Prediction of Genetic Factors of Hyperthyroidism Based on Gene Interaction Network - Shen, Fei; Cai, Wensong; Gan, Xiaoxiong; Feng, Jianhua; Chen, Zhen; Guo, Mengli; Wei, Fang; Cao, Jie; Xu, Bo (Frontiers in Cell and Developmental Biology, 2021)
The number of hyperthyroidism patients is increasing these years. As a disease that can lead to cardiovascular disease, it brings great potential health risks to humans. Since hyperthyroidism can induce the occurrence of many diseases, studying its genetic factors will promote the early diagnosis and treatment of hyperthyroidism and its related diseases. Previous studies have used genome-wide association analysis (GWAS) to identify genes related to hyperthyroidism. However, these studies only identify significant sites related to the disease from a statistical point of view and ignore the complex regulation relationship between genes. In addition, mutation is not the only genetic factor of causing hyperthyroidism. Identifying hyperthyroidism-related genes from gene interactions would help researchers discover disease mechanism. In this paper, we purposed a novel machine learning method for identifying hyperthyroidism-related genes based on gene interaction network. Random Walk was implemented to encode the gene interaction network. The features of genes were the regulation relationship between genes and non-coding RNAs. Finally, multiple Relevance Vector Machines (RVM) were applied to identify hyperthyroidism-related genes.