#17 · Systems biology

Modeling choices alter conclusions drawn from gene regulatory network analyses

The dominant thread across these three preprints is that analytic choices shape the conclusions drawn from regulatory and genomic data. One study introduces a diagnostic framework that quantifies ranking reversals across evaluation protocol axes in single-cell GRN inference benchmarks. A second proposes asymmetric Shapley values as an importance metric for high-dimensional genomic features in mixed-dimensional clinical prediction. A third surveys Boolean network control problems, offering a taxonomy and a framework that compares tool solutions, along with a mutation co-occurrence score for prioritization. These items do not share a single method. What is new is explicit attention to how evaluation protocol, importance attribution, and problem formulation alter outcomes, favoring comparative evaluation before adoption.