GRN Topology Identification Using Likelihood Maximization and Relative Expression Level Variations

摘要：

Structure identification is investigated in this paper for a gene regulatory network (GRN) using knock out/down steady state experimental data. Through incorporating sparsity of a large scale GRN, estimates are derived respectively for the wild-type expression level of a gene and the variance of its measurement errors by means of likelihood maximization. Using these estimates, relative expression level variations (RELV) of a gene are further estimated that are due to gene knock out/down experiments. An algorithm is suggested through normalizing and modifying the magnitude of this RELV to identify direct causal regulations of a GRN. Computation results with the Size 100 sub-challenges of both DREAM3 and DREAM4 show that, compared with some well known Z-score based methods, prediction performances are substantially improved by the suggested method, especially the AUPR specification. Moreover, this method can even outperform the best team of both DREAM3 and DREAM4.

关键词： gene regulatory network knock out/down experiment likelihood maximization power law topology estimation

作者: Tong Zhou Jie Xiong Ya-Li Wang

作者单位: Department of Automation, Tsinghua University, Beijing, 100084, P. R. China Tsinghua National Labora Department of Automation, Tsinghua University, Beijing, 100084, P. R. China

会议类型: 国际会议

会议名称: The 31st Chinese Control Conference(第三十一届中国控制会议)

会议地点: 合肥

会议语种:英文

页码: 7408-7414

在线出版日期: 2012-07-01（万方平台首次上网日期，不代表论文的发表时间）