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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...
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| Publicado no: | BMC Bioinformatics |
|---|---|
| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
BioMed Central
2019
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6788053/ https://ncbi.nlm.nih.gov/pubmed/31601178 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12859-019-3110-0 |
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