  Accurately inferring Gene Regulatory Networks (GRNs) is a critical and
challenging task in biology. GRNs model the activatory and inhibitory
interactions between genes and are inherently causal in nature. To accurately
identify GRNs, perturbational data is required. However, most GRN discovery
methods only operate on observational data. Recent advances in neural
network-based causal discovery methods have significantly improved causal
discovery, including handling interventional data, improvements in performance
and scalability. However, applying state-of-the-art (SOTA) causal discovery
methods in biology poses challenges, such as noisy data and a large number of
samples. Thus, adapting the causal discovery methods is necessary to handle
these challenges. In this paper, we introduce DiscoGen, a neural network-based
GRN discovery method that can denoise gene expression measurements and handle
interventional data. We demonstrate that our model outperforms SOTA neural
network-based causal discovery methods.
