  Background: In recent years, researchers have made significant strides in
understanding the heterogeneity of breast cancer and its various subtypes.
However, the wealth of genomic and proteomic data available today necessitates
efficient frameworks, instruments, and computational tools for meaningful
analysis. Despite its success as a prognostic tool, the PAM50 gene signature's
reliance on many genes presents challenges in terms of cost and complexity.
Consequently, there is a need for more efficient methods to classify breast
cancer subtypes using a reduced gene set accurately.
  Results: This study explores the potential of achieving precise breast cancer
subtype categorization using a reduced gene set derived from the PAM50 gene
signature. By employing a "Few-Shot Genes Selection" method, we randomly select
smaller subsets from PAM50 and evaluate their performance using metrics and a
linear model, specifically the Support Vector Machine (SVM) classifier. In
addition, we aim to assess whether a more compact gene set can maintain
performance while simplifying the classification process. Our findings
demonstrate that certain reduced gene subsets can perform comparable or
superior to the full PAM50 gene signature.
  Conclusions: The identified gene subsets, with 36 genes, have the potential
to contribute to the development of more cost-effective and streamlined
diagnostic tools in breast cancer research and clinical settings.
