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<p>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.</p>

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KEGNI: knowledge graph enhanced framework for gene regulatory network inference

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月22日

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  • Pengxiao Li,
  • Lin Li,
  • Jingminjie Nan,
  • Jiahuan Chen,
  • Jielin Sun,
  • Yanan Cao

摘要

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.