Accelerating returns in gene panel design: A completion theory for regulatory network identification.
Measurement panel design for regulatory network inference requires choosing which genes to observe. We prove that under random gene selection, expected marginal identifiability is non-decreasing and strictly increasing for sufficiently large k (once nontrivial regulatory interactions can be completed)-a property we ter...