Motivation: We propose a reverse engineering scheme to discover genetic regulation from genome-wide transcription data that monitors the dynamic transcriptional response after a change in cellular environment. The interaction network is estimated by solving a linear model using simultaneous shrinking of the least absolute weights and the prediction error. Results: The proposed scheme has been applied to the murine C2C12 cell-line stimulated to undergo osteoblast differentiation. Results show that our method discovers genetic interactions that display significant enrichment of co-citation in literature. More detailed study showed that the inferred network exhibits properties and hypotheses that are consistent with current biological knowledge. © The Author 2005. Published by Oxford University Press. All rights reserved.
CITATION STYLE
van Someren, E. P., Vaes, B. L. T., Steegenga, W. T., Sijbers, A. M., Dechering, K. J., & Reinders, M. J. T. (2006). Least absolute regression network analysis of the murine osteoblast differentiation network. Bioinformatics, 22(4), 477–484. https://doi.org/10.1093/bioinformatics/bti816
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