  Motivation: Laboratory gene regulatory data for a species are sporadic.
Despite the abundance of gene regulatory network algorithms that employ single
data sets, few algorithms can combine the vast but disperse sources of data and
extract the potential information. With a motivation to compensate for this
shortage, we developed an algorithm called GENEREF that can accumulate
information from multiple types of data sets in an iterative manner, with each
iteration boosting the performance of the prediction results.
  Results: The algorithm is examined extensively on data extracted from the
quintuple DREAM4 networks and DREAM5's Escherichia coli and Saccharomyces
cerevisiae networks and sub-networks. Many single-dataset and multi-dataset
algorithms were compared to test the performance of the algorithm. Results show
that GENEREF surpasses non-ensemble state-of-the-art multi-perturbation
algorithms on the selected networks and is competitive to present
multiple-dataset algorithms. Specifically, it outperforms dynGENIE3 and is on
par with iRafNet. Also, we argued that a scoring method solely based on the
AUPR criterion would be more trustworthy than the traditional score.
  Availability: The Python implementation along with the data sets and results
can be downloaded from github.com/msaremi/GENEREF
