A toolbox for functional analysis and the systematic identification of diagnostic and prognostic gene expression signatures combining meta-analysis and machine learning

Vey J, Kapsner L, Fuchs M, Unberath P, Veronesi G, Kunz M (2019)

Publication Type: Journal article

Publication year: 2019

Journal

Cancers MDPI

Book Volume: 11

Article Number: 1606

Journal Issue: 10

DOI: 10.3390/cancers11101606

Abstract

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.

Authors with CRIS profile

Lorenz Kapsner Professur für Diagnostische Molekularpathologie Philipp Unberath Lehrstuhl für Medizinische Informatik Meik Kunz Lehrstuhl für Medizinische Informatik

Involved external institutions

Julius-Maximilians-Universität Würzburg

Germany (DE) Humanitas Research Hospital / IRCCS Istituto Clinico Humanitas

Italy (IT)

How to cite

APA:

Vey, J., Kapsner, L., Fuchs, M., Unberath, P., Veronesi, G., & Kunz, M. (2019). A toolbox for functional analysis and the systematic identification of diagnostic and prognostic gene expression signatures combining meta-analysis and machine learning. Cancers, 11(10). https://doi.org/10.3390/cancers11101606

MLA:

Vey, Johannes, et al. "A toolbox for functional analysis and the systematic identification of diagnostic and prognostic gene expression signatures combining meta-analysis and machine learning." Cancers 11.10 (2019).

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