Paper published in Bioinformatics

We'll also be presenting this work at ISMB 2026 in Washington, D.C.: find us in the NetBio COSI track this Wednesday! 
 
Complex diseases are rarely driven by single genes; they emerge from interconnected molecular mechanisms across the interactome. Network-based methods expand disease-associated "seed" genes into disease modules, but the many available algorithms follow very different strategies, making it hard to know which modules are most reliable or biologically meaningful.
With nf-core/diseasemodulediscovery, developed within the RePo4EU consortium, we built an all-in-one, reproducible Nextflow pipeline that handles installation, input prep, execution, and systematic evaluation of disease module discovery methods, assessing topology, functional coherence, robustness, and seed recovery, with drug prioritization via Drugst.One.
Applying it across 50 disease–network combinations revealed substantial variability driven by both network and algorithm choices: methods are robust to minor perturbations but struggle to recover omitted seeds and are heavily affected by the choice of input network. None consistently outperforms the rest, underscoring the need for careful method selection. Integrated into nf-core, it is built as an extendable, long-term resource for reproducible network medicine research.

👏 Huge thanks and congrats to all co-authors and collaborators, especially our DaiSyBio members, Johannes Kersting, Lisa Marie Spindler, Quirin Manz, Mo Tan, and Markus List.

🧬 We're also grateful to the nf-core community (https://nf-co.re/community) for their support in making this pipeline possible.

🔗 Links:
📄 Paper: https://doi.org/10.1093/bioinformatics/btag223
💻 Pipeline: https://nf-co.re/diseasemodulediscovery/dev/
📅 Talk at ISMB 2026, NetBio COSI: https://www.iscb.org/ismb2026/scientific-programme/cosi-tracks-other-abstracts/netbio