A Comparative Evaluation of Gene Set Analysis Techniques in Predictive Classification of Expression Samples

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F10%3A00169638" target="_blank" >RIV/68407700:21230/10:00169638 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
A Comparative Evaluation of Gene Set Analysis Techniques in Predictive Classification of Expression Samples
Original language description
We demonstrate how some recently developed techniques of set level gene expression data analysis may be exploited in the context of predictive classification of gene expression samples for the tasks of attribute selection and extraction. With four benchmark gene expression datasets, we empirically test the influence of these method on the predictive accuracy of constructed classification models in a comparative setting. Our results mainly indicate that gene set selection methods (SAM GS and the global test) can boost the predictive accuracy if used with caution.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
International Conference on Bioinformatics, Computational Biology, Genomics and Chemoinformatics
ISBN
978-1-60651-017-9
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
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Publisher name
International Society for Research in Science and Technology (ISRST)
Place of publication
Orlando
Event location
Orlando
Event date
Jul 12, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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