Supervised, semi-supervised and unsupervised inference of gene regulatory networks.
Web of Science
AuthorMaetschke, SR; Madhamshettiwar, PB; Davis, MJ; Ragan, MA
Source TitleBriefings in Bioinformatics
PublisherOxford University Press (OUP)
University of Melbourne Author/sDavis, Melissa
AffiliationMedical Biology (W.E.H.I.)
Document TypeJournal Article
CitationsMaetschke, S. R., Madhamshettiwar, P. B., Davis, M. J. & Ragan, M. A. (2014). Supervised, semi-supervised and unsupervised inference of gene regulatory networks.. Brief Bioinform, 15 (2), pp.195-211. https://doi.org/10.1093/bib/bbt034.
Access StatusOpen Access
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3956069
Inference of gene regulatory network from expression data is a challenging task. Many methods have been developed to this purpose but a comprehensive evaluation that covers unsupervised, semi-supervised and supervised methods, and provides guidelines for their practical application, is lacking. We performed an extensive evaluation of inference methods on simulated and experimental expression data. The results reveal low prediction accuracies for unsupervised techniques with the notable exception of the Z-SCORE method on knockout data. In all other cases, the supervised approach achieved the highest accuracies and even in a semi-supervised setting with small numbers of only positive samples, outperformed the unsupervised techniques.
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