Computational analysis of microarray gene expression profiles of lung cancer

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F16%3A43887623" target="_blank" >RIV/44555601:13440/16:43887623 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.biopolymers.org.ua/pdf/32/1/070/biopolym.cell-2016-32-1-070-en.pdf" target="_blank" >http://www.biopolymers.org.ua/pdf/32/1/070/biopolym.cell-2016-32-1-070-en.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.7124/bc.00090F" target="_blank" >10.7124/bc.00090F</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Computational analysis of microarray gene expression profiles of lung cancer

Popis výsledku v původním jazyce

The article is dedicated to optimization of the DNA microarray data processing, which is aimed at improving the quality of object clustering. Methods. Data preprocessing was performed with program R using Bioconductor package. Modelling the clustering process was made in the software environment KNIME using the program WEKA functions. Results. The data preprocessing is shown to be optimal while using such techniques as the background correction rma method, quantile normalization, mas PM correction and summarization by mas method. The simulation results have demonstrated a high effectiveness of the clustering algorithm Sota for this category of data. Conclusion. Improvement of the quality of biological object clustering is possible by means of hybridization and optimization of the methods and algorithms at different stages of data processing.

Název v anglickém jazyce

Computational analysis of microarray gene expression profiles of lung cancer

Popis výsledku anglicky

The article is dedicated to optimization of the DNA microarray data processing, which is aimed at improving the quality of object clustering. Methods. Data preprocessing was performed with program R using Bioconductor package. Modelling the clustering process was made in the software environment KNIME using the program WEKA functions. Results. The data preprocessing is shown to be optimal while using such techniques as the background correction rma method, quantile normalization, mas PM correction and summarization by mas method. The simulation results have demonstrated a high effectiveness of the clustering algorithm Sota for this category of data. Conclusion. Improvement of the quality of biological object clustering is possible by means of hybridization and optimization of the methods and algorithms at different stages of data processing.

Druh

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

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

Biopolymers and Cell

ISSN

0233-7657

e-ISSN

—

Svazek periodika

2016

Číslo periodika v rámci svazku

Vol. 32. N 1.

Stát vydavatele periodika

UA - Ukrajina

Počet stran výsledku

9

Strana od-do

70-79

Kód UT WoS článku

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EID výsledku v databázi Scopus

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