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.
- genetic regulatory networks
- expression data
van Someren, E. P., Vaes, B. L. T., Steegenga, W. T., Sijbers, A. M., & Dechering, K. J. (2006). Least Absolute Regression Network Analysis of the Murine Osteoblast Differentation Network. Bioinformatics, 22(4), 477-484. https://doi.org/10.1093/bioinformatics/bti816