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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 340758, 7 pages
Research Article

Integrating Gene Expression and Protein Interaction Data for Signaling Pathway Prediction of Alzheimer’s Disease

1Information Engineering College, Shanghai Maritime University, Shanghai 201306, China
2DNJ Pharma and Rowan University, Glassboro, NJ 08028, USA
3Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received 23 February 2014; Accepted 18 March 2014; Published 9 April 2014

Academic Editor: Tao Huang

Copyright © 2014 Wei Kong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Discovering the signaling pathway and regulatory network would provide significant advance in genome-wide understanding of pathogenesis of human diseases. Despite the rich transcriptome data, the limitation for microarray data is unable to detect changes beyond transcriptional level and insufficient in reconstructing pathways and regulatory networks. In our study, protein-protein interaction (PPI) data is introduced to add molecular biological information for predicting signaling pathway of Alzheimer’s disease (AD). Combining PPI with gene expression data, significant genes are selected by modified linear regression model firstly. Then, according to the biological researches that inflammation reaction plays an important role in the generation and deterioration of AD, NF-κB (nuclear factor-kappa B), as a significant inflammatory factor, has been selected as the beginning gene of the predicting signaling pathway. Based on that, integer linear programming (ILP) model is proposed to reconstruct the signaling pathway between NF-κB and AD virulence gene APP (amyloid precursor protein). The results identify 6 AD virulence genes included in the predicted inflammatory signaling pathway, and a large amount of molecular biological analysis shows the great understanding of the underlying biological process of AD.