  In this paper, we tested several sparse optimization algorithms based on the
public dataset of the DREAM5 Gene Regulatory Network Inference Challenge. And
we find that introducing 20% of the regulatory network as a priori known data
can provide a basis for parameter selection of inference algorithms, thus
improving prediction efficiency and accuracy. In addition to testing common
sparse optimization methods, we also developed voting algorithms by bagging
them. Experiments on the DREAM5 dataset show that the sparse optimization-based
inference of the moderation relation works well, achieving better results than
the official DREAM5 results on three datasets. However, the performance of
traditional independent algorithms varies greatly in the face of different
datasets, while our voting algorithm achieves the best results on three of the
four datasets.
