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<p>Single-cell transcriptomics enables precise characterization of cellular heterogeneity, but current pre-trained models relying solely on expression data fail to capture gene associations. We present scKGBERT, a knowledge-enhanced foundation model integrating 41 M single-cell RNA-seq profiles and 8.9 M protein–protein interactions to jointly learn gene and cell representations. scKGBERT employs Gaussian attention to emphasize key genes and improve biomarker identification, achieving superior performance across gene annotation, drug response, and disease prediction tasks. scKGBERT enhances biological interpretability and offers a powerful resource for precision medicine and disease mechanism discovery.</p>

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scKGBERT: a knowledge-enhanced foundation model for single-cell transcriptomics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月24日

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  • Yang Li,
  • Guanyu Qiao,
  • Hongli Du,
  • Xin Gao,
  • Guohua Wang

摘要

Single-cell transcriptomics enables precise characterization of cellular heterogeneity, but current pre-trained models relying solely on expression data fail to capture gene associations. We present scKGBERT, a knowledge-enhanced foundation model integrating 41 M single-cell RNA-seq profiles and 8.9 M protein–protein interactions to jointly learn gene and cell representations. scKGBERT employs Gaussian attention to emphasize key genes and improve biomarker identification, achieving superior performance across gene annotation, drug response, and disease prediction tasks. scKGBERT enhances biological interpretability and offers a powerful resource for precision medicine and disease mechanism discovery.