Recurrent high order neural networks for identification of the EGFR signaling pathway

Christodoulou, MA; Zarkogianni, D

dc.contributor.author	Christodoulou, MA	en
dc.contributor.author	Zarkogianni, D	en
dc.date.accessioned	2014-03-01T02:50:52Z
dc.date.available	2014-03-01T02:50:52Z
dc.date.issued	2006	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35170
dc.subject	EGFR	en
dc.subject	Identification	en
dc.subject	Recurrent high order neural networks	en
dc.subject.other	Epidermal Growth Factor-Receptor	en
dc.subject.other	Identification	en
dc.subject.other	Recurrent high order neural networks	en
dc.subject.other	Computational methods	en
dc.subject.other	Dynamical systems	en
dc.subject.other	Learning algorithms	en
dc.subject.other	Proteins	en
dc.subject.other	Signal systems	en
dc.subject.other	Recurrent neural networks	en
dc.title	Recurrent high order neural networks for identification of the EGFR signaling pathway	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/MED.2006.328819	en
heal.identifier.secondary	http://dx.doi.org/10.1109/MED.2006.328819	en
heal.identifier.secondary	1700728	en
heal.publicationDate	2006	en
heal.abstract	The present work deals with a specific signaling pathway called EGFR PATHWAY (Epidermal Growth Factor-Receptor) which is composed of twenty three proteins and their interactions. It is an essential part of the cell since it affects metabolism, growth and dimerization. The pathway can be modelled by an autonomous ODE. The aim is the construction of a computational model which predicts the dynamic behavior of each protein in the EGFR PATHWAY. The mathematical tool used, is the so called Recurrent High Order Neural Network (RHONN). RHONN is a recurrent neural network with dynamical components distributed throughout its body in the form of dynamical neurons. It is applicable for the identification of dynamical systems. The RHONN model consists of twenty three neurons and it is trained by a set containing various initial conditions and the dynamical output of each protein. We use three different learning algorithms concluding to three different RHONN models. When the training stops the appropriate weights are calculated and frozen so as to produce reliable models to identify the EGFR Pathway.	en
heal.journalName	14th Mediterranean Conference on Control and Automation, MED'06	en
dc.identifier.doi	10.1109/MED.2006.328819	en