Integrative Gene Selection on Gene Expression Data: Providing Biological Context to Traditional Approaches

Journal of Integrative Bioinformatics
Cindy PerscheidMatthias Uflacker

Abstract

The advance of high-throughput RNA-Sequencing techniques enables researchers to analyze the complete gene activity in particular cells. From the insights of such analyses, researchers can identify disease-specific expression profiles, thus understand complex diseases like cancer, and eventually develop effective measures for diagnosis and treatment. The high dimensionality of gene expression data poses challenges to its computational analysis, which is addressed with measures of gene selection. Traditional gene selection approaches base their findings on statistical analyses of the actual expression levels, which implies several drawbacks when it comes to accurately identifying the underlying biological processes. In turn, integrative approaches include curated information on biological processes from external knowledge bases during gene selection, which promises to lead to better interpretability and improved predictive performance. Our work compares the performance of traditional and integrative gene selection approaches. Moreover, we propose a straightforward approach to integrate external knowledge with traditional gene selection approaches. We introduce a framework enabling the automatic external knowledge integration, gen...Continue Reading

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Methods Mentioned

BETA
RNAseq

Software Mentioned

Pathway Commons
R
VB
IG
SoFoCles
Python
WEKA
mathbb
SVM
DisGeNET

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