Fantine Mordelet

TL;DR: A comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data defines the performance, data requirements and inherent biases of different inference approaches, and provides guidelines for algorithm application and development.

TL;DR: A novel, robust and accurate scoring technique for stability selection, which improves the performance of feature selection with LARS, is introduced, which was ranked among the top GRN inference methods in the DREAM5 gene network inference challenge and was evaluated to be the best linear regression-based method in the challenge.

TL;DR: TIGRESS (Trustful Inference of Gene Regression using Stability Selection) as discussed by the authors is the state-of-the-art method for gene regulatory network inference using least angle regression (LARS) and stability selection.

TL;DR: It is shown theoretically and experimentally that the proposed method can match and even outperform the performance of state-of-the-art methods for PU learning, particularly when the number of positive examples is limited and the fraction of negatives among the unlabeled examples is small.

TL;DR: SIRENE (Supervised Inference of Regulatory Networks), a new method for the inference of gene regulatory networks from a compendium of expression data, is proposed and test it on a benchmark experiment aimed at predicting regulations in Escherichia coli, and it retrieves of the order of 6 times more known regulations than other state-of-the-art inference methods.