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cff-version: 1.2.0
message: 'To cite package "CytoMethIC" in publications use:'
type: software
license: Artistic-2.0
title: 'CytoMethIC: DNA methylation-based machine learning models'
version: 1.9.0
doi: 10.32614/CRAN.package.CytoMethIC
abstract: This package provides model data and functions for easily using machine
  learning models that use data from the DNA methylome to classify cancer type and
  phenotype from a sample. The primary motivation for the development of this package
  is to abstract away the granular and accessibility-limiting code required to utilize
  machine learning models in R. Our package provides this abstraction for RandomForest,
  e1071 Support Vector, Extreme Gradient Boosting, and Tensorflow models. This is
  paired with an ExperimentHub component, which contains models developed for epigenetic
  cancer classification and predicting phenotypes. This includes CNS tumor classification,
  Pan-cancer classification, race prediction, cell of origin classification, and subtype
  classification models. The package links to our models on ExperimentHub. The package
  currently supports HM450, EPIC, EPICv2, MSA, and MM285.
authors:
- family-names: Zhou
  given-names: Wanding
  email: zhouwanding@gmail.com
  orcid: https://orcid.org/0000-0001-9126-1932
- family-names: Fanale
  given-names: Jacob
  email: jfanale@seas.upenn.edu
  orcid: https://orcid.org/0009-0002-0490-3269
repository: https://bioc.r-universe.dev
repository-code: https://github.com/zhou-lab/CytoMethIC
commit: 193109c97eee860a2acb38e1f8bbcdb1096c8200
url: https://github.com/zhou-lab/CytoMethIC
date-released: '2026-04-28'
contact:
- family-names: Fanale
  given-names: Jacob
  email: jfanale@seas.upenn.edu
  orcid: https://orcid.org/0009-0002-0490-3269