Online bias-aware disease module mining with ROBUST-Web.
Bioinformatics
; 39(6)2023 06 01.
Article
in En
| MEDLINE
| ID: mdl-37233198
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Software
Language:
En
Journal:
Bioinformatics
Journal subject:
INFORMATICA MEDICA
Year:
2023
Type:
Article
Affiliation country:
Germany