Dysregulated pathway identification of Alzheimer’s disease based on internal correlation analysis of genes and pathways

Wei Kong, Xiaoyang Mou, Benteng Di, Jin Deng, Ruxing Zhong, Shuaiqun Wang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Background: Dysregulated pathway identification is an important task which can gain insight into the underlying biological processes of disease. Current pathway-identification methods focus on a set of co-expression genes and single pathways and ignore the correlation between genes and pathways. Objective: This study takes into account the internal correlations not only between genes but also pathways to explore the underlying dysregulated pathways of Alzheimer’s disease (AD), the most common form of dementia. Methods: In order to find the significantly differential genes for AD, mutual information (MI) is used to measure interdependencies between genes other than expression valves. Then, by integrating the topology information from KEGG, the significant pathways involved in the feature genes are identified. Next, the distance correlation (DC) is applied to measure the pairwise pathway crosstalks since DC has the advantage of detecting nonlinear correlations when compared to Pearson correlation. Finally, the pathway pairs with significantly different correlations between normal and AD samples are known as dysregulated pathways. Results: We identified 33 dysregulated pathway pairs related to AD in which the crosstalks score calculated by DC greatly changed from normal to AD samples. The molecular biology analysis demonstrated that many dysregulated pathways related to AD pathogenesis have been discovered successfully by the internal correlation detection. Conclusion: Our studies on the identification of the dysregulated pathways in the development and deterioration of AD will help to find new effective target genes which are closely related to the pathogenesis of AD and provide important theoretical guidance for drug design.

Original languageEnglish (US)
Pages (from-to)896-904
Number of pages9
JournalCombinatorial Chemistry and High Throughput Screening
Issue number10
StatePublished - Dec 1 2017

All Science Journal Classification (ASJC) codes

  • Drug Discovery
  • Computer Science Applications
  • Organic Chemistry


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