title: Reverse engineering large-scale genetic networks: synthetic versus real data.
abstract: Development of microarray technology has resulted in an exponential rise in gene expression data. Linear computational methods are of great assistance in identifying molecular interactions, and elucidating the functional properties of gene networks. It overcomes the weaknesses of in vivo experiments including high cost, large noise, and unrepeatable process. In this paper, we propose an easily applied system, Stepwise Network Inference (SWNI), which integrates deterministic linear model with statistical analysis, and has been tested effectively on both simulated experiments and real gene expression data sets. The study illustrates that connections of gene networks can be significantly detected via SWNI with high confidence, when single gene perturbation experiments are performed complying with the algorithm requirements. In particular, our algorithm shows efficiency and outperforms the existing ones presented in this paper when dealing with large-scale sparse networks without any prior knowledge.
authors.affiliation: School of Computer Engineering and Science, Shanghai University, 149 Yanchang Road, Zhabei District, Shanghai 200072, People's Republic of China.
authors.forename: Luwen
authors.initials: L
authors.lastname: Zhang
authors.forename: Mei
authors.initials: M
authors.lastname: Xiao
authors.forename: Yong
authors.initials: Y
authors.lastname: Wang
authors.forename: Wu
authors.initials: W
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics; D030541:Databases, Genetic; D004926:Escherichia coli / Q000235:genetics; D053263:Gene Regulatory Networks / Q000235:genetics*; D005821:Genetic Techniques; D008957:Models, Genetic; D012015:Reference Standards