Integration of multiple types of genetic markers for neuroblastoma may contribute to improved prediction of the overall survival

Biology Direct
Aneta Polewko-KlimWitold R Rudnicki

Abstract

Modern experimental techniques deliver data sets containing profiles of tens of thousands of potential molecular and genetic markers that can be used to improve medical diagnostics. Previous studies performed with three different experimental methods for the same set of neuroblastoma patients create opportunity to examine whether augmenting gene expression profiles with information on copy number variation can lead to improved predictions of patients survival. We propose methodology based on comprehensive cross-validation protocol, that includes feature selection within cross-validation loop and classification using machine learning. We also test dependence of results on the feature selection process using four different feature selection methods. The models utilising features selected based on information entropy are slightly, but significantly, better than those using features obtained with t-test. The synergy between data on genetic variation and gene expression is possible, but not confirmed. A slight, but statistically significant, increase of the predictive power of machine learning models has been observed for models built on combined data sets. It was found while using both out of bag estimate and in cross-validation pe...Continue Reading

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Citations

Nov 2, 2019·Biology Direct·Chengsheng ZhuYana Bromberg
Apr 18, 2019·Interdisciplinary Sciences, Computational Life Sciences·Xin FengFengfeng Zhou
Nov 29, 2020·Biology Direct·Alexei VerkhratskyYufang Shi
Dec 11, 2020·Cell Death Discovery·Ivano AmelioGerry Melino
Nov 18, 2020·Biochemistry. Biokhimii︠a︡·G Melino
Dec 11, 2020·Cell Death Discovery·Alessio ButeraGerry Melino
Oct 16, 2020·Biology Direct·Ivano AmelioGerry Melino

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Datasets Mentioned

BETA
GSE49711
GSE56109

Methods Mentioned

BETA
RNA-seq
classifier prediction

Software Mentioned

Forest
AceView
Magic
R
2D
Random
SVA
IG
Affymetrix
CAMDA

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