Approaches for recognizing disease genes based on network.
Diseases are closely related to genes, thus indicating that genetic abnormalities may lead to certain diseases. The recognition of disease genes has long been a goal in biology, which may contribute to the improvement of health care and understanding gene functions, pathways, and interactions. However, few large-scale gene-gene association datasets, disease-disease association datasets, and gene-disease association datasets are available. A number of machine learning methods have been used to recognize disease genes based on networks. This paper states the relationship between disease and gene, summarizes the approaches used to recognize disease genes based on network, analyzes the core problems and challenges of the methods, and outlooks future research direction.
School of Information Science and Technology, Xiamen University, Xiamen 361005, China.
Quan
Q
Zou
School of Information Science and Technology, Xiamen University, Xiamen 361005, China.
Jinjin
J
Li
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
Chunyu
C
Wang
School of Information Science and Technology, Xiamen University, Xiamen 361005, China.
Xiangxiang
X
Zeng
D001185:Artificial Intelligence; D030541:Databases, Genetic; D004194:Disease / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks / Q000235:genetics*; D055106:Genome-Wide Association Study / Q000379:methods; D006801:Humans