Least Absolute Regression Network Analysis of the Murine Osteoblast Differentation Network

E.P. van Someren, B.L.T. Vaes, W.T. Steegenga, A.M. Sijbers, K.J. Dechering

Research output: Contribution to journalArticleAcademicpeer-review

54 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)477-484
JournalBioinformatics
Volume22
Issue number4
DOIs
Publication statusPublished - 2006

Keywords

  • genetic regulatory networks
  • expression data
  • cells
  • bone
  • identification
  • protein
  • reconstruction
  • growth

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