Pe'er D, Regev A, Elidan G, Friedman N
School of Computer Science & Engineering, Hebrew University, Jerusalem, 91904, Israel.
Bioinformatics. 2001;17 Suppl 1:S215-24. doi: 10.1093/bioinformatics/17.suppl_1.s215.
Genome-wide expression profiles of genetic mutants provide a wide variety of measurements of cellular responses to perturbations. Typical analysis of such data identifies genes affected by perturbation and uses clustering to group genes of similar function. In this paper we discover a finer structure of interactions between genes, such as causality, mediation, activation, and inhibition by using a Bayesian network framework. We extend this framework to correctly handle perturbations, and to identify significant subnetworks of interacting genes. We apply this method to expression data of S. cerevisiae mutants and uncover a variety of structured metabolic, signaling and regulatory pathways.
遗传突变体的全基因组表达谱提供了细胞对扰动反应的各种测量值。对此类数据的典型分析可识别受扰动影响的基因，并使用聚类方法对功能相似的基因进行分组。在本文中，我们使用贝叶斯网络框架发现了基因之间更精细的相互作用结构，如因果关系、介导作用、激活和抑制作用。我们扩展了这个框架，以正确处理扰动，并识别相互作用基因的重要子网。我们将此方法应用于酿酒酵母突变体的表达数据，揭示了各种结构化的代谢、信号传导和调节途径。
