Your browser doesn't support javascript.
loading
An 8-gene machine learning model improves clinical prediction of severe dengue progression.
Liu, Yiran E; Saul, Sirle; Rao, Aditya Manohar; Robinson, Makeda Lucretia; Agudelo Rojas, Olga Lucia; Sanz, Ana Maria; Verghese, Michelle; Solis, Daniel; Sibai, Mamdouh; Huang, Chun Hong; Sahoo, Malaya Kumar; Gelvez, Rosa Margarita; Bueno, Nathalia; Estupiñan Cardenas, Maria Isabel; Villar Centeno, Luis Angel; Rojas Garrido, Elsa Marina; Rosso, Fernando; Donato, Michele; Pinsky, Benjamin A; Einav, Shirit; Khatri, Purvesh.
Afiliación
  • Liu YE; Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, CA, Stanford, USA.
  • Saul S; Cancer Biology Graduate Program, School of Medicine, Stanford University, CA, Stanford, USA.
  • Rao AM; Division of Infectious Diseases and Geographic Medicine, Department of Medicine, School of Medicine, Stanford University, CA, Stanford, USA.
  • Robinson ML; Division of Infectious Diseases and Geographic Medicine, Department of Medicine, School of Medicine, Stanford University, CA, Stanford, USA.
  • Agudelo Rojas OL; Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, CA, Stanford, USA.
  • Sanz AM; Immunology Graduate Program, School of Medicine, Stanford University, CA, Stanford, USA.
  • Verghese M; Division of Infectious Diseases and Geographic Medicine, Department of Medicine, School of Medicine, Stanford University, CA, Stanford, USA.
  • Solis D; Department of Pathology, School of Medicine, Stanford University, CA, Stanford, USA.
  • Sibai M; Clinical Research Center, Fundación Valle del Lili, Cali, Colombia.
  • Huang CH; Clinical Research Center, Fundación Valle del Lili, Cali, Colombia.
  • Sahoo MK; Department of Pathology, School of Medicine, Stanford University, CA, Stanford, USA.
  • Gelvez RM; Department of Pathology, School of Medicine, Stanford University, CA, Stanford, USA.
  • Bueno N; Department of Pathology, School of Medicine, Stanford University, CA, Stanford, USA.
  • Estupiñan Cardenas MI; Department of Pathology, School of Medicine, Stanford University, CA, Stanford, USA.
  • Villar Centeno LA; Department of Pathology, School of Medicine, Stanford University, CA, Stanford, USA.
  • Rojas Garrido EM; Centro de Atención y Diagnóstico de Enfermedades Infecciosas (CDI), Bucaramanga, Colombia.
  • Rosso F; Centro de Atención y Diagnóstico de Enfermedades Infecciosas (CDI), Bucaramanga, Colombia.
  • Donato M; Centro de Atención y Diagnóstico de Enfermedades Infecciosas (CDI), Bucaramanga, Colombia.
  • Pinsky BA; Centro de Atención y Diagnóstico de Enfermedades Infecciosas (CDI), Bucaramanga, Colombia.
  • Einav S; Centro de Atención y Diagnóstico de Enfermedades Infecciosas (CDI), Bucaramanga, Colombia.
  • Khatri P; Clinical Research Center, Fundación Valle del Lili, Cali, Colombia.
Genome Med ; 14(1): 33, 2022 03 29.
Article en En | MEDLINE | ID: mdl-35346346
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Dengue Grave Tipo de estudio: Diagnostic_studies / Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Genome Med Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Dengue Grave Tipo de estudio: Diagnostic_studies / Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Genome Med Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido