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

Gonzalo A. Ruz, Eric Goles

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 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 languageEnglish
Title of host publicationProceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010
Pages889-894
Number of pages6
DOIs
StatePublished - 2010
Event9th International Conference on Machine Learning and Applications, ICMLA 2010 - Washington, DC, United States
Duration: 12 Dec 201014 Dec 2010

Publication series

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

Conference

Conference9th International Conference on Machine Learning and Applications, ICMLA 2010
Country/TerritoryUnited States
CityWashington, DC
Period12/12/1014/12/10

Keywords

  • Attractors
  • Boolean networks
  • Gene regulatory networks
  • Simulated annealing

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