Inferring gene correlation networks from transcription factor binding sites
Gene expression is a highly regulated biological process that is fundamental to the existence of phenotypes of any living organism. The regulatory relations are usually modeled as a network; simply, every gene is modeled as a node and relations are shown as edges between two related genes. This pape...
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| Veröffentlicht in: | Genes & Genetic Systems 2013/10/01, Vol.88(5), pp.301-309 |
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| container_title | Genes & Genetic Systems |
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| creator | Mahdevar, Ghasem Nowzari-Dalini, Abbas Sadeghi, Mehdi |
| description | Gene expression is a highly regulated biological process that is fundamental to the existence of phenotypes of any living organism. The regulatory relations are usually modeled as a network; simply, every gene is modeled as a node and relations are shown as edges between two related genes. This paper presents a novel method for inferring correlation networks, networks constructed by connecting co-expressed genes, through predicting co-expression level from genes promoter’s sequences. According to the results, this method works well on biological data and its outcome is comparable to the methods that use microarray as input. The method is written in C++ language and is available upon request from the corresponding author. |
| doi_str_mv | 10.1266/ggs.88.301 |
| format | Article |
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Syst.</addtitle><description>Gene expression is a highly regulated biological process that is fundamental to the existence of phenotypes of any living organism. The regulatory relations are usually modeled as a network; simply, every gene is modeled as a node and relations are shown as edges between two related genes. This paper presents a novel method for inferring correlation networks, networks constructed by connecting co-expressed genes, through predicting co-expression level from genes promoter’s sequences. According to the results, this method works well on biological data and its outcome is comparable to the methods that use microarray as input. The method is written in C++ language and is available upon request from the corresponding author.</description><subject>Algorithms</subject><subject>Binding Sites</subject><subject>Gene Expression</subject><subject>Gene Regulatory Networks</subject><subject>neighbor joining</subject><subject>neural networks</subject><subject>Neural Networks (Computer)</subject><subject>Promoter Regions, Genetic</subject><subject>Protein Binding</subject><subject>Saccharomyces cerevisiae - genetics</subject><subject>Saccharomyces cerevisiae - metabolism</subject><subject>Saccharomyces cerevisiae Proteins - genetics</subject><subject>Saccharomyces cerevisiae Proteins - metabolism</subject><subject>self – organizing maps</subject><subject>Software</subject><subject>Transcription Factors - genetics</subject><subject>Transcription Factors - metabolism</subject><issn>1341-7568</issn><issn>1880-5779</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkV1rFDEUhoNY7Ife-ANkwBspzJrPmeTCi1LaWlrwRq9DJnMyzTqbrEkW6b83425X8CYnJE-ec3iD0HuCV4R23edpyispVwyTV-iMSIlb0ffqdd0zTtpedPIUnee8xphiJdkbdEp5pzhT7Aw93AcHKfkwNRMEaGxMCWZTfAxNgPI7pp-5cSlumpJMyDb57d87Z2yJqRl8GJe32RfIb9GJM3OGd4d6gX7c3ny__to-fru7v756bG3HWWmVlb1yvRWSCyyhJz3QcSB8GIRzeFDE8t4RJ5kFTkcwRhE5WjMSPnaYAmYX6NPeu03x1w5y0RufLcyzCRB3WRNBGOYd60hFP_6HruMuhTrdQlFJCZGiUpd7yqaYcwKnt8lvTHrWBOslYl0j1lLqGnGFPxyUu2ED4xF9ybQCd3ug3npr5hhmH-BfY_vEqu45a1ptGuP6YaIWrnHVL4uilDFFFtOXvWmdi5ng2Mqk4u0ML1OJw2jHc_tkkobA_gAgVqQn</recordid><startdate>2013</startdate><enddate>2013</enddate><creator>Mahdevar, Ghasem</creator><creator>Nowzari-Dalini, Abbas</creator><creator>Sadeghi, Mehdi</creator><general>The Genetics Society of Japan</general><general>Japan Science and Technology Agency</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SS</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>2013</creationdate><title>Inferring gene correlation networks from transcription factor binding sites</title><author>Mahdevar, Ghasem ; 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| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; J-STAGE (Japan Science & Technology Information Aggregator, Electronic) Freely Available Titles - Japanese |
| subjects | Algorithms Binding Sites Gene Expression Gene Regulatory Networks neighbor joining neural networks Neural Networks (Computer) Promoter Regions, Genetic Protein Binding Saccharomyces cerevisiae - genetics Saccharomyces cerevisiae - metabolism Saccharomyces cerevisiae Proteins - genetics Saccharomyces cerevisiae Proteins - metabolism self – organizing maps Software Transcription Factors - genetics Transcription Factors - metabolism |
| title | Inferring gene correlation networks from transcription factor binding sites |
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