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by Xiaobo Zhou, Xiaodong Wang, Ranadip Pal, Ivan Ivanov, Michael Bittner, Edward R. Dougherty
http://gsplab.tamu.edu/Publications/PDFpapers/pap_Bio_connectivity.pdf
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Abstract:
Motivation: We have hypothesized that the construction of transcriptional regulatory networks using a method that optimizes connectivity would lead to regulation consistent with biological expectations. A key expectation is that the hypothetical networks should produce a few, very strong attractors, highly similar to the original observations, mimicking biological state stability and determinism. Another central expectation is that, since it is expected that the biological control is distributed and mutually reinforcing, interpretation of the observations should lead to a very small number of connection schemes. Results: We propose a fully Bayesian approach to constructing probabilistic gene regulatory networks (PGRNs) that emphasizes network topology. The method computes the possible parent sets of each gene, the corresponding predictors
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