Chinese Academy of Sciences, Academy of Mathematics and Systems Science, Beijing, People's Republic of China.
IET Syst Biol. 2009 Nov;3(6):475-86. doi: 10.1049/iet-syb.2008.0155.
The identification of genes and pathways involved in biological processes is a central problem in systems biology. Recent microarray technologies and other high-throughput experiments provide information which sheds light on this problem. In this article, the authors propose a new computational method to detect active pathways, or identify differentially expressed pathways via integration of gene expression and interactomic data in a sophisticated and efficient manner. Specifically, by using signal-to-noise ratio to measure the differentially expressed level of networks, this problem is formulated as a mixed integer linear programming problem (MILP). The results on yeast and human data demonstrate that the proposed method is more accurate and robust than existing approaches.
在系统生物学中，识别涉及生物过程的基因和途径是一个核心问题。最近的微阵列技术和其他高通量实验提供了一些信息，有助于解决这个问题。在本文中，作者提出了一种新的计算方法，通过以一种复杂而有效的方式整合基因表达和相互作用组数据，来检测活性途径或识别差异表达途径。具体来说，通过使用信噪比来测量网络的差异表达水平，将这个问题表示为一个混合整数线性规划问题（MILP）。在酵母和人类数据上的结果表明，与现有方法相比，所提出的方法更准确、更稳健。
