The first frames network inference as a series of regression problems wherein the expression level of each target gene is predicted by a combination of the expression levels across a set of potential upstream regulators. The second casts the problem of finding regulators as a classification problem in which new targets of a TF are predicted by comparing each potential target gene’s expression profile to the profiles of known “true” and “false” targets.
