TY  - JOUR
A1  - Perscheid, Cindy
T1  - Integrative biomarker detection on high-dimensional gene expression data sets
BT  - a survey on prior knowledge approaches
JF  - Briefings in bioinformatics
N2  - Gene expression data provide the expression levels of tens of thousands of genes from several hundred samples. These data are analyzed to detect biomarkers that can be of prognostic or diagnostic use. Traditionally, biomarker detection for gene expression data is the task of gene selection. The vast number of genes is reduced to a few relevant ones that achieve the best performance for the respective use case. Traditional approaches select genes based on their statistical significance in the data set. This results in issues of robustness, redundancy and true biological relevance of the selected genes. Integrative analyses typically address these shortcomings by integrating multiple data artifacts from the same objects, e.g. gene expression and methylation data. When only gene expression data are available, integrative analyses instead use curated information on biological processes from public knowledge bases. With knowledge bases providing an ever-increasing amount of curated biological knowledge, such prior knowledge approaches become more powerful. This paper provides a thorough overview on the status quo of biomarker detection on gene expression data with prior biological knowledge. We discuss current shortcomings of traditional approaches, review recent external knowledge bases, provide a classification and qualitative comparison of existing prior knowledge approaches and discuss open challenges for this kind of gene selection.
KW  - gene selection
KW  - external knowledge bases
KW  - biomarker detection
KW  - gene
KW  - expression
KW  - prior knowledge
Y1  - 2021
U6  - https://doi.org/10.1093/bib/bbaa151
SN  - 1467-5463
SN  - 1477-4054
VL  - 22
IS  - 3
PB  - Oxford Univ. Press
CY  - Oxford
ER  - 
TY  - GEN
A1  - Perscheid, Cindy
A1  - Faber, Lukas
A1  - Kraus, Milena
A1  - Arndt, Paul
A1  - Janke, Michael
A1  - Rehfeldt, Sebastian
A1  - Schubotz, Antje
A1  - Slosarek, Tamara
A1  - Uflacker, Matthias
T1  - A tissue-aware gene selection approach for analyzing multi-tissue gene expression data
T2  - 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
N2  - High-throughput RNA sequencing (RNAseq) produces large data sets containing expression levels of thousands of genes. The analysis of RNAseq data leads to a better understanding of gene functions and interactions, which eventually helps to study diseases like cancer and develop effective treatments. Large-scale RNAseq expression studies on cancer comprise samples from multiple cancer types and aim to identify their distinct molecular characteristics. Analyzing samples from different cancer types implies analyzing samples from different tissue origin. Such multi-tissue RNAseq data sets require a meaningful analysis that accounts for the inherent tissue-related bias: The identified characteristics must not originate from the differences in tissue types, but from the actual differences in cancer types. However, current analysis procedures do not incorporate that aspect. As a result, we propose to integrate a tissue-awareness into the analysis of multi-tissue RNAseq data. We introduce an extension for gene selection that provides a tissue-wise context for every gene and can be flexibly combined with any existing gene selection approach. We suggest to expand conventional evaluation by additional metrics that are sensitive to the tissue-related bias. Evaluations show that especially low complexity gene selection approaches profit from introducing tissue-awareness.
KW  - RNAseq
KW  - gene selection
KW  - tissue-awareness
KW  - TCGA
KW  - GTEx
Y1  - 2018
SN  - 978-1-5386-5488-0
U6  - https://doi.org/10.1109/BIBM.2018.8621189
SN  - 2156-1125
SN  - 2156-1133
SP  - 2159
EP  - 2166
PB  - IEEE
CY  - New York
ER  -