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A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning
Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tri...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070530/ https://www.ncbi.nlm.nih.gov/pubmed/33921457 http://dx.doi.org/10.3390/biom11040565 |
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author | Takahashi, Satoshi Takahashi, Masamichi Tanaka, Shota Takayanagi, Shunsaku Takami, Hirokazu Yamazawa, Erika Nambu, Shohei Miyake, Mototaka Satomi, Kaishi Ichimura, Koichi Narita, Yoshitaka Hamamoto, Ryuji |
author_facet | Takahashi, Satoshi Takahashi, Masamichi Tanaka, Shota Takayanagi, Shunsaku Takami, Hirokazu Yamazawa, Erika Nambu, Shohei Miyake, Mototaka Satomi, Kaishi Ichimura, Koichi Narita, Yoshitaka Hamamoto, Ryuji |
author_sort | Takahashi, Satoshi |
collection | PubMed |
description | Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques. |
format | Online Article Text |
id | pubmed-8070530 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80705302021-04-26 A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning Takahashi, Satoshi Takahashi, Masamichi Tanaka, Shota Takayanagi, Shunsaku Takami, Hirokazu Yamazawa, Erika Nambu, Shohei Miyake, Mototaka Satomi, Kaishi Ichimura, Koichi Narita, Yoshitaka Hamamoto, Ryuji Biomolecules Review Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques. MDPI 2021-04-12 /pmc/articles/PMC8070530/ /pubmed/33921457 http://dx.doi.org/10.3390/biom11040565 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Takahashi, Satoshi Takahashi, Masamichi Tanaka, Shota Takayanagi, Shunsaku Takami, Hirokazu Yamazawa, Erika Nambu, Shohei Miyake, Mototaka Satomi, Kaishi Ichimura, Koichi Narita, Yoshitaka Hamamoto, Ryuji A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning |
title | A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning |
title_full | A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning |
title_fullStr | A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning |
title_full_unstemmed | A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning |
title_short | A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning |
title_sort | new era of neuro-oncology research pioneered by multi-omics analysis and machine learning |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070530/ https://www.ncbi.nlm.nih.gov/pubmed/33921457 http://dx.doi.org/10.3390/biom11040565 |
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