title: Supervised, semi-supervised and unsupervised inference of gene regulatory networks.
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.
authors.affiliation: Institute for Molecular Bioscience and ARC Centre of Excellence in Bioinformatics, Brisbane, QLD 4072, Australia, Tel.: 61 7 3346 2616; Fax: 61 7 3346 2101; m.ragan@uq.edu.au.
authors.forename: Stefan R
authors.initials: SR
authors.lastname: Maetschke
authors.forename: Piyush B
authors.initials: PB
authors.lastname: Madhamshettiwar
authors.forename: Melissa J
authors.initials: MJ
authors.lastname: Davis
authors.forename: Mark A
authors.initials: MA
authors.lastname: Ragan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030541:Databases, Genetic / Q000706:statistics & numerical data; D004926:Escherichia coli / Q000235:genetics; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D053263:Gene Regulatory Networks; D005798:Genes, Bacterial; D005800:Genes, Fungal; D012441:Saccharomyces cerevisiae / Q000235:genetics; D012984:Software; D060388:Support Vector Machine; D049490:Systems Biology
keywords: gene expression data; gene regulatory networks; machine learning; simulation