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These scores may be used to\ntrain machine learning models and to predict clinical features\non new data. For this, several machine learning methods are\nevaluated in order to select the best method based on internal\nvalidation and to tune the hyperparameters. Performance metrics\nand a ready-to-use model to predict the outcomes for new\npatients are returned.", "VignetteBuilder": "knitr", "biocViews": "Pathways, Classification, FeatureExtraction, Transcriptomics", "BugReports": "https://github.com/jordimartorell/pathMED/issues", "URL": "https://github.com/jordimartorell/pathMED", "License": "GPL-2", "Encoding": "UTF-8", "LazyData": "false", "RoxygenNote": "7.3.2", "Config/pak/sysreqs": "cmake make libgsl0-dev libmagick++-dev gsfonts\nlibicu-dev libpng-dev libuv1-dev libxml2-dev libssl-dev\nzlib1g-dev", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-06-03 12:47:15 UTC", "RemoteUrl": "https://github.com/bioc/pathMED", "RemoteRef": "HEAD", "RemoteSha": "07b9fe20731fe15348a63c83e82ba163cadafebb", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-03 18:30:49 UTC", "User": "root" }, "Author": "Jordi Martorell-Marugán [cre, aut] (ORCID:\n),\nDaniel Toro-Domínguez [aut] (ORCID:\n),\nRaúl López-Domínguez [aut] 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