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.

翻译：准确推断基因调控网络（GRN）是生物学中一项关键且具有挑战性的任务。GRN描述了基因之间激活和抑制的相互作用，本质上具有因果性。为了准确识别GRN，需要使用扰动数据。然而，大多数GRN发现方法仅基于观测数据运行。近年来，基于神经网络的因果发现方法取得了显著进展，包括处理干预数据、改进性能和可扩展性。然而，将最先进的因果发现方法应用于生物学领域面临着诸多挑战，例如噪声数据和大规模样本数量。因此，需要调整这些因果发现方法以应对这些挑战。本文提出了DiscoGen，一种基于神经网络的GRN发现方法，该方法能够对基因表达测量进行去噪，并处理干预数据。我们证明，该模型的性能优于最先进的基于神经网络的因果发现方法。