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Significance Discovery of gene regulatory networks (GRNs) is crucial for gaining insights into biological processes involved in development or disease. Although time-resolved, high-throughput data are increasingly available, many algorithms do not account for temporal delays underlying regulatory systems—such as protein synthesis and posttranslational modifications—leading to inaccurate network inference. To overcome this challenge, we introduce Sliding Window Inference for Network Generation (SWING), which uniquely accounts for temporal information. We validate SWING in both in silico and in vitro experimental systems, highlighting improved performance in identifying time-delayed edges and illuminating network structure. SWING performance is robust to user-defined parameters, enabling identification of regulatory mechanisms from time-series gene expression data.

Windowed Granger causal inference strategy improves discovery of gene regulatory networks