Least Absolute Regression Network Analysis of the murine osteoblast differentiation network

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Title:	Least Absolute Regression Network Analysis of the murine osteoblast differentiation network
Author(s):	Someren, E.P. van ; Vaes, B.L.T. ; Steegenga, W.T. ; Sijbers, A.M.; Dechering, K.J. ; Reinders, M.J.
Publication year:	2005
Source:	Bioinformatics, vol. 22, iss. 4, (2005), pp. 477-484
ISSN:	1367-4803
Related links:	http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16332709
DOI:	https://doi.org/10.1093/bioinformatics/bti816
Publication type:	Article / Letter to editor
Please use this identifier to cite or link to this item : https://hdl.handle.net/2066/32676
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Subject:	Applied Biology Cell Biology
Organization:	Cell biology Applied Biology
Journal title:	Bioinformatics
Volume:	vol. 22
Issue:	iss. 4
Page start:	p. 477
Page end:	p. 484
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. AVAILABILITY: Software is freely available for academic use as a Matlab package, called GENLAB: http://genlab.tudelft.nl/genlab.html. SUPPLEMENTARY INFORMATION: Additional data, results and figures can be found at http://genlab.tudelft.nl/larna.html.