Supervised, semi-supervised and unsupervised inference of gene regulatory networks.

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Author
Maetschke, SR; Madhamshettiwar, PB; Davis, MJ; Ragan, MADate
2014-03Source Title
Briefings in BioinformaticsPublisher
Oxford University Press (OUP)University of Melbourne Author/s
Davis, MelissaAffiliation
Medical Biology (W.E.H.I.)Metadata
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Journal ArticleCitations
Maetschke, 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 Status
Open AccessOpen Access at PMC
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3956069Abstract
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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