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

Marietta KoklaJyrki VirtanenMarjukka KolehmainenJussi PaananenKati Hanhineva
Published in: BMC bioinformatics (2019)
Type and rate of missingness affects the performance and suitability of imputation methods. RF-based imputation method performs best in most of the tested scenarios, including combinations of different types and rates of missingness. Therefore, we recommend using random forest-based imputation for imputing missing metabolomics data, and especially in situations where the types of missingness are not known in advance.
Keyphrases
  • climate change
  • mass spectrometry
  • electronic health record
  • big data
  • artificial intelligence