Package: DrDimont
Type: Package
Title: Drug Response Prediction from Differential Multi-Omics Networks
Version: 0.1.6
Authors@R: c(
  person("Katharina", "Baum", email = "katharina.baum@fu-berlin.de", role = "cre",
         comment = c(ORCID = "0000-0001-7256-0566")),
  person("Pauline", "Hiort", email = "pauline.hiort@hpi.de", role = "aut",
         comment = c(ORCID = "0000-0002-3530-7358")),
  person("Julian", "Hugo", role = "aut",
         comment = c(ORCID = "0000-0003-3355-1071")),
  person("Spoorthi", "Kashyap", role = "aut", 
         comment = c(ORCID = "0000-0002-5474-8183")),
  person("Nataniel", "Müller", role = "aut",
         comment = c(ORCID = "0000-0002-0275-3992")),
  person("Justus", "Zeinert", role = "aut",
         comment = c(ORCID = "0000-0003-3918-0507"))
  )
Description: While it has been well established that drugs affect and
        help patients differently, personalized drug response
        predictions remain challenging. Solutions based on single omics
        measurements have been proposed, and networks provide means to
        incorporate molecular interactions into reasoning. However, how
        to integrate the wealth of information contained in multiple
        omics layers still poses a complex problem. We present a novel
        network analysis pipeline, DrDimont, Drug response prediction
        from Differential analysis of multi-omics networks. It allows
        for comparative conclusions between two conditions and
        translates them into differential drug response predictions.
        DrDimont focuses on molecular interactions. It establishes
        condition-specific networks from correlation within an omics
        layer that are then reduced and combined into heterogeneous,
        multi-omics molecular networks. A novel semi-local, path-based
        integration step ensures integrative conclusions. Differential
        predictions are derived from comparing the condition-specific
        integrated networks. DrDimont's predictions are explainable,
        i.e., molecular differences that are the source of high
        differential drug scores can be retrieved. Our proposed
        pipeline leverages multi-omics data for differential
        predictions, e.g. on drug response, and includes prior
        information on interactions. The case study presented in the
        vignette uses data published by Krug (2020)
        <doi:10.1016/j.cell.2020.10.036>. The package license applies
        only to the software and explicitly not to the included data.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
LazyDataCompression: xz
RoxygenNote: 7.3.3
VignetteBuilder: knitr
Imports: igraph, dplyr, stringr, WGCNA, Rfast, readr, tibble, tidyr,
        magrittr, rlang, utils, stats, reticulate
Suggests: GO.db, rmarkdown, knitr
Depends: R (>= 3.5.0)
NeedsCompilation: no
Packaged: 2026-07-02 09:19:27 UTC; root
Author: Katharina Baum [cre] (ORCID:
        <https://orcid.org/0000-0001-7256-0566>), Pauline Hiort [aut]
        (ORCID: <https://orcid.org/0000-0002-3530-7358>), Julian Hugo
        [aut] (ORCID: <https://orcid.org/0000-0003-3355-1071>),
        Spoorthi Kashyap [aut] (ORCID:
        <https://orcid.org/0000-0002-5474-8183>), Nataniel Müller [aut]
        (ORCID: <https://orcid.org/0000-0002-0275-3992>), Justus
        Zeinert [aut] (ORCID: <https://orcid.org/0000-0003-3918-0507>)
Maintainer: Katharina Baum <katharina.baum@fu-berlin.de>
Config/pak/sysreqs: cmake libglpk-dev make libicu-dev libpng-dev
        libuv1-dev libxml2-dev python3 libx11-dev
Repository: https://kathbaum.r-universe.dev
Date/Publication: 2025-11-08 13:00:02 UTC
RemoteUrl: https://github.com/cran/DrDimont
RemoteRef: HEAD
RemoteSha: 13b9430ca8719965376296f42574df87004366e0