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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F05%3A03115158%21RIV07-AV0-21230___
rdf:type
n3:Vysledek skos:Concept
dcterms:description
Není k dispozici We propose a methodology for predictive classification from gene expression data, able to combine the robustness of high-dimensional statistical classification methods with the comprehensibility and interpretability of simple logic-based models. We first construct a robust classifier combining contributions of a large number of gene expression values, and then search for compact summarizations of subgroups among genes associated in the classifier with a given class. The subgroups are described by means of relational logic features extracted from publicly available gene annotations. The curse of dimensionality pertaining to the gene expression based classification problem due to the large number of attributes (genes) is turned into an advantage in the secondary subgroup discovery task, as here the original attributes become learning examples. We propose a methodology for predictive classification from gene expression data, able to combine the robustness of high-dimensional statistical classification methods with the comprehensibility and interpretability of simple logic-based models. We first construct a robust classifier combining contributions of a large number of gene expression values, and then search for compact summarizations of subgroups among genes associated in the classifier with a given class. The subgroups are described by means of relational logic features extracted from publicly available gene annotations. The curse of dimensionality pertaining to the gene expression based classification problem due to the large number of attributes (genes) is turned into an advantage in the secondary subgroup discovery task, as here the original attributes become learning examples.
dcterms:title
Relational Subgroup Discovery for Gene Expression Data Mining Není k dispozici Relational Subgroup Discovery for Gene Expression Data Mining
skos:prefLabel
Relational Subgroup Discovery for Gene Expression Data Mining Není k dispozici Relational Subgroup Discovery for Gene Expression Data Mining
skos:notation
RIV/68407700:21230/05:03115158!RIV07-AV0-21230___
n4:aktivita
n13:P
n4:aktivity
P(1ET101210513), P(KJB201210501)
n4:dodaniDat
n8:2007
n4:domaciTvurceVysledku
n6:5523036 n6:9942904
n4:druhVysledku
n9:A
n4:duvernostUdaju
n15:S
n4:entitaPredkladatele
n12:predkladatel
n4:idSjednocenehoVysledku
540635
n4:idVysledku
RIV/68407700:21230/05:03115158
n4:jazykVysledku
n10:eng
n4:klicovaSlova
gene expression data mining
n4:klicoveSlovo
n5:gene%20expression%20data%20mining
n4:kodPristupu
n16:L
n4:kontrolniKodProRIV
[A3DCEDACFB71]
n4:mistoVydani
Praha
n4:nosic
neuvedeno
n4:obor
n18:JC
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
4
n4:projekt
n14:1ET101210513 n14:KJB201210501
n4:rokUplatneniVysledku
n8:2005
n4:tvurceVysledku
Železný, Filip Štěpánková, Olga Tolar, J. Lavrač, N.
n17:organizacniJednotka
21230