Least absolute regression network analysis of the murine osteoblast differentiation network

Journal article (2006)

Authors

BLT Vaes External organisation
WT Steegenga External organisation
AM Sijbers External organisation
KJ Dechering External organisation
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Published Date

2006

Language

Undefined/Unknown

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Abstract

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.