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Inferring the topology of a gene-regulatory network (GRN) from genome-scale time-series measurements of transcriptional change has proved useful for disentangling complex biological processes. To address the challenges associated with this inference, a number of competing approaches have previously been used, including examples from information theory, Bayesian and dynamic Bayesian networks (DBNs), and ordinary differential equation (ODE) or stochastic differential equation. The performance ofdoi:10.1098/rsfs.2011.0053 pmid:23226586 pmcid:PMC3262295 fatcat:glhjailtdngwzmi3zcnb4keo5y