Identifying differentially expressed pathways via a mixed integer linear programming model

IET Systems Biology
Y-Q QiuLuonan Chen

Abstract

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.

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Citations

Mar 1, 2012·IET Systems Biology·Z-P LiuL Chen
Jan 31, 2012·Briefings in Bioinformatics·Jinlian WangHabtom W Ressom
Jun 21, 2011·Bioinformatics·Phuong DaoS Cenk Sahinalp
Jul 5, 2012·Cold Spring Harbor Perspectives in Medicine·Sonja W ScholzHoward J Federoff
Sep 25, 2010·BMC Systems Biology·Zikai WuLuonan Chen
Feb 2, 2013·Advanced Drug Delivery Reviews·Haisu Ma, Hongyu Zhao
Sep 15, 2014·Computational Biology and Chemistry·Naruemon Pratanwanich, Pietro Lio
Mar 16, 2013·Molecular BioSystems·Zikai WuLuonan Chen

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