DiscoGen: Learning to Discover Gene Regulatory Networks
This work addresses a critical challenge in biology for researchers needing precise GRN inference from perturbational data, though it appears incremental as an adaptation of existing methods.
The paper tackles the problem of accurately inferring Gene Regulatory Networks (GRNs) from noisy and large-scale biological data, introducing DiscoGen, a neural network-based method that outperforms state-of-the-art causal discovery approaches.
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.