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This review covers DNA microarray databases for bioinformatics and machine learning, highlighting challenges like high feature numbers and small sample sizes in gene expression analysis for disease diagnosis.

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Area of Science:

  • Bioinformatics
  • Machine Learning
  • Genomics

Background:

  • DNA microarray datasets enable gene expression analysis for disease diagnosis and tumor classification.
  • High dimensionality and limited sample sizes present significant challenges in microarray data classification.

Purpose of the Study:

  • To review commonly used DNA microarray databases in scientific literature.
  • To highlight data characteristics problematic for machine learning, including imbalance, complexity, and dataset shift.

Main Methods:

  • Literature review of prominent DNA microarray databases.
  • Discussion of inherent data complexities and their impact on machine learning models.

Main Results:

  • Identified frequently utilized microarray databases.
  • Detailed the challenges posed by high feature counts, small sample sizes, data imbalance, complexity, and dataset shift.

Conclusions:

  • Understanding these data characteristics is crucial for developing effective machine learning approaches for microarray analysis.
  • Further research is needed to address the identified challenges in bioinformatics and machine learning applications.