Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment.

Garbulowski, Mateusz; Smolinska, Karolina; Çabuk, Ugur; Yones, Sara A; Celli, Ludovica; Yaz, Esma Nur; Barrenäs, Fredrik; Diamanti, Klev; Wadelius, Claes; Komorowski, Jan

Garbulowski, Mateusz; Smolinska, Karolina; Çabuk, Ugur; Yones, Sara A; Celli, Ludovica; Yaz, Esma Nur; Barrenäs, Fredrik; Diamanti, Klev; Wadelius, Claes; Komorowski, Jan.

Afiliação

Garbulowski M; Department of Cell and Molecular Biology, Uppsala University, 752 37 Uppsala, Sweden.
Smolinska K; Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 106 91 Solna, Sweden.
Çabuk U; Department of Cell and Molecular Biology, Uppsala University, 752 37 Uppsala, Sweden.
Yones SA; Department of Cell and Molecular Biology, Uppsala University, 752 37 Uppsala, Sweden.
Celli L; Polar Terrestrial Environmental Systems, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, 14473 Potsdam, Germany.
Yaz EN; Institute of Biochemistry and Biology, University of Potsdam, 14469 Potsdam, Germany.
Barrenäs F; Department of Cell and Molecular Biology, Uppsala University, 752 37 Uppsala, Sweden.
Diamanti K; Department of Cell and Molecular Biology, Uppsala University, 752 37 Uppsala, Sweden.
Wadelius C; Institute of Molecular Genetics Luigi Luca Cavalli-Sforza, National Research Council, 27100 Pavia, Italy.
Komorowski J; Department of Biology and Biotechnology, University of Pavia, 27100 Pavia, Italy.

Cancers (Basel) ; 14(4)2022 Feb 17.

Article em En | MEDLINE | ID: mdl-35205761

Palavras-chave

TCGA; batch effect; co-enrichment; glioma; machine learning; rough sets

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Texto completo: 1 Bases de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Cancers (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Suécia

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