DiscoGen: Learning to Discover Gene Regulatory Networks

• URL: http://arxiv.org/abs/2304.05823v1
• Date: Wed, 12 Apr 2023 13:02:49 GMT
• Title: DiscoGen: Learning to Discover Gene Regulatory Networks
• Authors: Nan Rosemary Ke, Sara-Jane Dunn, Jorg Bornschein, Silvia Chiappa, Melanie Rey, Jean-Baptiste Lespiau, Albin Cassirer, Jane Wang, Theophane Weber, David Barrett, Matthew Botvinick, Anirudh Goyal, Mike Mozer, Danilo Rezende
• Abstract summary: Accurately inferring Gene Regulatory Networks (GRNs) is a critical and challenging task in biology. Recent advances in neural network-based causal discovery methods have significantly improved causal discovery. Applying state-of-the-art causal discovery methods in biology poses challenges, such as noisy data and a large number of samples. We introduce DiscoGen, a neural network-based GRN discovery method that can denoise gene expression measurements and handle interventional data.
• Score: 30.83574314774383
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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