A Network Integration-based Method For Identification Of Complex Disease Risk Genes

In recent years,genome-wide association research(GWAS)has become one of the main research methods in genetics,and has been successfully applied to the risk identification of multiple complex diseases and identified a number of related association genomes.These findings provide more opportunities for the research of complex diseases.But biological information extraction based on the all-factor group is not very simple,and most biological genes are located in non-coding regions and are not closely linked to other genes.In this regard,with the help of network integration,the sorting of priority genes,connecting the gene network with GWAS,can effectively solve the identification problem of risk genes.In this paper,the REGENT method and gene/microRNA identification algorithm as the dominant research method.REGENT is a method of integrating multiple genetic networks and GWAS data,which prioritizes complex disease risk genes,exporting effective inference al-gorithms through hierarchical statistical models and combining GWAS data from complex diseases to correctly identify complex disease pathogenic genes,which takes precedence over existing methods.The application of the gene/microRNA recognition algorithm can not only systematically analyze the gene data of complex diseases,but also express the gene/microRNA,both of which are highly innovative.