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

Gonzalo A. Ruz, Eric Goles

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

19 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaProceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010
Páginas889-894
Número de páginas6
DOI
EstadoPublicada - 2010
Evento9th International Conference on Machine Learning and Applications, ICMLA 2010 - Washington, DC, Estados Unidos
Duración: 12 dic. 201014 dic. 2010

Serie de la publicación

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

Conferencia

Conferencia9th International Conference on Machine Learning and Applications, ICMLA 2010
País/TerritorioEstados Unidos
CiudadWashington, DC
Período12/12/1014/12/10

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