Matrix analysis
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
CMSB '03 Proceedings of the First International Workshop on Computational Methods in Systems Biology
Combining Microarrays and Biological Knowledge for Estimating Gene Networks via Bayesian Networks
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
An algorithm to analyze stability of gene-expression patterns
Discrete Applied Mathematics - Special issue: Discrete mathematics & data mining II (DM & DM II)
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Robust filtering for gene expression time series data with variance constraints
International Journal of Computer Mathematics - Bioinformatics
A mathematical program to refine gene regulatory networks
Discrete Applied Mathematics
An algorithm to analyze stability of gene-expression patterns
Discrete Applied Mathematics - Special issue: Discrete mathematics & data mining II (DM & DM II)
Extracting gene regulation information from microarray time-series data using hidden markov models
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
A scalable approach for inferring transcriptional regulation in the yeast cell cycle
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
CMSB'04 Proceedings of the 20 international conference on Computational Methods in Systems Biology
An integer optimization approach for reverse engineering of gene regulatory networks
Discrete Applied Mathematics
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Spurred by advances in cDNA microarray technology, gene expression data are increasingly becoming available. In time-ordered data, the expression levels are measured at several points in time following some experimental manipulation. A gene regulatory network can be inferred by fitting a linear system of differential equations to the gene expression data. As biologically the gene regulatory network is known to be sparse, we expect most coefficients in such a linear system of differential equations to be zero. In previously proposed methods to infer such a linear system, ad hoc assumptions were made to limit the number of nonzero coefficients in the system. Instead, we propose to infer the degree of sparseness of the gene regulatory network from the data, where we determine which coefficients are nonzero by using Akaike's Information Criterion.