PyRforest: a comprehensive R package for genomic data analysis featuring scikit-learn Random Forests in R.

ABSTRACT: Random Forest models are widely used in genomic data analysis and can offer insights into complex biological mechanisms, particularly when features influence the target in interactive, nonlinear, or nonadditive ways. Currently, some of the most efficient Random Forest methods in terms of computational speed are implemented in Python. However, many biologists use R for genomic data analysis, as R offers a unified platform for performing additional statistical analysis and visualization. Here, we present an R package, pyRforest, which integrates Python scikit-learn "RandomForestClassifier" algorithms into the R environment. pyRforest inherits the efficient memory management and parallelization of Python, and is optimized for classification tasks on large genomic datasets, such as those from

SUBMITTER: Kolisnik T 

PROVIDER: S-EPMC11735746 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature