Li X, Feltus FA, Sun X, Wang JZ, Luo F. Identifying differentially expressed genes in cancer patients using a non-parameter Ising model.
Proteomics 2011;
11:3845-52. [PMID:
21761563 DOI:
10.1002/pmic.201100180]
[Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2011] [Revised: 05/22/2011] [Accepted: 06/10/2011] [Indexed: 11/06/2022]
Abstract
Identification 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.
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