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NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning.
Klausen, Michael Schantz; Jespersen, Martin Closter; Nielsen, Henrik; Jensen, Kamilla Kjaergaard; Jurtz, Vanessa Isabell; Sønderby, Casper Kaae; Sommer, Morten Otto Alexander; Winther, Ole; Nielsen, Morten; Petersen, Bent; Marcatili, Paolo.
Afiliación
  • Klausen MS; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.
  • Jespersen MC; Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.
  • Nielsen H; Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.
  • Jensen KK; Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.
  • Jurtz VI; Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.
  • Sønderby CK; The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
  • Sommer MOA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.
  • Winther O; The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
  • Nielsen M; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.
  • Petersen B; Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.
  • Marcatili P; Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina.
Proteins ; 87(6): 520-527, 2019 06.
Article en En | MEDLINE | ID: mdl-30785653
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Bases de Datos de Proteínas / Aprendizaje Profundo Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Proteins Asunto de la revista: BIOQUIMICA Año: 2019 Tipo del documento: Article País de afiliación: Dinamarca
Texto completo
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XML
PubMed Links
Buscar en Google

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Bases de Datos de Proteínas / Aprendizaje Profundo Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Proteins Asunto de la revista: BIOQUIMICA Año: 2019 Tipo del documento: Article País de afiliación: Dinamarca