TY  - JOUR
A1  - Vey, Johannes
A1  - Kapsner, Lorenz A.
A1  - Fuchs, Maximilian
A1  - Unberath, Philipp
A1  - Veronesi, Giulia
A1  - Kunz, Meik
T1  - A toolbox for functional analysis and the systematic identification of diagnostic and prognostic gene expression signatures combining meta-analysis and machine learning
JF  - Cancers
N2  - The identification of biomarker signatures is important for cancer diagnosis and prognosis. However, the detection of clinical reliable signatures is influenced by limited data availability, which may restrict statistical power. Moreover, methods for integration of large sample cohorts and signature identification are limited. We present a step-by-step computational protocol for functional gene expression analysis and the identification of diagnostic and prognostic signatures by combining meta-analysis with machine learning and survival analysis. The novelty of the toolbox lies in its all-in-one functionality, generic design, and modularity. It is exemplified for lung cancer, including a comprehensive evaluation using different validation strategies. However, the protocol is not restricted to specific disease types and can therefore be used by a broad community. The accompanying R package vignette runs in ~1 h and describes the workflow in detail for use by researchers with limited bioinformatics training.
KW  - bioinformatics tool
KW  - R package
KW  - machine learning
KW  - meta-analysis
KW  - biomarker signature
KW  - gene expression analysis
KW  - survival analysis
KW  - functional analysis
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193240
SN  - 2072-6694
VL  - 11
IS  - 10
ER  -