About: Empirical Evidence of the Applicability of Functional Clustering through Gene Expression Classification

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About: Empirical Evidence of the Applicability of Functional Clustering through Gene Expression Classification Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://delivery.acm.org/10.1145/2190000/2189819/ttb2012030788.pdf?ip=147.32.80.13&acc=ACTIVE%20SERVICE&CFID=105155070&CFTOKEN=54614029&__acm__=1337931276_cc8c68fee89e41aac8fe30bb2da302e7
Description	The availability of a great range of prior biological knowledge about the roles and functions of genes and gene-gene interactions allows us to simplify the analysis of gene expression data to make it more robust, compact and interpretable. Here, we objectively analyze the applicability of functional clustering for the identification of groups of functionally related genes. The analysis is performed in terms of gene expression classification and uses predictive accuracy as an unbiased performance measure. Features of biological samples that originally corresponded to genes are replaced by features that correspond to the centroids of the gene clusters and are then used for classifier learning. Using ten benchmark datasets, we demonstrate that functional clustering significantly outperforms random clustering without biological relevance. We also show that functional clustering performs comparably to gene expression clustering, which groups genes according to the similarity of their expression profiles. Finally, the suitability of functional clustering as a feature extraction technique is evaluated and discussed. The availability of a great range of prior biological knowledge about the roles and functions of genes and gene-gene interactions allows us to simplify the analysis of gene expression data to make it more robust, compact and interpretable. Here, we objectively analyze the applicability of functional clustering for the identification of groups of functionally related genes. The analysis is performed in terms of gene expression classification and uses predictive accuracy as an unbiased performance measure. Features of biological samples that originally corresponded to genes are replaced by features that correspond to the centroids of the gene clusters and are then used for classifier learning. Using ten benchmark datasets, we demonstrate that functional clustering significantly outperforms random clustering without biological relevance. We also show that functional clustering performs comparably to gene expression clustering, which groups genes according to the similarity of their expression profiles. Finally, the suitability of functional clustering as a feature extraction technique is evaluated and discussed. (en)
Title	Empirical Evidence of the Applicability of Functional Clustering through Gene Expression Classification Empirical Evidence of the Applicability of Functional Clustering through Gene Expression Classification (en)
skos:prefLabel	Empirical Evidence of the Applicability of Functional Clustering through Gene Expression Classification Empirical Evidence of the Applicability of Functional Clustering through Gene Expression Classification (en)
skos:notation	RIV/68407700:21230/12:00193189!RIV13-MSM-21230___
http://linked.open...avai/riv/aktivita	S Z
http://linked.open...avai/riv/aktivity	S, Z(MSM6840770012)
http://linked.open...iv/cisloPeriodika	9
http://linked.open...vai/riv/dodaniDat	2013
http://linked.open...aciTvurceVysledku	Kléma, Jiří Krejník, Miloš
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	134225
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/12:00193189
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Gene Expression; Gene Set Analysis; Clustering; Feature Extraction; Classification (en)
http://linked.open.../riv/klicoveSlovo	Gene Expression Feature Extraction Clustering Gene Set Analysis Classification
http://linked.open...odStatuVydavatele	US - Spojené státy americké
http://linked.open...ontrolniKodProRIV	[3EC66E642F72]
http://linked.open...i/riv/nazevZdroje	IEEE Transactions on Computational Biology and Bioinformatics
http://linked.open...in/vavai/riv/obor	EB
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...UplatneniVysledku	2012
http://linked.open...v/svazekPeriodika	3
http://linked.open...iv/tvurceVysledku	Kléma, Jiří Krejník, Miloš
http://linked.open...ain/vavai/riv/wos	000301293900014
http://linked.open...n/vavai/riv/zamer	Transdisciplinární výzkum v oblasti biomedicínského inženýrství II
issn	1545-5963
number of pages	11 (xsd:int)
http://bibframe.org/vocab/doi	10.1109/TCBB.2012.23
http://localhost/t...ganizacniJednotka	21230

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