Construction And Identification Model Of Abnormal Expression Genes In Plateau Hypertension

Hypertension is a disease with high morbidity and mortality in the world.It is one of the most common cardiovascular diseases at present.Since 1990 s,the research of hypertension gene becomes a hot topic in the world.The molecular biology research of it is mainly found with primary high blood pressure related genes in a particular race.In addition to genetic factors,environmental factors also have a critical impact on the incidence of essential hypertension.High altitude hypertension is a unique disease in plateau area,which is the result of interaction of environment,diet and heredity.Although the pathogenesis of the disease is still under exploration,it is one of the important factors to induce high altitude and low altitude hypoxia in plateau area.In order to adapt to the environment of high temperature and low oxygen,the people who live in plateau area have a higher degree of adaptive changes in gene and physiological function than people in plain area.In recent years,high-throughput microarray technology has been developed rapidly,especially the wide application of gene chip technology,a large number of gene expression data has been engendered.In order to effectively organize and manage such a large number of gene expression data,various databases have been established.Therefore,the construction of hypertension information database will contributes to the integration of gene expression data related to hypertension,which with a strong pertinence,and can contribute to the study of hypertension diseases for biology and medical workers.It has a great scientific significance and application value in the prevention,diagnosis and treatment of hypertension.The focus of this thesis is to construct a model for identifying and detecting differently expressed genes in R compiler environment,and on the basis of this model,a visual platform for online detection and data analysis of differently expressed genes was constructed,Specific methods are listed as follows: 1,get the GDS data sets and platform data from the GEO database,using R compiler environment to preprocessing,including background processing,log conversion,missing values filling,normalization,etc.preprocessing of the source data is mainly to eliminate background noise and bias system of original data,make the data more accord with the distribution of biological significance,and ensure the accuracy of data analysis;2,We screened the differentially expressed genes with multiple method and t test method,and analysis the cluster rendering image;3,the detection model was constructed using R package,with MySQL and PHP,JavaScript and other web development tools to build online detection and data analysis of differential gene expression visualization platform,the data processing module,data analysis module and visualization module is displayed through the browser.In this paper,we take the GDS2258 and GDS5083 data set as an example,use the construction of the differential expression of gene identification and detection model and online platform for analysis,obtain the data of the differentially expressed genes,and all these show that the model and the online platform are effective and reliable,and can be used for screening differentially expressed genes in other hypertension related data sets.