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

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Title: Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences.
Authors: Kaden, Marika1,2, Bohnsack, Katrin Sophie1,2, Weber, Mirko1,2, Kudła, Mateusz1,3, Gutowska, Kaja3,4,5, Blazewicz, Jacek3,4,5, Villmann, Thomas1,2, thomas.villmann@hs-mittweida.de
Source: Neural Computing & Applications; Jan2022, Vol. 34 Issue 1, p67-78, 12p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 154580935
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PubType: Academic Journal
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  Data: Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences.
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s00521-021-06018-2
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      – Code: eng
        Text: English
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        PageCount: 12
        StartPage: 67
    Titles:
      – TitleFull: Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences.
        Type: main
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            NameFull: Kaden, Marika
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            NameFull: Bohnsack, Katrin Sophie
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            NameFull: Weber, Mirko
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            NameFull: Kudła, Mateusz
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            NameFull: Gutowska, Kaja
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            NameFull: Blazewicz, Jacek
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            – D: 01
              M: 01
              Text: Jan2022
              Type: published
              Y: 2022
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              Value: 34
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            – TitleFull: Neural Computing & Applications
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