ArticleNature communications2018
Network enhancement as a general method to denoise weighted biological networks.
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 52 papers.
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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.
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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
52 citing papers in PubMed.
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- Single cell transcriptomic analysis by deep learning identifies novel microglial transcription regulations in Alzheimer's Disease.AI in neuroscience · 2026Article
- scIMGCN: an Automatic Single-Cell Type Annotation Method Based on Interpretable Graph Convolutional Network.Interdisciplinary sciences, computational life sciences · 2026Article
- scSCCNIA: similarity matrix based contrastive clustering with neighbor information aggregation for single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- In vivo Proximity & Spatial Proteomics with CRISPR Screening Identify STXBP1 as a Protective Modifier of α-synuclein Toxicity in Dopamine Neurons.bioRxiv : the preprint server for biology · 2026Article
- Differentiable graph clustering with structural grouping for single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2025Article
- Stress, loneliness, depression, anxiety and problematic smartphone use among a sample of Syrian refugee adolescents: a network approach.Child and adolescent psychiatry and mental health · 2025Article
- TRENDY: gene regulatory network inference enhanced by transformer.Bioinformatics (Oxford, England) · 2025Article
- Genome-Wide Detection of Leukemia Biomarkers from lincRNA-Protein-Coding Gene Interaction Networks in the Three-Dimensional Chromatin Structure.Current issues in molecular biology · 2025Article
- Conserved transcription factors coordinate synaptic gene expression through repression.bioRxiv : the preprint server for biology · 2025Article
- scHNTL: single-cell RNA-seq data clustering augmented by high-order neighbors and triplet loss.Bioinformatics (Oxford, England) · 2025Article
- KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning.Frontiers in pharmacology · 2025Article
- Elucidating simulated equivalence responding through dynamic visualization of structural connectivity and relational density.Frontiers in artificial intelligence · 2025Article
- Arsenic modifies the microbial community assembly of soil-root habitats inISME communications · 2025Article
- scVAG: Unified single-cell clustering via variational-autoencoder integration with Graph Attention Autoencoder.Heliyon · 2024Article
- A mini-review of single-cell Hi-C embedding methods.Computational and structural biotechnology journal · 2024Review
- PartIES: a disease subtyping framework with Partition-level Integration using diffusion-Enhanced Similarities from multi-omics Data.Briefings in bioinformatics · 2024Article
- PhyImpute and UniFracImpute: two imputation approaches incorporating phylogeny information for microbial count data.Briefings in bioinformatics · 2024Article
- A metagene based similarity network fusion approach for multi-omics data integration identified novel subtypes in renal cell carcinoma.Briefings in bioinformatics · 2024Article
- scVGATAE: A Variational Graph Attentional Autoencoder Model for Clustering Single-Cell RNA-seq Data.Biology · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from molecular to the biome. However, biological networks are noisy due to the limitations of measurement technology and inherent natural variation, which can hamper discovery of network patterns and dynamics. We propose Network Enhancement (NE), a method for improving the signal-to-noise ratio of undirected, weighted networks. NE uses a doubly stochastic matrix operator that induces sparsity and provides a closed-form solution that increases spectral eigengap of the input network. As a result, NE removes weak edges, enhances real connections, and leads to better downstream performance. Experiments show that NE improves gene-function prediction by denoising tissue-specific interaction networks, alleviates interpretation of noisy Hi-C contact maps from the human genome, and boosts fine-grained identification accuracy of species. Our results indicate that NE is widely applicable for denoising biological networks.
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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.