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

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    Author
    Maetschke, SR; Madhamshettiwar, PB; Davis, MJ; Ragan, MA
    Date
    2014-03
    Source Title
    Briefings in Bioinformatics
    Publisher
    Oxford University Press (OUP)
    University of Melbourne Author/s
    Davis, Melissa
    Affiliation
    Medical Biology (W.E.H.I.)
    Metadata
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    Document Type
    Journal Article
    Citations
    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 Access
    URI
    http://hdl.handle.net/11343/255624
    DOI
    10.1093/bib/bbt034
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3956069
    Abstract
    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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