ArticleBMC biology2025
AnomalGRN: deciphering single-cell gene regulation network with graph anomaly detection.
Article in BMC biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- iProDNet: integrated probabilistic differential network inference under heterogeneous biological conditions.Briefings in bioinformatics · 2026Article
- A comprehensive survey on graph neural networks for gene regulatory network inference.Briefings in bioinformatics · 2026Review
- Network security situation prediction method based on KG and Parsimonious memory unit.Scientific reports · 2026Article
- AMDRP: adaptive drug feature fusion and multihead bidirectional cross-attention network for drug-cancer cell response prediction.Molecular diversity · 2026Article
- CaHoT-GRN: context-aware high-order topology learning for robust single-cell gene regulatory network inference.Briefings in bioinformatics · 2026Article
- Multi-view knowledge-guided flow subgraphs with substructure initialization for explainable DDI prediction.Briefings in functional genomics · 2026Article
- scMFF: a machine learning framework with multiple feature fusion strategies for cell type identification.BMC bioinformatics · 2025Article
- Assessment and applications of joint profiling of single-cell chromatin accessibility and transcriptome.Briefings in bioinformatics · 2025Review
- GT-GRN: a graph transformer framework for enhanced gene regulatory network inference via multimodal embedding of expression data and existing network knowledge.Briefings in bioinformatics · 2025Article
- ESAE-SDA: ensemble sparse autoencoder framework for epigenomics-informed snoRNA-disease associations prediction.BMC bioinformatics · 2025Article
- Denoising self-supervised learning for disease-gene association prediction.BMC bioinformatics · 2025Article
- Cancer detection via one-shot learning: integrating gene expression and genomic mutation analysis.BMC bioinformatics · 2025Article
- Machine learning methods for gene regulatory network inference.Briefings in bioinformatics · 2025Review
- DualNetM: an adaptive dual network framework for inferring functional-oriented markers.BMC biology · 2025Article
- Drug repurposing for Alzheimer's disease using a graph-of-thoughts based large language model to infer drug-disease relationships in a comprehensive knowledge graph.BioData mining · 2025Article
- IRGL-RRI: interpretable graph representation learning for plant RNA-RNA interaction discovery.Frontiers in plant science · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundSingle-cell RNA sequencing (scRNA-seq) is now essential for cellular-level gene expression studies and deciphering complex gene regulatory mechanisms. Deep learning methods, when combined with scRNA-seq technology, transform gene regulation research into graph link prediction tasks. However, these methods struggle to mitigate the impact of noisy data in gene regulatory networks (GRNs) and address the significant imbalance between positive and negative links.
resultsConsequently, we introduce the AnomalGRN model, focusing on heterogeneity and sparsification to elucidate complex regulatory mechanisms within GRNs. Initially, we consider gene pairs as nodes to construct new networks, thereby converting gene regulation prediction into a node prediction task. Considering the imbalance between positive and negative links in GRNs, we further adapt this issue into a graph anomaly detection (GAD) task, marking the first application of anomaly detection to GRN analysis. Introducing the cosine metric rule enables the AnomalGRN model to differentiate between homogeneity and heterogeneity among nodes in the reconstructed GRNs. The adoption of graph structure sparsification technology reduces noisy data impact and optimizes node representation.
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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.