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  - 
TY  - JOUR
A1  - Appel, Mirjam
A1  - Scholz, Claus-Jürgen
A1  - Müller, Tobias
A1  - Dittrich, Marcus
A1  - König, Christian
A1  - Bockstaller, Marie
A1  - Oguz, Tuba
A1  - Khalili, Afshin
A1  - Antwi-Adjei, Emmanuel
A1  - Schauer, Tamas
A1  - Margulies, Carla
A1  - Tanimoto, Hiromu
A1  - Yarali, Ayse
T1  - Genome-Wide Association Analyses Point to Candidate Genes for Electric Shock Avoidance in Drosophila melanogaster
JF  - PLoS ONE
N2  - Electric shock is a common stimulus for nociception-research and the most widely used reinforcement in aversive associative learning experiments. Yet, nothing is known about the mechanisms it recruits at the periphery. To help fill this gap, we undertook a genome-wide association analysis using 38 inbred Drosophila melanogaster strains, which avoided shock to varying extents. We identified 514 genes whose expression levels and/or sequences covaried with shock avoidance scores. We independently scrutinized 14 of these genes using mutants, validating the effect of 7 of them on shock avoidance. This emphasizes the value of our candidate gene list as a guide for follow-up research. In addition, by integrating our association results with external protein-protein interaction data we obtained a shock avoidance- associated network of 38 genes. Both this network and the original candidate list contained a substantial number of genes that affect mechanosensory bristles, which are hairlike organs distributed across the fly's body. These results may point to a potential role for mechanosensory bristles in shock sensation. Thus, we not only provide a first list of candidate genes for shock avoidance, but also point to an interesting new hypothesis on nociceptive mechanisms.
KW  - functional analysis
KW  - disruption project
KW  - natural variation
KW  - complex traits
KW  - networks
KW  - behavior
KW  - flies
KW  - temperature
KW  - genetics
KW  - painful
Y1  - 2015
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-152006
VL  - 10
IS  - 5
ER  -