We propose a framework that uses the available gene/protein interaction databases of the literature as a universal benchmark in order to globally assess the inference performances of gene network inference algorithms. We also developed an R software package for convenient use of the framework, which can also be used in general as a quick tool to search in the literature for available validations of interactions. We applied the proposed approach to 2 publicly available prostate cancer gene expression datasets and a large breast cancer gene expression dataset. The results revealed different aspects and superiority of algorithms that had not been compared previously in the available literature. Our approach allowed the assessing and comparing of the algorithms on a real dataset of a size of around 30,000 probes, which showed the strengths and weaknesses of the algorithms from different points of view rather than conventional approaches. We further show that our approach provides a unique advantage in assessing the performance of an inference method when applied to a new dataset and thus sheds light on the results of a de novo application, which would be obscure without our approach.