Quantifying Ranking Instability Across Evaluation Protocol Axes in Gene Regulatory Network Benchmarking
Benchmark rankings are routinely used to justify scientific claims about method quality in gene regulatory network (GRN) inference, yet the stability of these rankings under plausible evaluation protocol choices is rarely examined. We present a systematic diagnostic framework for measuring ranking instability under protocol shift, including decomposition tools that separate base rate effects from discrimination effects. Using existing single cell GRN benchmark outputs across three human tissues and six inference methods, we quantify pairwise reversal rates across four protocol axes: candidate set restriction (16.3 percent, 95 percent CI 11.0 to 23.4 percent), tissue context (19.3 percent)...
| Authors | Ihor Kendiukhov |
|---|---|
| Primary category | q-bio.MN |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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