ArticleNature communications2018
Prioritizing network communities.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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
24 citing papers in PubMed.
- Inductive-Associative Meta-learning Pipeline with Human Cognitive Patterns for Unseen Drug-Target Interaction Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Loop Catalog: a comprehensive HiChIP database of human and mouse samples.Genome biology · 2025Article
- Learning the therapeutic targets of acute myeloid leukemia through multiscale human interactome network and community analysis.BioData mining · 2025Article
- A multidimensional recommendation framework for identifying biological targets to aid the diagnosis and treatment of liver metastasis in patients with colorectal cancer.Molecular cancer · 2024Article
- MuDCoD: multi-subject community detection in personalized dynamic gene networks from single-cell RNA sequencing.Bioinformatics (Oxford, England) · 2023Article
- Review
- Enhancing drug property prediction with dual-channel transfer learning based on molecular fragment.BMC bioinformatics · 2023Article
- Repurposing Approved Drugs for Sarcopenia Based on Transcriptomics Data in Humans.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Assessment of community efforts to advance network-based prediction of protein-protein interactions.Nature communications · 2023Article
- Assessing network-based methods in the context of system toxicology.Frontiers in pharmacology · 2023Article
- Deepening the knowledge of rare diseases dependent on angiogenesis through semantic similarity clustering and network analysis.Briefings in bioinformatics · 2022Article
- Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer.Nature communications · 2022Article
- Looking at COVID-19 from a Systems Biology Perspective.Biomolecules · 2022Review
- Network-based prioritization of cancer biomarkers by phenotype-driven module detection and ranking.Computational and structural biotechnology journal · 2022Article
- Drug repositioning by merging active subnetworks validated in cancer and COVID-19.Scientific reports · 2021Article
- Network medicine framework for identifying drug-repurposing opportunities for COVID-19.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Recovery patterns and physics of the network.PloS one · 2021Article
- Finding disease modules for cancer and COVID-19 in gene co-expression networks with the Core&Peel method.Scientific reports · 2020Article
- Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules.Frontiers in genetics · 2020Article
- Nine quick tips for analyzing network data.PLoS computational biology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Uncovering modular structure in networks is fundamental for systems in biology, physics, and engineering. Community detection identifies candidate modules as hypotheses, which then need to be validated through experiments, such as mutagenesis in a biological laboratory. Only a few communities can typically be validated, and it is thus important to prioritize which communities to select for downstream experimentation. Here we develop CRANK, a mathematically principled approach for prioritizing network communities. CRANK efficiently evaluates robustness and magnitude of structural features of each community and then combines these features into the community prioritization. CRANK can be used with any community detection method. It needs only information provided by the network structure and does not require any additional metadata or labels. However, when available, CRANK can incorporate domain-specific information to further boost performance. Experiments on many large networks show that CRANK effectively prioritizes communities, yielding a nearly 50-fold improvement in community prioritization.
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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.