Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study

BACKGROUND: LC-MS technology makes it possible to measure the relative abundance of numerous molecular features of a sample in single analysis. However, especially non-targeted metabolite profiling approaches generate vast arrays of data that are prone to aberrations such as missing values. No matte...

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Detalles Bibliográficos
Autores principales: Kokla, Marietta, Virtanen, Jyrki, Kolehmainen, Marjukka, Paananen, Jussi, Hanhineva, Kati
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6788053/
https://www.ncbi.nlm.nih.gov/pubmed/31601178
http://dx.doi.org/10.1186/s12859-019-3110-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6788053/
https://www.ncbi.nlm.nih.gov/pubmed/31601178
http://dx.doi.org/10.1186/s12859-019-3110-0

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