ArticleBMC bioinformatics2021
Hypergraph models of biological networks to identify genes critical to pathogenic viral response.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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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, 1 synthesis or guideline pooled it, 100 citations in OpenAlex.
- Pooled it
- Graph and Hypergraph Theories Applied to Dynamic Protein-Protein Interaction Network Analysis, and Deep-Learning Frameworks for Protein Complex Network Prediction.International journal of molecular sciences · 2026Review
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- The microscale organization of directed hypergraphs.Communications physics · 2026Article
- Hypergraph Representation Learning with Weighted- and Clustering-Biased Random Walks.Entropy (Basel, Switzerland) · 2025Article
- A Hypergraph powered approach to Phenotype-driven Gene Prioritization and Rare Disease Prediction.Scientific reports · 2025Article
- Enriched knowledge representation in biological fields: a case study of literature-based discovery in Alzheimer's disease.Journal of biomedical semantics · 2025Article
- Hypergraph-based analysis of weighted gene co-expression hypernetwork.Frontiers in genetics · 2025Article
- Using deep neural networks and LASSO regression to predict miRNA expression changes based on mRNA data.Frontiers in bioinformatics · 2025Article
- Investigating the Potential Shared Molecular Mechanisms between COVID-19 and Alzheimer's Disease via Transcriptomic Analysis.Viruses · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- The simpliciality of higher-order networks.EPJ data science · 2024Article
- SARS-CoV-2-infected human airway epithelial cell cultures uniquely lack interferon and immediate early gene responses caused by other coronaviruses.Clinical & translational immunology · 2024Article
- Peripheral nerve repair is associated with augmented cross-tissue inflammation following vascularized composite allotransplantation.Frontiers in immunology · 2023Article
- Identifying Hub Genes and miRNA-mRNA Regulatory Networks in Mice Infected with H1N1 Influenza Virus.Disease markers · 2023Article
- What's next for computational systems biology?Frontiers in systems biology · 2023Review
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- Examining organic acid production potential and growth-coupled strategies in Issatchenkia orientalis using constraint-based modeling.Biotechnology progress · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
28 authors at 6 institutions in 2 countries.
Funding
Abstract
backgroundRepresenting biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets.
resultsWe compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality.
conclusionsOur results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.
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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.