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

arXiv: https://arxiv.org/abs/1301.1083 · HTML: https://mlsanity.com/paper/1301.1083/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2013.

## Built on by (most cited first, 1 of 1)

- [Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information](https://mlsanity.com/paper/2210.00116/) (2022)

## Abstract (arXiv)

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.