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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00193189" target="_blank" >RIV/68407700:21230/12:00193189 - isvavai.cz</a>

Výsledek na webu

<a href="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" target="_blank" >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</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TCBB.2012.23" target="_blank" >10.1109/TCBB.2012.23</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 functionalclustering 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 expres

Název v anglickém jazyce

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

Popis výsledku anglicky

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 functionalclustering 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 expres

Druh

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

CEP obor

EB - Genetika a molekulární biologie

OECD FORD obor

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Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Computational Biology and Bioinformatics

ISSN

1545-5963

e-ISSN

—

Svazek periodika

3

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

788-798

Kód UT WoS článku

000301293900014

EID výsledku v databázi Scopus

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