  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
expression data. The results reveal very low prediction accuracies for
unsupervised techniques with the notable exception of the z-score method on
knock-out 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.
