Unravelling gene networks from steady-state ezperimental perturbation data

摘要：

Reverse engineering gene networks exclusively from microarray expression data sets trough computational analysis is a difficult but important task. We present a method (SNI) for deriving gene interactions among genes and reconstruction gene regulatory networks from steady-state experimental perturbation data. The predictive power of our approach is tested and verified on both simulated data generated from artificial scale-free networks and Escherichia coli gene profiling data. Comparing with other inferring approaches, the analyzed results illustrate that SNI is a useful tool and outperform other approaches for predicting regulatory genes especially when the network is very sparse.

关键词： Gene Regulatory network steady-state linear regression sparse network significance test

作者: Luwen Zhang Wu Zhang Mei Xiao Jiang Xie Zikai Wu

作者单位: School of Computer Engineering and Science Shanghai University Shanghai,China Institute of Systems B School of Computer Engineering and Science Shanghai University Shanghai,China Institute of Systems Biology School of Communication and Information Engineering Shanghai University

会议类型: 国际会议

会议名称: The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2009-06-11（万方平台首次上网日期，不代表论文的发表时间）