Data-driven Discovery of Genetic Network Models

Electrical Engineering, Mathematics and Computer Scienc

Least Absolute Regression Network Analysis of the murine osteoblast differentiation network

Item does not contain fulltextMOTIVATION: 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

The effect of pkc activation and inhibition on osteogenic differentiation of human mesenchymal stem cells

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Effect of chordin-like 1 on mc3t3-e1 and human mesenchymal stem cells

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Microarray analysis on Runx2-deficient mouse embryos reveals novel Runx2 functions and target genes during intramembranous and endochondral bone formation

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