Research Progress of Gliomas in Machine Learning
In the field of gliomas research, the broad availability of genetic and image information originated by computer technologies and the booming of biomedical publications has led to the advent of the big-data era. Machine learning methods were applied as possible approaches to speed up the data mining...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Cells |
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| Online Access: | https://www.mdpi.com/2073-4409/10/11/3169 |
| _version_ | 1797510835553173504 |
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| author | Yameng Wu Yu Guo Jun Ma Yu Sa Qifeng Li Ning Zhang |
| author_facet | Yameng Wu Yu Guo Jun Ma Yu Sa Qifeng Li Ning Zhang |
| author_sort | Yameng Wu |
| collection | DOAJ |
| description | In the field of gliomas research, the broad availability of genetic and image information originated by computer technologies and the booming of biomedical publications has led to the advent of the big-data era. Machine learning methods were applied as possible approaches to speed up the data mining processes. In this article, we reviewed the present situation and future orientations of machine learning application in gliomas within the context of workflows to integrate analysis for precision cancer care. Publicly available tools or algorithms for key machine learning technologies in the literature mining for glioma clinical research were reviewed and compared. Further, the existing solutions of machine learning methods and their limitations in glioma prediction and diagnostics, such as overfitting and class imbalanced, were critically analyzed. |
| first_indexed | 2024-03-10T05:36:56Z |
| format | Article |
| id | doaj.art-fda032d5ca40407d805d0a84a6287f7a |
| institution | Directory Open Access Journal |
| issn | 2073-4409 |
| language | English |
| last_indexed | 2024-03-10T05:36:56Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cells |
| spelling | doaj.art-fda032d5ca40407d805d0a84a6287f7a2023-11-22T22:52:18ZengMDPI AGCells2073-44092021-11-011011316910.3390/cells10113169Research Progress of Gliomas in Machine LearningYameng Wu0Yu Guo1Jun Ma2Yu Sa3Qifeng Li4Ning Zhang5Tianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin 300000, ChinaTianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin 300000, ChinaTianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin 300000, ChinaTianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin 300000, ChinaTianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin 300000, ChinaTianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin 300000, ChinaIn the field of gliomas research, the broad availability of genetic and image information originated by computer technologies and the booming of biomedical publications has led to the advent of the big-data era. Machine learning methods were applied as possible approaches to speed up the data mining processes. In this article, we reviewed the present situation and future orientations of machine learning application in gliomas within the context of workflows to integrate analysis for precision cancer care. Publicly available tools or algorithms for key machine learning technologies in the literature mining for glioma clinical research were reviewed and compared. Further, the existing solutions of machine learning methods and their limitations in glioma prediction and diagnostics, such as overfitting and class imbalanced, were critically analyzed.https://www.mdpi.com/2073-4409/10/11/3169gliomasmachine learningpredictionradiomicsgene expression |
| spellingShingle | Yameng Wu Yu Guo Jun Ma Yu Sa Qifeng Li Ning Zhang Research Progress of Gliomas in Machine Learning Cells gliomas machine learning prediction radiomics gene expression |
| title | Research Progress of Gliomas in Machine Learning |
| title_full | Research Progress of Gliomas in Machine Learning |
| title_fullStr | Research Progress of Gliomas in Machine Learning |
| title_full_unstemmed | Research Progress of Gliomas in Machine Learning |
| title_short | Research Progress of Gliomas in Machine Learning |
| title_sort | research progress of gliomas in machine learning |
| topic | gliomas machine learning prediction radiomics gene expression |
| url | https://www.mdpi.com/2073-4409/10/11/3169 |
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