Global trends in machine learning applications for single-cell transcriptomics research
ML-scRNA-seq integration has advanced cellular heterogeneity analysis and precision medicine development. Future directions should optimize deep learning architectures, enhance model generalization capabilities, and promote technical translation through multi-omics and clinical data integration. Interdisciplinary collaboration represents key to overcoming current limitations (e.g., data standardization, algorithm interpretability), ultimately realizing deep integration between single-cell technologies and precision medicine.
Authors
- Yongyan Song (ORCID: https://orcid.org/0000-0001-6886-0873)
- Chao Tan
- Yuan Li (ORCID: https://orcid.org/0000-0003-4263-9569)
- Yuhang Ai (ORCID: https://orcid.org/0000-0001-7682-7782)
- Xinyu Liu (ORCID: https://orcid.org/0000-0003-0854-8559)
- Zhen Zhang (ORCID: https://orcid.org/0000-0002-4510-372X)
- Hao Liu (ORCID: https://orcid.org/0000-0001-7448-7024)
- Jin Yang (ORCID: https://orcid.org/0000-0002-7120-5682)
Institutions
- Chengdu Medical College (CN)
- Chengdu University (CN)
- First Affiliated Hospital of Chengdu Medical College (CN)
Publication Details
- Journal
- Hereditas
- Published
- 2025-08-16
- DOI
- https://doi.org/10.1186/s41065-025-00528-y
- Citations
- 2
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 1.61
Funders
- Natural Science Foundation of Sichuan Province