Abstract
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.
Keywords: Dysregulated pathway identification, internal correlation, Alzheimer's disease, mutual information, pathway crosstalk, distance correlation.
Combinatorial Chemistry & High Throughput Screening
Title:Dysregulated Pathway Identification of Alzheimer's Disease Based on Internal Correlation Analysis of Genes and Pathways
Volume: 20 Issue: 10
Author(s): Wei Kong*, Xiaoyang Mou, Benteng Di, Jin Deng, Ruxing Zhong and Shuaiqun Wang
Affiliation:
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306,China
Keywords: Dysregulated pathway identification, internal correlation, Alzheimer's disease, mutual information, pathway crosstalk, distance correlation.
Abstract: 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.
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Cite this article as:
Kong Wei *, Mou Xiaoyang , Di Benteng, Deng Jin , Zhong Ruxing and Wang Shuaiqun, Dysregulated Pathway Identification of Alzheimer's Disease Based on Internal Correlation Analysis of Genes and Pathways, Combinatorial Chemistry & High Throughput Screening 2017; 20 (10) . https://dx.doi.org/10.2174/1386207320666171121112235
DOI https://dx.doi.org/10.2174/1386207320666171121112235 |
Print ISSN 1386-2073 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5402 |
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