ArticlebioRxiv : the preprint server for biology2026
FERRET: Framework to Evaluate Robustness in Regulatory Networks Using Heterogeneous Cell Types.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
2 authors.
Funding
Abstract
Techniques for evaluating gene regulatory network (GRN) inference methods typically focus on recovering small ground-truth networks or on benchmarking against simulated data. However, both approaches have important limitations and fail to capture the biological variability present in real datasets. FERRET is a framework for benchmarking single-cell GRN inference methods based on a simple biological assumption: independent estimates of the regulatory network from the same cellular state should resemble one another more closely than estimates from distinct cellular states. Rather than relying on incomplete or simulated ground truth, FERRET quantifies network robustness using two complementary metrics:
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Registered trials
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