PathMED: An R toolkit for single-sample molecular scoring and machine learning with omics data

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ID: 322344
2026
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Abstract
MOTIVATION: Molecular scoring is a popular approach for studying pathway-level functional alterations with omics data. Using molecular scores for tasks such as single-sample molecular characterisation, phenotype prediction or disease stratification has several advantages compared to using omics data directly. Molecular scores provide biological interpretability and are more generalisable across datasets, facilitating data integration and machine learning applications. However, numerous scoring methods are available through different software packages, and currently there is a lack of tools to easily use these scores for model training and prediction. RESULTS: We developed pathMED, an R/Bioconductor package that unifies various scoring methods in a simple framework. Furthermore, pathMED also contains a machine learning module to train and test models that use the calculated molecular scores to predict clinical outcomes. We demonstrate some of its potential applications in three use cases using public omics data. We showed the generalisability of machine learning models trained on transcriptomic scores in predicting clinical outcomes when deploying on proteomic scores. We also demonstrated the application of transcriptomics scores in predicting breast cancer treatment response and identifying pathways strongly associated to tumour biology and treatment response. Finally, we demonstrated the benefit of integrating a novel gene set dissection step into the analysis pipeline to resolve disease heterogeneity at the pathway level. AVAILABILITY: PathMED is freely available in the Bioconductor repository (https://bioconductor.org/packages/release/bioc/html/pathMED.html). Code to reproduce the analyses is publicly available at https://github.com/GENyO-BioInformatics/pathMED_article. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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openalex_W7170334984 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jordi Martorell‐Marugán, Ivan Ellson, Raúl López-Domínguez, Pablo Pedro Jurado-Bascón, Juan Antonio Villatoro-García, Chang Wang, Frédéric Baribaud, Daniel Toro-Domínguez, Pedro Carmona-Sáez
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag519
URL
https://doi.org/10.1093/bioinformatics/btag519
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