Drug Response Prediction from Differential Multi-Omics Networks
Overview
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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 30 (1 internal)
- Examples that run
- 100%14 run in a plain check
- Exported functions with examples
- 88%
- Documented parameters
- 94%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Dependencies
Installing DrDimont adds 88 packages (49 with compiled code, 70 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB. 2 more not on CRAN are not measured.
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- unarchivedReturned to CRAN2026-07-22
- 0.1.7Latest
- archivedRemoved from CRAN2026-05-16issues were not corrected in time
- RR 4.6.0 released · 2026-04-24
- 0.1.62025-11-08 · diff ↗ ·
removed 26 exports
calculate_interaction_score, check_connection, check_drug_target, check_drug_targets_in_layers, check_layer, check_sensible_connections, chunk, chunk_2gether, combine_graphs, corPvalueStudentParallel, create_unique_layer_node_ids, find_targets, generate_reduced_graph, get_layer, get_layer_setting, graph_metrics, inter_layer_edgelist_by_id, inter_layer_edgelist_by_table, load_interaction_score_output, network_reduction_by_p_value, network_reduction_by_pickHardThreshold, sample_size, set_cluster, shutdown_cluster, target_edge_list, write_interaction_score_input - RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- 0.1.42022-09-23 · diff ↗
- 0.1.32022-05-17
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2022-05-17
- Total releases
- 4 / 4 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 622 KB / 14 files
- Download size
- 1.2 MB
Cite
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citation("DrDimont")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1093/bioinformatics/btac477.
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