Alexandru George Floares

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Automatically inferring drug gene regulatory networks models from microarray time series data is a challenging task. The ordinary differential equations models are sensible, but difficult to build. We extended our reverse engineering algorithm for gene networks (RODES), based on genetic programming, by adding a neural networks feedback linearization(More)
Pharmacogenomic systems (PG) are very high dimensional, nonlinear, and stiff systems. Mathematical modeling 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(More)
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(More)
Modeling neural networks with ordinary differential equations systems is a sensible approach, but also very difficult. This paper describes a new algorithm based on linear genetic programming which can be used to reverse engineer neural networks. The RODES algorithm automatically discovers the structure of the network, including neural connections, their(More)
The ordinary differential equations approach to neutral networks modeling is one of the most sensible approach but also very difficult. We proposed a reverse engineering algorithm for neural networks based on linear genetic programming. This algorithm allows the automatic discovery of the structure, estimation of the parameters, and even identification of(More)
2134 Background: An important problem in cancer chemotherapy is the design of drug dosage regimens such that at the end of treatment, the tumor burden is minimized, and the benefits are balanced against the toxic effects. We propose an approach based on neural networks (NN) control to optimize chemotherapy regimens. METHODS The tumor growth and the effect(More)
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