Comparative Evaluation of Set-level Techniques in Predictive Classification of Gene Expression Samples

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00193325" target="_blank" >RIV/68407700:21230/12:00193325 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.biomedcentral.com" target="_blank" >http://www.biomedcentral.com</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/1471-2105-13-S10-S15" target="_blank" >10.1186/1471-2105-13-S10-S15</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparative Evaluation of Set-level Techniques in Predictive Classification of Gene Expression Samples

Popis výsledku v původním jazyce

Background: Analysis of gene expression data in terms of a priori defined gene sets has recently received significant attention as this approach typically yields more compact and interpretable results than those produced by traditional methods that relyon individual genes. The set level strategy can also be adopted with similar benefits in predictive classification tasks accomplished with machine learning algorithms. Initial studies into the predictive performance of set level classifiers have yieldedrather controversial results. The goal of this study is to provide a more conclusive evaluation by testing various components of the set level framework within a large collection of machine learning experiments. Results: Genuine curated gene sets constitute better features for classification than sets assembled without biological relevance. For identifying the best gene sets for classification, the Global test outperforms the gene set methods GSEA and SAM GS as well as two generic featur

Název v anglickém jazyce

Comparative Evaluation of Set-level Techniques in Predictive Classification of Gene Expression Samples

Popis výsledku anglicky

Background: Analysis of gene expression data in terms of a priori defined gene sets has recently received significant attention as this approach typically yields more compact and interpretable results than those produced by traditional methods that relyon individual genes. The set level strategy can also be adopted with similar benefits in predictive classification tasks accomplished with machine learning algorithms. Initial studies into the predictive performance of set level classifiers have yieldedrather controversial results. The goal of this study is to provide a more conclusive evaluation by testing various components of the set level framework within a large collection of machine learning experiments. Results: Genuine curated gene sets constitute better features for classification than sets assembled without biological relevance. For identifying the best gene sets for classification, the Global test outperforms the gene set methods GSEA and SAM GS as well as two generic featur

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

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

2012

Kód důvěrnosti údajů

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

Název periodika

BMC Bioinformatics

ISSN

1471-2105

e-ISSN

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Svazek periodika

12

Číslo periodika v rámci svazku

13

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

29

Strana od-do

"S15"

Kód UT WoS článku

000306140100015

EID výsledku v databázi Scopus

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