Sunday, January 18, 2026

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

Authors: Liu X, Zhang Z, Tan C, Ai Y, Liu H, Li Y, Yang J, Song Y.

DOI: 10.1186/s41065-025-00528-y

Abstract Summary

Machine learning integration with single-cell RNA sequencing has transformed cellular analysis, with China and the US leading 65% of 3,307 publications. Research evolved from algorithm development to clinical applications like tumor microenvironment analysis. Key challenges include data heterogeneity and model interpretability. Future advances require optimized deep learning and multi-omics integration for precision medicine.

Why Brain? 🧠

Machine learning transforms single-cell RNA analysis, enabling precise cellular profiling. Study reveals China-US research dominance and shift from algorithm development to clinical cancer applications.

License: CC BY.


The image is AI-generated for illustrative purposes only. Courtesy of Midjourney.

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