Title:A Heterogeneous Networks Fusion Algorithm Based on Local Topological Information for Neurodegenerative Disease
VOLUME: 12 ISSUE: 5
Author(s):Xue Jiang, Han Zhang*, Xiongwen Quan and Yanbin Yin
Affiliation:College of Computer and Control Engineering, Nankai University, Tianjin, College of Computer and Control Engineering, Nankai University, Tianjin, P.O. Box 300350, College of Computer and Control Engineering, Nankai University, Tianjin, Department of Biological Sciences, Northern Illinois University, DeKalb, Illinois
Keywords:Disease-related gene prediction, gene co-expression, heterogeneous networks fusion, disease-specific gene network,
local topological structure, neurodegenerative disease.
Abstract:Background: Predicting disease-related genes based on gene network, is helpful for revealing
the interactions between genes under complex disease phenotypes. There usually exist numerous
noisy connections in gene co-expression network, making the simulation results greatly depart from the
real situation. Most research focus on developing better similarity measures between genes to construct
more accurate gene co-expression network. However, with the emergence of various types of biological
networks and the urgent needs of precision medicine, the single source gene co-expression network is
no longer able to meet the accuracy requirement for disease-related gene prediction.
Objective: We have proposed a heterogeneous networks fusion algorithm based on local topological information
(HNFLTI) to reconstruct a disease-specific gene network. We have also designed a novel
framework based on the HNFLTI to identify the disease-related genes.
Method: Firstly, HNFLTI modifies the weight of each edge that connects any two nodes in the gene coexpression
network according to the topological structure similarity between the local sub-networks in
different source networks. Secondly, HNFLTI filters out redundancy connections by a filtration step,
obtaining the disease-specific gene network. Finally, we conduct label progradation on the diseasespecific
gene network to predict the disease-related genes.
Results: Experimental results demonstrate that the prediction accuracy of disease-related genes is significantly
improved using the disease-specific gene network compared with that of the gene coexpression
network.
Conclusion: Since the molecular mechanisms of neurodegenerative disease are very complex, it is difficult
to identify the disease-related genes using traditional computational methods. We reconstruct a
disease-specific gene network using the HNFLTI to improve the prediction accuracy of disease-related
genes and to conduct exploratory analysis of the molecular mechanism of the disease. The method
might be one of the best choices when user wants to obtain reliable interactions between genes under
complex disease phenotype.
