Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research.
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In recent years, the diversity of cancer cells in tumor tissues as a result of intratumor heterogeneity has attracted attention. In particular, the development of single-cell analysis technology has made a significant contribution to the field; technologies that are centered on single-cell RNA sequencing (scRNA-seq) have been reported to analyze cancer constituent cells, identify cell groups responsible for therapeutic resistance, and analyze gene signatures of resistant cell groups. However, although single-cell analysis is a powerful tool, various issues have been reported, including batch effects and transcriptional noise due to gene expression variation and mRNA degradation. To overcome these issues, machine learning techniques are currently being introduced for single-cell analysis, and promising results are being reported. In addition, machine learning has also been used in various ways for single-cell analysis, such as single-cell assay of transposase accessible chromatin sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq) analysis, and multi-omics analysis; thus, it contributes to a deeper understanding of the characteristics of human diseases, especially cancer, and supports clinical applications. In this review, we present a comprehensive introduction to the implementation of machine learning techniques in medical research for single-cell analysis, and discuss their usefulness and future potential.


Gene Expression
machine learning
multi-omics analysis
next-generation sequencing
single-cell analysis


Asada, Ken
Takasawa, Ken
Machino, Hidenori
Takahashi, Satoshi
Shinkai, Norio
Bolatkan, Amina
Kobayashi, Kazuma
Komatsu, Masaaki
Kaneko, Syuzo
Okamoto, Koji
Hamamoto, Ryuji

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