Background:
Finding the pathogenic gene is very important for understanding the pathogenesis
of the disease, locating effective drug targets and improving the clinical level of medical treatment.
However, the existing methods for finding the pathogenic genes still have limitations, for instance
the computational complexity is high, and the combination of multiple genes and pathways has
not been considered to search for highly related pathogenic genes and so on.
Methods:
We propose a pathogenic genes selection model of genetic disease based on Network Motifs
Slicing Feedback (NMSF). We find a point set which makes the conductivity of the motif minimum
then use it to substitute for the original gene pathway network. Based on the NMSF, we propose a new
pathogenic genes selection model to expand pathogenic gene set.
Results:
According to the gene set we have obtained, selection of key genes will be more accurate and
convincing. Finally, we use our model to screen the pathogenic genes and key pathways of liver cancer
and lung cancer, and compare the results with the existing methods.
Conclusion:
The main contribution is to provide a method called NMSF which simplifies the gene
pathway network to make the selection of pathogenic gene simple and feasible. The fact shows our result
has a wide coverage and high accuracy and our model has good expeditiousness and robustness.
