Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP)
Abstract The evaluation of pharmacological data using machine learning requires high data quality. Therefore, data preprocessing, that is, cleaning analytical laboratory errors, replacing missing values or outliers, and transforming data adequately before actual data analysis, is crucial. Because cu...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Wiley
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/850b9423372b45f3bd26413a3af60024 |
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