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...

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Autores principales:	Yameng Wu, Yu Guo, Jun Ma, Yu Sa, Qifeng Li, Ning Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	gliomas machine learning prediction radiomics gene expression Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/fda032d5ca40407d805d0a84a6287f7a
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spelling	oai:doaj.org-article:fda032d5ca40407d805d0a84a6287f7a2021-11-25T17:12:17ZResearch Progress of Gliomas in Machine Learning10.3390/cells101131692073-4409https://doaj.org/article/fda032d5ca40407d805d0a84a6287f7a2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4409/10/11/3169https://doaj.org/toc/2073-4409In 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.Yameng WuYu GuoJun MaYu SaQifeng LiNing ZhangMDPI AGarticlegliomasmachine learningpredictionradiomicsgene expressionBiology (General)QH301-705.5ENCells, Vol 10, Iss 3169, p 3169 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	gliomas machine learning prediction radiomics gene expression Biology (General) QH301-705.5
spellingShingle	gliomas machine learning prediction radiomics gene expression Biology (General) QH301-705.5 Yameng Wu Yu Guo Jun Ma Yu Sa Qifeng Li Ning Zhang Research Progress of Gliomas in Machine Learning
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.
format	article
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
title	Research Progress of Gliomas in Machine Learning
title_short	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_sort	research progress of gliomas in machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/fda032d5ca40407d805d0a84a6287f7a
work_keys_str_mv	AT yamengwu researchprogressofgliomasinmachinelearning AT yuguo researchprogressofgliomasinmachinelearning AT junma researchprogressofgliomasinmachinelearning AT yusa researchprogressofgliomasinmachinelearning AT qifengli researchprogressofgliomasinmachinelearning AT ningzhang researchprogressofgliomasinmachinelearning
_version_	1718412690864472064

Research Progress of Gliomas in Machine Learning

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