Selected article for: "cumulative distribution function and distribution function"

Author: Marcus Ludwig; Louis-Félix Nothias; Kai Dührkop; Irina Koester; Markus Fleischauer; Martin A. Hoffmann; Daniel Petras; Fernando Vargas; Mustafa Morsy; Lihini Aluwihare; Pieter C. Dorrestein; Sebastian Böcker
Title: ZODIAC: database-independent molecular formula annotation using Gibbs sampling reveals unknown small molecules
  • Document date: 2019_11_16
  • ID: 03uonbrv_102
    Snippet: We use identical parameters for all ve datasets, see (5) above: We weight fragments and root losses when comparing fragmentation trees of molecular formula candidates. Here, we use the SIRIUS 4 noise intensity scoring as importance ι in (7) . The probability that a peak p that corresponds to a fragment and root loss is not noise is ι = 1 − par(int(p)), where par is the Pareto cumulative distribution function with x min = 0.002, x median = 0.0.....
    Document: We use identical parameters for all ve datasets, see (5) above: We weight fragments and root losses when comparing fragmentation trees of molecular formula candidates. Here, we use the SIRIUS 4 noise intensity scoring as importance ι in (7) . The probability that a peak p that corresponds to a fragment and root loss is not noise is ι = 1 − par(int(p)), where par is the Pareto cumulative distribution function with x min = 0.002, x median = 0.015 and int(p) ∈ [0, 1] the relative peak intensity, see ref. 24 . To establish a threshold on the minimal similarity of fragmentation trees, we decrease score s and tree sizes n 1 and n 2 each by 1.0, see (6) .

    Search related documents:
    Co phrase search for related documents
    • formula candidate and molecular formula candidate: 1, 2, 3, 4
    • fragmentation tree and molecular formula candidate: 1
    • noise intensity and peak intensity: 1
    • peak intensity and relative peak intensity: 1, 2