Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research

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 seque...

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Autores principales:	Ken Asada, Ken Takasawa, Hidenori Machino, Satoshi Takahashi, Norio Shinkai, Amina Bolatkan, Kazuma Kobayashi, Masaaki Komatsu, Syuzo Kaneko, Koji Okamoto, Ryuji Hamamoto
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	single-cell analysis next-generation sequencing machine learning multi-omics analysis Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/98e97ae928d2494d80ad06936e7c2a72
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spelling	oai:doaj.org-article:98e97ae928d2494d80ad06936e7c2a722021-11-25T16:48:25ZSingle-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research10.3390/biomedicines91115132227-9059https://doaj.org/article/98e97ae928d2494d80ad06936e7c2a722021-10-01T00:00:00Zhttps://www.mdpi.com/2227-9059/9/11/1513https://doaj.org/toc/2227-9059In 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.Ken AsadaKen TakasawaHidenori MachinoSatoshi TakahashiNorio ShinkaiAmina BolatkanKazuma KobayashiMasaaki KomatsuSyuzo KanekoKoji OkamotoRyuji HamamotoMDPI AGarticlesingle-cell analysisnext-generation sequencingmachine learningmulti-omics analysisBiology (General)QH301-705.5ENBiomedicines, Vol 9, Iss 1513, p 1513 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	single-cell analysis next-generation sequencing machine learning multi-omics analysis Biology (General) QH301-705.5
spellingShingle	single-cell analysis next-generation sequencing machine learning multi-omics analysis Biology (General) QH301-705.5 Ken Asada Ken Takasawa Hidenori Machino Satoshi Takahashi Norio Shinkai Amina Bolatkan Kazuma Kobayashi Masaaki Komatsu Syuzo Kaneko Koji Okamoto Ryuji Hamamoto Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research
description	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.
format	article
author	Ken Asada Ken Takasawa Hidenori Machino Satoshi Takahashi Norio Shinkai Amina Bolatkan Kazuma Kobayashi Masaaki Komatsu Syuzo Kaneko Koji Okamoto Ryuji Hamamoto
author_facet	Ken Asada Ken Takasawa Hidenori Machino Satoshi Takahashi Norio Shinkai Amina Bolatkan Kazuma Kobayashi Masaaki Komatsu Syuzo Kaneko Koji Okamoto Ryuji Hamamoto
author_sort	Ken Asada
title	Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research
title_short	Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research
title_full	Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research
title_fullStr	Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research
title_full_unstemmed	Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research
title_sort	single-cell analysis using machine learning techniques and its application to medical research
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/98e97ae928d2494d80ad06936e7c2a72
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Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research

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