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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2019
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| Materias: | |
| 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 |