Stability of ranked gene lists in large microarray analysis studies.

Journal of Biomedicine & Biotechnology
Gregor Stiglic, Peter Kokol

Abstract

This paper presents an empirical study that aims to explain the relationship between the number of samples and stability of different gene selection techniques for microarray datasets. Unlike other similar studies where number of genes in a ranked gene list is variable, this study uses an alternative approach where stability is observed at different number of samples that are used for gene selection. Three different metrics of stability, including a novel metric in bioinformatics, were used to estimate the stability of the ranked gene lists. Results of this study demonstrate that the univariate selection methods produce significantly more stable ranked gene lists than the multivariate selection methods used in this study. More specifically, thousands of samples are needed for these multivariate selection methods to achieve the same level of stability any given univariate selection method can achieve with only hundreds.

References

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Citations

Apr 20, 2012·Bioinformatics·Juliane SiebourgNiko Beerenwinkel
Apr 6, 2012·PloS One·Gregor StiglicPeter Kokol
Jul 20, 2016·Artificial Intelligence in Medicine·Utku SirinReda Alhajj
Aug 25, 2017·Computational and Mathematical Methods in Medicine·Andrea BommertMichel Lang
Jan 1, 2019·Knowledge and Information Systems·Roman Feldbauer, Arthur Flexer
Mar 23, 2018·Neuroinformatics·James Deraeve, William H Alexander
Oct 19, 2020··Robin CugnyFranck Ravat

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Datasets Mentioned

BETA
GSE2109

Software Mentioned

GEMLeR
WEKA
RFE
Weka environment
InfoGainAttributeEval
SVM
IG

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