Comparative Evaluation of Set-Level Techniques in Microarray Classification

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F11%3A00181929" target="_blank" >RIV/68407700:21230/11:00181929 - isvavai.cz</a>
Result on the web
<a href="http://labe.felk.cvut.cz/~zelezny/pubs/isbra11b.pdf" target="_blank" >http://labe.felk.cvut.cz/~zelezny/pubs/isbra11b.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Comparative Evaluation of Set-Level Techniques in Microarray Classification
Original language description
Analysis of gene expression data in terms of a priori-defined gene sets typically yields more compact and interpretable results than those produced by traditional methods that rely on individual genes. The set-level strategy can also be adopted in predictive classification tasks accomplished with machine learning algorithms. Here, sample features originally corresponding to genes are replaced by a much smaller number of features, each corresponding to a gene set and aggregating expressions of its members into a single real value. Classifiers learned from such transformed features promise better interpretability in that they derive class predictions from overall expressions of selected gene sets (e.g. corresponding to pathways) rather than expressions of specific genes. In a large collection of experiments we test how accurate such classifiers are compared to traditional classifiers based on genes.
Czech name
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Czech description
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Project
<a href="/en/project/GA201%2F09%2F1665" target="_blank" >GA201/09/1665: Bridging the gap between system biology and machine learning</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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