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Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning.
Orlenko, Alena; Kofink, Daniel; Lyytikäinen, Leo-Pekka; Nikus, Kjell; Mishra, Pashupati; Kuukasjärvi, Pekka; Karhunen, Pekka J; Kähönen, Mika; Laurikka, Jari O; Lehtimäki, Terho; Asselbergs, Folkert W; Moore, Jason H.
Afiliación
  • Orlenko A; Department of Biostatistics, Epidemiology and Informatics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.
  • Kofink D; Department of Cardiology, Division Heart and Lungs, Utrecht, The Netherlands.
  • Lyytikäinen LP; Department of Cardiology, Division Heart and Lungs, Utrecht, The Netherlands.
  • Nikus K; Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland.
  • Mishra P; Department of Cardiology, Tampere University Hospital, Tampere, Finland.
  • Kuukasjärvi P; Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland.
  • Karhunen PJ; Department of Cardiology, Tampere University Hospital, Tampere, Finland.
  • Kähönen M; Department of Cardio-Thoracic Surgery, Heart Center, Tampere University Hospital, Tampere, Finland.
  • Laurikka JO; Department of Forensic Medicine, Fimlab Laboratories, Tampere, Finland.
  • Lehtimäki T; Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland.
  • Asselbergs FW; Department of Cardio-Thoracic Surgery, Heart Center, Tampere University Hospital, Tampere, Finland.
  • Moore JH; Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland.
Bioinformatics ; 36(6): 1772-1778, 2020 03 01.
Article en En | MEDLINE | ID: mdl-31702773
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Enfermedad de la Arteria Coronaria Tipo de estudio: Diagnostic_studies / Guideline / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Enfermedad de la Arteria Coronaria Tipo de estudio: Diagnostic_studies / Guideline / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos