System for detecting hypertension-related genes and analyzing gene functions

Abstract

The invention discloses a system for detecting hypertension related genes and analyzing gene functions. Comprising a gene preprocessing unit used for preprocessing hypertension gene expression data to obtain a gene expression matrix; the gene sample analysis unit is used for analyzing a sample distribution condition based on the gene expression matrix; the gene difference analysis unit is used for screening differential genes with significant changes on the basis of the gene expression matrix to obtain a gene difference matrix; the gene module analysis unit is used for determining gene modules and screening key modules through a weighted gene co-expression network method based on the gene expression matrix; the Hub gene analysis unit is used for screening Hub genes in the key genes; and the gene function analysis unit is used for carrying out function enrichment analysis and network analysis of a pathway and gene relationship on the differentially expressed gene or the gene module. The method can assist scientific researchers who are not familiar with hypertension gene related databases and analysis processes in accurately and effectively finding genes and pathways related to complex diseases such as hypertension.

Description

technical field [0001] The invention belongs to the fields of bioinformatics and data mining, and in particular relates to a system for detecting hypertension-related genes and gene function analysis. Background technique [0002] Hypertension is a chronic disease with high morbidity and mortality worldwide, and is an important factor leading to the death of cardiovascular patients. About 17 million people die from cardiovascular diseases in the world every year, and more than 50% of them are caused by high blood pressure. Despite the continuous advancement of medical technology, effective prevention and treatment of hypertension has not yet been achieved, and the morbidity and mortality are still on the rise, and the etiology has not yet been fully elucidated. Therefore, more tools and methods are needed to identify biomarkers associated with hypertension. [0003] At present, using existing technologies to analyze hypertension gene expression data has become an important...

AI Technical Summary

Problems solved by technology

However, many researchers engaged in hypertension-related research do not have a background in bioinformatics, programming skills and statistics. Although most of the massive data resources are open and free, they have different standards and uneven quality levels, and cannot be used directly. It brings great inconvenience to scientific researchers. Although there are some programs or websites that can analyze and visualize the results of gene expression data, they have some limitations such as single function, complex operation or certain programming ability.

In recent years, genomics studies have shown that there are complex relationships between genes and related expression products. The regulation of gene expression is not isolated. They regulate and correlate with each other to form an interactive network. The single gene or differential gene of the expression data cannot meet the actual needs. How to discover more new methods and can assist researchers, especially experiments without bioinformatics background, and clinical personnel to effectively identify key genes is one of the current problems

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