Inference of switched biochemical reaction networks using sparse bayesian learning

Reference : Inference of switched biochemical reaction networks using sparse bayesian learning


	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Multidisciplinary, general & others
	To cite this reference:	http://hdl.handle.net/10993/20819

Title :	Inference of switched biochemical reaction networks using sparse bayesian learning
Language :	English
Author, co-author :	Pan, Wei []
	Yuan, Y. []
	Sootla, A. []
	Stan, G.B []
Publication date :	2014
Main document title :	The proceedings of the 7th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD)
Publisher :	Springer
Pages :	p.51-60
Peer reviewed :	Yes
Event name :	7th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD)
Event date :	15-19 September, 2014
Event place (city) :	Nancy
Event country :	France
Keywords :	[en] Switched system ; Sparse Bayesian Learning ; Convex-Concave programming
Abstract :	[en] 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.
Permalink :	http://hdl.handle.net/10993/20819

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