Estimating Gene Networks from Expression Data and Binding Location Data via Boolean Networks
In this paper, we propose a computational method for estimating gene networks by the Boolean network model. The Boolean networks have some practical problems in analyzing DNA microarray gene expression data: One is the choice of threshold value for discretization of gene expression data, since expre...
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| creator | Hirose, Osamu Nariai, Naoki Tamada, Yoshinori Bannai, Hideo Imoto, Seiya Miyano, Satoru |
| description | In this paper, we propose a computational method for estimating gene networks by the Boolean network model. The Boolean networks have some practical problems in analyzing DNA microarray gene expression data: One is the choice of threshold value for discretization of gene expression data, since expression data take continuous variables. The other problem is that it is often the case that the optimal gene network is not determined uniquely and it is difficult to choose the optimal one from the candidates by using expression data only. To solve these problems, we use the binding location data produced by Lee et al.[8] together with expression data and illustrate a strategy to decide the optimal threshold and gene network. To show the effectiveness of the proposed method, we analyze Saccharomyces cerevisiae cell cycle gene expression data as a real application. |
| doi_str_mv | 10.1007/11424857_38 |
| format | Conference Proceeding |
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The Boolean networks have some practical problems in analyzing DNA microarray gene expression data: One is the choice of threshold value for discretization of gene expression data, since expression data take continuous variables. The other problem is that it is often the case that the optimal gene network is not determined uniquely and it is difficult to choose the optimal one from the candidates by using expression data only. To solve these problems, we use the binding location data produced by Lee et al.[8] together with expression data and illustrate a strategy to decide the optimal threshold and gene network. 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The Boolean networks have some practical problems in analyzing DNA microarray gene expression data: One is the choice of threshold value for discretization of gene expression data, since expression data take continuous variables. The other problem is that it is often the case that the optimal gene network is not determined uniquely and it is difficult to choose the optimal one from the candidates by using expression data only. To solve these problems, we use the binding location data produced by Lee et al.[8] together with expression data and illustrate a strategy to decide the optimal threshold and gene network. To show the effectiveness of the proposed method, we analyze Saccharomyces cerevisiae cell cycle gene expression data as a real application.</description><subject>Applied sciences</subject><subject>Boolean Function</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Expression Data</subject><subject>Gene Expression Data</subject><subject>Gene Network</subject><subject>Score Function</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540258620</isbn><isbn>9783540258629</isbn><isbn>9783540258605</isbn><isbn>3540258604</isbn><isbn>9783540320456</isbn><isbn>3540320458</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNkDlPw0AUhJdLIoRU_IFtKCgMe3mPEkIISBE00CFZz_ZzZJLsWrsWx7_HUQDxmlfMNyPNEHLG2SVnzFxxroSyuSmk3SMTZ6zMFZOCqVzvkxHXnGdSKndATraCyK0W7JCMmGQic0bJYzJJ6Y0NJ7m2Uo7I6yz17Qb61i_pHD3SR-w_Qlwl2sSwobPPLmJKbfD0Fnqg4Gt60_p6iy9CNfh-lfcW6E0IawT_l3FKjhpYJ5z8_DF5uZs9T--zxdP8YXq9yDrBXZ-B4wioFTbKlkxznSMoy02TlxLc0MRaVwqJBqG2yqGp66pqmlIoZXSFtRyT811uB6mCdRPBV20qujg0i18F184xx_XAXey4NEh-ibEoQ1ilgrNiO2_xb175DQWfZ_A</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Hirose, Osamu</creator><creator>Nariai, Naoki</creator><creator>Tamada, Yoshinori</creator><creator>Bannai, Hideo</creator><creator>Imoto, Seiya</creator><creator>Miyano, Satoru</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2005</creationdate><title>Estimating Gene Networks from Expression Data and Binding Location Data via Boolean Networks</title><author>Hirose, Osamu ; Nariai, Naoki ; Tamada, Yoshinori ; Bannai, Hideo ; Imoto, Seiya ; Miyano, Satoru</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p219t-a91eae64ef48b06165ea4817f5b3a9354889b23e7ead849e7ddccffb24476ced3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Applied sciences</topic><topic>Boolean Function</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Expression Data</topic><topic>Gene Expression Data</topic><topic>Gene Network</topic><topic>Score Function</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hirose, Osamu</creatorcontrib><creatorcontrib>Nariai, Naoki</creatorcontrib><creatorcontrib>Tamada, Yoshinori</creatorcontrib><creatorcontrib>Bannai, Hideo</creatorcontrib><creatorcontrib>Imoto, Seiya</creatorcontrib><creatorcontrib>Miyano, Satoru</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hirose, Osamu</au><au>Nariai, Naoki</au><au>Tamada, Yoshinori</au><au>Bannai, Hideo</au><au>Imoto, Seiya</au><au>Miyano, Satoru</au><au>Gervasi, Osvaldo</au><au>Gavrilova, Marina L.</au><au>Taniar, David</au><au>Laganà, Antonio</au><au>Mun, Youngsong</au><au>Lee, Heow Pueh</au><au>Tan, Chih Jeng Kenneth</au><au>Kumar, Vipin</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Estimating Gene Networks from Expression Data and Binding Location Data via Boolean Networks</atitle><btitle>Computational Science and Its Applications – ICCSA 2005</btitle><date>2005</date><risdate>2005</risdate><spage>349</spage><epage>356</epage><pages>349-356</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540258620</isbn><isbn>9783540258629</isbn><isbn>9783540258605</isbn><isbn>3540258604</isbn><eisbn>9783540320456</eisbn><eisbn>3540320458</eisbn><abstract>In this paper, we propose a computational method for estimating gene networks by the Boolean network model. The Boolean networks have some practical problems in analyzing DNA microarray gene expression data: One is the choice of threshold value for discretization of gene expression data, since expression data take continuous variables. The other problem is that it is often the case that the optimal gene network is not determined uniquely and it is difficult to choose the optimal one from the candidates by using expression data only. To solve these problems, we use the binding location data produced by Lee et al.[8] together with expression data and illustrate a strategy to decide the optimal threshold and gene network. To show the effectiveness of the proposed method, we analyze Saccharomyces cerevisiae cell cycle gene expression data as a real application.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11424857_38</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Computational Science and Its Applications – ICCSA 2005, 2005, p.349-356 |
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| language | eng |
| recordid | cdi_pascalfrancis_primary_16990916 |
| source | Springer Books |
| subjects | Applied sciences Boolean Function Computer science control theory systems Exact sciences and technology Expression Data Gene Expression Data Gene Network Score Function |
| title | Estimating Gene Networks from Expression Data and Binding Location Data via Boolean Networks |
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