Abstract
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.
Highlights
The human genome project has been accomplished and has achieved great success, and new methods that verify gene function with high-throughput have been applied, studying genetic problems that induce diseases is still one of the major challenges facing humanity [1]
Most recent studies at recognizing disease gene that involves linkage analysis or association studies have resulted in a genomic interval of 0.5 cm to 10 cm, which contains 300 genes [2, 3]
The selection of functional candidate genes and prioritization candidate genes has been one of the keys in recognizing disease genes because several reorganization approaches are based on the functions of these genes
Summary
The human genome project has been accomplished and has achieved great success, and new methods that verify gene function with high-throughput have been applied, studying genetic problems that induce diseases is still one of the major challenges facing humanity [1]. The traditional gene mapping method is based on family genetic disease. Genes inducing diseases are located in a chain interval. Using the biological experiment method to identify each gene located in a chain interval requires a large number of human resources and capital support [4]. The study of candidate association works well when using a set of known functional candidate genes, which have a clear biological relationship to the disease [6]. The selection of functional candidate genes and prioritization candidate genes has been one of the keys in recognizing disease genes because several reorganization approaches are based on the functions of these genes
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