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A network-based method for predicting disease-associated enhancers
1School of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam.
Plos One
|December 8, 2021
Summary
We developed RWDisEnh, a computational method to predict disease-enhancer associations by analyzing shared target genes. This approach identifies novel disease-associated enhancers, advancing our understanding of gene regulation in disease.
Area of Science:
- Genomics
- Computational Biology
- Disease Association Studies
Background:
- Enhancers regulate gene transcription, and their aberrant activity is linked to diseases.
- A limited number of identified enhancers are currently associated with diseases, necessitating new predictive methods.
Purpose of the Study:
- To develop a computational method for predicting associations between diseases and enhancers.
- To leverage enhancer-target gene relationships for disease association prediction.
Main Methods:
- Constructed an enhancer functional interaction network based on shared target genes.
- Developed RWDisEnh, a network diffusion method using random walks with restart.
- Integrated disease similarities and known disease-enhancer associations into a heterogeneous network.
Main Results:
- RWDisEnh effectively measures disease-enhancer associations using a network diffusion approach.
- The method outperformed existing network diffusion (PageRank with Priors) and neighborhood-based (MaxLink) methods.
- RWDisEnh successfully predicted novel enhancers associated with diseases.
Conclusions:
- RWDisEnh presents a promising computational approach for predicting disease-enhancer associations.
- This method can aid in identifying novel regulatory elements involved in disease pathogenesis.
