We tackle the challenge of evaluating various gene expression data to rebuild gene–gene interaction networks. Many datasets can be integrated, which is typically thought to boost statistical power and improve characterization of the system being studied. However, tasks related to reverse engineering are especially difficult because of the systematic diversity across research. We compare and contrast two commonly used methods in the literature to address systematic biases. First is data-merging method, which analyzes the pooled data directly after biases are removed and second is meta-analysis methods, which compute appropriate statistics on individual datasets first and then summarize them. Both synthetic and actual data are used in this comparison analysis using two different microarray datasets that include numerous Escherichia coli and yeast investigations. Additionally, a case study on the rebuilding of the regulatory network of Ikaros using transcription factor in human peripheral blood mononuclear cells (PBMCs) is provided. We conducted experiments using both synthetic and actual data, and the meta-analysis and data-merging strategies produced equivalent results. Moreover, the two methods performed better than (a) the naive solution of combining data without considering potential biases and (b) the anticipated outcomes of analyzing a single dataset exclusively. When ranking candidate interactions appropriately, correlation statistics turned out to be a more useful method than p-values. The outcomes of the PBMC case study show that the current study’s conclusions apply to other kinds of network reconstruction strategies generally.