Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems
This work addresses causal inference in GRNs for biomedical research, offering a scalable solution that handles cyclic dynamics, though it appears incremental by building on existing GFlowNet methods.
The paper tackles the problem of causal discovery in Gene Regulatory Networks (GRNs), which is challenging due to cyclic feedback loops and scalability issues, by introducing Swift-DynGFN, a framework that enhances causal structure learning and scalability through gene-wise independence for parallelization, showing advancements in experiments on real and synthetic datasets.
Understanding causal relationships within Gene Regulatory Networks (GRNs) is essential for unraveling the gene interactions in cellular processes. However, causal discovery in GRNs is a challenging problem for multiple reasons including the existence of cyclic feedback loops and uncertainty that yields diverse possible causal structures. Previous works in this area either ignore cyclic dynamics (assume acyclic structure) or struggle with scalability. We introduce Swift-DynGFN as a novel framework that enhances causal structure learning in GRNs while addressing scalability concerns. Specifically, Swift-DynGFN exploits gene-wise independence to boost parallelization and to lower computational cost. Experiments on real single-cell RNA velocity and synthetic GRN datasets showcase the advancement in learning causal structure in GRNs and scalability in larger systems.