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

doi:10.1093/bioinformatics/bti816

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 journal › Article › Academic › peer-review

65 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 language	English
Pages (from-to)	477-484
Journal	Bioinformatics
Volume	22
Issue number	4
DOIs	https://doi.org/10.1093/bioinformatics/bti816
Publication status	Published - 2006

Keywords

genetic regulatory networks
expression data
cells
bone
identification
protein
reconstruction
growth

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10.1093/bioinformatics/bti816

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title = "Least Absolute Regression Network Analysis of the Murine Osteoblast Differentation Network",

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.",

keywords = "genetic regulatory networks, expression data, cells, bone, identification, protein, reconstruction, growth",

author = "{van Someren}, E.P. and B.L.T. Vaes and W.T. Steegenga and A.M. Sijbers and K.J. Dechering",

year = "2006",

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AU - van Someren, E.P.

AU - Vaes, B.L.T.

AU - Steegenga, W.T.

AU - Sijbers, A.M.

AU - Dechering, K.J.

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

KW - genetic regulatory networks

KW - expression data

KW - cells

KW - bone

KW - identification

KW - protein

KW - reconstruction

KW - growth

U2 - 10.1093/bioinformatics/bti816

DO - 10.1093/bioinformatics/bti816

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VL - 22

SP - 477

EP - 484

JO - Bioinformatics

JF - Bioinformatics

IS - 4

ER -

Least Absolute Regression Network Analysis of the Murine Osteoblast Differentation Network

Abstract

Keywords

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