Inferring and Evaluating Network Medicine-Based Disease Modules with Nextflow
Bibliographic record
Abstract
MOTIVATION: Most diseases result from complex molecular interactions of genes and proteins. Various network-based methods characterize these mechanisms by expanding seed genes into disease modules. Their underlying algorithmic strategies differ, making it difficult to determine which of the created modules are most useful or biologically plausible. RESULTS: To address this challenge, we developed an all-in-one pipeline that handles installation, input preparation, execution, and systematic evaluation of six widely used module detection tools, considering module topology, functional coherence, robustness, and the capacity to recover seeds. To showcase the value of our pipeline and provide guidance to potential users, we conducted a comprehensive evaluation across 50 different disease-network combinations, revealing substantial variability among the derived disease modules, driven by both network and algorithm choices. We show that methods are robust to minor perturbations but struggle to recover omitted seeds. None consistently outperforms all others, underscoring the need for careful method selection. Our work enables the systematic comparison of disease module discovery approaches and promotes reproducible network medicine research. Integrated into the nf-core project, it is intended as an extendable, long-term resource for tracking progress in the field. AVAILABILITY AND IMPLEMENTATION: The pipeline is implemented in Nextflow. Code and documentation are available through GitHub (https://github.com/nf-core/diseasemodulediscovery) and the nf-core website (https://nf-co.re/diseasemodulediscovery). Code and data used for demonstrating the pipeline are available through GitHub (https://github.com/REPO4EU/modulediscovery_demonstration).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".