Comparative Evaluation of Set-Level Techniques in Microarray Classification

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

Comparative Evaluation of Set-Level Techniques in Microarray Classification

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Comparative Evaluation of Set-Level Techniques in Microarray Classification

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

<a href="/cs/project/GA201%2F09%2F1665" target="_blank" >GA201/09/1665: Překonání propasti mezi systémovou biologií a strojovým učením</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Bioinformatics Research and Applications

ISBN

978-3-642-21259-8

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

12

Strana od-do

274-285

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Changsha

Datum konání akce

27. 5. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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