{"title":"结合不同的数据类型:蛋白质序列和蛋白质结构","authors":"Kejue Jia, R. Jernigan","doi":"10.4172/2153-0602.1000E117","DOIUrl":null,"url":null,"abstract":"With the development of high-throughput, next-generation sequencing and other advanced technologies, a large number of gene expression profiles have been produced. Many of these profiles are available from public databases [1-3]. A challenging research problem that has drawn a lot of attention in the past is to infer gene regulatory networks from the expression data. A gene regulatory network is represented by a directed graph, in which nodes represent transcription factors or mRNA with edges showing transcriptional regulatory relationships between two nodes.","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"10 3 1","pages":"1-2"},"PeriodicalIF":0.0000,"publicationDate":"2015-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Combining Disparate Data Types: Protein Sequences and Protein Structures\",\"authors\":\"Kejue Jia, R. Jernigan\",\"doi\":\"10.4172/2153-0602.1000E117\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the development of high-throughput, next-generation sequencing and other advanced technologies, a large number of gene expression profiles have been produced. Many of these profiles are available from public databases [1-3]. A challenging research problem that has drawn a lot of attention in the past is to infer gene regulatory networks from the expression data. A gene regulatory network is represented by a directed graph, in which nodes represent transcription factors or mRNA with edges showing transcriptional regulatory relationships between two nodes.\",\"PeriodicalId\":15630,\"journal\":{\"name\":\"Journal of Data Mining in Genomics & Proteomics\",\"volume\":\"10 3 1\",\"pages\":\"1-2\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-01-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Data Mining in Genomics & Proteomics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4172/2153-0602.1000E117\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Data Mining in Genomics & Proteomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4172/2153-0602.1000E117","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Combining Disparate Data Types: Protein Sequences and Protein Structures
With the development of high-throughput, next-generation sequencing and other advanced technologies, a large number of gene expression profiles have been produced. Many of these profiles are available from public databases [1-3]. A challenging research problem that has drawn a lot of attention in the past is to infer gene regulatory networks from the expression data. A gene regulatory network is represented by a directed graph, in which nodes represent transcription factors or mRNA with edges showing transcriptional regulatory relationships between two nodes.