{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T05:01:58Z","timestamp":1782622918098,"version":"3.54.5"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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.<\/jats:p><jats:p>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.<\/jats:p><jats:p>Availability: Software is freely available for academic use as a Matlab package called GENLAB:<\/jats:p><jats:p>Contact: \u00a0E.P.vanSomeren@tudelft.nl<\/jats:p><jats:p>Supplementary information: Additional data, results and figures can be found at<\/jats:p>","DOI":"10.1093\/bioinformatics\/bti816","type":"journal-article","created":{"date-parts":[[2005,12,7]],"date-time":"2005-12-07T03:03:48Z","timestamp":1133924628000},"page":"477-484","source":"Crossref","is-referenced-by-count":60,"title":["Least absolute regression network analysis of the murine osteoblast differentiation network"],"prefix":"10.1093","volume":"22","author":[{"given":"E. 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