System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Linear Modeling of Genetic Networks from Experimental Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Multi-criterion optimization for genetic network modeling
Signal Processing - Special issue: Genomic signal processing
Inferring Connectivity of Genetic Regulatory Networks Using Information-Theoretic Criteria
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Bayesian inference of gene regulatory networks using gene expression time series data
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
Isolasso: a lasso regression approach to RNA-seq based transcriptome assembly
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
Inferring Gene Regulatory Networks via Nonlinear State-Space Models and Exploiting Sparsity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Reverse engineering of gene regulatory networks from biological data
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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We construct a gene-to-gene regulatory network from time-series data of expression levels for the whole genome of the yeast Saccharomyces cerevisae, in a case where the number of measurements is much smaller than the number of genes in the network. This network is analyzed with respect to present biological knowledge of all genes (according to the Gene Ontology database), and we find some of its large-scale properties to be in accordance with known facts about the organism. The linear modeling employed here has been explored several times, but due to lack of any validation beyond investigating individual genes, it has been seriously questioned with respect to its applicability to biological systems. Our results show the adequacy of the approach and make further investigations of the model meaningful.