ArticleGenome biology2025
KEGNI: knowledge graph enhanced framework for gene regulatory network inference.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- CaHoT-GRN: context-aware high-order topology learning for robust single-cell gene regulatory network inference.Briefings in bioinformatics · 2026Article
- Inferring Gene Regulatory Networks From Single-Cell RNA Sequencing Data by Dual-Role Graph Contrastive Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- KEGNI: knowledge graph enhanced framework for gene regulatory network inference.Genome biology · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Inference of cell type-specific gene regulatory networks (GRNs) is a fundamental step in investigating complex regulatory mechanisms. Here, we present KEGNI (Knowledge graph-Enhanced Gene regulatory Network Inference), a knowledge-guided framework that employs a graph autoencoder to capture gene regulatory relationships and incorporates a knowledge graph to infer GRNs based on scRNA-seq data. KEGNI shows superior performance compared to multiple methods using scRNA-seq data or paired scRNA-seq and scATAC-seq data. KEGNI can identify driver genes and elucidate the regulatory mechanisms underlying distinct cellular contexts. The modular design of KEGNI supports the integration of various knowledge graphs for context-specific tasks.
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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.