Computation Intelligence Tools for Modeling and Controlling Pharmacogenomic Systems: Genetic Programming and Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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 =        "7510--7517",
  address =      "Vancouver",
  month =        "16-21 " # 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 =         "0-7803-9487-9",
  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,
                 3820--3827, but his own IASTED-2007
                 ISBN:978-0-88986-694-2 says CEC 7510--7517.

                 WCCI 2006 - A joint meeting of the IEEE, the EPS, and
                 the IEE.

                 IEEE Catalog Number: 06TH8846D",
}

Genetic Programming entries for Alexandru Floares

Citations