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Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data
Missing values are known to be problematic for the analysis of gas chromatography-mass spectrometry (GC-MS) metabolomics data. Typically these values cover about 10%–20% of all data and can originate from various backgrounds, including analytical, computational, as well as biological. Currently, the...
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| Auteurs principaux: | , , , , , , , |
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| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
MDPI
2014
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| Sujets: | |
| Accès en ligne: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4101515/ https://ncbi.nlm.nih.gov/pubmed/24957035 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/metabo4020433 |
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