Benchmarking Gene Regulatory Network Inference Methods on Simulated and Experimental Data.

ABSTRACT: Although the challenge of gene regulatory network inference has been studied for more than a decade, it is still unclear how well network inference methods work when applied to real data. Attempts to benchmark these methods on experimental data have yielded mixed results, in which sometimes even the best methods fail to outperform random guessing, and in other cases they perform reasonably well. So, one of the most valuable contributions one can currently make to the field of network inference is to benchmark methods on experimental data for which the true underlying network is already known, and report the results so that we can get a clearer picture of their efficacy. In this paper, we report results from the first, to our knowledge, benchmarking of network inference methods on single ce

SUBMITTER: Saint-Antoine M 

PROVIDER: S-EPMC10197678 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature