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Accurate somatic variant detection using weakly supervised deep learning.
Krishnamachari, Kiran; Lu, Dylan; Swift-Scott, Alexander; Yeraliyev, Anuar; Lee, Kayla; Huang, Weitai; Leng, Sim Ngak; Skanderup, Anders Jacobsen.
Afiliación
  • Krishnamachari K; Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.
  • Lu D; School of Computing, National University of Singapore, Singapore, Singapore.
  • Swift-Scott A; Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.
  • Yeraliyev A; Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.
  • Lee K; Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.
  • Huang W; Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.
  • Leng SN; Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.
  • Skanderup AJ; Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.
Nat Commun ; 13(1): 4248, 2022 07 22.
Article en En | MEDLINE | ID: mdl-35869060
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / Neoplasias Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2022 Tipo del documento: Article País de afiliación: Singapur Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
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XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / Neoplasias Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2022 Tipo del documento: Article País de afiliación: Singapur Pais de publicación: Reino Unido