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Clustering algorithms in biomedical research: a review.

Rui Xu1, Donald C Wunsch

  • 1Industrial Artificial Intelligence Laboratory, GE Global Research Center, Niskayuna, NY 12309, USA. rxu@ieee.org

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Clustering algorithms are vital in biomedical research for analyzing complex data. This paper guides researchers in selecting appropriate clustering methods for diverse applications like gene expression and MRI analysis.

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Area of Science:

  • Biomedical data science
  • Computational biology
  • Medical imaging analysis

Background:

  • Clustering algorithms are widely used in biomedical research for analyzing gene expression, genomic sequences, biomedical documents, and MRI images.
  • The variety of clustering algorithms, their differing terminologies, goals, and assumptions can be confusing for researchers.
  • Selecting the correct clustering algorithm for specific biomedical applications is crucial for accurate analysis.

Purpose of the Study:

  • To provide a comprehensive overview of current clustering algorithms for biomedical researchers.
  • To present illustrative examples of cluster analysis applications in the biomedical field.
  • To assist researchers in choosing the most appropriate clustering algorithms for their specific research needs.

Main Methods:

  • Literature review of clustering algorithms and their biomedical applications.
  • Categorization of clustering techniques based on their underlying principles and data types.
  • Case study analysis of successful clustering implementations in various biomedical domains.

Main Results:

  • An overview of the current landscape of clustering algorithms relevant to biomedical research.
  • Demonstration of how clustering aids in gene expression analysis, genomic sequence analysis, document mining, and MRI image analysis.
  • Identification of key factors to consider when matching clustering algorithms with specific biomedical data and research questions.

Conclusions:

  • Clustering is an indispensable tool in modern biomedical research, offering insights across diverse data types.
  • Understanding the nuances of different clustering algorithms is essential for effective application.
  • This work serves as a practical guide to facilitate informed algorithm selection for biomedical researchers.