Comparison of deep learning models with simple method to assess the problem of antimicrobial peptides prediction.

Lobanov, M Y; Slizen, M V; Dovidchenko, N V; Panfilov, A V; Surin, A A; Likhachev, I V; Galzitskaya, O V

Lobanov, M Y; Slizen, M V; Dovidchenko, N V; Panfilov, A V; Surin, A A; Likhachev, I V; Galzitskaya, O V.

Afiliación

Lobanov MY; Laboratory of Bioinformatics and Proteomics, Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia.
Slizen MV; Laboratory of Bioinformatics and Proteomics, Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia.
Dovidchenko NV; Laboratory of Bioinformatics and Proteomics, Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia.
Panfilov AV; Laboratory of Bioinformatics and Proteomics, Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia.
Surin AA; Faculty of Applied math, MIREA - Russian Technological University, Moscow, 119454, Russia.
Likhachev IV; Laboratory of Bioinformatics and Proteomics, Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia.
Galzitskaya OV; Institute of Mathematical Problems of Biology branch of Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, 142290, Pushchino, Russia.

Mol Inform ; : e2200181, 2023 Mar 24.

Article en En | MEDLINE | ID: mdl-36961202

Palabras clave

amino acid composition; antimicrobial peptide; deep learning; random forest; visualization

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Mol Inform Año: 2023 Tipo del documento: Article País de afiliación: Rusia Pais de publicación: Alemania

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