An ensemble method for reconstructing gene regulatory network with jackknife resampling and arithmetic mean fusion.

Abstract

During the past decades, numerous computational approaches have been introduced for inferring the GRNs. PCA-CMI approach achieves the highest precision on the benchmark GRN datasets; however, it does not recover the meaningful edges that may have been deleted in an earlier iterative process. To recover this disadvantage and enhance the precision and robustness of GRNs inferred, we present an ensemble method, named as JRAMF, to infer GRNs from gene expression data by adopting two strategies of resampling and arithmetic mean fusion in this work. The jackknife resampling procedure were first employed to form a series of sub-datasets of gene expression data, then the PCA-CMI was used to generate the corresponding sub-networks from the sub-datasets, and the final GRN was inferred by integrating these sub-networks with an arithmetic mean fusion strategy. Compared with PCA-CMI algorithm, the results show that JRAMF outperforms significantly PCA-CMI method, which has a high and robust performance.