2023/04/12 by Nan Rosemary Ke, Ke, Nan Rosemary, Sara-Jane Dunn +25
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #FOS: Biological sciences #FOS: Computer and information sciences #Gene Regulatory Network Analysis #Gene expression and cancer classification #Genomics (q-bio.GN) #Machine Learning (cs.LG) #Molecular Networks (q-bio.MN)
paper · pdf · doi:10.48550/arxiv.2304.05823
openalex publication_date 2023/04/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.