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Open AccessArticle

A Toolbox for Functional Analysis and the Systematic Identification of Diagnostic and Prognostic Gene Expression Signatures Combining Meta-Analysis and Machine Learning

1
Functional Genomics and Systems Biology Group, Department of Bioinformatics, University of Würzburg, 97074 Würzburg, Germany
2
Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 130.3, 69120 Heidelberg, Germany
3
Center of Medical Information and Communication Technology, Erlangen University Hospital, 91054 Erlangen, Germany
4
Chair of Medical Informatics, Friedrich-Alexander University of Erlangen-Nürnberg, 91058 Erlangen, Germany
5
Unit of Thoracic Surgery, Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano (Milan), Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally.
Cancers 2019, 11(10), 1606; https://doi.org/10.3390/cancers11101606
Received: 30 September 2019 / Revised: 14 October 2019 / Accepted: 15 October 2019 / Published: 21 October 2019
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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. View Full-Text
Keywords: Bioinformatics tool; R package; machine learning; meta-analysis; biomarker signature; gene expression analysis; survival analysis; functional analysis Bioinformatics tool; R package; machine learning; meta-analysis; biomarker signature; gene expression analysis; survival analysis; functional analysis
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Vey, J.; Kapsner, L.A.; Fuchs, M.; Unberath, P.; Veronesi, G.; Kunz, M. A Toolbox for Functional Analysis and the Systematic Identification of Diagnostic and Prognostic Gene Expression Signatures Combining Meta-Analysis and Machine Learning. Cancers 2019, 11, 1606.

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