ArticleNature methods2019
Assessment of network module identification across complex diseases.
Article in Nature methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 159 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
159 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- MKLNID: Identifying Melanoma-related Pathogenic Genes Through Multiple Kernel Learning and Network Impulsive Dynamics.Interdisciplinary sciences, computational life sciences · 2026Article
- A Framework for Benchmarking Pathway Reconstruction Algorithms.bioRxiv : the preprint server for biology · 2026Article
- A module-based approach for post-omics, post-GWAS network-based gene classification.Bioinformatics (Oxford, England) · 2026Article
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- Correlation-Induced Accessibility Bridges in Biomedical Networks: A Proof-of-Concept Relational Graph Model.Entropy (Basel, Switzerland) · 2026Article
- Inferring and evaluating network medicine-based disease modules with nextflow.Bioinformatics (Oxford, England) · 2026Article
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- Host-Directed Antiviral Strategies Against Influenza Viruses: Host Targets, Multi-Omics Approaches and AI-Assisted Discovery.Veterinary sciences · 2026Review
- Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer.Scientific reports · 2026Article
- Multi-omics reveal phosphatidic acid phosphatases modify Niemann-Pick type C disease severity.iScience · 2026Article
- Targeting protein protein interactions and their modulators to enable new therapeutic strategies for human diseases.NPJ systems biology and applications · 2026Review
- Finding Significant Hits in Networks: a network-based tool for analyzing gene-level P-values to identify significant genes missed by standard methods.Briefings in bioinformatics · 2026Article
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- Decoding the dark genome reveals its organisation into modular disease networks.Scientific reports · 2026Article
- Large-scale manual curation and harmonization of metadata from metagenomic and cancer genomic repositories: challenges and solutions.Database : the journal of biological databases and curation · 2026Article
- Multilayer network approaches to omics data integration in digital twins for cancer research.Frontiers in systems biology · 2026Review
- A Roadmap for the Future of Systems Biology in Cancer Research.Cancer research · 2025Review
- Unraveling the Network Signatures of Oncogenicity in Virus-Human Protein-Protein Interactions.Entropy (Basel, Switzerland) · 2025Article
99 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors.
Funding
Abstract
Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.