References of "Stan, G.B."
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See detailAnalysis of synchronizing biochemical networks via incremental dissipativity
Hamadeh, A.; Goncalves, Jorge UL; Stan, G.B.

in Kulkarni, V.; Stan, G.; Raman, K. (Eds.) A Systems Theoretic Approach to Systems and Synthetic Biology II: Analysis and Design of Cellular Systems (2014)

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See detailInference of switched biochemical reaction networks using sparse bayesian learning
Pan, Wei UL; Yuan, Y.; Sootla, A. et al

in The proceedings of the 7th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) (2014)

This paper proposes an algorithm to identify biochemical reaction networks with time-varying kinetics. We formulate the problem as a nonconvex optimisation problem casted in a sparse Bayesian learning ... [more ▼]

This paper proposes an algorithm to identify biochemical reaction networks with time-varying kinetics. We formulate the problem as a nonconvex optimisation problem casted in a sparse Bayesian learning framework. The nonconvex problem can be efficiently solved using Convex-Concave programming. We test the effectiveness of the method on a simulated example of DNA circuit realising a switched chaotic Lorenz oscillator. [less ▲]

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See detailIn-silico Robust Reconstruction of the Per-Arnt-Sim Kinase Pathway using Dynamical Structure Functions
Chetty, V.; Adebayo, J.; Mathis, A. et al

Scientific Conference (2012, October)

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See detailClinical data based optimal STI strategies for HIV: a reinforcement learning approach
Ernst, D.; Stan, G.B.; Goncalves, Jorge UL et al

in Proceedings of the IEEE Conference on Decision and Control (2006)

This paper addresses the problem of computing optimal structured treatment interruption strategies for HIV infected patients. We show that reinforcement learning may be useful to extract such strategies ... [more ▼]

This paper addresses the problem of computing optimal structured treatment interruption strategies for HIV infected patients. We show that reinforcement learning may be useful to extract such strategies directly from clinical data, without the need of an accurate mathematical model of HIV infection dynamics. To support our claims, we report simulation results obtained by running a recently proposed batch-mode reinforcement learning algorithm, known as fitted Q iteration, on numerically generated data. [less ▲]

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