Online bias-aware disease module mining with ROBUST-Web.
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ABSTRACT: Summary
We present ROBUST-Web which implements our recently presented ROBUST disease module mining algorithm in a user-friendly web application. ROBUST-Web features seamless downstream disease module exploration via integrated gene set enrichment analysis, tissue expression annotation, and visualization of drug-protein and disease-gene links. Moreover, ROBUST-Web includes bias-aware edge costs for the underlying Steiner tree model as a new algorithmic feature, which allow to correct for study bias in protein-protein interaction networks and further improves the robustness of the computed modules.Availability and implementation
Web application: https://robust-web.net. Source code of web application and Python package with new bias-aware edge costs: https://github.com/bionetslab/robust-web, https://github.com/bionetslab/robust_bias_aware.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Sarkar S
PROVIDER: S-EPMC10246579 | biostudies-literature | 2023 May
REPOSITORIES: biostudies-literature
Publications
Online bias-aware disease module mining with ROBUST-Web.
Sarkar Suryadipto S Lucchetta Marta M Maier Andreas A Abdrabbou Mohamed M MM Baumbach Jan J List Markus M Schaefer Martin H MH Blumenthal David B DB
Bioinformatics (Oxford, England) 20230601 6
<h4>Summary</h4>We present ROBUST-Web which implements our recently presented ROBUST disease module mining algorithm in a user-friendly web application. ROBUST-Web features seamless downstream disease module exploration via integrated gene set enrichment analysis, tissue expression annotation, and visualization of drug-protein and disease-gene links. Moreover, ROBUST-Web includes bias-aware edge costs for the underlying Steiner tree model as a new algorithmic feature, which allow to correct for ...[more]
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