Abstract
In this paper a novel method is presented for the identification of sparse dynamical interaction networks, such as gene regulatory networks. This method uses mixed L 2/L 1 minimization: nonlinear least squares optimization to achieve an optimal fit between the model in state space form and the data, and L 1-minimization of the parameter vector to find the sparsest such model possible. In this approach, in contrast to previous research, the dynamical aspects of the model are taken into account, which gives rise to a nonlinear estimation problem. The set-up allows for the identification of structured or partially sparse models, so that available prior knowledge on interactions can be incorporated. To investigate the potential for applications, the algorithm is tested on artificial gene regulatory networks.
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Peeters, R.L.M., Zeemering, S. (2007). Sparse Gene Regulatory Network Identification. In: Tuyls, K., Westra, R., Saeys, Y., Nowé, A. (eds) Knowledge Discovery and Emergent Complexity in Bioinformatics. KDECB 2006. Lecture Notes in Computer Science(), vol 4366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71037-0_12
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DOI: https://doi.org/10.1007/978-3-540-71037-0_12
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