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

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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 http://hdl.handle.net/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 1700728 en
heal.identifier.secondary http://dx.doi.org/10.1109/MED.2006.328819 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

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