Furthermore, many of the predicted novel associations by tREMAP are supported by evidence from literature. 
THERM
Here, we developed a new one-class collaborative filtering algorithm tREMAP that is based on regularized, weighted nonnegative matrix tri-factorization.  Our benchmark study shows that tREMAP significantly outperforms its counterpart REMAP, a bi-factorization-based algorithm, for transcription factor target gene prediction in all four performance metrics AUC, MAP, MPR, and HLU.  Furthermore, many of the predicted novel associations by tREMAP are supported by evidence from literature.  Although we only use canonical TF-target gene interaction data in this study, tREMAP can be directly applied to tissue-specific data sets.  tREMAP provides a framework to integrate multiple omics data for the further improvement of TF target gene prediction.  Thus, tREMAP is a potentially useful tool in studying gene regulatory networks.  The benchmark data set and the source code of tREMAP are freely available at https://github.com/hansaimlim/REMAP/tree/master/TriFacREMAP.
tREMAP