Related Experiment Videos
Intrinsic-overlapping co-expression module detection with application to Alzheimer's Disease
Hazel Nicolette Manners1, Swarup Roy2, Jugal K Kalita3
1Department of Information Technology, North Eastern Hill University, Shillong, Meghalaya, India.
Computational Biology and Chemistry
|November 23, 2018
Summary
This study introduces CluViaN, a novel method for identifying overlapping functional gene modules in networks. It pinpoints key genes in Alzheimer's Disease (AD) pathways, offering potential for targeted therapies.
Area of Science:
- Genomics
- Systems Biology
- Computational Biology
Background:
- Complex diseases arise from intricate gene interactions within molecular pathways.
- Functional modules (gene subnetworks) share biological functions and are implicated in disease pathogenesis.
- Identifying central genes within these modules can aid in understanding disease mechanisms and developing personalized medicine.
Purpose of the Study:
- To develop a method for detecting intrinsic and overlapping functional gene modules.
- To identify key genes associated with Alzheimer's Disease (AD) through functional motif analysis.
- To propose a novel non-exclusive clustering approach for gene co-expression networks.
Main Methods:
- Developed CluViaN (Clustering Via Network), a non-exclusive clustering algorithm for gene co-expression networks.
- Constructed networks using microarray expression profiles.
- Applied topological analysis to identify hub genes within disease-specific modules.
- Validated findings using two distinct AD phenotype datasets.
Main Results:
- CluViaN successfully detects intrinsic and overlapping motifs across species, outperforming existing methods.
- Identified significant AD-specific modules and ranked them by disease pathway gene involvement.
- Pinpointed central genes like PSEN1, APP, and NDUFB2, and novel hub genes PML and MUC4, implicated in AD.
- Demonstrated the utility of CluViaN in uncovering disease-relevant genetic architecture.
Conclusions:
- CluViaN offers an integrated approach to identify overlapping and intrinsic functional gene modules.
- The identified hub genes, including novel candidates like PML and MUC4, warrant further investigation for their role in AD.
- This approach facilitates the discovery of potential therapeutic targets for complex diseases like Alzheimer's.