Exploiting temporal genetic correlations for enhancing regulatory network optimization

Ahammed Sherief Kizhakkethil Youseph; Madhu Chetty; Gour Karmakar

doi:10.1007/978-3-319-46687-3_53

Exploiting temporal genetic correlations for enhancing regulatory network optimization

Ahammed Sherief Kizhakkethil Youseph, Madhu Chetty, Gour Karmakar

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

Abstract

Inferring gene regulatory networks (GRN) from microarray gene expression data is a highly challenging problem in computational and systems biology. To make GRN reconstruction process more accurate and faster, in this paper, we develop a technique to identify the gene having maximum in-degree in the network using the temporal correlation of gene expression profiles. The in-degree of the identified gene is estimated applying evolutionary optimization algorithm on a decoupled S-system GRN model. The value of in-degree thus obtained is set as the maximum in-degree for inference of the regulations in other genes. The simulations are carried out on in silico networks of small and medium sizes. The results show that both the prediction accuracy in terms of well known performance metrics and the computational time of the optimization process have been improved when compared with the traditional S-system model based inference.

Original language	English
Title of host publication	Neural Information Processing
Subtitle of host publication	23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part IV
Publisher	Springer
Pages	479-487
Number of pages	9
Volume	9947 LNCS
ISBN (Print)	9783319466866
DOIs	https://doi.org/10.1007/978-3-319-46687-3_53
Publication status	Published - 2016
Event	International Conference on Neural Information Processing 2016 - Kyoto, Japan Duration: 16 Oct 2016 → 21 Oct 2016 Conference number: 23rd https://link.springer.com/book/10.1007/978-3-319-46687-3 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9947 LNCS
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	International Conference on Neural Information Processing 2016
Abbreviated title	ICONIP 2016
Country/Territory	Japan
City	Kyoto
Period	16/10/16 → 21/10/16
Internet address	https://link.springer.com/book/10.1007/978-3-319-46687-3 (Proceedings)

Keywords

Differential evolution
Discrete cosine transform
Gene regulatory network
S-system
Temporal correlation

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10.1007/978-3-319-46687-3_53

Cite this

Youseph, A. S. K., Chetty, M., & Karmakar, G. (2016). Exploiting temporal genetic correlations for enhancing regulatory network optimization. In Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part IV (Vol. 9947 LNCS, pp. 479-487). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9947 LNCS). Springer. https://doi.org/10.1007/978-3-319-46687-3_53

Youseph, Ahammed Sherief Kizhakkethil ; Chetty, Madhu ; Karmakar, Gour. / Exploiting temporal genetic correlations for enhancing regulatory network optimization. Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part IV. Vol. 9947 LNCS Springer, 2016. pp. 479-487 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Exploiting temporal genetic correlations for enhancing regulatory network optimization",

abstract = "Inferring gene regulatory networks (GRN) from microarray gene expression data is a highly challenging problem in computational and systems biology. To make GRN reconstruction process more accurate and faster, in this paper, we develop a technique to identify the gene having maximum in-degree in the network using the temporal correlation of gene expression profiles. The in-degree of the identified gene is estimated applying evolutionary optimization algorithm on a decoupled S-system GRN model. The value of in-degree thus obtained is set as the maximum in-degree for inference of the regulations in other genes. The simulations are carried out on in silico networks of small and medium sizes. The results show that both the prediction accuracy in terms of well known performance metrics and the computational time of the optimization process have been improved when compared with the traditional S-system model based inference.",

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author = "Youseph, {Ahammed Sherief Kizhakkethil} and Madhu Chetty and Gour Karmakar",

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Youseph, ASK, Chetty, M & Karmakar, G 2016, Exploiting temporal genetic correlations for enhancing regulatory network optimization. in Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part IV. vol. 9947 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9947 LNCS, Springer, pp. 479-487, International Conference on Neural Information Processing 2016, Kyoto, Japan, 16/10/16. https://doi.org/10.1007/978-3-319-46687-3_53

Exploiting temporal genetic correlations for enhancing regulatory network optimization. / Youseph, Ahammed Sherief Kizhakkethil; Chetty, Madhu; Karmakar, Gour.
Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part IV. Vol. 9947 LNCS Springer, 2016. p. 479-487 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9947 LNCS).

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

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AU - Karmakar, Gour

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N2 - Inferring gene regulatory networks (GRN) from microarray gene expression data is a highly challenging problem in computational and systems biology. To make GRN reconstruction process more accurate and faster, in this paper, we develop a technique to identify the gene having maximum in-degree in the network using the temporal correlation of gene expression profiles. The in-degree of the identified gene is estimated applying evolutionary optimization algorithm on a decoupled S-system GRN model. The value of in-degree thus obtained is set as the maximum in-degree for inference of the regulations in other genes. The simulations are carried out on in silico networks of small and medium sizes. The results show that both the prediction accuracy in terms of well known performance metrics and the computational time of the optimization process have been improved when compared with the traditional S-system model based inference.

AB - Inferring gene regulatory networks (GRN) from microarray gene expression data is a highly challenging problem in computational and systems biology. To make GRN reconstruction process more accurate and faster, in this paper, we develop a technique to identify the gene having maximum in-degree in the network using the temporal correlation of gene expression profiles. The in-degree of the identified gene is estimated applying evolutionary optimization algorithm on a decoupled S-system GRN model. The value of in-degree thus obtained is set as the maximum in-degree for inference of the regulations in other genes. The simulations are carried out on in silico networks of small and medium sizes. The results show that both the prediction accuracy in terms of well known performance metrics and the computational time of the optimization process have been improved when compared with the traditional S-system model based inference.

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Youseph ASK, Chetty M, Karmakar G. Exploiting temporal genetic correlations for enhancing regulatory network optimization. In Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part IV. Vol. 9947 LNCS. Springer. 2016. p. 479-487. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46687-3_53

Exploiting temporal genetic correlations for enhancing regulatory network optimization

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