{"title":"A network-based analysis of ischemic stroke using parallel microRNA-mRNA expression profiles","authors":"Yingying Wang, Yunpeng Cai","doi":"10.1109/GlobalSIP.2014.7032361","DOIUrl":null,"url":null,"abstract":"Ischemic stroke is one of the leading causes of death and disability worldwide with inflammatory-immune responses in blood and brain damage. To analyze the severity of ischemic stroke, many studies were performed to find biomarkers based on samples from animal brain tissue models. In this work, we used parallel microRNA-mRNA expression profile from rat brain tissues to construct a network based on negative correlation calculation. PageRank algorithm was used to calculate the importance of network nodes. 14 genes were chosen as featured biomarkers. Results showed these genes were significant on biological levels which indicated us that the biomarkers chosen based on animal models may be helpful in stroke diagnosis, etiology and pathogenesis, thus guiding acute treatment and development of new treatments in the future.","PeriodicalId":362306,"journal":{"name":"2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GlobalSIP.2014.7032361","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Ischemic stroke is one of the leading causes of death and disability worldwide with inflammatory-immune responses in blood and brain damage. To analyze the severity of ischemic stroke, many studies were performed to find biomarkers based on samples from animal brain tissue models. In this work, we used parallel microRNA-mRNA expression profile from rat brain tissues to construct a network based on negative correlation calculation. PageRank algorithm was used to calculate the importance of network nodes. 14 genes were chosen as featured biomarkers. Results showed these genes were significant on biological levels which indicated us that the biomarkers chosen based on animal models may be helpful in stroke diagnosis, etiology and pathogenesis, thus guiding acute treatment and development of new treatments in the future.
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