The gene expression matrix: towards the extraction of genetic network architectures
Proceedings of the second world congress on Nonlinear analysts: part 3
Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)
Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)
On sparse representations in arbitrary redundant bases
IEEE Transactions on Information Theory
The pattern memory of gene-protein networks
AI Communications - Network Analysis in Natural Sciences and Engineering
On Phase Transitions in Learning Sparse Networks
ECML '07 Proceedings of the 18th European conference on Machine Learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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In this study we will focus on piecewise linear state space models for gene-protein interaction networks. We will follow the dynamical systems approach with special interest for partitioned state spaces. From the observation that the dynamics in natural systems tends to punctuated equilibria, we will focus on piecewise linear models and sparse and hierarchic interactions, as, for instance, described by Glass, Kauffman, and de Jong. Next, the paper is concerned with the identification (also known as reverse engineering and reconstruction) of dynamic genetic networks from microarray data. We will describe exact and robust methods for computing the interaction matrix in the special case of piecewise linear models with sparse and hierarchic interactions from partial observations. Finally, we will analyze and evaluate this approach with regard to its performance and robustness towards intrinsic and extrinsic noise.