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  "Title": "DNA methylation-based machine learning models",
  "Description": "This package provides model data and functions for easily\nusing machine learning models that use data from the DNA\nmethylome to classify cancer type and phenotype from a sample.\nThe primary motivation for the development of this package is\nto abstract away the granular and accessibility-limiting code\nrequired to utilize machine learning models in R. Our package\nprovides this abstraction for RandomForest, e1071 Support\nVector, Extreme Gradient Boosting, and Tensorflow models. This\nis paired with an ExperimentHub component, which contains\nmodels developed for epigenetic cancer classification and\npredicting phenotypes. This includes CNS tumor classification,\nPan-cancer classification, race prediction, cell of origin\nclassification, and subtype classification models. The package\nlinks to our models on ExperimentHub. The package currently\nsupports HM450, EPIC, EPICv2, MSA, and MM285.",
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  "License": "Artistic-2.0",
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  "URL": "https://github.com/zhou-lab/CytoMethIC",
  "BugReports": "https://github.com/zhou-lab/CytoMethIC/issues",
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  "Repository": "Bioconductor 3.23",
  "Date/Publication": "2026-04-28 12:37:28 UTC",
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  "Author": "Wanding Zhou [aut] (ORCID: <https://orcid.org/0000-0001-9126-1932>),\nJacob Fanale [aut, cre] (ORCID:\n<https://orcid.org/0009-0002-0490-3269>)",
  "Maintainer": "Jacob Fanale <jfanale@seas.upenn.edu>",
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  "_created": "2026-10-10T11:18:27.000Z",
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