Novel Gene Sets Improve Set-level Classification of Prokaryotic Gene Expression Data
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00233472" target="_blank" >RIV/68407700:21230/15:00233472 - isvavai.cz</a>
Výsledek na webu
<a href="http://www.biomedcentral.com/1471-2105/16/348" target="_blank" >http://www.biomedcentral.com/1471-2105/16/348</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1186/s12859-015-0786-7" target="_blank" >10.1186/s12859-015-0786-7</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Novel Gene Sets Improve Set-level Classification of Prokaryotic Gene Expression Data
Popis výsledku v původním jazyce
Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation.Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate c
Název v anglickém jazyce
Novel Gene Sets Improve Set-level Classification of Prokaryotic Gene Expression Data
Popis výsledku anglicky
Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation.Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate c
Klasifikace
Druh
J<sub>x</sub> - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)
CEP obor
JC - Počítačový hardware a software
OECD FORD obor
—
Návaznosti výsledku
Projekt
<a href="/cs/project/GAP202%2F12%2F2032" target="_blank" >GAP202/12/2032: Predikce vlastností bílkovin prostorovým statistickým relačním strojovým učením</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2015
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
BMC Bioinformatics
ISSN
1471-2105
e-ISSN
—
Svazek periodika
16
Číslo periodika v rámci svazku
1
Stát vydavatele periodika
GB - Spojené království Velké Británie a Severního Irska
Počet stran výsledku
18
Strana od-do
—
Kód UT WoS článku
000363616400004
EID výsledku v databázi Scopus
2-s2.0-84945582665