Learning gene regulatory networks with predefined attractors for sequential updating schemes using simulated annealing

Gonzalo A. Ruz; Eric Goles

doi:10.1109/ICMLA.2010.139

Learning gene regulatory networks with predefined attractors for sequential updating schemes using simulated annealing

Gonzalo A. Ruz, Eric Goles

Faculty of Engineering and Science

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

20 Scopus citations

Abstract

A simulated annealing framework is presented for learning gene regulatory networks with predefined attractors, under the threshold Boolean network model updated sequentially. The proposed method is used to study the robustness of the networks, defined as the number of different updating sequences they can have without loosing the attractor. The results suggests a power law between the frequency of the networks and the number of the updating sequences, also, a decrease of the networks' robustness as the cycle length grows. In general, the proposed simulated annealing framework is effective for reverse engineering problems.

Original language	English
Title of host publication	Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010
Pages	889-894
Number of pages	6
DOIs	https://doi.org/10.1109/ICMLA.2010.139
State	Published - 2010
Event	9th International Conference on Machine Learning and Applications, ICMLA 2010 - Washington, DC, United States Duration: 12 Dec 2010 → 14 Dec 2010

Publication series

Name	Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010

Conference

Conference	9th International Conference on Machine Learning and Applications, ICMLA 2010
Country/Territory	United States
City	Washington, DC
Period	12/12/10 → 14/12/10

Keywords

Attractors
Boolean networks
Gene regulatory networks
Simulated annealing

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10.1109/ICMLA.2010.139

Cite this

Ruz, G. A., & Goles, E. (2010). Learning gene regulatory networks with predefined attractors for sequential updating schemes using simulated annealing. In Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010 (pp. 889-894). Article 5708962 (Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010). https://doi.org/10.1109/ICMLA.2010.139

@inproceedings{8bb0409d15084ce08777866ae264aa9f,

title = "Learning gene regulatory networks with predefined attractors for sequential updating schemes using simulated annealing",

abstract = "A simulated annealing framework is presented for learning gene regulatory networks with predefined attractors, under the threshold Boolean network model updated sequentially. The proposed method is used to study the robustness of the networks, defined as the number of different updating sequences they can have without loosing the attractor. The results suggests a power law between the frequency of the networks and the number of the updating sequences, also, a decrease of the networks' robustness as the cycle length grows. In general, the proposed simulated annealing framework is effective for reverse engineering problems.",

keywords = "Attractors, Boolean networks, Gene regulatory networks, Simulated annealing",

author = "Ruz, {Gonzalo A.} and Eric Goles",

year = "2010",

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language = "English",

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series = "Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010",

pages = "889--894",

booktitle = "Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010",

note = "9th International Conference on Machine Learning and Applications, ICMLA 2010 ; Conference date: 12-12-2010 Through 14-12-2010",

}

Ruz, GA & Goles, E 2010, Learning gene regulatory networks with predefined attractors for sequential updating schemes using simulated annealing. in Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010., 5708962, Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010, pp. 889-894, 9th International Conference on Machine Learning and Applications, ICMLA 2010, Washington, DC, United States, 12/12/10. https://doi.org/10.1109/ICMLA.2010.139

Learning gene regulatory networks with predefined attractors for sequential updating schemes using simulated annealing. / Ruz, Gonzalo A.; Goles, Eric.
Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010. 2010. p. 889-894 5708962 (Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010).

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

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AU - Goles, Eric

PY - 2010

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N2 - A simulated annealing framework is presented for learning gene regulatory networks with predefined attractors, under the threshold Boolean network model updated sequentially. The proposed method is used to study the robustness of the networks, defined as the number of different updating sequences they can have without loosing the attractor. The results suggests a power law between the frequency of the networks and the number of the updating sequences, also, a decrease of the networks' robustness as the cycle length grows. In general, the proposed simulated annealing framework is effective for reverse engineering problems.

AB - A simulated annealing framework is presented for learning gene regulatory networks with predefined attractors, under the threshold Boolean network model updated sequentially. The proposed method is used to study the robustness of the networks, defined as the number of different updating sequences they can have without loosing the attractor. The results suggests a power law between the frequency of the networks and the number of the updating sequences, also, a decrease of the networks' robustness as the cycle length grows. In general, the proposed simulated annealing framework is effective for reverse engineering problems.

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Ruz GA, Goles E. Learning gene regulatory networks with predefined attractors for sequential updating schemes using simulated annealing. In Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010. 2010. p. 889-894. 5708962. (Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010). doi: 10.1109/ICMLA.2010.139

Learning gene regulatory networks with predefined attractors for sequential updating schemes using simulated annealing

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Keywords

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