The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo

TitleThe Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo
Publication TypeJournal Article
Year of Publication2006
AuthorsBonneau R, Reiss DJ, Shannon P, Facciotti M, Hood L, Baliga NS, Thorsson V
JournalGenome Biol
Volume7
PaginationR36
Date PublishedMay 10
PMID16686963
AbstractABSTRACT : We present a method (the Inferelator) for deriving genome-wide transcriptional regulatory interactions, and apply the method to predict a large portion of the regulatory network of the archaeon Halobacterium NRC-1. The Inferelator uses regression and variable selection to identify transcriptional influences on genes based on the integration of genome annotation and expression data. The learned network successfully predicted Halobacterium's global expression under novel perturbations with predictive power similar to that seen over training data. Several specific regulatory predictions were experimentally tested and verified.

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