Experimental noise cutoff boosts inferability of transcriptional networks in large-scale gene-deletion studies

Abstract

Reliable inference of gene interactions from perturbation experiments remains a challenge. Here, the authors quantify the upper limits of transcriptional network inference from knockout screens, identify the key determinants of accuracy, and introduce an unbiased and scalable inference algorithm.