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

Maetschke, Stefan R., Madhamshettiwar, Piyush B., Davis, Melissa J. and Ragan, Mark A. (2014) Supervised, semi-supervised and unsupervised inference of gene regulatory networks. Briefings in Bioinformatics, 15 2: 195-211. doi:10.1093/bib/bbt034

Author Maetschke, Stefan R.
Madhamshettiwar, Piyush B.
Davis, Melissa J.
Ragan, Mark A.
Title Supervised, semi-supervised and unsupervised inference of gene regulatory networks
Journal name Briefings in Bioinformatics   Check publisher's open access policy
ISSN 1467-5463
Publication date 2014-03
Year available 2013
Sub-type Article (original research)
DOI 10.1093/bib/bbt034
Open Access Status DOI
Volume 15
Issue 2
Start page 195
End page 211
Total pages 17
Place of publication Oxford, United Kingdom
Publisher Oxford University Press
Collection year 2014
Language eng
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.
Keyword Gene regulatory networks
Gene expression data
Machine learning
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

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Created: Thu, 01 Aug 2013, 10:53:48 EST by Susan Allen on behalf of Institute for Molecular Bioscience