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- Xumeng Li, Frank A Feltus, Xiaoqian Sun, James Z Wang, and Feng Luo.
- School of Computing, Clemson University, Clemson, SC, USA.
- Proteomics. 2011 Oct 1; 11 (19): 3845-52.
AbstractIdentification of genes and pathways involved in diseases and physiological conditions is a major task in systems biology. In this study, we developed a novel non-parameter Ising model to integrate protein-protein interaction network and microarray data for identifying differentially expressed (DE) genes. We also proposed a simulated annealing algorithm to find the optimal configuration of the Ising model. The Ising model was applied to two breast cancer microarray data sets. The results showed that more cancer-related DE sub-networks and genes were identified by the Ising model than those by the Markov random field model. Furthermore, cross-validation experiments showed that DE genes identified by Ising model can improve classification performance compared with DE genes identified by Markov random field model.Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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