Least absolute regression network analysis of the murine osteoblast differentiation network

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

Least absolute regression network analysis of the murine osteoblast differentiation network

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

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Research output: Contribution to journal › Article › Scientific › peer-review

60 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	Undefined/Unknown
Pages (from-to)	477-484
Number of pages	8
Journal	Bioinformatics
Volume	22
Issue number	4
Publication status	Published - 2006

Keywords

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

Cite this

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title = "Least absolute regression network analysis of the murine osteoblast differentiation 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 = "Wiskunde en Informatica, Techniek, technische Wiskunde en Informatica, academic journal papers, CWTS JFIS >= 2.00",

author = "{van Someren}, EP and BLT Vaes and WT Steegenga and AM Sijbers and KJ Dechering and MJT Reinders",

year = "2006",

language = "Undefined/Unknown",

volume = "22",

pages = "477--484",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

number = "4",

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TY - JOUR

T1 - Least absolute regression network analysis of the murine osteoblast differentiation network

AU - van Someren, EP

AU - Vaes, BLT

AU - Steegenga, WT

AU - Sijbers, AM

AU - Dechering, KJ

AU - Reinders, MJT

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 - Wiskunde en Informatica

KW - Techniek

KW - technische Wiskunde en Informatica

KW - academic journal papers

KW - CWTS JFIS >= 2.00

UR - http://bioinformatics.oxfordjournals.org/cgi/reprint/22/4/477.pdf

M3 - Article

SN - 1367-4803

VL - 22

SP - 477

EP - 484

JO - Bioinformatics

JF - Bioinformatics

IS - 4

ER -

Least absolute regression network analysis of the murine osteoblast differentiation network

Abstract

Keywords

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