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...
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| Format: | Article |
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MDPI AG
2021-04-01
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| Series: | Biomolecules |
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| Online Access: | https://www.mdpi.com/2218-273X/11/4/565 |
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| author | Satoshi Takahashi Masamichi Takahashi Shota Tanaka Shunsaku Takayanagi Hirokazu Takami Erika Yamazawa Shohei Nambu Mototaka Miyake Kaishi Satomi Koichi Ichimura Yoshitaka Narita Ryuji Hamamoto |
| author_facet | Satoshi Takahashi Masamichi Takahashi Shota Tanaka Shunsaku Takayanagi Hirokazu Takami Erika Yamazawa Shohei Nambu Mototaka Miyake Kaishi Satomi Koichi Ichimura Yoshitaka Narita Ryuji Hamamoto |
| author_sort | Satoshi Takahashi |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T12:24:33Z |
| format | Article |
| id | doaj.art-e0cab93e2509473cbceb9bcf24ef6c86 |
| institution | Directory Open Access Journal |
| issn | 2218-273X |
| language | English |
| last_indexed | 2024-03-10T12:24:33Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj.art-e0cab93e2509473cbceb9bcf24ef6c862023-11-21T15:14:15ZengMDPI AGBiomolecules2218-273X2021-04-0111456510.3390/biom11040565A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine LearningSatoshi Takahashi0Masamichi Takahashi1Shota Tanaka2Shunsaku Takayanagi3Hirokazu Takami4Erika Yamazawa5Shohei Nambu6Mototaka Miyake7Kaishi Satomi8Koichi Ichimura9Yoshitaka Narita10Ryuji Hamamoto11Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo 104-0045, JapanDepartment of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, Tokyo 104-0045, JapanDepartment of Neurosurgery, Faculty of Medicine, The University of Tokyo, Tokyo 113-8655, JapanDepartment of Neurosurgery, Faculty of Medicine, The University of Tokyo, Tokyo 113-8655, JapanDepartment of Neurosurgery, Faculty of Medicine, The University of Tokyo, Tokyo 113-8655, JapanDepartment of Neurosurgery, Faculty of Medicine, The University of Tokyo, Tokyo 113-8655, JapanDepartment of Neurosurgery, Faculty of Medicine, The University of Tokyo, Tokyo 113-8655, JapanDepartment of Diagnostic Radiology, National Cancer Center Hospital, Tokyo 104-0045, JapanDepartment of Diagnostic Pathology, National Cancer Center Hospital, Tokyo 104-0045, JapanDivision of Brain Tumor Translational Research, National Cancer Center Research Institute, Tokyo 104-0045, JapanDepartment of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, Tokyo 104-0045, JapanDivision of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo 104-0045, JapanAlthough 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.https://www.mdpi.com/2218-273X/11/4/565multi-omics analysismachine learningneuro-oncologyglioma |
| spellingShingle | Satoshi Takahashi Masamichi Takahashi Shota Tanaka Shunsaku Takayanagi Hirokazu Takami Erika Yamazawa Shohei Nambu Mototaka Miyake Kaishi Satomi Koichi Ichimura Yoshitaka Narita Ryuji Hamamoto A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning Biomolecules multi-omics analysis machine learning neuro-oncology glioma |
| 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 | multi-omics analysis machine learning neuro-oncology glioma |
| url | https://www.mdpi.com/2218-273X/11/4/565 |
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