Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A Practical Approach to Microarray Data Analysis
A Practical Approach to Microarray Data Analysis
Adaptive control using neural networks and approximate models
IEEE Transactions on Neural Networks
Free lunches for neural network search
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Automatic reverse engineering algorithm for drug gene regulating networks
ASC '07 Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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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.