Optimized cell type signatures revealed from single-cell data by combining principal feature analysis, mutual information, and machine learning.

Caliskan, Aylin; Caliskan, Deniz; Rasbach, Lauritz; Yu, Weimeng; Dandekar, Thomas; Breitenbach, Tim

Caliskan, Aylin; Caliskan, Deniz; Rasbach, Lauritz; Yu, Weimeng; Dandekar, Thomas; Breitenbach, Tim.

Afiliación

Caliskan A; Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.
Caliskan D; Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.
Rasbach L; Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.
Yu W; Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.
Dandekar T; Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.
Breitenbach T; Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.

Comput Struct Biotechnol J ; 21: 3293-3314, 2023.

Article en En | MEDLINE | ID: mdl-37333862

Palabras clave

Explainability of machine learning; Feature analysis; Feature selection; Machine learning; Model reduction; Principal; Single cell analysis

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Comput Struct Biotechnol J Año: 2023 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Países Bajos

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