Least absolute regression network analysis of the murine osteoblast differentiation network.
basic_science · Level V
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Abstract
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. 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.
Medical subject headings
- Gene Expression Profiling
- Gene Expression Regulation
- Oligonucleotide Array Sequence Analysis
- Osteoblasts
- Signal Transduction
- Transcription Factors