{"title":"重建肝细胞癌发生过程的动态基因调控网络","authors":"Hailong Zhu, R. Rao, Luonan Chen","doi":"10.1109/BIBMW.2012.6470298","DOIUrl":null,"url":null,"abstract":"A crucial work of investigating the mechanisms of cancer development is to unraveling the dynamic nature of gene regulation during the disease process. However, reconstruction of dynamic gene regulatory network requires time-sequence samples of a biological process, which are not available for many bio-medical problems. In this paper, we propose a dynamic cascaded method to reconstruct dynamic gene networks from sample-based transcriptional data. Our method is based on two biologically plausible assumptions, which can characterise the dynamic and continuous nature of gene transcription. Our approach was successfully applied to reconstruct the dynamic gene networks of hepatocellular carcinoma (HCC) progression. The derived HCC networks were verified by functional analysis and network enrichment analysis.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Reconstructing dynamic gene regulatory network for the development process of hepatocellular carcinoma\",\"authors\":\"Hailong Zhu, R. Rao, Luonan Chen\",\"doi\":\"10.1109/BIBMW.2012.6470298\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A crucial work of investigating the mechanisms of cancer development is to unraveling the dynamic nature of gene regulation during the disease process. However, reconstruction of dynamic gene regulatory network requires time-sequence samples of a biological process, which are not available for many bio-medical problems. In this paper, we propose a dynamic cascaded method to reconstruct dynamic gene networks from sample-based transcriptional data. Our method is based on two biologically plausible assumptions, which can characterise the dynamic and continuous nature of gene transcription. Our approach was successfully applied to reconstruct the dynamic gene networks of hepatocellular carcinoma (HCC) progression. The derived HCC networks were verified by functional analysis and network enrichment analysis.\",\"PeriodicalId\":6392,\"journal\":{\"name\":\"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBMW.2012.6470298\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBMW.2012.6470298","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Reconstructing dynamic gene regulatory network for the development process of hepatocellular carcinoma
A crucial work of investigating the mechanisms of cancer development is to unraveling the dynamic nature of gene regulation during the disease process. However, reconstruction of dynamic gene regulatory network requires time-sequence samples of a biological process, which are not available for many bio-medical problems. In this paper, we propose a dynamic cascaded method to reconstruct dynamic gene networks from sample-based transcriptional data. Our method is based on two biologically plausible assumptions, which can characterise the dynamic and continuous nature of gene transcription. Our approach was successfully applied to reconstruct the dynamic gene networks of hepatocellular carcinoma (HCC) progression. The derived HCC networks were verified by functional analysis and network enrichment analysis.