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pathMED

Scoring Personalized Molecular Portraits

Bioconductor version: 3.23 · Package version: 1.4.0

PathMED is a collection of tools to facilitate precision medicine studies with omics data (e.g. transcriptomics). Among its funcionalities, genesets scores for individual samples may be calculated with several methods. These scores may be used to train machine learning models and to predict clinical features on new data. For this, several machine learning methods are evaluated in order to select the best method based on internal validation and to tune the hyperparameters. Performance metrics and a ready-to-use model to predict the outcomes for new patients are returned.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("pathMED")

Details

MaintainerJordi Martorell-Marugán <jmartorellm@gmail.com>
AuthorJordi Martorell-Marugán [cre, aut] (ORCID: <https://orcid.org/0000-0002-5186-0735>), Daniel Toro-Domínguez [aut] (ORCID: <https://orcid.org/0000-0001-8440-312X>), Raúl López-Domínguez [aut] (ORCID: <https://orcid.org/0000-0001-8634-117X>), Iván Ellson [aut] (ORCID: <https://orcid.org/0000-0001-6307-3141>)
LicenseGPL-2
URLhttps://github.com/jordimartorell/pathMED
Bug Reportshttps://github.com/jordimartorell/pathMED/issues
Downloads rank102
Source branchRELEASE_3_23
biocViewsClassification, FeatureExtraction, Pathways, Software, Transcriptomics

Documentation

Download

Dependencies

Depends: R (>= 4.5.0)

Imports: BiocParallel, caret, caretEnsemble, decoupleR, ggplot2, GSVA, factoextra, FactoMineR, magrittr, matrixStats, methods, metrica, pbapply, reshape2, singscore, stats, stringi, dplyr

Suggests: ada, AUCell, Biobase, BiocGenerics, BiocStyle, fgsea (>= 1.15.4), gam, GSEABase, import, kernlab, klaR, knitr, mboost, MLeval, randomForest, ranger, rmarkdown, RUnit, SummarizedExperiment, utils, xgboost