<?xml version="1.0" encoding="utf-8" ?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:r="https://r-universe.dev"><channel><title>jfanale26.r-universe.dev</title><link>https://jfanale26.r-universe.dev</link><description>Recent package updates in jfanale26</description><generator>R-universe</generator><image><url>https://github.com/jfanale26.png</url><title>R packages by jfanale26</title><link>https://jfanale26.r-universe.dev</link></image><lastBuildDate>Tue, 28 Apr 2026 12:37:28 GMT</lastBuildDate><item><title>[bioc] CytoMethIC 1.9.0</title><author>jfanale@seas.upenn.edu (Jacob Fanale)</author><description>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.</description><link>https://github.com/r-universe/bioc/actions/runs/38045776531</link><pubDate>Tue, 28 Apr 2026 12:37:28 GMT</pubDate><r:package>CytoMethIC</r:package><r:version>1.9.0</r:version><r:status>failure</r:status><r:repository>https://bioc.r-universe.dev</r:repository><r:upstream>https://github.com/bioc/CytoMethIC</r:upstream></item></channel></rss>