Identifying differentially expressed pathways via a mixed integer linear programming model.
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.
Qiu
Y-Q
YQ
Chinese Academy of Sciences, Academy of Mathematics and Systems Science, Beijing, People's Republic of China. yqqiu@amss.ac.cn
Zhang
S
S
Zhang
X-S
XS
Chen
L
L
D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics; D005260:Female; D053263:Gene Regulatory Networks / Q000502:physiology*; D006801:Humans; D009362:Neoplasm Metastasis / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D012372:ROC Curve; D015398:Signal Transduction / Q000502:physiology*; D049490:Systems Biology / Q000379:methods*; D015003:Yeasts / Q000235:genetics