A New Method for Predicting Disease-Causing Genes
Author Information
Author(s): Guo Xingli, Gao Lin, Wei Chunshui, Yang Xiaofei, Zhao Yi, Dong Anguo
Primary Institution: Xidian University
Hypothesis
Can integrating heterogeneous networks improve the prediction of disease-causing genes?
Conclusion
The proposed method significantly outperforms existing methods in predicting disease-causing genes.
Supporting Evidence
- The method ranked the correct gene as one of the top ten in 622 of 1,428 cases.
- It was tested with 10-fold cross-validation on 1,126 diseases.
- The method was applied to study breast cancer, Alzheimer disease, and diabetes mellitus type 2.
Takeaway
This study created a new way to find genes that cause diseases by looking at how diseases and genes are connected in networks.
Methodology
The method integrates disease similarity and protein-protein interaction networks to compute disease-gene association scores using an iterative algorithm.
Potential Biases
The method may be biased towards well-connected genes in the protein-protein interaction network.
Limitations
The method relies on protein-protein interaction data, which may have low coverage and high false positive rates.
Statistical Information
P-Value
0.0001
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website