Deep learning shapes single-cell data analysis
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Development of single-cell multi-omics methods and growth of single-cell datasets created a well-suited scenario for applying deep learning methods in single-cell analysis, which could further lead to advancements in drug discovery, therapeutic targets, and digital health.
“The development of single-cell multi-omics methods and the growth of single-cell datasets created a well-suited scenario for applying deep learning methods in single-cell analysis, which could further lead to advancements in the field of drug discovery, therapeutic targets, and digital health [2,131].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [131]
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Benchmarking atlas-level data integration in single-cell genomics
Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01336-8 · Cited by 1,438 · see citing works
Hu, J., Li, X., Coleman, K., Schroeder, A., Ma, N., Irwin, D. J., Lee, E. B., Shinohara, R. T., & Li, M. (2021) · Nature Methods · doi:10.1038/s41592-021-01255-8 · Cited by 1,113 · see citing works
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Research works printed but not linked: 8
- [1]Nath A Bild AH Leveraging single-cell approaches in cancer precision medicine Trends Cancer 2021 7 359 372 10.1016/j.trecan.2021.01.007 33563578 PMC7969443 · DOI
- [2]Mogilenko DA Shchukina I Artyomov MN Immune ageing at single-cell resolution Nat. Rev. Immunol. 2021 10.1038/s41577-021-00646-4 34815556 PMC8609266 · DOI
- [3]Tian Y Single-cell immunology of SARS-CoV-2 infection Nat. Biotechnol. 2022 40 30 41 10.1038/s41587-021-01131-y 34931002 PMC9414121 · DOI
- [4]Ma A McDermaid A Xu J Chang Y Ma Q Integrative methods and practical challenges for single-cell multi-omics Trends Biotechnol. 2020 38 1007 1022 10.1016/j.tibtech.2020.02.013 32818441 PMC7442857 · DOI
- [5]Amodio M Exploring single-cell data with deep multitasking neural networks Nat. Methods 2019 16 1139 1145 10.1038/s41592-019-0576-7 31591579 PMC10164410 · DOI
- [6]Wang J scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses Nat. Commun. 2021 12 1882 10.1038/s41467-021-22197-x 33767197 PMC7994447 · DOI
- [8]Saelens W Cannoodt R Todorov H Saeys Y A comparison of single-cell trajectory inference methods Nat. Biotechnol. 2019 37 547 554 10.1038/s41587-019-0071-9 30936559 · DOI · Paper page
- [10]Greener JG Kandathil SM Moffat L Jones DT A guide to machine learning for biologists Nat. Rev. Mol. Cell Biol. 2021 23 40 55 10.1038/s41580-021-00407-0 34518686 · DOI · Paper page
Cited by
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Transformer for one stop interpretable cell type annotation
Chen, J., Xu, H., Tao, W., Chen, Z., Zhao, Y., & Han, J.-D. J. (2023) · Nature Communications · doi:10.1038/s41467-023-35923-4 · Cited by 189 · see citing works
Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis
Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75 · see citing works
Harnessing the deep learning power of foundation models in single-cell omics
Ma, Q., Jiang, Y., Cheng, H., & Xu, D. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00756-6 · Cited by 34 · see citing works
Self-citation
- Authors
- Ma, Q., & Xu, D.
- Affiliations
- The Ohio State University Comprehensive Cancer Center – Arthur G. James Cancer Hospital and Richard J. Solove Research Institute (US)
- University of Missouri (US)
- Corresponding author
- Ma, Qin
- Venue
- Nature Reviews Molecular Cell Biology
- Published
- 2022-02-23
- Volume
- 23
- Issue
- 5
- Pages
- 303-304
- ISSN
- 1471-0072 (ISSN-L)
- 1471-0080
- PMC
- PMC8864973
- OpenAlex
- W4212836561
- Cited by
- 123
- References
- 2
- Citations by year
- 2026: 21 · 2025: 36 · 2024: 32 · 2023: 22 · 2022: 12
- FWCI
- 8.33
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Reviews Molecular Cell Biology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ohio State University
- University of Missouri
- National Institutes of Health
- National Institute of General Medical Sciences (R01GM131399, R35GM126985)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- deep learning, single-cell data analysis, single-cell RNA sequencing, best practices, computational biology
- MeSH terms
- Deep Learning, Data Analysis, Algorithms, Single-Cell Analysis
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