TY  - JOUR
A1  - Perscheid, Cindy
A1  - Grasnick, Bastien
A1  - Uflacker, Matthias
T1  - Integrative Gene Selection on Gene Expression Data
BT  - Providing Biological Context to Traditional Approaches
JF  - Journal of Integrative Bioinformatics
N2  - 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, gene selection, and evaluation. Evaluation results prove our framework to be a useful tool for evaluation and show that integration of external knowledge improves overall analysis results.
KW  - Gene Expression Data Analysis
KW  - Integrative Gene Selection
KW  - Pattern Recognition
KW  - Prior Knowledge
KW  - Knowledge Bases
Y1  - 2019
U6  - https://doi.org/10.1515/jib-2018-0064
SN  - 1613-4516
VL  - 16
IS  - 1
PB  - De Gruyter
CY  - Berlin
ER  -