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.
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.
Authors: Giusy Della Gatta; Mukesh Bansal; Alberto Ambesi-Impiombato; Dario Antonini; Caterina Missero; Diego di Bernardo Journal: Genome Res Date: 2008-04-25 Impact factor: 9.043
Authors: Kenneth Lo; Adrian E Raftery; Kenneth M Dombek; Jun Zhu; Eric E Schadt; Roger E Bumgarner; Ka Yee Yeung Journal: BMC Syst Biol Date: 2012-08-16