Abstract
Modern pharmacology, combining pharmacokinetic, pharmacodynamic, and pharmacogenomic data, is dealing with high dimensional, nonlinear, stiff systems. Mathematical modeling of these systems is very difficult, but important for understanding them. At least as important is to adequately control them through inputs – drugs’ dosage regimens. Genetic programming (GP) and neural networks (NN) are alternative techniques for these tasks. We use GP to automatically write the model structure in C++ and optimize the model’s constants. This gives insights into the subjacent molecular mechanisms. We also show that NN feedback linearization (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 modeling and NN modeling and control.
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Floares, A. (2006). Genetic Programming and Neural Networks Feedback Linearization for Modeling and Controlling Complex Pharmacogenomic Systems. In: Bloch, I., Petrosino, A., Tettamanzi, A.G.B. (eds) Fuzzy Logic and Applications. WILF 2005. Lecture Notes in Computer Science(), vol 3849. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11676935_22
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DOI: https://doi.org/10.1007/11676935_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32529-1
Online ISBN: 978-3-540-32530-7
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