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Reconstruct transcription networks by combining gene expression correlations with TF binding sites
One of the major challenges in molecular biology is to understand the precise mechanism by which gene expression is regulated. Reconstruction of transcription networks is essential to modelling this mechanism. We describe a novel approach for building transcription networks from transcription module...
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creator | Ying-Zhe Hsu Yuh-Jyh Hu |
description | One of the major challenges in molecular biology is to understand the precise mechanism by which gene expression is regulated. Reconstruction of transcription networks is essential to modelling this mechanism. We describe a novel approach for building transcription networks from transcription modules by combining expression profile correlations with probabilistic element assessment. To demonstrate its performance, we systematically tested it on 27 transcription modules and reconstructed the transcription network for 6 transcription factors and 15 genes involved in the yeast cell cycle. The experimental results show that our combinatorial approach can better filter false positives to increase the selectivity in prediction of target genes. The regulatory control relationships described by the network reconstructed also mostly agree with those in earlier studies. |
doi_str_mv | 10.1109/MMSE.2003.1254450 |
format | conference_proceeding |
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Reconstruction of transcription networks is essential to modelling this mechanism. We describe a novel approach for building transcription networks from transcription modules by combining expression profile correlations with probabilistic element assessment. To demonstrate its performance, we systematically tested it on 27 transcription modules and reconstructed the transcription network for 6 transcription factors and 15 genes involved in the yeast cell cycle. The experimental results show that our combinatorial approach can better filter false positives to increase the selectivity in prediction of target genes. The regulatory control relationships described by the network reconstructed also mostly agree with those in earlier studies.</description><identifier>ISBN: 0769520316</identifier><identifier>ISBN: 9780769520315</identifier><identifier>DOI: 10.1109/MMSE.2003.1254450</identifier><language>eng</language><publisher>IEEE</publisher><subject>Bioinformatics ; Biological system modeling ; Biology computing ; DNA ; Gene expression ; Genetics ; Genomics ; Information science ; Large-scale systems ; Sequences</subject><ispartof>Fifth International Symposium on Multimedia Software Engineering, 2003. 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Proceedings</title><addtitle>MMSE</addtitle><description>One of the major challenges in molecular biology is to understand the precise mechanism by which gene expression is regulated. Reconstruction of transcription networks is essential to modelling this mechanism. We describe a novel approach for building transcription networks from transcription modules by combining expression profile correlations with probabilistic element assessment. To demonstrate its performance, we systematically tested it on 27 transcription modules and reconstructed the transcription network for 6 transcription factors and 15 genes involved in the yeast cell cycle. The experimental results show that our combinatorial approach can better filter false positives to increase the selectivity in prediction of target genes. The regulatory control relationships described by the network reconstructed also mostly agree with those in earlier studies.</description><subject>Bioinformatics</subject><subject>Biological system modeling</subject><subject>Biology computing</subject><subject>DNA</subject><subject>Gene expression</subject><subject>Genetics</subject><subject>Genomics</subject><subject>Information science</subject><subject>Large-scale systems</subject><subject>Sequences</subject><isbn>0769520316</isbn><isbn>9780769520315</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2003</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkM1OwzAQhC0hJKD0ARAXv0DCOrbzc0RVC0itkKCcK8feFEPrVF6j0rcnEZ3LzOGbOQxjdwJyIaB5WK3e53kBIHNRaKU0XLAbqMpGFyBFecWmRF8wSGmhK3XN2je0faAUf2ziKZpANvpD8n3gAdOxj9_E2xO3_b71wYct32JAjr-HiEQjZfsYcWfGBvGjT598veAD60aYfEK6ZZed2RFOzz5hH4v5evacLV-fXmaPy8yLSqfMdE2HILUsXem0QQBnCi3qGlUzJGWdQeNEDaAVVKYWTrnWmrK2snO6QDlh9_-7HhE3h-j3Jp425xvkH1mGVnE</recordid><startdate>2003</startdate><enddate>2003</enddate><creator>Ying-Zhe Hsu</creator><creator>Yuh-Jyh Hu</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2003</creationdate><title>Reconstruct transcription networks by combining gene expression correlations with TF binding sites</title><author>Ying-Zhe Hsu ; Yuh-Jyh Hu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-af9fe03536d6d5ae00da25188e49da24cdaead18005407a81d4dbca68c3fd52e3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Bioinformatics</topic><topic>Biological system modeling</topic><topic>Biology computing</topic><topic>DNA</topic><topic>Gene expression</topic><topic>Genetics</topic><topic>Genomics</topic><topic>Information science</topic><topic>Large-scale systems</topic><topic>Sequences</topic><toplevel>online_resources</toplevel><creatorcontrib>Ying-Zhe Hsu</creatorcontrib><creatorcontrib>Yuh-Jyh Hu</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ying-Zhe Hsu</au><au>Yuh-Jyh Hu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Reconstruct transcription networks by combining gene expression correlations with TF binding sites</atitle><btitle>Fifth International Symposium on Multimedia Software Engineering, 2003. Proceedings</btitle><stitle>MMSE</stitle><date>2003</date><risdate>2003</risdate><spage>257</spage><epage>264</epage><pages>257-264</pages><isbn>0769520316</isbn><isbn>9780769520315</isbn><abstract>One of the major challenges in molecular biology is to understand the precise mechanism by which gene expression is regulated. Reconstruction of transcription networks is essential to modelling this mechanism. We describe a novel approach for building transcription networks from transcription modules by combining expression profile correlations with probabilistic element assessment. To demonstrate its performance, we systematically tested it on 27 transcription modules and reconstructed the transcription network for 6 transcription factors and 15 genes involved in the yeast cell cycle. The experimental results show that our combinatorial approach can better filter false positives to increase the selectivity in prediction of target genes. The regulatory control relationships described by the network reconstructed also mostly agree with those in earlier studies.</abstract><pub>IEEE</pub><doi>10.1109/MMSE.2003.1254450</doi><tpages>8</tpages></addata></record> |
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subjects | Bioinformatics Biological system modeling Biology computing DNA Gene expression Genetics Genomics Information science Large-scale systems Sequences |
title | Reconstruct transcription networks by combining gene expression correlations with TF binding sites |
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