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

TL;DR

This paper evaluates various gene regulatory network inference methods, finding supervised approaches generally outperform unsupervised and semi-supervised methods, especially with limited positive samples, based on extensive simulation data analysis.

Contribution

It provides a comprehensive comparison of inference methods across supervised, semi-supervised, and unsupervised categories, offering practical guidelines for their application.

Findings

Unsupervised methods show low prediction accuracy, except z-score on knock-out data.

Supervised methods outperform others in most scenarios.

Semi-supervised methods with few positive samples still outperform unsupervised techniques.

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 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.