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

Please always quote using this URN: urn:nbn:de:bvb:20-opus-193240
  • 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 survivalThe 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.show moreshow less

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Metadaten
Author: Johannes Vey, Lorenz A. Kapsner, Maximilian Fuchs, Philipp Unberath, Giulia Veronesi, Meik Kunz
URN:urn:nbn:de:bvb:20-opus-193240
Document Type:Journal article
Faculties:Fakultät für Biologie / Theodor-Boveri-Institut für Biowissenschaften
Language:English
Parent Title (English):Cancers
ISSN:2072-6694
Year of Completion:2019
Volume:11
Issue:10
Article Number:1606
Source:Cancers (2019) 11:10, 1606; https://doi.org/10.3390/cancers11101606
DOI:https://doi.org/10.3390/cancers11101606
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Tag:R package; bioinformatics tool; biomarker signature; functional analysis; gene expression analysis; machine learning; meta-analysis; survival analysis
Release Date:2022/03/18
Date of first Publication:2019/10/21
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International