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An enriched approach to combining high-dimensional genomic and low-dimensional phenotypic data.
Cabrera, Javier; Emir, Birol; Cheng, Ge; Duan, Yajie; Alemayehu, Demissie; Cherkas, Yauheniya.
Afiliación
  • Cabrera J; Department of Statistics, Rutgers University, Piscataway Jersey, USA.
  • Emir B; Statistical Research and Data Science Center, Pfizer Research & Development, Pfizer Inc, New York, USA.
  • Cheng G; Department of Statistics, Rutgers University, Piscataway Jersey, USA.
  • Duan Y; Department of Statistics, Rutgers University, Piscataway Jersey, USA.
  • Alemayehu D; Statistical Research and Data Science Center, Pfizer Research & Development, Pfizer Inc, New York, USA.
  • Cherkas Y; Statistics and Decision Sciences Janssen R&D, Pennsylvania, USA.
J Biopharm Stat ; : 1-7, 2024 Apr 05.
Article en En | MEDLINE | ID: mdl-38578223
ABSTRACT
We describe an approach for combining and analyzing high-dimensional genomic and low-dimensional phenotypic data. The approach leverages a scheme of weights applied to the variables instead of observations and, hence, permits incorporation of the information provided by the low dimensional data source. It can also be incorporated into commonly used downstream techniques, such as random forest or penalized regression. Finally, the simulated lupus studies involving genetic and clinical data are used to illustrate the overall idea and show that the proposed enriched penalized method can select significant genetic variables while keeping several important clinical variables in the final model.
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Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: J Biopharm Stat Asunto de la revista: FARMACOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: J Biopharm Stat Asunto de la revista: FARMACOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos