title: Predicting nucleotide sequence efficiency
body: <p>I am new to machine learning and I am wondering whether it would be possible to use my available biological data for clustering. I want to find out whether a group of DNA sequences can be clustered into two groups, efficient and not efficient.</p>
  
  <p>I have five sets, each containing about 480 short sequences (lets call them samples). Each set is having an effect with different strength:</p>
  
  <p>Set1 - Very good effect</p>
  
  <p>Set2 - Good effect</p>
  
  <p>Set3 - Minor effect</p>
  
  <p>Set4 - Very minor effect</p>
  
  <p>Set5 - No effect</p>
  
  <p>Each sample has some features, e.g. free energy,starting with a specific nucleotide...</p>
  
  <p>Now my question is whether I can find out which type of sample in my sets are playing a role for the effect of the whole set. My only assumption is that in set1 I have more efficient samples then in set5 (either none or very few). A very simple (not realistic) result could be, all samples which start with nucleotide 'A' end end with nucleotide 'C' are causing the effect. </p>
  
  <p>Is it possible to use machine learning to find out?</p>
  
  <p>Thanks!</p>