Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/60803
DC FieldValueLanguage
dc.contributor.authorHuang, H.Y.en_US
dc.contributor.author劉俊吉zh_TW
dc.contributor.authorLiu, C.C.en_US
dc.contributor.authorZhou, X.J.en_US
dc.date2010zh_TW
dc.date.accessioned2014-06-09T06:00:59Z-
dc.date.available2014-06-09T06:00:59Z-
dc.identifier.issn0027-8424zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/60803-
dc.description.abstractThe rapid accumulation of gene expression data has offered unprecedented opportunities to study human diseases. The National Center for Biotechnology Information Gene Expression Omnibus is currently the largest database that systematically documents the genome-wide molecular basis of diseases. However, thus far, this resource has been far from fully utilized. This paper describes the first study to transform public gene expression repositories into an automated disease diagnosis database. Particularly, we have developed a systematic framework, including a two-stage Bayesian learning approach, to achieve the diagnosis of one or multiple diseases for a query expression profile along a hierarchical disease taxonomy. Our approach, including standardizing cross-platform gene expression data and heterogeneous disease annotations, allows analyzing both sources of information in a unified probabilistic system. A high level of overall diagnostic accuracy was shown by cross validation. It was also demonstrated that the power of our method can increase significantly with the continued growth of public gene expression repositories. Finally, we showed how our disease diagnosis system can be used to characterize complex phenotypes and to construct a disease-drug connectivity map.en_US
dc.language.isoen_USzh_TW
dc.relationProceedings of the National Academy of Sciences of the United States of Americaen_US
dc.relation.ispartofseriesProceedings of the National Academy of Sciences of the United States of America, Volume 107, Issue 15, Page(s) 6823-6828.en_US
dc.relation.urihttp://dx.doi.org/10.1073/pnas.0912043107en_US
dc.subjectmicroarray dataen_US
dc.subjectclassificationen_US
dc.subjectomnibusen_US
dc.subjectsearchen_US
dc.subjectcanceren_US
dc.titleBayesian approach to transforming public gene expression repositories into disease diagnosis databasesen_US
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1073/pnas.0912043107zh_TW
Appears in Collections:基因體暨生物資訊學研究所
文件中的檔案:

取得全文請前往華藝線上圖書館



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.