A new multiple regression approach for the construction of genetic regulatory networks
Artificial Intelligence in Medicine
Reconstructing linear gene regulatory networks
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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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 Contact: E.P.vanSomeren@tudelft.nl Supplementary information: Additional data, results and figures can be found at http://genlab.tudelft.nl/larna.html