Proceedings - Twentieth IEEE International Symposium on Computer-Based Medical Systems, CBMS'07
Pages	383-388
Number of pages	6
DOIs	https://doi.org/10.1109/CBMS.2007.60
State	Published - 2007
Externally published	Yes
Event	20th IEEE International Symposium on Computer-Based Medical Systems, CBMS'07 - Maribor, Slovenia Duration: Jun 20 2007 → Jun 22 2007

Publication series

Name	Proceedings - IEEE Symposium on Computer-Based Medical Systems
ISSN (Print)	1063-7125

Other

Other	20th IEEE International Symposium on Computer-Based Medical Systems, CBMS'07
Country/Territory	Slovenia
City	Maribor
Period	06/20/07 → 06/22/07

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging
Computer Science Applications

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10.1109/CBMS.2007.60

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Tung, T. Q., Ryu, T., Lee, K. H., & Lee, D. (2007). Inferring gene regulatory networks from microarray time series data using transfer entropy. In Proceedings - Twentieth IEEE International Symposium on Computer-Based Medical Systems, CBMS'07 (pp. 383-388). Article 4262679 (Proceedings - IEEE Symposium on Computer-Based Medical Systems). https://doi.org/10.1109/CBMS.2007.60

@inproceedings{1326e45e2291490588c09cfe0e8a8f54,

title = "Inferring gene regulatory networks from microarray time series data using transfer entropy",

abstract = "Reverse engineering of gene regulatory networks from microarray time series data has been a challenging problem due to the limit of available data. In this paper, a new approach is proposed based on the concept of transfer entropy. Using this information theoretic measure, causal relations between pairs of genes are assessed to draw a causal network. A heuristic rule is then applied to differentiate direct and indirect causality. Simulation on a synthetic network showed that the transfer entropy can identify both linear and nonlinear causality. Application of the method in a biological data identified many causal interactions with biological information supports.",

author = "Tung, {Thai Quang} and Taewoo Ryu and Lee, {Kwang H.} and Doheon Lee",

year = "2007",

doi = "10.1109/CBMS.2007.60",

language = "English (US)",

isbn = "0769529054",

series = "Proceedings - IEEE Symposium on Computer-Based Medical Systems",

pages = "383--388",

booktitle = "Proceedings - Twentieth IEEE International Symposium on Computer-Based Medical Systems, CBMS'07",

note = "20th IEEE International Symposium on Computer-Based Medical Systems, CBMS'07 ; Conference date: 20-06-2007 Through 22-06-2007",

}

Tung, TQ, Ryu, T, Lee, KH & Lee, D 2007, Inferring gene regulatory networks from microarray time series data using transfer entropy. in Proceedings - Twentieth IEEE International Symposium on Computer-Based Medical Systems, CBMS'07., 4262679, Proceedings - IEEE Symposium on Computer-Based Medical Systems, pp. 383-388, 20th IEEE International Symposium on Computer-Based Medical Systems, CBMS'07, Maribor, Slovenia, 06/20/07. https://doi.org/10.1109/CBMS.2007.60

Inferring gene regulatory networks from microarray time series data using transfer entropy. / Tung, Thai Quang; Ryu, Taewoo; Lee, Kwang H. et al.
Proceedings - Twentieth IEEE International Symposium on Computer-Based Medical Systems, CBMS'07. 2007. p. 383-388 4262679 (Proceedings - IEEE Symposium on Computer-Based Medical Systems).

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

TY - GEN

T1 - Inferring gene regulatory networks from microarray time series data using transfer entropy

AU - Tung, Thai Quang

AU - Ryu, Taewoo

AU - Lee, Kwang H.

AU - Lee, Doheon

PY - 2007

Y1 - 2007

N2 - Reverse engineering of gene regulatory networks from microarray time series data has been a challenging problem due to the limit of available data. In this paper, a new approach is proposed based on the concept of transfer entropy. Using this information theoretic measure, causal relations between pairs of genes are assessed to draw a causal network. A heuristic rule is then applied to differentiate direct and indirect causality. Simulation on a synthetic network showed that the transfer entropy can identify both linear and nonlinear causality. Application of the method in a biological data identified many causal interactions with biological information supports.

AB - Reverse engineering of gene regulatory networks from microarray time series data has been a challenging problem due to the limit of available data. In this paper, a new approach is proposed based on the concept of transfer entropy. Using this information theoretic measure, causal relations between pairs of genes are assessed to draw a causal network. A heuristic rule is then applied to differentiate direct and indirect causality. Simulation on a synthetic network showed that the transfer entropy can identify both linear and nonlinear causality. Application of the method in a biological data identified many causal interactions with biological information supports.

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DO - 10.1109/CBMS.2007.60

M3 - Conference contribution

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SN - 0769529054

SN - 9780769529059

T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems

SP - 383

EP - 388

BT - Proceedings - Twentieth IEEE International Symposium on Computer-Based Medical Systems, CBMS'07

T2 - 20th IEEE International Symposium on Computer-Based Medical Systems, CBMS'07

Y2 - 20 June 2007 through 22 June 2007

ER -

Inferring gene regulatory networks from microarray time series data using transfer entropy

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