Learning with positive only examples occurs when the training set of a binary classifier is composed of examples known to be positive, and examples where the label category is unknown. Such a condition largely affects the task of learning gene regulatory networks as biologists does not aware the information whether two genes does not interact. We introduce the problem of learning new gene–gene interactions from positive and unlabeled data and propose a roadmap of possible approches.