Computation Intelligence Tools for Modeling and Controlling Pharmacogenomic Systems: Genetic Programming and Neural Networks
Created by W.Langdon from
gpbibliography.bib Revision:1.6217
 @InProceedings{Floares:2006:CEC,

author = "Alexandru G. Floares",

title = "Computation Intelligence Tools for Modeling and
Controlling Pharmacogenomic Systems: Genetic
Programming and Neural Networks",

booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",

year = "2006",

editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and
Simon M. Lucas",

pages = "75107517",

address = "Vancouver",

month = "1621 " # jul,

publisher = "IEEE Press",

keywords = "genetic algorithms, genetic programming, computational
intelligences tools, computation intelligence tools,
computer programming language, differential genes
expression, neural networks, nonlinear coupled ordinary
differential equations, pharmacogenomic systems,
genetics, medical control systems, neurocontrollers,
nonlinear differential equations, nonlinear equations",

ISBN = "0780394879",

DOI = "doi:10.1109/IJCNN.2006.246876",

size = "8 pages",

abstract = "Pharmacogenomic systems (PG) are very high
dimensional, nonlinear, and stiff systems. Mathematical
modelling of these systems, as systems of nonlinear
coupled ordinary differential equations (ODE), is
considered important for understanding them;
unfortunately, it is also a very difficult task. At
least as important is to adequately control them
through inputs, which are drugs' dosage regimes. In
this paper, we investigate new approaches based on
computational intelligences tools  genetic programming
(GP), and neural networks (NN)  for these difficult
tasks. We use GP to automatically write the model
structure in a computer programming language (C+t) and
to optimise the model's constants. In some
circumstances, the proposed methods not only give an
accurate mathematical model of the PG system, but they
also give insights into the subjacent molecular
mechanisms. We also show that NN feedback linearisation
(FBL) can adequately control these systems, with or
without a mathematical model. The drug dosage regimen
will determine the output of the system to track very
well a therapeutic objective. To our knowledge, this is
the first time when a very large class of complex
pharmacological problems are formulated and solved in
terms of GP modelling and NN modeling and control.",

notes = "May 2010
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1716624&tag=1
\cite{conf/ijcnn/Floares06} says this is in IJCNN 2006,
38203827, but his own IASTED2007
ISBN:9780889866942 says CEC 75107517.
WCCI 2006  A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D",
 }
Genetic Programming entries for
Alexandru Floares
Citations