text: user12307: Note: this question has also been asked on Biostars
  I am currently trying to complete a Breast Cancer Classification task using Neural Networks. I have experimented with using my full dataset of gene expressions(1000+) and managed to improve my results by using only the PAM50 genes. I was wondering though what other genes I could add to the list to even better improve my results.
  I have tried looking for papers about extending the PAM50, but could not find anything. I would greatly appreciate any advice.
  
  user12324: There is a CSV table in this paper with 33 sets of genes.
  
original_text: 12307: <p><em>Note: this question has also been asked on <a href="https://www.biostars.org/p/9471832/" rel="nofollow noreferrer">Biostars</a></em></p>
  <p>I am currently trying to complete a Breast Cancer Classification task using Neural Networks. I have experimented with using my full dataset of gene expressions(1000+) and managed to improve my results by using only the PAM50 genes. I was wondering though what other genes I could add to the list to even better improve my results.</p>
  <p>I have tried looking for papers about extending the PAM50, but could not find anything. I would greatly appreciate any advice.</p>
   12324: <p>There is a <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5921990/bin/12885_2018_4388_MOESM2_ESM.csv" rel="nofollow noreferrer">CSV table</a> in <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5921990/" rel="nofollow noreferrer">this paper</a> with 33 sets of genes.</p>
  