Research Progress of Gliomas in Machine Learning.

Journal: Cells
Published Date:

Abstract

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.

Authors

  • Yameng Wu
    Tianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin 300000, China.
  • Yu Guo
    Animal Disease Control Center of Inner Mongolia, Hohhot, China.
  • Jun Ma
    State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China.
  • Yu Sa
    Department of Biomedical Engineering, Tianjin University, 92 Weijin Rd., Tianjin, 300072, China. sayu@tju.edu.cn.
  • Qifeng Li
    Tianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin 300000, China.
  • Ning Zhang
    Institute of Nuclear Agricultural Sciences, Zhejiang University, Hangzhou, 310058, China.