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Identification of disease-distinct complex biomarker patterns by means of unsupervised machine-learning using an interactive R toolbox (Umatrix)

Crossref DOI link: https://doi.org/10.1186/s41044-018-0032-1

Published Online: 2018-04-20

Published Print: 2018-12

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Lötsch, Jörn https://orcid.org/0000-0002-5818-6958
Lerch, Florian

Djaldetti, Ruth

Tegder, Irmgard

Ultsch, Alfred
Funding

Funding for this research was provided by:

Landesoffensive zur Entwicklung wissenschaftlich - ökonomischer Exzellenz (LOEWE), LOEWE-Zentrum für Translationale Medizin und Pharmakologie (n/a)

European Union Seventh Framework Programme (602919)
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Text and Data Mining valid from 2018-04-20
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Article History

Received: 15 January 2018

Accepted: 6 April 2018

First Online: 20 April 2018

Ethics approval and consent to participate

: The study followed the Declaration of Helsinki and was approved by the Ethics Committee of the Medical Faculty of the Goethe – University Frankfurt am Main, Germany (reference number 197/13).

: Informed written consent into study participation and publication of the results in an anonymized form was obtained from all subjects.

: The authors declare that they have no competing interests.

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