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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "MetNet" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'MetNet: Inferring metabolic networks from untargeted high-resolution mass
  spectrometry data'
version: 1.23.0
abstract: MetNet contains functionality to infer metabolic network topologies from
  quantitative data and high-resolution mass/charge information. Using statistical
  models (including correlation, mutual information, regression and Bayes statistics)
  and quantitative data (intensity values of features) adjacency matrices are inferred
  that can be combined to a consensus matrix. Mass differences calculated between
  mass/charge values of features will be matched against a data frame of supplied
  mass/charge differences referring to transformations of enzymatic activities. In
  a third step, the two levels of information are combined to form a adjacency matrix
  inferred from both quantitative and structure information.
authors:
- family-names: Naake
  given-names: Thomas
  email: thomasnaake@googlemail.com
preferred-citation:
  type: article
  title: 'MetNet: Metabolite Network Prediction from High-Resolution Mass Spectrometry
    Data in R Aiding Metabolite Annotation'
  authors:
  - family-names: Naake
    given-names: Thomas
    email: thomasnaake@googlemail.com
  - family-names: Fernie
    given-names: Alisdair R.
  journal: Analytical Chemistry
  year: '2019'
  volume: '91'
  start: 1768-1772
repository: https://bioc.r-universe.dev
date-released: '2022-11-23'
contact:
- family-names: Naake
  given-names: Thomas
  email: thomasnaake@googlemail.com