Claudia Skok Gibbs
A gene regulatory network describes which transcription factor proteins switch which genes on or off. Reconstructing one from genome-wide measurements is a long-standing problem, and this work is as interested in how the field evaluates its answers as in the answers themselves.
Three constraints get named. Methods tend to weld their modelling assumptions to one particular inference procedure, so you cannot swap a better assumption into an existing pipeline. Model selection often rests on heuristics. And the reference networks used to score results are themselves incomplete, meaning a predicted link marked wrong may simply be one biology has not confirmed yet.
The point about point estimates is the one with teeth. A method that outputs a single best network with no uncertainty gives a biologist no way to tell a confident edge from a coin flip, which is precisely the information needed to decide which prediction is worth an experiment.
Gene regulatory networks (GRNs) link transcription factor (TF) proteins to their target genes, yet reconstructing these networks from genome-wide data remains challenging under practical and methodological constraints. Many methods couple modeling assumptions to a specific inference procedure and rely on heuristic model selection, while evaluation is constrained by incomplete reference networks and point-estimate outputs that lack uncertainty. GRN reconstruction also depends…
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