The synergism of spatial metabolomics and morphometry improves machine learning‐based renal tumour subtype classification | Zendy

Prade Verena M. | Zendy; Sun Na | Zendy; Shen Jian | Zendy; Feuchtinger Annette | Zendy; Kunzke Thomas | Zendy; Buck Achim | Zendy; Schraml Peter | Zendy; Moch Holger | Zendy; Schwamborn Kristina | Zendy; Autenrieth Michael | Zendy; Gschwend Jürgen E. | Zendy; Erlmeier Franziska | Zendy; Hartmann Arndt | Zendy; Walch Axel | Zendy

Open Access

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

Author(s) -

Prade Verena M.,

Sun Na,

Shen Jian,

Feuchtinger Annette,

Kunzke Thomas,

Buck Achim,

Schraml Peter,

Moch Holger,

Schwamborn Kristina,

Autenrieth Michael,

Gschwend Jürgen E.,

Erlmeier Franziska,

Hartmann Arndt,

Walch Axel

Publication year - 2022

Publication title -

clinical and translational medicine

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.125

H-Index - 1

ISSN - 2001-1326

DOI - 10.1002/ctm2.666

Subject(s) - subtyping , chromophobe cell , clear cell , renal cell carcinoma , biomarker discovery , pathology , metabolomics , mass spectrometry imaging , biomarker , medicine , immunohistochemistry , clear cell renal cell carcinoma , kidney cancer , computational biology , oncology , proteomics , bioinformatics , biology , mass spectrometry , computer science , chemistry , biochemistry , chromatography , gene , programming language

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