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

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

Abstract Background Single-cell RNA sequencing (scRNA-seq) has revolutionized cellular heterogeneity analysis by decoding gene expression profiles at individual cell level, while machine learning (ML) has emerged as core computational tool for clustering analysis, dimensionality reduction modeling a...

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Bibliographic Details
Main Authors: Xinyu Liu, Zhen Zhang, Chao Tan, Yinquan Ai, Hao Liu, Yuan Li, Jin Yang, Yongyan Song
Format: Article
Language:English
Published: BMC 2025-08-01
Series:Hereditas
Subjects:
Single-cell transcriptomics
Machine learning
Bibliometric analysis
Random forest
Deep learning
Online Access:https://doi.org/10.1186/s41065-025-00528-y
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https://doi.org/10.1186/s41065-025-00528-y

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