Online bias-aware disease module mining with ROBUST-Web

• Published in: Bioinformatics (Oxford, England) Vol. 39; no. 6
• Main Authors: Sarkar, Suryadipto, Lucchetta, Marta, Maier, Andreas, Abdrabbou, Mohamed M, Baumbach, Jan, List, Markus, Schaefer, Martin H, Blumenthal, David B
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.06.2023
• Subjects: Applications Note
• ISSN: 1367-4811; 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btad345

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.