摘要：AbstractIn this paper, a sparse reconstruction framework is proposed on the basis of steadystate experiment data to identify Gene Regulatory Networks (GRNs) structure. Different from traditional methods, this approach is adopted which is well suitable for a large-scale underdetermined problem in inferring a sparse vector. We investigate how to combine the noisy steady-state experiment data and a sparse reconstruction algorithm to identify causal relationships. Efficiency of this method is tested by an artificial linear network and the DREAM networks. The performance of the suggested approach is compared with two state-of-the-art algorithms, the widely adopted total least-squares (TLS) method and those available results on the DREAM project website. Actual results show that with a lower computational cost, the proposed method can significantly enhance estimation accuracy
