Regulatory Network

Gene regulatory networks (GRNs) describe the complex interactions governing gene expression, crucial for understanding cellular processes and disease. Current research focuses on developing robust methods to infer these networks from diverse data types, including single-cell transcriptomics and perturbation experiments, employing machine learning approaches such as graph neural networks, transformers, and Bayesian methods. These advancements improve the accuracy and scalability of GRN inference, leading to better understanding of biological mechanisms and informing applications in personalized medicine and drug discovery. The field is also increasingly emphasizing causal inference and the incorporation of prior biological knowledge to enhance model interpretability and predictive power.