Evidence map›Paper›PMID 41286991›Full record

ArticleGenome biology2025

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

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yang Li *College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China.
Guanyu Qiao *College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China.
Hongli DuSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China.
Xin GaoComputer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
Guohua WangCollege of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China. ghwang@nefu.edu.cn.

Funding

National Key Research and Development Program of China 2024YFF1206602National Natural Science Foundation of China 32470687National Natural Science Foundation of China Original Exploration Program 62450112National Science Fund for Distinguished Young Scholars 62225109the Fundamental Research Funds for the Central Universities 2572025JT05
6 · The paper itself

Abstract

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.

Indexed as

Gene Expression ProfilingSingle-Cell AnalysisTranscriptomeHumansKnowledge graphPre-trained language modelPre-training modelSingle-cell transcriptomics

Identifiers

PMID41286991
PMCPMC12642280

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.