TIGER: technical variation elimination for metabolomics data using ensemble learning architecture

Large metabolomics datasets inevitably contain unwanted technical variations which can obscure meaningful biological signals and affect how this information is applied to personalized healthcare. Many methods have been developed to handle unwanted variations. However, the underlying assumptions of m...

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Detalles Bibliográficos
Autores principales: Han, Siyu, Huang, Jialing, Foppiano, Francesco, Prehn, Cornelia, Adamski, Jerzy, Suhre, Karsten, Li, Ying, Matullo, Giuseppe, Schliess, Freimut, Gieger, Christian, Peters, Annette, Wang-Sattler, Rui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Problem Solving Protocol
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921617/
https://www.ncbi.nlm.nih.gov/pubmed/34981111
http://dx.doi.org/10.1093/bib/bbab535

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921617/
https://www.ncbi.nlm.nih.gov/pubmed/34981111
http://dx.doi.org/10.1093/bib/bbab535

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