Least absolute regression network analysis of the murine osteoblast differentiation network

EP van Someren, BLT Vaes, WT Steegenga, AM Sijbers, KJ Dechering, MJT Reinders

Research output: Contribution to journalArticleScientificpeer-review

60 Citations (Scopus)


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 languageUndefined/Unknown
Pages (from-to)477-484
Number of pages8
Issue number4
Publication statusPublished - 2006


  • Wiskunde en Informatica
  • Techniek
  • technische Wiskunde en Informatica
  • academic journal papers
  • CWTS JFIS >= 2.00

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