A Computational Framework for Pattern Detection on Unaligned Sequences: An Application on SARS-CoV-2 Data.

Pechlivanis, Nikolaos; Togkousidis, Anastasios; Tsagiopoulou, Maria; Sgardelis, Stefanos; Kappas, Ilias; Psomopoulos, Fotis

Pechlivanis, Nikolaos; Togkousidis, Anastasios; Tsagiopoulou, Maria; Sgardelis, Stefanos; Kappas, Ilias; Psomopoulos, Fotis.

Afiliação

Pechlivanis N; Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.
Togkousidis A; Department of Genetics, Development and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Tsagiopoulou M; Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.
Sgardelis S; Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.
Kappas I; Department of Ecology, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Psomopoulos F; Department of Genetics, Development and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece.

Front Genet ; 12: 618170, 2021.

Article em En | MEDLINE | ID: mdl-34122498

Palavras-chave

SARS-CoV-2; feature selection; k-mers; phylogenetics; unsupervised learning

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Front Genet Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Grécia

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