The synergism of spatial metabolomics and morphometry improves machine learning-based renal tumour subtype classification.

Prade VM, Sun N, Shen J, Feuchtinger A, Kunzke T, Buck A, Schraml P, Moch H, Schwamborn K, Autenrieth M, Gschwend JE, Erlmeier F, Hartmann A, Walch A (2022)

Publication Type: Journal article, Letter

Publication year: 2022

Journal

Clinical and Translational Medicine Wiley

Book Volume: 12

Journal Issue: 2

DOI: 10.1002/ctm2.666

Authors with CRIS profile

Franziska Erlmeier Institute of Pathology Arndt Hartmann Lehrstuhl für Allgemeine Pathologie und Pathologische Anatomie

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE) Universitätsspital Zürich (USZ) CH Switzerland (CH) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Prade, V.M., Sun, N., Shen, J., Feuchtinger, A., Kunzke, T., Buck, A.,... Walch, A. (2022). The synergism of spatial metabolomics and morphometry improves machine learning-based renal tumour subtype classification. Clinical and Translational Medicine, 12(2). https://doi.org/10.1002/ctm2.666

MLA:

Prade, Verena M., et al. "The synergism of spatial metabolomics and morphometry improves machine learning-based renal tumour subtype classification." Clinical and Translational Medicine 12.2 (2022).

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