Supervised, semi-supervised and unsupervised inference of gene regulatory networks
This provides guidelines for practical application in computational biology, but it is incremental as it focuses on evaluation rather than introducing new methods.
The paper tackled the problem of inferring gene regulatory networks from expression data by evaluating unsupervised, semi-supervised, and supervised methods, finding that supervised approaches achieved the highest accuracies, with unsupervised techniques showing very low prediction accuracies except for the z-score method on knock-out data.
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.