Chris K. Lewis

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Gene expression time course data can be used not only to detect differentially expressed genes but also to find temporal associations among genes. The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from transcriptomic data is addressed. A network reconstruction algorithm was(More)
1 Problem statement. We address the computational problems of discretizing continuous random variables and identifying a logical network model to account for temporal dependencies among genes and environmental stimuli from high-throughput transcriptome data. These new algorithms were applied to real biological data from an analysis of the molecular response(More)
The problem of computing logical network models to account for temporal dependencies among interacting genes and environmental stimuli from high-throughput transcriptomic data is addressed. A logical network reconstruction algorithm was developed that uses the statistical significance as a criterion for network selection to avoid false interactions arising(More)
Gene expression time course data can be used not only to detect differentially expressed genes, but also to find temporal associations among different genes. The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from high-throughput transcriptomic data is addressed. A network(More)
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