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August 16, 2025HereditasOpen Access

Global trends in machine learning applications for single-cell transcriptomics research

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Authors

XLXinyu LiuZZZhen ZhangCTChao Tan

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Overview

Bibliometric analysis uncovers ML applications and challenges in single-cell rna sequencing, suggesting future directions in precision medicine.

Key Points

  • Machine learning integration in single-cell transcriptomics significantly improves cellular heterogeneity analysis, and supports advancements in precision medicine.
  • Analysis of 3,307 publications revealed a focus on clustering analysis and gene expression in single-cell studies over two decades.
  • Bibliometric methods utilized included CiteSpace and VOSviewer, highlighting key research themes such as immunotherapy and bioinformatics.
  • International collaboration is pivotal, especially between China and the US, to tackle challenges like data heterogeneity and model interpretability.

Cite This Study

Liu et al. (2025) studied this question.

synapsesocial.com/papers/68af2d7ccf1dd9ea359e58f2https://doi.org/10.1186/s41065-025-00528-y
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