Selected article for: "mean squared error and squared error"

Author: Lara Urban; Andre Holzer; J Jotautas Baronas; Michael Hall; Philipp Braeuninger-Weimer; Michael J Scherm; Daniel J Kunz; Surangi N Perera; Daniel E Martin-Herranz; Edward T Tipper; Susannah J Salter; Maximilian R Stammnitz
Title: Freshwater monitoring by nanopore sequencing
  • Document date: 2020_2_7
  • ID: 77nsidzc_55
    Snippet: For the mock community classification benchmark, we assessed the number of unclassified reads, misclassified reads (i.e. sequences not assigned to any of the seven bacterial families), and the root mean squared error (RMSE) between observed and expected taxon abundance of the seven bacterial families. Following the detection of a strong bias towards the Enterobacteriaceae family across all classification tools, we also analysed RMSE values after .....
    Document: For the mock community classification benchmark, we assessed the number of unclassified reads, misclassified reads (i.e. sequences not assigned to any of the seven bacterial families), and the root mean squared error (RMSE) between observed and expected taxon abundance of the seven bacterial families. Following the detection of a strong bias towards the Enterobacteriaceae family across all classification tools, we also analysed RMSE values after exclusion of this family (Extended Data Figure 1b -c).

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