Novel Gene Sets Improve Set-level Classification of Prokaryotic Gene Expression Data

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Novel Gene Sets Improve Set-level Classification of Prokaryotic Gene Expression Data
Original language description
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
Czech name
—
Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JC - Computer hardware and software
OECD FORD branch
—

Project
<a href="/en/project/GAP202%2F12%2F2032" target="_blank" >GAP202/12/2032: Predicting protein properties through spatial statistical relational machine learning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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