tkolisnik/Rf2pval: A comprehensive approach to genomic analysis using scikit-learn's Random Forest models and rank-based feature reduction in R

Welcome to Rf2pval, a comprehensive tool designed to revolutionize your approach to genomic data analysis using Random Forest Models in R. Tailored for expression data, such as RNA-seq or Microarray, Rf2pval is built for bioinformaticians and researchers looking to explore the relationship between biological features and a matched binary outcome variable using Random Forest models. Please see our vignette for instructions that will guide you through Rf2pval's seamless integration of scikit-learn's Random Forest methodologies (imported to R via reticulate) for model development, evaluation, and our custom feature reduction approach by way of rank-based permutation. It will also direct you through our integration with SHAP and gProfiler.

install.packages("remotes")
remotes::install_github("tkolisnik/Rf2pval")