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Stable feature selection for biomarker discovery.

Zengyou He1, Weichuan Yu

  • 1School of Software, Dalian University of Technology, China. zyhe@dlut.edu.cn

Computational Biology and Chemistry
|August 13, 2010
PubMed
Summary
This summary is machine-generated.

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Feature selection stability is crucial for biomarker discovery but often overlooked. This review categorizes existing stable feature selection methods for better understanding and future research in this growing field.

Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Statistical Genetics

Background:

  • Feature selection is a cornerstone of biomarker discovery.
  • The stability of feature selection methods concerning sampling variations has been historically under-addressed.
  • Recent research increasingly highlights the importance of feature selection stability.

Purpose of the Study:

  • To provide a comprehensive overview of stable feature selection methods for biomarker discovery.
  • To categorize existing stable feature selection techniques within a hierarchical framework.
  • To establish a foundation for future research and development in stable feature selection.

Main Methods:

  • Systematic literature review of stable feature selection techniques.

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  • Development of a generic hierarchical framework for categorizing methods.
  • Analysis and synthesis of existing stable feature selection approaches.

    • Main Results:

    • Identification and overview of various stable feature selection methods.
    • Categorization of these methods based on the proposed hierarchical framework.
    • Highlighting the growing attention and advancements in the field.

    Conclusions:

    • Stable feature selection is critical for reliable biomarker discovery.
    • The proposed framework offers a structured approach to understanding and advancing the field.
    • Further research is needed to explore and refine stable feature selection techniques.