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PrognoScan: a new database for meta-analysis of the prognostic value of genes
In cancer research, the association between a gene and clinical outcome suggests the underlying etiology of the disease and consequently can motivate further studies. The recent availability of published cancer microarray datasets with clinical annotation provides the opportunity for linking gene ex...
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Published in: | BMC medical genomics 2009-04, Vol.2 (1), p.18-18, Article 18 |
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description | In cancer research, the association between a gene and clinical outcome suggests the underlying etiology of the disease and consequently can motivate further studies. The recent availability of published cancer microarray datasets with clinical annotation provides the opportunity for linking gene expression to prognosis. However, the data are not easy to access and analyze without an effective analysis platform.
To take advantage of public resources in full, a database named "PrognoScan" has been developed. This is 1) a large collection of publicly available cancer microarray datasets with clinical annotation, as well as 2) a tool for assessing the biological relationship between gene expression and prognosis. PrognoScan employs the minimum P-value approach for grouping patients for survival analysis that finds the optimal cutpoint in continuous gene expression measurement without prior biological knowledge or assumption and, as a result, enables systematic meta-analysis of multiple datasets.
PrognoScan provides a powerful platform for evaluating potential tumor markers and therapeutic targets and would accelerate cancer research. The database is publicly accessible at http://gibk21.bse.kyutech.ac.jp/PrognoScan/index.html. |
doi_str_mv | 10.1186/1755-8794-2-18 |
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To take advantage of public resources in full, a database named "PrognoScan" has been developed. This is 1) a large collection of publicly available cancer microarray datasets with clinical annotation, as well as 2) a tool for assessing the biological relationship between gene expression and prognosis. PrognoScan employs the minimum P-value approach for grouping patients for survival analysis that finds the optimal cutpoint in continuous gene expression measurement without prior biological knowledge or assumption and, as a result, enables systematic meta-analysis of multiple datasets.
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To take advantage of public resources in full, a database named "PrognoScan" has been developed. This is 1) a large collection of publicly available cancer microarray datasets with clinical annotation, as well as 2) a tool for assessing the biological relationship between gene expression and prognosis. PrognoScan employs the minimum P-value approach for grouping patients for survival analysis that finds the optimal cutpoint in continuous gene expression measurement without prior biological knowledge or assumption and, as a result, enables systematic meta-analysis of multiple datasets.
PrognoScan provides a powerful platform for evaluating potential tumor markers and therapeutic targets and would accelerate cancer research. The database is publicly accessible at http://gibk21.bse.kyutech.ac.jp/PrognoScan/index.html.</description><subject>Cancer</subject><subject>DNA microarrays</subject><subject>Gene expression</subject><subject>Genetic aspects</subject><subject>Online databases</subject><subject>Prognosis</subject><issn>1755-8794</issn><issn>1755-8794</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNp1kk1v1DAQhiNERUvhyhFF4oA4pNhO4g8OSKuKj5WKQBTO1sQZp66SeLGzhf57nO6qbUSRD7beeefRjGey7AUlJ5RK_paKui6kUFXBCiofZUe3wuN778PsaYyXhHBSK_okO6SqVCVR4ij78i34bvTnBsZ3OeQj_s5bmKCBiLn1IR9wggJG6K-ji7m3-XSB-eYmJ07O5FfQb3HWOxwxPssOLPQRn-_v4-znxw8_Tj8XZ18_rU9XZ0XDJZmKthUCGJjaAiNJMi0nAoW0UHGDlaS2UrxFlExwi9aUKDm3ghMJ1IoayuNsveO2Hi71JrgBwrX24PSN4EOnIaTyetSU1mglq1JLpFKtVIYLYTkoqxrLLCbW-x1rs20GbA2OU4B-AV1GRnehO3-lGZdKCpIAqx2gcf4_gGXE-EHPo9HzaDTTVCbG630Rwf_aYpz04KLBvocR_TZqUZaMqlrUyflq5-wgdedG6xPTzG69YoQoyaWgyXXygCudFgdn_IjWJX2R8GaRkDwT_pk62Mao1-ffH4Sb4GMMaG97pUTPW_lvdy_vf_Gdfb-G5V_Z2tzy</recordid><startdate>20090424</startdate><enddate>20090424</enddate><creator>Mizuno, Hideaki</creator><creator>Kitada, Kunio</creator><creator>Nakai, Kenta</creator><creator>Sarai, Akinori</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20090424</creationdate><title>PrognoScan: a new database for meta-analysis of the prognostic value of genes</title><author>Mizuno, Hideaki ; Kitada, Kunio ; Nakai, Kenta ; Sarai, Akinori</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b680t-dd77a2ac5fa20b68cd607e78fa46ce481f496dee8276fefc3e866f7608a1f75a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Cancer</topic><topic>DNA microarrays</topic><topic>Gene expression</topic><topic>Genetic aspects</topic><topic>Online databases</topic><topic>Prognosis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mizuno, Hideaki</creatorcontrib><creatorcontrib>Kitada, Kunio</creatorcontrib><creatorcontrib>Nakai, Kenta</creatorcontrib><creatorcontrib>Sarai, Akinori</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Open Access: DOAJ - Directory of Open Access Journals</collection><jtitle>BMC medical genomics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mizuno, Hideaki</au><au>Kitada, Kunio</au><au>Nakai, Kenta</au><au>Sarai, Akinori</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>PrognoScan: a new database for meta-analysis of the prognostic value of genes</atitle><jtitle>BMC medical genomics</jtitle><addtitle>BMC Med Genomics</addtitle><date>2009-04-24</date><risdate>2009</risdate><volume>2</volume><issue>1</issue><spage>18</spage><epage>18</epage><pages>18-18</pages><artnum>18</artnum><issn>1755-8794</issn><eissn>1755-8794</eissn><abstract>In cancer research, the association between a gene and clinical outcome suggests the underlying etiology of the disease and consequently can motivate further studies. The recent availability of published cancer microarray datasets with clinical annotation provides the opportunity for linking gene expression to prognosis. However, the data are not easy to access and analyze without an effective analysis platform.
To take advantage of public resources in full, a database named "PrognoScan" has been developed. This is 1) a large collection of publicly available cancer microarray datasets with clinical annotation, as well as 2) a tool for assessing the biological relationship between gene expression and prognosis. PrognoScan employs the minimum P-value approach for grouping patients for survival analysis that finds the optimal cutpoint in continuous gene expression measurement without prior biological knowledge or assumption and, as a result, enables systematic meta-analysis of multiple datasets.
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subjects | Cancer DNA microarrays Gene expression Genetic aspects Online databases Prognosis |
title | PrognoScan: a new database for meta-analysis of the prognostic value of genes |
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