Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences.

Kaden, Marika; Bohnsack, Katrin Sophie; Weber, Mirko; Kudla, Mateusz; Gutowska, Kaja; Blazewicz, Jacek; Villmann, Thomas

Kaden, Marika; Bohnsack, Katrin Sophie; Weber, Mirko; Kudla, Mateusz; Gutowska, Kaja; Blazewicz, Jacek; Villmann, Thomas.

Afiliación

Kaden M; University of Applied Sciences Mittweida, Technikumplatz 17, 09648 Mittweida, Germany.
Bohnsack KS; Saxon Institute for Computational Intelligence and Machine Learning, Technikumplatz 17, 09648 Mittweida, Germany.
Weber M; University of Applied Sciences Mittweida, Technikumplatz 17, 09648 Mittweida, Germany.
Kudla M; Saxon Institute for Computational Intelligence and Machine Learning, Technikumplatz 17, 09648 Mittweida, Germany.
Gutowska K; University of Applied Sciences Mittweida, Technikumplatz 17, 09648 Mittweida, Germany.
Blazewicz J; Saxon Institute for Computational Intelligence and Machine Learning, Technikumplatz 17, 09648 Mittweida, Germany.
Villmann T; University of Applied Sciences Mittweida, Technikumplatz 17, 09648 Mittweida, Germany.

Neural Comput Appl ; 34(1): 67-78, 2022.

Article en En | MEDLINE | ID: mdl-33935376

Palabras clave

Interpretable models; Genomic sequence analysis; Learning vector quantization; Reject options

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Neural Comput Appl Año: 2022 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido

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