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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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scKGBERT: a knowledge-enhanced foundation model for single-cell transcriptomics.

Yang Li1, Guanyu Qiao1,2, Hongli Du3

  • 1College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China.

Genome Biology
|November 25, 2025
PubMed
Summary
This summary is machine-generated.

We developed scKGBERT, a novel foundation model for single-cell analysis. It integrates gene expression and protein interactions to improve cell characterization and disease prediction.

Keywords:
Knowledge graphPre-trained language modelPre-training modelSingle-cell transcriptomics

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell transcriptomics offers detailed cellular heterogeneity insights.
  • Existing models struggle to incorporate gene associations, limiting biological understanding.

Purpose of the Study:

  • To develop a knowledge-enhanced foundation model for single-cell analysis.
  • To improve gene and cell representation by integrating diverse biological data.

Main Methods:

  • Integrated 41 million single-cell RNA sequencing profiles with 8.9 million protein-protein interactions.
  • Developed scKGBERT, a foundation model utilizing Gaussian attention for gene emphasis.
  • Jointly learned gene and cell representations for enhanced biological insights.

Main Results:

  • Achieved superior performance in gene annotation, drug response prediction, and disease prediction tasks.
  • Demonstrated improved biomarker identification through emphasized key genes.
  • Enhanced biological interpretability of single-cell data.

Conclusions:

  • scKGBERT provides a powerful resource for precision medicine.
  • The model facilitates deeper discovery of disease mechanisms.
  • Integrating interaction data significantly improves single-cell data analysis.